Standards or guidance related to cord blood and umbilical cord-derived cells.
\r\n\tWe accept scientific papers which can be presented as original research papers and review papers. The required length of the full chapters is 10-20 pages and the chapters should be original works (not republished).
\r\n\tAs a self-contained collection of scholarly papers, the book will target an audience of practicing researchers, academics, Ph.D. students and other scientists. Since it will be published as an Open Access publication, it will allow unrestricted online access to chapters with no reading or subscription fees.
Quality is regarded as an important build-in feature of a product, whose function is to fit ones needs, while showing no defects. In addition to this, the price has to be right, so that the product may serve its designed life span. Another aspect of quality is that the product has to show the user a favorable characteristic while used other than the aim of the user to buy the product. When all the mentioned measures are met within the product, it may be regarded as a quality product.
\nSome types of production are done by one person, let us say a tailor; this person is responsible of every step of the product either machine performed, or by hand. As such, that person would take care of any defects as soon as these occur. In addition to this, that one person would add or deduct so-called “excitement” features in the product according to the consumer’s taste. This kind of production is called the mentor-protege type. Henry Ford introduced and implemented mass production, where every step of the production is done by someone else with a different machine. Since many people and machines are involved in the production, the chance of deviations and defects increases and may cause quality-specific problems. Furthermore, since the operators do not know who the consumer they are producing for in mass production, research and development departments were created to fulfill the “excitement” features, since this work had to be done by other people than the operator; additionally, research and development departments serve to aim produced goods to different markets.
\nIn order to effectively manage and eliminate quality-specific problems, a number of quality control methods were developed. The following encapsulates some of these:
Quality control,
Total quality control,
Total quality management,
Quality management,
Quality improvement,
Six-sigma quality management, etc.
Statistical quality control is used widely in the modern business world. Indeed, control charts are deemed as one of the primary techniques to enhance quality. Gathering data to prepare a control chart is done according to many national and international standards like British Standards (BS), American Standards (ASTM), German Standards (DIN), Turkish Standards (TSE), etc. Variations in due time or sample order are examined by control charts in order to keep production under control according to the product’s desired properties. The purpose of this chapter is to highlight the arising benefits of using control charts and elaborate their impetus on industrial case studies such as to keep production under control, to eliminate defects, and to increase profit, if not, a full understanding of what is going on in production or service will not be conceived.
\nA process is a system of bonds worked altogether to produce a specific outcome or factors which affect the production and the quality of a product or a function. In order a process to achieve the intended result, the causes of the mentioned process have to be kept under control. To this end, control charts are used [1]. The latter is prepared with numerical data of a particular characteristic of the product, which is controlled. Additionally, control charts provide visual support about the deviations in the characteristics [2]. In doing so, they prevent the formation of defects and increase and develop the efficiency of the processes.
\nQuality improvement tools are mainly process flow diagrams, cause-and-effect (fishbone) diagrams, check sheets, histograms, scatter plots, Pareto diagrams, and control charts. The aim of this chapter is to focus on the use of only the control charts and provide a qualitative and quantitative insight. As such, it will present industrial cases regarding their use and type. In addition to this, it will discuss on how they are designed, prepared, and interpreted together with research concerning control charts.
\nIn doing so, this work will include:
Presentation of control charts in the area of quality control;
Design of a control chart;
Types of control charts;
General guidelines to prepare control charts;
Control charts for variables, that is, individual measurements control charts, means control charts, ranges control charts, and standard deviation control charts with industrial applications;
Control charts for attributes, that is, control charts for fraction nonconforming, control charts for the number of nonconforming items, control charts for conformities per unit, and control charts for nonconformities with industrial applications;
Special cases for control charts;
Interpretation of control charts;
Research on control charts.
Control charts provide higher efficiency in production, decrease defects and faulty production, increase profit, and diminish costs. These are some of the reasons why control charts are widely used in industry. Indeed, their area of application is quite wide and covers nearly everything from service organizations and providers to financial consulting offices, as well as in various other applications in daily life.
\nIt is worth mentioning at this point that in nature as well as in service and production companies, no two products of the same substance are exactly the same. This implies that at least two of the same substance or characteristic are always different, or at least there is a small difference between them. This, however, is normal as long as it affects small variations. To produce every piece in a lot exactly to the specified nominal characteristic is both hard and costly. The measurements of some quality characteristic like length, width, temperature, weight, etc., vary slightly and maybe unavoidable. This variability depends on equipment, machinery, materials, equipment, environment, people, etc., and is acceptable. These types of variability are referred to as “normal”, “random”, or “natural”.
\nIn view of the above, it is preferred that the variability has to be reduced as much as possible in the process, if it is not eliminated. The distances of the points from the mean line give the user information about its variability. There are chance causes of variation in statistical control, but there are also assignable causes which are not a part of the chance causes. These show important, large, and unusual differences. The reasons for this may be:
Material is taken from a different lot,
The machine setter makes a new setting,
Any kind of “operator error”.
The above may cater for the “abnormal” or “unnatural” variations. In a production where the aim is to achieve quality and to meet the consumers’ requirements, the presence of assignable causes may draw the process out-of-control. Since the objective of studied quality characteristic is to be stable and repeatable, the occurrence of assignable causes must be detected instantly and the investigation of the process and corrective action ought to take place before further nonconforming units are manufactured. Control charts are widely used in order to interpret the variability a characteristic possesses between nominal and actual settings. The differences between “normal” and “abnormal” variations are detected, and the characteristic is kept under control by taking all the necessary measures. The purpose of control charts in quality control is prevention, which is better than cure [3].
\nThe amount of variation to be allowed in any manufacturing process is of paramount importance. It is impossible to examine the records of past data and evaluate data by looking and thinking without doing statistical calculations.
\nIn some factories, technical staff checks out the data and estimates on an ad hoc level the limits of the process. These may be too wide or too narrow, which in turn, may be both affecting the production negatively causing it to go out-of-control. If the limits are too wide, the process will posses an excess of variation; if it is too narrow, extra work may be required so as to maintain set limits. It is worth noting at this point, both of them prevent corrective action to take place which is suitable for production. On the other hand, when the limits are calculated on a scientific basis, the exact amount of expected variation in a product will be determined and will be confidently used, so guesswork will be eliminated [4]. Examples for limits can be seen in Figures 6–12.
\nShewhart developed the control charts first in 1924 and are as such called Shewhart control charts. The usage of control charts became common as its benefits were recognized in due time. Its benefits can be listed as:
Knowing how the production proceeds,
Diminishing costs,
Increasing production by doing it right the first time so to prevent defects,
Being aware of the effects of raw material, machine, worker, and environmental factors by analyzing the patterns occurring on the control chart
Saving time by preventing the error of searching for special reasons that effect the processes even they do not exist;
Making it easier to find the factors that negatively affect the process;
Used to seek if the desired efficiency of a machine is achieved;
Useful in decreasing the variations in a product or in a process;
Useful in decreasing the number of rejected pieces or waste;
Ensuring to decrease the cost of testing and control;
Enabling the specifications and orders at a more realistic level;
Helpful in making the processes more stable;
Advantageous in preparation of reports near to real about the processes or operations to present to the managers;
Expedient in keeping sensitive and reliable records;
Used in deciding the renewal time of the production machines;
Substantial reference in research and development practices;
Helpful in cost and financial analysis;
Used in stocks control [5].
The areas where control charts can be used are areas such as production-not least on say, count of the yarn or the weight of fabric in textiles, costs, sales, circulation of workers, material, chemicals, etc., in a certain period of time.
\nQuality control charts are statistical technique tools which have a wide application in scientific research, in industry, and even in daily life. This concept makes the use of control charts as important as cost control and material control. Information about the design and types of control charts and general guidelines to prepare control charts, and the likes are given below.
\nA control chart is a graph mainly derived from a normal distribution curve. The y-axis denotes a quality characteristic or a particular characteristic of the product or process, which is controlled and is marked in units, in which the test value is expressed. The x-axis consists of time intervals or sample number. There is a center line, which is the average of the value of the studied matter or may also indicate the nominal value. The upper boundary characterizes the upper control limit (UCL), while the lower designates the lower control limit (LCL), respectively. The gathered data are plotted in sequence, and then, the pattern occurring on the chart is interpreted. A sample of a control chart is given in Figure 1.
\nSample of a control chart.
As can be seen from Figure 1, there is a close relationship between the normal distribution curve and the control chart. Control charts are constructed on the basis of expanding the sigma limits above and below of the mean. By taking a deeper look, it can be expressed that expansion of 1.962\n
Warning and action limits for large samples.
Warning and action limits for small samples.
The center line of a control chart stands from the past data or new data got from the measurements in the process or applied from what the consumer wants. If the clients have specified limits for their orders, production has to be done according to the specification limits of the client. In this case, the UCL and LCL have to be in the specification limits (Figure 4). If the control limits take place out of the specification limits (Figure 4), then that is an undesirable condition because the product will be manufactured with a quality characteristic range that the client does not want, thus resulting in an inferior quality product.
\nPlacement of UCL and LCL according to specification limits.
3\n
Representation of 99.73% probability.
Control charts are quality technique tools that may trigger an alarm. If a value exceeds the warning limit above or below, production may continue, but the reason for this variation must be investigated and corrective action must be taken.
\nIn obtaining data from a process, sampling is performed by using small sample sizes. Concerning small samples, sensitivity of the control chart is increased by statistical methods, and the warning limit and the action limit are combined to be expressed as UCL and LCL. So, if a value gets close to one of these limits, it is understood that it is not needed to stop production but search for the reason of this variation and to correct it. Likewise, if a value crosses one of these limits, action has to be taken and production must be stopped before searching. Sensitivity, sample size, and sampling frequency (specific and equal time intervals) are important factors regarding the performance of the control chart. Sampling frequency must be in accordance with the production processes.
\nControl charts have two main types according to the way the values used are obtained. Values can be obtained by measuring on a numerical scale, that is, counting, calculating, by using a testing instrument, or by deriving proportions of judgments. If they are conforming or nonconforming, one would look at their certain attributes they have to possess so as to express a case. If the values used are obtained by measuring, then they are called control charts for variables. If the values used are obtained by deriving proportions, then they are called control charts for attributes. These charts apply for different process-specific cases in processes, so that each can be evaluated on its own.
\nEach type has different kinds of control charts particularly for the case studied. The most important kinds of control charts for variables are mainly
\nIndividual measurements control chart (x),
Means control chart (\n
Ranges control chart (R),
Standard deviation control chart (s).
Others are s2 control chart, moving range control charts, and regression control chart. The main kinds of control charts for attributes are foremost p-, np-, u-, and c-control charts. Others are standardized control charts, g control charts, and h control charts.
\nThere are control charts for special uses in literature which can be listed as cumulative sum control charts, moving average control chart, x-bar and R-control charts for short production runs, attributes control charts for short production runs, modified and acceptance control charts, group control charts for multiple-stream processes, chi-square control chart, difference control charts, control charts for contrasts, run sum and zone control charts, adaptive control charts, moving average control charts, residual control charts, control charts for six-sigma processes, acceptance control charts, T2 control charts, Hotelling T2 control charts, Exponentially Weighted Moving Average (EWMA) control charts, exponentially weight means square control charts, multivariate EWMA control charts, one-sided EWMA control charts, moving centerline EWMA control charts, and one-sided CUSUM control chart [7].
\nSteps to prepare control charts in general are as follows:
Obtain a set of values;
Decide which kind of a control chart to prepare;
Do the needed calculations;
Draw the control chart;
Plot the values in Step 1 on the control chart;
Continue to plot the new values collected in due time on the chart;
Interpret the pattern occurring on the chart.
It is apparent from the last step, as the production proceeds, new values accumulate and these new values should be plotted on the same control chart with the UCL and LCL calculated from the first values of the same production. This procedure guarantees that the properties of the first products and the rest lie in the same control limits.
\nIn the first preparation of the control chart, if an assignable cause is found in the data collected, that point is discarded and the trial control limits are recalculated, using only the remaining points.
\nControl charts for variables are widely used because they enable more effectual control and provide more information about the performance of the processes. These charts are preferred because they provide the user with an estimation of the central tendency and the distribution of the studied case [8]. The most commonly used ones as stated above are individual measurements control chart (x), means control chart (\n
Control charts prepared with individual measurements are called individual measurements control charts. These charts are used in cases where only one value measured on a numerical scale is to be defined, that is, counting, calculating, or with a testing instrument. Examples would be the number of workers for successive months, paid taxes over years (in economics), effective staple length for similar fiber batches, fiber fineness obtained from air flow principle (in textiles), etc. [9].
\nPreparation steps for an X control chart are:
There has to be at least 10 values, 20 is better, but if there is a large time gap between obtaining the values 8 serves as well;
Average value of X (\n
The absolute value of the differences between two consecutive values of X is called the moving range. Moving range (MR) is calculated and average of MR (\n
UCL and LCL are calculated by \n
Control chart lines are drawn with the center line (\n
The values used in calculation are plotted on the chart;
The values coming up in due time are plotted on the same chart and interpreted as will be explained later.
An application of the X chart to yarn irregularity quality characteristic (U%) of the Kaynak Group Cotton Yarn Factory’s regular measurements is given in Figure 6 [10].
\nApplication of X chart to yarn irregularity quality characteristic (U%) of the Kaynak group cotton yarn factory’s regular measurements.
When sampling is done, n-repeats are taken at once to analyze the case under study in specified intervals. Control charts prepared with the means of the samples taken at once are called means control charts. These charts are used in cases where repeated measurements on a numerical scale of small sample sizes are done. Sample size is usually 5. Means of the samples possess a normal distribution. The basis of this system depends on finding how close the means of the samples measured are to the nominal or average value. Examples would be yarn count, fabric weight per unit area (in textiles), etc.
\nPreparation steps for an X-bar (\n
There has to be at least 10 different repeated measurement groups of a sample size of 5; 12 is better, but it should never be 8;
Mean of sample size (usually 5) is calculated for each different repeated measurement group, where each mean of sample size is indicated as \n
Range is the difference between the maximum and the minimum value in a sample (like size of 5). Range (R) for each sample size is calculated;
The averages of \n
UCL and LCL are calculated by \n
Control chart lines are drawn with the center line (\n
The values calculated in Step 2 (\n
The values coming up in due time are plotted on the same chart and interpreted as will be explained later.
An application of \n
Application of \n\n\nx\n¯\n\n\n chart to yarn maximum breaking strength quality characteristic (gf) of the Kaynak group cotton yarn factory’s regular measurements.
Control charts prepared with the range values are called range control charts. These charts are used together with means control charts (\n
The preparation steps for a R-control chart are:
The same R values obtained in Step 3 of \n
The same average of R (\n
The UCL is calculated by D4 \n
Control chart lines are drawn with the center line (\n
The values used in Step 1 are plotted on the chart;
The values coming up in due time are plotted on the same chart and interpreted [9].
When interpreting the pattern occurring on the R-control chart, it is ideal when the points are located near to the LCL.
\nAn application of R chart to yarn maximum breaking strength quality characteristic (gf) of the Kaynak Group Cotton Yarn Factory’s regular measurements is given in Figure 8.
\nApplication of R chart to yarn maximum breaking strength quality characteristic (gf) of the Kaynak group cotton yarn factory’s regular measurements.
Control charts prepared with the standard deviation values are called standard deviation control charts. These charts are used together with the means control charts (\n
It is suggested here to use \n
The preparation steps for a s-control chart are similar with range control charts, that is, average value of s (\n
An application of the s chart to yarn maximum breaking strength quality characteristic (gf) of the Kaynak Group Cotton Yarn Factory’s regular measurements is given in Figure 9.
\nApplication of s chart to yarn maximum breaking strength quality characteristic (gf) of the Kaynak group cotton yarn Factory’s regular measurements.
Control charts for attributes are used in cases, where the studied matter is not represented by measuring on a numerical scale but defined as a conforming (nondefective) or nonconforming (defective) to specifications. Then, different proportions suitable to each case are obtained, and control charts are drawn. The most commonly used ones as stated above are control chart for fraction nonconforming (p), control chart for the number of nonconforming items (np), control chart for conformities per unit (u), and control chart for nonconformities (c).
\nControl charts are developed by dividing the amount of nonconforming pieces to the total production amount are called p-control charts. The p-control charts possess binomial distribution. Since the total amount will be changing from one lot, batch, or party to the other, proportions are used to bring all to the same denominator. In the case where p charts will be used, 100% of the production must be controlled, otherwise the nonconformities which are not controlled may reach the end user. Examples would be the proportion of number of defective skirts to total produced skirts in 1 day, the proportion of number of defective yarn cones to total produced cones in one shift (in textiles), etc.
\nThe preparation steps for a p-control chart are:
There has to be at least 10 values;
The proportions (p) are calculated by dividing the nonconformity amount to the total amount;
The average value of p (\n
The UCL and LCL are calculated by \n
The control chart lines are drawn with the center line (\n
An application of the p chart to nonconforming pants in the Çağla Textile Ready-wear Factory’s regular measurements is given in Figure 10 [11].
\nApplication of p chart to nonconforming pants in the Çağla textile ready-wear Factory’s regular measurements.
Control charts prepared with the number of nonconforming items is called a np-control chart. A proportion is not done because the total production amount in these cases is the same in every day or shift, etc. There is no need to divide like in p-control charts every time. Examples would be the number of defective skirts in 1 day for a constant produced amount, the number of defective yarn cones in one shift for a constant produced amount (in textiles), etc.
\nThe preparation steps for a np-control chart are similar with the p-control charts, that is, the average value of np (n\n
An application of the np chart to the nonconforming skirts in the Çağla Textile Ready-wear Factory’s regular measurements is given in Figure 11.
\nApplication of np chart to nonconforming skirts in the Çağla textile ready-wear factory’s regular measurements.
Control charts prepared with the number of nonconformities per unit are called u-control charts. The unit here is different from the production amount mentioned in the p- and np- charts, being changing or constant, respectively. The unit here is the restricting factor, where the main pronounced value is the nonconformity. A unit may be length, weight, etc. As such, it is mentioned as number of defects per unit length or number of conformities per unit weight, facilitating the status change of the unit.
\nThe number of nonconformities is divided to the unit to find the “per unit” value of the defects to bring all to the same comparison ground. An example would be the number of defects per 100 m length of fabric (fixed width) (in textiles).
\nThe preparation steps for a u- control chart are:
There has to be at least a set of 10 values;
The number of defects per unit is calculated for a specified unit for every value;
The average value of u (\n
The UCL and LCL are calculated by \n
The control chart lines are drawn with the center line (\n
When interpreting the pattern occurring on the u-control chart, it would be preferably when the points are located near to the LCL.
\nApplications of u charts to defects in fabrics of fixed width in the Özer Textile Weaving Factory’s regular measurements are given in Figure 12 [12].
\nApplication of u charts to defects in fabrics of fixed width in the Özer textile weaving factory’s regular measurements. (a) Application of u chart to thin weft yarn defect calculated per 100 m. Of fabric (considered normal), (b) application of u chart to thick warp yarn defect calculated per 100 m of fabric (gives alarm).
The control charts prepared with the number of nonconformities per constant unit are called c-control charts. The unit here is again the restricting factor, where the main pronounced value is the nonconformity. In these cases, the unit will be constant for all the data collected. There is no need to divide every time, since they are all on the same ground of comparison. Examples would be the imperfections (thick place in yarn, thin place in yarn, and neps) in yarn (number of an imperfection per 1 km. of yarn; in textiles).
\nThe preparation steps for a c- control chart are similar with the u-control charts, that is, the average value of c (\n
The applications of c charts in the Gülçağ Textile Yarn Factory and the Yıldırımlar Printing & Dying Factory are given in Figure 13.
\nApplication of a c chart. (a) Applications of c chart to thin place imperfection per 1 km, of yarn in the Gülçağ textile yarn Factory’s regular measurements [13], (b) applications of c chart to spot defects per fixed fabric roll of 80 m. In the Yıldırımlar printing & dying factory’s regular measurements [14].
This chapter will detail and analyze industrial applications of control charts for variables and attributes.
\nControl charts are widely used in industry nowadays. The information obtained from them helps production to be monitored effectively. Some examples of control charts for variables taken from industry are given in Figures 14–17.
\nIndividual measurements control chart for the number of rolls daily of Sarıkılıç.
Individual measurements control chart for the production weight daily of Sarıkılıç textile nonwoven factory.
Means control chart, ranges control chart, and standard deviation control chart for open-end yarns’ hairiness values of the Kaynak group yarn factory.
Means control chart, ranges control chart, and standard deviation control chart for nonwoven thickness values of the Sarıkılıç textile nonwoven factory.
Individual measurements control charts for number of rolls of nonwoven fabric and daily production weight of Sarıkılıç Textile Nonwoven Factory are given in Figures 14 and 15, respectively. By observing mentioned figures, one might see that at the beginning, both the number of rolls and production weight are high, but toward the end, even the number of rolls are near to average and production weight is high. This is because the weight of the unit area of nonwoven fabric increased. This is a typical case seen in textile factories, and as such, it can be said that production is under control.
\nIn Figure 16, the means control chart, ranges control chart, and standard deviation control chart are given for open-end yarns’ hairiness values, which are supplied from Kaynak Group Yarn Factory. A closer look at the charts may reveal an improvement in hairiness values, as the production proceeded but for a short time. The factory searched for the reason of this improvement and found out that it was because of the better condition of air suction and applied that condition afterward.
\nIn Figure 17, means control chart, ranges control chart, and standard deviation control chart are given for nonwoven thickness of nonwoven fabric values of the Sarıkılıç Textile Nonwoven Factory. It is seen in the charts that the thickness values have increased. The same applies to the range and standard deviation values too. The factory searched for the reason, and it was determined that different unit weights of nonwoven rolls were plotted on the same charts and were corrected.
\nSome examples of control charts for attributes applied in industry are given in Figures 18-21.
\np control chart for nonconforming socks in different amounts of production in the Tekstüre textile socks factory.
np-control chart for nonconforming socks in constant amount of production in the Tekstüre textile socks factory.
u-control chart for double weft fault in different lengths of fabric rolls in the Ne-Ke textile weaving factory.
c-control chart for cracks in dying department of the Özer textile weaving factory.
In the Tekstüre Textile Socks Factory in İstanbul, there are nonconforming socks produced during manufacturing. A p-control chart for nonconforming socks in different amounts of production is given in Figure 18. As can be seen in the figure, there is an increase in nonconformities toward the end. The factory searched for the underlying reason and found out that new employees had not taken enough training regarding socks production. A np-control chart for nonconforming socks in constant amount of production is given in Figure 19. As depicted in the figure, there is a decrease in nonconformities toward the end. The factory searched for its reason and found out that new machines were bought, which had started production.
\nIn the Ne-Ke Textile Weaving Factory, there is a double weft fault in weaving of bed placemats. A u-control chart for different lengths of fabric rolls is given in Figure 20. As seen in the figure, the pattern seemed normal, and the factory did not take any action for this case. In Figure 21, a c-control chart is given for the cracks occurred in the dying department of the Özer Textile Weaving Factory. By observing the corresponding figure, it can be seen that there is a sharp increase and then a fall. The factory searched for its reason and found that the worker had forgotten to add the anticrack chemical into the dying bath in the night shift and gave more training to the worker.
\nSome special cases for control charts are listed below:
The formulae for calculation of the UCL and LCL change in \n
If there are variable sample sizes, then the UCL and the LCL will be varying also and another approach to dealing with variable sample size is to use a “standardized” control chart
There can be subgroups for a case studied in the control chart. An example would be individual machines producing the same lot, yarn producing machines or fabric producing machines. In this case, there may be different control charts to control every machine under control even if they produce the same lot
Process capability analysis can be done using a control chart
There may be variable sample sizes on control charts
There may be variable sampling interval on control charts
The details for these special cases are not included here.
\nThe distribution of the points on a control chart is important, and the patterns occurring on the control chart have to be examined and interpreted. Since the values distribute at a distance around the mean value and support visually the variation in the spread of the test results, they provide useful information about the process so as to make modifications in order to reduce variability. For interpreting the control charts, the principles of the control charts have to be known, and their users must be familiar with the process. It is the author’s view that during the interpretation of control charts, not only statistics but also experience and common sense have to be combined with it. If there is a run toward the warning limit, this may suggest that a change has to be made. On the other hand, a similar run would also mean that a change in time may prevent the next item from lying outside the limits. This has to be evaluated for every occasion on its own.
\nTwo examples of a typical control chart where production is under control or a normal behavior is noticed is seen in Figure 22.
\nTwo examples of a typical control chart.
The main interpretation of control charts is that all the points should lie in between the UCL and LCL. If sample points fall in between the control limits in a continued production, then the process is in control, and as such, no action has to be taken. If a point falls out of them, then the process is out-of-control, and further investigative and corrective action ought to be taken. If, however, points get close to the UCL and LCL’s, one has to search for the root of the problem and solve it without stopping production. If on the other hand, points cross the UCL and LCL’s, production must be stopped and the problem must be investigated and solved. Faulty production is worse than no production.
\nOn the other hand, even if none of the points lie out of the control limits, this does not mean that the chance factor had played a role. All the points on the control chart may lie in between the UCL and LCL’s like a typical chart in Figure 22, but this does not mean that production is under control. Incidentally, they may well be out-of-control soon. The reason for this is the pattern occurring on the control chart. Patterns give information about the condition of the process, and their early identification may trigger the alarm for the user to investigate their causes and to prevent any faults before they occur. Patterns having deviations from normal behavior are indicators of raw material, machine (setting, adjustment, tool abrasion, and systematic causes of deterioration) or measuring method, human, and environmental factors starting to change the quality characteristic of the product. To interpret control charts, every cause has to be studied one by one and investigated and corrective action ought to be taken.
\n\n\n
Cyclic patterns: Two examples of control charts showing a cyclic pattern are given in Figure 23. An \n
Two examples of control charts showing a cyclic pattern.
Mixture: An example of a control chart showing a mixture pattern is given in Figure 24. In a mixture pattern, the plotted points gather around the UCL and LCL, but few points fall near the center line. In this outline, there are two or more overlapping distributions generating the process output. An \n
Example of a control chart showing a mixture pattern.
Shift in process level: An example of a control chart showing a shift in process level pattern is given in Figure 25. An \n
Example of a control chart showing a shift in process level pattern.
Trend: An example of a control chart showing a trend pattern is given in Figure 26. In a trend pattern, the plotted points continuously move in one direction. An \n
Example of a control chart showing a trend pattern.
Stratification: An example of a control chart showing a stratification pattern is given in Figure 27. In a stratification pattern, the plotted points tend to cluster around the center line, and there is a lack of natural variability in the pattern. An \n
Example of a control chart showing a stratification pattern.
Approaching LCL: An example of a control chart showing an approach to LCL pattern is given in Figure 28. A p-control chart having an approach to LCL pattern may represent a real improvement in process quality. But, downward shifts are not always attributable to improved quality. This is due to the fact that errors in the inspection process may be resulting from inadequately trained or inexperienced inspectors or from improperly calibrated test and inspection equipment during that particular shift. Besides, inspection may pass nonconforming units owing to a lack in training. The same interpretation is valid for np-control charts also.
\nExample of a control chart showing an approach to LCL pattern.
Approaching UCL or LCL: An example of a control chart showing an approach to UCL or LCL pattern is given in Figure 29. A c-control chart having approaching the UCL line may be because of temperature control and an approach to the LCL may be due to inspection error.
\nExample of a control chart showing an approach to UCL or LCL pattern.
Some definitive guidelines are developed to interpret control charts. Keeping in mind that the main principle is none of the points should cross UCL or LCL, the developed standards can be grouped as follows showing that process is out-of-control:
\nPoint/Points crossing the control limits: Examples of control charts showing point/points crossing the control limits are given in Figure 30. If there is an assignable cause in the \n
Examples of control charts showing point/points crossing the control limits.
Many points very near to the control limits: An example of a control chart showing many points that are very near to the control limits is given in Figure 31. This pattern may be toward UCL or LCL.
\nExample of a control chart showing many points that are very near to the control limits.
Points gather around a value: An example of a control chart showing points gathering around a value is given in Figure 32.
\nExample of a control chart showing points gathering around a value.
Consecutive points: All the consecutive seven points which are placed on one side of the center line is given in Figure 33. About 10 out of 11 consecutive points that are placed on one side of the center line is shown in Figure 34.
\nAll of the consecutive 7 points are placed on one side of the center line.
10 out of 11 consecutive points that are placed on one side of the center line.
This expression can be widened as 12 out of 14 consecutive points, 14 out of 17 consecutive points, 16 out of 20 consecutive points, and 19 out of 25 consecutive points (Figure 35) are placed on one side of the center line. They all indicate very nonrandom appearance and an out-of-control production.
\n19 out of 25 consecutive points are placed on one side of the center line.
Runs: Average run length is the average number of points that must be plotted assignable before it can be said that it is an out-of-control condition. They describe the performance of the control charts. Some examples are:
\nA run of 2 points out of 3 near the control limits is given in Figure 36.
\nA run of 2 points out of 3 is near the control limits.
Others may be a run of 4 points out of 5 at a 1σ distance from the center line, a run of 8 points lie at one side of the center line, and a run of 7 points rises or falls (Figure 37).
\nA run of 7 points rises or falls.
The placement of the points according to the center line is also important. 2/3 of the points have to lie between the inner 1/3 distance between the UCL and LCL’s and 1/3 of the points have to lie between the outer 2/3 distance between the limits. If more or less of the 2/3 of points lie near the center line, then this means either the limits were calculated wrong or the points are placed erratically on the chart, successive measurements may have been from different parties in production but located on the same chart by fault, or the machine adjustments have changed but the control operator was not aware of it and located the points on the same chart by fault instead of preparing a new chart.
\nExamples of less than 2/3 of points lie in the middle 1/3 of the control limits are given in Figure 38.
\nLess than 2/3 of points lie in the middle 1/3 of the control limits.
An example of clear shifts for different periods is given in Figure 39. The reason for these shifts would be that the process is changing periodically, and so, different limits have to be calculated for different periods. Another reason would be that the lot had been changed, but the person in charge is not aware of it and continues to plot two different lots on the same chart rather than preparing a new one for the new lot.
\nExample of clear shifts for different periods.
A number of researches have been performed on the topic of control charts. Indeed, the majority of studied works emphasize the early prediction of defects and on different areas like poultry, health, etc., other than manufacturing which is the main area these are used. A short survey of new developments in control charts is given below.
\nThere are some statistics software packages which also include preparations of control charts like SPSS, MATLAB, STATISTICA, etc. These software packages utilize the usage of control charts in companies, service, and official applications. With the help of computers, much of the work done by hand is performed very quickly, and results are obtained right away. The results are interpretated fast, and corrective action is taken to increase efficiency and profit in the enterprise. Furthermore, new techniques like artificial neural networks are applied in modern quality control methods and techniques.
\nThe authors use Shewhart control charts to maintain the quality of raisins (dried grapes) and dried figs within acceptable limits and make it possible to readjust storage conditions, if the acceptable limits should be violated. This occurs since the Shewhart control charts they use are constructed by using the Hunter Lab color scale parameters to assure maintenance of the color and flavor of raisins and dried figs during storage in modified atmosphere packages, vacuum packages, or nylon bags. Changing the storage conditions after the fruits have deteriorated cannot improve quality because deterioration of raisins or figs is irreversible. In the early stages of storage, violation of the control limits will warn the operators, and the storage conditions will be improved [15].
\nThe authors used the program which was designed by Montgomery to prevent errors and wastage of resources during sampling process in order to determine the economic design of parameters. The economical design of Shewhart control charts improves the principle of balancing between control efficiency and its costs. They did an application in a fruit soda producing factory. It is worth noting at this point that although staff was trained about total quality control, they were not adequately trained in statistical quality control. After the work of the authors, with this program and by paying attention to the lost functions and unit costs, design parameters, sample size, sampling interval, and the control limits were determined, resulting in a reduction of errors [16].
\nIt is the author’s view that coal properties are variable even within a single coal seam due to coalification history, mining method, etc. To control the variability of coal quality is important from the points of efficiency and production costs of power plants. These are negatively affected by nonconsistent coal characteristics such as calorific value, moisture content, and ash content and profitability of the coal producer. Variations in coal properties of the Tuncbilek Power Plant were studied by means of control charts, and process capability analysis of the statistical quality control methods was found to be very high. The latter showed variation within short intervals and away from contract specifications. It was suggested that the coal should be blended to reduce the variability in coal characteristics before selling to power plants, so that the efficiency of the power plant and the income of the coal producer can be increased [17].
\nThe authors obtained the control limits of \n
It is worth noting however that statistical quality control charts (SQCCs) are widely used in manufacturing processes so as to keep fluctuations within the acceptable limits; nonetheless, no application is done to weight management studies. In this paper, the author proves that using the mean Body Mass Index (BMI) values as the only indicator to assess the weight status of populations might be misleading in clinical weight management studies. For healthy aging, the author suggests to introduce a powerful tool, SQCCs, to keep fluctuations in BMIs within acceptable limits in a given population and makes a cross-sectional design. The distributions of individual BMIs and the pattern of BMI which change by age were studied using X charts, tolerance charts, and a capability analysis was performed. It is concluded by the author that the mean BMI increased in both genders by age as seen in Figure 40. Likewise, the individual weights were out-of-control limits, the mean BMI values were within the limits, and although the number of overweight individuals was greater in some groups, their mean BMIs were lower compared to the groups with fewer overweight individuals. Capability tests concluded that each group, even the groups with a mean BMI in the normal weight ranges and also the groups which are referred as being “under control” according to the X charts, was not within the so-called energy balance (Cp < 1 and Cpk < 1). The results suggest that by using the mean BMIs as the only indicator might be misleading in weight management studies. This work introduces SQCCs as a potential tool for clinical nutrition studies to maintain the fluctuations of individual BMIs within acceptable limits for healthy aging populations [19].
\nMean BMI increase in females by age.
The authors constructed modified Shewhart charts incorporating weight loss, Haugh units and yolk index for storage of untreated, soda lime, water glass, or oil coated and thermostabilized eggs. The data obtained showed differences between particular treatments. Control charts derived from them illustrate that maintenance lies within the limits of the inevitable quality loss, and this comes from the storage of eggs. Knowing the trends made it possible to readjust the storage conditions so as to prolong the period before the violation of acceptable limits [20].
\nIn determining the quality of the egg shell, broken or cracked eggs are important factors. The manufacturers need control charts throughout production to keep the number of broken or cracked eggs under control. The authors in this paper used p-control charts prepared with 52 weeks data in poultry business. They used three methods to draw control charts and concluded that the process was not under control because the number of broken or cracked eggs often crossed the upper control limit [21].
\nWhen the vector of means of several quality characteristics are monitored, the most widely used multivariate control chart is the Hotelling’s χ2 control chart, which is a Shewhart-type control chart, and it is relatively insensitive to detect small magnitude shifts quickly. The authors study the performance of the Hotelling’s χ2 control chart supplemented with a r-out-of-m runs rule. Their new control chart exhibits an improved performance over other competitive runs rules based control charts [22].
\nWhen the quality characteristics cannot be measured on a continuous scale, attribute control charts are very useful for monitoring different processes. Some cases involve the monitoring of multiple attributes simultaneously. This leads to multinomial and multiattribute quality control methods, which are better than the simultaneous use of multiple uniattribute methods. The authors equally studied research previously conducted on multiattribute quality control, regarding the design, performance, and applications of multiattribute control charts (MACCs), as well as multiattribute sampling plans. They also reviewed comparisons of the MACCs, as well as MADM research. They also emphasized the need of neural networks, the design of artificial neural network in attributes monitoring for an out-of-control signal, the detection of the magnitude of the shifts in parameters, the determination of the shape of the membership functions in linguistic terms, the appropriate degree of fuzziness of the membership functions, the exact relationship between the degree of fuzziness and sensitivity of control charts, and in nonhomogenous cases, where the distribution is no longer binomial what the properties of the process p chart should and as such may form a subject for further investigation [23].
\nThe authors have examined the problem of the statistical and economics-based design of fully adaptive Shewhart control charts for monitoring finite-horizon processes, where the production horizon for a specific product can be limited to a few hours or shifts. They propose a Markov chain model to design a fully adaptive Shewhart control chart for such cases. Their Markov chain model allows the exact computation of several statistical performance metrics, as well as the expected cost of the monitoring and operation process for any adaptive Shewhart control chart with an unknown but finite number of inspections. The implementation of the Vp X chart in short runs shows the production of a finite batch of products. They also support two models, namely one that is economics based and one that is aimed for the statistical design. These charts can also be used to optimize the performance of any adaptive control chart (VSSI, VSI, VSS, and Vp) in a finite-horizon context. They derived some properties of the economics-based model, which facilitates economic optimization and CUSUM adaptive control charts can also be developed [24].
\nThe authors presented the economic design of \n
A Shewhart-type chart with fixed parameters and,
An adaptive chart with variable sampling intervals and/or sample size.
They aimed to improve the statistical control scheme employed for monitoring quality characteristics and minimize the relevant costs. They also tested and confirmed the applicability of the theoretical models supporting the economic design of control charts with fixed and variable parameters and evaluated the economic benefits of moving from the broadly used static charts to the application of the more flexible and effective adaptive control charts. They concluded that by re-designing the currently employed Shewhart chart using economic criteria, the quality-related cost is expected to decrease by approximately 50% without increasing the implementation complexity.It is the author’s view that by monitoring the process by means of an adaptive \n
The author studied the factors that affect the Brix value and the volatile acidity of the final product in the bio-production of grape molasses, considering the ground used for cultivation and the variety of grapes. The author applied off-line statistical quality control techniques and discussed the outcomes in detail, concluding that Corinthian and Camborne varieties of grapes seemed to lead to the optimum result because the Brix value is optimum and grape molasses, while Phocean and Corinthian varieties of grapes were the best choices in order to decrease their volatile acidity, and mountain ground was better [26].
\nThe author indicates that Statistical Process Control procedures are based on the assumption that the process subject to monitoring consists of independent observations. Many nonindustrial processes besides chemical processes exhibit autocorrelation, where the assumption is not valid. The author has developed a methodology for monitoring autocorrelated processes. The main idea here is to compare the performance of the time series model against an alternative which works with departures from it. A phase II control procedure is proposed, which is a time-varying auto-regressive (AR) model for autocorrelated and locally stationary processes. That model is optimized during phase I, and as a result, the model describes the process accurately. The phase II control procedure is based on a comparison of the current time series model with the alternative model which is measuring deviations from it, using Bayes factors where its threshold rules enable a binomial-type control procedure. This model can equally be used in local nonstationarities via the dynamic evolution of the AR coefficients, and so it describes stable and nonstable processes. In particular, this method can be used in nonindustrial process monitoring, where nonstable or nonstationary processes are typical (finance, environmentrics, etc.). Temperature measurements at two different stages in the manufacturing of a plastic mold are used as data sets [27].
\nIn statistical quality control, control charts are the most widely used and are regarded as an effective tool. This work presented recent developments in the design of the adaptive control charts, especially in univariate control charts because they allow some of their parameters to change during production. They also act as an extension of the study of Tagaras. Based on performed literature review, it may be stated that the adaptive control charts may result to faster detection of a process shift and thus may contribute to improving overall economic performance. However, they are harder to administer, and their application may run up against technical difficulties. The design parameters which are the sample size, the sampling interval, and the control limit coefficient can be changed in adaptive control charts, while warning limits are added and improvements are gained. This study has equally shown that the more parameters are adaptive, the more improvement is obtained, hence, making the implementation of the control chart more difficult. The performance measures of the adaptive control charts which are derived from the Markov chain approach are discussed in this paper. The authors are interested in monitoring the process dispersion instead of the process mean. They indicate that in the S or R chart and the conforming run length chart, modification can be applied in order to detect variance shifts, and these shifts prove to detect increase in σ better than the decrease and are useful to monitor both the process mean and the process variance shift. It is the author’s view that users may misuse the cause-selecting chart in production steps because of unsatisfactory training, and this may lead to unnecessary adjustment that could increase the variability and as such the cost of the products. In view of the above, the dependent processes can be extended to the VP charts as well as to multiple process steps, multiple assignable causes, and dependent assignable causes. EWMA and the CUSUM charts are more effective than the standard Shewhart charts because they take into account both the present and previous samples. The adaptive control charts for attributes are also studied in this paper, and it is shown that by adding the adaptive feature, the detection ability of the charts is increased [28].
\nIn today’s world, the pursuit of high quality production is one of the main topics. The need for use of the specific software products so as to control the production process quality is the result of the variety and complexity of the production characteristics. SPSS is the most widely used software, which provides increased deliverables for a basic quality control analysis. A critical review of SPSS quality control functions and features is done, which contributes to enhanced quality management. It is worth mentioning at this point though, that aforementioned software package is facing competition from Minitab and Statistica to name but a few. In the future, it is hoped to find a universal all-in-one tool for the data processing without any insufficiencies concerning quality control functions and statistical analyses [29].
\nPerformed literature research indicates that pattern recognition technology is used to automatically judge the changing modes of control chart, which reveal potential problems. They propose a neural network-numerical fitting (NN-NF) model to recognize different control chart patterns with the purpose of improving the recognition rate and the efficiency of control chart patterns. They first use a back propagation (BP) network and then Monte Carlo simulation to generate training and testing of the data samples. If the control chart patterns are recognized with the general run rules, the abnormal report is directly generated, if not, the NN-NF model is activated. Training time of their NN-NF model is less, and the recognition rate is also improved [30].
\nIn addition to the above, a skewness correction (SC) method is proposed for constructing the \n
This paper emphasizes that control chart pattern recognition (CCPR) is a important task in statistical process control (SPC). Abnormal patterns in control charts can be associated with certain assignable cause adversely affecting the process stability. Work is aimed at reviewing and analyzing research on CCPR. In conjunction with this, a new conceptual classification scheme emerges, based on a content analysis method, so as to classify past and current developments in CCPR research done in more than 120 papers within the period 1991–2010. It was found that most of the CCPR studies dealt with independently and identically distributed process data; some recent studies pertaining to the identification of mean shifts or/and variance shifts of a multivariate process were based on innovative techniques. It is worth mentioning at this point though that there is an increase in the percentage of studies that address concurrent pattern identification as well as in Artificial Neural Network (ANN) approaches for improving the recognition of pattern together with hybrid, modular, and integrated ANN recognizer designs. The latter may be combined with decision tree learning, particle swarm optimization, etc. There are two main categories of performance criteria used to evaluate CCPR approaches: statistical criteria that are related to two conventional average run length (ARL) measures and recognition-accuracy criteria, which are not based on these ARL measures mainly for ANN-based approaches. Performance criteria with ARL measures are insufficient and inappropriate in the case of concurrent pattern identification. The authors also discuss some future research directions and their perspectives [32].
\nControl charts are important tools of statistical quality control that enhance quality. Quality improvement methods like flow diagrams, cause-and-effect (fishbone) diagrams, check sheets, histograms, scatter plots, and Pareto diagrams have also been applied so as to fulfill the needs of consumers with the desired properties and the least possible defects in the output, while maximizing producers’ profit. There are natural variations in production but also assignable causes which are not a part of chance but may be attributable to a number of internal and/or external factors like raw material, machine setting (or adjustment, tool abrasion, systematic causes of deterioration) or measuring method, human, and environmental effects.
\nThis paper provided a qualitative and quantitative insight into the use of only the control charts. Based on a number of industrial cases, it showed that the implementation of control charts can indeed contribute to defects minimization and, hence, reduce warranty and other costs.
\nControl charts mainly used are control charts for variables, that is, individual measurements control chart (x), means control chart (\n
The case studies presented herein showcase that control charts result in higher production efficiency and are as such used widely in industry. This work has equally highlighted performed research in the area of control charts. Indeed, research on control charts is done on a global basis, and from the findings discussed in this work, statistical methods and techniques are further empowered by the use of computer technology and, in particular, dynamic software packages and artificial neural networks, to name a few. In view of the above, it may be stated that Statistics may further assist its users by refined and selected methods to improve quality in a modern way besides control charts.
\nThe author kindly exhibits her deep regards to Kaynak Group Cotton Yarn Factory, Çağla Textile Ready-wear Factory, Özer Textile Weaving Factory, Gülçağ Textile Yarn Factory, Sarıkılıç Textile Nonwoven Factory, Yıldırımlar Printing & Dying Factory, and Ne-Ke Textile Weaving Factory which are located in Uşak and Teksüre Textile Socks Factory which is located in İstanbul-Turkey.
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Umbilical cord blood (CB) has been utilized as a source of hematopoietic stem cells for three decades. It could potentially also serve as the optimum source of immune cells, such as mononuclear cells (MNC), regulatory T cells, NK cells, and mesenchymal stromal cells (MSCs) with or without genetic modifications, for immunotherapy and neurogenic regeneration in some cases. In addition, UB could be prepared as readily available products [1].
It is well-known that human mesenchymal stromal cells (MSCs) can be harvested from various tissues that include the bone marrow (BM) [2], cord blood (CB) [3], adipose tissue [4], placenta [5], and umbilical cord (UC) [6]. Recently, clinical trials using MSCs for various diseases have been conducted, and some of them were approved. The BM is considered the traditional source of MSCs, and the characteristics and applications of BM-derived MSCs (BM-MSCs) have been studied in detail. However, the harvesting of these cells is associated with an invasive procedure, and it may cause hemorrhage, infection, and chronic pain. In addition, BM-MSCs exhibit accelerated senescence as the donors’ age [7].
On the other hand, both CB and UC are routinely discarded as medical waste. The harvesting of CB and UC-derived MSCs (UC-MSCs) is therefore noninvasive and painless. The CB drawn from the UC and placenta is well-known as the source of hematopoietic stem cells for CB transplantations. However, in this article, we focus on the CB as the source for immune cells and regenerative medicine, such as regulatory T cells (Treg), NK cells, MSCs, and so on. The UC is the conduit between the developing embryo and placenta and consists of two umbilical arteries and one umbilical vein buried in the Wharton’s jelly. UC-MSCs have the abilities of multipotency and self-renewal properties comparable or superior to MSCs derived from other tissues in some papers. For this reason, several private CB banks have begun to collect CB and UC. We have thus established the cord blood/cord bank, “IMSUT CORD”, as a new type of biobank, to supply both frozen UC tissues and master cells for research and clinical uses.
In this chapter, we will introduce the overall flow from collection to shipment as taking the example of IMSUT CORD and discuss several issues that need to be resolved in unrelated allogeneic off-the-shelf stable supply system at present.
There are many public and private CB banks in the world, in which procedures are nearly standardized intended for hematopoietic stem cell transplantation (HSCT) as shown in Section 5. The procedures include informed consent acquisition from the mother, collection of CB, processing, storage, to shipment, which have been already established. In the case of UC bank for unrelated allogeneic uses, the International Council for Harmonization of Technical Requirements for Pharmaceuticals for Human Use (ICH) which issued ICH Q5A as the regulation of materials for biological products requires the second blood test from the baby’s mother, to deny viral infection in window period at delivery. Figure 1 shows the overall process of banking in the mother’s side. We deal with both CB and UC. In the CB and UC collection hospitals, the purpose, overall process, private information policy, the right to withdrawal, 6-month health check, and second blood test for the mother to confirm the negative study of infection are explained to the mother, and she gives written consent as the guardian of the baby. In addition to obtaining informed consent, questionnaires about medical history, genetic history of the baby donor’s family, and history for the mother’s communicable disease risk behavior are conducted to survey their health. The CB and UC are then collected, and the mother’s blood is tested for infections. These documentations and tests in CB bank can be referred to UC banking as well, although additional safety tests for UC banking shall be required strictly. UC-MSCs from one donor can be delivered and administered to many patients. Especially when the CB and UC passed the safety and some quality tests at clinical grade, the mother is asked to receive the blood test to make sure that all infection tests are negative in 6 months after delivery. These second tests are demanded by the Pharmaceuticals and Medical Devised Agency (PMDA) like the FDA and EMA, because it should be proven that the donor’s mother and baby were not in the window period of viral infections at delivery. On the other hand, bacterial contamination is also taken care because the baby and placenta with UC come out from nonsterile vagina. We collect UC in the case of a scheduled cesarean section to reduce the possibility of contamination due to the exposure to the vaginal bacterial/fungal flora.
Overall flow from informed consent acquisition to shipment.
CB and UC are then transported from the collection hospitals to the CB/UC bank under controlled and validated temperature. CB is transported at validated temperature range (2–25°C) to protect cell viability, and the UC is cooled at 2–10°C in our facility.
Among the processing methods to obtain nucleated cells from CB for hematopoietic stem cell transplantation, the hydroxyethyl starch (HES) centrifugation method (HES method) is the most efficient and common. The HES method originated from the New York Blood Center CB bank [8]. Recently, automated CB processing SEPAX® (GE Healthcare Life Sciences) [9] and AutoXpress Platform® (Cesca Therapeutics, Inc.) [10] have been developed. For CB cryopreservation, DMSO and Dextran 40 together with CB-plasma are used worldwide [8].
On the other hand, no processing method of fresh or frozen CB not for hematopoietic stem cell transplantation has been settled as standard. The use of mononuclear cells (MNCs) obtained by the Ficoll-Paque centrifugation method (Ficoll method) or cell sorting with antibody-conjugated magnetic beads might be a new candidate for further processing and culture method. CB processed by HES method resulted in whole nucleated cells including neutrophils, monocytes, lymphocytes, and nucleated red blood cells with some amount of red blood cells (RBC). The recovery rate of hematopoietic stem cells and mononuclear cells processed by HES method is superior to those by Ficoll method. That is why HES method is introduced by CB banks in the world [8]. However, neutrophils in the nucleated cells and residual RBC may cause the aggregation resulting in the difficulty of further processing, when the frozen and thawed cells are diluted with large volume of isotonic solutions such as medium. Only frozen-thawed CB nucleated cells can be diluted with dextran and albumin/saline solution [8]. On the other hand, frozen-thawed MNCs processed by Ficoll method does not require such a special solution and can be diluted with medium and PBS, although the recovery rate of MNCs from the fresh CB by Ficoll method is less than that by HES methods.
MSCs derived from fresh CB are difficult to expand. Only one company, Medipost Co., Ltd., in Korea, has succeeded in expanding CB-derived MSCs. Their product, Cartistem®, has been approved by the Ministry of Food and Drug Safety in Korea for the treatment of osteoarthritis [11].
There are diverse procedures and culture methods for the isolation of MSCs from the various compartments of UC, such as Wharton’s jelly, veins, arteries, UC lining membrane, subamnion, and perivascular regions [12]. The isolation methods of MSCs from the Wharton’s jelly, vein, and arteries of UC are reported previously, although the marked differences were not found as far as the 10% fetal bovine serum (FBS) and α minimum essential medium (MEM) [13]. There are several papers to obtain MSCs from whole UC versus Wharton’s jelly [14] or different parts of the same UC [15], but we suggest that to process from whole UC seems sufficient and simple for further processing [15]. Despite the wide range of isolation and culture procedures, the different groups seem to agree on the cryopreservation of UC tissue [16] and explant method [17] followed by the harvest of migrating cells from tissue. However, large-scale culture methods remain to be determined. Figure 2 shows the example of scheme of CB and UC collection and process and shipping to clinical use.
IMSUT CORD scheme of CB and UC from collection to shipping for clinical use.
It is known that the UC tissue can be frozen in a cryoprotectant. This possibility of cryopreservation is the advantages of UC tissue for both clinical and research uses. The reasons of the advantages are:
UC tissue processing can be started after the donor’s health and infection statuses are confirmed well. This leads to initial cost-effectiveness because unnecessary works using inappropriate materials are eliminated. In addition, we can thaw a small amount of the UC to survey, before culturing MSCs in a large scale.
Storage of the tissues of origin allows us to keep traceability and to check the quality as the biological resources at a later date.
When new reagents or techniques were developed in the future, we can isolate novel cells from the cryopreserved UC tissues.
If the donor, the baby, has diseases that can be treated with autologous cells, including iPS cells or gene-modified cells, or autologous UC-MSCs, the cryopreserved UC tissues would be the appropriate source.
Several animal serum-free cryoprotectants containing 5–10% dimethyl sulfoxide (DMSO) are available. Whether the use of serum originating from animals, such as fetal bovine serum (FBS), is required, is critical, because it adds the risk of the transmission of zoonotic infections, immunological reactions, and additional regulatory issues [18]. There are several reports of cryopreservation of the UC tissue, using serum-free and xenogeneic animal-free (xeno-free) cryoprotectants. Ennis et al. introduced CryoStor CS10® (BioLife Solutions Inc., WA) for isolating human UC perivascular cells (HUPVCs). However, they did not show the comparative test results to those of fresh UC [19]. Roy et al. reported the cryopreservation of the UC tissue in 10% DMSO and 0.2 M sucrose solution, but the cumulative cell yield derived from the frozen-thawed UC-MSCs in their solution was inferior to that of fresh UC-MSCs [20]. We previously reported the cryopreservation of UC tissue, with no impact on viability, using a serum- and animal origin-free cryoprotectant, STEM-CELLBANKER® [16]. We demonstrated that cells derived from UC cryopreserved in this manner retained the phenotypic characteristics of MSCs, including the immunosuppressive activity in allogeneic mixed lymphocyte reactions, as well as differentiation potential. As shown in Figure 2, with the cryopreservation of UC tissue, UC processing might be altered.
There are two major approaches after frozen-thawed UC tissue: explant and enzymatic digestion methods. Frozen-thawed UC tissue is manually minced into small fragments approximately 1–2 mm3 in size. Mincing is preferred to using a surgical scalpel or the use of an autologous mixer. These fragments are aligned and seeded regularly on a tissue culture-treated dish. After the tissue fragments attach to the bottom of the dish, culture media is added, slowly and gently in order not to detach the fragments [21, 22, 23, 24]. Media is then refreshed every 3–7 days for 2–4 weeks until the fibroblast-like adherent cells reach 80–90% confluence. Subsequently, adherent cells and tissue fragments are rinsed once with PBS, detached using trypsin, and washed with media. The culture is then filtered to remove tissue fragments. The disadvantage in the explant method is that tissue fragments often float in media, resulting in the poor recovery of cells. To protect the exfoliation of tissue fragments from the bottom of the culture dish, we introduced stainless steel mesh (Cellamigo®; Tsubakimoto Chain Co.) shown in Figure 2, No. 8. In this manner, we can plate source tissue more quickly and harvest more MSCs. In addition, the incubation time required to reach 80–90% confluence is reduced [17].
In the enzymatic digestion method, UC is minced into small pieces and immersed in the media containing enzymes such as collagenase, or a combination of collagenase and hyaluronidase with or without trypsin [21, 24, 25, 26]. The cells dissociated by the enzymes are then cultured until they reach full confluence. However, the digestion method is costly and time-consuming and may result in decreased cell viability due to lytic activity and varying sensitivity of the cells to collagenase. In addition, the initial harvested cells include more of the other types of cells compared with that harvested using the explant method.
It is critical to consider how much we can expand the UC-MSCs to allow allogeneic “off-the-shelf” industrial availability, because the proliferation of adherent cells needs a large surface area. The conventional method uses multilayered flasks, and the cells are cultured in incubators installed in cleanrooms. These multilayer flasks can consistently support the expansion of UC-MSCs, and the state of cell confluence can be examined under the microscope. However, this method requires the considerable involvement of operators because the processes of seeding, refreshment, passage, and harvest require individual and manual works. Several companies have introduced the spinner bioreactor with a microcarrier made of plastic, dextran, denatured collagen-coated beads, and other components. The bioreactor system may reduce the number of operators required and may allow to reduce the clean levels of facility since it is a closed system. On the contrary, several critical problems of the bioreactor system exist. The cost of equipment is high and it is difficult to evaluate cell proliferation environment such as pH, lactate, and so on. When some microcarriers are torn off by spinner, or undigested microcarriers are residual in the final products, we have no ideas to remove the residual microcarriers completely. Recently, a plastic bag bioreactor system with a microcarrier in gentle locking was reported [27]. The most critical problem is that the cells produced by the flask-based culture method may be different from those by bioreactors. Harvesting cells on a large scale is still not easy. Recently, filter-based cell concentration and washing systems were introduced (
The academic culture level such as IMSUT CORD is at small to middle scale. Only the company may have the ability to expand the cells at extra-large scale and maintain to control and supply the cell product for clinic constantly.
Master and product cells of UC-MSCs for clinical use are usually required for long-term cryopreservation, together with records on the donor infant and the mother. There are several cryoprotectants for long-term cryopreservation. The most popular cryoprotectant consists of 5–10% dimethyl sulfoxide with human albumin. Recently, serum-free cryoprotectants, described in Section 4.1, have been commercialized and are thought to be more ideal compared to those containing human-derived serum. In addition to cryoprotectants, it is important to build an adequate record preservation system. Those who manage the long-term cryopreservation should preserve the records that include the documentation relating to the collection including donors, processing, results of quality tests, and instruments directly related to the products. The kinds of records and the length to be preserved are in accordance with the bank policies and standards and the corresponding domestic laws and regulations. It is necessary to discuss how long we should or we can cryopreserve CB and UC tissue, UC master cells, and product cells, in the technical and ethical aspects. In the technical aspect, the cell-preserving vessel to accommodate the cell suspension for long-term freezing should be durable in a liquid nitrogen. In the ethical aspect, we do not expect whether the babies can recapture their ownership of CB and UC even though their mother waived the ownership of them, when they grow up to be adult.
The Mesenchymal and Tissue Stem Cell Committee of the International Society for Cellular Therapy proposed the minimal criteria for defining human MSCs [28, 29]. First, MSCs must be plastic-adherent when maintained in standard culture conditions. Second, MSC must express CD105, CD73, and CD90 but not CD45, CD34, CD14 or CD11b, CD79α or CD19, and HLA-DR surface molecules. Third, MSCs must differentiate into adipocytes, chondroblasts, and osteoblasts in vitro [30, 31, 32]. Immunosuppressive effects have now become the most popular property of MSCs for potential clinical use [12]. Defect in HLA-class II expression with negative CD80 and CD86in UC-MSCs even in the presence of inflammatory cytokines such as IFN-γ can theoretically rescue them from immune recognition by CD4+ T cells [33]. MSCs can also inhibit proliferation of and cytokine secretion by immune cells, as well as alter subtypes of macrophage from M1 to M2 in vitro [34, 35, 36, 37]. This immunomodulation is linked mainly to soluble factors such as indoleamine 2,3-dioxygenase (IDO), PGE2, and HLA-G5 [38], hepatocyte growth factor, and transforming growth factor-β1 released from MSCs [39]. Further quality tests are dependent on clinical applications and characteristics of MSCs.
The safety tests required differ according to the risks of clinical applications. For example, the tests of CB banking for hematopoietic stem cell transplantation are different from those of UC-MSCs. Donor-recipient relation of the former is one-to-one, and the risk is limited to one patient. On the other hand, that relationship of the latter, as UC-MSCs master cells and product cells, is one-to-many, so hundreds of patients may suffer health injuries by one donor. Thus, the vials of UC-MSCs are tested thoroughly at a designated time not only known viruses but also unknown viruses. Those tests should follow the local, national or international applicable laws and regulations. More precise safety tests for CB and UC shall be described elsewhere for the respective products for clinical application.
There are international standards/guidance for CB collection, banking, and release of hematopoietic stem cell transplantation, such as the Foundation for the Accreditation of Cellular Therapy (FACT)/NETCORD [40], American association of Blood Banks (AABB), US Food and Drug Administration (FDA) shown in Table 1, and local standards or regulations under the applicable laws in respective countries. A CB/UC bank, facility, or individual should implement if the standard of practice in the community or applicable law establish additional requirements. International standards/guidance for biobanking process for UC collection, processing, culture, and release has not been settled, but collection and banking protocols can follow the CB banking standards and good tissue practice. Each CB/UC bank, facility, and individual should analyze its practices and procedures to determine whether additional standards apply. Compliance with the standards is not an exclusive means of complying with the standard of care in the industry or community or with local, national, or international laws or regulations [40]. Allogeneic public CB banks requested US FDA accreditation with FACT/NETCORD or AABB in the USA, while the CB banks in Europe (EU) required FACT/NetCord with additional requirements like FACT/JACIE standards, when it is requested by the respective national regulation affairs. There are many private or private-public combined CB banks in the world, which tend to follow the AABB standards and have the inspection and accreditation (
Items | Accreditation organization | Standards or guidance titles |
---|---|---|
Cord blood (CB) processing for hematopoietic stem cell transplantation | FACT/NETCORD FACT/JACIE | International Standards for Cord Blood Collection, Banking, and Release for Administration International Standards for Hematopoietic Cellular Therapy Product Collection, Processing, and Administration |
FDA in the USA | Guidance for Industry: Minimally Manipulated, Unrelated Allogeneic Placental/Umbilical Cord Blood Intended for Hematopoietic Reconstitution for Specified Indications Guidance for Industry and FDA Staff: Investigational New Drug Applications for Minimally Manipulated, Unrelated Allogeneic Placental/Umbilical Cord Blood Intended for Hematopoietic and Immunologic Reconstitution in Patients with Disorders Affecting the Hematopoietic System | |
AABB | Standards for Cellular Therapy Services | |
Umbilical cord-derived cells including mesenchymal stromal cells (UC-MSCs)/somatic cell or other derivative cells CB not intended for hematopoietic stem cell transplantation | FACT | Common standards for Cellular Therapies |
FDA in the USA | Good Tissue Practice 21CFR 1271.210 Current Good Tissue Practice (CGTP) and Additional Requirements for Manufacturers of Human Cells, Tissues, and Cellular and Tissue-Based Products (HCT/Ps) Guidance for Industry: Preclinical Assessment of Investigational Cellular and Gene Therapy Products Guidance for FDA Reviewers and Sponsors Content and Review of Chemistry, Manufacturing, and Control (CMC) Information for Human Somatic Cell Therapy Investigational New Drug Applications (INDs) | |
AABB | Standards for Cellular Therapy Services | |
EMA in EU | Tissues and Cells Directives: Guideline on human cell-based medicinal products (EMEA/CHMP/410896/2006) for ATMP | |
PMDA (Japan) | Good Gene, Cellular, and Tissue-based Products Manufacturing Practice (GCTP) | |
Quality management system | ISO | ISO9001 ISO/TC276 (Draft) |
Standards or guidance related to cord blood and umbilical cord-derived cells.
Foundation for the Accreditation of Cellular Therapy, FACT; US Food and Drug Administration, FDA; American Association of Blood Banks, AABB; advanced therapy medicinal products, Pharmaceutical and medical devices agency (PMDA). This table does not include the law defined in each country. These standards, guidance, guidelines, and practices are not intended to apply all cell therapies using CB and UC. The CB/UC bank carefully chooses and implements them for your intended products under the applicable law.
The number of clinical trials using CB and UC-MSCs in the fields of immune cell therapies and regenerative medicine has been increasing. On the other hand, CB as a source of hematopoietic stem cell transplantation is less used recently, because the cell number is limited, the engraftment of HSC is delayed, and HLA haplo-identical HSCT is induced and controlled. These clinical trials are aimed uses that include severe acute graft-versus-host disease (GVHD) treatment, rapid engraftment of HSCT, and the prevention of severe acute GVHD. Clinical trials using CB- and UC-MSCs are summarized in Tables 2 and 3, respectively. We started a sponsor-investigator clinical trial using UC-derived MSCs for patients with treatment-resistant severe acute GVHD supported by the research fund of the Japan Agency for Medical Research and Development (AMED). Consistent supply is the critical key to conduct clinical trials and for marketing. For the stable supply of frozen CB and UC, or UC-derived MSCs, we have established a CB and UC bank, named IMSUT CORD, in our institute. This bank also provides CB and UC-MSCs for immunotherapy and regenerative medicine products to hospitals and pharmaceutical companies shown in Figure 2. The bank also provides frozen CB, frozen UC, master cells, and the cells after master cells as an intermediate products requiring further processing or more culture in the companies.
Authors | Cell type | Disease | Patients number | Age (range) year | Route and procedure of administration | Cell number/kg or body | Results | Adverse events |
---|---|---|---|---|---|---|---|---|
Brunstein et al. [79] | CB-NC-derived Treg (CB from The New York Blood Center) | Grade II–IV acute GVHD | Treg: 11 | 61 (45–68) | IV | 3–100 × 106 Treg/kg | aGVHD: Treg group 9%, control 45% cGVHD: Treg 0%, control 14% | No dose-limiting infusion adverse events |
Control: 22 | 60 (34–69) | — | ||||||
Kellner et al. [80] | Fucosylated or non-fucosylated UCB-Tregs | HSCT | 5 | IV (−1 day of HSCT) | 1 × 106/kg | No infusion reactions | ||
Zhu et al. [81] | CB-MNC | Chronic complete spinal cord injury | 8 in Hong Kong | 42.6 ± 2.7 | IT (dorsal entry zone) | 1.6–3.2 × 106 | Walk 10 m, 15/20 pts. (p = 0.001), no necessity of assistance for bladder management, 12/20 (p = 0.001) and bowel management (p = 0.002) | 1 neuropathic pain;1 subarachnoid hematoma and pneumocephalus due to cerebrospinal loss;1 arachnoid hemorrhage I HK group, |
Phase I–II | 20 in Kunming | 36.9 ± 2.4 | 68 AEs including postoperative wound swelling; 9 pain Overall 5 severe AE in 28 patients | |||||
Shah et al. [82] | CB-MNC-derived NK cells (CB from MD Anderson Cord Blood Bank) | Multiple myeloma undergoing autologous PBSCT | 12 | 48–70 | 5 × 106, 1 × 107, 5 × 107 and 1 × 108 CB-NK cells/kg | 10 achieved VGPR (8 near CR) as the best response | No infusional toxicities and no GVHD | |
Lv et al. [83] | CB-MSC + UC-MSCs | Autism | 14 CB-MNC9 CB-MNC and UC-MSCs14 no cells therapy | CB-MNC: 7.08 (3.29–12.01) CB-MNC + UC-MSCs: 6.51 (3.98–9.83)Control: 5.02 (3.51–10.02) | IV | Proximately 2 × 106/kg CB-MNCs1 × 106/kg of UC-MSCs 4 times in 5–7 day | Improvement of CARS, ABC scores, and CGI evaluation at 24 weeks in CB-MNCs with UC-MSCs | No treatment-related and no severe adverse effects |
Dolstra et al. [84] | CB-CD34-derived NK cells (CB from Cord Blood Bank Nijmegen) | AML in old patients | 10 | 68–76 | IV | 3 and 30 × 106/kg | NK cell maturation in vivo, MRD become negative in 2/4 with MRD before IV | No GVHD, no toxicity |
Park et al. [85] | CB-derived MSCs | Rheumatoid arthritis | 9 | 57.4 ± 10.0 | IV | 2.5 × 107, 5 × 107, or 1 × 108 | DAS2/-ESR decreased, inflammatory cytokine levels are reduced | No DLT, no major toxicity |
Laskowitz et al. [86] | CB-NC (CB from Carolinas Cord Blood Bank or MD Anderson Cord Blood Bank) | Cerebral stroke | 10 | 65.5 (45–79) | IV on 3–9 days poststroke | Cell dose 1.54 (0.84–3.34) × 107/kg, CD34+ 2.03 (0.10–6.80) × 105/kg | All improved by at least one grade in Modified Rankin Score | AE tolerated no serious AE |
Huang et al. [87] | CB-MSCs | Cerebral palsy (age: 3–12) | 27 (CB-MSCs) | CB-MSCs: 7 (3–12) | IV | 27:4 CB-MSCs IV at 5 × 107 with basic rehabilitation treatment | Significant improved of GMFM-88 evaluation | No serious AE |
27 (control) | Control: 7 (3–12) | |||||||
Kim et al. [88] | CB-MSCs | Moderate-to-severe atopic dermatitis | 34 (7 in phase I, 27 in phase IIa) | 29.07 ± 2.03 (n = 14) | SC | 2.5 × 107 | Improved atopic dermatitis scores, pruritus score, serum IgE and eosinophil number | No serious AE |
28.08 ± 1.07 (n = 11) | 5.0 × 107 |
Clinical trials using allogeneic cord blood.
AE, adverse event; AML, acute myeloid leukemia; CB, cord blood; UC, umbilical cord; MSCs, mesenchymal stromal cells; MNCs, mononuclear cells; NK cells, natural killer cells; Treg, regulatory T cells; GVHD, graft-versus-host disease; PBSCT, peripheral blood stem cell transplantation; IV, intravenous injection; SC, subcutaneous injection.
Authors | Disease | Patients number | Age (range) year | Route and procedure of administration | Cell number/kg or body | Frequency interval | Results | Adverse events |
---|---|---|---|---|---|---|---|---|
Engraftment facilitation and graft-versus-host disease (GVHD) in hematopoietic stem cell transplantation | ||||||||
Wu et al. [41] | Severe steroid-resistant aGVHD | 2 | Pt 1:4 | IV | Pt 1: 3.3, 7.2, 8.0 × 106/kg | 3 | Improved | No |
Pt 2:6 | IV | Pt 2: 4.1 × 106/kg | 1 | |||||
Si et al. [42] | Severe aplastic anemia | 37 | 5 | IV (7–10 days after HSCT) | 1 × 106/kg | 1 | aGVHD II–IV; 17 of 37 (45.9%) | No |
(0.75–11.58) | cGVHD, 7 of 37 (18.9%) | |||||||
Wu et al. [43] | Refractory/relapsed hematologic malignancy | 50 | 26 (9–58) | IV (4 h before haploidentical HSCT) | 5 × 105/kg | 1 | aGVHD II–IV, 12 of 50 (24.0%) cGVHD, 17 of 45 (37.7%) (3 extended) | No |
Wu et al. [44] | Severe AA | 21 | 18 (4–31) | IV (4 h before HSCT) | 5 × 105/kg | 1 | aGVHD II-IV;12 of 21 (57.1%) 3 of 9 extended cGVHD | No |
Fu et al. [45] | Refractory severe AA | 5 | 15.2 (9–22) | IV (2 days after PBSCT) | 1 × 106/kg | 1 | No severe aGVHD or cGVHD | No |
Gao et al. [46] | Prophylaxis of chronic GVHD after HLA-haploidentical stem cell transplantation | 62 | Age < 8, 15 pts.; 18–40, 39; >40, 8 | IV | 3 × 107 cells | Until cGVHD occurred, leukemia relapsed, or 4 cycles | cGVHD at 2 yr.: MSCs group 27.4%, control 49.0% (P = .021). Severe lung cGVHD: MSCs group 0, control 7 (P = .047) | No |
Zhu et al. [47] | High-risk acute leukemia | 25 | 11.2 (4–17) | IV (before haploidentical HSCT) | Median 1.14 × 106/kg (1.03–1.39 × 106/kg) | 4 (over 7 days intervals) | aGVHDI, 8 of 25 (32.0%) cytomegalovirus viremia, 23 of 25 (92.0%) | No |
Pan et al. [48] | Extensive bone marrow necrosis of a chronic myeloid leukemia patient | 1 | 10 | iBM | iBM: 2 × 107/kg | 1 | BM recovered | No |
IV | IV: 2 pp. × 106/kg | |||||||
Neurogenic injuries | ||||||||
Wang et al. [49] | Traumatic brain injury | 20 | 27.5 (5–48) | Intrathecal (IT) | 1 × 107 | 4 (5–7 days intervals) | Motor functional recovery after 6 months | No |
Jin et al. [50] | Hereditary spinocerebellar ataxia | 16 | 39.9 (21–56) | IV + intrathecal | IV; 4 × 107 | 4 (over 7 days interval) | Motor functional recovery after 6 months | No |
IT; 2 × 107 cells | ||||||||
Wang et al. [51] | Cerebral palsy | 16 (8 twins) | 6.29 (3–12) | IT | 1–1.5 × 107 cells | 4 (3–5 days intervals) | Motor functional recovery after 1 and 6 months | No |
Diabetes mellitus | ||||||||
Guan et al. [52] | DM (type 2) | 6 | 40.5 (27–51) | IV | 1 × 106/kg | 2 (2 weeks interval) | Insulin-independent for 25–43 Mo, 3 dose reduction of insulin, others | No |
Hu et al. [53] | DM (type 1) | 15 | 17.6 | IV | 2.6 ± 1.2 × 107/kg | 2 (4 weeks interval) | HbA1c and C-peptide improvement in MSCs group | No |
Cai et al. [54] | DM (type 1) | 21 | 18–29 (5–28) at onset | Supraselective pancreatic artery cannulation | 1.1 × 106/kg, with autologous BM-MNC | 1 | Moderate improvement of metabolic measures | 1 transient abdominal pain |
Kong et al. [55] | DM (type 2) | 18 | IV | 1 × 106/kg | Day 0 and until Day 90 if effective | FBS reduced plasma C-peptide and regulatory T cells increased | 4/18: slight fever | |
Heart and angioplasty | ||||||||
Cai et al. [56] | Avascular necrosis of the femoral head | 30 | 41.6 (19–63) | Femoral head artery (co transplant with autologous BM) | Autologous BM-BM-MNCs, 60.7 ± 11.5 × 106/kg UC-MSCs, 1 × 106/kg | 1 | Improved | No |
Can et al. [57] | Myocardial ischemia | 39 | 30–80 | Intracoronary | 2 × 107/kg | 1 | Ongoing | No |
Zhao et al. [58] | Severe systolic heart failure | 30 | 52.9 (20–79) | Intracoronary | Unknown | 1 | Cardiac remodeling and function improved with reduced mortality rate | No |
Li et al. [59] | Coronary chronic total occlusion | 15 | Unknown | Intracoronary | 3 × 106/4 × 106/5 × 106/kg | 1 | Infarcted size reduced with improved left ventricular EF | No |
Musialek et al. [60] | Acute myocardial infarction | 10 | 55.6 (32–65) | Intracoronary | 3 × 107/body | 1 | Feasible and procedural safe as off-the-shelf cellular therapy | Transient fever (38.9°C) |
Bartolucci [61] | Heart failure | 15 | 57.33 ± 10.05 | IV | 1 × 106 cells/kg | 1 | Significant improvements in LVEF, NYHA functional class, Minnesota Living with Heart Failure Questionnaire | No |
Liver | ||||||||
Xue et al. [62] | Decompensated liver cirrhosis | 50 | Unknown | Intrahepatic artery | 3 × 107/body | 1 | Increase of serum albumin | No |
Wang et al. [63] | Primary biliary cirrhosis | 7 | 49 (33–58) | IV | 5 × 105/kg at 4 weeks interval | 3 | ALP and γ-GTP | No |
Shi et al. [64] | Prevention of acute liver allograft rejection | 14 (13, single dose, 1 multiple dose) | 57 ± 12 | IV | 1 × 106 cells | Single (13 pts), 3 times every 4w (1 pt) | Decreases of ALT, AST, T-BIL Histologic improvements, MSCs 6, control 0. | No |
Liang et al. [65] | Liver cirrhosis caused by autoimmune diseases | 23 (2 CB-MSC, 1 BM MSC) | 53.4 (35–70) | IV | 1 × 106 cells/kg | 1 | Not statistically significant improvement | 2, fever; 3, mild fidgetiness, suffered from insomnia |
Zhang et al. [66] | Ischemic-type biliary lesions following liver transplantation | 12 | 47.3 ± 10.1 | IV | 1 × 106 cells/kg | 6 (1, 2, 4, 8, 12, 16 weeks) | Significantly decreased need for interventional therapies. 1-, 2-yr graft survival rates: MSCs group (100%, 83.3%), control group (72.9%, 68.6%) | No |
Xu et al. [67] | Hepatitis B virus-related acute-on-chronic liver failure | 30:UC-MSC | UC-MSC: 40.67 ± 9.89 | IV | 105 cells/kg | UC-MSC, once a week, 4 times | No significant improvement of short-term prognosis | Fever, UC-MSC 11 pts., PE + UC-MSC 6 pts |
20, UC-MSC + plasma exchange | UC-MSC/plasma exchange, 42.00 ± 6.55 | IV | UC-MSC/PE: first 2 UC-MSC: 2nd day after 1st, 3rd PE treatments | |||||
Gastrointestinal tract | ||||||||
Zhang et al. [68] | Crohn’s disease | 41 | 32.7 (20–41) | IV | 1 × 106 cells/kg | Once a week, four times | Decreases of CDAI, HBI, corticosteroid dosage | Fever 4, upper respiratory tract infection, 7 |
Hu et al. [69] | Ulcerative colitis | 34 | 42.9 ± 23.1 | IV then IA | 0.5 × 106 cells/kg | 2, 7 days | Decreases of median Mayo score, histology score. Improvement of IBDQ scores | No |
Skin | ||||||||
Hashemi et al. [70] | Chronic skin ulcer | 5 | 30–60 | Covered by acellular amniotic membrane seeded with WJSCs | About 2 × 106 cells were seeded | Epithelial surface of acellular amniotic membrane | Significantly decreased wound healing time, wound size. Significantly declined wound size after 6, 9 days | Not stated |
Kidney | ||||||||
Sun et al. [71] | Prevention of delayed graft function and acute rejection in renal transplantation | 21 | 41.0 ± 11.5 | IV | 2 × 106 cells/kg (before transplantation), 5 × 106 (during surgery) | ← | No significant improvement | No |
Deng et al. [72] | Lupus nephritis | 12 MSC, 6 placebo | 29 ± 10 | IV | 1 × 108 cells | 2 times 1 wk. interval | Not statistically significant improvement | 1: leucopenia, pneumonia, subcutaneous abscess, 1: severe pneumonia |
Autoimmune diseases | ||||||||
Wang et al. [73] | Active and refractory SLE | 40 | 17–54 | IV | 1 × 106 cells/kg on day 0 and 7 | 2 | MCR (13 of 40, 32.5%), PCR (11 of 40, 27.5%) during 12 months, although several patients relapse after 6 months | No |
Wang et al. [74] | RA | 136 | 46.1 | IV | 4 × 107 cells, 2nd in 3 months later | 1 (n = 112) | Decreases of serum TNF-α, IL-6, increase of regulatory T cells. Significant remission for 3–6 months | Mild fever (<38.5°C) without treatment at injection, 6 patients |
2 (n = 24) | ||||||||
Riordan et al. [75] | Multiple sclerosis | 20 | 41.2 (24–55) | IV | 2 × 107 UC-MSC | 7 (1–4 days) | Significant improvements of various symptoms. Inactive lesions by MRI in 15/18 patients. (83.3%) after 1 year | Headache, fatigue |
Others | ||||||||
He et al. [76] | Severe sepsis | 15 (3 cohorts) | 56 (25–72) | IV | 1 × 106 cells/kg | 1 | System clinical outcomes are not changed | No |
2 × 106 cells/kg | ||||||||
3 × 106 cells/kg | ||||||||
Cao et al. [77] | Recurrent intrauterine adhesions | 27 | 35.1 ± 3.8 (27–42) | Loaded onto a collagen scaffold | 1 × 107 | 1 | Pregnant, 10 of 26 patients | No |
Clinical trials using allogeneic umbilical cord-derived mesenchymal stromal cells.
aGVHD, acute graft-versus-host disease; cGVHD, chronic GVHD; HSCT, hematopoietic stem cell transplantation; AA, aplastic anemia; BM, bone marrow; IT, intrathecal injection; AE, adverse event; AML, acute myeloid leukemia; CB, cord blood; UC, umbilical cord; BM, bone marrow; PE, plasma exchange; RA, rheumatoid arthritis; MSCs, mesenchymal stromal cells; DM, diabetes mellitus; FBS, fast blood sugar; ES, ejection fraction; IV, intravenous injection; SC, subcutaneous injection; DM, diabetes mellitus.
The following are also the major points for managing CB and UC banking.
First, to build an adequate quality management system to serve the resource of cell therapy products, we have introduced the concept of the ISO 9001 and obtained its certification, and as a result, we introduced the concept of PDCA cycle. Second, involvements of various kinds of specialists must be considered. There are many procedures, such as collection, obtaining informed consent, application to ethics review committee, and document management. Third, health check and infection test of the donor’s mother are required to ensure that no infection is detected after window period of infections. In this process, both traceability and personal information protection must be satisfied. Fourth, we respect the right of decision to donate, rejection, or withdrawal. Donor’s mother should be explained the policy of the bank that the consented withdrawal time is set at the initiation of processing for clinical use. Although the CB and UC belong to the baby, we obtain informed consent from the donor’s mother as guardianship and ownership are asked to be transferred to the bank. Fifth, there is also the issue of how long the UC tissue and UC-MSCs can be cryopreserved. For example, in the Japanese public CB bank for HSCT, the CB is cryopreserved for 10 years as a clinical grade of HSC source. After this period, they are used for basic research or preclinical studies or discarded if they are not used for research. A cryopreservation period of 10 years for UC and UC-MSCs may be the first threshold to be checked. In addition, we disclose the information in website for the mothers who have not been explained about the new researches or new clinical trials at the first IC acquisition. Lastly, because unlike CB, the UC is a tissue considered as a part of the perinatal appendage, we must follow the tissue transport and medical disposal/waste regulations under the applicable laws or local rules and ethical standards.
Recently, there are an increasing number of private CB banks, which have initiated to serve the cryopreservation of UC, i.e., private CB and UC bank. Using private autologous CB, clinical trials for cerebral palsy caused by hypoxic ischemic encephalopathy (HIE) reported their efficacy [78], although the collection of CB is difficult for the baby in such a severe situation of delivery, resulting in the limited application entry. Recently, we obtained the proof of concept that the UC-MSCs attenuated the neurogenic and functional damage caused by intraventricular hemorrhage (IVH) in newborn model mice. Duke University implemented the clinical trial using allogeneic UC tissue-derived cells for the patients with HIE. Allogeneic off-the-shelf UC-MSCs are a promising source; however, we do not know the adverse events such as HLA antibody induction caused by long-term repeated injections of allogeneic cells. Therefore, autologous use of CB and UC is still challenged to be discussed continuously.
Although several problems still remain to be dissolved, operation of adequate CB and UC bank should be considered as the provider of cell source for regenerative and immune cell therapy, because of their prominent characteristics and convenient and noninvasive collection.
This study was supported by Grants-in-Aids for Scientific Research from the Japan Agency for Medical Research and Development (AMED) (19be0504001h0002). We thank Kamisato A. PhD. for ethical support. We specially thank Takahashi A. MT, Ms. Hori A., Yamamoto Y. MT., Mr. Miharu Y. MT, Ms. Izawa, and MS. Nagaya N., for processing and quality management. We also thank the staff of NTT Medical Center Hospital and Yamaguchi Hospital, Tokyo, for their assistance with the collection of UC and CB.
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