The Measured Parameters for Analysis and Simulation
\\n\\n
More than half of the publishers listed alongside IntechOpen (18 out of 30) are Social Science and Humanities publishers. IntechOpen is an exception to this as a leader in not only Open Access content but Open Access content across all scientific disciplines, including Physical Sciences, Engineering and Technology, Health Sciences, Life Science, and Social Sciences and Humanities.
\\n\\nOur breakdown of titles published demonstrates this with 47% PET, 31% HS, 18% LS, and 4% SSH books published.
\\n\\n“Even though ItechOpen has shown the potential of sci-tech books using an OA approach,” other publishers “have shown little interest in OA books.”
\\n\\nAdditionally, each book published by IntechOpen contains original content and research findings.
\\n\\nWe are honored to be among such prestigious publishers and we hope to continue to spearhead that growth in our quest to promote Open Access as a true pioneer in OA book publishing.
\\n\\n\\n\\n
\\n"}]',published:!0,mainMedia:null},components:[{type:"htmlEditorComponent",content:'
Simba Information has released its Open Access Book Publishing 2020 - 2024 report and has again identified IntechOpen as the world’s largest Open Access book publisher by title count.
\n\nSimba Information is a leading provider for market intelligence and forecasts in the media and publishing industry. The report, published every year, provides an overview and financial outlook for the global professional e-book publishing market.
\n\nIntechOpen, De Gruyter, and Frontiers are the largest OA book publishers by title count, with IntechOpen coming in at first place with 5,101 OA books published, a good 1,782 titles ahead of the nearest competitor.
\n\nSince the first Open Access Book Publishing report published in 2016, IntechOpen has held the top stop each year.
\n\n\n\nMore than half of the publishers listed alongside IntechOpen (18 out of 30) are Social Science and Humanities publishers. IntechOpen is an exception to this as a leader in not only Open Access content but Open Access content across all scientific disciplines, including Physical Sciences, Engineering and Technology, Health Sciences, Life Science, and Social Sciences and Humanities.
\n\nOur breakdown of titles published demonstrates this with 47% PET, 31% HS, 18% LS, and 4% SSH books published.
\n\n“Even though ItechOpen has shown the potential of sci-tech books using an OA approach,” other publishers “have shown little interest in OA books.”
\n\nAdditionally, each book published by IntechOpen contains original content and research findings.
\n\nWe are honored to be among such prestigious publishers and we hope to continue to spearhead that growth in our quest to promote Open Access as a true pioneer in OA book publishing.
\n\n\n\n
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Radio Frequency Identification (RFID) networks use radio signal broadcast to automatically identify items with attached RFID tags. A tag consists of a microchip that stores a unique identifier and an antenna. The tag’s antenna is attached to the chip and can transmit a unique tag identifier to a reader (also called interrogator). The reader is capable of learning the set of tags within its interrogation range. The process of learning in-range tags is called a census. After an initial census is completed, the reader can answer queries about the presence of specific tag(s) within its range sent to it from other type of devices.
RFID systems have abundant benefits as compared to the barcode and smart card systems. RFID networks use radio frequency as a method of data transmission. Thus, unlike barcode labels, a tag does not need to be placed in a line of sight position from the reader, or even get in contact with a reader as smart cards, in order to be identified successfully. Depending on whether they use low, high, or ultrahigh transmission frequencies, RFID tags are identifiable within 3 meters span in case of a typical far-field reader [Want06] or at even further distances. Therefore, RFID tags are used more flexibly and conveniently than existing barcode and smart card implementations.
Moreover, some commercial implementations of RFID tags can store data in the amount of 16bytes - 64Kbytes [Finkenzeller03]. RFID tags can hold the same amount of data compared to smart cards, and much larger volume than barcodes. In addition, RFID tags are getting less expensive. The cost of RFID chips at the time of this study is less than 10 cents, while back in 1999, for example, was around 2 US dollars. Since tag readers have limits on their operations range imposed by the frequency of the wireless signal used, when RFID networks need to cover large spaces, multiple readers need to be used. The cost of current reader implementations is hundreds of US dollars. As a result, RFID networks may not be yet suitable to track large inventories of inexpensive items, but they are certainly becoming more affordable and can be used to track different types of items, e.g. live stock, pets, and valuable goods. Due to these advantages RFID systems are emerging as one of the alternative technologies of our time.
One of the world biggest supply chains Wal-Mart has required suppliers to implement RFID networks in at least 12 of its 137 distribution centres by the end of 2006. The Proctor & Gamble Co. is the first of about 100 suppliers to conform to Wal-Mart’s requirements to tag its products with RFID chips [Computerworld07]. The US Navy finished its pilot of a passive RFID system to support the loading of supplies into cargo containers in May 2004. According to the related final report the RFID process increased the speed and efficiency of the cargo checking process, while less people were needed to support the new RFID based system as compared to the legacy implementation [Weinstein05].
An RFID system is made up of an application, a reader and tags.
The application is a program installed on a (proxy) computer which can control readers.
The reader is a device which runs functions such as reading, writing and authentication.When the reader gathers data from tags it transmits to the computer application.
The tag is used to identify an object and is located on (or in) the object itself.
A reader is connected to the computer and has a transmitter and receiver, while a tag has a control unit (chip) and a coupling element (antenna).
RFID Physical Composition [Finkenzeller03]
RFID tags can be passive, i.e. not having an internal energy source or active,internal battery powered. A reader typically charges a set of passive tags within its interrogation zone using inductive coupling; the reader broadcasts electromagnetic signal then the tag’s antenna absorbs and stores the signal’s energy into an on-board capacitor. This technique is called load modulation for near-field coupling and back scatteringfor far-field coupling. After charging its battery it can be activated.
The ALOHA algorithm is a collision resolution algorithm based on Time Division Multiple Access (TDMA). There are three flavors of the original ALOHA algorithm: (Pure) ALOHA, SlottedALOHA and FrameSlotted ALOHA [Zürich04].
In Figure 2, X and Y axis represents the read cycle and tags respectively. The read cycle is the time interval between neighboring two REQUEST commands and it can be repeated until all tags in the interrogation range are identified. Note that there no slots are used in the (Pure) ALOHA algorithm (Figure 2: (a)) while the read cycle is divided into several continuous slots in the Slotted ALOHA (Figure 2: (b)) and Framed Slotted ALOHA algorithm. Furthermore, a frame is comprised of the number of slots in the Framed Slotted ALOHA algorithm (Figure 3: A slot is a discrete time intervals synchronized by the reader, sufficiently long in duration to allow a tag to transmit its ID and the ID’s 16-bit CRC code. A set of slots are grouped into frames. When size is fixed, each consecutively transmitted frame has the same number of slots.
Pure and Slotted ALOHA Algorithms
The reader broadcasts the REQUEST command to the tags located in the reader’s interrogation range during the downlink while the tags transmit their data to the reader during the uplink. As all activated tags share the uplink partial or complete collision can occur in the (Pure) ALOHA algorithm. However, ifthe data is transmittedusing the slot of frame the partial collision can be eliminated. Furthermore, to reduce the fraction of collision occurrence tags send their data no more than once within a frame, which is the Frame Slotted ALOHA algorithm. We next present in more detail the operation of the three ALOHA algorithms introduced above.
A tag itself decides the data transmission time randomly as soon as it is activated. The transmission time is not synchronized with both the reader and the other tags at all. When the electricity is charged by the reader’s electromagnetic wave tags transmit data after receiving the REQUEST command from the reader. If multiple tags transmit data imminently (whether earlier or later) then a complete or partial collision occurs (Fig.2 (a)). Retransmitting after random delay is the solution for a collision. During the read cycle the reader receives the data and identifies tags sent data without collision. When a read cycle is done then the reader broadcasts the SELECT command with the tag’s unique identifier received from the tag. Once tags are selected the tags stop responding for the request command i.e. the selected tags keep silence until whether they receive other commands e.g. authenticate, read and write or the tag’s power is off by being located out of the reader’s power range. When the tag is reentered into the reader’s interrogation range it restart transmitting its data to the reader.The advantage of this algorithm is simplicity.
It is obtained by the addition of a constraint to the (Pure)ALOHA. The read cycle is divided into discrete time intervals called slotand which is synchronized with the entire tags by the reader. Thus, tagsmust choose one of the slots randomly and transmit data within a single slot. Transmission begins right after a slot delimiter (Fig.2 (b)). This causes that packets either collide completely or don’t collide at all i.e. there is no partial collision in the Slotted ALOHA algorithm. This reduces wasting the read cycle relatively as compared with the (Pure) ALOHA algorithm. However, the empty slot can be occurred in the read cycle and the disadvantage is that it requires a synchronization mechanism in order for the slot-begin to occur simultaneously at all tags.
Framed SlottedALOHAalgorithm uses the frame which is the discrete time interval of the read cycle and each frame is divided into the same number of slots. There are multiple frames in a single read cycle and the frame size is decided by the reader (Figure 3: There is a constraint that the tags can transmit data only once in each frame. It may reduce the number of collided slots and it shows the best performance among them.
FSA (Framed Slotted Aloha) can be classified into the BFSA (Basic Framed Slotted Aloha) and the DFSA (Dynamic Framed Slotted Aloha) according to whether which uses fixed frame size or variable frame size [Klair04]. If the number of actual tags is unknown DFSA can identify tags efficiently rather than BFSA by changing frame size since BFSA uses fixed frame size. In addition, BFSA and DFSA can be further classified based on whether they support muting or/and early-end features [Klair04]. The muting makes tags remain silent after being identified by the reader while the early-end allows a reader close an idle slot early when no response is detected. Figure 4 is shown for the classification of the FSA.
Framed Slotted ALOHA Algorithms
Classification of FSA
Framed Slotted ALOHA and Binary Tree are the two most widely used multi tags identifying anti-collision protocols. Fabio Cappelletti et al. simulated the Binary Tree protocol of RFID by using the OPNET IT Guru 11.0 in 2005 [Cappelletti06]. In the paper, they measured the network throughputand the census delay through the simulation. And they compared simulation performance and analytical results. What they measure is shown in Table 1.
Analytical Parameters | Simulation parameters | Unit |
Network throughput | Network throughput | (%) |
Throughput per node | Throughput per node | (%) |
Total census delay Lower bound Upper bound Arithmetic average Heuristic | Total census delay | Number of slots |
Time required to detect a single tag Lower bound Upper bound Arithmetic average Heuristic | Time required to detect a single tag | Number of slots |
Number of transmitted packets (Total, Average) | Number of packets |
The Measured Parameters for Analysis and Simulation
The network throughput represents the ratio between the number of successfully transmitted packets and the total number of packets sent by the tags while the throughput per node denotes the average number of packets sent by a single tag. The results of the paper showed that the analytical performance was in good agreement with the simulation results.
It is commercial product of RFID system which is comprised of the actual tags and a reader connected to the computer. The application is installed in the computer to control the reader collecting data from tags. The built-in Framed Slotted ALOHA protocol is provided by the system. The memory size of an I-Code tag is the total of 64 bytes which are available for 46 bytes application data, 8 bytes serial number and 10 bytes functionalities such as write protection, maintaining quiet state of tag and reset quite state, etc. And, the reader provides the interface for setting configuration parameters such as the serial connection speed and commands for handling communication with tags. Examples of commands are following:
Anti-collision/select (ACS): After broadcasting this command, tags begins transmittingtheir serial numbers. Once tags become “selected” status then remain quite in following ACS commands.
Read: This command makes the selected tags transmit their data to the reader.
Write: This command makes the selected tags write data transmitted by the reader.
The procedure of tag identification is different based on the classification of RFID [Finkenzeller03]. There are two types of procedure based on the tags characteristic in the Frame Slotted ALOHA protocol. One of them switches off when read while the other not switches off but replays transmission. The I-Code system uses the switching off tag. Figure 6 depicts the census procedure used in the I-Code System implementation.
Tag Identification Procedure of the I-Code System
The reader broadcasts the ACS command with a random value, frame size to make tags transmit their serial number to the reader. When the tag receives a command from the reader first of all it identifies the type of command, and if it is ACS and the status of tag is not ‘selected’ then it computes the response slot number using the random value and frame size (0 ≤ response slot < frame size) as parameters. Random value is used to prevent the same collisions from occurring repeatedly. The serial number in the computed slot is transmitted to the reader by the tag. During an uplink, data transmission from tags to reader, the reader can identify tags transmitted without collision. The results of a read cycle (the number of empty slots, the number of slots filled with one tag and the number of collided slots) are used for analysis. Once the tag is identified the tag remains quiet until other command e.g. read or write is broadcasted with serial number. And if the tag re-enters the reader’s power zone after moving out, that needs re-identification. The read cycle is repeated till reaching the assurance level, the probability of identifying all tags in the reader’s range.
Through simulation we can measure the total census delay by varying the frame size for given number of tags. Then we can find the optimal frame size which results in the minimum total census delay, for given number of tags (see Figure 7).
The optimal frame size, resulting in minimum census delay, can be determined according to the total number of tags. Figure 8 presents an example of the relationship between total number of tags and frame size. For example, the optimal frame size for 80 active tags is 45, while for 30 passive tags it is 40.
To maximize network throughput frame size (the number of allocated slots in the read cycle) should be chosen in accordance with the number of tags since for the same fixed slot size, number of total collisions during a census increases with increase in total number of tags.
Example of Total Census Delay (Tag Collection Time) Using Static Frame Size [Bin05]
Due to the nature of the Framed Slotted ALOHA protocol, the read cycle time is divided by the number of slots in a frame and packet data (tag ID number plus CRC) to be transmitted should occupy a single slot. If there are lots of tags and frame size is small then the probability of collisions will be increased and the number of identified tags is decreased, because tags will be competing for a lesser number of slots within a frame. On the contrary, if the reader reads few tags with too big frame size then the probability of collision is decreased, but at the expense of the response time being increased. There is optimal frame size that makes minimum number of read cycles for certain number of tags. The dynamic slot allocation is choosing the optimal frame size (in number of fixed slots) during the tag read cycle. However, the problem is that the number of tags is unknown. So, the reader should estimate the number of tags in each read cycle with the result of the previous read cycle (number of empty slots, number of slots filled with one tag, number of collided slots) and current frame size.
The key difference of the developed Framed Slotted ALOHA protocol is how they estimate the number of tags and what they estimate.
In this scheme, they estimate the number of tags using the current frame size and the result of read cycle. And then updates the current frame size using the estimated number of tags and previous frame size. The procedure of this algorithm is shown in Figure 9. Variable ‘N’, ‘N0’, ‘n_est’ and ‘stepN’ represent the frame size, temporary frame size, the estimated number of tags and the counter for the cycle performed with currently estimated framed size, respectively. Variable ‘c’, ‘t’ represents the result of a read cycle comprised of three integers; number of empty slots, number of slots filled with one tag and number of collided slots, and variable ‘t’ represents the temporary number of estimated number of tags.
In order to estimate the number of tags two estimation functions are used; lower bound and Chebyshev’s inequality. Lower bound simply estimates the number of tags is bigger than the summation of the number of slots filled with one tag and two-times of the number of collided slots:
When the lower bound is used the real value of number of tags is underestimated.
Example of Optimal Frame Size For Minimum Census Delay [Bin05]
Chebyshev’s inequality measures the difference the real values and expected values to estimate the number of tags for which the difference becomes minimal [Vogt02]. The number of tags are estimated using the currently used frame size (N) and the results of previous read cycle <c0, c1, ck> representing the number of empty slots, the number of slots filled with one tag and the number of collided slots respectively. And
* Function: VogtAlgorithm () - Variable: integer : N, N0, n_est, stepN, c, t - N = minimum frame size - repeat begin while stepN is smaller than maxStep: stepN++; Perform a read cycle with N and store the result in c Estimate number of tags with N and c, and then store result in t If t is bigger than n_est then save t in n_est. Call adaptFrameSize(N, n_est) and save value in N0. If N0 is bigger than N then reset stepN with 0. And, save N0 in N. - repeat end * Function: adaptFrameSize(N, n_est) - Return type : integer - repeat begin while n_est is smaller than low value for choice of N: Store N/2 in N. - repeat end - repeat begin while n_est is bigger than high value for choice of N: Store 2*N in N. - repeat end |
Pseudo Code of H. Vogt’s Algorithm
Figure 8 presents the optimal frame size resulting from the execution of function adaptiveFrameSize (N, n_est) using as an input the estimated number of tags and the value of the current frame size N. The optimal frame size is selected for a range of tags, based on a low and high margin.
Choosing Optimal Frame Size [Vogt02]
This algorithm estimates the number of tags using the posteriori probability. The posteriori probability of k tags for an observed slot is as below:
The a posteriori expected value of the number of tags is respectively, 0 for an empty slot, 1 for a slot filled with one tag, and
Where K = 2.39 is a constant for collided slots [Bin05]. And, the frame size (N) in the (i+1)th read cycle is
where H is a constant, which maps the tags to the frame size. The update of the frame size occurs when
* Function: BinZHENAlgorithm () -Variable: integer : N, i, c, s, nest(i) N = minimum frame size i = 0 - repeat begin while i is smaller than the number of read cycle for confidence level: i ++; Perform a read cycle with N and store the result in c for collided slots, s for success slots. Estimate number of tags with N, c and s, and then store result in nest(i). If nest(i) is bigger than γ * nest(i - 1) then Call calculateFrameSize(nest(i), tagType) and save value in N. i = 0. - repeat end * Function: calculateFrameSize(nest(i), tagType) - Return type : integer -If tagType is passive then N(i) = (random value of 1 – 1.4) * nest(i) else N(i) = (random value of 0.8 – 1) * nest(i) - return N(i) |
Pseudo Code of Bin ZHEN et al.’s Algorithm
This algorithm estimates the number of unread tags instead of number of tags to determine the frame size. H. Vogt’s algorithm shows poor performance when the number of tags becomes large because the variance of the tag number estimation is increased according to the number of tags increase [Rom90]. Therefore, to handle the poor performance of large number of tag identification EDFSA algorithm restricts the number of responding tags as much as the frame size. Conversely, if the number of tags is too small as compared with the frame size it reduces the frame size. To estimate the number of unread tags equation (2) is used. The procedure of EDFSA algorithm’s read cycle is shown in Figure 9.
Read Cycle of EDFSA Algorithm
To evaluate the implementation of the BFSA protocol I first evaluated the total census delay of the tag reading process. It is comprised of three different delays; success delay,collision delay and idle delay. Thus, the total census delay is defined as
where n is success delay, C[n]is collision delay and I[n] is idle delay [Cappelletti06]. The unit of delay can be defined as a slot duration T (sec) and it is defined as,
where ID (bits) is the size of the packet containing tag’s ID, and data_rate (bps) is the data rate from tag to reader.
It is necessary that evaluating of the read cycles satisfying the confidence level α since it is used to determine total census delay. The assurance level α is the probability of identifying all tags in the reader’s interrogation range [Vogt02] e.g. if α = 0.99 which means one or more missing tags, less than 1% of all, are allowed. The probability of r tags responding in a slot in the
where N is the given frame size (slots) and n is the number of tags to be read in the
Then the expected number of the successful transmissions in the
R represents the number of required read cycles to identify a set of tags with a confidence level α. As the number of tags n and the frame size N are the same for all read cycle, p1(i) is constant. That makes equation 13 as,
If we solve the equation 13 for R we can obtain the condition of R as below: [Klair04]
The ceil function is used since R is the integral value. By using R and if the number of tags is known, we can evaluate the theoretical delay of successful (n), idle (I[n]), and collision (C[n]) transmission as follows [Klair04]:
where N is a frame size, T is slot duration. The summation of those three delays represents the total census delay.
Muting decreases the number of tag’s responses after every read cycle. Hence, the number of responding tags in the
where
By using Rmin, if the number of tags is known, we can evaluate the theoretical minimum delay of successful (n), idle (I[n]), and collision (C[n]) transmission by using the equation 15, 16, and 17. And, their summation yields the minimum total census delay.
Network throughput can be defined as the ratio between the number of successfully transmitted packets (one per tag) and the total number of packets sent by the tags during the census [Cappelletti06]. Suppose that there are n tags to be read. Then, the total number of packets sent by n tags during a census for non-muting BFSA is
where R is the number of required read cycles needed to identify a set of tags with a confidence level α. Since tags can transmit only once in a read cycle. Now we can calculate the network throughput as
where α is assurance level, n is total number of identified tags, and P[n] is the total number of packets sent by the tags during the census.
In this project, I implemented two Aloha models; BFSA-Muting and BFSA-Non-Muting. To validate the model I analyzed the log file [appendix A, B] of the models and compared with the pseudo code. For easy comparison I put the figures describing the events of the simulation comes from the log file.
For the simplicity I put a reader and eight tags, and the same given conditions are used between two simulations. The reader and tags being used in the simulation are shown in Figure 10 (a) while the given conditions are shown in Figure 10 (b).
Simulation Information
The time required for the packet transmission can be calculated by using the given packet size and the data rate among reader and tags. They are shown in Figure 11.
Packet Transmission Time
I assume that the propagation delay is negligible since in case of a typical far-field reader has 3 meters span interrogation range [Want06]. Consider the speed of light is 299,792,458 m/s then the delay of 3 meters will be 1-8 seconds. And I also assume the calculation delay of the reader and of the tag is negligible as simplicity is the strong point i.e. it does not need complex calculation both for the reader and for the tag.
For the validation of the simulation model we compared the analytical results (obtained based on an algorithm presented in [Klair04] (see Figure 12)) with our simulation results.
When the reader starts a census procedure the number of unread tags is initialized to the number of actual tags in range. While the census is performed to identify unread tags the number of identified tags, collided slots, idle slots, and the current frame size are stored as a running total. If there is no collision from tags the total delay, collision delay, and idle delay are calculated. T represents the duration of a single slot.
The log from the BFSA-Muting simulation is shown in Appendix A. Figure 13 depicts the sequence of events during the BFSA-Muting simulation. Through analyzing the log we can check the correctness of the implementation.
Pseudo Code of the BFSA Muting
BFSA-Muting Simulation Log
As we can see from Figure 13 (a), when the census begins the reader broadcasts a REQUEST packet to all tags. The transmission delay of a REQUEST packet is 0.000176 seconds since the size of the packet is 88 bits while the data rate is 500,000 bps. We assume propagation and calculation delay are negligible, since events are generated at slot boundaries and propagation delay and computation time will not have an effect on census delay and throughput. As soon as tags receive the REQUEST packet they start their timer to synchronize the read cycle between the reader and tags. Tags can select only one of the slots in the read cycle randomly and transmit a RESPONSE packet which contains tag’s ID and CRC to the reader by occupying a single slot, e.g. as we see from Figure 13 (a) each tag send its ID only once in a read cycle based on the definition of the FSA protocol. There are eight slots in a frame in this simulation. And we can see every slot durations in the read cycle is identical. The delay for the transmitting of the RESPONSE packet is the definition of the slot duration. As you see at Figure 13 (b) the size of RESPONSE packet is 80 bits while data rate is 500,000 bps. That makes the transmission time of the REQUEST packet to 0.00016 seconds. When multiple tags transmit their ID to the reader with the same slot it causes a collision then the reader can’t identify tag’s ID successfully. Two collisions occur in the first read cycle, see Figure 13 (a). Three tags (IDs: 1, 2, and 6) transmits their ID by occupying the second slot and two tags (IDs: 3 and 7) are also transmitting during the third slot. Both of them collide and are being discarded. However, a single tag transmission without collision is identified by the reader successfully as can be seen from the fourth, sixth and seventh slot. The first, fifth and eighth slots are idle slots in the first read cycle (frame). When a read cycle (frame) is finished tags can’t transmit their ID until the next read cycle begins and the number of identified tags, collided slots, and idle slots are computed and stored by the reader. If there is no collision during a read cycle the census will be completed.
SELECT packets are transmitted together with the tag’s ID identified by the reader as soon as a read cycle has completed (as shown in Figure 13 (b)). The purpose of sending SELECT packet is to mute the already identified tags, i.e. forcing them to stop transmitting their IDs. This reduces collisions.
Three SELECT packets are transmitted as shown in Figure 13 (b) with a tag’s ID identified in the previous read cycle. The size of the SELECT packet is 72 bits and because of the data rate being 500,000 bps the transmission delay will be 0.000144 seconds. After transmitting SELECT packets the REQUEST packet is broadcasted to all tags. However, selected tags will disregard this message. Only unread tags will response to the REQUEST packet.
When the REQUEST packet is delivered to all tags the read cycle is started again. The reader can synchronize the start time of the read cycle with tags since reader can calculate the transmission delay of SELECT and REQUEST packet with packets size and data rate. Once the read cycle is started, the procedure of transmit tag’s ID, of detecting collision, and of identifying tag’s ID is same with ones in the previous read cycle. When the reader detects no collision during a read cycle the census will be finished shown in Figure 13 (e).
There are two major differences from BFSA-Muting; identified tags are not muted and the assurance level is used for finishing the census. For measuring the assurance level after finishing every read cycle and finishing the census successfully, the line 9 of Figure 15 would be replaced with Figure 14.
Computing Assurance Level of BFSA-Non-Muting
In the BFSA-Non-Muting, tags are not muted at all. Thus, the probability of collision occurrence is higher than the BFSA-Muting and SELECT packet is not necessary to be transmitted to tags.
BFSA-Non-Muting Simulation Log
In BFSA-Non-Muting when the census begins the reader transmits REQUEST packet to all tags and they start transmitting their IDs (once in a read cycle). Tags are never muted, so that all tags continue to transmit for the duration of a census, once every read cycle. Another difference between BFSA-Non-Muting and BFSA-Muting is that they have different behavior at the end of each read cycle. As can be seen from Figure 15, the assurance level is measured at the end of every read cycle and is changed according to the total number of identified tags given the total number of actual tags. As shown in Figure 15 (a) the identified tag (ID: 4) sends its ID again during the next frame. Census completes when the assurance level is satisfied, as shown in Figure 15 (d).
In this Section, we evaluate two parameters: total census delay and network throughput. We compare our simulation results with analytical results, computed by using the equations from Section 3.
Total census delay varies depending on the frame size and the number of actual tags in the BFSA model. If a frame size is either too big or too small as compared to the total number of tags the delay will be longer because of the increased number of idle slots and collision slots respectively, i.e. there is an optimal frame size resulting in the least total census delay for given number of tags. Thus, we first measure the optimal frame size to find the minimal total census delay for given fixed number of tags to be read (identified). Simulation runs were conducted by varying the initial number of tags from 10 to 100 with step of 5 while the given static frame size varies from 10 to 120 with step of 5. 10 census procedures were simulated for each frame size and given a specific number of tags.
The minimal total census delay for given static number of tags is shown in Figure 19. Triangle line represents the analytical result of BFSA-Non-Muting, ‘x’ line represents simulation result of BFSA-Non-Muting, square line represents analytical result of BFSA-Muting, and ‘+’ line represents simulation result of BFSA-Muting. For computing the analytical result of BFSA-Non-Muting and BFSA-Muting a computing program was developed [appendix C] and equation 6, 7, 9, 10, 11, 14, 15, 16, 17, 18 and 21 in Section 3 are used.
The minimum total census delay was increased linearly with the number of tags and 100 tag set was identified within 0.25 sec using BFSA-Non-Muting with assurance level 0.99 and 500 Kbps data rate. BFSA-muting took less than 0.1 sec with the same given conditions with BFSA-Non-Muting. The simulation result of BFSA-Muting shows approximately 70% shorter minimum total census delay than BFSA-Non-Muting simulation result. Both BFSA simulation results show about less than 15% shorter minimum total census delay than its analytical results in the experiment.
The optimal frame size is acquired from the simulation being used for computing the minimum total census delay in Figure 16. The symbols in Figure 17 are identical with Figure 16. Figure 17 shows us good agreement between the simulation result and the analytical result. The optimal frame size was increased linearly with the number of tags and BFSA-Muting has smaller optimal frame size than the one BFSA-Non-Muting has.
Minimum Total Census Delay for Given Number of Tags
Optimum Frame Size for Given Number of Tags
We evaluate three types of network throughput: maximum throughput, minimum throughput, and mean throughput. Network throughput represents the ratio between successfully transmitted number of packets and total number of transmitted packets during census. All of them show good agreement between analytical result and simulation result and they are shown in Figure 18.
Network Throughput
In Figure 18, network throughput shows good agreement between analytical and simulation result and simulation throughput shows slightly lower than analytical one. Figure 18 tell us that the network throughput of the two BFSA models is getting lower according to the increment of the fixed total number of tags. In Figure 18 (c), we can see mean network throughput of BFSA-muting is 200 - 400% greater than the throughput of BFSA-Non-Muting. Since in the BFSA-Muting the identified tags keep silent thus the total number of transmitted packet would be reduced while in the BFSA-Non-Muting the identified tags never stop transmitting its ID. Thus the difference between two RFID models makes network throughput different.
To evaluate the performance of RFID protocols we implemented two BFSA models (Muting and Non-muting). Wehave used the simulation tool, OPNET Modeler 14. The simulation models were validated by analyzing the log in the validation Section. In addition, we compared the simulation resultsagainst analytical results, generated by using the equations presented in Section 3.
In Section 4, we evaluated total census delay and network throughput by comparingsimulation and analytical results. Our simulation results show good agreement with analytical results both for total census delay and for network throughput. We also could see the performance difference of the two BFSA models in terms of the total census delay and the network throughput. As expected, BFSA-Muting performed better in terms of both network throughputand total census delay as compared to BFSA-Non-Muting due to reduction in the total number of transmitted packets.
The epidemic of the Acquired Immune Deficiency Syndrome (AIDS) was first recognized as a clinical entity in 1981 [1]. HIV-1 was identified as the causative agent for AIDS in 1983 [2]. Globally, 37.9 million people were living with HIV, with 1.7 million new infections and 770,000 people having died of AIDS in 2018. Sub-Saharan Africa is the most severely affected by HIV infection in which 1 in every 20 adults (4.9%) are living with HIV [3].
HIV may be classified into types, groups and subtypes based on genetic similarities. There are two types of HIV: HIV-1 and HIV-2. Both types can be transmitted by sexual contact, blood contact, and vertical from mother to child [4]. HIV-1 is the predominant type worldwide with high genetic diversity due to extremely high mutation and recombination rates and high cell turnover [5]. The strains of HIV-1 can be classified into four groups: the “major” group M, the “outlier” group O and two new groups, N and P, representing the four separate introductions of simian immunodeficiency virus into humans [6]. The majority (more than 90%) of HIV-1 infections are caused by HIV-1 group M strains. Within group M there are nine genetically distinct subtypes (or clades) of HIV-1. These are subtypes A, B, C, D, F, G, H, J and K [7].
HIV disease progression and clinical manifestations of diseases may differ between women and men because of biological and socioeconomic factors [8]. The progression of AIDS reflects the chronic nature of the disease, the hallmark of which is a gradual deterioration of the host immune system. Previous investigations found different rates of HIV disease progression and of virological and immunological response to antiretroviral therapy (ART) among HIV-infected women compared with men [9]. The rate of increase in viral load over time is presumably greater for women than men given that women and men progress to AIDS and death at similar rates [10]. Baseline clinical and immunologic status was found to be predictors of HIV related mortality and morbidity in HIV outcome studies conducted in both high income and low-income countries. In the same studies, Men were found to have a significant higher rate of loss to follow-up and ART non-adherence [11]. More evidence suggests that HIV positive men have worse treatment outcomes than their women counterparts in Africa [12]. One study found that humans show strong sex differences in immunity to infection and autoimmunity, suggesting sex hormones modulate immune responses [13]. Similarly, evaluation of the gender difference in the outcome of other viral diseases like the COVID-19, revealed a higher mortality rate of male patients as compared with female patients, suggesting the protective role of estrogen [14].
Some studies have documented the differences in clinical progression of HIV between women and men due to hormones and age at the time of HIV infection [15], some studies suggesting that the differences in immunological responses to ART and mortality risk are due to biologic differences between men and women. The hormonal fluctuations in ovulating females, may affect immune function, and thus can cause variation on viral loads in women, where HIV RNA levels can decrease to a median of 0.16 log10 from the time of early follicular to the mid-luteal phase [10]. Studies involving male-to-female transsexuals have shown decrease in CCR5 expression when female hormones are administered.
Women living with HIV have limited access to care and treatment in several countries, most of who belong to ethnic or racial minorities, despite having better treatment outcomes and higher enrolment to ART care when compared to men [12]. However, women may delay ART initiation because of social obligations, pregnancy and socioeconomic factors [16].
Highly Active Antiretroviral Therapy (HAART) for treatment of HIV infection has improved the lives of HIV infected people by reducing the morbidity and mortality in patients receiving treatment. This has transformed a chronic fatal infectious disease into a manageable chronic infectious disease. Despite the use of HAART, numerous reports indicate failure of therapy due to lack of potency of some drugs or drug combinations, insufficient drug adherence and transmission of drug resistant virus. In a number of patients, HAART is not sufficiently effective thus results into virological, clinical and immunological decay [17]. The success of HAART in HIV infected patients may be influenced by other host factors; however, there is no enough information with combined assessment of a variety of factors that can influence treatment efficacy in clinical routine practice. One of the poorly understood factors that may influence disease progression and treatment response is gender. Men and women are affected differently with HIV and women’s immune systems may respond differently to the virus because of hormonal influence. Women may also experience stronger side effects when using ARV drugs, such as central nervous system and gastrointestinal symptoms, which could then lower their ARV adherence [18]. Survival of HIV infected patient from the point of HIV diagnosis to AIDS within a comparable clinical care setting is affected mostly by the time of HIV diagnosis (WHO stage I and II), and other differences like age, gender, race and behavioral factors may also play a role on survival [19]. Also the differential use of ARV drugs for purely social economic reasons may lead to survival disadvantages for women [10].
Some patients, especially males diagnosed with HIV in developing countries, are not always successfully linked to onward treatment services, resulting in delays on initiating ART, or prophylaxis for opportunistic infections. Delayed presentation to care and treatment, and late HIV diagnosis, can result into late initiation of ART which affects adherence to treatment and results into poor prognosis in a disadvantageous group in the society [20]. More studies will be of benefit to explore the possibility of initiating ART at lower viral loads in women, especially during the early stages of infection.
As the use of ARV drugs in resource-limited countries increases, it is important to understand the effect of several factors on disease progression and outcome of ART. Combined analyses of various factors are scarce. Several studies except trials on specific regimens, have addressed aspects such as baseline clinical characteristics and adherence as predictors of treatment success. For limited resource settings, where ARV drugs are not sufficient for all patients, it would be cost effective to issue the drugs to patients who will ultimately adhere to the therapy. Cheap alternative methods are needed, to help in guiding therapy, especially in this era where the use of ARV has become widespread. If not controlled, emergence of HIV drug resistance will further complicate the epidemiology and transmission patterns of HIV especially during the time when ARV resources are limited. Various studies came up with different results on the survival difference between male and female HIV patients, and some of the differences were a result of inadequate medical care rather than biological differences. Some of the differences were related to the time to development of AIDS and opportunistic infections, viral load, ARV drug resistance and CD4 counts over time of observation. HIV disease progression can be determined by viral and host factors and sex differences in immune modulation. The purpose of this study was to assess the socio-demographic and virological factors predicting HIV disease progression among HIV patients in Dar es Salaam, Tanzania.
We conducted a cohort study in which we followed up HIV-infected ARV naïve patients for 1 year. We enrolled ARV naïve, HIV infected patients who were due to initiate ART, if they were 18 years and above, with available medical records from previous year. All patients were enrolled after providing written informed consent. We used a structured questionnaire to collect social demographic variables and anthropometric information, while patient record files and CTC database were used to collect data with respect to HIV diagnosis, clinical and ARV treatment information.
We categorized patients according to the clinical and performance scales of the staging system for HIV-1 patients [21]. The differences on these variables were assessed between males and females. All patients were followed up for a period of 1 year after starting treatment where treatment outcome was evaluated after 12 months from the time of initiation of therapy.
Ethical clearance to conduct the study was obtained from the National Institute for Medical Research in Tanzania and permission was seek from Hospital administration. All patient identifying information was de-linked from the collected data.
We collected blood in EDTA collection tubes, CD4 level was estimated using Becton Dickinson FACSCalibur, and viral load using TaqMan Viral-Load Assay COBAS® AmpliPrep.
Whole blood was separated by centrifugation at 4000 revolutions per minute for 20 minutes at −15°C, and plasma aliquoted and stored for testing. HIV RNA was extracted from plasma using Qiagen QIAamp Kit (Qiagen, Hilden, Germany) according to the manufacturer’s instructions. cDNA synthesis was performed using SS III RT-PCR System (Life Technologies, USA) according to the manufacturer’s instructions with the primers PrtM-F1 and RT-R1. Nested PCR was performed from obtained RT-PCR product, using primers Prt-F2 and RT-R2. The QIA quick PCR purification kit (Qiagen, Hilden, Germany) was used to purify the nested PCR products and quantified by agarose gel visualization. The ABI Prism Big Dye Terminator Cycle Sequencing Ready Reaction kit was used for sequencing together with the following primers PRT-F2, RT-R2, SeqF3 (35V), SeqR3 (90v1), SeqF4 (36V) and SeqR4 (AV44). Sequences were aligned using Clustal X [22], manually edited with BioEdit [23].
Data were analyzed using Epi Info version 3.5.1 and STATA 11. Clinical progression was assessed by comparing clinical characteristics at hospital registration, baseline and at the time of the study, taking into account the change in clinical characteristics over time. Median percentage weight gain was adjusted for amount of time on treatment. Gender differences were assessed using the Kruskal-Wallis test for continuous variables and the chi-square test for categorical data. Descriptive analysis was done for the basic demographic, clinical and immunological characteristics of patients as well as continuous variables like CD4 counts, age, plasma viral load levels and BMI for both males and females. Survival distributions for male and female patients were estimated using the Kaplan-Meier method. Patients who were lost to follow up were censored at the date when they were last seen. Patients who were still alive on the date when the study ended were censored on this date. Survival times were expressed in days. Cox’s proportional hazards regression models were used to assess the associations between patient characteristics and outcomes. All other variables were included in multivariable models to assess their impact on the association between gender and outcomes.
In a study of 234 patients recruited to initiate ARVs in Tanzania, 164 (70%) of them were females and 70 (30%) were males, with a refusal rate of 6 and 25%, respectively. Significantly more males had attended secondary schools than females; had a higher income and a better knowledge of ARV use (Table 1). There was no significant difference in median age (36 years), alcohol intake and use of traditional medicine and use of intravenous drugs (Table 1). Significantly more males tested for HIV following a chronic illness, in contrast to females who tested without signs of AIDS. Consequently, the disease stage at HIV diagnosis was significantly more advanced in males: more males had CD4 count <100 cells/ml at baseline; they had a significantly lower Body Mass Index and higher mean Log10 viral load (males 5.5; females 5.1) (Table 1).
Characteristics | Females (N = 164) | Males (N = 70) | P value |
---|---|---|---|
Categorical variables | N (%) | N (%) | |
Primary level education | 156 (95.1) | 59 (84.3) | 0.005 |
Above 100 US Dollar monthly income | 57 (34.8) | 43 (61.4) | 0.0002 |
Having relative to remind to take medication | 133 (81.1) | 59 (843) | 095 |
Alcohol intake | 33 (20.1) | 18 (25.7) | 04 |
Use of traditional medicine | 97 (592) | 45 (652) | 05 |
History of intravenous drug abuse | 8 (4.9) | 7(10) | 02 |
Knowledge on ARV use and side effects | 10 (6.9) | 14 (20) | 0.005 |
HIV testing due to chronic illness | 105 (64) | 55 (78.6) | 0.04 |
Starting treatment with in 1 year of HIV diagnosis | 130 (793) | 55 (78.6) | 0.96 |
Presence of 2 or more opportunistic infections | 75 (45.7) | 37 (519) | 03 |
WHO staging at initiation of therapy | |||
Stage I | 16 (9.8) | 5 (7.1) | 0.6 |
Stage II | 49 (29.9) | 16 (22.9) | 0.3 |
Stage III | 82 (50.0) | 34 (48.6) | 0.8 |
Stage IV | 17(10.4) | 15 (21.4) | 0.04 |
CD4 < 100 cells/μl at ART initiation | 50 (333)* | 31 (47.0)* | 0.05 |
Continuous variables | Median (IQR) | Median (IQR) | P value |
Age (Years) | 35 (30.5–43.5) | 37 (33.5–42.0) | 0.38 |
CD4 (cells/μl) at ART initiation | 149 (6–148; 75–218) | 102 (3–221; 47–184) | 0.02 |
Continuous variables | Mean (SD) | Mean (SD) | P value |
BMI at initiation of therapy | 22 (5) | 20 (4) | 0.002 |
Log 10 viral load (RNA copies/ml) at initiation of therapy | 5.1 (1.3) | 5.5 (1.1) | 0.05 |
Baseline characteristics of 234 HIV-1 infected naïve patients, Dar es Salaam 2010.
Reproduced with permission from [24].
The prevalence of adherence to ART as measured by consistence in keeping appointment was not different between females (62.8%) and males (62.9%) (Table 2). After 1 year of treatment with ART, the virological response was significantly better in females than in males (females 69%; males 45% with undetectable viral load) but the mean CD4 increase was significantly higher in males (230 cells/ml) than females (202 cells/ml) (Table 3).
Characteristics | Females (N = 164) | Males (N = 70) | P value |
---|---|---|---|
N (%) | N (%) | ||
Death | 21 (12.8) | 14 (20) | 0.2 |
Alive | 101 (61.6) | 35 (50.0) | 0.1 |
Lost to follow up | 42 (25.6) | 21 (30.0) | 0.5 |
Missed appointments | 103 (62.8) | 44 (62.9) | 0.99 |
One year outcomes of 234 HIV infected patients after starting ART from September 2010 to August 2011.
Reproduced with permission from [24].
Characteristics | Females (N = 101) | Males (N = 53) | P value |
---|---|---|---|
Percentage with undetectable viral load | 70 (69%) | 24 (45%) | 0.003 |
Continuous variables | Median (IQR) | Median (IQR) | |
CD4 (cells/μl) count after 1 year | 312 (252–413) | 321 (110–480) | 0.6 |
Continuous variables | Mean (SD) | Mean (SD) | |
Percentage BMI increase (from Baseline) | 10.5 (14.2) | 9.8 (17.5) | 0.3 |
BMI after 1 year | 24.5 (4.8) | 22.5 (4.1) | 0.02 |
Percentage weight gain | 10.4(14.3) | 9.3 (17.3) | 0.2 |
CD4 (cells/μl) increase from baseline | 202 (516; 35–163) | 230 (272; 86–181) | 0.05 |
The BMI was still significantly higher in females (24.5) compared to males (22.5), but the percentage increase of BMI was not significantly different. Also, more females (61.6%) survived than males (50%) with more deaths occurring in males. The unadjusted relative hazard for death for males at 1 year of ART was 1.94 with a confidence interval of 0.91 to 4.11, p = 0.08 (Figure 1). Cox proportional hazards (of a model containing social demographic variables) showed no significant difference in the survival rate after 1 year on treatment between males and females (relative hazard 1.02, 95% CI 0.75, 1.38). The reported opportunistic infections during 1 year of follow up were not significantly different (Figure 2).
Kaplan-Meier survival curves on time to death for 234 patients, Dar es Salaam (reproduced with permission from [24]).
Observed opportunistic infection during 1 year of follow up (reproduced with permission from [24]).
A total of 67 patients were found to have detectable viral load at enrolment (females 43 (64%); Males 24 (36%)) during 1 year of treatment. Among which, 31 females and 29 males (60 total), were alive after 1 year of follow up. The status of 7 could not be confirmed after 1 year of treatment, as they were either died or Loss to follow up. The main subtypes identified were C 18 (27%), A 14 (21%) and D 13 (19%) (Figure 3). There was no significance difference on subtype distribution between males and Females.
Subtype distribution of the detected sequences [24].
A total of 6 (9%) patients had detected NRTI resistance mutations, 4 females and 2 males, among which 3 were alive after 1 year of therapy. There was no significance difference between males and females with regard to NRTI resistance mutations. Two of the patients had both NRTI and Protease Inhibitor (PI) resistance mutations. One patient-initiated treatment at WHO stage IV, four at WHO stage III and one at WHO stage II of disease staging. All patients started treatment with CD4 below 100 cells/μl and Viral Load above 5000 copies/ml.
A total of 2 (3%) patients had detected NNRTI resistance mutations, all females and were alive after 1 year of therapy. One patient-initiated treatment at WHO stage III and one at WHO stage II of disease staging. All patients started treatment with CD4 below 100 cells/μl and Viral Load above 5000 copies/ml.
There was no patient with PI major resistance mutations. A total of 15 (22%) patients had detected PI minor resistance mutations, 6 females and 9 males, among which 6 were alive after 1 year of therapy. There was no significance difference between males and females with regard to PI minor resistance mutations. One patient-initiated treatment at WHO stage IV, nine at WHO stage III and five at WHO stage II of disease staging. Eight patients started treatment with CD4 below 100 cells/μl and all with Viral Load above 5000 copies/ml.
Our analysis of gender difference found significant clinical and social-demographic variations between females and males on HIV disease progression after 1 year of treatment. Overall, females were found to start CTC with higher CD4 count and BMI, and lower viral loads than males. Similar findings were reported by other studies, which found that women had higher CD4 cell count at ART initiation than men [9].
There was no statistical significant difference in survival between males and females in our study population after 1 year of follow up; this is in contrast with the findings of another study which found better survival among females and less disease progression among females after 3 years follow up [25]. The difference could be due to a shorter follow up (1 year) in our study compared to 3 years. Despite the fact that more males died, the ones who survived were found to have higher mean CD4 increase than females and more females had undetectable plasma viral load. Our findings are similar to a study, which found a significant better survival of female HIV-1 infected patients on HAART compared to male patients [10].
The fact that women did not fare worse than men is encouraging considering that females were found to be less educated with lower monthly income. This is consistent with the current data available in Tanzania where males earn more than females and are more educated [12]. However, males delayed reporting to care, with an advanced disease. Several studies have also indicated differences in health seeking behavior, between men and women, where more women were reporting to health facilities earlier [26]. This could be a result of differences in social responsibilities giving women more entry points to HIV services, like during pregnancy. However, we did not have similar results in this study.
The most common reason for HIV testing was presence of AIDS related symptoms, rather than voluntary testing, especially for males, and this denied the patients time for care at CTC. Despite the fact that both groups had delayed registering for care at CTC, 66% of males understood more about the use of ARVs than females. Immune Reconstitution Inflammatory Syndrome (IRIS) was associated with poor response to HIV patients initiated ART with low CD4 [26], which could also have happened to some of the patients in this cohort, as also observed in another study that CD4 cell count at ART initiation was a strong predictor of mortality [27].
Significant number of patients was using alternative medicines, illicit drugs and alcohol prior to starting ARVs, more observed among males than females. The use of alternative medicines and alcohol could contribute to the delay in seeking health care and late presentation to the care and treatment centers with advanced disease and also predispose the patients to poor adherence and poor prognosis [28]. However, there was no significant difference on ART adherence as measured by consistency in keeping appointment. There was also no significant difference in development of ARV drug resistance mutations. Excessive alcohol consumption can exacerbate immunosuppression, enhance the toxicity of ARV on liver cells and accelerating liver damage and may also depress the immune system leading to increased multiplication of the virus in mononuclear cells [29]. A significant number of patients reported use of injection-based illicit drugs in the past and present, this could have implications on ARV adherence and disease progression. Several studies have associated the use of illicit drugs with non-adherence to ART and poor prognosis. People who inject drugs (PWID) are also challenged with poor social and economic conditions, mental illnesses, which may affect their access and adherence to ART [30].
Women had a lower median viral load at initiation of therapy compared to men, despite the fact that there was no much difference on the period of illness before starting ART between the two groups. However, after 1 year of treatment, more females had undetectable plasma viral load and lower mean CD4 cell increase than males. This will need further evaluation, as this may need redefining the time to initiate ART in the two groups. Further studies are needed to understand the benefits of initiating ART, earlier with lower viral loads. This is because the absolute viral load seems to confer different risks for AIDS between men and women, which is not the case with relative viral loads [10]. Because HIV related morbidity is influenced by both viral and host factors, sex differences in immune modulation will likely play instrumental roles in determining the course of disease. Despite an observed high number of both males and females patients presenting with opportunistic infections; females reported more fever and oral candidiasis than males. The reason could be late presentation to CTC and thus could not benefit from the care and treatment services like prophylaxis against opportunistic infections. This may predispose the patients to poor prognosis and poor adherence after starting treatment [31]. Patients receiving HIV diagnosis late in the course of infection are usually more severely immune compromised and are more likely to present with co-morbidities like tuberculosis, which may be part of the immune reconstitution syndrome.
Consistent with our previous report, the most prevalent subtypes were A, C and D, and recombinants [32]. Our study found no contradiction to previous studies that found no association between subtype and therapy response, although our sample size was too small to conclude that [33].
Of concern was the detection of resistance to the first line ARV in Tanzania, in individuals who have been on treatment for only 1 year. This ARV resistance pattern was not limited to a particular subtype or gender. We observed minor PI mutations, which could be naturally occurring polymorphisms with no clinical significance. Interestingly, similar resistance mutations in the protease inhibitor genes were also observed in two different studies in Tanzania [34, 35].
We assessed the gender differences on HIV disease progression and outcomes after 1 year of ART among HIV infected patients and whether this potential difference is influenced by social, virological and immunological differences among patients starting ART. We observed some differences in clinical disease progression between males and females before starting ART and after 1 year of treatment. Male HIV patients delay seeking care and enter into treatment at a more advanced stage of HIV infection, which predisposed them to increased mortality. We also observed social factors that can affect future ART success in these patients. We recommend continuous follow up of this and other cohort of patients to understand responses to ART and the differences between males and females, together with advocating early HIV diagnosis and treatment to males. The observed gender difference between males and females will need further evaluation, as there may be a need to redefine the time to initiate ART in the two groups. The possibility of initiating ART at lower viral loads in women, especially during the early stages of infection merits further study. We recommend continuous follow up of this and more cohort of patients to understand responses to ART and the differences between males and females, together with advocating early HIV diagnosis and treatment to males.
It is important to monitor the viral response to patients on ART for early detection of treatment failure, together with understanding the ARV resistance pattern to ART Naïve patients and ART experienced patients. ARV resistance monitoring will help avoid unnecessary costs on use of ineffective treatment. The observed mutations within the pol region are of considerable concern because they may increase the development and spread of ARV resistant strains.
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\n\nPlease note that refunded amounts will not always be exactly the same as original payment amounts due to bank transaction fees and expenses. Any such costs will be split evenly between IntechOpen and the Author.
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