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1. Introduction
Since the first implementation of synthetic herbicides in crop protection systems, weeds have continuously developed resistance. As a main reason of such evolution, long-lasting exploitation of herbicides with one target site in plants is considered. This has been the case with the first widely-used triazine herbicides, photosynthesis inhibitors, which have effectively eliminated a wide range of weeds. Unfortunately, inappropriate adjustment of herbicides to weed species occupying fields, application of herbicides at the incorrect developmental stage and in unsuitable weather conditions have contributed to the accumulation of active compounds in the soil, accumulation of weed species and acceleration evolution of resistant biotypes [1]. To date, there have been 211 species and 393 biotypes of herbicide resistant weeds identified [2]. Most of them are resistant to B, C1 and A groups of herbicides, inhibitors of: acetolactate synthase (ALS), photosystem II and acetyl CoA carboxylase, respectively. Ten species pose the biggest threat for crops due to causing yield losses, including the most important herbicide-resistant species which are characterized by multiple resistances: rigid ryegrass (Lolium rigidum Gaud.), wild oat (Avena fatua L.) and redroot pigweed (Amaranthus retroflexus L.).
Evolution of weeds resistant to herbicides demands new solutions to cope with the problem since economic losses generated by weeds can be higher than those caused by other pests. Due to the fact that abandoning chemical weed control is, with current agricultural practices, rather impossible, it is necessary to create new classes of herbicides with new mechanisms of action and target sites not previously exploited. Presently used synthetic herbicides are not approved for use in organic agriculture. Moreover, using crop protection chemicals also need public acceptance. [3]. The number of synthetic chemicals with new target sites are decreasing dramatically. Eco-friendly trends in weed management force scientists to reach for innovative sources and tools. Natural compounds pose a great field for the discovery of new environmentally safe herbicides, so called “bioherbicides”, which are based on compounds produced by living organisms. According to the CAS (Chemical Abstracts Service) registry, among the 24 million organic compounds, a large group of secondary plant metabolites is represented. Some of these compounds take part in allelopathic interactions.
2. Allelopathic interactions and allelopathic compounds
Allelopathy is considered a multi-dimensional phenomenon occurring constantly in natural and anthropogenic ecosystems [4]. It is defined as the interaction between plants and microorganisms by a variety of compounds usually referred to as allelopathins, allelochemicals, or allelopathic compounds. This review is focused mainly on compounds taking part in complex allelopathic interactions between higher plants. However, determination of quality, quantity, direct or indirect effects of allelopathins on plant or microorganism communities in the natural environment is very difficult owing to the multi-dimensional character of those interactions. The development of analytical techniques allowing better specification of direct effects of allelopathins, have moved the exploration (or the research on) of this phenomenon from fields into laboratories. The term “allelopathy” refers rather to interactions occurring in the natural environment [5]. For studies with plant extracts, allelopathins isolated from plant tissue, collected from exudates or even synthetic compounds identical to natural ones, it was established the term ‘‘phytotoxicity’’ to distinguish allelopathy (as a phenomenon occurring in natural environment) from studies conducted in laboratory.
Allelopathins are products of the secondary metabolism and are non-nutritional primary metabolites [6,7]. These compounds belong to numerous chemical groups including: triketones, terpenes, benzoquinones, coumarins, flavonoids, terpenoids, strigolactones, phenolic acids, tannins lignin, fatty acids and nonprotein aminoacids. A wide range of these biochemicals are synthesized during the shikimate pathway [8] or, in the case of essential oils, from the soprenoid pathway. Allelochemicals can be classified into 10 categories [9] according to their different structures and properties:
water-soluble organic acids, straight-chain alcohols, aliphatic aldehydes, and ketones;
simple lactones;
long-chain fatty acids and polyacetylenes;
quinines (benzoquinone, anthraquinone and complex quinines);
phenolics;
cinnamic acid and its derivatives;
coumarins;
flavonoids;
tannins;
steroids and terpenoids (sesquiterpene lactones, diterpenes, and triterpenoids).
Allelochemicals are released into the environment by plant organs such as roots, rhizomes, leaves, stems, bark, flowers, fruits and seeds (Figure 1a). The huge number of allelopathic interactions is typically negative in character, with positive relations being rare. Allelopathic compounds affect germination and growth of neighboring plants by disruption of various physiological processes including photosynthesis, respiration, water and hormonal balance. The underlying cause of their action is mainly inhibition of enzyme activity. Ability of an allelochemical to inhibit or delay plant growth and/or seed germination is usually defined as its “allelopathic (or phytotoxic) potential”. An excellent example of allelopathic interaction is seen in soil exhaustion due to the accumulation of allelopathins that can be prevented by using fertilizers and rotating crops. Plants producing allelopathins are considered as “donor” organisms while the plants which allelopathins are directed to are referred to as “target” plants or “acceptors”. The after-effects and strength of allelopathic interactions are diverse due to modifications of the allelopathins taking place in soil (Fig 1b). Most of the allelochemicals penetrate the soil as already plant-active compounds, e.g. phenolic acids, cyanamide, momilactones, heliannuols etc. Some have to be modified into the active form by microorganisms or by specific environmental conditions (pH, moisture, temperature, light, oxygen etc.), e.g. juglone, benzoxazolin-2-one (BOA), 2-amino-3-H-phenoxazin-3-one (APO).
3. Advantages and disadvantages of allelopathins as bioherbicides
Mode of action of some allelochemicals is similar to synthetic herbicides. These features have allowed them to be considered for possible use in weed management as bioherbicides. However, the field of knowledge is poorly studied but it is a very attractive area to explore.
Figure 1.
Multi-dimensional nature of allelopathic interacions. (1a) Plant A releases allelochemicals X and F which directly affect growth of plant B. (1b) left side; Plant A releases allelochemical X which is modified or activated by microorganisms to allelochemical Y that affects growth of plant B. (1b) right side; Plant A releases allelochemical X which stimulates microorganisms to produce allelochemical Z that affects growth of plant B.
Allelochemicals are highly attractive as new classes of herbicides due to a variety of advantages. However, in the perspective of bioherbicides based on allelopathins, effects caused by these compounds on target plants are also classified as “phytotoxic”.
Most of allelopathins are totally or partially water-soluble which makes them easier to apply without additional surfactants [3, 10]. Their chemical structure is more environmentally friendly than synthetic ones. They possess higher oxygen- and nitrogen-rich molecules with relatively few so called ‘heavy atoms’, a halogen substitute, and are characterized by the absence of ‘unnatural’ rings. These properties decrease a chemical’s environmental half-life, prevent accumulation of the compound in soil and eventual influence on non-target organisms. On the other hand, these properties are an allelochemical’s Achille’s heel due to less than satisfactory duration of activity. Structure complexity generates more stereocenters making them more reactive and unstable. Therefore, rapid degradation of one of the chemical groups can significantly decrease bioactivity of the whole compound.
The diversity of allelopathins makes them promising tools possessing specific properties in discovering novel, specific target sites in acceptor plants. Even if they inhibit photosynthesis or respiration, they may also bind to proteins at different sites than synthetic herbicides [11, 12]. This provides the opportunity to eliminate weeds that are already resistant to commercialized herbicides with the same mode of action. Allelochemicals are also characterized by multi-site action in plants without high specificity which is achieved in the case of synthetic herbicides. Therefore, this feature excludes the application of an allelopathic compound as a selective herbicide or totally prohibits its usage in weed management. On the other hand, effects of allelopathins in acceptor plants are highly dose-dependent [13]. This allows the opportunity to search out compounds exhibiting selectivity. Generally, monocotyledonous plants are more resistant to allelochemicals than dicotyledonous ones. Therefore, usage of a compound as a potential herbicide is possible but rather restricted to cultivation of exact crops with a defined weed composition.
The route of discovery is much more complicated with allelopathins. In contrast to synthetic herbicides where synthesis, bioassay, evaluation and quantitative structure-active relationship follow Quantitative Structure-Activity Relationship (QSAR), allelochemicals have to be first isolated from plant extracts [14]. The amount of recovered compounds is usually low in comparison to chemical synthesis. After extraction, purification and selection of the most attractive compound and determination of its mode of action in plants is done. At the end of the process, similar to synthetic herbicides, allelpathins are subjected to QSAR. The long discovery process is usually offset by a shorter, less expensive track of registration [15]. It is worth noting that before an allelochemical can become an herbicide, the following conditions have to be performed: phytotoxic activity at the range between 10-5 and 10-7 M, identified chemical structure, known mode of action in plants, time of residence in soil, possible influence on microbial ecology and non-target plants, possible toxic properties on human health and profitability of production on a commercial scale [16].
A high number of limitations does not exclude allelochemicals as possible herbicides. In particular, they can be alternatives in weed management strategy. Widely developed bioinformatics and cheminformatics support development of new herbicides [3, 15, 16]. Identified chemical structure of a particular allelochemical is a starting point to design a product with the compound-like properties using computer programs. Thanks to cheminformatics we are able to predict the potential structure of analogues and make several modifications, which make it more or less active, with higher environmental stability, as it was done for leptospermone. We may also predict the target site of compound action in plants due to comparison studies. Similar structure of a compound to a commercialized herbicide or other natural compound whose mode of action is well-known may allow us to predict the target site.
4. Allelopathic plant extracts as bioherbicides
Plant protection is effective but rather costly and problematic due to environmental pollution. Exploration of the allelopathic potential of some species allows the introduction of alternative techniques for weed management, e.g. extracts from allelopathic plants can be applied as foliar sprays. Apart from decreasing the costs of herbicide application, this method also improves crop production.
The best known examples of natural bioherbicides are phytotoxic water extracts from herbage of sorghum (Sorghum bicolor (L.) Moench.) (sorgaab) and sunflower (Helianthus annuus L.) (sunfaag) which can be effectively used in plant protection without yield losses.
Effects of sorgaab on weeds is time- and dose-depend but is typically used at 5% or 10% (w/v) concentration as double spray 20/30 and 40/60 days after sowing (DAS) or after seedling transplantation (AT) [17-19]. The best results to account for net profits have been elicited with a double spray of 10% extract in cotton (Gossypim hirsutum L.), soybean (Glycine max L.), wheat (Triticum aestivum L.) or rice (Oryza sativa L.). The highest efficacy of such extract applications has been verified in rice on reduction of barnyard grass (Echinochloa cruss-galli L.) biomass by 40%, without significant changes in weed density and accompanied yield increase by 18%.
Sunfaag has been widely used in wheat. The extract has been usually applied three times at 7-day intervals starting between 3-4 weeks post-emergence. This system of application has reduced biomass of the two most commonly occurring weeds, lambsquarters (Chenopodium album L.) and toothed dock (Rumex dentatus L.), by 70% and 97% respectively, although it has not eliminated all weed species in field. It has improved wheat biomass by 7-8% in comparison to weed free control without significant changes in number of tillers and total seed biomass. The herbicidal efficiency calculated as the effectiveness of sunfaag in comparison to synthetic herbicides showed a quite high value, 60% efficiency index. Weed management systems require high concentrations of sunfaag ranging up to 80% and can generate economic losses due to the necessity of cultivating higher amounts of sorghum or sunflower that also required an appropriate cultivation system [20, 21]. Therefore, sunfaag can be applied as a pre-emergence herbicide with much lower doses. The most promising application system has considered usage of 10% (w/v) extract at pre-emergence + 25 DAS + 35 DAS. Following the application, there has been noted a remarkably reduced population of wild oat, lesser swinecress (Coronopus\n\t\t\t\tdidymus L.) and littleseed canarygrass (Phalaris minor Retz.) without affecting germination of wheat and increased wheat yield in 7% [22]. However, the inhibitory effect on weed growth and crop yield is selective and highly dependent on duration or term of sorgaab and sunfaag application.
Aqueous extracts of sorghum and sunflower are effective on weed growth but unfortunately might not be profitable enough in crop production; however, crop allelopathy can be manipulated for achieving sustainable weed management. Combination of phytotoxic crop water extracts with lower rates of herbicides may provide reduced weed control levels with reduced herbicide usage. The interesting review of allelopathic crop plants in weed management strategy is presented in reference [23]. Two field studies were conducted utilizing water extracts of sorghum, sunflower and rapeseed (Brassica napus L.) with reduced glyphosate dosage for controlling purple nutsedge (Cyperus rotundus L) in cotton [24]. Sorghum and rapeseed water extracts were tank mixed (at 15 or 18 L ha-1) in different combinations with reduced rates of glyphosate by 767 and 575 g active substance (a.s.) ha-1 and sprayed as directed post emergence at 21 DAS. Purple nutsedge density and dry weight were suppressed by 78% to 95% and 83% to 95%, respectively, when different crop water extracts were used in combination with a reduced rate of glyphosate. Seed cotton yield was improved from 15-21% in sorgaab and rape water extract combinations with reduced rates of glyphosate (67-75%). Similar research has been conducted on water extracts of sorghum with sunflower in combination with herbicides in wheat, soybean, rice, and canola (Brassica sp.) [25, 26]. Both extracts, in combination with herbicides, have the same or even better effect on inhibition of growth of the following weeds: littleseed canarygrass and lesser swinecress, compared to single synthetic herbicide applications [25, 26]. Spraying of wheat seedlings 30 DAS with sorgaab+sunfaag (18 L each ha-1) with mesosulfuron+idosulfuron (4.32 g a.s. ha-1) has the same effect on total weed density (reduction up to 90% in relation to control) as application of mesosulfuron+idosulfuron used alone, but with higher doses (120 g a.s. ha-1). Herbicidal solution has also improved yield parameters, both in relation to control and in relation to single herbicide application: fertile tillers (10%), spikelets per spike (11%) and grains per spike (10%) [26]. In cotton, application of both extracts at 18 L ha-1 each with glyphosate (767 g a.s. ha-1) 21 DAS has been the most effective in density reduction of the highly competitive weed purple nutsedge up to 93% [24]. However, the greatest benefit in wheat is the usage of a sorgaab/sunfaag combination which lowered by 70% doses of metribuzin and phenaxaprop (at 57 g a.s. ha-1), applied at 18 L each ha-1. In turn, in cotton, application of the same rates of extracts per ha with glyphosate (767 g a.s. ha-1) seems to be the most economically reasonable costs of following weed management method [24, 25].
Selectivity of plant extracts on weeds without any negative implications on crop productivity is probably due to differences in the physiological stage of plants and following plant competition. Sunfaag has been applied when wheat seedlings were 3-4 weeks old while lambsquarters and toothed dock 1-week old at the stage of three to four leaf [20, 21].
High allelopathic potential conditioned by glucosinolates and isothiocyanates is present in Brassica sp. [27, 28]. Isothiocyanates have been strong suppressants of germination of spiny sowthistle (Sonchus asper L. Hill), scentless mayweed (Matricaria inodora L.), smooth pigweed (Amaranthus\n\t\t\t\thybridus L.), barnyardgrass, blackgrass (Alopecurus myosuroides Huds.) and wheat [28]. Black mustard (Brassica nigra L.) extract of different plant parts like leaf, stem, flower and root have inhibited germination and radicle length of wild oat. Inhibitory effects on germination increased with increasing concentration of extract solution of the fresh plant parts [29]. Some experiments were conducted also using garden radish (Raphanus sativus L.) extract on germination of 25 weed and 32 crop species [30]. Garden radish extracts totally inhibited germination of 11 weeds such as Johnsongrass (Sorghum halelense L. Pers.), Alhagi spp., black-grass (Alopecurus myosuroides Huds.), shepherd\'s-purse (Capsella bursa-pastoris L. Medik.), field bindweed (Convolvulus arvensis L.), dodder (Cuscuta sp.), carrot (Daucus carota L.), shortpod mustard (Hirschfeldia incana L.), Ochtodium aegyptiacum (L.), and shortfruit hedgemustard (Sisymbrium polyceratium L.), and 4 crop species namely lettuce (Lactuca sativa L.), tobacco (Nicotiana tabacum L.), bean (Phaseolus vulgaris L.), and clover (Trifolium sp.). Garden radish extracts at different rates (100, 66, 50 and 33% of pure extract) did not affect germination of wheat, cotton, and maize (Zea mays L.), but affected soybean germination at the 100% extract rate in vitro. Rhizome regeneration of Johnsongrass was inhibited by 54-99% depending on extract concentration. Regeneration of bermudagrass (Cynodon dactylon L. Pers.) rhizomes was inhibited to a lower extent at all concentrations; for instance, 54% inhibition occurred at the highest extract concentration. Lower extract rates stimulated redroot pigweed germination, while 66 and 100% extracts inhibited germination by 21 and 42%, respectively. Inhibition reached only 56 and 49% at the highest extract concentration for common purslane (Portulaca oleracea L.) and cocklebur (Xanthium strumarium L.), respectively. Garden radish residues which were cut into pieces and incorporated into the growing medium decreased weed intensity and increased maize yield [31].
Legumes crops may also be applied as a source of allelochemicals useful in weed suppression. Mulch of dead pea plants could be used to control growth of weeds. Pea cover crop has regulated germination and growth of lady’s thumb (Polygonum persicaria L.), smooth pigweed, smallflower galinsoga, and common lambsquarters. Similarly, the aqueous leachates (1%) of all four legumes, velvetbean (Mucuna deeringiana (Bort.) Merr.), jackbean (Canavalia ensiformis (L.) DC.), jumbiebean (Leucaena leucocephala (Lam.) de Wit), and wild tamarind (Lysiloma latisiliquum (L.) Benth.), have been shown to suppress weeds [32]. These plants exhibited strong phytotoxic effects on the radicle growth of barnyardgrass, alegría (Amaranthus ssp.) and amaranth (Amaranthus hypochondriacus L.) [33]. Russian knapweed (Acroptilon repens) control is difficult in many crops. Allelopathic effects of extracts and plant parts of alfalfa (Medicago sativa L.) on Russian knapweed were reported both in Petri dishes and pot experiments [34]. Alfalfa has been recommended in fields with high mugwort (Artemisia vulgaris L.) infestation, as it decreased mugwort to 89% under field conditions, while extracts of alfalfa vegetative parts inhibited mugwort germination up to 83% in Petri dish assays.
Application of plant extracts as pre-emergence or as early post emergence herbicides resulted in reduction of doses of synthetic herbicide due to their synergistic or additive action. However, not all phytotoxic extracts are effective enough to inhibit weed growth or germination when applied as spray even when plants show high allelopathic potential as mulch, intercropping system or in rotation. This may be the result of masking the activity of one compound by another in water solution or other factors such as impossibility of extract penetration through the cuticle [12]. A new opportunity to enhance effectiveness of usage of bioherbicides based on natural extracts is associated with extraction of individual allelochemicals and/or its comparison with synthetic herbicides. The extraction of sesquiterpene lactone, dehydrozaluzanin C (DHZ) produced among Compositae family serves as an example [34]. Comparison studies of isolated DHZ (1 mM) and the commercial herbicide Logran® showed high inhibitory activity of DHZ on dicotyledonous plants while the synthetic herbicide showed no activity [34]. Also pure 2-benzoxazolinone (BOA) isolated from several graminaceous crops such as rye (Secale cereale L.), maize and wheat was active similarly as herbicide but its stability in the environment was much shorter than the synthetic herbicide [35].
5. Plant allelopathins as sources of bioherbicides
Plant phytotoxic extracts, after evaluation, can be successfully used in integrated weed management. However, as was aforementioned, not all systems of its application under field conditions are suitable and profitable enough. To circumvent masking effects of one allelopathin by another in plant extract, research is now focused on isolation and application of a single, specific compound for the purpose of weed elimination. The list of allelochemicals isolated from various plants that may act as inhibitors of weed seed germination and/or weed growth are summarized in Table 1. A purified allelopathic compound may act on target plants with much higher or much lower strength. Even in situations when an allelopathin is active at unprofitably high doses but has a favorable environmental profile, it still may be a source to explore due to several reasons such as biodegradability. Modifications of chemical structure can make a compound more active on target plants while preserving desire properties.
Herein, examples of purified allelopathins with possible roles as herbicides are described. Some herbicides based on modified allelopathins already launched on the market are also included.
bottle brush (Callistemon citrinus), manuka (Leptospermum scoparium J.R., G. Forst)
\n\t\t\t
barnyard grass, hairy crabgrass, yellow foxtail (Setaria glauca L.), california red oat (Avena sativa L.), Indian mustard (Brassica juncea L.), curly dock (Rumex crispus L.)
barnyard grass, redroot pigweed, crabgrass, Sprangletop (Leptochloa filiformis Lam.), dandelion (Taraxacum sp.), lambsquarter or wild spinach (Chenopodium album L.), annual bluegrass or poa, morning glory or bindweed, wild mustard, curly dock
\n\t\t
\n\t
Table 1.
Allelopathic compounds isolated from plants that exhibit inhibitory potential on seed germination and growth of weeds
5.1. Sorgoleone
The inhibitory effect of sorghum on various plant species has been known for many years. Accumulation of sorghum phytotoxins in soil affects crop growth and imposes the need for a crop rotation system. Besides crops, weeds are also vulnerable to its allelopathic influence [16, 36]. Sorghum toxicity is mainly determined by both hydrophilic phenols in herbage, as well as hydrophobic sorgoleone and its analogs exuded by the root hairs [37, 38]. Therefore, sorghum herbage reach can be successfully used against weeds as a foliar spray as it is discussed in detail in the previous chapter.
However, allelochemical sorgoleone has enormous potential as an herbicide due to its high activity against various weed species. Studies conducted under laboratory conditions have shown that low doses of sorgoleone (100 μM) inhibit growth of the following weeds by 80%, black nightshade (Solanum nigrum L.), redroot pigweed, common ragweed (Ambrosia atrtemisiflora L.), and by 40% of sicklepod (Cassia obtusifolia L.), hairy crabgrass (Digitaria sanguinalis L.), velvetleaf (Abutilon\n\t\t\t\t\ttheophrasti Medik.), barnyardgrass and tef (Eragrostis tef Zucc., Trotter) [11, 16].
Sorgoleone released into the soil may act as a pre-emergence herbicide. Its persistence in the soil during or after sorghum cultivation inhibits germination and growth of small-seeded weeds, e.g. hairy crabgrass and green bristlegrass (Setaria viridis (L.) Beauv.), due to its better absorption and translocation within the small seeds than in large seeds [39]. However, strength and final effect on seeds or seedling physiology is multifactor-dependent. Sorgoleone sorbs strongly to the organic matter. This allows an extended persistence in the soil but unfortunately, significantly reduces its bioavailability. Moreover, the dynamics of decomposition significantly influences sorgoleone bioactivity, e.g. the methoxy- group of the aromatic ring is decomposed by 26% 48 h after exudation; however, some amounts of sorgoleone are also extractable after 6 weeks [40, 41]. Nevertheless, constitutive production of the compound allows a continuous supply and accumulation in the soil around 1.5 cm of root zone [42].
Inhibition of H+-ATPase in plant roots makes sorgoleone an effective growth inhibitor and potential post-emergence herbicide [43]. Decreased activity of that enzyme affects ion uptake and water balance by decreasing water uptake and affecting plant growth. Redroot pigweed, Jimson weed (Datura stramonium L.) and tef grown in hydroponic culture with 10 μM sorgoleone were characterized by lower H+-ATPase activity in roots. Presence of sorgoleone in nutrient solution significantly suppressed growth and evoked brown coloration and necrosis [43, 44].
Sorgoleone may be taken up by roots but cannot be translocated acropetally by xylem due to high lipophilic properties. Therefore, its application as a post-emergence herbicide may be limited. However, as a spray (0.6 kg ha-1), it has inhibited growth by 12% of green foxtail (Setaria faberi Herrm.), by 40-50% purslane, hairy crabgrass and velvetleaf, and up to 80-90% of common ragweed, redroot pigweed, and black nightshade [40].
Due to the structural similarity of sorgoleone to plastoquinon, it acts as a photosystem II (PSII) inhibitor [11, 43]. It binds to the niche of the D1 protein in PSII, gathers electrons and does not allow reoxidation of plastoquinon A by the secondary electron acceptor, plastoquinone B. Competition studies under sorgoleone versus synthetic herbicides such as atrazine, diuron, metribuzin and bentazon have shown that sorgoleone is an atrazine competitive inhibitor [11, 12]. Moreover, the I50 of sorgoleone is 0.1 μM and similar to other PSII inhibitors. It is worth mentioning that sorgoleone belongs to the His215 family of PSII inhibitors, while atrazine belongs to Ser264. Mutation in Ser264 of the D1 protein is responsible for resistance to triazines as well as other non-triazine herbicides, leading to cross-resistance. However, plants resistant to atrazine, with a QB binding site on PSII mutation (Ser264), are not resistant to sorgoleone. Application of sorgoleone is particularly justified in the case of triazine-resistant biotypes of redroot pigweed, due to the same physiological effects as applications of atrazine in redroot pigweed-susceptible biotypes [11]. These properties make sorgoleone a potential early post-emergence herbicide when applied as a spray with much less environmental implications than atrazine. Therefore, inhibition of photosynthesis is the main target site of sorgoleone action in young seedlings but its mode of action in older plants may be different [12]. Sorgoleone can be a useful inhibitor of p-hydroxyphenylpyruvate dioxygenase (HPPD), which takes part in α-tocopherol and plastoquinone synthesis. Inhibition of that enzyme leads to a decreased pool of available plastoquinone and indirectly affects activity of phytoene desaturase, a key enzyme in carotenoid synthesis. Such sequence of events causes declining carotenoid levels and affects photosynthesis [45]. Currently used triketone herbicides (e.g. sulcotrione, isoxaflutole) have the same mechanism of action on HPPD as sorgoleone, irreversible competitive inhibition, with I50 = 0.4 μM. Triketone herbicides are considered by the U.S. Environmental Protection Agency (EPA) to be a low environmental risk. They are usually utilized as selective herbicides to eliminate broadleaf weeds in corn [10]. It follows, due to similar action and chemical structure and environmental friendly profile, sorgoleone might also be useful as a selective herbicide; however, such comparison studies have yet to be conducted. Then, its mode of action also cannot explain whether it is more or less active on broadleaf or grass weeds species [44].
5.2. Momilactones
Extracts and residues of rice, the well-known cereal plant, also have allelopathic potential. Among isolated secondary metabolites, phenolic acids, hydroxamic acids, fatty acids, terpenes and indoles were identified [46]. The key role in rice allelopathy plays momilactone A and B isolated from root exudates. High allelopathic rice varieties release up to 2-3 μg of momilactone B per day [3]. These compounds inhibited the growth of typical weeds in rice, e.g. barnyard grass and awnless barnyard grass (Echinochloa colona (L.) Link.) at concentrations higher than 1 μM and 10 μM, respectively. Furthermore, phytotoxic abilities of momilacton A and B were also demonstrated on livid pigweed (Amaranthus lividus L.), hairy crabgrass and annual bluegrass (Poa annua L.) at concentrations higher than 60 μM and 12 μM, respectively [47]. The experiment has shown that momilactone B is secreted by rice roots into the rhizosphere over the entire life cycle [48]. Momilactone A and B belong to the diterpenoid phytoalexins which are known as antimicrobial secondary metabolites generated in response to signal molecules called elicitors (especially biotic elicitors) [49]. Both compounds thought to be unique to rice, recently have been found in the moss (Hypnum plumaeforme Wils.), a taxonomically distinct plant [49]. Despite the ability of momilactone A and B to inhibit plant growth, its mode of action in plants is still unknown.
5.3. Artemisinin
Artemisinin is a sesquiterpenoid lactone of annual wormwood (Artemisia annua L.). It is synthesized and sequestered in glandular trichomes located on the leaves and flowers [51]. It can also be excreted by the roots or root hairs, but only at the beginning of the growing season; therefore, dead leaves are the major source of artemisinin in soils [52]. Artemisinin is also lost from annual wormwood by rain runoff but to a minor degree (<0.5%),. This allelopathin is well known as a promising anti-malaric agent but also as a phytotoxin selective mainly to broadleaf weeds. Artemisinin (at 33 µM) significantly reduced shoot and root growth of lettuce, redroot pigweed, pitted morning-glory (Ipomoea lacunose L.) common purslane and annual wormwood [53]. However, the same treatment had no effect on sorghum or velvetleaf. Several studies have been aimed at identifying the molecular target site of this compound as well as the structural requirements for herbicidal activity [53-55].The effect of artemisinin is most evident on root growth and chlorophyll content. In onion root tips, artemisinin (10 - 100 μM) decreased the mitotic index, provoked abnormal mitotic figures and caused structural modifications of chromosomes [55]. However, no definite target site has yet been identified. The most recent studies on rice sprayed with 1.86 μM artemisinin indicated its inhibiting abilities on photosynthetic electron transport [56]. Artemisinin site of action is probably plastoquinone B in photosystem II. Interestingly, as authors suggest, this effect is caused not directly by artemisinin itself, but rather by an unidentified artemisinin-metabolite occured in the plant after artemisinin application [56].
Other controversies around the phytotoxic potential of artemisinin arose when the dichloromethane extracts of annual wormwood leaves containing artemisinin showed a stronger phytotoxic effect on redroot pigweed seed germination and seedling growth than pure artemisinin [57]. Moreover, aqueous extract with disposed artemisinin had equal inhibitory effects on both physiological processes as allelopathin alone. This experiment suggests a marginal role of artemisinin in plant extract and joint action of other allelochemicals. Although, most studies analyzing allelopathic weed–crop interferences using annual wormwood were conducted under laboratory and greenhouse conditions [58].
Toxic studies on duckweed (Lemna minor L.) and the fresh water algae Pseudokirchneriella subcapitata (Korshikov) had EC50 values 0.24 and 0.19 mg L−1 respectively, with growth rate as endpoint corresponding to those of the herbicide atrazine [59]. These profiles questioned environmental safety of artemisinin for the purpose as a bioherbicide. It may be a result of its complex chemical structure, but this compound may be used as the basis for a new herbicide, based on artemisinin chemical structure. Such attempts have already been made using artemisinin’s analogues [55]. Four of the tested 12 analogues inhibited germination and root growth of lettuce, Arabidopsis thaliana (L.) and duckweed at extremely low concentrations (3 μM).
5.4. Leptospermone
Leptospermone (1-hydroxy-2-isovaloryl-4,4,6,6-tetramethyl cyclohexen-3,5-dione) is a natural triketone produced by the roots of the bottlebrush (Callistemon citrinus Curtis) [60]. In its pure form, it was tested both pre- and post-emergence on a range of plant species including: hairy crabgrass, yellow foxtail (Setaria glauca (L.) P. Beauv.), barnyard grass, California red oat (Avena sativa L.), redroot pigweed, Indian mustard (Brassica juncea L.) and curly dock (Rumex crispus L.). Leptospermone is a strong p- hydroxyphenylpyruvate dioxygenase (HPPD) inhibitor with I50 values 3 μg mL-1[61]. Inhibition of this enzyme leads to disruption in carotenoid biosynthesis and loss of chlorophyll. Unfortunately, a pure compound rate of 9000 g a.s. ha-1 was required to give acceptable weed control. Such high doses excluded leptospermone from commercial development. The structure of this allelochemical was used as a basis for development of synthetic analogues including mesotrione (trade name Callisto), an herbicide produced by Syngenta AG. Mesotrione is applied for control of broadleaved weeds in maize. The rates of mesotrione are in the range from 75 to 225 g a.s. ha-1 (around 100 times more potent than leptospermone) [60].
However, leptospermone has lately been found as the main herbicidal component of manuka oil (Leptospermum scoparium J.R., G. Forst) [61]. Manuka oil (1%) applied as post-emergence spray, significantly decreased growth and dry weight of redroot pigweed, barnyardgrass, velvetleaf and hairy crabgrass. Though, hairy crabgrass seedlings that emerged after manuka oil application were totally blanched. Pre-emergence application of 0.17% manuka oil which corresponds to 0.2 L ha-1 of leptospermone inhibited hairy crabgrass growth by 50%. The pre-emergence effects are mainly dependent on its persistence in soil. Average time of leptospermone half-life in soil was calculated at 15 days while applied as a compound of manuka oil time extended by 3 days. This clearly shows that half-life of active compounds may be longer in mixture than applied alone due to additive or synergistic action. This type of leptospermone application poses another possibility of usage for this compound in its natural form without chemical modification of the structure [61].
5.5. Essential oils
Lately, there has been a growing interest for using essential oils as allelopathins with bioherbicide potential. Some of them have already been commercialized and successfully launched in organic agriculture. They disrupt the cuticle and contribute to desiccation or burn down young tissues. Examples of this are the commercially available bioherbicide with the trade name of GreenMatch EX which consists of lemongrass (Cymbopogon sp.) oils or InterceptorTM with 10% pine (Pinus sylvestris L.) oil [3]. Essential oils are complex mixtures of monoterpenes, sesquiterpenes, and aromatic phenols, oxides, ethers, alcohols, esters, aldehydes and ketones [62]. The main terpenoids of volatile essential oils are monoterpenes (C10) and sesquiterpenes (C15). It has been well documented that essential oils found in foliage of eucalyptus (Eucalyptus sp.) show phytotoxic potential. During field experiments it has been reported that common weeds such as coffee senna (Cassia occidentalis L.) and barnyardgrass sprayed with different concentrations of eucalyptus oil (from 5 % to 10 % v/v with 0.05 % v/v Tween-80) exhibited dose-dependent and species-dependent levels of injury. Coffee senna plants were more sensitive to the eucalyptus oil than barnyardgrass [62]. Phytotoxicity of eucalyptus oil is due to the components such as 1,8-cineole, citronellal, citronellol, citronellyl acetate, p-cymene, eucamalol, limonene, linalool, α-pinene, γ-terpinene, α-terpineol, alloocimene, and aromadendrene [62]. Pre-emergence herbicidal activity of 1,8-cineole 3, and 1,4-cineole 4 were tested against rigid ryegrass and garden radish var. Long Scarlet in laboratory-based bioassays. 1,8-cineole and its derivatives showed a dose-dependent herbicidal activity against both weed species [64]. Laboratory studies [64, 65] also have shown that soil-applied 1,8-cineole suppressed the growth of several weeds. However, field reports demonstrated that 1,8-cineole alone has poor herbicidal activity [67, 68]. The commercial herbicide cinmethylin is a 2-benzyl ether substituted analog of the monoterpene 1,4-cineole (1-methyl-4-(1-methylethyl)-7-oxabicyclo heptane). This compound was discovered and partially developed by Shell Chemicals as a derivative of the allelopathic natural monoterpene, 1,8-cineole [69]. The benzyl ether substitution appears to decrease the volatility of the cineole ring by several orders of magnitude thereby rendering it more suitable for herbicide use [70]. Cinmethylin is a moderately effective growth inhibitor used for monocot weed control [71]. Despite the fact that it has been used commercially in both Europe and Japan and has been studied experimentally for several decades, the mechanism of action of this herbicide is still unknown [54, 72]. Cinmethylin was commercialized outside the United States in 1982 under the trade names of Cinch and Argold. Cinmethylin is active on several important grasses in rice; Echinochloa sp., Cyperus sp. and heartshape false pickerelweed (Monochoria viginalis Burm.f.) at rates from 25 to 100 g a.s. ha-1 [73].
5.6. Sarmentine
Sarmentine was first isolated from long pepper (Piper longum L.) fruits [74] but is also present in varied organs of other Piper species (Huang and Asolkan patent). It has been known as a medicinal plant with many beneficial multidirectional properties on human health. However, methanol extract of long pepper dry fruits has been shown to be suppressive to lettuce [75]. Purification and fractioning of long pepper crude extract allows the dissection of the active compound – sarmentine, a molecule with a long unsaturated fatty acid chain and pyrrolidine. Due to the hydrophobic properties, sarmentine is suspended with surfactants, 0.2 % glycospere O-20, 2% ethanol and 0.1% sodium lauryl sulfate. As a foliar spray, it is active at 2.5 mg mL-1, but its high phytotoxicity is manifested at 5 mg mL-1. Higher concentrations of sarmentine caused almost 100% mortality of redroot pigweed, barnyardgrass, bindweed (Convonvulus sp.), hairy crabgrass, sprangletop (Leptochloa sp.), annual bluegrass, wild mustard (Sinapis arvensis L.), curly dock with impaired effects on horseweed (Conyza canadensis (L.) Cronquist) and sedge (Carex sp.) growth under laboratory conditions. First phytotoxic symptoms such as bent stems and contact necrosis, have been visible 30 minutes after application; however full-blown implications were seen 7 h after spraying. The most likely mechanism of sarmentine action on plants is disruption of the plant cuticle which leads to disruption of cell membranes and lipid peroxidation followed by formation of radicals [76, 77].
As an herbicide, sarmentine and its derivatives may be both obtained from fruits of long pepper and successfully chemically synthesized [75]. Despite the fact that the compound is active under laboratory conditions, its chemical and biological instability under field conditions may limit its application as an herbicide. However, it has been shown that crucial for sarmentine herbicidal activity is the presence of an amine bond with a secondary amine. Replacement of the acid moiety with structurally similar fatty acids has not changed its phytotoxic potential. Moreover, natural herbicides based on sarmentine may also contain other derivatives with similar modes of action on plants but higher environmental stability [75]. Sarmentine may be successfully applied in combination with synthetic herbicides, e.g. aryloxyphenoxypropionic, benzoic acid, dicarboximide, organophosphorus, triazine, sulfonamide herbicides and with many others. This gives an opportunity to further the structural modification that makes the compound more stable without any disadvantages on bioherbicide action in plants. It is worth noting that sarmentine has already been patented as an herbicide but not commercialized yet [75].
6. Biotechnology in bioherbicide investigation
A lot of effort has been done to explore the nature of allelopathic interactions. Studies on allelopathic compounds greatly increased thanks to chemical and biochemical techniques, which improved identification and knowledge about its mode of action. Since then, the crucial role of secondary metabolites synthesized and released by plants became better understood. It has been clearly demonstrated that allelopathins may take part in very complex inter- and intra-specific ecological interactions including soil microorganisms. However, despite the extensive research carried out under laboratory conditions, the higher level of such interactions at the ecosystem level has not been sufficiently explored. Structure, chemical properties, and mode of action in plants of multitude allelochemicals are already known but, unfortunately, only a part-per thousand of them have been successfully introduced in agricultural practices. This is mainly due to limitations of compounds as plant protection agents but also due to extended field experiments. A very important aspect that allows the introduction of allelopathy to natural weed management is knowledge about biology of donor and target plants and the exact chemicals responsible for the interaction [78]. All formerly described limitations of natural compounds as bioherbicides decreasing in case of plant extracts as herbicides due to simple and low cost of application. However, separation of one, specific compound that is the most interesting for us among hundreds synthesized by plants often required information about its synthesis in vivo.
One of the problems is to obtain adequate amounts of the compound, when its chemical synthesis is impossible or collection of plants, unprofitable. Increased synthesis of an allelopathin gives triple profits. First of all, enhanced allelopathic potential of a plant makes it more competitive against weeds. Second of all, increased concentration of a compound makes plant extract more active. Thirdly, this allows collection of the compound at a sufficient amount and makes it more profitable. However, it is much easier to obtain active compounds from the crop species than wild living ones. Difficulties in introducing plants to cultivation are due to the low ability to grow outside their natural ecosystem [79].
Cells and organ cultures provide opportunities to circumvent these limitations. Abilities of undifferentiated and differentiated cells to produce allelochemicals may be commercialized in bioreactors using cell suspension cultures [79]. Such attempts have been made on Artemisia suspension culture for artemisinin production; however, obtained amounts of that compound were insufficient. The addition of β-cyclodextrins to the growing medium has increased artemisinin synthesis up to 300% [80]. Allelochemicals produced by roots may be obtained from hairy root cultures, both via callogenesis or infection with Agrobacterium tumefaciens. Transgenic hairy roots are characterized by high genetic stability and facility to accumulate metabolites. The hairy root system already has been applied to increased production of phenolic compounds of nettleleaf goosefoot (Chenopodium murale Linn.) [81] and gossypol of cotton [82]. Active growth of roots and rapid colonization of the bioreactor allows rapidly reaching target weight, necessary to obtain an adequate quantity of the compound extracted from plants or growing medium.
The recombinant DNA technology can be useful to improve allelochemical production. Enhancing or suppression of gene expression, metabolic engineering and genetic transformation are promising new tools for allelochemical synthesis [79]. This approach is based on elucidation of the metabolic pathway, enzyme activities and identification of genes encoding crucial enzymes, associated with metabolite (allelochemical) synthesis.
Allelopathy is a quantitative trait. A genetic analysis of quantitative trait loci (QTL) is a promising approach to identify genes underlying this trait. Only a few crops are under genetic screening for its allelopathic properties including: rice, wheat, barley and oat [83, 84]. The first QTL map associated with allelopathic properties was developed in rice. A segregating population derived from a cross of two cultivars varying with allelopathic potential against barnyardgrass. The map contained 140 DNA markers with four main-effects QTL located on chromosome 2, 3 and 8 [85]. Proteomic studies on allelopathy of rice against barnyardgrass confirmed the crucial role of three enzymes: phenylalanine ammonia-lyse (PAL), thioredoxin and 3-hydroxy-3-methilglutarilcoenzyme A reductase 3 (HMGR) is highly involved in phenols biosynthesis [86]. Such a genetic approach may allow the location of the gene in the genome and better understanding of its function in plant allelopathy and create the chance of applying marker assisted selection (MAS) to enhance allelopathic abilities.
Just like breeding programs allow improved crop production, they may also improve production of allelopathic compounds increasing allelopathic potential.
Scopoletin has been known as allelopathic root exudates of oats (Avena sp.) that affects growth of neighboring plants. Screening of 3000 of Avena accessions has shown varying ability to scopoletin production. Twenty five of them have exuded higher amounts of scopoletin than control cultivar Garry, of which 4 were threefold more than the control [87]. Variation in allelopathin production was also discovered for sorgoleone of seven sorghum accessions [38] nomilacton A and B of 8 rice accessions [88] DIBOA and DIMBOA of 14 rye cultivars [88], gramine of 43 lines of modern cultivar of barley (Hordeum vulgare L.) and wild progenitor H. spontaneum (C. Koch) [90]. Enhanced production of active compounds from allelopathic plants can be developed by efficient breeding - selection of individuals with high allelopathic ability. Identification of a single gene, arranged in synthesis of allelopathin already has been performed for sorgoleone. SOR1 (or compatible SbDES3) expression is specific for root hairs of two species of sorghum (S. bicolor and S. halepense) and associated with sorgoleone synthesis, while it is not expressed in other organs of sorghum SOR1 encodes novel fatty acid desaturase (FAD), involved in the formation of a specific bond at 16:3Δ9,12,15 pattern [91, 92]. Comparative studies of FAD derived from sorghum with other desaturases showed high similarity to omega-3 fatty acid desaturases (FAD3) [93]. However, none of the hitherto known desaturases can synthesize double bonding at this unique pattern along the aliphathic chain of the sorgoleone molecule. Characterization of this gene allows an overexpression of SOR1 and increased sorgoleone synthesis and improved allelopathic potential of sorghum, as well as easier collection of the compound. Moreover, the well-known pathway of sorgoleone synthesis and characteristic of candidate genes may be a promising source of introducing sorgoleone production to grass crops [94].
The situation becomes more complicated when more than one gene encoding special enzymes is required to increase synthesis of a plant compound. Such difficulties have been encountered for DIBOA, synthesized by various grass species [95]. In maize, biosynthesis of this compound is determined by five genes (Bx1 to Bx5) encoding three enzymes: tryptophan synthase α homolog, cytochrome P-450-dependent monooxygenase [95].
Monoterpenes are a large family of compounds produced by a varied family of aromatic plants. Some of the monoterpenes also take part in allelopathic interactions, e.g. linalool, cineole camphene, pinene, limonene, etc. Currently, metabolic engineering allows improved production of specific compounds in heterologous systems [96]. The most interesting are monoterpene synthases which catalyzed geranyl diphosphate (GPP) into output structure of numerous monoterpenes family, e.g. enhanced expression of limonene synthase in transgenic peppermint (Mentha piperita L.) has increased yield of monoterpenes. An alternative approach is to change the density of secretory structures by both plant hormone and transcriptional factors manipulation. Such attempts already have been made in annual wormwood and A. thaliana. It was recently found that the number of glandular trichomes increased in response to jasmonic acid. Spraying of annual wormwood with this hormone significantly increased density of these structures on leaves what was accompanied with higher artemisinin content [51]. This was an effect of enhanced expression of gene encoding enzymes taking part in artemisinin biosynthesis. On the other hand, in Arabidopsis, co-expression of two positive transcriptional factors (GL1, and R protein of maize) has significantly improved the number of trichomes [96].
However, we have to bear in mind that biosynthesis of natural compounds can be limited to organs, tissues or even cells. Specific locations of compound synthesis, accumulation or secretion often make that compound toxic to other tissues within the same plant organism. Moreover, even successful transformation of a plant does not guarantee successful and sufficient production of a desirable compound. The gene of (S)-linalool synthase (Lis) of fairy fans (Clarkia breweri Gray), constitutively expressed in transgenic petunia (Petunia hybrida Hook.), has produced linalool but in its glycosylated, non-volatile form [96].
All presented techniques provide greater knowledge on allelopathy. However, better understanding of such complex interactions among this phenomenon bring us one step forward to development of new strategies in weed management and finding new herbicides and new herbicidal target sites.
7. Conclusions
The phenomena of allelopathy and phytotoxic interactions between plants are strongly expanding branches of biological science. Allelochemicals, as a group of substances also called biocommunicators, seem to be a fruitful challenge for combining traditional agricultural practices and new approaches in pest management strategies. Allelochemicals have already been used to defend crops against pathogens, insects or nematodes, parallel to some attempts to use them for weed control. Crop rotation, cover crops, dead and living mulches are being employed in agriculture. Both in natural and agricultural ecosystems allelopathic interactions are involved in practically every aspect of plant growth, as they can play the role of stimulants and suppressants. Complex plant-plant and plant-microbe interactions in ecosystems and currently developing studies on molecular, cytological and physiological levels bring us to a better understanding of processes occurring around us. The ancient knowledge of well-known toxic properties of water extracts of a variety of allelopathic plants give us a basis that could be used in the creation of a novel approach in weed control.
Some allelochemicals, mainly these that are mentioned in the text above, may act as a starting point for production of new bioherbicides with novel target sites, not previously exploited, as the understanding of their mode of action is still growing. Creation of bioherbicides based on allelochemicals generates the opportunity to exploit natural compounds in plant protection and shows the possibility to cope with evolved weed resistance to herbicides. Despite the fact that we have extensive knowledge about the chemical nature of natural compounds, we can synthesize its analogues, and we have basically explored its phytotoxic potential, we still have insufficient data. Until recently, most studies on phytotoxicity have been conducted under laboratory conditions due to the ability to eliminate other environmental factors such us temperature, soil texture and its chemical and physical properties. Such approach allows the recognition of only direct effects of allelochemical action. There is still a great need to transfer laboratory data into field conditions. Such experiments are not willing to be taken on due to troublesome field experiments dependent on environmental conditions and a few year repetitions. New tools of molecular genetics, proteomics and metabolomics profiling as well as modern and sophisticated methods of chemistry and biochemistry will lead to the creation of substances, maybe based on the structure of particular compounds occurring in nature, which could be used without any risks as selective and eco-friendly herbicides.
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Introduction",level:"1"},{id:"sec_2",title:"2. Allelopathic interactions and allelopathic compounds",level:"1"},{id:"sec_3",title:"3. Advantages and disadvantages of allelopathins as bioherbicides ",level:"1"},{id:"sec_4",title:"4. Allelopathic plant extracts as bioherbicides ",level:"1"},{id:"sec_5",title:"5. Plant allelopathins as sources of bioherbicides",level:"1"},{id:"sec_5_2",title:"5.1. Sorgoleone ",level:"2"},{id:"sec_6_2",title:"5.2. Momilactones ",level:"2"},{id:"sec_7_2",title:"5.3. Artemisinin",level:"2"},{id:"sec_8_2",title:"5.4. Leptospermone",level:"2"},{id:"sec_9_2",title:"5.5. Essential oils",level:"2"},{id:"sec_10_2",title:"5.6. Sarmentine",level:"2"},{id:"sec_12",title:"6. Biotechnology in bioherbicide investigation",level:"1"},{id:"sec_13",title:"7. Conclusions",level:"1"}],chapterReferences:[{id:"B1",body:'RolaHMarczewskaKKucharskiMZjawisko odporności chwastów na herbicydy w uprawach rolniczych. 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Laboratory of Biotechnology, Plant Breeding and Acclimatization Institute - National Research Institute, Mlochow, Poland
Department of Plant Physiology, Warsaw University of Life Sciences – SGGW, Warsaw, Poland
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1. Introduction
Facing the unavoidable aging population, the demands for long-term care services are increasing and need to be addressed in modern society [1, 2]. In order to effectively provide long-term care to the elderly in the community, the Taiwan government proposed a 10-year long-term care project, namely Long-term Care Project 1.0 (LTCP 1.0), in 2007. The goal of the project was to establish a comprehensive community-based care system for (i) providing appropriate services to the elderly, (ii) improving the independence of the elderly, (iii) enhancing the quality of life, and (iv) maintaining autonomy and dignity [3]. Difference in gender, level of urbanization, culture, economy and health are also considered in this system. Eight services items are covered in LTCP 1.0: daily care services, transportation services, meal services, home and community-based rehabilitation services, respite care services, home nursing, rental of equipment and long-term care institution services [4]. Subsequently, the government launched a more extensive public framework, i.e., LTCP 2.0, in 2017 to increase the services coverage in the community [5]. Table 1 shows the differences between LTCP 1.0 and LTCP 2.0, in which LTCP 2.0 expands the scope of services to optimize the front-end preventive care and to provide back-end connections to multi-target support services and home hospice services.
LTCP 1.0
LTCP 2.0
Services targets
4
8
Services scopes
8
17
Financial resources
Government funding
Medical development funding
Government funding
Social welfare funding
Long-term service development funding
Innovative service
N/A
ABC community care model
Table 1.
Differences between LTCP 1.0 and LTCP 2.0.
In order to provide high quality and affordable services, the ABC community care model with 3 tiers was established to clearly define the roles and responsibilities of healthcare parties involved in LTCP 2.0, as shown in Figure 1 [6]. Tier A refers to a community integrated care service center for coordinating and allocating the resources of care services based on the care plan formulated by care managers. Moreover, Tier A also provides a localized delivery system that connects to Tier B and Tier C. Based on the assessment results from Tier A, Tier B, i.e. a multiple service center, provides corresponding diverse healthcare services for the public. Information from Tier A and Tier B is then sent to the long-term care station in Tier C for providing various care functions to the elderly. Therefore, the care managers in Tier A play an important role in LTCP 2.0 in evaluating the needs and formulating care plans [7]. However, it is complicated for care managers to perform the tasks of health assessment, reviewing historical health records and resources planning in a short time. In addition, it requires care managers with adequate knowledge and experience to handle these tasks. Due to the fact that in healthcare resources are as shortages in terms of staff and equipment, the implementation of care planning in LTCP 2.0 becomes challenging to achieve the goals of providing accurate and fast-responsive healthcare services.
Figure 1.
ABC community care model.
To address the above problems, the objective of this article is to present an intelligent clinical decision support system (ICDSS) for care planning. The case-based reasoning (CBR) technique is adopted to provide the knowledge support for decision-making in care planning. By extracting the relevant knowledge from similar past cases, the care plans can be formulated in a cost-effective and time-efficient manner so as to maintain the high quality of services. The rest of article is structured as follows. Related studies and background are discussed in Section 2. Section 3 describes the architecture of the proposed system while Section 4 discusses the case study and findings. Section 5 shows the future research directions. Conclusions are drawn in Section 6.
2. Related work
Due to the development of medical technology and increased life expectancy, the number of elderly people in Taiwan is expected to increase continuously annually. According to the statistics [8], the percentage of the elderly population aged 65 or above is 13.2% in 2016. It is estimated that this population in 2026 will reach to 20.6%. Associated with the fast growth of the aging population, the reporting of chronic diseases has also increased significantly. As a consequence, the needs for long-term care services have become more demanding and urgent. In response to the increased demands for long-term care services, the government of Taiwan has considered long-term care services in healthcare industry as one of eight key industries to minimize the threats of chronic diseases [9]. Therefore, the Long-term Care Project 1.0 (LTCP 1.0) was launched in 2007 and was an initiative to integrate the home and residential-based services in Taiwan. Up to the end of 2015, over 160,000 people had received the services offered by LTCP and there are nearly 2800 institutions providing care services following the principles of LTCP. In view of the benefits offered by LTCP 1.0, a revised version of the original LTCP, i.e. LTCP 2.0, was created to further facilitate the integration of preventative care, social care and medical care in the community. In order to facilitate the implementation of LTCP 2.0 and coordinate the operation of long-term services and resources, researchers have been focusing on improving the performance of LTCP. Liu and Yao [10] adopted latent class analysis to examine the interrelationship among health indicators so as to determine the level of needs of the elderly care recipients. Lin et al. [11] studied the performance of LTCP according to the dimensions of the workforce, sources of funding, application of technology, service nature and norms. The aims of their study is to identify problems in LTCP and develop a continuous improvement strategies so as to improve the long-term care services. However, attention is rarely paid to the field of LTCP as well as providing knowledge support for decision-making. In fact, due to the shortage of knowledge manpower, it is time-consuming and tedious for care managers to effectively provide an integrated “one-stop” consultation for applications, health evaluations, care plans formulation and the coordination and delivery of long-term care services. Therefore, it is essential to provide knowledge support for decision-making processes in the care planning of LTCP.
In recent years, decision support systems have become increasingly popular for providing decision support in various industries [12, 13], and are designed to facilitate precise decision-making through the use of accurate and timely data, information and knowledge management. In the healthcare industry, the clinical decision support system is a specific term for the health customized version of decision support system [14]. The emerging clinical decision support system allows healthcare professionals to manage and manipulate the massive amount of data in an effective and efficient manner. In this situation, automatic decision-making can be provided for evaluating the health status of patients and providing corresponding treatment. Case-based reasoning (CBR) is one of the well-known artificial intelligent techniques commonly embedded in the clinical decision support system and adopts previous experience and knowledge for solving new problems [15]. The general CBR model has been formalized for computer reasoning as a four-step process: (i) retrieve the most similar case, (ii) reuse the retrieved case for solving the new problem, (iii) revise the content in the solutions if necessary, and (iv) retain the solutions as the new case stored in the case library [16]. The predictive process in the CBR allows users to takes less effort and time to generate solutions. CBR has been widely adopted in the healthcare industry in disease diagnosis, planning and resources allocations [17, 18]. Chang et al. [19] adopted CBR to develop a continuous care information system for facilitating discharge planning. By the adoption of computer-reasoning in the evaluation process of care planning, accurate continuing care solutions can be formulated in a timely manner. Wang et al. [20] developed a hybrid CBR approach to shorten the time for providing the treatment planning for the people with mental problems. Petrovic et al. [21] applied CBR in radiotherapy treatment planning to reduce the errors in planning and in recommending radiotherapy solutions. The above studies show that CBR is a promising approach in providing knowledge support for generating the solutions in the healthcare industry.
In summary, from the above literature, it is found that care managers play an essential role in the LTCP for generating appropriate and personalized long-term care solutions and in coordinating care services resources. Due to the needs for accurate and fast-responsive healthcare services, the adoption of a clinical decision support system using CBR is a feasible solution to shorten the evaluation time and improve the service quality in LTCP.
3. Methodology
In order to facilitate the decision-making of care managers, an intelligent clinical decision support system (ICDSS) is developed. The ICDSS architecture is shown in Figure 2 and consists of three modules: (i) data collection module, (ii) case-based reasoning module, and (iii) care plan formulation module. With a systematic method to provide knowledge support for care managers, the effectiveness and efficiency in the processes of assessing the health information and formulating care plans can be improved. Consequently, accurate and fast-responsive healthcare services can be delivered to the elderly so as to maintain a high quality of care.
Figure 2.
An architecture of ICDSS.
3.1 Data collection module
In the data collection module, the elderly with chronic diseases or disabilities can apply for the community care service through the online platform. Three types of data, historical data, medical records and personal information, are collected and uploaded to the cloud databases in electronic format. Historical data refers to past health data such as vaccination records, surgery records, and the historical data from the past public system. The medical record is the biometric data for reflecting the psychological and physiological aspects of the elderly. Heart rate, blood sugar index, vision and muscle strength are examples of the medical record. Personal information of the elderly including name, age, gender, family relationships, personality, joint condition, sleeping ability and living environment are also collected. Apart from uploading the three types of data, an interview is conducted to understand the problems of the elderly in daily living. By doing so, care managers can collected both subjective and objective data for further data analysis in the case-based reasoning.
3.2 Case-based reasoning module
Traditionally, care managers have to review the massive amount of health data one by one for evaluating the needs of the elderly. The use of ICDSS allows care managers to effectively formulate appropriate care plans according to the needs. In this module, CBR is adopted to retrieve the most similar care records for generating the care solutions. To begin with the first stage in the CBR, i.e. case retrieval, past care records are firstly stored in the case library. An indexing tree is constructed to cluster past care records according to the key attributes that may affect the types of service provided. Along the searching path of the indexing tree, a small group of past case records are retrieved. In the reuse stage, retrieved case records are ranked descending order according to their similarity value. Eq. (1) is the expression for calculating the total similarity value.
Total similarity value=∑i=1nwisimfiIfiR∑i=1nwiE1
where wi is the weighting of the attribute i, sim is the function for calculating the similarity value of attributes, and, fiI and fiR are the values of attributes fi in the new and past cases. The care record with the highest similarity value is selected and considered as the most significant reference to generate care solutions for solving the new problem. After that, care managers can make modifications to the retrieved care records so as to meet the real-life situations of the elderly. Therefore, a new care plan is formulated for serving the elderly.
Differing to the traditional CBR process, a rating scheme is deployed in the case retention process to assess and monitor the effectiveness of the care plan. Figure 3 shows the rating scheme in the case retention of CBR. As it is a long-term care service, regular meetings between care managers and the elderly or their families is required so as to collect their feedback. In addition, feedback from direct service providers, i.e. social workers and nursing staff, are also collected for the performance evaluation. Only care plans with good quality are retained in the case library for continuously improving the quality of the care records.
Figure 3.
Rating scheme in the case retention of CBR.
3.3 Care plan formulation module
After the case revision process of CBR, a new care plan with specific goals is generated. The care plan consists of several elements: (i) type of care, (ii) meals & nutrition, (iii) transportation and (iv) community center/activity recommend. According to the different nature of healthcare services, i.e. home care services or residential care services, different levels of healthcare services are provided for the elderly. In addition, details of the care plan are shared and transferred to the healthcare parties in Tier B and Tier C in the ABC community care model. Based on the care plan, operational guidelines can be provided to caregivers so as to deliver the corresponding healthcare services. Considering the health deterioration occurred in the elderly, the needs of healthcare services will move from less intensive care towards more intensive care via ABC model. Therefore, the re-evaluation of care plan is required every month to ensure its appropriateness.
4. Case example
In this section, a case example is illustrated to demonstrate the application of the ICDSS for providing knowledge support for decision-making in care planning. The case company is one of integrated community service centers located in Taichung, Taiwan. The main objective of the case company is to bring “Health and Happiness” for the elderly so as to maintain their quality of life, and physical and mental health in the community. The main staff members in the case company are care managers and supervisors such as social workers, nurses, occupational therapists and pharmacists for formulating care plan and coordinating care service resources. Figure 4 shows the existing operation flow in the case company. The current practice of information flow, elderly health evaluation, healthcare service suggestions and follow up services are done manually. Care managers base on their experience to provide suggestions for the elderly. However, it is time-consuming and ineffective for care managers to implement these complicated steps in care planning. In addition, human errors easily occurs in these evaluation processes, resulting in high complaint rates and poor service satisfaction in the case company.
Figure 4.
Existing operation flow in the case company.
4.1 Implementation of ICDSS
In order to tackle the above mentioned problems, the case company decided to implement the proposed system in providing knowledge support in care planning. Instead of the traditional in-person application method, the online platform is developed to collect the information. As mentioned in Section 3.1, data such as historical data, medical record and personal information are inputted by the elderly and then stored in the cloud database of the Data Collection Module. After submitting the application, care managers can note the new application in the ICDSS. An interview can be arranged to understand their current health situation and problems faced in daily living. The interview results are also stored in the database. In order to reduce the errors in the care management processes, care managers have to verify the accuracy of the data provided by the elderly.
Relevant information is then extracted and transfer to the Case-based Reasoning Module for further data analysis. In the case-based reasoning module, care managers have to identify the key attributes for constructing the indexing tree and retrieving the past care records. In this situation, five attributes: (i) kind of mobility, (ii) self-care ability, (iii) type of neuropsychiatric condition, (iv) communication method and (v) age are defined as key attributes that may significantly influence the type of services provided in care planning. Figure 5 shows the user interface for case retrieval. According to the structure of the indexing tree, the small group of past case records which match the specifications are retrieved. After that, the retrieved past case records are ranked using Eq. (1) so as to distinguish the most suitable case record. A total of 17 attributes are used to calculate the total similarity value of care records, as shown in Table 2. Based on the results from the case reuse, the care record with highest similarity value is extracted. An applicant is selected to illustrate the mechanism of CBR in ICDSS and details of the applicant are shown in Table 3. It is found that the elderly needs assistive devices for performing the movement and self-care activities. She does not have any problem of cognitive decline. A comprehensive review is implemented for deciding on the type of services. After the processes of case retrieval and reuse, the past care record (ID: 0082) has the highest similarity value (91.21%) to the new applicant. Therefore, care managers can select the past care record (ID: 0082) as the most significant reference for generating the new long-term care solutions.
Figure 5.
The user interface for case retrieval.
No.
Attributes
Weighting
No.
Attributes
Weighting
1
Living condition
1
9
Percentage of falling
2
2
Height
1
10
Drinking
2
3
Weight
1
11
Smoking
2
4
Glucose
1
12
Arthritis
2
5
Heart rate
1
13
Cancer
2
6
Upper blood pressure
1
14
Dysphagia
2
7
Lower blood pressure
1
15
Liver disease
2
8
Body temperature
1
16
Mental disease
2
17
Urinary
2
Table 2.
Attributes for calculating the similarity value of care records.
Items
Detail
Items
Detail
Name
JL
Gender
Female
Age
69
Weight (kg)
57
Communication method
Oral
Height (cm)
160
Mobility
Move with the aid of assistive devices
Blood pressure (mmHg)
108/46
Self-care ability
Function with the aid of assistive devices
Heart rate (bpm)
80
Neuro-psychiatric
No cognitive decline
Body temperature (°C)
36.4
Living condition
Living with family
Fall (%)
18
Table 3.
Details of the applicant.
To formulate a tailor-made care plan according to the needs of the elderly, modifications are made by care managers to add additional healthcare information and revise the content of the past care record. Therefore, a new care plan with services details can be generated, as shown in Figure 6. In the new care plan, home care services are provided to the new applicant three times per week. Therefore, this information is sent to the caregivers in Tier B and Tier C for allocating the corresponding healthcare resources. In each visit, caregivers are required to measure the biometric data including blood pressure, heart rate, blood glucose level and body temperature. They have to assist the elderly in bathing and filling the drug organizer. In addition, transportation service is provided for pick from their home to the hospital for regular checking. By doing so, community-based long-term care services can be delivered.
Figure 6.
The details of the retrieved past care record.
In order to ensure the quality of care provided, a feedback survey is conducted to measure the performance of the care plan, as shown in Figure 7. In addition, a regular meeting is held for nursing staff participating in serving this elderly so as to discuss and understand the problems faced in delivering the healthcare services. The care plan with good quality is then retained in case library for continuously quality improvement purposes.
Figure 7.
Feedback survey for the elderly.
4.2 Findings
Through the pilot study in the case company, the ICDSS contributes to (i) improve the efficiency in care planning, and (ii) enhance the quality of long-term care services.
With the implementation of the ICDSS, it found that ICDSS offers several benefits to the case company. Firstly, it improves the efficiency in care planning. Instead of the traditional manual approach in care planning processes, ICDSS allows the care managers to formulate personalized long-term care solutions based on past explicit knowledge. Table 4 shows the performance improvements after the use of ICDSS. With the use of ICDSS, care managers can review the health records provided by the elderly in the online platform rather than finding such information from separate data files. Therefore, the time for reviewing health records is significant reduced by 80.77% Moreover, the time for formulating the details of the care plan is also reduced from 156 to 22 min. Since the knowledge in past care records is extracted for solving new cases with similar problems, care managers can effectively generate solutions for providing the appropriate healthcare services and promoting the preventive health.
Performance indicator
Without ICDSS (min)
With ICDSS (min)
Improvement (%)
Time for reviewing health records
52
10
80.77
Time for formulating the details of care plan
156
22
85.90
Table 4.
Performance improvement after the use of ICDSS.
Furthermore, ICDSS prevents knowledge loss for formulating care plans. Since care managers with different levels of experience are employed in the case company, there may be some variations in the context of care plans. The adoption of ICDSS allows the valuable knowledge to be stored and shared in the form of past care records. Care managers, especially junior care managers, can make use of this knowledge to facilitate their decision-making. Thus, consistent long-term care solutions can be generated by different care managers. In addition, the number of complaints per week is reduced from 8 to 2. With the decrease in the number of complaints, the service satisfaction is significantly improved. Not only can the integrated community service center in Tier A enhance the service satisfaction, but also the healthcare parties in Tier B and Tier C.
5. Future research directions
5.1 Explore the adoption of the Internet of Things (IoT) in residential care services
According to Zhao et al. [22], there is growing evidence that chronic diseases are the major issue associated with aging population. In the last decade, the Internet of Things (IoT) is a newly emerging technology for the healthcare industry [23]. Under the IoT platform, information can be gathered, processed and analyzed to serve individual and healthcare organizations. IoT has been widely adopted in hospitals and home care services for remote monitoring and disease diagnosis. It is not only to help increase the data accuracy of the clinical decision support system, but also to provide early detection of any abnormalities occurring. In fact, its application can be further explored in residential care services. Current adoption of the IoT in nursing homes are lagging behind the hospitals and home care services providers. As one of the important long-term care service providers in LTCP 2.0, future research effort can be paid in extending the adoption of IoT in nursing homes so as to speed up its daily routine processes. Considering that numerous healthcare parties are involved in the LTCP 2.0, the IoT allows the caregivers to real-time collect and monitor the biometric data of the elderly through the equipment of the sensors. It also facilitates the information exchange among various parties. Once the abnormalities occurred, instant actions can be generated by corresponding healthcare parties to prevent the further health deterioration.
5.2 Explore a big data analytics platform for data management and manipulation
On the other hand, due to the application of smart devices and social media in healthcare services, there is an exponentially increase of health-related big data [24]. It is necessary to develop big data analytics platforms with text mining and machine learning abilities for facilitating data management and manipulation. For example, with the use of IoT, massive health data can be collected and stored in the cloud platform. The data analytic techniques such as artificial intelligence help discover the hidden pattern of available data and generate invaluable knowledge for supporting the proactive healthcare services in the LTCP 2.0. Criteria, including the ability to manipulate, continuity, ease of use, availability, quality assurance, privacy and security, should be considered in designing of this platform [25]. In addition, any data lag between data collection and processing should be avoided in this platform for achieving real-time big data analytics. Therefore, how to integrate the mentioned elements in the big data analytics platforms should be considered in future research so as to improve the effectiveness of LTCP.
6. Conclusions
To cope with the aging population, the needs for healthcare services as well as community care services are demanding. With the purpose of reducing the burden on caregivers, healthcare parties are seeking ways to better utilize the limited resources to improve the service quality. In Taiwan, LTCP 2.0 has been launched to create a comprehensive community-based care and health support system for the elderly. Care managers in Tier A play crucial roles in LTCP 2.0 for deciding on the types of service provided and in coordinating with care service resources. However, the traditional manual approach relying on care manager experience to perform the evaluation tasks and care plan formulation is ineffective. Without a knowledge-based decision support system, it is difficult for care managers, in a timely manner, to generate personalized long-term care solutions as well as coordinating care resources in Tier B and Tier C. Therefore, in this chapter, the ICDSS is designed to provide the knowledge support for decision-making in care planning of LTCP 2.0. The adoption of CBR in ICDSS approach allows care managers to disseminate the experience gained from the past similar care records. By doing so, it enables the successful execution of care planning so that fast-responsive and accurate healthcare services can be delivered. Furthermore, it enables data sharing and communication among healthcare parties in the LTCP 2.0, so that correct caring guidelines and knowledge can be transferred in a timely way to caregivers who provide direct care to the elderly timely. By so doing, the service quality can be greatly enhanced.
Acknowledgments
The authors would like to thank Research Office and Faculty of Engineering of the Hong Kong Polytechnic University through the Engineering Doctorate programme for supporting this project (Project Code: RU8J).
\n',keywords:"long-term care, personalized care services, clinical decision support system, artificial intelligence techniques, care plan",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/69527.pdf",chapterXML:"https://mts.intechopen.com/source/xml/69527.xml",downloadPdfUrl:"/chapter/pdf-download/69527",previewPdfUrl:"/chapter/pdf-preview/69527",totalDownloads:165,totalViews:0,totalCrossrefCites:0,dateSubmitted:"May 14th 2019",dateReviewed:"September 11th 2019",datePrePublished:"October 16th 2019",datePublished:"February 3rd 2021",dateFinished:"October 11th 2019",readingETA:"0",abstract:"With the global aging population, providing effective long-term care has been promoted and emphasized for reducing the hospitalizations of the elderly and the care burden to hospitals and governments. Under the scheme of Long-term Care Project 2.0 (LTCP 2.0), initiated in Taiwan, two types of long-term care services, i.e., institutional care and home care, are provided for the elderly with chronic diseases and disabilities, according to their personality, living environment and health situation. Due to the increasing emphasis on the quality of life in recent years, the elderly expect long-term care service providers (LCSP) to provide the best quality of care (QoC). Such healthcare must be safe, effective, timely, efficiently, diversified and up-to-date. Instead of supporting basic activities in daily living, LCSPs have changed their goals to formulate elderly-centered care plans in an accurate, time-efficient and cost-effective manner. In order to ensure the quality of the care services, an intelligent clinical decision support system (ICDSS) is proposed for care managers to improve their efficiency and effectiveness in assessing the long-term care needs of the elderly. In the ICDSS, artificial intelligence (AI) techniques are adopted to distinguish and formulate personalized long-term care plans by retrieving relevant knowledge from past similar records.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/69527",risUrl:"/chapter/ris/69527",signatures:"Paul Kai Yuet Siu, Valerie Tang, King Lun Choy, Hoi Yan Lam and George To Sum Ho",book:{id:"9134",title:"Recent Advances in Digital System Diagnosis and Management of Healthcare",subtitle:null,fullTitle:"Recent Advances in Digital System Diagnosis and Management of Healthcare",slug:"recent-advances-in-digital-system-diagnosis-and-management-of-healthcare",publishedDate:"February 3rd 2021",bookSignature:"Kamran Sartipi and Thierry Edoh",coverURL:"https://cdn.intechopen.com/books/images_new/9134.jpg",licenceType:"CC BY 3.0",editedByType:"Edited by",editors:[{id:"29601",title:"Dr.",name:"Kamran",middleName:null,surname:"Sartipi",slug:"kamran-sartipi",fullName:"Kamran Sartipi"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"}},authors:[{id:"296597",title:"Dr.",name:"K.L.",middleName:null,surname:"Choy",fullName:"K.L. Choy",slug:"k.l.-choy",email:"kl.choy@polyu.edu.hk",position:null,institution:{name:"Hong Kong Polytechnic University",institutionURL:null,country:{name:"China"}}},{id:"296613",title:"Dr.",name:"H.Y.",middleName:null,surname:"Lam",fullName:"H.Y. Lam",slug:"h.y.-lam",email:"cathy.lam@connect.polyu.hk",position:null,institution:{name:"Hong Kong Polytechnic University",institutionURL:null,country:{name:"China"}}},{id:"305062",title:"Ms.",name:"Valerie",middleName:null,surname:"Tang",fullName:"Valerie Tang",slug:"valerie-tang",email:"v.tang@connect.polyu.hk",position:null,institution:null},{id:"305098",title:"Dr.",name:"G.T.S.",middleName:null,surname:"Ho",fullName:"G.T.S. Ho",slug:"g.t.s.-ho",email:"georgeho@hsu.edu.hk",position:null,institution:null},{id:"305099",title:"Mr.",name:"Paul K.Y.",middleName:null,surname:"Siu",fullName:"Paul K.Y. Siu",slug:"paul-k.y.-siu",email:"paul.ky.siu@connect.polyu.hk",position:null,institution:{name:"Hong Kong Polytechnic University",institutionURL:null,country:{name:"China"}}}],sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. Related work",level:"1"},{id:"sec_3",title:"3. Methodology",level:"1"},{id:"sec_3_2",title:"3.1 Data collection module",level:"2"},{id:"sec_4_2",title:"3.2 Case-based reasoning module",level:"2"},{id:"sec_5_2",title:"3.3 Care plan formulation module",level:"2"},{id:"sec_7",title:"4. Case example",level:"1"},{id:"sec_7_2",title:"4.1 Implementation of ICDSS",level:"2"},{id:"sec_8_2",title:"4.2 Findings",level:"2"},{id:"sec_10",title:"5. Future research directions",level:"1"},{id:"sec_10_2",title:"5.1 Explore the adoption of the Internet of Things (IoT) in residential care services",level:"2"},{id:"sec_11_2",title:"5.2 Explore a big data analytics platform for data management and manipulation",level:"2"},{id:"sec_13",title:"6. Conclusions",level:"1"},{id:"sec_14",title:"Acknowledgments",level:"1"}],chapterReferences:[{id:"B1",body:'He A, Chou K. Long-term care service needs and planning for the future: A study of middle-aged and older adults in Hong Kong. Ageing and Society. 2017;1:1-33. DOI: 10.1017/S0144686X17000824'},{id:"B2",body:'Beard HPJR, Bloom DE. Towards a comprehensive public health response to population ageing. Lancet (London, England). 2015;385(9968):658'},{id:"B3",body:'Executive Yuan. Long-Term Care 2.0 Plan for Greater Peace of Mind. Taipei, Taiwan: Executive Yuan; 2018'},{id:"B4",body:'Wang, H. H., & Tsay, S. F. (2012). Elderly and long-term care trends and policy in Taiwan: Challenges and opportunities for health care professionals. The Kaohsiung Journal of Medical Sciences. 28(9):465-469'},{id:"B5",body:'Ministry of Health and Welfare. Achievements of Ten-Year Long Term Care Program. Ministry of Health and Welfare of Taiwan: Taipei, Taiwan; 2017'},{id:"B6",body:'Government of Taiwan. Earning Statistics in June 2018. In: Bureau S, editor. Statistics from Statistical Bureau. Taipei, Taiwan: 2018'},{id:"B7",body:'Yeh MJ. Long-term care system in Taiwan: The 2017 major reform and its challenges. Ageing & Society. 2019;1:1-18'},{id:"B8",body:'Hou SI. A Taiwan study abroad program on aging, culture, and healthcare. Educational Gerontology. 2018;44(1):18-27'},{id:"B9",body:'Department of Health. Taiwan Public Health Report. Taipei: Department of Health, Taiwan; 2010'},{id:"B10",body:'Liu LF, Yao HP. Examining the need assessment process by identifying the need profiles of elderly care recipients in the ten-year long-term care project (TLTCP) of Taiwan. Journal of the American Medical Directors Association. 2014;15(12):946-954'},{id:"B11",body:'Lin PJ, Shiue YC, Tzeng GH, Huang SL. Developing a sustainable long-term ageing health care system using the DANP-mV model: Empirical case of Taiwan. International Journal of Environmental Research and Public Health. 2019;16(8):1349'},{id:"B12",body:'Arnott D, Pervan G. A critical analysis of decision support systems research revisited: The rise of design science. In: Enacting Research Methods in Information Systems. Cham: Palgrave Macmillan; 2016. pp. 43-103'},{id:"B13",body:'Shibl R, Lawley M, Debuse J. Factors influencing decision support system acceptance. Decision Support Systems. 2013;54(2):953-961'},{id:"B14",body:'Bose R. Knowledge management-enabled health care management systems: Capabilities, infrastructure, and decision-support. Expert Systems with Applications. 2003;24(1):59-71'},{id:"B15",body:'Kolodner J. Case-Based Reasoning. San Mateo: Morgan Kaufmann; 1993'},{id:"B16",body:'Prentzas J, Hatzilygeroudis I. Assessment of life insurance applications: An approach integrating neuro-symbolic rule-based with case-based reasoning. Expert Systems. 2016;33(2):145-160'},{id:"B17",body:'Gu D, Liang C, Zhao H. A case-based reasoning system based on weighted heterogeneous value distance metric for breast cancer diagnosis. Artificial Intelligence in Medicine. 2017;77:31-47'},{id:"B18",body:'Marling C, Montani S, Bichindaritz I, Funk P. Synergistic case-based reasoning in medical domains. Expert Systems with Applications. 2014;41(2):249-259'},{id:"B19",body:'Chang CL, Cheng BW, Su JL. Using case-based reasoning to establish a continuing care information system of discharge planning. Expert Systems with Applications. 2004;26(4):601-613'},{id:"B20",body:'Wang WM, Cheung CF, Lee WB, Kwok SK. Knowledge-based treatment planning for adolescent early intervention of mental healthcare: A hybrid case-based reasoning approach. Expert Systems. 2007;24(4):232-251'},{id:"B21",body:'Petrovic S, Khussainova G, Jagannathan R. Knowledge-light adaptation approaches in case-based reasoning for radiotherapy treatment planning. Artificial Intelligence in Medicine. 2016;68:17-28'},{id:"B22",body:'Zhao C, Wong L, Zhu Q , Yang H. Prevalence and correlates of chronic diseases in an elderly population: A community-based survey in Haikou. PLoS One. 2018;13(6):e0199006'},{id:"B23",body:'Yuehong YIN, Zeng Y, Chen X, Fan Y. The internet of things in healthcare: An overview. Journal of Industrial Information Integration. 2016;1:3-13'},{id:"B24",body:'Raghupathi W, Raghupathi V. Big data analytics in healthcare: Promise and potential. Health Information Science and Systems. 2014;2(1):3'},{id:"B25",body:'Ohlhorst F. Big Data Analytics: Turning Big Data into Big Money. Vol. 65. Canada: John Wiley & Sons; 2012'}],footnotes:[],contributors:[{corresp:null,contributorFullName:"Paul Kai Yuet Siu",address:null,affiliation:'
Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hong Kong
Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hong Kong
'},{corresp:null,contributorFullName:"Hoi Yan Lam",address:null,affiliation:'
Department of Supply Chain and Information Management, The Hang Seng University of Hong Kong, Hong Kong
'},{corresp:null,contributorFullName:"George To Sum Ho",address:null,affiliation:'
Department of Supply Chain and Information Management, The Hang Seng University of Hong Kong, Hong Kong
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S6 kinase activity has been linked to different cellular processes like glucose homeostasis, translational and transcriptional regulation. Hence any dysregulation in S6K1 leads to the emergence of various pathological conditions like diabetes, cancer and obesity. It is as such S6 kinase has emerged as a potential target for therapeutic interventions employed in curing such diseases. 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The Open Access model is applied to all of our publications and is designed to eliminate subscriptions and pay-per-view fees. This approach ensures free, immediate access to full text versions of your research.
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The Open Access Publishing Fee (OAPF) is payable only after your full chapter, monograph or Compacts monograph is accepted for publication.
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OAPF Publishing Options
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1,400 GBP Chapter - Edited Volume
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10,000 GBP Monograph - Long Form
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4,000 GBP Compacts Monograph - Short Form
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*These prices do not include Value-Added Tax (VAT). Residents of European Union countries need to add VAT based on the specific rate in their country of residence. Institutions and companies registered as VAT taxable entities in their own EU member state will not pay VAT as long as provision of the VAT registration number is made during the application process. This is made possible by the EU reverse charge method.
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Exceeds 20 pages (for chapters in Edited Volumes), an additional fee of 40 GBP per page will be required
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Your Author Service Manager will inform you of any items not covered by the OAPF and provide exact information regarding those additional costs before proceeding.
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Open Access Funding
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For Authors who are still unable to obtain funding from their institutions or research funding bodies for individual projects, IntechOpen does offer the possibility of applying for a Waiver to offset some or all processing feed. Details regarding our Waiver Policy can be found here.
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