In a search for new potential AChE inhibitors, 31 selected medicinal plants from Perlis were collected gathered, air dried and successively extracted using hexane, dichloromethane, and alcohol. The dichloromethane and alcoholic extracts were screened for AChE inhibitory activity using Ellman's method. Out of 31 plant species, the methanol extracts of Rhapis excelsa leaves (97.03 ± 3.71 %), Diospyros blancoi leaves (95.80 ± 1.57 %) and Phyllantus elegans root (83.22 ± 3.08 %) showed the highest AChE inhibitory activity at the concentration of 100 μg/mL.
Depression is the most common illness observed in the elderly, adults, and children. Antidepressants prescribed are usually synthetic drugs and these can sometimes cause a wide range of unpleasant side effects. Current research is focussed on natural products from plants as they are a rich source of potent new drug leads. Besides Hypericum perforatum (St. John's wort), the plants studied include Passiflora incarnata L. (passion flower), Mitragyna speciosa (kratom), Piper methysticum G. Forst (kava) and Valeriana officinalis L. Harman, harmol, harmine, harmalol and harmaline are indole alkaloids isolated from P. incarnata, while mitragynine is isolated from M. speciosa. The structure of isolated compounds from P. methysticum G. Forst and V. officinalis L. contains an indole moiety. The indole moiety is related to the neurotransmitter serotonin which is widely implicated for brain function and cognition as the endogenous receptor agonist. An imbalance in serotonin levels may influence mood in a way that leads to depression. The moiety is present in a number of antidepressants already on the market. Hence, the objective of this review is to discuss bioactive compounds containing the indole moiety from plants that can serve as potent antidepressants.
The effect of oven drying at 50ᵒC ± 1ᵒC for 9 hour, 70ᵒC ± 1ᵒC for 5 hour and freeze drying on retention of chlorophyll, riboflavin, niacin, ascorbic acid and carotenoids in herbal preparation consisting of 8 medicinal plants was evaluated. The medicinal plants selected were leaves of Apium graveolens (saderi), Averrhoa bilimbi (belimbing buluh), Centella asiatica (pegaga), Mentha arvensis (pudina), Psidium guajava (jambu batu), Sauropus androgynous (cekor manis), Solanum nigrum (terung meranti) and Polygonum minus (kesum ). Results revealed that both type and conditions of the drying treatments affected retention of all phytochemicals analysed. Herbal preparation developed using oven drying was found to have inferior phytochemicals content compared to that obtained by freeze dryer. Nevertheless, the herbal preparation developed using all treatments still retain appreciable amount of phytochemicals studied, especially carotenoids, ascorbic acid, niacin and riboflavin and thus have potential for commercial purposes.
Osteoporosis is characterized by skeletal degeneration with low bone mass and destruction of microarchitecture of bone tissue which is attributed to various factors including inflammation. Women are more likely to develop osteoporosis than men due to reduction in estrogen during menopause which leads to decline in bone-formation and increase in bone-resorption activity. Estrogen is able to suppress production of proinflammatory cytokines such as IL-1, IL-6, IL-7, and TNF-α. This is why these cytokines are elevated in postmenopausal women. Studies have shown that estrogen reduction is able to stimulate focal inflammation in bone. Labisia pumila (LP) which is known to exert phytoestrogenic effect can be used as an alternative to ERT which can produce positive effects on bone without causing side effects. LP contains antioxidant as well as exerting anti-inflammatory effect which can act as free radical scavenger, thus inhibiting TNF-α production and COX-2 expression which leads to decline in RANKL expression, resulting in reduction in osteoclast activity which consequently reduces bone loss. Hence, it is the phytoestrogenic, anti-inflammatory, and antioxidative properties that make LP an effective agent against osteoporosis.
Tiger’s Milk mushrooms (Lignosus rhinocerus) are polypores with three distinct parts: cap (pileus), stem (stipe) and tuber (sclerotium). The stem of this medicinal mushroom is centrally connected to the brownish woody cap that grows out from the tuber underground rather than from the wood. To date, the biotic and abiotic factors that induce the growth of this mushroom are unclear and information regarding its development is scanty. Hence, the differential protein expressions of vegetative dikaryotic mycelial and primordial cells of this mushroom were investigated. Six two dimensional-sodium dodecyl sulfate-polyacrylamide gel electrophoresis (2D-SDS-PAGE) of 13 cm with pH3-10 containing the intracellular proteins of vegetative mycelial and primordial cells of L. rhinocerus were obtained. Analysis of 2D-SDS-PAGE using Progenesis Samespot version 4.1 yielded approximately 1000 distinct protein spots in the proteome of vegetative mycelial cells, while primordial proteome contained nearly 100 spots. Further comparison between the vegetative mycelial and primordial proteomes yielded significant up-regulation of protein expression of 5 primordial cells proteins that were labeled as P1, P2, P3, P4 and P5. These protein spots were excised, trypsin digested and submitted to mass spectrometry. Protein identification through MASCOT yielded significant identification with P1 and P2 as DnaJ domain protein, P3 and P5 as hypothetical protein while P4 as AP-2rep transcription factor. The present results suggested that P3, P4 and P5 are novel proteins that involved in the initiation of L. rhinocerus primordia. Our findings also suggested that stress response mechanism is present during fruitification of this mushroom.
Scientific communication is facilitated by a data-driven, scientifically sound taxonomy that considers the end-user's needs and established successful practice. In 2013, the Fusarium community voiced near unanimous support for a concept of Fusarium that represented a clade comprising all agriculturally and clinically important Fusarium species, including the F. solani species complex (FSSC). Subsequently, this concept was challenged in 2015 by one research group who proposed dividing the genus Fusarium into seven genera, including the FSSC described as members of the genus Neocosmospora, with subsequent justification in 2018 based on claims that the 2013 concept of Fusarium is polyphyletic. Here, we test this claim and provide a phylogeny based on exonic nucleotide sequences of 19 orthologous protein-coding genes that strongly support the monophyly of Fusarium including the FSSC. We reassert the practical and scientific argument in support of a genus Fusarium that includes the FSSC and several other basal lineages, consistent with the longstanding use of this name among plant pathologists, medical mycologists, quarantine officials, regulatory agencies, students, and researchers with a stake in its taxonomy. In recognition of this monophyly, 40 species described as genus Neocosmospora were recombined in genus Fusarium, and nine others were renamed Fusarium. Here the global Fusarium community voices strong support for the inclusion of the FSSC in Fusarium, as it remains the best scientific, nomenclatural, and practical taxonomic option available.
Biomass wastes produced from oil palm mills and plantations include empty fruit bunches (EFBs), shells, fibers, trunks, and oil palm fronds (OPF). EFBs and shells are partially utilized as boiler fuel while the rest of the biomass materials like OPF have not been utilized for energy generation. No previous study has been reported on gasification of oil palm fronds (OPF) biomass for the production of fuel gas. In this paper, the effect of moisture content of fuel and reactor temperature on downdraft gasification of OPF was experimentally investigated using a lab scale gasifier of capacity 50 kW. In addition, results obtained from equilibrium model of gasification that was developed for facilitating the prediction of syngas composition are compared with experimental data. Comparison of simulation results for predicting calorific value of syngas with the experimental results showed a satisfactory agreement with a mean error of 0.1 MJ/Nm³. For a biomass moisture content of 29%, the resulting calorific value for the syngas was found to be only 2.63 MJ/Nm³, as compared to nearly double (4.95 MJ/Nm³) for biomass moisture content of 22%. A calorific value as high as 5.57 MJ/Nm³ was recorded for higher oxidation zone temperature values.
Biomass co-firing with coal can be adopted in the electricity sector to promote greenhouse gas reduction, renewable energy production, and resource efficiency improvement toward environmental sustainability. This realization, however, requires effective management of supply chain issues, such as the collection of biomass feedstock, the transportation of biomass, and the localization of biomass processing plants to deliver the co-firing scales needed. This work addresses these issues by providing a techno-economic assessment conducted in a spatially-explicit manner to investigate the opportunity for scaling up the co-firing deployment at the national scale. The modeling approach is applied to the case of Malaysia's coal and palm oil biomass industries. The number of cases involving the impact of energy decarbonization targets, economic policy instrument, and supply chain cost parameter variations on the co-firing scales deployed are assessed. The findings show that densified biomass feedstock can substitute significant shares of coal capacities to deliver up to 29 MtCO2/year of carbon dioxide reduction. Nevertheless, this would cause a surge in the electricity system cost by up to 2 billion USD/year due to the substitution of up to 40% of the coal plant capacities. In facilitating the maximal deployment of co-firing at the national scale, more than 100 solid biofuel production plants would need to be built to support a maximum of 41 TWh/year of co-firing capacity. Actions to minimize the specific cost elements of the biomass co-firing supply chain are thus needed in the near term to increase the effectiveness of economic policy instrument to promote co-firing and reduce environmental emissions.
The fruit and leaf of God's crown (Phaleria macrocarpa) have been traditionally used to treat a wide variety of diseases. However, the proteins of this tropical plant are still heavily understudied. Three protein extraction methods; phenol (Phe), trichloroacetic acid (TCA)-acetone-phenol (TCA-A-Phe), and ultrasonic (Ult) were compared on the fruit and leaf of P. macrocarpa. The Phe extraction method showed the highest percentage of recovered protein after the resolubilization process for both leaf (12.24%) and fruit (30.41%) based on protein yields of the leaf (6.15 mg/g) and fruit (36.98 mg/g). Phe and TCA-A-Phe extraction methods gave well-resolved bands over a wide range of molecular weights through sodium dodecyl sulfate-polyacrylamide gel electrophoresis. Following liquid chromatography-tandem mass spectrometry analysis, proteins identified through the Phe extraction method were 30%-35% enzymatic proteins, including oxidoreductases, transferases, hydrolases, lyases, isomerases, and ligases that possess various biological functions. PRACTICAL APPLICATIONS: Every part of God's crown plant is traditionally consumed to treat various illnesses. While plant's benefits are well known and have led to a plethora of health products, the proteome remains mostly unknown. This study compares three protein extraction methods for the leaf and fruit of P. macrocarpa and identifies their proteins thru LC-MS/MS coupled with PEAKS. These method comparisons can be a guide for works on other plants as well. In addition, the proteomics data from this study may shed light on the functional properties of these plant parts and their products.
Gout is a type of arthritis that causes painful inflammation in one or more joints. In gout, elevation of uric acid in the blood triggers the formation of crystals, causing joint pain. Malaysia is a mega-biodiversity country that is rich in medicinal plants species. Therefore, its flora might offer promising therapies for gout. This article aims to systematically review the anti-gout potential of Malaysian medicinal plants. Articles on gout published from 2000 to 2017 were identified using PubMed, Scopus, ScienceDirect, and Google Scholar with the following keyword search terms: "gout," "medicinal plants," "Malaysia," "epidemiology," "in vitro," and "in vivo." In this study, 85 plants were identified as possessing anti-gout activity. These plants had higher percentages of xanthine oxidase inhibitory activity (>85%); specifically, the Momordica charantia, Chrysanthemum indicum, Cinnamomum cassia, Kaempferia galanga, Artemisia vulgaris, and Morinda elliptica had the highest values, due to their diverse natural bioactive compounds, which include flavonoids, phenolics, tannin, coumarins, luteolin, and apigenin. This review summarizes the anti-gout potential of Malaysian medicinal plants but the mechanisms, active compounds, pharmacokinetics, bioavailability, and safety of the plants still remain to be elucidated.
A proof-of-concept medicinal herbs identification scheme using machine learning classifiers is proposed in the form of an automated computational package. The scheme makes use of two-dimensional correlation Fourier Transformed Infrared (FTIR) fingerprinting maps derived from the FTIR of raw herb spectra as digital input. The prototype package admits a collection of 11 machine learning classifiers to form a voting pool. A common set of oversampled dataset containing 5 different herbal classes is used to train the pool of classifiers on a one-verses-others manner. The collections of trained models, dubbed the voting classifiers, are deployed in a collective manner to cast their votes to support or against a given inference fingerprint whether it belongs to a particular class. By collecting the votes casted by all voting classifiers, a logically designed scoring system will select out the most probable guess of the identity of the inference fingerprint. The same scoring system is also capable of discriminating an inference fingerprint that does not belong to any of the classes the voting classifiers are trained for as the 'others' type. The proposed classification scheme is stress-tested to evaluate its performance and expected consistency. Our experimental runs show that, by and large, a satisfactory performance of the classification scheme of up to 90 % accuracy is achieved, providing a proof-of-concept viability that the proposed scheme is a feasible, practical, and convenient tool for herbal classification. The scheme is implemented in the form of a packaged Python code, dubbed the "Collective Voting" (CV) package, which is easily scalable, maintained and used in practice.
A survey of plants used in Malaysia for treating female diseases was made by consulting books, journals and traditional healers. In this report on the survey, forty-four plants are described. Information on plant parts used, methods of preparation and administration, and other usages of plants are given for each species.
In this paper, we present a new method to recognise the leaf type and identify plant species using phenetic parts of the leaf; lobes, apex and base detection. Most of the research in this area focuses on the popular features such as the shape, colour, vein, and texture, which consumes large amounts of computational processing and are not efficient, especially in the Acer database with a high complexity structure of the leaves. This paper is focused on phenetic parts of the leaf which increases accuracy. Detecting the local maxima and local minima are done based on Centroid Contour Distance for Every Boundary Point, using north and south region to recognise the apex and base. Digital morphology is used to measure the leaf shape and the leaf margin. Centroid Contour Gradient is presented to extract the curvature of leaf apex and base. We analyse 32 leaf images of tropical plants and evaluated with two different datasets, Flavia, and Acer. The best accuracy obtained is 94.76% and 82.6% respectively. Experimental results show the effectiveness of the proposed technique without considering the commonly used features with high computational cost.