Search

The ginger oil traded worldwide could come from various sources. Standard quality is the most critical aspect of ensuring customer safety. This study aims to develop an analytical method for red ginger oil (RGO) authentication. Chemical compositions of red ginger oil were determined by Gas Chromatography-Mass Spectrometry (GC-MS). The Fourier Transform Infrared Spectroscopy (FTIR) coupled with multivariate analysis (discriminant analysis (DA), partial least square (PLS), and principal component regression (PCR) were used to identify and quantify the adulterant. The total terpenoid compounds were 55.72%, with the percentage of monoterpenes at 34.29% and sesquiterpenes at 21.43%. E-Citral (19.01%), Z-Citral (14.82%), Geranyl Acetate (11.90%), Geraniol (9.56%), 1,8-Cineole (5.84%), and camphene (4.92%) were identified as the main constituents. The best PLS model for quantifying the level of palm oil in RGO was at the wavenumber 3100–2700 cm–1, while the region of 3100 – 2700 and 1850 – 650 cm–1 was suitable for detection of soybean adulterants. FTIR spectroscopy coupled with chemometrics produced accurate and fast authentication of red ginger oil without the used solvent. Then, the GC-MS technique could identify the chemical constituents present in the red ginger oil.

In several industries such as food and detergent, enzymes are commonly used out of which microorganisms' proteolytic enzymes are dominant. Microorganisms from marine sources have advantageous commercial characteristics when it comes to protease production. Protease producing bacterial strains were isolated from four different stations in the South China Sea, located at Kuantan, Pahang-Malaysia. The temperature was ranging from 29 to 36.2°C; salinity varied from 36,18 to 37,9 ppt, turbidity from 10.21 to 16.4 NTU. Dissolved Oxygen had an average of 4.44 mg/mL, and pH was around 8. All the bacterial strains were screened for protease activity using Skim Milk agar plates. Out of 18 isolates, ten strains formed a clear zone on skim milk agar plates. Molecular identification by 16S rRNA results revealed that Bacillus spp was the most dominant bacteria, four isolates belong to Bacillus cereus, another 04 identified as Bacillus licheniformis in addition of one isolate was Bacillus safensis. They were followed by one isolate Staphylococcus warneri. Furthermore, a phenotypic characterization is conducted using The Biolog GENIII MicroPlate with 94 phenotypic tests. These tests consist of 71 carbon source utilization assays and 23 chemical sensitivity assays. The proteolytic activity was confirmed through a quantitative protease assay. Where the strain B. cereus-MD152 was the highest yield, followed by B. licheniformis-ABN13. On the contrary, the lowest yield was secreted by B. cereus-IIUM6 with no more than 16.5 U/ml.

Background: Metformin has been added in the Malaysian clinical practice guideline (CPG) as one of the first-line options in the management of gestational diabetes mellitus (GDM); however, the uptake on this practice among healthcare professionals is unknown. Objective: The objective of the study is to determine the awareness, attitude, and practice and their predictors on the use of metformin for GDM among healthcare professionals in Malaysia. Materials and Methods: This was a multicenter, cross-sectional study in three tertiary hospitals in Malaysia. Medical doctors and pharmacists working in several departments were invited to participate in a survey using self-administered questionnaires. Results: From 350 questionnaires distributed, 225 were completed by medical doctors (43.1%), pharmacists (40.4%), and specialists (7.5%). Less than 30% of them were aware on the option of using metformin as one of the first-line agents in GDM. Education level, department, and profession were found to be associated with the awareness level (P = 0.016, P = 0.004, and P = 0.001, respectively). 70.2% of the respondents showed a positive attitude toward metformin use in GDM. Only 64 (28.4%) of the respondents have prescribed/dispensed metformin for GDM before, although more than half will consider doing so in the future. Having postgraduate qualifications increased the likelihood of having a good awareness (odds ratio [OR]: 2.44, 95% confidence interval [CI] 1.23–4.85) and to consider prescribing/dispensing metformin for GDM patients (OR: 2.27, 95% CI 1.08–4.78). Conclusion: Despite a positive attitude toward metformin use in GDM among healthcare professionals in Malaysia, their awareness level on this practice was low as they currently prefer the use of insulin over metformin.

In this research, lipase recovered from the skim latex of Hevea brasiliensis was immobilized via cross-linked enzyme aggregates (CLEA) technology, while supported by magnetic nanoparticles (MNPs), for properties enhancement. Hybrid immobilization may have affected the kinetic performances of the biocatalysts. The kinetic performance of both MNP supported and unsupported CLEA-lipase, were evaluated based on the Michaelis-Menten model using p-nitrophenyl palmitate as the substrate. Three different linearization model equations were used to compute the kinetic properties, v_max and K_m, and a hyperbolic regression was conducted with computer software. Based on the best fitted model, v_max of MNP-CLEA-lipase, obtained from the Lineweaver-Burk plot (R2=0.9823), was 0.0023 µmol/min.mL, which is higher than CLEA-lipase (0.0015 µmol/min.mL), indicating it needs much higher substrate concentration to saturate the enzymatic sites to reach its maximum velocity. K_m for MNP-CLEA-lipase was 0.4400 µmol, compared to 0.5188 µmol for CLEA-lipase, inferring that it has a higher affinity towards substrates, whereby its rate will approach v_max with lower substrate concentration. Overall, this research demonstrated that wasteful by-products such as skim latex can be converted to useful value-added biocatalyst. A better understanding of the kinetic parameters of this newly produced MNP immobilized biocatalyst is necessary for its further development.

Skim latex from Hevea brasiliensis (rubber tree) contains many useful enzymes that can be utilized to produce value-added products. Lipase recovered from skim latex serum was immobilized via cross-linked enzyme aggregates (CLEA) technology, while supported by magnetic nanoparticles termed ‘MNP-CLEA-lipase’. One of the main advantages of this method is, the biocatalyst can be easily recovered by a magnetic field for recycling. In this research, dialdehydic pectin was used as the cross-linking agent, replacing glutaraldehyde, and soy protein was used as an additive, replacing bovine serum albumin (BSA). The operating conditions for the preparation of the most active MNP-CLEA-lipase were optimized statistically. Dialdehydic pectin and soy protein showed optimum operating conditions at 180 mg/mL and 0.6% (w/v) respectively and with (NH4)2SO4 at 80% saturation, giving a Residual Activity (RA) of 154%. The optimum temperature for MNP-CLEA-lipase was at 40°C (127% RA) while the optimum pH was at 8 (127% RA). It retained 20% RA after 100 days of storage. The reusability test showed that it maintained 7% RA after the 10th cycle. This research showed two advantages; first, that value-added products can be recovered from agricultural waste, and second, by alternating to low-cost food-grade cross-linkers and additives, enhanced biocatalyst can be produced which can be safely used in the beverage and food industries

Skim latex from Hevea brasiliensis consists of many useful proteins and enzymes that can be utilized to produce value-added products for industrial purposes. In this project, to enhance the properties of the recovered lipase from skim latex serum, this enzyme was immobilized via cross-linked enzyme aggregates (CLEA) technology, and supported by magnetic nanoparticles (MNP-CLEA-lipase). MNP-CLEA-lipase was prepared by chemical cross-linking of enzyme aggregates with amino functionalized MNP, which can easily be separated from the spent media after use by magnetic field. The performances of the newly produced MNP-CLEA-lipase were compared with its unsupported counterpart, CLEA-lipase. The optimum conditions for the preparation of CLEA-lipase was carried out by using 45% saturated (NH4)2SO4 and 50 mM glutaraldehyde (GA), whereas for MNP-CLEA-lipase, it was carried out by using 70% saturated (NH4)2SO4 and 60 mM GA. The optimum pH changes from 6 to 8 when CLEA-lipase is supported, while achieving a maximum of 69.92% residual activity (RA) compared to 20.66% for the unsupported CLEA-lipase. However, the optimum temperature for both are the same (35°C), with MNP-CLEA-lipase achieving 85.89% RA compared to 31.91% for the unsupported ones. Stability studies carried out on both showed that MNP-CLEA-lipase has higher thermal (25–60°C) and pH (5–10) stabilities compared to its unsupported counterparts. As for the reusability of enzymes, MNP-CLEA-lipase retained 56.46% of residual activity after six cycles of reuse. The FESEM results showed that the MNP-CLEA-lipase is a Type 2, which is less structured, and this has bearings towards its activities. FTIR analysis showed the presence of Amide I and Amide II bands in MNP-CLEA-lipase, originating from the newly formed cross-linked between the silanized MNP and CLEA-lipase. Overall, MNP supported CLEA-lipase showed better performance compared to the unsupported biocatalyst.

Objectives. The current health technology assessment used to evaluate respiratory inhalers is associated with limitations that have necessitated the development of an explicit formulary decision-making framework to ensure balance between the accessibility, value, and affordability of medicines. This study aimed to develop a multiple-criteria decision analysis (MCDA) framework, apply the framework to potential and currently listed respiratory inhalers in the Ministry of Health Medicines Formulary (MOHMF), and analyze the impacts of applying the outputs, from the perspective of listing and delisting medicines in the formulary. Methods. The overall methodology of the framework development adhered to the recommendations of the ISPOR MCDA Emerging Good Practices Task Force. The MCDA framework was developed using Microsoft Excel 2010 and involved all relevant stakeholders. The framework was then applied to 27 medicines, based on data gathered from the highest levels of available published evidence, pharmaceutical companies, and professional opinions. The performance scores were analyzed using the additive model. The end values were then deliberated by an expert committee. Results. A total of eight main criteria and seven subcriteria were determined by the stakeholders. The economic criterion was weighted at 30%. Among the noneconomic criteria, “patient suitability” was weighted the highest. Based on the MCDA outputs, the expert committee recommended one potential medicine (out of three; 33%) be added to the MOHMF and one existing medicine (out of 24; 4%) be removed/delisted from the MOHMF. The other existing medicines remained unchanged. Conclusions. Although this framework was useful for deciding to add new medicines to the formulary, it appears to be less functional and impactful for the removal/delisting existing medicines from the MOHMF. The generalizability of this conclusion to other formulations remains to be confirmed.

Cancer is one of the deadliest diseases in the world and is responsible for around 13% of all deaths worldwide. Cancer incidence rate is growing at an alarming rate in the world. Despite the fact that cancer is preventable and curable in early stages, the vast majority of patients are diagnosed with cancer very late. Furthermore, cancer commonly comes back after years of treatment. Therefore, it is of paramount importance to predict cancer recurrence so that specific treatments can be sought. Nonetheless, conventional methods of predicting cancer recurrence rely solely on histopathology and the results are not very reliable. The microarray gene expression technology is a promising technology that could predict cancer recurrence by analyzing the gene expression of sample cells. The microarray technology allows researchers to examine the expression of thousands of genes simultaneously. This paper describes a stateof-the-art machine learning based approach called averaged one-dependence estimators with subsumption resolution to tackle the problem of predicting, from DNA microarray gene expression data, whether a particular cancer will recur within a specific timeframe, which is usually 5 years. To lower the computational complexity, we employ an entropy-based gene selection approach to select relevant prognostic genes that are directly responsible for recurrence prediction. This proposed system has achieved an average accuracy of 98.9% in predicting cancer recurrence over 3 datasets. The experimental results demonstrate the efficacy of our framework.

Mardina V, Harmawan T, Fitriani, Sufriadi E, Febriani, Yusof F. 2020. Euthynnus affinis viscera-an alternative source for protease and lipase enzymes: Characteristic and potential application as destainer agent. Biodiversitas 21: 5858-5864. This study described the characterization of protease and lipase, extracted from the viscera of Euthynnus affinis and their applications as stains removal. Enzyme increased a few folds (3.99 folds for protease and 2.45 folds for lipase) after the submission to partial purification processes by ammonium sulfate precipitation and dialysis. Molecular mass assessment by SDS-PAGE showed that the enzyme solution contains three protein bands, estimated at 70, 42, and 24 kDa for protease and 32 kDa for lipase. Zymography confirmed the presence of protease and lipase in the sample. The protease and lipase exhibited optimum activities at pH 7.0 and pH 8.0 respectively, active at temperatures from 35 to 75oC with optimal activities at 65oC for both. The enzymes were stable at alkaline pH after 90 minutes (mins) of incubation and were also stable up to 65oC, showing the remaining activities of more than 70%. Samples containing protease and lipase as de-staining agents were tested by their ability to remove blood and palm oil stains, with results proving they were effective. Overall, the study showed that crude fish visceral extract contains protease and lipase and is useful in removing household stains, and could substitute for commercial detergents.

Mardina V, Harmawan T, Fitriani, Sufriadi E, Febriani, Yusof F. 2020. Euthynnus affinis viscera-an alternative source for protease and lipase enzymes: Characteristic and potential application as destainer agent. Biodiversitas 21: 5858-5864. This study described the characterization of protease and lipase, extracted from the viscera of Euthynnus affinis and their applications as stains removal. Enzyme increased a few folds (3.99 folds for protease and 2.45 folds for lipase) after the submission to partial purification processes by ammonium sulfate precipitation and dialysis. Molecular mass assessment by SDS-PAGE showed that the enzyme solution contains three protein bands, estimated at 70, 42, and 24 kDa for protease and 32 kDa for lipase. Zymography confirmed the presence of protease and lipase in the sample. The protease and lipase exhibited optimum activities at pH 7.0 and pH 8.0 respectively, active at temperatures from 35 to 75oC with optimal activities at 65oC for both. The enzymes were stable at alkaline pH after 90 minutes (mins) of incubation and were also stable up to 65oC, showing the remaining activities of more than 70%. Samples containing protease and lipase as de-staining agents were tested by their ability to remove blood and palm oil stains, with results proving they were effective. Overall, the study showed that crude fish visceral extract contains protease and lipase and is useful in removing household stains, and could substitute for commercial detergents.

The ability of microalgae to utilize CO2 during photosynthesis and grow rapidly shows their potential in CO2 bio-fixation to capture and store the gas. However, CO2 capture by this biological approach is very slow compared to chemical reaction-based processes such as absorption using amine or aqueous ammonia. Integration between chemical (aqueous ammonia) and biological (microalgae) aspects might enhance the capturing process and at the same time the microalgae can assimilate CO2 for beneficial bioproduct formation. Thus, it is important to assess the growth of the microalgae in various concentrations of ammonia with CO2 supply. Hence, the main objective of this study is to investigate Chlorella vulgaris growth and its kinetics in aqueous ammonia. To achieve that, C. vulgaris was cultivated in various concentrations of aqueous ammonia between 0 to 1920 mg/L at room temperature (i.e. 27 °C) and supplied with 15% (v/v) of CO2 under illumination of 3500 lux of white fluorescent light. Result shows that the maximum growth capacity (Xmax) of C. vulgaris is deteriorating from 1.820 Au to 0.245 Au as the concentration of aqueous ammonia increased. However, no significant change in maximum specific growth rate (µmax) was observed. The growth data was then fitted into the logistic growth model. The model coefficient of determination (R2) is decreasing, which suggests modification of the model is required. ABSTRAK: Keupayaan alga-mikro untuk menggunakan CO2 semasa proses fotosintesis dan pembiakannya yang pesat menunjukkan potensi dalam penggunaan dan penyimpanan gas ketetapan-biologi. Walau bagaimanapun, penggunaan CO2 melalui cara ini adalah sangat perlahan berbanding proses tindak balas kimia melalui penyerapan amina ataupun cecair ammonia. Percampuran antara tindak balas kimia (cecair ammonia) dan tindak balas biologi, memungkinkan penambahan proses percampuran dan pada masa sama alga-mikro akan menyerap CO2 bagi kepentingan pembentukan hasil biologi. Dengan itu, adalah sangat penting untuk mengawasi pertumbuhan alga-mikro dalam pelbagai ketumpatan ammonia bersama kandungan CO2. Oleh itu, objektif utama penyelidikan ini adalah untuk menyiasat pertumbuhan Chlorella vulgaris dan proses kinetik dalam cecair ammonia. Bagi memperoleh hasil tersebut, C. vulgaris telah dikulturkan pada ketumpatan cecair berbeza antara 0 ke 1920 mg/L pada suhu bilik (iaitu 27 °C) dan dibekalkan dengan 15% (v/v) CO2 di bawah cahaya putih flurosen 3500 lux. Keputusan menunjukkan kapasiti pertumbuhan terbanyak (Xmax) C. vulgaris telah berkurang daripada 1.820 Au kepada 0.245 Au apabila ketumpatan cecair ammonia dikurangkan. Walau bagaimanapun, tiada perubahan ketara pada kadar pertumbuhan (µmax) dapat dilihat. Data kadar pertumbuhan kemudiannya dikemas kini pada model pertumbuhan logistik. Model pekali penentu (R2) telah direndahkan di mana cadangan untuk mengubah model adalah diperlukan.

Collagens are an important and ubiquitous family of proteins. They have many functions in the human body and similarly have found numerous, potent applications in various industries including the manufacture of biomaterials. The ever-increasing demand for collagen has made necessary the exploration of alternative sources such as bacterial collagen-like proteins which have a triple-helical domain of Gly-X-Y amino acid repeats. Detection and quantification of native collagens have been well-established. However, collagen-like proteins differ in their composition and do not have the unique abundance of hydroxyproline and hydroxylysine found in vertebrate collagens. Thus, this poses a problem in the detection and quantification of collagen-like proteins. This paper evaluates reports on the detection and quantification of collagens and collagen-like proteins. A systematic search of the PubMed database was conducted in May 2021, to which five additional papers were added. The 310 unique search results were then subjected to a screening and elimination process, at the end of which 22 papers were included in the study. The findings were summarized and presented in a table that highlights progress in this field. While novel methods have been developed for the detection and quantitation of collagens in general, mainly using enzyme digestion, hybridization, and fluorescence, there is a need for a rapid, one-step method that selectively and sensitively detects and quantitates collagen and collagen-like protein samples with ease.

The roles of multi-walled carbon nanotubes (MWNTs) and functionalised multiwalled carbon nanotubes (fMWNTs) in enhancing the efficacy of urea fertilizer (UF) as plant nutrition for local MR219 paddy variety was investigated. The MWNTs and fMWNTs were grafted onto UF to produce UF-MWNTs fertilizer with three different conditions, coded as FMU1 (0.6 wt. % fMWNTs), FMU2 (0.1 wt. % fMWNTs) and MU (0.6 wt. % MWNTs. The batches of MR219 paddy were systematically grown in accordance to the general practice performed by the Malaysian Agricultural Research and Development Institute (MARDI). The procedure was conducted using a pot under exposure to natural light at three different fertilization times; after a certain number of days of sowing (DAS) at 14, 35 and 55 days. Interestingly, it was found that the crop growth of plants treated with FMU1 and FMU2 significantly increased by 22.6% and 38.5% compared to plants with MU addition. Also, paddy treated with FMU1 produced 21.4% higher number of panicles and 35% more grain yield than MU while paddy treated with FMU2 gave 28.6% more number of panicles and 36% higher grain yield than MU, which implies the advantage of fMWNTs over MWNTs to be combined with UF as plant nutrition. The chemical composition and morphology of UF-MWNTs fertilizers which is further characterised by FTiR and FESEM confirmed the successful and homogeneous grafting of UF onto the fMWNTs.Peranan tabung karbon nano berdinding banyak (MWNTs) dan

In this article, we have studied the process of silver nanoparticles (AgNPs) aggregation and to stop aggregation 0.3% Polyvinylpyrrolidone (PVP) was used. Aggregation study carried out via UV-vis spectroscopy and it is reported that the absorption spectrum of spherical silver nanoparticles were found a maximum peak at 420 nm wavelength. Furthermore, Transmission Electron Microscopy (TEM) were used to characterized the size and shape of AgNPs, where the average particle size is around 10 to 25 nm in diameter and the AgNPs shape is spherical. Next, Dynamic Light Scattering (DLS) were used, owing to observed size distribution and self-correlation of AgNPs.

Objective The activity of enzymes participating in the systems of antioxidant protection was assayed in the peel and pulp of sunflower. The essential roles of proteases in food stimulate research to find other sources of the enzyme especially from non-conventional sources. In the present work, we study several biochemical parameters in the pulp and peel of sunflower. Methods Pulp and peel of sunflower was extracted, antioxidant enzymes and non-enzymatic antioxidant were measured. Alkaline protease was measured and purified from pulp in sunflower. Results High carbohydrate concentration, beta-carotene, catalase and ascorbate peroxidase activities, free radical scavenging capacity and free flavonoid content were observed in the peel of sunflower. Whereas, MDA and ceruloplasmin activities were high in the pulp of sunflower. Conclusions The present study concluded that peel in sunflower are strong radical scavengers and can be considered as good sources of natural antioxidants for medicinal and commercial uses. Further analysis showed that protease activity was a significantly high in the pulp compared to the peel.

Enzymes are preferred over chemical catalysts in a myriad of applications owing to their high specificity, selectivity and moderate operating conditions. Enzymes in the soluble state are susceptible to instability and difficult in separation. Immobilization of enzyme onto a support increases its physical and thermal stability, reusability and recovery from the reaction broth. Different support materials such as polymers, hydrogels, nanoparticles, nanofibers and nano-scaffolds are being used for enzyme immobilization. Carbon-based nanomaterials have gained high popularity among different support materials. Here, we present the research findings on the immobilization of β-glucosidase onto two novel support materials, i.e., glutaraldehyde-activated multi-walled carbon nanotubes (MWCNTs) and Ca-alginate beads. The relative merits of the two supports are compared in terms of the performance of the enzyme in each case. β-Glucosidase immobilized on glutaraldehyde-modified MWCNTs exhibited higher residual activity and stability compared to the enzyme encapsulated in Ca-alginate beads. Kinetic study shows a higher enzyme affinity for the substrate for enzyme immobilized onto MWCNT support compared to Ca-alginate. The overall results demonstrate that despite a general decrease in the enzyme activity due to immobilization, there is greater retention of activity of the immobilized enzyme upon multiple cycles of hydrolysis. This study provides distinct economic advantage of employing nanoparticles as support for enzyme immobilization for large-scale industrial applications.

Dietary polyphenols exist in two forms; extractable polyphenols (EPP) or compounds solubilised by aqueous/organic solvents, and non-extractable polyphenols (NEPP) or compounds remain in the corresponding residues after the extraction. At present, most researchers focus on EEP fractions, while NEPP is neglected. Thus, this study aimed to release NEPP from the remaining powder residue of Barhi date palm kernels (BDPK) with acid hydrolysis. The related extraction conditions were determined and optimised using response surface methodology (RSM) for maximisation of NEPP with highest cytotoxic and antioxidant activities. The face-centred central composite design (FCCCD) was used to establish treatments based on three independent variables, namely; extraction temperature, time, and solvent/sample ratio. Under the optimal conditions, the experimental values for DPPH radical-scavenging capacity of NEPP (IC50=57.52µg/mL), and cytotoxicity of NEPP against A549 and HT29 cells were IC50=17.4 µg/mL and 31.4µg/mL, respectively. The experimental values were in agreement with those predicted by RSM models, confirming the suitability of the model employed and the success of RSM for optimisation of the extraction conditions for NEPP from BDPK. These results indicate that NEPP from industrial date fruit waste could be a promising candidate as natural antioxidants with significant antiproliferation effect against A549 and HT29 cancer cells in-vitro.

Background The genetic features indicate a crucial role in nephrolithiasis. The present study was aimed to investigate the role of Glutathione-S-transferase Mu (GSTM1), Glutathione-S- transferase Theta (GSTT1) and endothelial nitric oxide synthase (eNOs) gene polymorphism in nephrolithiasis. Methods We involved a case-control study in which 480 individuals were divided into 240 healthy control and 240 patients with nephrolithiasis. For each patient and control, we measured biochemical criteria, levels of glutathione S-transferase, eNOs, GSTM1, GSTT1genes and eNOS genes polymorphism by PCR-RFLP. Results GSTM1 and GSTT1 null genotypes are not a risk features for nephrolithiasis. The eNOS frequency GG, GT, and TT genotypes by using Ban II enzyme as restriction enzyme were found to be (48.33, 36.67, and 15.00) %. The eNOS frequency TT, GT, and GG genotypes by using the Ban II enzyme as restriction enzyme were found to be 15.84, 25.83, and 58.33%, respectively. The result showed an increase in serum eNOs levels were in the patient's group comparing to control. Conclusions This work is the first in the literature to study the relation between eNOs genes polymorphisms and nephrolithiasis. The results conclude that TT genotypes in the eNOs genes are associated with an increase the oxidative stress in patients.

Background and objectives: The initiation of apoptotic death in cancer cells is the aim of many chemotherapeutic strategies. As many anticancer agents have been discovered in natural plant sources, the search for novel phytochemicals displaying anticancer activities continues at a tremendous rate. This study was conducted to evaluate the effect of extractable polyphenols (EPP); non-extractable polyphenols (NEPP); nobiletin (NOB); tectorigenin (TEC) and persicogenin (PERS) from Barhi date palm kernels (BDPK) extracts on the induction of apoptosis in human lung cancer A549 and colon cancer HT29 cell lines. Materials and Methods: The anticancer activities were determined using MTT and trypan blue exclusion assays. Flow cytometry analysis was performed to study the cell viability, cell cycle arrest and apoptosis. The underlying mechanism of apoptosis induced by crude extracts and the purified compounds was investigated using caspase-3, -8 and -9 assays and the mitochondrial membrane potential assay. Results: The findings indicate that both BDPK extracts and the purified compounds do exert induced cell death on A549 and HT29 cells. The results observed from MTT- assay and trypan blue exclusion indicate that the cytotoxic effects of both plant extracts and the three isolated flavonoids are dose-dependent with higher cell death after 72 hours treatment. Treatment of human lung and colon cancer cells with EPP, NEPP, NOB, TEC and PERSI induced late stages of apoptosis as there was evidence of the DNA degradation and high percentage of the cells population was situated at sub-G1 phase, indicating a high population of apoptotic cells. Conclusions: Study of the apoptotic mechanism demonstrated that EPP and NEPP exhibited dependent mitochondrial signalling pathway as seen with caspase-9 and induced receptor-mediated (extrinsic) apoptotic pathway as seen with caspase-8. Therefore, our results suggest that BDPK extracts and the three isolated flavonoids could be a good candidate for developing anticancer agents.

Natural polymeric gel such as alginate and carrageenan, and carbon nanomaterials, especially MWCNT and graphene are gaining increasing popularity as immobilization support owing to their biocompatibility. Thus, this study attempts to make a relative characterization of the immobilized β-glucosidase on alginate and multiwalled carbon nanotubes (MWCNT) to assess their relative merits as biocatalyst. Acid functionalization of MWCNT lead to the formation of carboxyl groups at the MWCNT surface which seemed to have role in the stable attachment of the enzyme on the MWCNT surface. The effectiveness of the immobilized enzyme was evaluated using hydrolysis of p-nitrophenyl-β-D-glucopyranoside to p-nitrophenol. The optimum immobilization conditions were found to be 17 mg MWCNT and 4 h incubation time for MWCNT, and 3.5 wt% sodium alginate solution and 2 h incubation time for the Ca-alginate beads. The immobilization yield on MWCNT was found to be 96% and that for the Ca-alginate beads was 86%. The residual activity of the enzyme at the third cycle of subsequent reuse was 85% for the enzyme-MWCNT and almost 50% for the enzyme-alginate beads. Both, Ca-alginate and MWCNT immobilized enzyme were found to follow Michaelis-Menten kinetics. The kinetic data were best represented by the Langmuir plot. The kinetic constants Vmax and Km for the MWCNT immobilized enzyme were obtained as 1.324 mM/min and 0.801 mM, respectively, while for the alginate immobilized enzyme, the constants predicted were 1.240 mM/min and 0.524 mM, respectively. Thus MWCNT immobilized β-glucosidase exhibited greater stability in terms of activity and reusability compared to Ca-alginate beads.

Enzymatic reactions involving lipases as catalyst in transesterification can be an excellent alternative to produce environmental-friendly biodiesel. In this study, lipase extracted from Cocoa Pod Husk (CPH) and immobilized through cross linked enzyme aggregate (CLEA) technology catalysed the transesterification of Jatropha curcas oil successfully. Face centered central composite design (FCCCD) under response surface methodology (RSM) was used to get the optimal conditions of 3% (w/w) enzyme loading, 4h reaction time and 1:6 oil/ethanol ratio to achieve the highest conversion of free fatty acid and glycerides into biodiesel (93%). The reusability of CLEA-lipase was tested and after seven cycles, the conversion percentage reduced to 58%. The results revealed that CLEA lipase from CPH is a potential catalyst for biodiesel production.

Fungi are not classified as plants or animals. They resemble plants in many ways but do not produce chlorophyll or make their own food photosynthetically like plants. Fungi are useful for the production of beer, bread, medicine, etc. More complex than viruses or bacteria; fungi can be destructive human pathogens responsible for various diseases in humans. Most people have a strong natural immunity against fungal infection. However, fungi can cause diseases when this immunity breaks down. In the last few years, fungal infection has increased strikingly and has been accompanied by a rise in the number of deaths of cancer patients, transplant recipients, and acquired immunodeficiency syndrome (AIDS) patients owing to fungal infections. The growth rate of fungi is very slow and quite difficult to identify. A series of molecules with antifungal activity against different strains of fungi have been found in insects, which can be of great importance to tackle human diseases. Insects secrete such compounds, which can be peptides, as a part of their immune defense reactions. Active antifungal peptides developed by insects to rapidly eliminate infectious pathogens are considered a component of the defense munitions. This review focuses on naturally occurring antifungal peptides from insects and their challenges to be used as armaments against human diseases.

The functionalization of multi-walled carbon nanotubes (MWCNTs) is extremely important to increase their solubility. This study reported the efficient functionalization of MWCNTs via acid treatment introducing carboxyl group at the side wall of MWCNTs by mixing the concentrated nitric and sulfuric acid. Plant esterase enzyme extracted from wheat flour called alpha naphthyl acetate esterase (ANAE) was immobilized onto functionalized MWCNTs via covalent attachment of amine group in ANAE and carboxyl group of functionalized MWCNTs. In order to characterize the functionalization of MWCNTs and immobilization of enzyme, Fourier transform infrared (FTIR) spectroscopy have been used to confirm the existence of these functional groups. The FTIR spectrums revealed that the several peaks formed at 1992.39, 2324.14, 2880.85 and 3029.51 cm−1 which can be assigned to the C=O symmetric stretching, O−H stretch from strongly hydrogen-bonded –COOH, H−C stretch modes of H−C=O in the carboxyl group, and O-H stretch from carboxyl groups (O=C−OH and C−OH) respectively. The immobilization of ANAE on functionalized MWCNTs confirmed by observation of the peak at 3646.81 cm−1 and 3850.91 cm−1 introduced the strong amide linkages between carboxylic acid groups and amine group on the MWCNTs. This proposed framework can be applied for fabrication of screen printed electrode as biosensor for detection of pesticide.

The aim of this study was to develop an EQ-5D-5L value set reflecting the health preferences of the Malaysian adult population. Respondents were sampled with quotas for urbanicity, gender, age, and ethnicity to ensure representativeness of the Malaysian population. The study was conducted using a standardized protocol involving the EuroQol Valuation Technology (EQ-VT) computer-assisted interview system. Respondents were administered ten composite time trade-off (C-TTO) tasks and seven discrete choice experiment (DCE) tasks. Both linear main effects and constrained non-linear regression models of C-TTO-only data and hybrid models combining C-TTO and DCE data were explored to determine an efficient and informative model for value set prediction. Data from 1125 respondents representative of the Malaysian population were included in the analysis. Logical consistency was present in all models tested. Using cross-validation, eight-parameter models for C-TTO only and C-TTO + DCE hybrid data displayed greater out-of-sample predictive accuracy than their 20-parameter, main-effect counterparts. The hybrid eight-parameter model was chosen to represent the Malaysian value set, as it displayed greater out-of-sample predictive accuracy over C-TTO data than the C-TTO-only model, and produced more precise estimates. The estimated value set ranged from − 0.442 to 1. The constrained eight-parameter hybrid model demonstrated the best potential in representing the Malaysian value set. The presence of the Malaysian EQ-5D-5L value set will facilitate its application in research and health technology assessment activities.

Metallic silver (Ag) and some Ag compounds have long being used to fight microbial infection even before the emergence of antibiotics in the form of drugs. But lately, one of the problems facing the medical world is the occurrence of drug resistance microbes. Therefore, actions must be taken to overcome the problem, one of which is to continue studies to develop new drugs, either synthetic or natural. The ultimate goal is to offer appropriate and efficient antimicrobial drugs to patients. In this regard, with the advent of nanotechnology, it is now possible to synthesize nano scale Ag which can be used in diverse medical applications. The aim of this study is to investigate the effect of some important parameters towards the production of high yield Ag nanoparticles (AgNPs) from silver salts by making use of reducing agent. One-Factor-at-a-Time (OFAT) design of experiment was conducted, involving five important synthesis parameters. Based on the analyses of its yellow color, absorption by UV-vis in the range of 350 to 420 nm wavelength and surface plasmon resonance (SPR) at 420 nm, AgNPs produced were estimated to be in the range of 5 to 50 nm in diameter. Out of the five parameters tested, such as the concentration of silver nitrate (AgNO3), the initial pH of AgNO3 solution, the concentration of tri-sodium citrate (TSC), the reaction time between AgNO3 and TSC and stirring time (after reduction process), results showed that all factors except stirring time affect the production. Characterization of AgNPs, carried out using Transmission Electron Microscope (TEM), showed that the synthesized AgNPs were spherical with diameters less than 15 nm. Antimicrobial activity assays of AgNPs, carried out against selected microbes, namely Aspergillus niger, Microsporum canis and Staphylococcus aureus showed various degree of potencies.

In recent decades, nanoparticles research has garnered a lot of interest, especially in finding more effective ways for their synthesis. This study aims to investigate the chemical mediated synthesis of silver nanoparticles (AgNPs) using silver salt and tri-sodium citrate as the reducing agent. Processing parameters were statistically optimized in order to produce high yield AgNPs and optimization were analyzed using Response Surface Methodology (RSM). Based on AgNPs yield production, analyzed using UV-vis spectrophotometer in the range of 350 to 420nm, results show that out of the three parameters tested (AgNO3 and tri-sodium citrate concentrations and stirring time), all factors except stirring time contributed significantly to the production. Synthesized AgNPs at optimized condition (absorbance 0.9 AU at 420nm wavelength) were then characterized using TEM and UV-vis analysis.

This study focused on the production of a novel multi-CLEA comprising the enzyme activities of lipase and protease from fish viscera. A multi-CLEA is a single biocatalyst that can catalyze separate unrelated reactions, but these reactions can be conducted in one application. Tests pertaining to the effect of various additives on the multi-CLEA’s activity were performed. Response Surface Methodology’s Face Centered Central Composite Design (FCCCD) was employed to optimize the preparation parameters of the multi-CLEA in an aqueous medium. It was found that 55% (w/v) of ammonium sulfate, 65 mM of glutaraldehyde, and 0.113 mM of bovine serum albumin were the optimum levels of additives to prepare the multi-CLEA with the protease and lipase recovery activity of 43.82% and 99.91%, respectively. Multi-CLEAs were found to retain an average of more than 34% of the initial activity after five consecutive batches for both enzymes. Finally, the multi-CLEA was utilized to catalyze two reactions: improved washing process and biodiesel production. The stain removal percentage of a commercial detergent was improved by 67.78% after adding multi-CLEA. In addition, the multi-CLEA catalyzed biodiesel production from vegetable oil with a percentage conversion of 51.7%. Such results demonstrated that the multi-CLEA is a promising catalyst for biotechnological applications.

A tremendous amount of research has investigated carcinoma and looked into treatments to lower cancer mortality. Nevertheless, oral cancer is still a considerable health issue worldwide and mortality and incidence rates are rising. Oral cancer is ranked as the sixth most common carcinoma and is a prime health dilemma globally. Thus, it is increasingly important to have tools that allow for discrimination between oral dysplasia and squamous-cell carcinoma. Although conventional methods such as medical imaging can be helpful in prediction and diagnosis of oral cancer, these methods continue to have limitations. More recently, machine learning has been used as a complementary approach in biomedical research and it now plays a leading role in the emerging domains of computational biology and bioinformatics. Specifically, DNA chip gene expression technology is currently helping researchers to distinguish oral dysplasia and squamous-cell carcinoma thru analysis of gene expression in tissue samples. Nonetheless, one of the prime challenges in the computational process of microarray data is the curse of dimensionality due to the crushing number of features. Therefore, we applied a random projection (RP) feature construction technique to tackle the problem of high-dimensional gene expression data and to increase the efficiency of our proposed model. In addition, we combined a RP technique with a support vector machine (SVM) that employs a sequential minimal optimization training algorithm (SMO) in order to efficiently differentiate squamous-cell carcinoma and oral dysplasia. The highest classification accuracy recorded by our proposed model was 95.6332%. We show in this study that using a SMO machine-learning classifier with a RP dimensionality reduction tool can be effective for classifying oral cancer.

Over the past two decades, lung cancer has been a dominant malignant form of cancer. Around 80% of major lung cancers are non-small cell lung carcinoma (NSCLC). NSCLC is the major reason for death from a malignant disease worldwide. As a result, there is urgent interest in the improvement of innovative diagnostic noninvasive technologies that may enhance early diagnosis of the disease. One of the most promising techniques for early detection of cancerous cells depends on machine learning based on molecular cancer classification using gene expression profiling data. Current technological breakthroughs in gene expression profiling, specifically with DNA and oligonucleotide microarrays, permit the concomitant analysis of the expression of thousands of genes and also enables surveillance of disease prediction and progression of patient survival at the molecular level. For this reason, we attempted to come up with a machine-learning-based strategy called composite hypercubes on iterated random projections (CHIRP) in order to settle the problem of detection of early NSCLC from DNA biochip gene expression data. Furthermore, we utilized an unsupervised dimensionality reduction approach, named t-distributed stochastic neighbor embedding (T-SNE), to reduce computational complexity and to increase the efficiency of the developed system. The average accuracy obtained by the proposed system in terms of detection and diagnosis of early non-small cell lung cancer was 97.21871%. The empirical results prove that the combination of dimensionality reduction models with machine-learning algorithms can be effectively used for early detection of specific NSCLC tumor type.

Cervical carcinoma remains a prime cause of cancer-related deaths in woman globally. Research into the prediction of the survivability for cervical cancer has been a challenge for researchers. Survival rates increase with earlier detection of cancer of the cervix. Cancer research and associated domains have made significant strides over recent years. For example, cancer prognosis using machine learning techniques is now a promising area of research. Data mining and machine learning have found considerable application thru the use of microarray expression profiling inspection. Specifically, DNA chip gene expression technology is a promising tool that can identify cancerous cells in early phases of the disease by examining the gene expression of analyzed instances. Furthermore, microarray technology enables researchers to assay the expression of thousands of genes in parallel. In this paper, we present a Gaussian process regression model in order improve the prediction of survivability of patients with early cervical cancer. Additionally, stochastic proximity embedding (SPE) was applied to reducing the number of attributes by selecting the most informative genes of the input dataset. Consequently, the computational complexity was reduced and the performance of the proposed model was increased. Our results indicate that gene expression profiles combined with carefully chosen learning algorithms can predict patient survival for certain diseases.

Introduction and aims: It is necessary to ascertain current prescribing of antibiotics for upper respiratory tract infections (URTIs) to address potential overuse. A retrospective analysis was conducted of all prescriptions for URTIs among ten public primary healthcare centres in Kedah, Malaysia from 1st January to 31st March 2014. Results: A total of 123,524 prescriptions were screened and analysed. Of these, 7129 prescriptions were for URTI, with 31.8% (n=2269) containing antibiotics. Macrolides were the most commonly prescribed antibiotic constituting 61% (n=1403) of total antibiotics prescribed. There was a statistically significant association between different prescribers and diagnoses (p=0•001) and a weak positive trend suggesting family medicine specialists are more competent in antibiotic prescribing, followed by medical officers and assistant medical officers (τ=0•122).. Conclusions: Prescribing practices of some prescribers were inconsistent with current guidelines encouraging resistance development. National antimicrobial stewardship programmes and further educational initiatives are ongoing in Malaysia to improve antibiotic use

Background: Acute leukaemia is characterized by fast growth of abnormal clones of haemopoietic precursor cells inside bone marrow leading to undue accumulation in the bone marrow. Acute lymphoblastic leukemia (ALL) is the most common form of childhood cancer. Materials and Methods: The study concerned 50 children diagnosed with ALL (mean age, 8.55±2.54) compared to 40 healthy controls (mean age, 8.00±1.85). The Hb, serum copper , ceruloplasmin oxidase, advanced oxidation protein products (AOPPs), total antioxidant activity (TAA) and protein were measured in all groups.One proteinous component was isolated by gel filtration chromatography from the precipitate produced by polyethylene glycol. Results: Significantly higher levels of AOPP, copper and decrease in total antioxidant activity were noted in the cases. Statistical analysis also showed a significant increase (p