Four New Stilbene Derivatives Isolated from Gnetum latifolium var. funiculare Markgr. and Their Inhibition of NO Production in LPS-activated RAW264.7 Cells.

Gnetum latifolium var. funiculare Markgr. is a medicinal plant and widely distributed in mountainous areas of Vietnam. Phytochemical investigation on the trunks of this plant afforded eight stilbene derivatives (1-8) including for new compounds (1-4). Their structures were determined based on extensive analyses of HR-ESI-MS, 1D and 2D NMR spectra. Among the isolates, compounds 1-3 showed moderate NO production inhibition in LPS-activated RAW264.7 cells with the IC50 values ranging from 46.81 to 68.10 µM, compounds 4 and 6 showed weak effects with the IC50 values of 96.57 and 79.46 µM, respectively, compared to that of the positive control compound, dexamethasone (IC50 14.20 µM).

Association of PSORS1C3, CARD14 and TLR4 genotypes and haplotypes with psoriasis susceptibility.

Psoriasis is a common chronic, immune-mediated inflammatory disease of the skin. PSORS1C3 is a non-protein coding gene, of which the RNA transcript is found in psoriatic patients. CARD14 is mainly expressed in epidermal keratinocytes. TLR4 is a transmembrane protein to recognize microbial antigens. Our study aimed to assess the relationship among PSORS1C3, CARD14 and TLR4 polymorphisms, inflammatory expression and psoriasis susceptibility. To the end, 71 patients with psoriasis and 46 healthy individuals with the well-characterized clinical profiles were enrolled. Gene polymorphisms were determined by Sanger DNA sequencing and secretion of cytokines by ELISA. As a result, genetic analysis of PSORS1C3 gene identified nine SNPs and three haplotype blocks. Sequencing of the CARD14 gene determined eight SNPs and one haplotype block. Sequencing of TLR4 gene identified nine SNPs, in which a SNP rs1018673641 was found to exert deleterious effect. The linkage disequilibrium analysis showed that seven variants in PSORS1C3 gene and three SNPs in CARD14 gene were in tightly linked. More importantly, a significant association between IL-6 level and rs1018673641 AT genotype in TLR4 gene was detected in psoriatic patients. In conclusion, the PSORS1C3, CARD14 and TLR4 polymorphisms and haplotypes may be correlated with risk of suffering psoriasis and the IL-6-mediated chronic inflammation in psoriasis could be partially regulated by the TLR4 functional variant.

Imputation of missing monthly rainfall data using machine learning and spatial interpolation approaches in Thale Sap Songkhla River Basin, Thailand.

Missing rainfall data has been a prevalent issue and primarily interested in hydrology and meteorology. This research aimed to examine the capability of machine learning (ML) and spatial interpolation (SI) methods to estimate missing monthly rainfall data. Six ML algorithms (i.e. multiple linear regression (MLR), M5 model tree (M5), random forest (RF), support vector regression (SVR), multilayer perceptron (MLP), genetic programming (GP)) and four SI methods (i.e. arithmetic average (AA), inverse distance weighting (IDW), correlation coefficient weighted (CCW), normal ratio (NR)) were investigated and compared in their performance. The twelve rainfall stations, located in the Thale Sap Songkhla river basin and nearby basins, were considered as a study case. Tuning hyper-parameters for each ML method was conducted to get the most suitable model for the data sets considered. Three performance criteria matrices (i.e. NSE, OI, and r) were chosen, and the sum of those three performance criteria matrices was introduced for methods' performance comparison. The experimental results pointed out that selecting neighbouring stations were essential when applying SI methods, but not for the ML method. The overall performance showed ML better imputed missing monthly rainfall than SI due to overcoming spatial constraints. GP provided the highest performance by giving NSE = 0.825, OI = 0.877, and r = 0.909 for the training stage. Those values for the testing stage were 0.796, 0.852, and 0.902, respectively. It was followed by SVR-rbf, SVR-poly, and RF. NR provided the best performance among four SI methods, followed by CCW, AA, and IDW. When applying SI methods, it should contemplate a correlation between the target and neighbouring stations greater than 0.80.

Chemical constituents of Impatiens chapaensis Tard. and their α-glucosidase inhibition activities.

Sixteen compounds (1-16) were isolated from Impatiens chapaensis. Chemical structures were determined by spectroscopic analyses and comparisons with previously published data. This report is the first to identify compounds 1, 5-7, 10, 12-14, and 16 from the genus Impatiens. Seven chosen isolates (5, 7, 10, 11, 12, 15, and 16) were submitted for α-glucosidase inhibition assays with acarbose as the positive control (IC50 = 227.14 ± 13.71 µM). Flavonoid 5 exhibited a significant inhibitory effect (IC50 = 101.00 ± 9.01 µM).

Biologically Active Constituents from Plants of the Genus Xanthium.

Herbaceous annual plants of the genus Xanthium are widely distributed throughout the world and have been employed medicinally for millennia. This contribution aims to provide a systematic overview of the diverse structural classes of Xanthium secondary metabolites, as well as their pharmacological potential. On searching in various reference databases with a combination of three keywords "Xanthium", "Phytochemistry", and "Pharmacology", relevant publications have been obtained subsequently. From the 1950s to the present, phytochemical investigations have focused mainly on 15 Xanthium species, from which 300 compounds have been isolated and structurally resolved, primarily using NMR spectroscopic methodology. Xanthium constituents represent several secondary metabolite types, including simple phenols, sulfur and nitrogen-containing compounds, lignans, sterols, flavonoids, quinones, coumarins, and fatty acids, with terpenoids being the most common of these. Among the 174 terpenoids characterized, xanthanolide sesquiterpenoids are abundant, and most of the compounds isolated containing sulfur were found to be new in Nature. The ethnomedical uses of Xanthium crude extracts are supported by the in vitro and in vivo effects of their constituents, such as cytotoxicity, antioxidant, antibacterial, antifungal, antidiabetes, and hepatoprotective activities. Toxicological results suggest that Xanthium plant extracts are generally safe for use. In the future, additional phytochemical investigations, along with further assessments of the biological profiles and mechanism of action studies of the components of Xanthium species, are to be expected.

Biologically Active Constituents from Plants of the Genus Desmos.

The combination of traditional knowledge of medicinal plants with scientific rationale has yielded positive results in recent years. Bioactive compounds isolated from herbaceous plants have long been used as drugs that benefit human health, as well as providing useful compounds for drug development lead compound optimization. This chapter aims to provide a systematic overview of the structural types of Desmos secondary metabolites, along with their biological potential. Various chromatographic and spectroscopic methods have been utilized for isolating, purifying, and elucidating the structures of compounds from Desmos species. From 1982 to the present time, more than 200 metabolites have been isolated from members of this genus. Desmos spp. constituents include terpenoids, phytosterols, polyoxygenated cyclohexanes and cyclohexenes, oxepinones, fatty acids, with flavonoids, alkaloids, and miscellaneous phenols being the predominant compounds. The essential oils of Desmos species have also been investigated. Both crude plant extracts and isolated compounds from this genus have been evaluated for their biological activities. Desmos constituents have been found to exhibit cytotoxic, antimicrobial, antioxidative, anti-inflammatory, and aromatase and NFAT transcriptive inhibition effects.

The concentration of potentially toxic elements (PTEs) in sausages: a systematic review and meta-analysis study.

The contamination of fresh meat and meat products like sausages with potentially toxic elements (PTEs) is a worldwide health concern. Consequently, the related investigations concerning the levels of PTEs in sausages among databases such as Scopus, Cochrane, and PubMed were investigated 1 January 2000 to 20 August 2020. Furthermore, the concentration of PTEs in sausages was meta-analyzed based on the random effect model (REM). The findings indicated that the rank order of PTEs in sausage was Fe (432.154 µg/kg) > Cu (152.589 µg/kg) > Zn (93.813 µg/kg) > Cr (6.040 µg/kg) > Pb (1.524 µg/kg) > Ni (0.525 µg/kg) > Cd (0.115 µg/kg) > As (0.066 µg/kg). Our results showed that the PTE concentration in sausages was lower than the permitted limit except for Pb in samples reported from Nigeria, China, and Turkey. Therefore, continuous monitoring of PTEs in such products was recommended.

CuO-ZnO-CdWO4: a sustainable and environmentally benign photocatalytic system for water cleansing.

Currently, there is a major problem of water contaminations, especially of dyes, all over the world. A new technique is being developed daily for the treatment of contaminated water. In many ways, a photocatalytic degradation of a dye by a mixed metal oxide photocatalyst is counted as the best technique for water treatment. This paper also addresses the preparation and photocatalytic application of newly developed mixed metal oxide nanocomposite, CuO-ZnO-CdWO4. A novel mixed metal oxide CuO-ZnO-CdWO4 nanocomposite has been synthesized by a green route using Brassica Rapa leaves extract. The application of CuO-ZnO-CdWO4 as a photocatalyst in wastewater treatment has been thoroughly discussed. Several spectroscopic and microscopic techniques were used to characterize the prepared nanocomposite. The photocatalytic activity of CuO-ZnO-CdWO4 nanocomposite with a band gap of 3.13 eV was observed under the artificial visible light and sunlight for the degradation of Congo red dye. The results under sunlight show the 1.45 times greater removal efficiency than under the artificial visible light. Pseudo-first-order, diffusion, and Singh kinetics models were used to describe the kinetics of dye degradation. Pseudo-first-order model was found to be best fitted model for present study. The performance of CuO-ZnO-CdWO4 was estimated by significant parameters such as quantum yield, figure of merit, turnover number, and mean turnover frequency. The value these parameters were calculated as 1.70 × 10-8 molecules photon-1, 1.77 × 10-4, 2.98 × 108 s-1, and 3.31 × 10-4 s-1, respectively. These parameters revealed high potential of CuO-ZnO-CdWO4 for Congo red dye degradation.

Application of gamma radiation for disinfection of fungi in a large volume of historical archives damaged by flood following Typhoon Hagibis 2019, Japan: A case report.

A large volume of historical archives was extensively damaged by flood water, following Typhoon Hagibis in Fukushima, Japan, in October 2019. They were rescued from the stricken area within a week, however, the prolonged exposure of paper documents to water caused severe biodegradation by fungal growth. To disinfect fungi, the paper documents were exposed to gamma radiations emitted by a source of Cobalt 60 by the industrial irradiation service. The wet paper documents were mainly contaminated with hydrophilic and cellulolytic fungi, including Trichoderma, Stachybotrys, and Fusarium; no fungi grew after irradiation. These results indicated that the average absorbed dosage from 13.1 kGy to 16.1 kGy were sufficient to disinfect paper documents heavily contaminated with fungi. In the present study, we demonstrated the successful practical use of irradiation in fungi-damaged paper documents using a commercial gamma-irradiation facility.

Machine learning algorithm-based risk assessment of riparian wetlands in Padma River Basin of Northwest Bangladesh.

Wetland risk assessment is a global concern especially in developing countries like Bangladesh. The present study explored the spatiotemporal dynamics of wetlands, prediction of wetland risk assessment. The wetland risk assessment was predicted based on ten selected parameters, such as fragmentation probability, distance to road, and settlement. We used M5P, random forest (RF), reduced error pruning tree (REPTree), and support vector machine (SVM) machine learning techniques for wetland risk assessment. The results showed that wetland areas at present are declining less than one-third of those in 1988 due to the construction of the dam at Farakka, which is situated at the upstream of the Padma River. The distance to the river and built-up area are the two most contributing drivers influencing the wetland risk assessment based on information gain ratio (InGR). The prediction results of machine learning models showed 64.48% of area by M5P, 61.75% of area by RF, 62.18% of area by REPTree, and 55.74% of area by SVM have been predicted as the high and very high-risk zones. The results of accuracy assessment showed that the RF outperformed than other models (area under curve: 0.83), followed by the SVM, M5P, and REPTree. Degradation of wetlands explored in this study demonstrated the negative effects on biodiversity. Therefore, to conserve and protect the wetlands, continuous monitoring of wetlands using high resolution satellite images, feeding with the ecological flow, confining built up area and agricultural expansion towards wetlands, and new wetland creation is essential for wetland management.

Development of fuzzy analytic hierarchy process based water quality model of Upper Ganga river basin, India.

The ecological sustainability of rivers is in question due to severe pollution and lack of stringent regulations. Long term (1990-2016) water quality data of five stations namely Haridwar, Bareilly, Kanpur, Prayagraj and Varanasi of Upper Ganga river, India was considered for analysis using fuzzy analytical process (FAHP) based water quality index (WQI) to assess surface water quality. The value of water physical, biological and chemical parameters of temporal resolution (monthly, seasonal and yearly) indicate that value of electrical conductivity (EC), total dissolved solids (TDS), biological oxygen demand (BOD), chemical oxygen demand (COD), total alkalinity (Mg CaCO3), total hardness (Mg CaCO3), calcium (Ca), magnesium (Mg), sodium (Na), chlorine (Cl) and bicarbonate (HCO3) were observed very high compared to recommended value of Bureau of Indian Standards (BIS) and World Health Organization (WHO) at Kanpur, Prayagraj and Varanasi stations. However, low value of parameters is observed at Haridwar and Bareilly stations. Also, the high deviation was observed in water quality parameters during 1990-2010 whereas the deviation of parameters is decreased in 2011-2016. It is observed from the piper diagram that magnesium and bicarbonate at Haridwar, sodium, potassium and bicarbonate in Bareilly, Kanpur, Prayagraj and Varanasi stations are dominant during monthly and seasonal periods. The fuzzy based WQI value indicate that water quality is excellent to poor at Haridwar, while poor to unsuitable in Bareilly, Kanpur, Prayagraj and Varanasi during monthly and seasonal periods. The water quality ranges from poor to unsuitable during the 1990-2010 period and good to very poor during the 2011-2016 period at Bareilly, Kanpur, Prayagraj and Varanasi stations. Whereas very good to good during 1990-2010 and excellent to good during 2011-2016 at Haridwar. It was also determined that water quality parameters (Ca, Na+K, SO4, Hardness, Cl and Mg) and WQI values were increased with length of the stream. It indicates that drain discharge, urban growth, urban functions, ecological footprints and crop area increment were key sources of pollution.

Development of an integrated peri-urban wetland degradation assessment approach for the Chatra Wetland in eastern India.

The loss of peri-urban wetlands is a major side effect of urbanization in India in recent days. Timely and proper assessment of wetland area change is essential for the conservation of wetlands. This study follows the integrated way of the peri-urban wetland degradation assessment in the case of medium and small-size urban agglomerations with a special focus on Chatra Wetland. Analysis of land-use and land cover (LULC) maps of the past 28 years shows a decrease of 60% area of the wetland including marshy land. This has reduced the ecosystem services value by about 71.90% over the period 1991-2018. From this end, The Land Change Modeler of IDRISI TerrSet using the combination of MLPNN and Markov Chain has been used to predict the LULC map of this region. The scenario-based modeling following the LULC conversion and nine explanatory variables suggests the complete loss of this wetland by 2045. However, the authors have also tried to present a future LULC pattern of this region based on an environmental perspective. This proposed map suggests possible areas for built-up expansion on the western side of the city without significantly affecting the environment.

Flash-Flood Potential Mapping Using Deep Learning, Alternating Decision Trees and Data Provided by Remote Sensing Sensors.

There is an evident increase in the importance that remote sensing sensors play in the monitoring and evaluation of natural hazards susceptibility and risk. The present study aims to assess the flash-flood potential values, in a small catchment from Romania, using information provided remote sensing sensors and Geographic Informational Systems (GIS) databases which were involved as input data into a number of four ensemble models. In a first phase, with the help of high-resolution satellite images from the Google Earth application, 481 points affected by torrential processes were acquired, another 481 points being randomly positioned in areas without torrential processes. Seventy percent of the dataset was kept as training data, while the other 30% was assigned to validating sample. Further, in order to train the machine learning models, information regarding the 10 flash-flood predictors was extracted in the training sample locations. Finally, the following four ensembles were used to calculate the Flash-Flood Potential Index across the Bâsca Chiojdului river basin: Deep Learning Neural Network-Frequency Ratio (DLNN-FR), Deep Learning Neural Network-Weights of Evidence (DLNN-WOE), Alternating Decision Trees-Frequency Ratio (ADT-FR) and Alternating Decision Trees-Weights of Evidence (ADT-WOE). The model's performances were assessed using several statistical metrics. Thus, in terms of Sensitivity, the highest value of 0.985 was achieved by the DLNN-FR model, meanwhile the lowest one (0.866) was assigned to ADT-FR ensemble. Moreover, the specificity analysis shows that the highest value (0.991) was attributed to DLNN-WOE algorithm, while the lowest value (0.892) was achieved by ADT-FR. During the training procedure, the models achieved overall accuracies between 0.878 (ADT-FR) and 0.985 (DLNN-WOE). K-index shows again that the most performant model was DLNN-WOE (0.97). The Flash-Flood Potential Index (FFPI) values revealed that the surfaces with high and very high flash-flood susceptibility cover between 46.57% (DLNN-FR) and 59.38% (ADT-FR) of the study zone. The use of the Receiver Operating Characteristic (ROC) curve for results validation highlights the fact that FFPIDLNN-WOE is characterized by the most precise results with an Area Under Curve of 0.96.

New phenolics from Dianella ensifolia.

Phytochemical investigation and chromatographic separation of extracts from the aerial parts of Dianella ensifolia (L.) DC. (synonym Dianella nemorosa Lam. Ex. Schiler f.) led to the isolation of 10 compounds, the structures of which were determined by HR-ESI-MS and 1 D- and 2 D-NMR spectroscopies, and by comparisons with published studies. Among the isolated compounds were three flavans, a biflavan, a biflavone, a tetralone, a naphthalen glycoside, an aromatic compound, and two steroids. Six of these were known chemicals, while three were identified as new compounds: 7-acetyl-4R,8-dihydroxy-6-methyl-1-tetralone, 2(S),2',4'-dihydroxy-7-methoxyflavan, and diaensi-biflavan. 2(S),7,4'-dimethoxy flavan was obtained for the first time as a natural product.

Medicinal Plant Centipeda Minima: A Resource of Bioactive Compounds.

BACKGROUND: Centipeda minima (the family Asteraceae) is an annual herbaceous plant native to the tropical regions, especially in eastern tropical Asia. C. minima is well-known in the list of medicinal plants with capacities in treatment of whooping cough, nasal allergy, malaria, and asthma. More than sixty reports on phytochemical and pharmacological aspects of this plant are now available, but a supportive review is insufficient. OBJECTIVE: The current review aims to make a compilation of almost all of the isolated compounds from the title plant, together with their pharmacological activities. METHODOLOGY: Centipeda minima is a meaningful keyword to search for previous references, while the reliable databases, such as Sci-Finder, Google Scholar, Pub Med, Science Direct, the Web of Science, Scopus, Bentham science, Taylor Francis, Springer, IOP Science were utilized at most. CONCLUSION: More than one hundred secondary metabolites, classifying as terpenoids, flavonoids, mono-phenols, fatty acids, amides, and other types, were isolated from this plant. Among them, sesquiterpene lactones are dominant in either C. minima species or numerous plants of genus Centipeda. These phytochemical groups also possessed various biological results like anti-cancer, anti-bacteria, anti-allergy, anti-virus, anti-inflammation, and hepatoprotective activities. With many kinds of bioactive results such as anti-cancer and anti-inflammation, the use of C. minima plant extracts and isolated compounds for drug development seems to be a futuristic strategy.

Application of soft computing to predict water quality in wetland.

Prediction of water quality is a critical issue because of its significant impact on human and ecosystem health. This research aims to predict water quality index (WQI) for the free surface wetland using three soft computing techniques namely, adaptive neuro-fuzzy system (ANFIS), artificial neural networks (ANNs), and group method of data handling (GMDH). Seventeen wetland points for a period of 14 months were considered for monitoring water quality parameters including conductivity, suspended solid (SS), biochemical oxygen demand (BOD), ammoniacal nitrogen (AN), chemical oxygen demand (COD), dissolved oxygen (DO), temperature, pH, phosphate nitrite, and nitrate. The sensitivity analysis performed by ANFIS indicates that the significant parameters to predict WQI are pH, COD, AN, and SS. The results indicated that ANFIS with Nash-Sutcliffe Efficiency (NSE = 0.9634) and mean absolute error (MAE = 0.0219) has better performance to predict the WQI comparing with ANNs (NSE = 0.9617 and MAE = 0.0222) and GMDH (NSE = 0.9594 and MAE = 0.0245) models. However, ANNs provided a comparable prediction and the GMDH can be considered as a technique with an acceptable prediction for practical purposes. The findings of this study could be used as an effective reference for policy makers in the field of water resource management. Decreasing variables, reduction of running time, and high speed of these approaches are the most important reasons to employ them in any aquatic environment worldwide.

The optimal alternative for quantifying reference evapotranspiration in climatic sub-regions of Bangladesh.

Reference evapotranspiration (ETo) is a basic element for hydrological designing and agricultural water resources management. The FAO56 recommended Penman-Monteith (FAO56-PM) formula recognized worldwide as the robust and standard model for calculating ETo. However, the use of the FAO56-PM model is restricted in some data-scarce regions like Bangladesh. Therefore, it is imperative to find an optimal alternative for estimating ETo against FAO56-PM model. This study comprehensively compared the performance of 13 empirical models (Hargreaves-Samani, HargreavesM1, Hargreaves M2, Berti, WMO, Abtew, Irmak 1, Irmak 2, Makkink, Priestley-Taylor, Jensen-Haise, Tabari and Turc) by using statistical criteria for 38-years dataset from 1980 to 2017 in Bangladesh. The radiation-based model proposed by Abtew (ETo,6) was selected as an optimal alternative in all the sub-regions and whole Bangladesh against FAO56-PM model owing to its high accuracy, reliability in outlining substantial spatiotemporal variations of ETo, with very well linearly correlation with the FAO56-PM and the least errors. The importance degree analysis of 13 models based on the random forest (RF) also depicted that Abtew (ETo,6) is the most reliable and robust model for ETo computation in different sub-regions. Validation of the optimal alternative produced the largest correlation coefficient of 0.989 between ETo,s and ETo,6 and confirmed that Abtew (ETo,6) is the best suitable method for ETo calculation in Bangladesh.

The association between the lack of safe drinking water and sanitation facilities with intestinal Entamoeba spp infection risk: A systematic review and meta-analysis.

Intestinal protozoa infections are responsible for considerable morbidity and mortality, especially where the exposed population suffers from a lack of drinking water and sanitation facilities. In this study, the association between the lack of safe drinking water and sanitation (toilet) facilities with intestinal Entamoeba spp infection in the children (5-11 years), adult (18-55 years), and all age (5-55 years) were assessed. For this purpose, some of the international databases such as Scopus, PubMed, Web of Science, and Embase were screened to up to 7 June 2019 in order to retrieve the related citations. Also, the pooled odds ratios (ORs) following 95% confidence intervals (CIs) were calculated using a random-effects model. Twenty-nine articles with 36 studies were included while the OR extracted or calculated by using 2 × 2 contingency tables. However, the ingestion of contaminated water insignificantly can increase the odds ratio (OR) of Entamoeba spp infection (OR 1.01, (95% confidence interval [CI] 0.58 to 1.43), no access to sanitation (toilet) facilities significantly can increase odds of Entamoeba spp infection (OR 1.18, 95% CI 1.05 to 1.32). The meta-regression analysis showed that over time, odds of intestinal Entamoeba spp infection increased in both lack of safe drinking water (Coefficient: 3.24, P-value < 0.01) and sanitation (toilet) facilities (Coefficient: 2.36, P-value < 0.05) subgroups. Considering the findings, lack of safe drinking water resulted in a further increase in intestinal Entamoeba spp infection among adult (OR: 2.76), children (OR = 0.57) and all age groups (OR: 1.50), and also lack of sanitation (toilet) facilities resulted in further increase intestinal Entamoeba spp infection in children (OR: 1.06), adult (OR: 1.26) and all age (OR: 1.16). In this context, the lack of safe drinking water and sanitation facilities (toilet) was associated with a high risk of intestinal Entamoeba spp infection. Further attempts to providing public health facilities can control the prevalence of intestinal Entamoeba spp.