Green Synthesized Chitosan Nanoparticles for Controlling Multidrug-Resistant mecA- and blaZ-Positive Staphylococcus aureus and aadA1-Positive Escherichia coli.

Antimicrobial resistance has recently been considered an emerging catastrophe globally. The public health and environmental threats were aggravated by the injudicious use of antibiotics in animal farming, aquaculture, and croup fields, etc. Consequently, failure of antibiotic therapies is common because of the emergence of multidrug-resistant (MDR) bacteria in the environment. Thus, the reduction in antibiotic spillage in the environment could be an important step for overcoming this situation. Bear in mind, this research was focused on the green synthesis of chitosan nanoparticles (ChiNPs) using Citrus lemon (Assam lemon) extract as a cross-linker and application in controlling MDR bacteria to reduce the antibiotic spillage in that sector. For evaluating antibacterial activity, Staphylococcus aureus and Escherichia coli were isolated from environmental specimens, and their multidrug-resistant pattern were identified both phenotypically by disk diffusion and genotypically by detecting methicillin- (mecA), penicillin- (blaZ), and streptomycin (aadA1)-resistance encoding genes. The inhibitory zone's diameter was employed as a parameter for determining the antibacterial effect against MDR bacteria revealing 30 ± 0.4 mm, 34 ± 0.2 mm, and 36 ± 0.8 mm zones of inhibition against methicillin- (mecA) and penicillin (blaZ)-resistant S. aureus, and streptomycin (aadA1)-resistant E. coli, respectively. The minimum inhibitory concentration at 0.31 mg/mL and minimum bactericidal concentration at 0.62 mg/mL of yielded ChiNPs were used as the broad-spectrum application against MDR bacteria. Finally, the biocompatibility of ChiNPs was confirmed by showing a negligible decrease in BHK-21 cell viability at doses less than 2 MIC, suggesting their potential for future application in antibiotic-free farming practices.

A cross-sectional study on anxiety due to COVID-19 and its predictors among undergraduate medical students in a tertiary care teaching hospital of Kolkata, West Bengal.

Background: Medical education is recognized as stressful globally. COVID-19 pandemic is an additional source of anxiety to the medical students. Objectives: This study was conducted to assess the prevalence and to identify the factors associated with anxiety due to COVID-19 among undergraduate medical students in a teaching hospital of Kolkata, West Bengal. . Methods: An observational cross-sectional study was conducted among 363 undergraduate medical students using the stratified random sampling of a medical college from June to July 2021. Data were collected using a predesigned, pretested, and structured online questionnaire, including "Coronavirus Anxiety Scale." Descriptive statistics were used to estimate the prevalence of anxiety. Pearson's Chi-square test was performed to find out the factors associated with anxiety due to COVID-19. Results: About 25.6% of the medical students were found to have anxiety due to COVID-19. About 28.9% of them reported COVID-19 infection in family in recent past and 11.0% had themselves tested positive. Nearly 20% reported loss of family members, relatives, and close friends due to COVID-19. The factors associated with anxiety due to pandemic were socioeconomic status, social stigma, sleep disturbances, history of COVID-19 in family, loss of job. and vaccination status of family members missing practical classes and exam-related anxiety. Conclusion: The study found that one-fourth of the medical students had anxiety due to COVID-19. Social stigma due to COVID-19 and loss of job of parents were the most significant predictors. It is recommended that targeted psychological and clinical interventions need to be taken to alleviate students' anxiety due to COVID.

A computational approach to aid clinicians in selecting anti-viral drugs for COVID-19 trials.

The year 2020 witnessed a heavy death toll due to COVID-19, calling for a global emergency. The continuous ongoing research and clinical trials paved the way for vaccines. But, the vaccine efficacy in the long run is still questionable due to the mutating coronavirus, which makes drug re-positioning a reasonable alternative. COVID-19 has hence fast-paced drug re-positioning for the treatment of COVID-19 and its symptoms. This work builds computational models using matrix completion techniques to predict drug-virus association for drug re-positioning. The aim is to assist clinicians with a tool for selecting prospective antiviral treatments. Since the virus is known to mutate fast, the tool is likely to help clinicians in selecting the right set of antivirals for the mutated isolate. The main contribution of this work is a manually curated database publicly shared, comprising of existing associations between viruses and their corresponding antivirals. The database gathers similarity information using the chemical structure of drugs and the genomic structure of viruses. Along with this database, we make available a set of state-of-the-art computational drug re-positioning tools based on matrix completion. The tools are first analysed on a standard set of experimental protocols for drug target interactions. The best performing ones are applied for the task of re-positioning antivirals for COVID-19. These tools select six drugs out of which four are currently under various stages of trial, namely Remdesivir (as a cure), Ribavarin (in combination with others for cure), Umifenovir (as a prophylactic and cure) and Sofosbuvir (as a cure). Another unanimous prediction is Tenofovir alafenamide, which is a novel Tenofovir prodrug developed in order to improve renal safety when compared to its original counterpart (older version) Tenofovir disoproxil. Both are under trail, the former as a cure and the latter as a prophylactic. These results establish that the computational methods are in sync with the state-of-practice. We also demonstrate how the drugs to be used against the virus would vary as SARS-Cov-2 mutates over time by predicting the drugs for the mutated strains, suggesting the importance of such a tool in drug prediction. We believe this work would open up possibilities for applying machine learning models to clinical research for drug-virus association prediction and other similar biological problems.

Seasonal characteristics of boundary layer over a high-altitude rural site in Western India: implications on dispersal of particulate matter.

The temporal variability of the planetary boundary layer height (PBLH) over Mahabaleshwar was studied for a period of 1 year from 1 December 2015 to 30 November 2016 using microwave radiometer (MWR) observations. The PBLH over Mahabaleshwar was found to be the highest during the pre-monsoon (March-May) season and lowest during the winter (December-February) season. The seasonal mean of PBLH was estimated to be 339±88 m during winter, 485±70 m during pre-monsoon, 99±153 m during monsoon, and 438±24 m during post-monsoon season. Frequency distribution analysis of PBLH during pre-monsoon season revealed that the formation of turbulence internal boundary layer (TIBL) is evident. In contrast, cold and moist air mass during the monsoon season enhances the wind shear with lower buoyancy term which results in lowering of PBLH. The comparison of PBLH between MWR and radiosonde observations shows a good correlation (r2 = 0.66, p=0.001). The growth rate was observed to be 388 m/h during pre-monsoon, 206 m/h during winter, 57 m/h during monsoon, and 167 m/h during post-monsoon season. The seasonal mean concentration of PM2.5 was found to be 42.3±4.6 µg/m3during winter, 33.4±8.7 µg/m3 during pre-monsoon, 6.6±2.2 µg/m3 during monsoon, and 26.1±1.7 µg/m3during post-monsoon season. The effect of higher loading of scattering-type aerosol (dust particle) was also investigated as a case study. The analysis reveals the inverse relationship between the PBL height variability and the particulate loading indicating the importance of aerosol direct effect. Analysis of the ventilation coefficient (Vc) revealed that the dissipation potential was higher (1736 m2/s) during pre-monsoon season as compared to (1191 m2/s, 455m2/s, and 1580 m2/s) winter, monsoon, and post-monsoon seasons.

Non-target effect of bispyribac sodium on soil microbial community in paddy soil.

Bispyribac sodium is frequently used herbicide in the rice field. Though, it has been targeted to kill rice weeds, but its non-target effect on soil microbes in paddy soil was largely unknown. Therefore, in the present study, an attempt was made to assess the non-target effect of bispyribac sodium on alteration of functional variation of soil microbial community and their correlation with microbial biomass carbon (MBC) and soil enzymes. A microcosm experiment set up was made comprising three treatments viz., control (CON) (without application of bispyribac sodium), recommended dose of bispyribac sodium (35 g ha-1) (BS), and double the dose of BS (70 g ha-1) (DBS). Results indicated that the MBC and soil enzyme activities (dehydrogenase, alkaline phosphatase and urease) in BS and DBS-treated soil were significantly (p < 0.05) declined from 1st to 30th day after application as compared to CON. Counts of heterotrophic bacteria, actinomycetes and fungal population were also decreased in BS and DBS-treated soil. The average well color development (AWCD) values derived from Biolog®ecoplates followed the order of DBS ˂ BS ˂ CON. Shannon index value was high (p ≤ 0.05) in CON compared to soil-treated with BS and DBS. Principal component analysis (PCA) showed a clear distinction of the cluster of treatments between CON, BS and DBS. Biplot analysis and heatmap suggested that carboxylic compounds and amino acids showed positive response towards BS-treated soil, whereas phenolic compounds had positive correlation with DBS-treated soil. PCA analysis indicated that oligotrophs was rich in BS-treated paddy soil, whereas copiotrophs and asymbiotic nitrogen fixers were richer in DBS treatment. Overall, the present study revealed that application of recommended dose of BS and its double dose alter the soil microbial population, enzyme activities and functional microbial diversity in paddy soil.

Antigenic: An improved prediction model of protective antigens.

An antigen is a protein capable of triggering an effective immune system response. Protective antigens are the ones that can invoke specific and enhanced adaptive immune response to subsequent exposure to the specific pathogen or related organisms. Such proteins are therefore of immense importance in vaccine preparation and drug design. However, the laboratory experiments to isolate and identify antigens from a microbial pathogen are expensive, time consuming and often unsuccessful. This is why Reverse Vaccinology has become the modern trend of vaccine search, where computational methods are first applied to predict protective antigens or their determinants, known as epitopes. In this paper, we propose a novel, accurate computational model to identify protective antigens efficiently. Our model extracts features directly from the protein sequences, without any dependence on functional domain or structural information. After relevant features are extracted, we have used Random Forest algorithm to rank the features. Then Recursive Feature Elimination (RFE) and minimum redundancy maximum relevance (mRMR) criterion were applied to extract an optimal set of features. The learning model was trained using Random Forest algorithm. Named as Antigenic, our proposed model demonstrates superior performance compared to the state-of-the-art predictors on a benchmark dataset. Antigenic achieves accuracy, sensitivity and specificity values of 78.04%, 78.99% and 77.08% in 10-fold cross-validation testing respectively. In jackknife cross-validation, the corresponding scores are 80.03%, 80.90% and 79.16% respectively. The source code of Antigenic, along with relevant dataset and detailed experimental results, can be found at https://github.com/srautonu/AntigenPredictor. A publicly accessible web interface has also been established at: http://antigenic.research.buet.ac.bd.

Magnitude of Mental Morbidity and Its Correlates with Special Reference to Household Food Insecurity among Adult Slum Dwellers of Bankura, India: A Cross-Sectional Survey.

BACKGROUND: Mental disorders cause considerable morbidity and disability, and there is ample evidence that mental disorders are positively associated with household food insecurity. METHODS: A cross-sectional survey was conducted for a period of 2 months at Bakultala slum of Bankura town involving 152 people of ≥18 and ≤60 years of age selected using simple random sampling technique to estimate the prevalence of mental disorders and to find out its correlates. Information pertaining to socio-demographics and household food security (HHFS) and " potential psychiatric case" were collected through a house to house interview of the head of the household, using predesigned questionnaire, Bengali version of self-reporting questionnaire, and 6-item household food security scale (HFSS). RESULTS: In total, 45% of the study participants belonged to food unsecured households. Overall, 21% of the respondents were identified as "potential psychiatric case," which was found to be associated with higher age, illiteracy, divorcee female, and people living in households without food security. CONCLUSION: Study results reflecting high prevalence (21%) of "potential psychiatric case" with various correlates such as age, sex, education, marital status, and HHFS among the slum dweller of Bankura town may be helpful in formulating policies for combating mental health morbidities.

DPP-PseAAC: A DNA-binding protein prediction model using Chou's general PseAAC.

A DNA-binding protein (DNA-BP) is a protein that can bind and interact with a DNA. Identification of DNA-BPs using experimental methods is expensive as well as time consuming. As such, fast and accurate computational methods are sought for predicting whether a protein can bind with a DNA or not. In this paper, we focus on building a new computational model to identify DNA-BPs in an efficient and accurate way. Our model extracts meaningful information directly from the protein sequences, without any dependence on functional domain or structural information. After feature extraction, we have employed Random Forest (RF) model to rank the features. Afterwards, we have used Recursive Feature Elimination (RFE) method to extract an optimal set of features and trained a prediction model using Support Vector Machine (SVM) with linear kernel. Our proposed method, named as DNA-binding Protein Prediction model using Chou's general PseAAC (DPP-PseAAC), demonstrates superior performance compared to the state-of-the-art predictors on standard benchmark dataset. DPP-PseAAC achieves accuracy values of 93.21%, 95.91% and 77.42% for 10-fold cross-validation test, jackknife test and independent test respectively. The source code of DPP-PseAAC, along with relevant dataset and detailed experimental results, can be found at https://github.com/srautonu/DNABinding. A publicly accessible web interface has also been established at: http://77.68.43.135:8080/DPP-PseAAC/.

iProtGly-SS: Identifying protein glycation sites using sequence and structure based features.

Glycation is chemical reaction by which sugar molecule bonds with a protein without the help of enzymes. This is often cause to many diseases and therefore the knowledge about glycation is very important. In this paper, we present iProtGly-SS, a protein lysine glycation site identification method based on features extracted from sequence and secondary structural information. In the experiments, we found the best feature groups combination: Amino Acid Composition, Secondary Structure Motifs, and Polarity. We used support vector machine classifier to train our model and used an optimal set of features using a group based forward feature selection technique. On standard benchmark datasets, our method is able to significantly outperform existing methods for glycation prediction. A web server for iProtGly-SS is implemented and publicly available to use: http://brl.uiu.ac.bd/iprotgly-ss/.

iPHLoc-ES: Identification of bacteriophage protein locations using evolutionary and structural features.

Bacteriophage proteins are viruses that can significantly impact on the functioning of bacteria and can be used in phage based therapy. The functioning of Bacteriophage in the host bacteria depends on its location in those host cells. It is very important to know the subcellular location of the phage proteins in a host cell in order to understand their working mechanism. In this paper, we propose iPHLoc-ES, a prediction method for subcellular localization of bacteriophage proteins. We aim to solve two problems: discriminating between host located and non-host located phage proteins and discriminating between the locations of host located protein in a host cell (membrane or cytoplasm). To do this, we extract sets of evolutionary and structural features of phage protein and employ Support Vector Machine (SVM) as our classifier. We also use recursive feature elimination (RFE) to reduce the number of features for effective prediction. On standard dataset using standard evaluation criteria, our method significantly outperforms the state-of-the-art predictor. iPHLoc-ES is readily available to use as a standalone tool from: https://github.com/swakkhar/iPHLoc-ES/ and as a web application from: http://brl.uiu.ac.bd/iPHLoc-ES/.

Synthesis and SAR studies of marine natural products ma'edamines A, B and their analogues.

The synthesis of several analogues of ma'edamines A and B are reported. The synthesized compounds were tested on hormone receptor positive and HER2 positive breast cancer cell lines, by MTT assay. MED-114, 115, 117, 119, 120, 124, 128 and 131 were found to be equally active as Lapatinib on HER2 +ve cell line SKBR3.

The first total synthesis and biological evaluation of marine natural products ma'edamines A and B.

We have developed the first total syntheses of marine natural products ma'edamines A (18) and B (20). Structurally, they contain a pyrazine-2-(1H)-one core and were screened for antiproliferative activity on several cancer cell lines. Out of the six cell lines tested, ma'edamines A and B showed significant cytotoxicity against human colon cancer line COLO 205 (IC(50) 7.9 and 10.3 µM, respectively), breast cancer cell line MCF-7 (IC(50): 6.9 and 10.5 µM, respectively) and human lung adenocarcinoma cell line A549 (IC(50): 12.2 and 15.4 µM, respectively). The apoptotic effect of ma'edamines was confirmed by comet assay. Hence ma'edamines are likely to be useful as leads for development of a new class of anti-cancer agents.

Study comparing the management decisions by IMNCI algorithm and pediatricians in a teaching hospital for the young infants between 0 to 2 months.

Integrated management of neonatal and childhood illness (IMNCI) was already operational in many states of India, but there were very few studies in Indian scenario comparing its validity and reliability with the decisions of pediatricians. The general objective of the study is to compare the IMNCI decisions with the decisions of pediatricians and the specific objectives are to assess the agreement between IMNCI decisions and the decisions of pediatricians, to assess the under diagnosis and over diagnosis in IMNCI algorithm in comparison to the decisions of pediatricians and to assess the significance of multiple presenting symptoms in IMNCI algorithm. The study was conducted among the sick young infants presenting in pediatric department from January to March 2009. The IMNCI decision was compared with pediatrician's decisions by percent agreement, Kappa and weighted Kappa with the aids of SPSS version 10. The overall diagnostic agreement between IMNCI algorithm and pediatrician's decisions was 55.56%, (Kappa 0.32 and weighted Kappa 0.41) with 33.33% over diagnosis, and 11.11% under diagnosis. 71.88% young infants with multiple symptoms and 40% with single symptom were classified as red by IMNCI algorithm, which is statistically significant (P=0.004) whereas 56.25% young infants with multiple and 31.76% with single symptom were considered admissible by pediatricians, which is not statistically significant (P=0.052).

A study on cognitive status of 50 years and above aged non-demented women in a rural area of West Bengal.

The prevalence of ageing population is increasing not only in developed countries but also in developing world like India. Epidemiological reports about cognitive impairment or dementia in elderly people from developing countries are scarce. To study the cognitive status of women more than 50 years of age and to study the relationship of sociodemographic factors with cognitive status of the study subjects a descriptive epidemiological, community based cross-sectional survey was done involving 179 old women of 50 years and above in the rural field practice area of All India Institute of Hygiene and Public Health, Kolkata. The data were analysed using Epi-info 6.04, software packages. The mean age of the sample was 64.0 +/- 7.6 years. In the total sample, 53 subjects (29.6%) were in 50-59 years, 83 (43.4%) in 60-69 years, 34 (19%) in 70-79 years and 9 (5%) in women who were more than 80 years old. The cognitive defect was found to be 42.4% in elderly women .The variables like age > 70 years, widowhood, low per capita income, economic dependence, non-support from children, not staying with own children and having no satisfaction with life, were found to be significantly associated with cognitive defect. On (stepwise) multiple regression analysis these factors together contributed to 37% of cognitive impairment among these women. Prevalence of cognitive defect of more than 40% in the elderly women of this study emphasises the need for more attention and more social security measures for this neglected group.

A study of cluster behavioral abnormalities in Down syndrome.

BACKGROUND: The behavioral phenotype in Down syndrome follows a characteristic pattern. AIMS: To find the incidence of behavioral abnormalities in Down syndrome, to compare these findings with other causes of intellectual disability and normal population and to cluster these abnormalities. SETTINGS: One hundred forty mentally challenged people attending at tertiary care set up and from various non-governmental organizations were included in the study. Patients from both rural and urban set up participated in the study. The age-matched group from normal population was also studied for comparison. DESIGN: The study design is a cross-sectional survey done independently by four observers. MATERIALS AND METHODS: A semi-structured proforma for demographic profile has been used. The behavioral abnormalities are assessed by using DASH II (Diagnostic Assessment for the Severely Handicapped second modified version) scale. STATISTICAL ANALYSIS: Demographic comparison has been done by analysis of variance. Correlation matrix has been run to identify correlation between individual items. Principal component analysis has been used for grouping the behavioral pattern. RESULTS: Behavioral abnormalities as expected are more common in people having intellectual disability than the normal population. The Down syndrome group unlike other causes of intellectual disability shows higher scores in Stereotypy. Impulse control and Mania subscales. Factor analysis yields five characteristic factor structures, namely, hyperactive-impulsive, biological functions, affective, neurotic and organic-pervasive developmental disorder clusters. CONCLUSIONS: Contrary to the conventional belief of docile-fun and music loving prototype, individuals diagnosed with Down syndrome show clusters of behavioral abnormalities and management can vary depending on these target symptoms.