Exploring the Role of Non-synonymous and Deleterious Variants Identified in Colorectal Cancer: A Multi-dimensional Computational Scrutiny of Exomes.

Introduction: Colorectal cancers are the world's third most commonly diagnosed type of cancer. Currently, there are several diagnostic and treatment options to combat it. However, a delay in detection of the disease is life-threatening. Additionally, a thorough analysis of the exomes of cancers reveals potential variation data that can be used for early disease prognosis. Methods: By utilizing a comprehensive computational investigation, the present study aimed to reveal mutations that could potentially predispose to colorectal cancer. Ten colorectal cancer exomes were retrieved. Quality control assessments were performed using FastQC and MultiQC, gapped alignment to the human reference genome (hg19) using Bowtie2 and calling the germline variants using Haplotype caller in the GATK pipeline. The variants were filtered and annotated using SIFT and PolyPhen2 successfully categorized the mutations into synonymous, non-synonymous, start loss and stop gain mutations as well as marked them as possibly damaging, probably damaging and benign. This mutational profile helped in shortlisting frequently occurring mutations and associated genes, for which the downstream multi-dimensional expression analyses were carried out. Results: Our work involved prioritizing the non-synonymous, deleterious SNPs since these polymorphisms bring about a functional alteration to the phenotype. The top variations associated with their genes with the highest frequency of occurrence included LGALS8, CTSB, RAD17, CPNE1, OPRM1, SEMA4D, MUC4, PDE4DIP, ELN and ADRA1A. An in-depth multi-dimensional downstream analysis of all these genes in terms of gene expression profiling and analysis and differential gene expression with regard to various cancer types revealed CTSB and CPNE1 as highly expressed and overregulated genes in colorectal cancer. Conclusion: Our work provides insights into the various alterations that might possibly lead to colorectal cancer and suggests the possibility of utilizing the most important genes identified for wet-lab experimentation.

Computational molecular perspectives on novel carbazole derivative as an anti-cancer molecule against CDK1 of breast and colorectal cancers via gene expression studies, novel two-way docking strategies, molecular mechanics and dynamics.

With increase in cancer incidences, alternative strategies for disease management are of utmost importance. Carbazole, is a compound that is being studied extensively as an anti-cancer compound. In this work, we aimed to investigate a carbazole derivative against specific cancer types such as breast and colorectal, based on the off-target analyses of carbazole derivative. The present work shortlisted 6 proteins that have an association in both cancer types, and then employed two different molecular docking strategies to examine the binding stability of carbazole derivative: a blind-docking state, where the pockets were undefined and mutation-docking state, where possible mutations were induced within the proteins. The results showed that CDK1 bound best in both states to carbazole derivative, and performed better than an array of positive controls. Molecular dynamic simulations at 100 ns further proved its stability, with carbazole derivative-CDK1-blind and mutated complex having RMSD values between 3.2 and 3.6 Å, and 2.8-3.2 Šrespectively. Molecular-mechanics generalized born and surface area solvation disclosed free energy of binding for the complexes as -28.79 ± 3.97 kcal/mol and -31.86 ± 5.09 kcal/mol respectively, with carbazole derivative bound stably within the binding pocket at every 10 ns of the 100 ns trajectory. Radial distribution functions showed that the bell curve was well within 6 Å, thus showing that carbazole derivative and its atoms do not deviate away from the pocket, suggesting its ability to be used as a good anti-cancer compound against breast and colorectal.

Comprehending interaction mechanism of natural actives of Colchicum autumnale L. for rheumatoid arthritis using integrative chemoinformatic approaches.

This research delves into the realm of therapeutic potential within natural compounds derived from Colchicum autumnale L., emphasizing a holistic perspective on medications used in human therapy. Rather than confining the study to their primary actions, the research endeavors to unveil molecular targets for these natural compounds, with a specific focus on their potential applicability in the treatment of rheumatoid arthritis (RA). The study focuses on understanding interactions between specific natural actives that target RA. Fifteen RA target proteins were identified from OMIM, GeneScan and PharmaGKB. Their structures were downloaded from RCSB PDB. Two active components of C. autumnale L. were chosen for mass spectrometry investigation. Ligand characteristics were determined using the ADMETlab and SwissADME software tools. Molecular docking was performed, and the top three complexes were simulated for 200 ns, along with identification of free binding energies. The compounds ß-sitosterol-IL-10 (-6.50 kcal/mol), colchicine-IL-10 (-6.01 kcal/mol), linoleic acid-IL-10 (-7.22 kcal/mol) and linoleic acid-IL-10 (-7.22 kcal/mol) exhibited best binding energies. ß-Sitosterol and colchicine showed the highest stability in simulations, confirmed by molecular mechanics free energy binding calculations. This work provides insights into the molecular interaction of natural compounds against RA targets, offering potential therapeutic anti-RA medications.Communicated by Ramaswamy H. Sarma.

Investigating the Molecular Interactions of Quinoline Derivatives for Antibacterial Activity Against Bacillus subtilis: Computational Biology and In Vitro Study Interpretations.

Bacterial infections are evolving and one of the chief problems is emergence and prevalence of antibacterial resistance. Moreover, certain strains of Bacillus subtilis have become resistant to several antibiotics. To counteract this menace, the present work aimed to comprehend the antibacterial activity of synthesized two quinoline derivatives against Bacillus subtilis. Toxicity predictions via Protox II, SwissADME and T.E.S.T (Toxicity Estimation Software Tool) revealed that these derivatives were non-toxic and had little to no adverse effects. Molecular docking studies carried out in Schrodinger with two quinoline derivatives (referred Q1 and Q2) docked against selected target proteins (PDB IDs: 2VAM and1FSE) of B. subtilis demonstrated ideal binding energies (2VAM-Q1: - 4.63 kcal/mol and 2VAM-Q2: - 4.46 kcal/mol, and 1FSE-Q1: - 3.51 kcal/mol, 1FSE-Q2: - 6.34 kcal/mol). These complexes were simulated at 100 ns and the outcomes revealed their stability with slight conformational changes. Anti-microbial assay via disc diffusion method revealed zones of inhibition showing that B. subtilis was inhibited by both Q1 and Q2, with Q2 performing slightly better than Q1, pointing towards its effectiveness against this organism and necessitating further study on other bacteria in prospective studies. Thus, this study demonstrates that our novel quinoline derivatives exhibit antibacterial properties against Bacillus subtilis and can act as potent anti-bacterials.

Repurposing of known drugs for COVID-19 using molecular docking and simulation analysis.

We selected fifty one drugs already known for their potential disease treatment roles in various studies and subjected to docking and molecular docking simulation (MDS) analyses. Five of them showed promising features that are discussed and suggested as potential candidates for repurposing for COVID-19. These top five compounds were boswellic acid, pimecrolimus, GYY-4137, BMS-345541 and triamcinolone hexacetonide that interacted with the chosen receptors 1R42, 4G3D, 6VW1, 6VXX and 7MEQ, respectively with binding energies of -9.2 kcal/mol, -9.1 kcal/mol, -10.3 kcal/mol, -10.1 kcal/mol and -8.7 kcal/mol, respectively. The MDS studies for the top 5 best complexes revealed binding features for the chosen receptor, human NF-kappa B transcription factor as an important drug target in COVID-19-based drug development strategies.

Exploring the Synergistic Mechanism of AP2A2 Transcription Factor Inhibition via Molecular Modeling and Simulations as a Novel Computational Approach for Combating Breast Cancer: In Silico Interpretations.

Studies have shown that transcription factor AP2A2 (activator protein-2 alpha-2) is involved in the expression of DLEC1, a tumor suppressor gene, which, when mutated, will cause breast cancer and is thus an excellent target for breast cancer studies. Therefore, in the present research, a synergistic approach toward combating breast cancer is proposed by blocking AP2A2 factor, and allowing the cancer cells to be sensitive to anti-cancer drugs. The effect of AP2A2 on breast cancer was first understood via gene analysis from cBioPortal. AP2A2 was then modeled using RaptorX and its structure was validated from Ramachandran plots. Using all ligands from MolPort database, molecular docking was performed against AP2A2, from which the top three best docked ligands were studied for toxicity in humans using Protox-II. Once the ligands passed these tests, the best complexes were simulated at 200ns in Desmond Maestro, to comprehend their stabilities, followed by the computations of free energies of binding via Molecular mechanics- Generalized Born Solvent Accessibility method (MM-GBSA). The results showed that molecules MolPort-005-945-556 (sachharolipids), MolPort-001-741-124 (flavonoids), and MolPort-005-944-667 (lignan glycosides) with AP2A2 passed toxicity evaluation and belonged to toxicity classes 6, 5, and 5, respectively, with good docking energies. 200 ns simulations revealed stable complexes with slight conformational changes. Stability of ligands was confirmed via snapshots at every 20 ns of the trajectory. Radial distribution of these molecules against the protein revealed very slight deviation from binding pocket. Additionally, the free binding energies for these complexes were found to be - 54.93 ± 12.982 kcal/mol, - 44.39 ± 14.393 kcal/mol, and - 66.51 ± 13.522 kcal/mol, respectively. A preliminary computational validation of the inability of AP2A2 to bind to DLEC1 in the presence of ligands offers beneficial insights into the potential of these ligands. Therefore, this study sheds light on the potential natural molecules that could stably block AP2A2 with least deviation and act in synergy to aid anti-cancer drugs work on breast cancer cells.

Deciphering the interaction mechanism of natural actives against larval proteins of Aedes aegypti to identify potential larvicides: a computational biology analysis.

Aedes aegypti is the target for repellents to curb incidences of vector-borne diseases. Stopping breeding of this mosquito species at its larval stages helps in controlling spread of insect-borne diseases. Therefore, the present study focused on deciphering the mechanism of interaction of selected natural actives against larval proteins of A. aegypti to identify potential natural alternative larvicides. 65 larval proteins were identified from literature, whose structures were modelled and validated using RaptorX and ProCheck. 11 natural actives were selected for predicting their ligand properties and toxicities via Toxicity Estimation Software Tool and ProTox-II. Molecular docking studies were carried out using POAP followed by 100 ns molecular dynamic simulation studies for top three best docked complexes to better understand the robustness of docking, complex stabilities and molecular mechanisms of interactions. Toxicity predictions revealed that 6 molecules belonged to toxicity class 4, and five to toxicity class 5, implying that they were all safe to use. Complexes goniothalamin-translation elongation factor (-10 kcal/mol), andrographolide-acetyl-CoA C-myristoyltransferase (-9.2 kcal/mol) and capillin-translation elongation factor (-8.4 kcal/mol) showed best binding energies. When simulated, capillin-translation elongation factor showed most stability, while the remaining two also evidenced robust docking. Evolutionary studies for top two larval proteins disclosed 100 other insect species in which these proteins can be targeted using various larvicides. Protein-protein interaction network analysis revealed several protein pathways that might be affected due to aforesaid naturals. Therefore, this study provides computational insights into the molecular interaction of naturals against larval proteins, acting as potential natural larvicides.Communicated by Ramaswamy H. Sarma.

Decision Support System and Web-Application Using Supervised Machine Learning Algorithms for Easy Cancer Classifications.

Using a decision support system (DSS) that classifies various cancers provides support to the clinicians/researchers to make better decisions that can aid in early cancer diagnosis, thereby reducing chances of incorrect disease diagnosis. Thus, this work aimed at designing a classification model that can predict accurately for 5 different cancer types comprising of 20 cancer exomes, using the mutations identified from whole exome cancer analysis. Initially, a basic model was designed using supervised machine learning classification algorithms such as K-nearest neighbor (KNN), support vector machine (SVM), decision tree, naïve bayes and random forest (RF), among which decision tree and random forest performed better in terms of preliminary model accuracy. However, output predictions were incorrect due to less training scores. Thus, 16 essential features were then selected for model improvement using 2 approaches. All imbalanced datasets were balanced using SMOTE. In the first approach, all features from 20 cancer exome datasets were trained and models were designed using decision tree and random forest. Balanced datasets for decision tree model showed an accuracy of 77%, while with the RF model, the accuracy improved to 82% where all 5 cancer types were predicted correctly. Area under the curve for RF model was closer to 1, than decision tree model. In the second approach, all 15 datasets were trained, while 5 were tested. However, only 2 cancer types were predicted correctly. To cross validate RF model, Matthew's correlation co-efficient (MCC) test was performed. For method 1, the MCC test and MCC cross validation was found to be 0.7796 and 0.9356 respectively. Likewise, for second approach, MCC was observed to be 0.9365, corroborating the accuracy of the designed model. The model was successfully deployed using Streamlit as a web application for easy use. This study presents insights for allowing easy cancer classifications.

Polyurethane foams from vegetable oil-based polyols: a review.

Polyurethane is a versatile material that can be converted into various forms according to applications. PU foams or PUFs are the most commonly used polyurethanes. These are materials of low density and low thermal conductivity that make them highly suitable for thermal insulating applications. Most of the synthesis of PUFs is still based on the petrochemical industry. There are issues associated with the oil industry, such as environmental pollution, sustainability, and market instability. More recently, we have experienced the COVID-19 pandemic which has destroyed the global supply chain of raw materials. Such sudden disruption of the supply chain affects the global economy. To eliminate the reliance on special ingredients, it is important to find and produce alternate and domestic raw materials. Vegetable oils are organic, cost-effective, and economically viable and present in abundant amounts. The oil consists of triglycerides. It can be functionalized to provide polyol for PU foam synthesis. Herein, we review the literature on factors influencing the properties of PUFs depending on polyols from vegetable oil as well as present a glimpse of the conversion of vegetable oils into polyols for PUF synthesis.

Comprehensive Molecular Interaction Studies to Construe the Repellent/Kill Activity of Geraniol During Binding Event Against Aedes aegypti Proteins.

Aedes aegypti is an etiological agent for dengue, chikungunya, zika, and yellow fever viruses. With the advent of the use of natural alternatives as repellents, their precise mode of action during the event of binding is still unclear. Geraniol is one such bioactive natural that has been previously shown to have some insecticide properties. Thus, the present study aimed to understand the mechanism of the binding event of geraniol with the whole proteome of A. aegypti. Twenty protein target categories were shortlisted for the mosquito, wherein the proteins were downloaded with respect to the reference proteome. Conserved domain analysis was performed for the same using the CDD search tool to find the proteins that have common domains. 309 proteins were modeled using RaptorX standalone tool, and validated using Ramachandran plots from SAVES v6.0 from ProCheck. These modeled and validated proteins were then docked against geraniol, using POAP software, for understanding the binding energies. The top 3 best-docked complexes were then analyzed for their stabilities and event of binding via 100 ns simulation studies using DESMOND's Maestro environment. The docking results showed that the geraniol-voltage-gated sodium channel had the best energy of - 7.1 kcal/mol, followed by geraniol-glutathione-S-transferase (- 6.8 kcal/mol) and geraniol-alpha esterase (- 6.8 kcal/mol). The simulations for these 3 complexes revealed that several residues of the proteins interacted well with geraniol at a molecular level, and all three docked complexes were found to be stable when simulated (RMSD: 16-18 Å, 3.6-4.8 Å, 4.8-5.6 Å, respectively). Thus, the present study provides insights into the mechanism of the binding event of geraniol with the major A. aegypti targets, thereby, assisting the use of geraniol as a natural repellent.

Efficacy of covert sensitization for a patient with alcohol use disorder with memory impairment: A case report.

Alcohol use disorder is a chronic, relapsing and remitting illness with limited efficacy of pharmacotherapy in preventing relapse. Several non-pharmacological treatment options are usually used to improve treatment outcomes. In this case report, the patient with alcohol use disorder (as per Diagnostic and Statistical Manual 5th edition criteria) had memory impairments, which was seen to limit the effectiveness of cognitive approaches such as motivational enhancement therapy. Covert sensitization, a behavioral technique based on operant conditioning, was considered an alternative option along with pharmacotherapy to enhance abstinence and was seen to be effective in creating an association between alcohol use and unpleasant consequences in the patient. We observed an improvement in subjective craving and motivation to abstain following the therapy sessions. This highlights the use of covert sensitization as an alternative non-pharmacological intervention for patients with alcohol use disorder with cognitive impairments.

Implementation of ensemble machine learning algorithms on exome datasets for predicting early diagnosis of cancers.

Classification of different cancer types is an essential step in designing a decision support model for early cancer predictions. Using various machine learning (ML) techniques with ensemble learning is one such method used for classifications. In the present study, various ML algorithms were explored on twenty exome datasets, belonging to 5 cancer types. Initially, a data clean-up was carried out on 4181 variants of cancer with 88 features, and a derivative dataset was obtained using natural language processing and probabilistic distribution. An exploratory dataset analysis using principal component analysis was then performed in 1 and 2D axes to reduce the high-dimensionality of the data. To significantly reduce the imbalance in the derivative dataset, oversampling was carried out using SMOTE. Further, classification algorithms such as K-nearest neighbour and support vector machine were used initially on the oversampled dataset. A 4-layer artificial neural network model with 1D batch normalization was also designed to improve the model accuracy. Ensemble ML techniques such as bagging along with using KNN, SVM and MLPs as base classifiers to improve the weighted average performance metrics of the model. However, due to small sample size, model improvement was challenging. Therefore, a novel method to augment the sample size using generative adversarial network (GAN) and triplet based variational auto encoder (TVAE) was employed that reconstructed the features and labels generating the data. The results showed that from initial scrutiny, KNN showed a weighted average of 0.74 and SVM 0.76. Oversampling ensured that the accuracy of the derivative dataset improved significantly and the ensemble classifier augmented the accuracy to 82.91%, when the data was divided into 70:15:15 ratio (training, test and holdout datasets). The overall evaluation metric value when GAN and TVAE increased the sample size was found to be 0.92 with an overall comparison model of 0.66. Therefore, the present study designed an effective model for classifying cancers which when implemented to real world samples, will play a major role in early cancer diagnosis.

A Surgical Approach in the Management of Mucormycosis in Trauma Patients.

Introduction: Fungal infections are rare occurrence in the oral cavity. They are most often seen in other medical conditions such as the immunocompromised states, diabetes, on immunosuppressants, and more recently, among the COVID patients. There are various ways that are employed to manage these infections. The most usual of fungal infection in these conditions is mucormycosis, also called as zygomycosis. Hence, in our study, we aim to evaluate the management of the fungal infection mucormycosis in trauma patients by the surgical approach. Materials and Methods: We piloted a retrospective observational study among 50 subjects who were admitted to the department with oral fungal infections with mucormycosis. We analyzed various clinical and demographic parameters among the subjects. The data thus obtained were analyzed with proper statistical tools deliberating P < 0.05 as significant. Results: We observed that among the 50 subjects, the mean age was 41 ± 1.7 years. There was no significant difference between the genders and the age groups. The most common reason for the oral involvement was uncontrolled diabetes. This was followed by malignancy, specifically leukemia, AIDS, and COVID. The most common site of the involvement was the palate, followed by the mandibular region. All the subjects tested positive for the fungal hyphae of Rhizopus arrhizus which was the most common of the species. The surgical debridement along with the medical management showed satisfactory results, while one death was noted in our study. Conclusion: Although rare, oral involvement in the fungal infection with the mucormycosis is often easily managed when diagnosed early. The proper surgical debridement is the best method of treatment along with the appropriate medications. The management of the underlying medical conditions is the primary key for the success of the treatments.

Pancreatico-Gastrostomy: A Modified Two-Layered Technique.

INTRODUCTION: Pancreato-duodenal resections are commonly done for periampullary carcinomas. The outcome of the procedure is decided by pancreato-enterostomy. Pancreato-jejunostomy (PJ) has been employed largely as pancreato-enterostomy. Recently, there has been a renewed interest in pancreato-gastrostomy (PG). The debate continues on the choice of reconstruction. METHODS: A hundred cases of periampullary carcinoma were subjected to modified pancreatico-gastrostomy. The pancreatico-gastrectomy was evaluated by drain fluid amylase done on days 1, 3, and 5 post-operatively and clinical findings. The leaks were classified according to the International Study Group of Pancreatic Fistula (ISGPF) classification of biochemical leaks, postoperative pancreatic fistula (POPF) B and POPF C. The leaks were evaluated against pancreatic factors like duct diameter, consistency of the pancreas, the thickness of the pancreatic neck, and duct location.  Observations: Eighty percent of patients had no leaks. The biochemical leak was seen in 10% of cases. POPF B and C were observed at 5% each. Mortality was 3%. The diameter of the pancreatic duct of more than 3 mm and the firm consistency of the pancreas were favourable factors in the outcome of the anastomosis. CONCLUSION: A modified pancreatico-gastrostomy technique appears to be technically feasible and safe. The leak rates and mortality appear to be low. We need a higher number of patients to confirm the efficacy of this modified pancreatico-enterostomy.

Topical bilirubin-deferoxamine hastens excisional wound healing by modulating inflammation, oxidative stress, angiogenesis, and collagen deposition in diabetic rats.

AIM OF THE STUDY: The study was performed to understand the detailed mechanism of diabetic wound healing by bilirubin-deferoxamine (DFO) combination on topical application. MATERIALS AND METHODS: There were two study groups, control, and treatment. The granulation tissues collected on different days (3, 7, 14, and 19) were studied in detail for inflammatory mediators, angiogenesis markers, epithelialization, and oxidative stress parameters. RESULTS: A significant increase in wound contraction percentage was observed from day 7 in the bilirubin-DFO treatment group. The combinatorial treatment significantly reduced tumour necrosis factor-alpha (TNF-α) and interleukin-1 beta (IL-1ß), and enhanced IL-10 levels. Upregulated mRNAs of vascular endothelial growth factor (VEGF) and hypoxia-inducible factor-1 alpha (HIF-1 α) along with CD31 immunohistochemistry showed the pro-angiogenesis potential of the combination. Hematoxylin and Eosin (H and E) staining and Masson's trichrome staining showed reduced inflammatory cell infiltration, enhanced fibroblast proliferation, well-organized collagen fibers, and the development of new blood vessels. Collagen deposition is further supported by immunohistochemistry studies and Masson's trichrome staining. Bilirubin-DFO combination also reduced lipid peroxidation and elevated antioxidative enzymes. CONCLUSION: Topical application of bilirubin-DFO showed immense potential in augmenting skin wound regeneration in diabetes by upregulating the antioxidant status as well as increasing angiogenesis, collagen deposition, and modulating cytokines.

MutaXome: A Novel Database for Identified Somatic Variations of In silico Analyzed Cancer Exome Datasets.

Advancements in the field of cancer research have enabled researchers and clinicians to access a massive amount of data to aid cancer patients and to add to the existing knowledge of research. However, despite the existence of reliable sources for extricating this data, it remains a challenge to accurately comprehend and draw conclusions based on the entirety of available information. Therefore, the current study aimed to design and develop a database for the identified variants of 5 different cancer types using 20 different cancer exomes. The exome data were retrieved from NCBI SRA and an NGS data clean-up protocol was implemented to obtain the best quality reads. The reads which passed the quality checks were then used for calling the variants which were then processed and filtered. This data was used to normalize and the normalized data generated was used for developing the database. MutaXome, which stands for mutations in cancer exome was designed in SQL, with the front end in bootstrap and HTML, and backend in PHP. The normalized data containing the variants inclusive of Single Nucleotide Polymorphisms (SNPs), were added into MutaXome, which contains detailed information regarding each type of identified variant. This database, available online via http://www.vidyalab.rf.gd/, serves as a knowledge base for cancer exome variations and holds much potential for enriching it by linking it to a decision support system as prospective studies.

N-Glycoprofiling of immunoglobulin G and lactoferrin with site-specificity from goat milk using RP-UHPLC MS/MS.

Glycans present in glycoproteins are structurally diverse and contribute to the carbohydrate pool of the milk. Goat milk is a leading non-bovine milk source, wherein glycan diversity of several glycoproteins remains unexplored. Herein, site-specific N-glycoprofiling of two major glycoproteins - immunoglobulin G (IgG) and lactoferrin (Lf) from goat milk was performed through RP-UHPLC Q-Tof MS/MS approach. IgG revealed diverse complex glycans that were predominantly biantennary type with differential core fucosylation, bisecting GlcNAc, and mono/di- sialylation (NeuAc/NeuGc). The N-glycan repertoire of Lf at four sites indicated the range of high mannose, complex and hybrid types with varying abundances. High mannose glycans were specifically observed at N252NT and N564DT sites. Majorly complex glycans with fully sialylated were found at N387VT site. While N495QT site revealed complex and hybrid types with differential core fucosylation and sialylation. The glycan features observed in these glycoproteins would pave way for effective utilization as bioactive ingredients.

Household secondary attack rate in SARS-CoV-2 infection during the second wave of the COVID-19 pandemic in South India.

Background: Coronavirus disease 2019 (COVID-19) has become a major public health problem since its inception disrupting public life and crippling health systems. The mutated variant of the causative virus, Delta, has been notorious in causing rapid upsurge in cases compared to the Alpha variant. The current study tries to find out the household secondary attack rate (HSAR) of COVID-19 and factors associated with it during the second wave of cases in Kerala. Methodology: A retrospective cohort study was performed among 313 household contacts of 76 COVID-19 patients who had been admitted in Government Medical College, Thrissur, in the southern state of India, Kerala. Data from the participants were collected via phone using a semi-structured interview schedule, and analysis was performed with SPSS software. Results: The HSAR among household contacts was 59.1% (53.4-64.6%). The risk of acquiring COVID infection among household contacts was higher among contacts of symptomatic index cases with a P value of 0.001 and an odds ratio of 11 (3.7-32.4). index cases were having a home isolation P value of 0.001 and an odds ratio of 3.2 (2-5.1), with delay in COVID-19 testing for index cases with a P value of 0.006. Regarding characteristics of household contacts, higher age groups (p = 0.048), groups living in the same room with an index case P value of 0.021 and an odds ratio of [1.71 (1-2.8)], groups having physical contact with an index case P value of 0.001 and an odds ratio of [3.7 (2.1-7)], groups with touched or cleaned linen/articles with an index case P value of 0.02 and an odds ratio of [1.8 (1-3.1)], and groups having co-morbidities, especially diabetes mellitus (p = 0.0020), were significantly associated with chances of acquiring infection. However, the history of previous COVID positivity in household contacts was a protective factor against the infection P value of 0.009 with an odds ratio of [0.09 (0.01-0.78)]. Conclusion: The study concludes that the second wave of COVID-19 in Kerala was primarily caused by a high SAR, especially among household contacts, and this could have been the reason for the difficulty in control measures during the wave.

Prefrontal Cortex Transcranial Direct Current Stimulation Treatment in Alcohol Dependence Syndrome (PreCoTTA): Study Protocol for a Double- blind Randomized Sham-controlled Trial.

BACKGROUND: Alcohol dependence is a significant public health problem, contributing to the global health burden. Due to its immense socio-economic burden, various psychosocial, psychological, and pharmacological approaches have attempted to alter the behaviour of the patient misusing or abusing alcohol, but their efficacy is modest at best. Therefore, there is a search for newer treatment approaches, including non-invasive brain stimulation in the management of alcohol dependence. We plan to study the efficacy of Prefrontal Cortex Transcranial direct current stimulation Treatment in Alcohol dependence syndrome (PreCoTTA). METHODS: Two hundred twenty-five male patients with alcohol dependence syndrome will be randomized into the three study arms (2 active, left dorsolateral prefrontal cortex and left orbitofrontal cortex, and 1 sham) to receive a total of 14 tDCS sessions (10 continuous and 4 booster sessions). Data will be collected from these sessions at five different time points on clinical, neuropsychological and biochemical parameters. In addition, 225 healthy age and education matched controls will be administered the neuropsychological test battery at baseline for comparison with the patient group. DISCUSSION: The proposed study aims to explore the use of non-invasive brain stimulation; tDCS as a treatment alternative. We also aim to overcome the methodological gaps of limited sample sizes, fewer tDCS intervention sessions, lack of long-term follow-ups to measure the sustainability of gains, and lack of comprehensive measures to track changes in functioning and abstinence after tDCS intervention. The main outcomes include clinical (reduction in cue-induced craving, time to first drink, and QFI); neuropsychological (risk-taking, impulsivity, and other neuropsychological domains), and biochemical markers (BDNF, leptin and adiponectin). The findings of the study will have translational value as they may help to improve the clinician's ability to effectively manage craving in patients with alcohol dependence syndrome. Furthermore, we will have a better understanding of the neuropsychological and biochemical effects of non-invasive brain stimulation techniques which are of interest in the comprehensive treatment of addiction disorders. TRIAL REGISTRATION: The study has been registered with the Clinical Trials Registry-India (CTRI/ 2020/09/027582) on September 03rd 2020.

A review of the mud-dauber wasps of genus Sceliphron Klug (Hymenoptera: Sphecidae) from India.

The mud-dauber wasp genus Sceliphron Klug (Hymenoptera: Sphecidae) is reviewed, with six species and six subspecies reported from various parts of India. The Palaerctic species, S. destillatorium (Illiger), is recorded for the first time from the Indian subcontinent. The subspecies S. madraspatanum formosanum van der Vecht is also recorded for first time from the Indian subcontinent. The distribution records of species and subspecies within various states of India are augmented. A modified key to the species of Sceliphron occurring in India is provided. Intraspecific variation within nominal species is discussed.