A comprehensive genetic map of cytokine responses in Lyme borreliosis.

The incidence of Lyme borreliosis has risen, accompanied by persistent symptoms. The innate immune system and related cytokines are crucial in the host response and symptom development. We characterized cytokine production capacity before and after antibiotic treatment in 1,060 Lyme borreliosis patients. We observed a negative correlation between antibody production and IL-10 responses, as well as increased IL-1Ra responses in patients with disseminated disease. Genome-wide mapping the cytokine production allowed us to identify 34 cytokine quantitative trait loci (cQTLs), with 31 novel ones. We pinpointed the causal variant at the TLR1-6-10 locus and validated the regulation of IL-1Ra responses at transcritpome level using an independent cohort. We found that cQTLs contribute to Lyme borreliosis susceptibility and are relevant to other immune-mediated diseases. Our findings improve the understanding of cytokine responses in Lyme borreliosis and provide a genetic map of immune function as an expanded resource.

The futuristic applications of transition metal dichalcogenides for cancer therapy.

The second-most common cause of death resulting from genetic mutations in DNA sequences is cancer. The difficulty in the field of anticancer research is the application of the traditional methods, which also affects normal cells. Mutations, genetic replication alterations, and chromosomal abnormalities have a direct impact on the effectiveness of anticancer drugs at different stages. Presently, therapeutic techniques utilize nanotechnology, transition metal dichalcogenides (TMDCs), and robotics. TMDCs are being increasingly employed in tumor therapy and biosensing applications due to their biocompatibility, adjustable bandgap, versatile functionality, exceptional photoelectric properties, and wide range of applications. This study reports the advancement of nanoplatforms based on TMDCs that are specifically engineered for responsive and intelligent cancer therapy. This article offers a thorough examination of the current challenges, future possibilities for theranostic applications using TMDCs, and recent progress in employing TMDCs for cancer therapy. Currently, there is significant interest in two-dimensional (2D) TMDCs nanomaterials as ultrathin unique physicochemical properties. These materials have attracted attention in various fields, including biomedicine. Due to their inherent ability to absorb near-infrared light and their exceptionally large surface area, significant efforts are being made to prepare multifunctional nanoplatforms based on 2D TMDCs.

COVID-19 Vaccination in a Patient With Gluten Enteropathy: A Case Report.

In India, the COVID-19 vaccination for adolescents aged 15-17 years has been started since January 2022. Gluten enteropathy, also known as celiac or nontropical sprue, can arise as an autoimmune disease of the small intestines. We report a 15-year-old female with a history of allergy to gluten-containing products who came for the first dose of the COVID-19 vaccination to adult vaccination OPD at All India Institute of Medical Sciences, Jodhpur. After taking a detailed history, she had an allergy to gluten-containing products for five years. She had no previous history of allergic reactions to injections or medicines. The first dose of Covaxin was given to this female under proper supervision, and she was followed up for any adverse events. We did not find any evidence of adverse events following the COVID-19 vaccination in people with gluten enteropathy. The patient was discharged after one hour of observation. To date, no cases of Covaxin vaccination have been reported among gluten enteropathy patients. We discuss the current evidence relating to Covaxin vaccinations, highlighting that administering the vaccine to gluten-sensitive individuals did not cause any adverse reactions. However, proper history taking and other standard procedures should be followed while administering Covaxin to any known allergies.

A Comprehensive Review of the Diverse Spectrum Activity of 1,2,3-Triazole-linked Isatin Hybrids.

Heterocyclic compounds containing 1,2,3-triazole and isatin as core structures have emerged as promising drug candidates due to their diverse biological activities such as anti-cancer, antifungal, antimicrobial, antitumor, anti-epileptic, antiviral, and more. The presence of 1,2,3-triazoles and isatin heterocycles in these hybrids, both individually known for their medicinal significance, has increasingly piqued the interest of drug discovery researchers, as they seek to delve deeper into their extensive pharmacological potential for enhancing therapeutic efficacy. Moreover, these hybrid compounds are synthetically accessible using readily available materials. Therefore, there is a pressing need to provide a comprehensive overview of the existing knowledge in this field, offering valuable insights to readers and paving the way for the discovery of novel 1,2,3-triazole-linked isatin hybrids with therapeutic potential.

Supramolecular Organo/hydrogel-Fabricated Long Alkyl Chain α-Amidoamides as a Smart Soft Material for pH-Responsive Curcumin Release.

Low-molecular-mass gelators, due to their excellent biocompatibility, low toxicological profile, innate biodegradability and ease of fabrication have garnered significant interest as they self-assemble through non-covalent interactions. In this study, we have designed and synthesized a series of six α-amidoamides by varying the hydrophobic alkyl chain length (C12-C22), which were well characterized using different spectral techniques. These α-amidoamides formed self-assembled aggregates in a DMSO/water solvent system affording organo/hydrogels at 0.66% w/v, which is the minimum gelation concentration (MGC) making them as remarkable supergelators. The various functionalities present in these gelators such as amides and alkyl chain length pave the way toward excellent gelation mechanism through hydrogen bonding and van der Waals interaction as evidenced from FTIR spectroscopy. Notably, as the chain length increased, organo/hydrogels became more thermally stable. Rheological results showed that the stability and strength of these gelators were considerably impacted by variations in chain length. The SEM morphology revealed dense sheet architectures of the organo/hydrogel samples. Organo/hydrogels have a significant impact on the advancement of innovative drug delivery systems that respond to various stimuli, ushering in a new era in pharmaceutical technology. Inspired by this, we encapsulated curcumin, a chemopreventive medication, into the gel core and further released via gel-to-sol transition induced by pH variation at 37 °C, without any alteration in structure-activity relationship. The drug release behavior was observed by UV-vis spectroscopy. Moreover, cell viability and cell invasion experiments demonstrate that the gel formulations exhibit high biocompatibility and low cytotoxicity. Among the tested formulations, 5e+Cur exhibited remarkable efficacy in controlling A549 cell migration, suggesting significant potential for applications in the pharmaceutical industry.

Facile one-pot multicomponent synthesis of peptoid based gelators as novel scaffolds for drug incorporation and pH-sensitive release.

Infections caused by bacteria are the primary cause of illness and death globally, and antibiotics are the most commonly used medications to treat them. However, there are certain inherent problems in administering these drugs without any changes to their effectiveness. In order to sustain the targeted dosage over time, the use of a biocompatible local drug delivery system using low molecular mass gelators is preferred as a potential approach to reduce its side effects. Low molecular weight organic gelators (LMWOGs) have drawn a lot of attention due to their numerous and varied applications in multiple fields. But nowadays its quite a challenging task to synthesize new types of LMWOGs that can fill the significant gap towards potential applications. In this work, we have explored a multicomponent pathway for the synthesis of a small repertoire of peptoids from simple building blocks by a one-pot Ugi reaction. A variety of novel effective low molecular weight organic gelators have been synthesized, leading to the formation of stable self-assembled aggregates in various solvents such as DMSO, aqueous DMSO, and methanol. Consequently, these aggregates give rise to the creation of organogels and organo/hydrogels. The gels have a minimum gelation concentration (MGC) of 1-2% w/v with high thermal stability. Furthermore, successful encapsulation and release of metronidazole (MZ) were achieved within the gel matrix under physiological pH conditions at 37 °C, ensuring the preservation of its structural and functional properties. The results demonstrated that the release rate of MZ from the organo/hydrogels is contingent on pH, exhibiting a gradual and regulated release in mild alkaline environments. Moreover, the devised system displayed noteworthy antimicrobial efficacy against E. coli, underscoring the potential of these novel low molecular weight organic gels (LMWOGs) as effective drug delivery systems in the pharmaceutical industry. The gel formulations exhibit biocompatibility and negligible cytotoxicity, as evidenced by cell viability studies conducted using the MTT assay.

Assessment of the USG Detection of Abdominal Pathologies in HIV and Aids and its Association with CD4 Counts - An Original Research.

Introduction: In individuals with acquired immunodeficiency syndrome (HIV/AIDS), abdominal pathologies rank second in frequency only to pulmonary illnesses. An essential imaging method for assessing abdominal diseases is ultrasonography (USG). In this study, abdominal pathologies in HIV/AIDS patients were evaluated using USG, and their relationship to CD4 count was further examined. Materials and Techniques: 400 HIV+ subjects with aberrant abdominal USG participated in the current investigation. The subjects were assessed and graded as per the CD4 counts. Later the comparisons were drawn between the USG, and its relationship to CD4 count using SPSS 16.0 software, and all data were examined using appropriate statistical tools. Results: Men were over 60% of the 400 subjects. The average age of these subjects was 35.6 years; the range for this age group was 6 to 63 years. Spleen involvement was found on ultrasonographic examination in 45.1% of subjects, while liver and lymph node involvement was seen in 43.6% of subjects. Substantial correlations between CD4 counts and findings such as periportal & mesenteric lymphadenopathy, localized pancreatic lesion, splenic microabscess, splenomegaly, and hepatomegaly were found. One percent of individuals had lymphoma, which affected the retroperitoneal lymph nodes, pancreas, and liver. Conclusion: Present research demonstrates the significance of abdominal ultrasonographic examination in HIV+ patients. CD4 counts have a big impact on how an HIV/AIDS patient's differential diagnosis is determined. The interpretation of USG results in relation to CD4 levels may aid in accurate diagnosis.

In-silico identification of small molecule benzofuran-1,2,3-triazole hybrids as potential inhibitors targeting EGFR in lung cancer via ligand-based pharmacophore modeling and molecular docking studies.

Lung cancer is one of the most common and deadly types of cancer worldwide, and the epidermal growth factor receptor (EGFR) has emerged as a promising therapeutic target for the treatment of this disease. In this study, we designed a library of 1840 benzofuran-1,2,3-triazole hybrids and conducted pharmacophore-based screening to identify potential EGFR inhibitors. The 20 identified compounds were further evaluated using molecular docking and molecular dynamics simulations to understand their binding interactions with the EGFR receptor. In-silico ADME and toxicity studies were also performed to assess their drug-likeness and safety profiles. The results of this study showed the benzofuran-1,2,3-triazole hybrids BENZ-0454, BENZ-0143, BENZ-1292, BENZ-0335, BENZ-0332, and BENZ-1070 dock score of - 10.2, - 10, - 9.9, - 9.8, - 9.7, - 9.6, while reference molecule - 7.9 kcal/mol for EGFR (PDB ID: 4HJO) respectively. The molecular docking and molecular dynamics simulations revealed that the identified compounds formed stable interactions with the active site of the receptor, indicating their potential as inhibitors. The in-silico ADME and toxicity studies suggested that the compounds had good pharmacokinetic and safety profiles, further supporting their potential as therapeutic agents. Finally, performed DFT studies on the best-selected ligands to gain further insights into their electronic properties. The findings of this study provide important insights into the potential of benzofuran-1,2,3-triazole hybrids as promising EGFR inhibitors for the treatment of lung cancer. Overall, this study provides a valuable starting point for the development of novel EGFR inhibitors with improved efficacy and safety profiles. Supplementary Information: The online version contains supplementary material available at 10.1007/s40203-023-00157-1.

NREP contributes to development of NAFLD by regulating one-carbon metabolism in primary human hepatocytes.

Non-alcoholic fatty liver disease (NAFLD) is the most common cause of chronic liver disease. We recently discovered that neuronal regeneration-related protein (NREP/P311), an epigenetically regulated gene reprogrammed by parental metabolic syndrome, is downregulated in human NAFLD. To investigate the impact of NREP insufficiency, we used RNA-sequencing, lipidomics, and antibody microarrays on primary human hepatocytes. NREP knockdown induced transcriptomic remodeling that overlapped with key pathways impacted in human steatosis and steatohepatitis. Additionally, we observed enrichment of pathways involving phosphatidylinositol signaling and one-carbon metabolism. Lipidomics analyses also revealed an increase in cholesterol esters and triglycerides and decreased phosphatidylcholine levels in NREP-deficient hepatocytes. Signalomics identified calcium signaling as a potential mediator of NREP insufficiency's effects. Our results, together with the encouraging observation that several single nucleotide polymorphisms (SNPs) spanning the NREP locus are associated with metabolic traits, provide a strong rationale for targeting hepatic NREP to improve NAFLD pathophysiology.

A single-cell view on host immune transcriptional response to in vivo BCG-induced trained immunity.

Bacillus Calmette-Guérin (BCG) vaccination is a prototype model for the study of trained immunity (TI) in humans, and results in a more effective response of innate immune cells upon stimulation with heterologous stimuli. Here, we investigate the heterogeneity of TI induction by single-cell RNA sequencing of immune cells collected from 156 samples. We observe that both monocytes and CD8+ T cells show heterologous transcriptional responses to lipopolysaccharide, with an active crosstalk between these two cell types. Furthermore, the interferon-γ pathway is crucial in BCG-induced TI, and it is upregulated in functional high responders. Data-driven analyses and functional experiments reveal STAT1 to be one of the important transcription factors for TI shared in all identified monocyte subpopulations. Finally, we report the role of type I interferon-related and neutrophil-related TI transcriptional programs in patients with sepsis. These findings provide comprehensive insights into the importance of monocyte heterogeneity during TI in humans.

Standard workload-based estimation of nursing manpower requirement in the ICU of a tertiary care teaching hospital: A time and motion study.

BACKGROUND: The safety of patients remain at risk due to a higher workload and lower nurse-to-patient ratio. However, in India, most hospitals still adhere to long-known nurse staffing norms set by their statutory or accreditation bodies. Therefore, the present study was undertaken to recommend a standard workload-based estimation of nursing manpower requirement in the ICU of a tertiary care teaching hospital. MATERIALS AND METHODS: It was a descriptive, observational, time and motion study was conducted in the medicine ICU of a tertiary care teaching hospital. Data collection was done by using demographic and clinical profile sheet of patients, NPDS-H dependency assessment scale, time and activities record sheet, and WHO WISN tool. The nurses' activities were observed by nonparticipatory and non-concealment technique. Data analysis was done using descriptive statistics and the WHO WISN tool. RESULTS: The bed occupancy rate and the average length of stay in the medicine ICU were 93.23% and 7.18 days respectively. Distribution of dependency level of the medical ICU patients was very high (41.67%), low-high (33.33%), and medium-high (25.0%) dependency level. Considering available resources and workload in tertiary care hospitals in India, the study recommended a nurse-to-patient ratio of 1:1.2 in each shift for the medicine ICU of a tertiary care hospital. CONCLUSION: The study suggested minimum nurse-to-patient ratio in medical ICU should be 1:1.2 with provision of power to ICU incharge nurse to allocate nurses according to the workload in different shifts. Also, nurse staffing norms in hospitals need to be estimated or selected with serious consideration of health care demands when employing nurse staffing norms.

Identification and exploration of quinazoline-1,2,3-triazole inhibitors targeting EGFR in lung cancer.

Epidermal growth factor receptor (EGFR) enhances lung cancer development, due to their inability to permeate the cell membrane, secreted growth factors work through specialized signal transduction pathways. The purpose of this study is to find out a novel anticancer agent that inhibits EGFR and reduces the chances of lung cancer. A series of triazole-substituted quinazoline hybrid compounds were designed by Chemdraw software and docked against five different crystallographic EGFR tyrosine kinase domain (TKD). For docking and visualization PyRx, Autodock vina, and Discovery studio visualizer were used. Molecule-14, Molecule-16, Molecule-19, Molecule-20, and Molecule-38 showed significant affinity but Molecule-19 showed excellent binding affinity (-12.4 kcal/mol) with crystallographic EGFR tyrosine kinase. The superimposition of the co-crystalized ligand with the hit compound shows similar conformation at the active site of EGFR (PDB ID: 4HJO) indicating excellent coupling and pharmaceutically active. The hit compound showed a good bioavailability score (0.55) with no sign of carcinogenesis, mutagenesis, or reproductive toxicity properties. MD simulation and MMGBSA represent good stability and binding free energy demonstrating that the hit (Molecule-19) may be used as a lead compound. Molecule-19 also showed good ADME properties, bioavailability scores, and synthetic accessibility with fewer signs of toxicity. It was observed that Molecule-19 may be a novel and potential inhibitor against EGFR with fewer side effects than the reference molecule. Additionally, the molecular dynamics simulation revealed the stable nature of protein-ligand interaction and provided information about the amino acid residues involved in binding. Overall, this study led to the identification of potential EGFR inhibitors with favorable pharmacokinetic properties. We believe that the outcome of this study can help to develop more potent drug-like molecules to tackle human lung cancer.

Challenges Faced by Cancer Patients in Receiving Health Services During the COVID-19 Pandemic in a Tertiary Care Hospital in Rajasthan.

BACKGROUND: Amidst the COVID-19 pandemic, cancer patients may have faced difficulty accessing health care. This study explored the challenges experienced by cancer patients in availing of healthcare during the pandemic, as well as the vaccination status and prevalence of COVID-19 infection among cancer patients in the year 2021. METHOD: A cross-sectional study was conducted in a tertiary care hospital in Jodhpur, Rajasthan, to interview 150 patients from the oncology department using convenience sampling. Face-to-face interviews lasted for 20-30 minutes. The first segment of the pretested semi-structured questionnaire was directed at obtaining the patient's socio-demographic characteristics, while the second segment focused on the problems that patients encountered during the pandemic in receiving cancer care. The data were analyzed using Statistical Packages for Social Sciences (SPSS) software (IBM Corp., Armonk, NY). RESULTS: Several constraints, such as a lack of transportation services, difficulty in availing outpatient department (OPD) and teleconsultation services, long waiting times, and deferred surgeries and therapies, have hampered cancer care. COVID-19 mitigation measures further imposed additional stress and financial burden on cancer patients. Moreover, there was low vaccination coverage among cancer patients, which increases their probability of acquiring an infection. CONCLUSION: Policy reforms must prioritize cancer care in India to maintain a continuum of care by ensuring medication, teleconsultation, uninterrupted treatment, and complete vaccination to decrease the risk of COVID-19 infection and facilitate patient compliance with the healthcare delivery system.

AI Assisted Attention Mechanism for Hybrid Neural Model to Assess Online Attitudes About COVID-19.

COVID-19 is a novel virus that presents challenges due to a lack of consistent and in-depth research. The news of the COVID-19 spreads across the globe, resulting in a flood of posts on social media sites. Apart from health, social, and economic disturbances brought by the COVID-19 pandemic, another important consequence involves public mental health crises which is of greater concern. Data related to COVID-19 is a valuable asset for researchers in understanding people's feelings related to the pandemic. It is thus important to extract the early information evolving public sentiments on social platforms during the outbreak of COVID-19. The objective of this study is to look at people's perceptions of the COVID-19 pandemic who interact with each other and share tweets on the Twitter platform. COVIDSenti, a large-scale benchmark dataset comprising 90,000 COVID-19 tweets collected from February to March 2020, during the initial phases of the outbreak served as the foundation for our experiments. A pre-trained bidirectional encoder representations from transformers (BERT) model is fine-tuned and embeddings generated are combined with two long short-term memory networks to propose the residual encoder transformation network model. The proposed model is used for multiclass text classification on a large dataset labeled as positive, negative, and neutral. The experimental outcomes validate that: (1) the proposed model is the best performing model, with 98% accuracy and 96% F1-score; (2) It also outperforms conventional machine learning algorithms and different variants of BERT, and (3) the approach achieves better results as compared to state-of-the-art on different benchmark datasets.

First results of fast visible imaging diagnostic in Aditya-U tokamak.

A Fast Visible Imaging Diagnostic (FVID) system, measuring the visible light emission spectrum (400-1000 nm) from tokamak plasma, has been installed on the Aditya-U tokamak to monitor the two-dimensional dynamics of the poloidal cross section of the plasma. In this work, we present the design and installation of the FVID system on the Aditya-U tokamak. The evolution of plasma and plasma-wall interactions is described. The signature of the runaway electron beam in visible imaging and its correlation with other diagnostics is presented. The estimation of the electron cyclotron resonance layer position during pre-ionization is also discussed in this work.

Deep learning techniques for cancer classification using microarray gene expression data.

Cancer is one of the top causes of death globally. Recently, microarray gene expression data has been used to aid in cancer's effective and early detection. The use of DNA microarray technology to uncover information from the expression levels of thousands of genes has enormous promise. The DNA microarray technique can determine the levels of thousands of genes simultaneously in a single experiment. The analysis of gene expression is critical in many disciplines of biological study to obtain the necessary information. This study analyses all the research studies focused on optimizing gene selection for cancer detection using artificial intelligence. One of the most challenging issues is figuring out how to extract meaningful information from massive databases. Deep Learning architectures have performed efficiently in numerous sectors and are used to diagnose many other chronic diseases and to assist physicians in making medical decisions. In this study, we have evaluated the results of different optimizers on a RNA sequence dataset. The Deep learning algorithm proposed in the study classifies five different forms of cancer, including kidney renal clear cell carcinoma (KIRC), Breast Invasive Carcinoma (BRCA), lung adenocarcinoma (LUAD), Prostate Adenocarcinoma (PRAD) and Colon Adenocarcinoma (COAD). The performance of different optimizers like Stochastic gradient descent (SGD), Root Mean Squared Propagation (RMSProp), Adaptive Gradient Optimizer (AdaGrad), and Adaptive Momentum (AdaM). The experimental results gathered on the dataset affirm that AdaGrad and Adam. Also, the performance analysis has been done using different learning rates and decay rates. This study discusses current advancements in deep learning-based gene expression data analysis using optimized feature selection methods.

Impact of COVID-19 pandemic on obstetrics and gynecology residency training program in India: A national online survey.

OBJECTIVE: The goal of this study was to analyze how the COVID-19 pandemic affected the Obstetrics and Gynecology (OBG) residency program in India. STUDY DESIGN: This was a cross-sectional questionnaire-based online survey aimed to assess the impact of the pandemic on the residency training program in Obstetrics and Gynecology. The questionnaire consisted of five sections: demographic details, information regarding COVID-19 status, clinical work load, teaching and research, and psychological impact. RESULTS: The questionnaire was completed by 280 OBG trainees from different medical colleges from India. Training activity in general was reduced considerably during the pandemic, according to 79.6% (n = 223) respondents. According to 13.21% (n = 37) and 5% (n = 14) respondents, reduction in training activity were due to cancelation of elective operations and reduced patient foot fall respectively. In 74.3% (n = 208) of cases, trainees reported worry about meeting the goals of their specialty training. Logistic regression showed that the extent of training reduction was not significantly associated with residents' age (p = 0.806), gender (p = 0.982), marital status (p = 0.363), and status of their duty in COVID-19 dedicated hospitals (p = 0.110). However, year of residency was a significant predictor of the perception about degree of training reduction. CONCLUSION: The pandemic imposed a significant impact on OBG residency training in India. During the pandemic, exposure to learning opportunities, surgeries, and teaching were reduced, which may result in a decline in the quality of care offered to women in the future if training deficit is not overcome. At the same time, pandemic also gave birth to newer insights of learning and interaction by online mode.

Out-of-pocket spending on hypertension and diabetes among patients reporting in a health -care teaching institute of the Western Rajasthan.

Background: Across the globe, morbidity and mortality due to non-communicable diseases (NCDs) are major public health issues. The resulting concern is not just epidemiological but also about the economic consequences at the household level. Objective: To assess the various facets of out-of-pocket spending (OOPs) incurring on NCDs, namely hypertension and diabetes on patients attending a healthcare teaching institute in Rajasthan. Methodology: This cross-sectional study involves patients older than 18 years attending either out-patient clinics or who were admitted in the wards in a healthcare teaching institute for seeking care for diabetes or hypertension. Four hundred patients were chosen purposively and a pretested questionnaire was used to elicit information on incurring OOPs for NCDs. Descriptive statistics (like percentage, mean, median, and standard deviation) were calculated. Results: The study shows a significant expenditure other than out-patient, in-patient admissions, in the form of personal expenditure and loss of employment, amounting to 31.86 and 34.07%, respectively, of the mean total expenditure. In a quarter (3 months), the mean total expenditure is ₹ 9014.37 ± 6452.37. On average, the OOP expenditure per visit for an out-patient visit was ₹370.54 ± 237, while for the patients admitted to the hospital, the average OOPs was ₹1564.72 ± 1310.5. Conclusions: Health expenditures can contribute toward the impoverishment of many segments of the community. Undoubtedly, numerous people may tend to neglect the needed care for NCDs due to financial hurdles. Thus, there is a need to develop NCD care management centers with health insurance packages and make them accessible for all.

An hybrid deep learning approach for depression prediction from user tweets using feature-rich CNN and bi-directional LSTM.

Depression has become one of the most widespread mental health disorders across the globe. Depression is a state of mind which affects how we think, feel, and act. The number of suicides caused by depression has been on the rise for the last several years. This issue needs to be addressed. Considering the rapid growth of various social media platforms and their effect on society and the psychological context of a being, it's becoming a platform for depressed people to convey feelings and emotions, and to study their behavior by mining their social activity through social media posts. The key objective of our study is to explore the possibility of predicting a user's mental condition by classifying the depressive from non-depressive ones using Twitter data. Using textual content of the user's tweet, semantic context in the textual narratives is analyzed by utilizing deep learning models. The proposed model, however, is a hybrid of two deep learning architectures, Convolutional Neural Network (CNN) and bi-directional Long Short-Term Memory (biLSTM) that after optimization obtains an accuracy of 94.28% on benchmark depression dataset containing tweets. CNN-biLSTM model is compared with Recurrent Neural Network (RNN) and CNN model and also with the baseline approaches. Experimental results based on various performance metrics indicate that our model helps to improve predictive performance. To examine the problem more deeply, statistical techniques and visualization approaches were used to show the profound difference between the linguistic representation of depressive and non-depressive content.

Anti-SARS-CoV-2 IgG status among frontline health workers involved in COVID-19 survey at Jodhpur, Rajasthan, India.

Background: Asymptomatic carriers are responsible for the consistent spread of coronavirus disease 2019 (COVID-19) in the community. The Government of India has deputed house-to-house survey teams to aid in identifying asymptomatic individuals and their susceptible contacts. We selected door-to-door survey teams of a COVID-19 red zone in western India and determined their infectioncontrol practices and anti-severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) immunoglobin G (IgG) status. Materials and Methods: This single-day prospective cross-sectional study was conducted by the Department of Microbiology of a tertiary care hospital of Jodhpur, in collaboration with the Rajasthan State Health Services. Participants were asked to fill out a questionnaire regarding personal protective equipment (PPE) use after written informed consent. Venous blood samples were collected and Kavach enzyme-linked immunosorbent assay (ELISA) (J Mitra and Co.) was performed to determine anti-SARS-CoV-2 IgG status. Results: Out of the total 39 participants, IgG antibody was detected in four. Of them, three reported mild symptoms in the past. Out of two previously real-time polymerase chain reaction (RT-PCR) SARS-CoV-2-positive participants, only one had detectable IgG antibodies (Ab) in serum. Cloth mask was used by 24, N95 mask by 11, and surgical masks by four. Conclusion: Anti-SARS-CoV-2 IgG Abs were detected among four members of house-to-house COVID-19 survey teams in Jodhpur. Most of the team members used cloth masks, whereas the Government of India guidelines has recommended triple-layered surgical masks as minimum essential PPE for healthcare workers in India. More such studies should be conducted to ascertain infection prevention and control practices among such vulnerable frontline workers in our country.