Platelet-derived sTLT-1 is associated with platelet-mediated inflammation in coronary artery disease patients.

The development of coronary artery disease (CAD) depends heavily on platelet activation, and inflammation plays a major role in all stages of atherosclerosis. Platelet-specific soluble triggering receptor expressed on myeloid cells like transcript 1 (sTLT-1) facilitate clot formation and have been linked to chronic inflammation. In this study, we explored the role of platelet-derived sTLT-1 in platelet-mediated inflammation in CAD patients. Plasma levels of sTLT-1 were measured using enzyme-linked immunosorbent assay in CAD patients (n = 163) and healthy controls (n = 99). Correlation analysis was performed to determine the circulatory sTLT-1 levels with platelet activation markers, immune cells, and inflammatory cytokines/chemokines. Increased plasma sTLT-1 levels were observed in CAD patients compared with those in healthy controls (p < 0.0001). A positive correlation was observed between sTLT-1 and platelet activation markers (P-selectin, PAC-1), CD14++ CD16- cells (classical monocytes), Natural killer T (NKT) cells, and platelet-immune cell aggregates with monocytes, neutrophils, dendritic cells, CD11c+ cells, and NKT cells. In contrast, a significant negative correlation was observed with CD8 cells. Furthermore, a significant positive correlation was observed between sTLT-1 and inflammatory markers (TNF-α, IL-1ß, IL-2, IL-6, IL-12p70, IL-18, CXCL-12, and CCL-11). Logistic regression analysis identified sTLT-1 and triglycerides as predictors of CAD. Receiver operating characteristic curve (ROC) analysis showed that sTLT-1 had a higher sensitivity and specificity for predicting CAD. Our findings suggest that platelet activation induces the release of sTLT-1 into the circulation in CAD patients, which aggregates with immune cells and enhances inflammatory responses.

Nanometal surface energy transfer (NSET) from biologically active heterocyclic ligands to silver nanoparticles induces enhanced antimicrobial activity against gram-positive bacteria.

Herein we report the formation of a nanometal surface energy transfer (NSET) pair between a donor biologically active heterocyclic luminescent ligand such as 3-(1,3-Dioxoisoindolin-2-yl)-N, N-dimethylpropan-1-ammonium perchlorate (S4PNL; λem-408 nm) and an acceptor silver nanoparticle (Ag NP; λabs-406 nm). When the S4PNL ligand interacts with Ag NPs, the quenching in their luminescence intensity at 408 nm is noticed, with a Stern-Volmer constant of 0.8 × 104 M-1. The present donor-acceptor pair displays a binding constant of 2.8 × 104 M-1 and binding sites of 1.12. The current work shows the energy transfer from a molecular dipole (S4PNL) to a nanometal surface (Ag NP) and thus follows the nanometal surface energy transfer (NSET) ruler with an energy transfer efficiency of 80.0%, 50% energy transfer efficiency distance (d0) of 4.9 nm, donor-acceptor distance of 3.4 nm. The alteration in the zeta potential value of S4PNL upon interaction with AgNP clearly demonstrates the strong electrostatic interaction between donor and acceptor. Importantly, the current NSET pair shows enhanced antimicrobial activity against gram-positive bacteria such as Bacillus cereus (B. cereus) in comparison to their parent components i.e. S4PNL ligand and Ag NP. The NSET pair shows maximum inhibition against B. cereus (9202.21 ± 463.26 CFU/ml.) at 10% while minimum inhibition is observed at 0.01% of it (39,887.19 ± 242.67 CFU/ml.).

Discovery of new oxadiazolo pyridine derivatives as potent ghrelin O-acyltransferase inhibitors using molecular modeling techniques.

Diabesity is a major global health concern, and ghrelin O-acyltransferase (GOAT) acts as an important target for the development of new inhibitors of this disease. The present work highlights a detailed QSAR study using QSARINS software, which provides an excellent model equation using descriptors. Here, the best model equation developed has two variables, namely MLFER_E and XlogP, with statistical parameters R2 = 0.8433, LOF = 0.0793, CCCtr = 0.915, Q2LOO = 0.8303, Q2LMO = 0.8275, CCCcv = 0.9081, R2ext = 0.7712, and CCCext = 0.8668. A higher correlation of the key structural fragments with activity is validated by the developed QSAR model. Furthermore, molecular docking helped us identify the binding interactions. Thirty four new molecules with better predicted biological activity (pIC50) were designed. The binding energy of four compounds have shown higher binding activity into the membrane protein (PDB Id: 6BUG). Molecular dynamics simulation has established the stability of the protein-ligand complex over 100 ns. DFT and ADME-toxicity analyses also confirmed their drug-like properties. Based on our findings, we report that these new oxadiazolo pyridine derivatives lead to the development of potent candidates for further development. Graphical abstract: METTL3-mediated HOTAIRM1 promotes vasculogenic mimicry in glioma via regulating IGFBP2 expression. METTL3 expression is high in glioma cells and tissues that stabilize and enhance HOTAIRM1 expression. This HOTAIRM1 then interacts with IGFBP2 which in turn promotes glioma cell malignancy and vasculogenic mimicry (VM) formation, thus providing a new direction for glioma therapy. Supplementary Information: The online version contains supplementary material available at 10.1007/s40203-023-00167-z.

Sentiment analysis in multilingual context: Comparative analysis of machine learning and hybrid deep learning models.

This research paper investigates the efficacy of various machine learning models, including deep learning and hybrid models, for text classification in the English and Bangla languages. The study focuses on sentiment analysis of comments from a popular Bengali e-commerce site, "DARAZ," which comprises both Bangla and translated English reviews. The primary objective of this study is to conduct a comparative analysis of various models, evaluating their efficacy in the domain of sentiment analysis. The research methodology includes implementing seven machine learning models and deep learning models, such as Long Short-Term Memory (LSTM), Bidirectional LSTM (Bi-LSTM), Convolutional 1D (Conv1D), and a combined Conv1D-LSTM. Preprocessing techniques are applied to a modified text set to enhance model accuracy. The major conclusion of the study is that Support Vector Machine (SVM) models exhibit superior performance compared to other models, achieving an accuracy of 82.56% for English text sentiment analysis and 86.43% for Bangla text sentiment analysis using the porter stemming algorithm. Additionally, the Bi-LSTM Based Model demonstrates the best performance among the deep learning models, achieving an accuracy of 78.10% for English text and 83.72% for Bangla text using porter stemming. This study signifies significant progress in natural language processing research, particularly for Bangla, by enhancing improved text classification models and methodologies. The results of this research make a significant contribution to the field of sentiment analysis and offer valuable insights for future research and practical applications.

Mononuclear Zn(II) Complex Based on N2O Ligand Compartment: First Case to Detect Nitro Explosives.

This paper scrutinises the development of low-cost hypersensitive fluorescent probes manipulated chemically with Schiff base complexes and their prospective applicability for the detection of nitro explosives in light of the rapidly expanding demand for anti-trafficking measures. In this study, a new Zn(II) metal complex has been synthesized in one pot using the Schiff base ligand L= 2-methoxy-5-methyl-N-(2-pyridin-2-ylmethylene) aniline. The complex was also thoroughly characterised using various spectroscopic tools and subjected to single crystal XRD analysis. In the asymmetric unit, square pyramidal zinc (II) centre exist in the inner N2O compartment of the ligand L. The intermolecular Cg···Cg interactions exist between two different asymmetric residual units lead to supramolecular assembly along b axis. By turning off the fluorescence response, the complex serves as a sensor for the detection of nitro aromatics in CH3CN solution. A significant quenching efficiency has been reported with a quenching constant (KSV) 1.8 × 104 M-1 for 4-nitrobenzoic acid during investigation of sensing phenomenon in solution phase. In addition, determining the binding stoichiometry of the chemosensor with NO2 and the binding constant, the mechanism of fluorescence quenching has also been postulated. The detection limit of NO2 is 7.6×10 -7 M, with the binding constant k = 1.1021× 108 M-1. Additionally, the DFT calculation makes it easier to comprehend the appropriate binding process in light of the findings of experiments. We also designed a paper sensor strip for the visual detection of Nitro Explosive Residues in light of the sensor's potential used in forensic investigations.

Computational insights into pediatric adenovirus inhibitors: in silico strategies for drug repurposing.

Human adenovirus (HADV) infection can pose a serious threat to children, leading to a variety of respiratory illnesses and other complications. Particularly, children with weak immune systems are vulnerable to severe adenovirus infections with high mortality. The main focus of this study is to propose new antiviral agents as lead HADV inhibitors for children. So, several antiviral agents used in children were subjected to finding new HADV inhibitors using important computational methods of molecular docking, molecular dynamics (MD) simulation, Molecular Mechanics Poisson-Boltzmann Surface Area (MM-PBSA) binding free energy calculations, density functional theory (DFT), and pharmacokinetic analysis. Molecular docking of standard cidofovir along with other ligands, suggested that sofosbuvir has the highest binding energy (-10.8 kcal/mol), followed by baloxavir marboxil (-10.36 kcal/mol). Further, the analysis of molecular interactions using MD simulation (100 ns) and MM-PBSA indicated that baloxavir marboxil has formed the most stable protein-ligand complex with HADV, followed by sofosbuvir. The binding free energies of baloxavir marboxil and sofosbuvir were found to be -61.724 kJ/mol and -48.123 kJ/mol, respectively. The DFT and drug-likeness properties of these compounds were also investigated. Overall, two antiviral agents, such as baloxavir marboxil, and sofosbuvir are suggested as lead repurposed candidates against HADV.Communicated by Ramaswamy H. Sarma.

Design of porphyrin-based frameworks for efficient visible light-promoted reduction of CO2 from dilute gas: Combined experimental and theoretical investigation.

The photocatalytic carbon dioxide reduction (CO2R) coupled with hydrogen evolution reaction (HER) constitutes a promising step for a sustainable generation of syngas (CO + H2), an essential feedstock for the preparation of several commodity chemicals. Herein, visible light/sunlight-promoted catalytic reduction of CO2 and protons to syngas using rationally designed porphyrin-based 2D porous organic frameworks, POF(Co/Zn) is demonstrated. Indeed, POF(Co) showed superior catalytic performance over the Zn counterpart with CO and H2 generation rates of 1104 and 3981 µmol g-1h-1, respectively. The excellent catalytic performance of Co-based POF is aided by the favorable transfer of photo-excited electrons from Ru-sensitizer to the CoII catalytic site, which is not feasible in the case of POF(Zn), revealed from the theoretical investigation. More importantly, the POF(Co) catalyzes the reduction of CO2 even from dilute gas (13% CO2), surpassing most reported framework-based photocatalytic systems. Significantly, the catalytic performance of POF(Co) was increased under natural sunlight conditions suggesting sunlight-promoted enhancement in syngas generation. The in-depth theoretical investigation further unveiled the comprehensive mechanistic pathway of the light-promoted concurrent CO and H2 generation. This work showcases the advantages of porphyrin-based frameworks for visible light/sunlight-promoted syngas generation by utilizing greenhouse gas (CO2) and protons under mild eco-friendly conditions.

BTSD: A curated transformation of sentence dataset for text classification in Bangla language.

The Bangla Transformation of Sentence Classification dataset addresses the resource gap in natural language processing (NLP) for the Bangla language by providing a curated resource for Bangla sentence classification. With 3,793 annotated sentences, the dataset focuses on categorizing Bangla sentences into Simple, Complex, and Compound classes. It serves as a benchmark for evaluating NLP models on Bangla sentence classification, promoting linguistic diversity and inclusive language models. Collected from publicly accessible Facebook pages, the dataset ensures balanced representation across the categories. Preprocessing steps, including anonymization and duplicate removal, were applied. Three native Bangla speakers independently assessed the Transformation of Sentence labels, enhancing the dataset's reliability. The dataset empowers researchers, practitioners, and developers to build accurate and robust NLP models tailored to the Bangla language. It offers insights into Bangla syntax and structure, benefiting linguistic research. The dataset can be used to train models, uncover patterns in Bangla language usage, and develop effective NLP applications across domains.

Molecular iodine catalyzed C(sp2)-H sulfenylation of biologically active enaminone compounds under mechanochemical conditions and studies on their biocidal activity including molecular docking and DFT.

Here we demonstrated a solvent free, mechanochemical I2 catalyzed C(sp2)-H sulfenylation of enaminones under grinding condition. Only catalytic amount of I2 is required on silica surface without any external heating. The reaction time has reduced to a great extent in comparison to their solution based counterpart. The frictional energy created by ball-mill on mesoporous silica materials has attracted much attention towards this mechanochemical approach for molecular heterogeneous catalysis. Their large surface area and well defined porous architecture certainly increase the catalytic ability of iodine in this developed protocol. Anti-microbial activities of our synthesized compounds were investigated against two gram positive (Staphylococcus aureus and Bacillus cereus) and two gram negative (Escherichia coli and Klebsiella pneumonia) bacteria. To understand the potency of these compounds (3a-3m) as antimalarial agents, molecular docking studies were also performed. Density functional theory was also used to investigate the chemical reactivity and kinetic stability of the compound 3a-3m.

A Machine Learning Approach to Identify Potential miRNA-Gene Regulatory Network Contributing to the Pathogenesis of SARS-CoV-2 Infection.

Worldwide, many lives have been lost in the recent outbreak of coronavirus disease. The pathogen responsible for this disease takes advantage of the host machinery to replicate itself and, in turn, causes pathogenesis in humans. Human miRNAs are seen to have a major role in the pathogenesis and progression of viral diseases. Hence, an in-silico approach has been used in this study to uncover the role of miRNAs and their target genes in coronavirus disease pathogenesis. This study attempts to perform the miRNA seq data analysis to identify the potential differentially expressed miRNAs. Considering only the experimentally proven interaction databases TarBase, miRTarBase, and miRecords, the target genes of the miRNAs have been identified from the mirNET analytics platform. The identified hub genes were subjected to gene ontology and pathway enrichment analysis using EnrichR. It is found that a total of 9 miRNAs are deregulated, out of which 2 were upregulated (hsa-mir-3614-5p and hsa-mir-3614-3p) and 7 were downregulated (hsa-mir-17-5p, hsa-mir-106a-5p, hsa-mir-17-3p, hsa-mir-181d-5p, hsa-mir-93-3p, hsa-mir-28-5p, and hsa-mir-100-5p). These miRNAs help us to classify the diseased and healthy control patients accurately. Moreover, it is also found that crucial target genes (UBC and UBB) of 4 signature miRNAs interact with viral replicase polyprotein 1ab of SARS-Coronavirus. As a result, it is noted that the virus hijacks key immune pathways like various cancer and virus infection pathways and molecular functions such as ubiquitin ligase binding and transcription corepressor and coregulator binding.

Gastric Cancer Biomarker Candidates Identified by Machine Learning and Integrative Bioinformatics: Toward Personalized Medicine.

Gastric cancer (GC) is among the leading causes of cancer-related deaths worldwide. The discovery of robust diagnostic biomarkers for GC remains a challenge. This study sought to identify biomarker candidates for GC by integrating machine learning (ML) and bioinformatics approaches. Transcriptome profiles of patients with GC were analyzed to identify differentially expressed genes between the tumor and adjacent normal tissues. Subsequently, we constructed protein-protein interaction networks so as to find the significant hub genes. Along with the bioinformatics integration of ML methods such as support vector machine, the recursive feature elimination was used to select the most informative genes. The analysis unraveled 160 significant genes, with 88 upregulated and 72 downregulated, 10 hub genes, and 12 features from the variable selection method. The integrated analyses found that EXO1, DTL, KIF14, and TRIP13 genes are significant and poised as potential diagnostic biomarkers in relation to GC. The receiver operating characteristic curve analysis found KIF14 and TRIP13 are strongly associated with diagnosis of GC. We suggest KIF14 and TRIP13 are considered as biomarker candidates that might potentially inform future research on diagnosis, prognosis, or therapeutic targets for GC. These findings collectively offer new future possibilities for precision/personalized medicine research and development for patients with GC.

The SARS-COV-2 Seroprevalence among Oncology Patients.

Patients with cancer are presumed to be vulnerable to an increased risk of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and severe clinical outcomes due to the immunocompromised state mediated by their underlying malignancies and therapy. The aim of this study was to estimate the SARS-CoV-2 seroprevalence, following second to fourth waves in solid tumour patients attending the Steve Biko Academic Hospital (SBAH) for diagnosis and treatment of cancer. We used the single-prick COVID-19 IgG/IgM Rapid Test Cassettes to detect SARS-CoV-2 IgG/IgM antibodies in 760 patients with solid tumours who were asymptomatic and who had never tested positive for coronavirus disease 2019 (COVID-19). Out of the 760 patients, 277 were male (36.4%), 483 were female (63.6%), and the mean age was 55 years (range 18−92). The estimated total seroprevalence was 33.2%. The seroprevalence status of the COVID-19 IgG/IgM antibodies rose significantly from the second wave (11.3%) to the third (67.38%) and then the fourth (69.81%) waves with roughly similar counts. A significant number of the seropositive patients were asymptomatic to COVID-19 (96%). There was a higher rate of seropositivity in cancer patients with hypertension (p < 0.05). Patients with breast, gynaecologic, and prostate cancers exhibited increased SARS-CoV-2 seropositivity. Although oncology patients may be susceptible to SARS-CoV-2 infection, our data indicate that these patients remained asymptomatic throughout various waves with an overall COVID-19 IgG/IgM antibody seropositivity of 33.16%, suggesting no risk of severe or fatal cases of COVID-19.

Identification of novel lncRNA by reanalysis of RNA-seq data in Zika Virus Infected hiNPCs.

The Zika Virus (ZIKV) infection is a serious, public health concern with no vaccines or antiviral treatments. This study aims to identify the differentially expressed long non-coding RNAs (lncRNAs) in ZIKV infected human-induced neuroprogenitor cells (hiNPCs). Though lncRNA is well-known for its role in gene regulation, its role in ZIKV infection remains unclear. Thus, taking advantage of publicly available transcriptome data, BioProject PRJNA551246 was analysed. Performed the gene ontology and pathway analysis of differentially expressed lncRNAs were functionally interpreted based on the neighbouring protein-coding genes (100 kb upstream and downstream of each lncRNAs). The study revealed 19 novels and 237 differentially expressed lncRNAs in ZIKV infected hiNPCs. They are found to be significantly enriched in type I interferon signalling pathway, negative regulation of viral genome replication, defense response to the virus, pathways involved in Influenza A and Herpes simplex infection, tumor necrosis factor signalling pathway, and apoptosis. In ZIKV, associated microcephaly type I interferon act as potential modulating factors. Type-I interferon inhibits ZIKV replication in many human cell types. The results support future studies on understanding the structure and function of the novel lncRNAs and experimental approaches to determine the role of the lncRNAs in ZIKV induced infection. Supplementary Information: The online version contains supplementary material available at 10.1007/s13337-022-00771-1.

Strategic Design of Mg-Centered Porphyrin Metal-Organic Framework for Efficient Visible Light-Promoted Fixation of CO2 under Ambient Conditions: Combined Experimental and Theoretical Investigation.

The sunlight-driven fixation of CO2 into valuable chemicals constitutes a promising approach toward environmental remediation and energy sustainability over traditional thermal-driven fixation. Consequently, in this article, we report a strategic design and utilization of Mg-centered porphyrin-based metal-organic framework (MOFs) having relevance to chlorophyll in green plants as a visible light-promoted highly recyclable catalyst for the effective fixation of CO2 into value-added cyclic carbonates under ambient conditions. Indeed, the Mg-centered porphyrin MOF showed good CO2 capture ability with a high heat of adsorption (44.5 kJ/mol) and superior catalytic activity under visible light irradiation in comparison to thermal-driven conditions. The excellent light-promoted catalytic activity of Mg-porphyrin MOF has been attributed to facile ligand-to-metal charge transfer transition from the photoexcited Mg-porphyrin unit (SBU) to the Zr6 cluster which in turn activates CO2, thereby lowering the activation barrier for its cycloaddition with epoxides. The in-depth theoretical studies further unveiled the detailed mechanistic path of the light-promoted conversion of CO2 into high-value cyclic carbonates. This study represents a rare demonstration of sunlight-promoted sustainable fixation of CO2, a greenhouse gas into value-added chemicals.

Repurposing of anti-lung cancer drugs as multi-target inhibitors of SARS-CoV-2 proteins: An insight from molecular docking and MD-simulation study.

Herein we have selected seventeen anti-lung cancer drugs to screen against Mpro, PLpro and spike glycoproteins of SARS-CoV-2to ascertain the potential therapeutic agent against COVID-19. ADMET profiling were employed to evaluate their pharmacokinetic properties. Molecular docking studies revealed that Capmatinib (CAP) showed highest binding affinity against the selected proteins of SARS-CoV-2. Molecular Dynamics (MD) simulation and the analysis of RMSD, RMSF, and binding energy confirmed the abrupt conformational changes of the proteins due to the presence of this drug. These findings provide an opportunity for doing advanced experimental research to evaluate the potential drug to combat COVID-19.

Fuchs Endothelial Corneal Dystrophy associated risk variant, rs3768617 in LAMC1 shows allele specific binding of GFI1B.

AIMS: To investigate the genetic and functional association of an intronic variant of LAMC1, rs3768617 with Fuchs endothelial corneal dystrophy (FECD) in the Indian population. METHODS: Blood samples were collected from age and sex matched 356 controls and 120 FECD patients after a detailed assessment via specular microscopy. Genomic DNA was extracted and genotyping was done by fluorescence based capillary electrophoresis. The genetic association of rs3768617 polymorphisms was computed by the chi-square (χ2) test. Bioinformatics studies were performed to find the allele specific binding of different transcription factors in the region of rs3768617 and functional evaluation assessed by luciferase assay followed by Electrophoretic Mobility Shift Assay (EMSA) and Chromatin Immunoprecipitation assay (ChIP). Immunofluorescence assay was carried out to check for any differential expression of GFI1B between control and FECD endothelium samples. RESULTS: SNP rs3768617 {chr1:183123365 (GRCh38.p13)} was found to be genetically associated with FECD in Indian population (p = 2.646 × 10-8). Luciferase assay suggested that the rs3768617 locus has a regulatory role. In silico analysis showed that the transcription factor, GFI1B binds to the risk allele 'G' of rs3768617, but not to the protective allele 'A' which was also experimentally validated by EMSA. High enrichment of DNA flanking the surrounding region of rs3768617 was also found in presence of GFI1B specific antibody in ChIP assay. There was a 0.63 fold decrease in GFI1B expression in FECD affected corneal endothelium compared to control endothelium. CONCLUSIONS: The genetic association of rs3768617 in LAMC1 with FECD pathogenesis is mediated by GFI1B, thus finding the functional role of LAMC1 in FECD pathogenesis.

A nationwide dataset on the mental health of the Bangladeshi population due to the COVID-19 pandemic.

The data presented here concerns the article entitled "Impact of COVID-19 pandemic on mental health among general Bangladeshi population: a cross-sectional study" [1]. This article represents a unique dataset on the mental health status among the Bangladeshi population during the early stage of the COVID-19 pandemic. We collected the data from April 15 to May 10, 2020, when the government of Bangladesh imposed lockdown and quarantine approaches. Total 672 (381 men, 291 women) responses were collected using Google survey tools (Google Forms) from the Bangladeshi population aged between 15-65 years. We obtained electronic consent from all participants to participate in this study and publish their anonymous data. We assessed people's sociodemographic profiles and different psychometric measures in this study. We used UCLA-8, PHQ-9, GAD-7, and PSQI scales to assess loneliness, depression, anxiety, and sleep disturbance, respectively. This article provides a descriptive analysis of variables along with socioeconomic factors. The presented dataset gives a platform for future research for psychometric assessments of subjects using the above scales. We collected the survey data from the whole country regardless of socioeconomic factors. Therefore, policymakers of government and non-government organizations can use the data to develop different programs to promote the mental health of the Bangladeshi population.

Effect of COVID-19 pandemic on mental health among Bangladeshi healthcare professionals: A cross-sectional study.

The COVID-19 has been spreading across the world since December 2019. The pandemic has created tremendous fear of death from infection and awful psychological pressure on healthcare professionals (HCPs). The measures of psychological effects of the COVID-19 outbreak on the Bangladeshi HCPs are unknown. The present study aimed to assess the mental health outcomes of Bangladeshi HCPs and associated risk factors. We conducted this cross-sectional study from July 15 to September 20, 2020. A total of 355 HCPs aged between 20 and 60 years residing in Bangladesh participated in this study. All the participants completed a self-administered questionnaire through Google Forms consisting of socio-demographic characteristics and mental health outcomes. We measure loneliness, depression, anxiety, and sleep disturbance using the UCLA loneliness scale-8, patient health questionnaire-9, 7-item generalized anxiety disorder scale, Pittsburgh sleep quality index. The present study observed the prevalence of loneliness, depression, anxiety, and sleep disturbance among HCPs were 89%, 44%, 78%, and 87%, respectively. The factors significantly associated with the development of mental health problems among HCPs were working environment, economic condition, education level, area of residence, marital status, gender differences, professional category, body mass index, and smoking habit. Moreover, we have seen significant correlations among the different mental health outcomes. In Bangladesh, a large portion of HCPs reported mental health issues during the COVID-19 pandemic. COVID-19 pandemic incredibly impacted the psychological health of Bangladeshi healthcare professionals. Appropriate supportive programs and interventional initiatives might help the HCPs with mental health problems during and after this pandemic.

Impact of COVID-19 pandemic on mental health among general Bangladeshi population: a cross-sectional study.

OBJECTIVES: Mental health problems significantly increased worldwide during the coronavirus (COVID-19) pandemic. At the early stage of the outbreak, the government of Bangladesh imposed lockdown and quarantine approaches to prevent the spread of the virus, which impacted people's daily life and health. The COVID-19 pandemic has also affected people's economic status, healthcare facilities and other lifestyle factors in Bangladesh. We aimed to assess the impact of the COVID-19 pandemic on mental health among the Bangladeshi population. METHODS: We conducted an online cross-sectional survey among 672 Bangladeshi people aged between 15 and 65 years all over the country from 15 April to 10 May 2020. After obtaining electronic consent, we conducted a survey assessing people's sociodemographic profiles and psychometric measures. We used The University of California, Los Angeles (UCLA) Loneliness Scale-8, Patient Health Questionnaire-9, Generalized Anxiety Disorder 7-Item Scale and Pittsburgh Sleep Quality Index to assess loneliness, depression, anxiety and sleep disturbance, respectively. RESULTS: The prevalence of loneliness, depression, anxiety and sleep disturbance was estimated at 71% (mild: 32%, moderate: 29%, severe: 10%), 38% (mild: 24%, moderate: 11%, severe: 3%), 64% (mild: 30%, moderate: 17%, severe: 17%) and 73% (mild: 50%, moderate: 18%, severe: 5%), respectively. In Bangladesh, the key factors associated with poor mental health during COVID-19 were female sex, unemployment, being a student, obesity and living without a family. The present study also identified statistically significant interrelationships among the measured mental health issues. CONCLUSIONS: A large portion of respondents reported mental health problems during the COVID-19 pandemic in Bangladesh. The present study suggests longitudinal assessments of mental health among Bangladeshi people to determine the gravity of this issue during and after the pandemic. Appropriate supportive programmes and interventional approaches would address mental health problems in Bangladesh during the COVID-19 pandemic.