Bacterial and viral etiology of acute respiratory infection among the Forcibly Displaced Myanmar Nationals (FDMNs) in fragile settings in Cox's Bazar- a prospective case-control study.

The leading infectious cause of death in children worldwide is lower acute respiratory infection (LARI), particularly pneumonia. We enrolled a total of 538 acute respiratory infection (ARI) cases according to WHO criteria and age-sex matched 514 controls in the Forcibly Displaced Myanmar National (FDMN) refugee camps in Cox's Bazar, Bangladesh, between June 2018 and March 2020 to investigate the role of bacteria, viruses, and their co-infection patterns and observe Streptococcus pneumoniae (S. pneumoniae) serotype distribution. According to the etiological findings, children ≤5 years of age have a higher bacterial positivity (90%) and viral positivity (34%) in nasopharyngeal samples (NPS) compared to those >5 years of age, in both ARI cases as well as for the control group. Among the bacteria, S. pneumoniae was predominant in both cases and controls (85% and 88%). Adenovirus (ADV)(34), influenza virus A and B (IFV-A, B)(32,23), and respiratory syncytial virus (RSV)(26) were detected as the highest number among the viruses tested for the ARI cases. The total number of viruses was also found higher in ≤5 years of age group. Within this group, positive correlation was observed between bacteria and viruses but negative correlation was observed between bacteria. Both single and co-infection for viruses were found higher in the case group than the control group. However, co-infection was significantly high for Streptococcus aureus (S. aureus) and Haemophilus influenzae b (H. influenza b) (p<0.05). Additionally, semi-quantitative bacterial and viral load was found higher for the ARI cases over control considering Cycle threshold (Ct)≤30. Pathogen identification from blood specimens was higher by qRT-PCR than blood culture (16% vs 5%, p<0.05). In the S. pneumoniae serotype distribution, the predominant serotypes in ARI cases were 23F, 19A, 16F, 35B, 15A, 20 and 10F, while 11A, 10A, 34, 35A and 13 serotypes were predominant in the control group. Pathogen correlation analysis showed RSV positively correlated with human metapneumovirus (HMPV), S. aureus and H. influenza b while S. pneumoniae was negatively correlated with other pathogens in ≤5 years age group of ARI cases. However, in >5 years age group, S. aureus and H. influenza b were positively correlated with IFVs, and S. pneumoniae was positively correlated with HMPV and ADV. Logistic regression data for viruses suggested among the respondents in cases were about 4 times more likely to be RSV positive than the control. Serotype distribution showed 30% for PCV10 serotypes, 41% for PCV13 and 59% for other serotypes. Also, among the 40 serotypes of S. pneumoniae tested, the serotypes 22F, Sg24, 9V, 38, 8, and 1 showed strong positive correlation with viruses in the case group whereas in the control group, it was predominant for serotypes 14, 38, 17F and 39 ARI cases were prevalent mostly in monsoon, post-monsoon, and winter periods, and peaked in September and October. Overall these region-specific etiological data and findings, particularly for crisis settings representing the FDMNs in Cox's Bazar, Bangladesh, is crucial for disease management and disease prevention control as well as immunization strategies more generally in humanitarian crisis settings.

Reduced IFN-γ levels along with changes in hematologic and immunologic parameters are key to COVID-19 severity in Bangladeshi patients.

The manifestation of coronavirus disease 2019 (COVID-19) severity and mortality has been associated with dysregulation of the immune response, often influenced by racial disparities and conferred by changes in hematologic and immunologic parameters. These biological and hematologic parameters as well as cytokine profiles were investigated in a cohort of 61 COVID-19-positive patients (categorized into mild, moderate, and severe groups) from Bangladesh using standard analytical methods. The data reported that the interleukin (IL)-4 and IL-6 levels were significantly increased, whereas the levels of interferon (IFN)-γ were significantly reduced in patients with severe COVID-19 (p < 0.05) compared with those in patients with mild and/or moderate COVID-19. The extent of erythrocyte sedimentation rate (ESR); neutrophil count; and levels of ferritin, C-reactive protein (CRP), and D-dimer (p < 0.05) were found to be significantly increased, whereas the white blood cell (WBC), lymphocyte, eosinophil, and platelet counts (p < 0.05) were observed to be significantly reduced in patients with severe COVID-19 compared with those in the patients in other 2 groups. Our study exhibited a significantly higher IL-6-to-lymphocyte ratio in patients with severe COVID-19 than in those with mild and moderate COVID-19. The calculated neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), lymphocyte-to-monocyte ratio (LMR), and ferritin-to-ESR ratio were significantly increased in patients with severe COVID-19. The increase in the IL-4 and IL-6 levels along with CRP and D-dimer levels may envisage a hyperinflammatory environment and immune dysregulation, which contribute to prolonged viral persistence, leading to severe disease. However, the reduced level of IFN-γ can be attributed to a less fatality toll in Bangladesh compared with that in the rest of the world.

SARS-CoV-2 Seroprevalence before Delta Variant Surge, Chattogram, Bangladesh, March-June 2021.

A March-June 2021 representative serosurvey among Sitakunda subdistrict (Chattogram, Bangladesh) residents found an adjusted prevalence of severe acute respiratory syndrome coronavirus 2 antibodies of 64.1% (95% credible interval 60.0%-68.1%). Before the Delta variant surge, most residents had been infected, although cumulative confirmed coronavirus disease incidence was low.

Empirical Study on Classifiers for Earlier Prediction of COVID-19 Infection Cure and Death Rate in the Indian States.

Machine Learning methods can play a key role in predicting the spread of respiratory infection with the help of predictive analytics. Machine Learning techniques help mine data to better estimate and predict the COVID-19 infection status. A Fine-tuned Ensemble Classification approach for predicting the death and cure rates of patients from infection using Machine Learning techniques has been proposed for different states of India. The proposed classification model is applied to the recent COVID-19 dataset for India, and a performance evaluation of various state-of-the-art classifiers to the proposed model is performed. The classifiers forecasted the patients' infection status in different regions to better plan resources and response care systems. The appropriate classification of the output class based on the extracted input features is essential to achieve accurate results of classifiers. The experimental outcome exhibits that the proposed Hybrid Model reached a maximum F1-score of 94% compared to Ensembles and other classifiers like Support Vector Machine, Decision Trees, and Gaussian Naïve Bayes on a dataset of 5004 instances through 10-fold cross-validation for predicting the right class. The feasibility of automated prediction for COVID-19 infection cure and death rates in the Indian states was demonstrated.

Potential biodegradable face mask to counter environmental impact of Covid-19.

On the eve of the outbreak of the COVID-19 pandemic, there is a tremendous increase in the production of facemasks across the world. The primary raw materials for the manufacturing of the facemasks are non-biodegradable synthetic polymers derived from petrochemicals. Disposal of these synthetic facemasks increases waste-load in the environment causing severe ecological issues for flora and fauna. The synthesis processes of the polymers from the petrochemical by-products were also not eco-friendly, which releases huge greenhouse and harmful gases. Therefore, many research organizations and entrepreneurs realize the need for biodegradable facemasks to render similar performance as the existing non-biodegradable masks. The conventional textile fabrics made of natural fibers like cotton, flax, hemp, etc., can also be used to prepare facemasks with multiple layers in use for general protection. Such natural textile masks can be made anti-microbial by applying various herbal anti-microbial extracts like turmeric, neem, basil, aloe vera, etc. As porosity is the exclusive feature of the masks for arresting tiny viruses, the filter of the masks should have a pore size in the nanometre scale, and that can be achieved in nanomembrane manufactured by electrospinning technology. This article reviews the various scopes of electrospinning technology for the preparation of nanomembrane biomasks. Besides protecting us from the virus, the biomasks can be useful for skin healing, skincare, auto-fragrance, and organized cooling which are also discussed in this review article.

Clinical characteristics and short term outcomes after recovery from COVID-19 in patients with and without diabetes in Bangladesh.

BACKGROUND AND AIMS: This study investigated the clinical manifestations, outcomes and long-term complications of COVID-19 inpatients in southern part of Bangladesh while emphasizing on individuals having diabetes. METHODS: A cross-sectional study was conducted for a sample of COVID-19 inpatients across four different hospitals of Bangladesh between April 1and June 30, 2020. Variation in clinical characteristics, contact history, comorbidities, treatment patterns, and immediate post COVID complications were investigated. RESULTS: There were 734 COVID-19 presentations in this study of which 19.8% of patients had diabetes and 76% of the COVID-19 patients were male. Among biochemical parameters, plasma glucose, D-dimer, and Troponin-I levels were significantly elevated amidst the cohort with diabetes. The frequency of patients requiring insulin increased threefold during infection with SARS CoV-2. 1.4% patients developed new onset of diabetes mellitus. A number of COVID-19 patients with diabetes have been suffering from complications post-recovery including pain, discomfort, and sleep disturbance. CONCLUSION: Individuals with diabetes have experienced a severe manifestation of COVID-19 and post disease complications. Further in-depth studies focused on larger sample sizes are entailed to assess the relationships elaborately.

Pneumothorax In Covid-19 Pneumonia: A case series.

BACKGROUND: Coronavirus disease 2019 (Covid-19) is an infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). It mainly affects the lungs and common symptoms are fever, cough and shortness of breath. Pneumothorax has been noted to complicate Covid-19 cases requiring hospital admission, however the exact incidence and risk factors are still unknown. DISCUSSION: We present a series of 3 cases of primary spontaneous pneumothorax with Covid-19 pneumonia. All cases in our series did not require positive pressure ventilation and none had any pre-existing lung disease. All were never smokers and had favourable outcomes despite having severe Covid-19 with a pneumothorax during the course of the disease. In our literature review we discuss several plausible mechanisms and risk factors resulting in a pneumothorax with Covid-19. CONCLUSION: Our cases are a reminder that an acute deterioration with hypoxia in a Covid-19 patient could indicate a pneumothorax. Pneumothorax is one of the reported complications in Covid-19 and clinician vigilance is required during assessment of patients, as both share the common symptom of breathlessness and therefore can mimic each other.