A mentored hands-on training model for scaling up implementation and intervention research in India: "connecting the dots".

Despite the high burden of mental disorders in low- and middle-income countries (LMICs), less than 25% of those in need have access to appropriate services, in part due to a scarcity of locally relevant, evidence-based interventions and models of care. To address this gap, researchers from India and the United States and the Indian Council of Medical Research (ICMR) collaboratively developed a "Grantathon" model to provide mentored research training to 24 new principal investigators (PIs). This included a week-long didactic training, a customized web-based data entry/analysis system and a National Coordination Unit (NCU) to support PIs and track process objectives. Outcome objectives were assessed via scholarly output including publications, awards received and subsequent grants that were leveraged. Multiple mentorship strategies including collaborative problem-solving approaches were used to foster single-centre and multicentre research. Flexible, approachable and engaged support from mentors helped PIs overcome research barriers, and the NCU addressed local policy and day-to-day challenges through informal monthly review meetings. Bi-annual formal review presentations by all PIs continued through the COVID-19 pandemic, enabling interim results reporting and scientific review, also serving to reinforce accountability. To date, more than 33 publications, 47 scientific presentations, 12 awards, two measurement tools, five intervention manuals and eight research grants have been generated in an open-access environment. The Grantathon is a successful model for building research capacity and improving mental health research in India that could be adopted for use in other LMICs.

A comprehensive overview of juvenile idiopathic arthritis: From pathophysiology to management.

Rheumatoid Arthritis (RA) is a progressive autoimmune disease. It is among the most widespread chronic illnesses in children, with an annual incidence of 1.6 to 23 new instances per 100,000 adolescents. About 1 child in every 1000 develops Juvenile Idiopathic Arthritis (JIA) type of chronic arthritis. The cause of JIA is not well known but what known is that it involves inflammation of the synovium and destruction of tissues in joints which can cause early-onset of oligo articular JIA. It is challenging to diagnose the condition in some children who initially complain of pain and joint swelling as there is no blood test discovered that can confirm the diagnoses of JIA. As JIA patients are immunosuppressed due to the use of drugs, making them vulnerable to catch infections like COVID-19 which can lead to cardiovascular diseases having high rate of morbidity and mortality. The comorbidity like Diabetes has higher incidence in these patients resulting in synergistic effect on inflammation. Currently, the connection of genetics in JIA provides evidence that HLA Class I and II alleles have a role in the pathophysiology of various subtypes of JIA which includes inflammation in the axial skeletal. The primary objective of therapy in juvenile idiopathic arthritis is the suppression of clinical symptoms. The pharmacological approach includes use of medications like DMARDs, NSAIDs etc. and non-pharmacological approach includes physiotherapy, which helps in restoring normal joint function and herbs as adjuvants which has the benefit of no side effects.

Low-Dose COVID-19 CT Image Denoising Using CNN and its Method Noise Thresholding.

Noise in computed tomography (CT) images may occur due to low radiation dose. Hence, the main aim of this paper is to reduce the noise from low dose CT images so that the risk of high radiation dose can be reduced. BACKGROUND: The novel corona virus outbreak has ushered in different new areas of research in medical instrumentation and technology. Medical diagnostics and imaging are one of the ways in which the area and level of infection can be detected. OBJECTIVE: The COVID-19 attacks people who have less immunity, so infants, kids, and pregnant women are more vulnerable to the infection. So they need to undergo CT scanning to find the infection level. But the high radiation diagnostic is also fatal for them, so the intensity of radiation needs to be reduced significantly, which may generate the noise in the CT images. METHOD: In this paper, a new denoising technique for such low dose Covid-19 CT images has been introduced using a convolution neural network (CNN) and the method noise-based thresholding. The major concern of the methodology for reducing the risk associated with radiation while diagnosing. RESULTS: The results are evaluated visually and also by using standard performance metrics. From comparative analysis, it was observed that proposed works gives better outcomes. CONCLUSIONS: The proposed low-dose COVID-19 CT image denoising model is therefore concluded to have a better potential to be effective in various pragmatic medical image processing applications in terms of noise suppression and clinical edge preservation.

Evaluation of environmental Mucorales contamination in and around the residence of COVID-19-associated mucormycosis patients.

Introduction: Recently, India witnessed an unprecedented surge of coronavirus disease 2019 (COVID-19)-associated mucormycosis (CAM) cases. In addition to patient management issues, environmental Mucorales contamination possibly contributed to the outbreak. A recent study evaluated environment contamination by Mucorales in the hospital setting. However, a considerable number of CAM patients were never admitted to a hospital before the development of the disease. The present study, therefore, planned to evaluate Mucorales contamination of patients' residences. Methods: The residential environment of 25 patients with CAM living in north India was surveyed. Air samples were collected from indoor and immediate outdoor vicinity of the patients' residence and cultured on Dichloran Rose-Bengal Chloramphenicol (DRBC) agar with benomyl for selective isolation of Mucorales. Surface swab samples were also collected from the air coolers fitted in those residences and cultured on DRBC agar. The isolates were identified by phenotypic and genotypic methods. Amplified fragment length polymorphism (AFLP) was employed to evaluate the genetic relatedness of the environmental and patients' clinical isolates. Results: The median spore count (mean ± SD, cfu/m3) of Mucorales in the air of patients' bedrooms was significantly higher than in the air in other rooms in those residences (3.55 versus 1.5, p = 0.003) or the air collected directly from the front of the air cooler (p < 0.0001). The Mucorales spore count in the environment did not correlate with either ventilation of the room or hygiene level of the patients' residences. Rhizopus arrhizus was isolated from the environment of all patients' residences (n = 25); other Mucorales species isolated were Cunninghamella bertholletiae (n = 14), Rhizopus microsporus (n = 6), Rhizopus delemar (n = 6), Syncephalastrum racemosum (n = 1), Lichtheimia corymbifera (n = 1), and Mucor racemosus (n = 1). Genetic relatedness was observed between 11 environmental isolates from the patients' bedrooms and respective clinical isolates from patients. Discussion: The study supported the view that the patients might have acquired Mucorales from the home environment during the post-COVID-19 convalescence period. Universal masking at home during patients' convalescence period and environmental decontamination could minimize exposure in those susceptible patients.

Efficacy of Bacillus Calmette-Guérin (BCG) Vaccination in Reducing the Incidence and Severity of COVID-19 in High-Risk Population (BRIC): a Phase III, Multi-centre, Quadruple-Blind Randomised Control Trial.

INTRODUCTION: Universal coverage of vaccines alone cannot be relied upon to protect at-risk populations in lower- and middle-income countries against the impact of the coronavirus disease 2019 (COVID-19) pandemic and newer variants. Live vaccines, including Bacillus Calmette-Guérin (BCG), are being studied for their effectiveness in reducing the incidence and severity of COVID-19 infection. METHODS: In this multi-centre quadruple-blind, parallel assignment randomised control trial, 495 high-risk group adults (aged 18-60 years) were randomised into BCG and placebo arms and followed up for 9 months from the date of vaccination. The primary outcome was the difference in the incidence of COVID-19 infection at the end of 9 months. Secondary outcomes included the difference in the incidence of severe COVID-19 infections, hospitalisation rates, intensive care unit stay, oxygen requirement and mortality at the end of 9 months. The primary analysis was done on an intention-to-treat basis, while safety analysis was done per protocol. RESULTS: There was no significant difference in the incidence rates of cartridge-based nucleic acid amplification test (CB-NAAT) positive COVID-19 infection [odds ratio (OR) 1.08, 95% confidence interval (CI) 0.54-2.14] in the two groups, but the BCG arm showed a statistically significant decrease in clinically diagnosed (symptomatic) probable COVID-19 infections (OR 0.38, 95% CI 0.20-0.72). Compared with the BCG arm, significantly more patients developed severe COVID-19 pneumonia (CB-NAAT positive) and required hospitalisation and oxygen in the placebo arm (six versus none; p = 0.03). One patient belonging to the placebo arm required intensive care unit (ICU) stay and died. BCG had a protective efficacy of 62% (95% CI 28-80%) for likely symptomatic COVID-19 infection. CONCLUSIONS: BCG is protective in reducing the incidence of acute respiratory illness (probable symptomatic COVID-19 infection) and severity of the disease, including hospitalisation, in patients belonging to the high-risk group of COVID-19 infection, and the antibody response persists for quite a long time. A multi-centre study with a larger sample size will help to confirm the findings in this study. CLINICAL TRIALS REGISTRY: Clinical Trials Registry India (CTRI/2020/07/026668).

The Bacillus Calmette­Guérin (BCG) vaccine has been studied previously in several settings, including reducing childhood mortalities due to viral infections and induction of trained immunity and reducing upper respiratory tract infections and pneumonia in older adults. This multi-centre trial has tried to evaluate the efficacy of BCG revaccination in reducing the incidence and severity of COVID-19 infections in adults between 18 and 60 years of age belonging to the high-risk group owing to the presence of comorbidities including diabetes, chronic kidney disease, chronic liver disease and chronic lung diseases. A single dose of BCG vaccine produced significantly high titres of BCG antibodies lasting for six months. While there was no significant reduction in the incidence of COVID-19 infection, there was an 8.4% reduction in the incidence of symptomatic COVID-19 disease at the end of 9 months of follow-up. In addition, there were significantly fewer severe COVID-19 infections requiring hospital stay and oxygen support. However, the overall numbers of severe COVID-19 infections were low. Thus, the study shows that BCG can protect against symptomatic and severe COVID-19 disease. However, it might not reduce the incidence of new infections. The study results are significant for low- and middle-income countries without adequate coverage of primary doses of COVID-19 vaccination, let alone the booster doses. Future studies should evaluate the BCG vaccine's efficacy as a booster compared with routine COVID-19 vaccine boosters.

Metformin: Activation of 5' AMP-activated protein kinase and its emerging potential beyond anti-hyperglycemic action.

Metformin is a plant-based drug belonging to the class of biguanides and is known to treat type-2 diabetes mellitus (T2DM). The drug, combined with controlling blood glucose levels, improves the body's response to insulin. In addition, trials have identified the cardioprotective potential of metformin in the diabetic population receiving the drug. Activation of 5' AMP-activated protein kinase (AMPK) is the major pathway for these potential beneficial effects of metformin. Historically, much emphasis has been placed on the potential indications of metformin beyond its anti-diabetic use. This review aims to appraise other potential uses of metformin primarily mediated by the activation of AMPK. We also discuss various mechanisms, other than AMPK activation, by which metformin could produce beneficial effects for different conditions. Databases including PubMed/MEDLINE and Embase were searched for literature relevant to the review's objective. Reports from both research and review articles were considered. We found that metformin has diverse effects on the human body systems. It has been shown to exert anti-inflammatory, antioxidant, cardioprotective, metabolic, neuroprotective, anti-cancer, and antimicrobial effects and has now even been identified as effective against SARS-CoV-2. Above all, the AMPK pathway has been recognized as responsible for metformin's efficiency and effectiveness. Owing to its extensive potential, it has the capability to become a part of treatment regimens for diseases apart from T2DM.

Evaluation of environmental Mucorales contamination in and around the residence of COVID-19-associated mucormycosis patients

Introduction Recently, India witnessed an unprecedented surge of coronavirus disease 2019 (COVID-19)-associated mucormycosis (CAM) cases. In addition to patient management issues, environmental Mucorales contamination possibly contributed to the outbreak. A recent study evaluated environment contamination by Mucorales in the hospital setting. However, a considerable number of CAM patients were never admitted to a hospital before the development of the disease. The present study, therefore, planned to evaluate Mucorales contamination of patients’ residences. Methods The residential environment of 25 patients with CAM living in north India was surveyed. Air samples were collected from indoor and immediate outdoor vicinity of the patients’ residence and cultured on Dichloran Rose–Bengal Chloramphenicol (DRBC) agar with benomyl for selective isolation of Mucorales. Surface swab samples were also collected from the air coolers fitted in those residences and cultured on DRBC agar. The isolates were identified by phenotypic and genotypic methods. Amplified fragment length polymorphism (AFLP) was employed to evaluate the genetic relatedness of the environmental and patients’ clinical isolates. Results The median spore count (mean ± SD, cfu/m3) of Mucorales in the air of patients’ bedrooms was significantly higher than in the air in other rooms in those residences (3.55 versus 1.5, p = 0.003) or the air collected directly from the front of the air cooler (p < 0.0001). The Mucorales spore count in the environment did not correlate with either ventilation of the room or hygiene level of the patients’ residences. Rhizopus arrhizus was isolated from the environment of all patients’ residences (n = 25);other Mucorales species isolated were Cunninghamella bertholletiae (n = 14), Rhizopus microsporus (n = 6), Rhizopus delemar (n = 6), Syncephalastrum racemosum (n = 1), Lichtheimia corymbifera (n = 1), and Mucor racemosus (n = 1). Genetic relatedness was observed between 11 environmental isolates from the patients’ bedrooms and respective clinical isolates from patients. Discussion The study supported the view that the patients might have acquired Mucorales from the home environment during the post-COVID-19 convalescence period. Universal masking at home during patients’ convalescence period and environmental decontamination could minimize exposure in those susceptible patients.

Comparison of safety and efficacy of convalescent plasma with fresh frozen plasma in severe covid-19 patients.

BACKGROUND: Role of Convalescent plasma (COPLA) to treat severe COVID-19 is under investigation. We compared efficacy and safety of COPLA with fresh frozen plasma (FFP) in severe COVID-19 patients. METHODS: One group received COPLA with standard medical care (n = 14), and another group received random donor FFP, as control with standard medical care (n = 15) in severe COVID-19 disease. RESULTS: The proportion of patients free of ventilation at day seven were 78.5% in COPLA group, and 93.3 % in control group were not significant (p= 0.258). However, improved respiratory rate, O2 saturation, SOFA score, and Ct value were observed in the COPLA group. No serious adverse events were noticed by plasma transfusion in both groups.

Development of a multiomics model for identification of predictive biomarkers for COVID-19 severity: a retrospective cohort study.

BACKGROUND: COVID-19 is a multi-system disorder with high variability in clinical outcomes among patients who are admitted to hospital. Although some cytokines such as interleukin (IL)-6 are believed to be associated with severity, there are no early biomarkers that can reliably predict patients who are more likely to have adverse outcomes. Thus, it is crucial to discover predictive markers of serious complications. METHODS: In this retrospective cohort study, we analysed samples from 455 participants with COVID-19 who had had a positive SARS-CoV-2 RT-PCR result between April 14, 2020, and Dec 1, 2020 and who had visited one of three Mayo Clinic sites in the USA (Minnesota, Arizona, or Florida) in the same period. These participants were assigned to three subgroups depending on disease severity as defined by the WHO ordinal scale of clinical improvement (outpatient, severe, or critical). Our control cohort comprised of 182 anonymised age-matched and sex-matched plasma samples that were available from the Mayo Clinic Biorepository and banked before the COVID-19 pandemic. We did a deep profiling of circulatory cytokines and other proteins, lipids, and metabolites from both cohorts. Most patient samples were collected before, or around the time of, hospital admission, representing ideal samples for predictive biomarker discovery. We used proximity extension assays to quantify cytokines and circulatory proteins and tandem mass spectrometry to measure lipids and metabolites. Biomarker discovery was done by applying an AutoGluon-tabular classifier to a multiomics dataset, producing a stacked ensemble of cutting-edge machine learning algorithms. Global proteomics and glycoproteomics on a subset of patient samples with matched pre-COVID-19 plasma samples was also done. FINDINGS: We quantified 1463 cytokines and circulatory proteins, along with 902 lipids and 1018 metabolites. By developing a machine-learning-based prediction model, a set of 102 biomarkers, which predicted severe and clinical COVID-19 outcomes better than the traditional set of cytokines, were discovered. These predictive biomarkers included several novel cytokines and other proteins, lipids, and metabolites. For example, altered amounts of C-type lectin domain family 6 member A (CLEC6A), ether phosphatidylethanolamine (P-18:1/18:1), and 2-hydroxydecanoate, as reported here, have not previously been associated with severity in COVID-19. Patient samples with matched pre-COVID-19 plasma samples showed similar trends in muti-omics signatures along with differences in glycoproteomics profile. INTERPRETATION: A multiomic molecular signature in the plasma of patients with COVID-19 before being admitted to hospital can be exploited to predict a more severe course of disease. Machine learning approaches can be applied to highly complex and multidimensional profiling data to reveal novel signatures of clinical use. The absence of validation in an independent cohort remains a major limitation of the study. FUNDING: Eric and Wendy Schmidt.

Th1/Th2/Th17 Cytokine Profile among Different Stages of COVID-19 Infection.

Aim: To characterize Th1/Th2/Th17 cytokine profile (IL-2, IL-4, IL-6, IL-10, TNF-α, IFN-γ, and IL-17A) among different stages of COVID-19 infection. Methods: This was a cross-sectional study which included six healthy individuals and 68 patients who were admitted with COVID-19 in the Department of Medicine, at All India Institute of Medical Sciences, New Delhi, from July 2020 to September 2020. Patients were categorized into mild, moderate, and severe COVID-19 groups, and serum samples were drawn for the measurement of Th1/Th2/Th17 cytokines (IL-4, IL-6, IL-10, TNF-α, IFN-γ, and IL-17A) which was done by BD™ Cytometric Bead Array. Results: All the cytokines showed dynamic expression in the COVID-19 group, of which only IL-6 was statistically significant. Among the three severity groups of COVID-19, increased severity did not transform into increased cytokine level, with the exception for IL-6, which was statistically significant. Conclusions: In our small sample study, six cytokines expressions were evaluated however most of them were elevated in COVID-19 patients but were not statistically significant except IL-6.

Machine Learning-Based Fragment Selection Improves the Performance of Qualitative PRM Assays.

Targeted mass spectrometry-based platforms have become a valuable tool for the sensitive and specific detection of protein biomarkers in clinical and research settings. Traditionally, developing a targeted assay for peptide quantification has involved manually preselecting several fragment ions and establishing a limit of detection (LOD) and a lower limit of quantitation (LLOQ) for confident detection of the target. Established thresholds such as LOD and LLOQ, however, inherently sacrifice sensitivity to afford specificity. Here, we demonstrate that machine learning can be applied to qualitative PRM assays to discriminate positive from negative samples more effectively than a traditional approach utilizing conventional methods. To demonstrate the utility of this method, we trained an ensemble machine learning model using 282 SARS-CoV-2 positive and 994 SARS-CoV-2 negative nasopharyngeal swabs (NP swab) analyzed using a targeted PRM method. This model was then validated using an independent set of 200 positive and 150 negative samples and achieved a sensitivity of 92% relative to results obtained by RT-PCR, which was superior to a traditional approach that resulted in 86.5% sensitivity when analyzing the same data. These results demonstrate that machine learning can be applied to qualitative PRM assays and results in superior performance relative to traditional methods.

Omicron sub-variants: is the world going to witness another wave?

Omicron, the new 'Variant of Concern' of SARS-CoV-2, is rapidly evolving into new sub-variants or sub-lineages (BA.1, BA.2 etc.). These sub-variants have higher transmissibility, decreased vaccine effectiveness and increased risk of reinfection. As a result, many nations across the globe are reporting surge in infections which is a matter of concern. Understanding Omicron and its sub-variants is vital for development of public health policy and preventing disease transmission. The present paper throws a spotlight on the newly detected sub-variants of Omicron as reported in ongoing researches which are available only in pre-print form and also the importance of a booster dose of the vaccine. Information regarding recent research on a new nasal vaccine formulation, which may be effective against the new variants, is also highlighted in the paper.

Impact of myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) on the quality of life of people with ME/CFS and their partners and family members: an online cross-sectional survey

ObjectivesThe aim of this study was to assess the impact of myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) on the quality of life (QoL) of people with ME/CFS and their relative or partner (family member).DesignA patient-partner, multinational, subject-initiated, cross-sectional online survey.SettingInternational survey using ME/CFS charities, support groups and social media.ParticipantsParticipants were self-selected with recruitment via social media. Inclusion criteria were aged 18 years or over and reported diagnosis of ME/CFS by health professional. 1418 people with ME/CFS and their 1418 family members from 30 countries participated in the survey. Participants with ME/CFS had a mean age of 45.8 years (range 18–81) and were predominantly women (1214 (85.6%) of 1418). Family members had a mean age of 51.9 years (range 18–87) and were predominantly men (women: 504 (35.5%) of 1418). 991 (70%) family members were partners of the people with ME/CFS.InterventionsEuroQoL-5 Dimension (EQ-5D-3L), completed by people with ME/CFS, and Family Reported Outcome Measure (FROM-16) questionnaire, completed by family members.ResultsThe mean overall health status on a Visual Analogue Scale for people with ME/CFS was 33.8 (0=worst, 100=best). People with ME/CFS were most affected by ability to perform usual activities, pain, mobility, self-care and least impacted by anxiety. For family members, the overall mean FROM-16 score was 17.9 (0=no impact, 32=worst impact), demonstrating a major impact on QoL. Impact on QoL was significantly correlated between the person with ME/CFS and their family member (p<0.0001). Family members were most impacted emotionally by worry, frustration and sadness and personally by family activities, holidays, sex life and finances.ConclusionsTo the best of our knowledge, this is the largest study on the impact of the QoL of persons with ME/CFS and their family members. While open participation surveys are limited by selection bias, this research has revealed a significant worldwide burden of ME/CFS on the QoL of people with ME/CFS and their family members.