Dynamic variations in mitochondrial shape have been related to function. However, tools to automatically classify and enumerate mitochondrial shapes are lacking, as are systematic studies exploring the relationship of such shapes to mitochondrial stress. Here we show that during increased generation of mitochondrial reactive oxygen species (mtROS), mitochondria change their shape from tubular to donut or blob forms, which can be computationally quantified. Imaging of cells treated with rotenone or antimycin, showed time and dose-dependent conversion of tubular forms to donut-shaped mitochondria followed by appearance of blob forms. Time-lapse images showed reversible transitions from tubular to donut shapes and unidirectional transitions between donut and blob shapes. Blobs were the predominant sources of mtROS and appeared to be related to mitochondrial-calcium influx. Mitochondrial shape change could be prevented by either pretreatment with antioxidants like N-acetyl cysteine or inhibition of the mitochondrial calcium uniporter. This work represents a novel approach towards relating mitochondrial shape to function, through integration of cellular markers and a novel shape classification algorithm.

Dengue virus, a mosquito-borne flavivirus, is a causative agent for dengue infection, which manifests with symptoms ranging from mild fever to fatal dengue shock syndrome. The presence of four serotypes, against which immune cross-protection is short-lived and serotype cross-reactive antibodies that might enhance infection, pose a challenge to further investigate the role of virus and immune response in pathogenesis. We evaluated the viral and immunological factors that correlate with severe dengue disease in a cohort of pediatric dengue patients in New Delhi. Severe dengue disease was observed in both primary and secondary infections. Viral load had no association with disease severity but high viral load correlated with prolonged thrombocytopenia and delayed recovery. Severe dengue cases had low Th1 cytokines and a concurrent increase in the inflammatory mediators such as IL-6, IL-8 and IL-10. A transient increase in CD14⁺CD16⁺ intermediate monocytes was observed early in infection. Sorting of monocytes from dengue patient peripheral blood mononuclear cells revealed that it is the CD14+ cells, but not the CD16+ or the T or B cells, that were infected with dengue virus and were major producers of IL-10. Using the Boruta algorithm, reduced interferon-α levels and enhanced aforementioned pro-inflammatory cytokines were identified as some of the distinctive markers of severe dengue. Furthermore, the reduction in the levels of IL-8 and IL-10 were identified as the most significant markers of recovery from severe disease. Our results provide further insights into the immune response of children to primary and secondary dengue infection and help us to understand the complex interplay between the intrinsic factors in dengue pathogenesis. Dengue virus is a human pathogen that causes dengue fever, which can either resolve after mild fever or lead to severe dengue hemorrhagic fever/dengue shock syndrome. The role of dengue virus levels in the blood and the kinetics of infection and immune response that results in severe dengue disease in humans is not well characterized. In this study, we analyzed 97 children with varying degrees of dengue disease, and we show that the dengue virus quantity in blood does not show any significant association with severe disease. However, most severe dengue patients had lower levels of interferons and Th1 cytokines and increased levels of secreted factors such as IL-6, IL-8 and IL-10 that could potentially cause leakage in blood capillaries. Our results indicate that monocytes, which are infected with dengue virus in patients, could possibly play a major role in dengue pathogenesis. Furthermore, using computational analysis we identified association of some of the secreted factors with severe disease and also predicted the markers that could serve as indicators of recovery from severe dengue.

As of August 6th, 2021, the World Health Organization has notified 200.8 million laboratory-confirmed infections and 4.26 million deaths from COVID-19, making it the worst pandemic since the 1918 flu. The main challenges in mitigating COVID-19 are effective vaccination, treatment, and agile containment strategies. In this review, we focus on the potential of Artificial Intelligence (AI) in COVID-19 surveillance, diagnosis, outcome prediction, drug discovery and vaccine development. With the help of big data, AI tries to mimic the cognitive capabilities of a human brain, such as problem-solving and learning abilities. Machine Learning (ML), a subset of AI, holds special promise for solving problems based on experiences gained from the curated data. Advances in AI methods have created an unprecedented opportunity for building agile surveillance systems using the deluge of real-time data generated within a short span of time. During the COVID-19 pandemic, many reports have discussed the utility of AI approaches in prioritization, delivery, surveillance, and supply chain of drugs, vaccines, and non-pharmaceutical interventions. This review will discuss the clinical utility of AI-based models and will also discuss limitations and challenges faced by AI systems, such as model generalizability, explainability, and trust as pillars for real-life deployment in healthcare.

India has one of the highest disease burdens in the world. A better understanding of what ails India will help policy makers plan appropriate health-care services and infrastructure development, design medical education curricula, and identify health research priorities that are relevant to the needs of the country. The POSEIDON study aimed to record the prevalence of symptoms and medical conditions for which patients visit a primary health-care practitioner in India. We randomly selected 12 000 general practitioners, general physicians, and paediatricians from 880 cities and towns and invited them to record demographic details, symptoms, and medical conditions for every patient they saw on Feb 1, 2011. A further 1225 practitioners volunteered to participate and their responses were included. We did simple descriptive analyses of prevalence rates and used χ(2) tests to study comorbid associations. Through application of systems biology methods, we visualised inter-relations between organ involvement of diseases and symptoms and deciphered how these associations change with age and gender. We included responses from 7400 health-care practitioners, which represented data for 204 912 patients, who presented with 554 146 reasons for visit. Fever (35·5%) was the most common presenting symptom. More than half of all patients presented with respiratory symptoms across all age groups and regions of India. Other common presentations were digestive system symptoms (25%), circulatory symptoms (12·5%), skin complaints (9%), and endocrine disorders (6·6%). Hypertension (14·52%), obstructive airways diseases (14·51%), and upper respiratory tract infections (12·9%) were the most common diagnoses reported. Of note was that 21·4% of all patients with hypertension reported by the primary health-care practitioners were younger than 40 years. Anaemia was the fourth most common disease reported by these health-care practitioners and was most common in women of menstrual age living outside metro cities. The POSEIDON study provides insight into the reasons that patients visit primary health-care practitioners in India; our results highlight important social and medical challenges in the developing world. Chest Research Foundation, Council of Scientific and Industrial Research-Institute of Genomics and Integrated Biology (CSIR-IGIB), and Cipla Ltd.