Ten-year trends in clinical characteristics and outcome of children hospitalized with severe wasting or nutritional edema in Malawi (2011-2021): Declining admissions but worsened clinical profiles.

Background: Severe acute malnutrition (SAM) constitutes a substantial burden in African hospitals. Despite adhering to international guidelines, high inpatient mortality rates persist and the underlying contributing factors remain poorly understood.

Objective: We evaluated the 10-year trend (2011-2021) in clinical factors and outcomes among children with severe wasting and/or nutritional edema at Malawi's largest nutritional rehabilitation unit (NRU).

Spatially resolved single-cell atlas unveils a distinct cellular signature of fatal lung COVID-19 in a Malawian population.

Postmortem single-cell studies have transformed understanding of lower respiratory tract diseases (LRTDs), including coronavirus disease 2019 (COVID-19), but there are minimal data from African settings where HIV, malaria and other environmental exposures may affect disease pathobiology and treatment targets. In this study, we used histology and high-dimensional imaging to characterize fatal lung disease in Malawian adults with (n = 9) and without (n = 7) COVID-19, and we generated single-cell transcriptomics data from lung, blood and nasal cells. Data integration with other cohorts showed a conserved COVID-19 histopathological signature, driven by contrasting immune and inflammatory mechanisms: in US, European and Asian cohorts, by type I/III interferon (IFN) responses, particularly in blood-derived monocytes, and in the Malawian cohort, by response to IFN-γ in lung-resident macrophages.

Development of machine learning models predicting mortality using routinely collected observational health data from 0-59 months old children admitted to an intensive care unit in Bangladesh: critical role of biochemistry and haematology data.

Introduction: Treatment in the intensive care unit (ICU) generates complex data where machine learning (ML) modelling could be beneficial. Using routine hospital data, we evaluated the ability of multiple ML models to predict inpatient mortality in a paediatric population in a low/middle-income country.

Method: We retrospectively analysed hospital record data from 0-59 months old children admitted to the ICU of Dhaka hospital of International Centre for Diarrhoeal Disease Research, Bangladesh.