Biology subjects

Asare, C.

Publications and source records attributed to Asare, C..

3 recordsLinked to original sources

bioRxiv2025

Development of the Early Childhood Duodenum across Ancestry, Geography and Environment

During early childhood, the proximal small intestinal mucosa plays a central role in growth, metabolism, immune priming, and neuronal development. Yet the cellular architecture and environmental responsiveness of the human small intestinal mucosa during this period remain poorly defined. Here, we generate a comprehensive cellular and spatial map of the duodenum from 87 children aged 6 months to 13 years, representing diverse ancestries and geographic contexts. This atlas integrates single-cell transcriptomic and spatial profiling with data on diet, social drivers of health, and environmental exposures. Using these data, we define mucosal cellular composition and chart its developmental trajectory in early childhood. Comparative analyses of children residing in the United States (US) and Pakistan reveal a differentiated enterocyte subset expressing the aquaglyceroporin, AQP10 (AQP10+ enterocyte), that is enriched in children from the US. We show that emergence of this enterocyte state depends on lipid exposure to intestinal stem cells and correlates with dietary fat intake. We also identify a previously-undescribed thyrotropin-releasing hormone (TRH+) enteroendocrine cell and provide evidence for a local endocrine-epithelial-lymphocyte circuit. Our work establishes a detailed framework for pediatric duodenal mucosal development and illuminates how intestinal cellular dynamics are shaped by age and environment.

developmental biology↗

bioRxiv2025

Hybrid Deep Learning and Lee-Carter Model for Mortality Forecasting: A Study of US Adults Aged 35-80

This study introduces a hybrid approach that enhances mortality forecasts by integrating machine learning techniques specifically Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM), and Neural Networks (NN) with the traditional Lee-Carter model. After deriving the time index{kappa} t from the Lee-Carter model, these deep learning models were employed to capture complex temporal patterns, which were then incorporated into the Lee-Carter frame-work to improve forecasting accuracy. The hybrid models were evaluated using historical mortality data from the United States, covering ages 35 to 80 years from 1975 to 2020. Among the models tested, the LSTM model outperformed all others, demonstrating superior capability in capturing time dependencies and producing more accurate mortality forecasts. The integration of LSTM with the Lee-Carter framework led to significant improvements in predictive accuracy. This research demonstrates that combining traditional statistical approaches with modern deep learning techniques, particularly LSTM, offers a powerful method for enhancing mortality forecasting by effectively modeling time-dependent patterns. These findings provide valuable insights and tools for policymakers, actuaries, and healthcare professionals to improve planning and decision-making.

bioinformatics↗

bioRxiv2024

Impact of variants and vaccination on nasal immunity across three waves of SARS-CoV-2

SARS-CoV-2 infection and COVID-19 disease vary with respect to viral variant and host vaccination status. However, how vaccines, emergent variants, and their intersection shift host responses in the human nasal mucosa remains uncharacterized. We and others have shown during the first SARS-CoV-2 wave that a muted nasal epithelial interferon response at the site of infection underlies severe COVID-19. We sought to further understand how upper airway cell subsets and states associate with COVID-19 phenotypes across viral variants and vaccination. Here, we integrated new single-cell RNA-sequencing (scRNA-seq) data from nasopharyngeal swabs collected from 67 adult participants during the Delta and Omicron waves with data from 45 participants collected during the original (Ancestral) wave in our prior study. By characterizing detailed cellular states during infection, we identified changes in epithelial and immune cells that are both unique and shared across variants and vaccination status. By defining SARS-CoV-2 RNA+ cells for each variant, we found that Delta samples had a marked increase in the abundance of viral RNA+ cells. Despite this dramatic increase in viral RNA+ cells in Delta cases, the nasal cellular compositions of Delta and Omicron exhibit greater similarity, driven partly by myeloid subsets, than the Ancestral landscapes associated with specialized epithelial subsets. We found that vaccination prior to infection was surprisingly associated with nasal macrophage recruitment and activation rather than adaptive immune cell signatures. While patients with severe disease caused by Ancestral or Delta variants had muted interferon responses, Omicron-infected patients had equivalent interferon responses regardless of disease severity. Our study defines the evolution of cellular targets and signatures of disease severity in the upper respiratory tract across SARS-CoV-2 variants, and suggests that intramuscular vaccines shape myeloid responses in the nasal mucosa upon SARS-CoV-2 infection.

immunology↗