The BAMBOO method for correcting batch effects in high throughput proximity extension assays for proteomic studies.

The proximity extension assay (PEA) enables large-scale proteomic investigations across numerous proteins and samples. However, discrepancies between measurements, known as batch-effects, potentially skew downstream statistical analyses and increase the risks of false discoveries. While implementing bridging controls (BCs) on each plate has been proposed to mitigate these effects, a clear method for utilizing this strategy remains elusive.

Comparable performance between automatic and manual laryngeal and hypopharyngeal GTV delineations validated with pathology.

Purpose: Deep learning is a promising approach to increase reproducibility and time-efficiency of GTV delineation in head and neck cancer, but model evaluation primarily relies on manual GTV delineations as reference annotation, which are subjective and tend to overestimate tumor volume. This study aimed to validate a deep learning model for laryngeal and hypopharyngeal GTV segmentation with pathology and to compare its performance with clinicians' manual delineations.

Materials And Methods: A retrospective dataset of 193 laryngeal and hypopharyngeal cancer patients was used to train a deep learning model with clinical GTV delineations as reference.

Immune responses to SARS-CoV-2 in sub-Saharan Africa and western Europe: a retrospective, population-based, cross-sectional study.

Background: SARS-CoV-2 has been associated with a higher proportion of asymptomatic infections and lower mortality in sub-Saharan Africa than high-income countries. However, there is currently a lack of data on cellular immune responses to SARS-CoV-2 in people living in Africa compared with people in high-income regions of the world. We aimed to assess geographical variation in peripheral and mucosal immune responses.