scaLR: a low-resource deep neural network-based platform for single cell analysis and biomarker discovery.

Single-cell ribonucleic acid (RNA) sequencing (scRNA-seq) produces vast amounts of individual cell profiling data. Its analysis presents a significant challenge in accurately annotating cell types and their associated biomarkers. Different pipelines based on deep neural network (DNN) methods have been employed to tackle these issues.

Toward a health-associated core keystone index for the human gut microbiome.

A robust index of gut microbiome taxa encompassing their association with host health and microbiome resilience would be valuable for development and optimization of microbiome-based therapeutics. Here we present a single ranked order for 201 taxa, the Health-Associated Core Keystone (HACK) index, derived using their association strengths with prevalence/community association in non-diseased subjects, longitudinal stability, and host health. The index was derived based on 127 discovery cohorts and 14 validation datasets (cumulatively encompassing 45,424 gut microbiomes, subject age >18 years, representing 42 countries, 28 disease categories, and 10,021 longitudinal samples).

Depletion of core microbiome forms the shared background against diverging dysbiosis patterns in Crohn's disease and intestinal tuberculosis: insights from an integrated multi-cohort analysis.

Background/aims: Crohn's disease (CD) and intestinal tuberculosis (ITB) are gastrointestinal (GI) inflammatory disorders with overlapping clinical presentations but diverging etiologies. The study aims to decipher CD and ITB-associated gut dysbiosis signatures and identify disease-associated co-occurring modules to evaluate whether this dysbiosis signature is a disease-specific trait or is a shared feature across diseases of diverging etiologies.

Methods: Disease-associated gut microbial modules were identified using statistical machine learning and co-abundance network analysis in controls, CD and ITB patients recruited as part of this study.

Tumor histoculture captures the dynamic interactions between tumor and immune components in response to anti-PD1 in head and neck cancer.

Dynamic interactions within the tumor micro-environment drive patient response to immune checkpoint inhibitors. Existing preclinical models lack true representation of this complexity. Using a Head and Neck cancer patient derived TruTumor histoculture platform, the response spectrum of 70 patients to anti-PD1 treatment is investigated in this study.