The development of food allergy (FA) and corresponding gut microbiome changes are influenced by the timing of infant solid food introduction. This microbiome analysis assessed how food selection and timing affected FA risk using the infant (N=198) longitudinal DIABIMMUNE cohort. Root vegetable and grain introduction influenced Streptococcus, Escherichia-Shigella, and Klebsiella genera abundance, all showing differential abundance in allergic infants (p<0.05). On average, earlier food introduction (<6 months) correlated with increased allergic activity. Individual food product introduction, for example, beetroot increased the Allantoin Degradation IV Pathway abundance, correlated with non-allergic infants (p<0.01). Microbiome-food item correlations suggest revisions to infant food introduction guidelines and potential pro-biotics to alter gut-microbiome tp reduce allergy risk.

Depression affects 5.7% of adults over 60, with over 50% of cases going undiagnosed. As the elderly population doubles by 2050, early depression detection is crucial. This study employs multiple machine learning models to predict early depression risk in adults aged 65+ based on demographic, physical, and cognitive health factors over various time periods. It also identifies and significantly reduces algorithmic bias in protected attributes including gender, race, and education through the method of reweighting. These findings confirm key contributing factors to depression-like clinical conditions, memory decline, and lower education suggesting the need to target these areas for maximized social and emotional improvement as the population ages.

Breast cancer (BC) recurrence is difficult to predict using machine learning techniques due to a lack of patient data. Currently, the Oncotype DX score is widely used for BC recurrence probability and relies on often challenging to obtain genetic data. This study aimed to develop a composite score for HER2-negative BC patients using easier-to-access data(clinical, histological, immunohistochemical, molecular biology). A calibrated Gradient Boosting Classifier was trained to predict recurrence scores ranging from 0-10, with lower scores indicating lower probability. The classifier accurately (90%) predicted BC recurrence risk scores, presenting it as a viable alternative to Oncotype DX to guide treatment decisions.

Type 1 Diabetes (T1D) is influenced by genetics and other factors, but the role of gut bacteria in its development remains unclear. This study investigates how Human Leukocyte Antigens (HLA) genes and gut bacteria interact to influence T1D risk. Children with high-risk HLA genes generally had fewer beneficial bacteria, like Lachnospira. Prevotella were more abundant in children who developed T1D, while Ruminococcus and others were more common in those who didn’t. Functional bacterial pathways, such as energy production pathways (PWY.5676 and PWY.7220/7222), were more common in T1D low-risk children. The findings suggest that disruptions in gut bacteria with accompanying genetic factors may contribute to the development of T1D.

Plastic production exceeds 359 million tons annually. Recent advances in plastic-degrading enzymes (PDE) have improved depolymerization, however, activity declines under industrial thermophilic conditions. To optimize PDE thermostability, identifying biological pathways in existing thermophilic PDEs is necessary. The genes of 26 thermophilic PDEs were clustered to identify thermostability overexpressed pathways. Pathways controlling cellular responses to environmental stress and DNA binding were overexpressed in 100% of thermophilic PDEs but only 20% of non-thermophilic PDEs (p<0.05). The findings suggest that pathway overexpression is conserved among taxonomy, plastisphere, and enzyme families and the optimization of thermostability by leveraging pathways key to stress response.