Development of a Deep Learning Tool to Support the Assessment of Thyroid Follicular Cell Hypertrophy in the Rat.

Thyroid tissue is sensitive to the effects of endocrine disrupting substances, and this represents a significant health concern. Histopathological analysis of tissue sections of the rat thyroid gland remains the gold standard for the evaluation for agrochemical effects on the thyroid. However, there is a high degree of variability in the appearance of the rat thyroid gland, and toxicologic pathologists often struggle to decide on and consistently apply a threshold for recording low-grade thyroid follicular hypertrophy.

Proteins Involved in Endothelial Function and Inflammation Are Implicated in Cerebral Small Vessel Disease.

Background: Endothelial dysfunction and inflammation have been implicated in the pathophysiology of cerebral small vessel disease (SVD). However, whether they are causal, and if so which components of the pathways represent potential treatment targets, remains uncertain.

Methods: Two-sample Mendelian randomization (MR) was used to test the association between the circulating abundance of 996 proteins involved in endothelial dysfunction and inflammation and SVD.

Genomic epidemiology of extended-spectrum beta-lactamase-producing from humans and a river in Aotearoa New Zealand.

In Aotearoa New Zealand, urinary tract infections in humans are commonly caused by extended-spectrum beta-lactamase (ESBL)-producing . This group of antimicrobial-resistant bacteria are often multidrug resistant. However, there is limited information on ESBL-producing found in the environment and their link with human clinical isolates.

Predicting purification process fit of monoclonal antibodies using machine learning.

In early-stage development of therapeutic monoclonal antibodies, assessment of the viability and ease of their purification typically requires extensive experimentation. However, the work required for upstream protein expression and downstream purification development often conflicts with timeline pressures and material constraints, limiting the number of molecules and process conditions that can reasonably be assessed. Recently, high-throughput batch-binding screen data along with improved molecular descriptors have enabled development of robust quantitative structure-property relationship (QSPR) models that predict monoclonal antibody chromatographic binding behavior from the amino acid sequence.

Illustrating the structures of bias from immortal time using directed acyclic graphs.

Background: Immortal time is a period of follow-up during which death or the study outcome cannot occur by design. Bias from immortal time has been increasingly recognized in epidemiological studies. However, the fundamental causes and structures of bias from immortal time have not been explained systematically.