Reactive astrocytes generated from human iPSC are pro-inflammatory and display altered metabolism.

Astrocytes are the most abundant type of glial cell in the central nervous system and they play pivotal roles in both normal health and disease. Their dysfunction is detrimental to many brain related pathologies. Under pathological conditions, such as Alzheimer's disease, astrocytes adopt an activated reactive phenotype which can contribute to disease progression.

Differentiation of SH-SY5Y neuroblastoma cells using retinoic acid and BDNF: a model for neuronal and synaptic differentiation in neurodegeneration.

There has been much interest in the use of cell culture models of neurones, to avoid the animal welfare and cost issues of using primary and human-induced pluripotent stem cell (hiPSC)-derived neurones respectively. The human neuroblastoma cell line, SH-SY5Y, is extensively used in laboratories as they can be readily expanded, are of low cost and can be differentiated into neuronal-like cells. However, much debate remains as to their phenotype once differentiated, and their ability to recapitulate the physiology of bona fide neurones.

Predicting CO production of lactating dairy cows from animal, dietary, and production traits using an international dataset.

Automated measurements of the ratio of concentrations of methane and carbon dioxide, [CH]:[CO], in breath from individual animals (the so-called "sniffer technique") and estimated CO production can be used to estimate CH production, provided that CO production can be reliably calculated. This would allow CH production from individual cows to be estimated in large cohorts of cows, whereby ranking of cows according to their CH production might become possible and their values could be used for breeding of low CH-emitting animals. Estimates of CO production are typically based on predictions of heat production, which can be calculated from body weight (BW), energy-corrected milk yield, and days of pregnancy.

Methane prediction equations including genera of rumen bacteria as predictor variables improve prediction accuracy.

Methane (CH) emissions from ruminants are of a significant environmental concern, necessitating accurate prediction for emission inventories. Existing models rely solely on dietary and host animal-related data, ignoring the predicting power of rumen microbiota, the source of CH. To address this limitation, we developed novel CH prediction models incorporating rumen microbes as predictors, alongside animal- and feed-related predictors using four statistical/machine learning (ML) methods.