Superposition of Parallel and Perpendicular Flow Velocity Shears in Magnetized Hybrid-Ion Plasmas

49th Annual Meeting of the Division of Plasma Physic

Influence of brain-derived neurotrophic factor on pathfinding of dentate granule cell axons, the hippocampal mossy fibers

Mossy fibers, the dentate granule cell axons, are generated throughout an animal's lifetime. Mossy fiber paths and synapses are primarily restricted to the stratum lucidum within the CA3 region. Brain-derived neurotrophic factor (BDNF), a neurotrophin family protein that activates Trk neurotrophin receptors, is highly expressed in the stratum lucidum in an activity-dependent manner. The addition of a Trk neurotrophin receptor inhibitor, K252a, to cultured hippocampal slices induced aberrant extension of mossy fibers into ectopic regions. BDNF overexpression in granule cells ameliorated the mossy fiber pathway abnormalities caused by a submaximal dose of K252a. A similar rescue was observed when BDNF was expressed in CA3 pyramidal cells, most notably in mossy fibers distal to the expression site. These findings are the first to clarify the role of BDNF in mossy fiber pathfinding, not as an attractant cue but as a regulator, possibly acting in a paracrine manner. This effect of BDNF may be as a signal for new fibers to fasciculate and extend further to form synapses with neurons that are far from active BDNF-expressing synapses. This mechanism would ensure the emergence of new independent dentate gyrus-CA3 circuits by the axons of new-born granule cells

Machine learning using clinical data at baseline predicts the medium-term efficacy of ustekinumab in patients with ulcerative colitis

Abstract Predicting the therapeutic response to biologics before administration is a key clinical challenge in ulcerative colitis (UC). We previously reported a model for predicting the efficacy of vedolizumab (VDZ) for UC using a machine-learning approach. Ustekinumab (UST) is now available for treating UC, but no model for predicting its efficacy has been developed. When applied to patients with UC treated with UST, our VDZ prediction model showed positive predictive value (PPV) of 56.3% and negative predictive value (NPV) of 62.5%. Given this limited predictive ability, we aimed to develop a UST-specific prediction model with clinical features at baseline including background factors, clinical and endoscopic activity, and blood test results, as we did for the VDZ prediction model. The top 10 features (Alb, monocytes, height, MCV, TP, Lichtiger index, white blood cell count, MCHC, partial Mayo score, and CRP) associated with steroid-free clinical remission at 6 months after starting UST were selected using random forest. The predictive ability of a model using these predictors was evaluated by fivefold cross-validation. Validation of the prediction model with an external cohort showed PPV of 68.8% and NPV of 71.4%. Our study suggested the importance of establishing a drug-specific prediction model