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Gliomas are a heterogeneous type of central nervous system tumor. The etiology of glioma formation remains elusive, with approximately 5% of gliomas being familial, underscoring the significance of understanding genetic susceptibility in glioma development. In this study, a dual germline PTCH2 mutation [Ser391*, Leu104Pro] was identified in a family with a history of glioma, and sequencing data from WES/SimcereDx Neuro-Onco 360 including 910 Chinese patients with glioma and 1666 patients with solid tumors were analyzed.

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Personalized theta-burst stimulation enhances social skills in young minimally verbal children with autism: a double-blind randomized controlled trial.

Biol Psychiatry

January 2025

Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, PR China; MOE Key Lab for Neuro information, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, 610054, PR China. Electronic address:

Background: Minimally verbal children with autism are understudied and lack effective treatment options. Personalized continuous theta-burst stimulation (cTBS) targeting the amygdala and its circuitry may be a potential therapeutic approach for this population.

Methods: In a double-blind randomized controlled trial, minimally verbal children with autism (ages 2-8 years) received 4 weeks of cTBS.

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The ability to infer a speaker's utterance within a particular context for the intended meaning is central to communication. Yet, little is known about the underlying neurocomputational mechanisms of pragmatic inference, let alone relevant differences among individuals. Here, using a reference game combined with model-based functional magnetic resonance imaging (fMRI), we showed that an individual-level pragmatic inference model was a better predictor of listeners' performance than a population-level model.

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Cardiac wall motion abnormalities (WMA) are strong predictors of mortality, but current screening methods using Q waves from electrocardiograms (ECGs) have limited accuracy and vary across racial and ethnic groups. This study aimed to identify novel ECG features using deep learning to enhance WMA detection, referencing echocardiography as the gold standard. We collected ECG and echocardiogram data from 35,210 patients in California and labeled WMA using unstructured language parsing of echocardiographic reports.

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