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Gene expression during brain development or abnormal development is a biological process that is highly dynamic in spatio and temporal. Previous studies have mainly focused on individual brain regions or a certain developmental stage. Our motivation is to address this gap by incorporating spatio-temporal information to gain a more complete understanding of brain development or abnormal brain development, such as Alzheimer's disease (AD), and to identify potential determinants of response. In this study, we propose a novel two-step framework based on spatial-temporal information weighting and multi-step decision trees. This framework can effectively exploit the spatial similarity and temporal dependence between different stages and different brain regions, and facilitate differential gene analysis in brain regions with high heterogeneity. We focus on two datasets: the AD dataset, which includes gene expression data from early, middle and late stages, and the brain development dataset, spanning fetal development to adulthood. Our findings highlight the advantages of the proposed framework in discovering gene classes and elucidating their impact on brain development and AD progression across diverse brain regions and stages. These findings align with existing studies and provide insights into the processes of normal and abnormal brain development. [ABSTRACT FROM AUTHOR]

Motivation: Structural variants (SVs) play an important role in genetic research and precision medicine. As existing SV detection methods usually contain a substantial number of false positive calls, approaches to filter the detection results are needed., Result: We developed a novel deep learning-based SV filtering tool, CSV-Filter, for both short and long reads. CSV-Filter uses a novel multi-level grayscale image encoding method based on CIGAR strings of the alignment results and employs image augmentation techniques to improve SV feature extraction. CSV-Filter also utilizes self-supervised learning networks for transfer as classification models, and employs mixed-precision operations to accelerate training. The experiments showed that the integration of CSV-Filter with popular SV detection tools could considerably reduce false positive SVs for short and long reads, while maintaining true positive SVs almost unchanged. Compared with DeepSVFilter, a SV filtering tool for short reads, CSV-Filter could recognize more false positive calls and support long reads as an additional feature., Availability and Implementation: https://github.com/xzyschumacher/CSV-Filter., (© The Author(s) 2024. Published by Oxford University Press.)

In recent decades, immune checkpoint inhibitors (ICIs) have emerged as safer and less disruptive alternatives to conventional chemotherapy and radiotherapy for certain patients with tumours. ICIs serve a synergistic role alongside conventional therapies by manipulating the immune system to recognise and target tumour cells. However, excessive activation of the immune system can lead to immune-related adverse events including pneumonia, myocarditis and colitis, which pose serious and even fatal risks. In the present case series, three patients with a thoracic tumour with an ICI-induced peptic ulcer triggered by programmed cell death protein 1 antibodies (diagnosed by gastrointestinal endoscopy) are presented. These cases were successfully treated with corticosteroids. The diagnostic and treatment processes undertaken for these patients underscore the requirement to comprehensively understand the mechanism of ICI-induced peptic ulcer. Moreover, the relevant literature was also reviewed in the present study., Competing Interests: The authors declare that they have no competing interests., (Copyright: © 2024 Wang et al.)