Impact of Post-Stroke Post-Traumatic Stress Disorder.

Introduction: Post-Traumatic Stress Disorder (PTSD) is associated with exposure to traumatic events, especially in the military setting. However, patients who experience stroke may develop anxiety about their stroke event and may re-experience transient neurological symptoms as a result. A significant portion develop the persistent and disabling symptoms of PTSD.

Integrative multi-omics analysis reveals a novel subtype of hepatocellular carcinoma with biological and clinical relevance.

Background: Hepatocellular carcinoma (HCC) is a highly heterogeneous tumor, and the development of accurate predictive models for prognosis and drug sensitivity remains challenging.

Methods: We integrated laboratory data and public cohorts to conduct a multi-omics analysis of HCC, which included bulk RNA sequencing, proteomic analysis, single-cell RNA sequencing (scRNA-seq), spatial transcriptomics sequencing (ST-seq), and genome sequencing. We constructed a tumor purity (TP) and tumor microenvironment (TME) prognostic risk model.

Up-regulated ITGB4 promotes hepatocellular carcinoma metastasis by activating hypoxia-mediated glycolysis and cancer-associated fibroblasts.

The pre-metastatic niche constructed by cancer-associated fibroblasts (CAFs) plays a key role in the hypoxic tumor microenvironment (TME), promoting hepatocellular carcinoma (HCC) metastasis. Integrin, which is involved in cell-to-cell or cell-to-matrix interactions and TME regulation, affects tumor metastasis. However, the complex interactions between integrin-mediated HCC cells and CAFs remain unclear.

Repurposing flubendazole for glioblastoma ferroptosis by affecting xCT and TFRC proteins.

New uses of old drugs hold great promise for clinical translation. Flubendazole, an FDA-approved antiparasitic drug, has been shown to target p53 and promote apoptosis in glioblastoma (GBM) cells. However, its damaging mechanism in GBM remains elusive.

Machine-learning-accelerated structure prediction of PtSnO nanoclusters under working conditions.

Credible property exploration or prediction can not be achieved without well-established compositions and structures of catalysts under working conditions. We construct surrogate models combination of machine learning (ML), genetic algorithm (GA) and thermodynamics (AITD) to accelerate global optimization of PtSn binary metal oxides, which are typically used for CO-assisted propane dehydrogenation to propylene. This challenging case illustrates that the subtle oxidized states of PtSnO clusters can be predicted in a large chemical space including a wide range of reaction conditions.