The First Hospital of Hunan University of Chinese Medicine

Hyperoxia-induced brain injury is a severe neurological complication that is often accompanied by adverse long-term prognosis. The pathogenesis of hyperoxia-induced brain injury is highly complex, with neuroinflammation playing a crucial role. The activation of the nucleotide-binding oligomerization domain-like receptor protein 3 (NLRP3) inflammasome, which plays a pivotal role in regulating and amplifying the inflammatory response, is the pathological core of hyperoxia-induced brain injury. Additionally, astrocytes actively participate in neuroinflammatory responses. However, there is currently no comprehensive overview summarizing the role of astrocytes in hyperoxia-induced brain injury and the NLRP3 signaling pathways in astrocytes.

This article aims to provide an overview of studies reported in the literature investigating the pathological role of astrocyte involvement during the inflammatory response in hyperoxia-induced brain injury, the mechanisms of hyperoxia activateing the NLRP3 inflammasome to mediate pyroptosis in astrocytes, and the potential therapeutic effects of drugs targeting the NLRP3 inflammasome to alleviate hyperoxia-induced brain injury.

We searched major databases (including PubMed, Web of Science, and Google Scholar, etc.) for literature encompassing astrocytes, NLRP3 inflammasome, and pyroptosis during hyperoxia-induced brain injury up to Oct 2024. We combined with studies found in the reference lists of the included studies.

In this study, we elucidated the transition of function in astrocytes and activation mechanisms under hyperoxic conditions, and we summarized the potential upstream of the trigger involved in NLRP3 inflammasome activation during hyperoxia-induced brain injury, such as ROS and potassium efflux. Furthermore, we described the signaling pathways of the NLRP3 inflammasome and pyroptosis executed by GSDMD and GSDME in astrocytes under hyperoxic conditions. Finally, we summarized the inhibitors targeting the NLRP3 inflammasome in astrocytes to provide new insights for treating hyperoxia-induced brain injury.

To investigate antidepressant active ingredients of XYP (Xiaoyao Pill), while predicting its primary pharmacodynamic material basis and underlying mechanisms of action.

UPLC-Q-TOF-MS/MS was used to identify the active ingredients of XYP. In addition, based on the analysis of components, network pharmacology and molecular docking were used to investigate potential therapeutic targets and possible signaling pathways of XYP in the treatment of depression.

A total of 102 chemical components, 10 prototype components and 16 metabolites absorbed in the brain were identified in XYP. Network pharmacology analysis showed that these compounds shared 420 common targets with depression, TP53, EGFR, PTGS2, ESR1, PPARG and other 68 targets were considered as core targets, mainly enriched in PI3K-Akt and MAPK signaling pathways. GO analysis unveiled associated apoptosis and inflammatory response. Molecular docking revealed that paeoniflorin, liquiritin, and atractylenolide III were found to have the highest binding energy to TP53, ESR1 and PPARG.

These findings suggested that XYP may exert antidepressant effects through atractylenolide III, paeoniflorin, saikosaponin D, liquiritin, formononetin, affecting the PIK3/AKT signaling pathway. This lays the foundation for the research on the quality standards and clinical rational application of traditional Chinese medicine formulas.

This multicenter study aimed to develop and validate a multiscale deep learning radiomics nomogram for predicting recurrence-free survival (RFS) in patients with pancreatic ductal adenocarcinoma (PDAC).

A total of 469 PDAC patients from four hospitals were divided into training and test sets. Handcrafted radiomics and deep learning (DL) features were extracted from optimal regions of interest, encompassing both intratumoral and peritumoral areas. Univariate Cox regression, LASSO regression, and multivariate Cox regression selected features for three image signatures (intratumoral, peritumoral radiomics, and DL). A multiscale nomogram was constructed and validated against the 8th AJCC staging system.

The 4 mm peritumoral VOI yielded the best radiomics prediction, while a 15 mm expansion was optimal for deep learning. The multiscale nomogram demonstrated a C-index of 0.82 (95 % CI: 0.78-0.85) in the training set and 0.70 (95 % CI: 0.64-0.76) in the external test 1 (high-volume hospital), with the external test 2 (low-volume hospital) showing a C-index of 0.78 (95 % CI: 0.65-0.91). These outperformed the AJCC system's C-index (0.54-0.57). The area under the curve (AUC) for recurrence prediction at 0.5, 1, and 2 years was 0.89, 0.94, and 0.89 in the training set, with AUC values ranging from 0.75 to 0.97 in the external validation sets, consistently surpassing the AJCC system across all sets.. Kaplan-Meier analysis confirmed significant differences in prognosis between high- and low-risk groups (P < 0.01 across all cohorts).

The multiscale nomogram effectively stratifies recurrence risk in PDAC patients, enhancing presurgical clinical decision-making and potentially improving patient outcomes.