Chinese Medical Journal最新文献

Background: Postoperative pulmonary complications (PPCs) are major adverse events in neurosurgical patients. This study aimed to develop and validate machine learning models predicting PPCs after neurosurgery.

Methods: PPCs were defined according to the European Perioperative Clinical Outcome standards as occurring within 7 postoperative days. Data of cases meeting inclusion/exclusion criteria were extracted from the anesthesia information management system to create three datasets: The development (data of Huashan Hospital, Fudan University from 2018 to 2020), temporal validation (data of Huashan Hospital, Fudan University in 2021) and external validation (data of other three hospitals in 2023) datasets. Machine learning models of six algorithms were trained using either 35 retrievable and plausible features or the 11 features selected by Lasso regression. Temporal validation was conducted for all models and the 11-feature models were also externally validated. Independent risk factors were identified and feature importance in top models was analyzed.

Results: PPCs occurred in 712 of 7533 (9.5%), 258 of 2824 (9.1%), and 207 of 2300 (9.0%) patients in the development, temporal validation and external validation datasets, respectively. During cross-validation training, all models except Bayes demonstrated good discrimination with an area under the receiver operating characteristic curve (AUC) of 0.84. In temporal validation of full-feature models, deep neural network (DNN) performed the best with an AUC of 0.835 (95% confidence interval [CI]: 0.805-0.858) and a Brier score of 0.069, followed by logistic regression (LR), random forest and XGBoost. The 11-feature models performed comparable to full-feature models with very close but statistically lower AUCs, with the top models of DNN and LR in temporal and external validations. An 11-feature nomogram was drawn based on the LR algorithm and it outperformed the minimally modified Assess respiratory RIsk in Surgical patients in CATalonia (ARISCAT) and Laparoscopic Surgery Video Educational Guidelines (LAS VEGAS) scores with a higher AUC (LR: 0.824, ARISCAT: 0.672, LAS: 0.663). Independent risk factors based on multivariate LR mostly overlapped with Lasso-selected features, but lacked consistency with the important features using the Shapley additive explanation (SHAP) method of the LR model.

Conclusions: The developed models, especially the DNN model and the nomogram, had good discrimination and calibration, and could be used for predicting PPCs in neurosurgical patients. The establishment of machine learning models and the ascertainment of risk factors might assist clinical decision support for improving surgical outcomes.

Trial registration: ChiCTR 2100047474; https://www.chictr.org.cn/showproj.html?proj = 128279.

Abstract: Poly(ADP-ribose) polymerase (PARP) inhibitors (PARPis) have emerged as critical agents for cancer therapy. By inhibiting the catalytic activity of PARP enzymes and trapping them in the DNA, PARPis disrupt DNA repair, ultimately leading to cell death, particularly in cancer cells with homologous recombination repair deficiencies, such as those harboring BRCA mutations. This review delves into the mechanisms of action of PARPis in anticancer treatments, including the inhibition of DNA repair, synthetic lethality, and replication stress. Furthermore, the clinical applications of PARPis in various cancers and their adverse effects as well as their combinations with other therapies and the mechanisms underlying resistance are summarized. This review provides comprehensive insights into the role and mechanisms of PARP and PARPis in DNA repair, with a particular focus on the potential of PARPi-based therapies in precision medicine for cancer treatment.

Background: Chronic kidney disease (CKD) is a global health issue, with renal fibrosis being a common pathway in CKD development. Histone modification plays crucial roles in transcriptional regulation, but their pathological functions and mechanisms in CKD are not well understood.

Methods: We utilized chromatin immunoprecipitation with next-generation DNA sequencing (ChIP-seq) and RNA-seq to evaluate the states and functions of H3 lysine 27 acetylation (H3K27ac) and H3 lysine 4 trimethylation (H3K4me3) in kidney of CKD mice. We identified epigenetic factors regulating H3K27ac through motif analysis. Expression of activating transcription factor 3 (ATF3) in CKD mouse models and patients' kidneys was validated via immunofluorescence staining or Western blot. We further generated the Atf3 deficient (Atf3-/-) mice to explore its effect in kidney function and fibrosis. ChIP-seq of H3K27ac from Atf3-/- CKD mice was employed to validate ATF3's regulatory effects. We explored how ATF3 maintains the state of H3K27ac by integrating the data sources from multiple databases.

Results: The states of H3K27ac and H3K4me3 were changed during CKD, and positively correlated with differential gene expression. ATF3 was highly expressed in kidney of both patients and mice with CKD, and co-localized with H3K27ac in genome, epigenetically regulating H3K27ac state. Atf3 deficient in CKD mice significantly ameliorated kidney dysfunction and fibrotic phenotype, and reduced H3K27ac levels at the ATF3 binding sites. Mechanically, ATF3 may recruit the histone acetyltransferases (HATs) network to maintain the H3K27ac state during CKD.

Conclusion: ATF3 promotes kidney injury and fibrosis in CKD by maintaining the state of H3k27ac via recruiting HATs network.