Clinical characteristics and influencing factors of anti-PD-1/PD-L1-related severe cardiac adverse event: based on FAERS and TCGA databases.

Combining the FDA Adverse Event Reporting System (FAERS) and the Cancer Genome Atlas (TCGA) databases, we aim to explore the factors that influence anti-programmed cell death protein-1 inhibitors/programmed death-ligand-1 (PD-1/PD-L1) related severe cardiac adverse events (cAEs). We obtained anti-PD-1/PD-L1 adverse event reports from January 2014 to December 2022 from the FAERS database. Disproportionality analysis was performed to find anti-PD-1/PD-L1-related cAEs using the proportional reporting ratio (PRR). We were exploring influencing factors based on multivariate logistic regression analysis. Finally, we utilized a strategy that combines FAERS and TCGA databases to explore the potential immune and genetic influencing factors associated with anti-PD-1/PD-L1-related severe cAEs. Reports of severe cAEs accounted for 7.10% of the overall anti-PD-1/PD-L1 adverse event reports in the FAERS database. Immune-mediated myocarditis (PRR = 77.01[59.77-99.23]) shows the strongest toxic signal. The elderly group (65-74: OR = 1.34[1.23-1.47], ≥ 75: OR = 1.64[1.49-1.81]), male (OR = 1.14[1.05-1.24]), anti-PD-L1 agents (OR = 1.17[1.03-1.33]), patients with other adverse events (OR = 2.38[2.17-2.60]), and the concomitant use of proton pump inhibitor (OR = 1.29[1.17-1.43]), nonsteroidal anti-inflammatory drugs (OR = 1.17[1.04-1.31]), or antibiotics (OR = 1.24[1.08-1.43]) may increase the risk of severe cAEs. In addition, PD-L1 mRNA (Rs = 0.71, FDR = 2.30 × 10- 3) and low-density lipoprotein receptor-related protein 3 (LRP3) (Rs = 0.82, FDR = 2.17 × 10- 2) may be immune and genetic influencing factors for severe cAEs. Severe cAEs may be related to antigen receptor-mediated signalling pathways. In this study, we found that age, gender, anti-PD-1/PD-L1 agents, concomitant other adverse events, concomitant medication, PD-L1 mRNA, and LRP3 may be influencing factors for anti-PD-1/PD-L1-related severe cAEs. However, our findings still require a large-scale prospective cohort validation.

A two-stage ensemble learning based prediction and grading model for PD-1/PD-L1 inhibitor-related cardiac adverse events: A multicenter retrospective study.

BACKGROUND: Immune-related cardiac adverse events (ircAEs) caused by programmed cell death protein-1 (PD-1) and programmed death-ligand-1 (PD-L1) inhibitors can lead to fulminant and even fatal consequences. This study aims to develop a prediction and grading model for ircAEs, enabling graded management of patients. METHODS: This study utilized medical record systems from two medical institutions to develop a prediction and grading model for ircAEs using ten machine learning algorithms and two variable screening methods. The model was developed based on a two-stage ensemble learning framework. In the first stage, the ircAEs and non-ircAEs cases were classified. In the second stage, ircAEs cases were grouped into grades 1-2 and 3-5. The experiments were evaluated using five-fold cross-validation. The model's prediction performance was assessed using accuracy, precision, recall, F1 value, Brier score, receiver operating characteristic curve area (AUC), and area under the precision-recall curve (AUPR). RESULTS: 615 patients were included in the study. 147 experienced ircAEs, and 44 experienced grade 3-5 ircAEs. The soft voting classifier trained using the variables screened by feature importance ranking performed better than other classifiers in both stages. The average AUC for the first and second stages is 84.18 % and 85.13 %, respectively. In the first stage, the three most important variables are N-terminal B-type natriuretic peptide (NT-proBNP), interleukin-2 (IL-2), and C-reactive protein (CRP). In the second stage, the patient's age, NT-proBNP, and left ventricular ejection fraction (LVEF) are the three most critical variables. CONCLUSIONS: The prediction and grading model of ircAEs based on two-stage ensemble learning established in this study has good performance and potential clinical application.

Evidence-based interventions to improve sleep quality after thoracic surgery:A retrospective analysis of clinical studies.

OBJECTIVE: To investigate and rank the evidence for the efficacy of interventions in improving sleep quality after cardiac surgery using comprehensive comparisons. BACKGROUND: Clinical evidence suggests that over 80 % of adult cardiac surgery patients experience sleep disturbances during the first week postoperatively. While certain interventions have been shown to improve post-thoracic surgery sleep quality, a systematic description of the effects of these varied interventions is lacking. METHODS: This systematic search was conducted across PubMed, Web of Science, Cochrane, Embase, and CINAHL databases to collate all published randomized clinical trials as evidence. Two researchers independently extracted pertinent information from eligible trials and assessed the quality of included studies. Based on statistical heterogeneity, traditional meta-analysis using fixed or random-effects models was employed to assess the efficacy of interventions, and a Frequentist network meta-analysis using a consistency model was conducted to rank the effectiveness of intervention protocols. RESULTS: Our review incorporated 37 articles (n = 3569), encompassing 46 interventions, including 9 reports on pharmacological interventions (24.3 %), 28 on non-pharmacological interventions (75.7 %), and 5 on anesthetic management interventions (13.5 %). The analysis indicated the efficacy of Benson's relaxation technique, Progressive muscle relaxation, Education, Aromatherapy, Acupressure, Massage, and Eye masks in enhancing postoperative sleep quality. Specifically, Benson's relaxation technique (cumulative ranking curve area: 0.80; probability: 98.3 %) and Acupressure (cumulative ranking curve area: 0.96; probability: 58.3 %) were associated with the highest probability of successfully improving postoperative sleep quality, while Progressive muscle relaxation (cumulative ranking curve area: 0.70; probability: 35.2 %) and Eye masks (cumulative ranking curve area: 0.81; probability: 78.8 %) were considered secondary options. Eye masks and Massage significantly reduced postoperative sleep latency, with Eye masks (cumulative ranking curve area: 0.82; probability: 51.0 %) being most likely to enhance sleep quality postoperatively, followed by Massage (cumulative ranking curve area: 0.60; probability: 27.2 %). Education, Music, Massage, Eye masks, and Handholding were effective in alleviating pain intensity, with Education being most likely to successfully reduce postoperative pain (cumulative ranking curve area: 0.92; probability: 54.3 %), followed by Music (cumulative ranking curve area: 0.91; probability: 54 %). CONCLUSIONS: Our findings can be utilized to optimize strategies for managing post-thoracic surgery sleep disturbances and to develop evidence-based approaches for this purpose. Benson's relaxation technique, Progressive muscle relaxation, Education, Aromatherapy, Acupressure, Massage, and Eye masks significantly improve sleep quality in postoperative patients. KEY: disorders of initiating and maintaining sleep, sleep wake disorders, thoracic surgical procedures, cardiac surgical procedures, sleep quality, pain, network meta-analysis.

Development and validation of a deep learning system to screen vision-threatening conditions in high myopia using optical coherence tomography images.

BACKGROUND/AIMS: To apply deep learning technology to develop an artificial intelligence (AI) system that can identify vision-threatening conditions in high myopia patients based on optical coherence tomography (OCT) macular images. METHODS: In this cross-sectional, prospective study, a total of 5505 qualified OCT macular images obtained from 1048 high myopia patients admitted to Zhongshan Ophthalmic Centre (ZOC) from 2012 to 2017 were selected for the development of the AI system. The independent test dataset included 412 images obtained from 91 high myopia patients recruited at ZOC from January 2019 to May 2019. We adopted the InceptionResnetV2 architecture to train four independent convolutional neural network (CNN) models to identify the following four vision-threatening conditions in high myopia: retinoschisis, macular hole, retinal detachment and pathological myopic choroidal neovascularisation. Focal Loss was used to address class imbalance, and optimal operating thresholds were determined according to the Youden Index. RESULTS: In the independent test dataset, the areas under the receiver operating characteristic curves were high for all conditions (0.961 to 0.999). Our AI system achieved sensitivities equal to or even better than those of retina specialists as well as high specificities (greater than 90%). Moreover, our AI system provided a transparent and interpretable diagnosis with heatmaps. CONCLUSIONS: We used OCT macular images for the development of CNN models to identify vision-threatening conditions in high myopia patients. Our models achieved reliable sensitivities and high specificities, comparable to those of retina specialists and may be applied for large-scale high myopia screening and patient follow-up.

Conformational antibody binding to a native, cell-free expressed GPCR in block copolymer membranes.

G-protein coupled receptors (GPCRs) play a key role in physiological processes and are attractive drug targets. Their biophysical characterization is, however, highly challenging because of their innate instability outside a stabilizing membrane and the difficulty of finding a suitable expression system. We here show the cell-free expression of a GPCR, CXCR4, and its direct embedding in diblock copolymer membranes. The polymer-stabilized CXCR4 is readily immobilized onto biosensor chips for label-free binding analysis. Kinetic characterization using a conformationally sensitive antibody shows the receptor to exist in the correctly folded conformation, showing binding behaviour that is commensurate with heterologously expressed CXCR4.