Tumor Necrosis Factor-Related Apoptosis-Inducing Ligand (TRAIL) in Patients after Acute Stroke: Relation to Stroke Severity, Myocardial Injury, and Impact on Prognosis

Mechanistic insight into airborne particulate matter PM10 as an environmental hazard for hemorrhagic stroke: Evidence from in vitro and in vivo studies

Airborne particulate matter less than 10 µm in diameter (PM10) is recognized as a significant environmental risk factor for hemorrhagic stroke (HS), as evidenced by epidemiological studies that link PM10 with the heightened cerebrovascular mortality related to HS. Nonetheless, the molecular mechanisms underlying this association remain unknown. Cerebral aneurysm (CA), an etiological factor of HS, is characterized by a bulge resulting from the abnormal loss of the muscular layer of a cerebral artery, comprising brain vascular endothelial cell (BVEC) and vascular smooth muscle cell (VSMC). BVEC exhibiting an inflammatory phenotype is critical for VSMC death within the cerebrovasculature. Here, we elucidate a molecular mechanism by which PM10 augments necroptotic death of VSMC as a consequence of intercellular effects arising from FasL inflammatory cytokine, which is derived from BVEC. Notably, BVEC exposed to PM10 upregulates FasL through ATM-NF-κB signaling, in response to oxidative DNA damage. This genotoxic stress is attributed to pro-oxidant action of aluminum, the prevalent element in PM10. Furthermore, respiratory exposure to PM10 in mice precipitates early onset of CA development through necroptotic VSMC death in cerebral artery, by activating FasL expression in BVEC. In conclusion, this study provides molecular evidence establishing a direct association between PM10 pollution and an elevated risk of stroke, particularly HS.

Stroke-heart syndrome: current progress and future outlook

Stroke can lead to cardiac complications such as arrhythmia, myocardial injury, and cardiac dysfunction, collectively termed stroke-heart syndrome (SHS). These cardiac alterations typically peak within 72 h of stroke onset and can have long-term effects on cardiac function. Post-stroke cardiac complications seriously affect prognosis and are the second most frequent cause of death in patients with stroke. Although traditional vascular risk factors contribute to SHS, other potential mechanisms indirectly induced by stroke have also been recognized. Accumulating clinical and experimental evidence has emphasized the role of central autonomic network disorders and inflammation as key pathophysiological mechanisms of SHS. Therefore, an assessment of post-stroke cardiac dysautonomia is necessary. Currently, the development of treatment strategies for SHS is a vital but challenging task. Identifying potential key mediators and signaling pathways of SHS is essential for developing therapeutic targets. Therapies targeting pathophysiological mechanisms may be promising. Remote ischemic conditioning exerts protective effects through humoral, nerve, and immune-inflammatory regulatory mechanisms, potentially preventing the development of SHS. In the future, well-designed trials are required to verify its clinical efficacy. This comprehensive review provides valuable insights for future research.

Prevalence of myocardial injury in patients after acute ischaemic stroke according to standard criteria

This study examined the prevalence of acute and chronic myocardial injury according to standard criteria in patients after acute ischaemic stroke (AIS) and its relation to stroke severity and short-term prognosis. Between August 2020 and August 2022, 217 consecutive patients with AIS were enrolled. Plasma levels of high-sensitive cardiac troponin I (hs-cTnI) were measured in blood samples obtained at the time of admission and 24 and 48 h later. The patients were divided into three groups according to the Fourth Universal Definition of Myocardial Infarction: no injury, chronic injury, and acute injury. Twelve-lead ECGs were obtained at the time of admission, 24 and 48 h later, and on the day of hospital discharge. A standard echocardiographic examination was performed within the first 7 days of hospitalization in patients with suspected abnormalities of left ventricular function and regional wall motion. Demographic characteristics, clinical data, functional outcomes, and all-cause mortality were compared between the three groups. The National Institutes of Health Stroke Scale (NIHSS) at the time of admission and the modified Rankin Scale (mRS) 90 days following hospital discharge were used to assess stroke severity and outcome. Elevated hs-cTnI levels were measured in 59 patients (27.2%): 34 patients (15.7%) had acute myocardial injury and 25 patients (11.5%) had chronic myocardial injury within the acute phase after ischaemic stroke. An unfavourable outcome, evaluated based on the mRS at 90 days, was associated with both acute and chronic myocardial injury. Myocardial injury was also strongly associated with all-cause death, with the strongest association in patients with acute myocardial injury, at 30 days and at 90 days. Kaplan-Meier survival curves showed that all-cause mortality was significantly higher in patients with acute and chronic myocardial injury than in patients without myocardial injury (P < 0.001). Stroke severity, evaluated with the NIHSS, was also associated with acute and chronic myocardial injury. A comparison of the ECG findings between patients with and without myocardial injury showed a higher occurrence in the former of T-wave inversion, ST segment depression, and QTc prolongation. In echocardiographic analysis, a new abnormality in regional wall motion of the left ventricle was identified in six patients. Chronic and acute myocardial injury with hs-cTnI elevation after AIS are associated with stroke severity, unfavourable functional outcome, and short-term mortality.

The Prognostic, Diagnostic, and Therapeutic Potential of TRAIL Signalling in Cardiovascular Diseases

TNF-related apoptosis-inducing ligand (TRAIL) was originally discovered, almost 20 years ago, for its ability to kill cancer cells. More recent evidence has described pleiotropic functions, particularly in the cardiovascular system. There is potential for TRAIL concentrations in the circulation to act as prognostic and/or diagnostic factors for cardiovascular diseases (CVD). Pre-clinical studies also describe the therapeutic capacity for TRAIL signals, particularly in the context of atherosclerotic disease and diseases of the myocardium. Because diabetes mellitus significantly contributes to the progression and pathogenesis of CVDs, in this review we highlight recent evidence for the prognostic, diagnostic, and therapeutic potential of TRAIL signals in CVDs, and where relevant, the impact of diabetes mellitus. A greater understanding of how TRAIL signals regulate cardiovascular protection and pathology may offer new diagnostic and therapeutic avenues for patients suffering from CVDs.

Identification of High-Risk Patients for Postoperative Myocardial Injury After CME Using Machine Learning: A 10-Year Multicenter Retrospective Study

Purpose

The occurrence of myocardial injury, a grave complication post complete mesocolic excision (CME), profoundly impacts the immediate and long-term prognosis of patients. The aim of this inquiry was to conceive a machine learning model that can recognize preoperative, intraoperative and postoperative high-risk factors and predict the onset of myocardial injury following CME.

Patients and Methods

This study included 1198 colon cancer patients, 133 of whom experienced myocardial injury after surgery. Thirty-six distinct variables were gathered, encompassing patient demographics, medical history, preoperative examination characteristics, surgery type, and intraoperative details. Four machine learning algorithms, namely, extreme gradient boosting (XGBoost), random forest (RF), multilayer perceptron (MLP), and k-nearest neighbor algorithm (KNN), were employed to fabricate the model, and k-fold cross-validation, ROC curve, calibration curve, decision curve analysis (DCA), and external validation were employed to evaluate it.

Results

Out of the four predictive models employed, the XGBoost algorithm demonstrated the best performance. The ROC curve findings indicated that the XGBoost model exhibited remarkable predictive accuracy, with an area under the curve (AUC) value of 0.997 in the training set and 0.956 in the validation set. For internal validation, the k-fold cross-validation method was utilized, and the XGBoost model was shown to be steady. Furthermore, the calibration curves demonstrated the XGBoost model's high predictive capability. The DCA curve revealed higher benefit rates for patients who underwent interventional treatment under the XGBoost model. The AUC value for the external validation set was 0.74, which indicated that the XGBoost prediction model possessed good extrapolative capacity.

Conclusion

The myocardial injury prediction model for patients undergoing CME that was developed using the XGBoost machine learning algorithm in this study demonstrates both high predictive accuracy and clinical utility.