Stroke is a leading cause of mortality and disability worldwide, expected to result in 61 million disability‐adjusted life‐years in 2020. Rapid diagnostics is the core of stroke management for early prevention and medical treatment. Serum metabolic fingerprints (SMFs) reflect underlying disease progression, predictive of patient phenotypes. Deep learning (DL) encoding SMFs with clinical indexes outperforms single biomarkers, while posing challenges with poor prediction to interpret by feature selection. Herein, rapid computer‐aided diagnosis of stroke is performed using SMF based multi‐modal recognition by DL, to combine adaptive machine learning with a novel feature selection approach. SMFs are extracted by nano‐assisted laser desorption/ionization mass spectrometry (LDI MS), consuming 100 nL of serum in seconds. A multi‐modal recognition is constructed by integrating SMFs and clinical indexes with an enhanced area under curve (AUC) up to 0.845 for stroke screening, compared to single‐modal diagnosis by only SMFs or clinical indexes. The prediction of DL is addressed by selecting 20 key metabolite features with differential regulation through a saliency map approach, shedding light on the molecular mechanisms in stroke. The approach highlights the emerging role of DL in precision medicine and suggests an expanding utility for computational analysis of SMFs in stroke screening.

Background: A systematic relational assessment of the global, regional, and national Ischemic heart disease (IHD) burden and its attributable risk factors is essential for developing more targeted prevention and intervention strategies. Methods: The GBD 2021 comparative risk assessment framework was employed to evaluate stroke burden attributable to environmental, behavioral, metabolic, and dietary risk factors, and a total of 25 risk factors were included. Specifically, we used the joinpoint regression model, decomposition analysis, and systematic fixed-effects analysis to reveal the global, regional, and national burden of IHD attributable to these 25 risk factors and their exposure value across 204 countries and territories with different socio-demographic index (SDI) levels from different perspectives. Results: Joinpoint regression revealed similar trends in summary exposure value (SEV) and attributable burdens for 25 IHD risk factors. From 1990 to 2021, SEV rankings increased for 12/25 risk factors, decreased for 10/25, and remained unchanged for 3/25. Decomposition analysis indicated that from 1990 to 2021, low SDI countries experienced the most significant increase in IHD burden attributable to 25 risk factors due to population growth, while upper-middle and high SDI countries were most affected by population aging, and high SDI countries demonstrated the greatest reduction in IHD burden attributed to epidemiological changes. Panel data analysis elucidated the impact of SEV, SDI, and quality-of-care index (QCI) on attributable IHD burden. Conclusion: This study emphasizing the critical role of risk factor control. Tailored interventions and exploration of country-specific factors are crucial for effectively reducing the global IHD burden.

Objective: Dehydration on admission is correlated with neurological deterioration (ND). The primary objective of our study was to use support vector machine (SVM) algorithms to identify an ND prognostic model, based on dehydration equations.Methods: This study included a total of 382 patients hospitalized with acute ischemic stroke. The following parameters were recorded: age, sex, laboratory values (serum sodium, potassium, chlorinum, glucose, and urea), and vascular risk factor data. Receiver operating characteristic (ROC) curve analysis was used to evaluate the discriminative performance of the BUN/Cr ratio as well as each of 38 equations for predicting ND. We used the Boruta algorithm for feature selection. After optimizing the SVM kernel parameters, we built an SVM model to predict ND and used the test set to obtain predictive values for assessing model accuracy.Results: In total, 102 of 382 patients (26.7%) with acute ischemic stroke developed ND. In all patients, the BUN/Cr ratio and each of 38 equations were significant predictors of ND. Equation 20 [1.86 × Na+ + glucose + urea + 9] yielded the maximum area under the ROC curve, and faired best in terms of prognostic performance (a cutoff value of 284.49 mM yielded a sensitivity of 94.12% and specificity of 61.43%). Equation 32 predicted ND poststroke across population groups, and worked well in older as well as young adults; (a cutoff value of 297.08 mM yielded a sensitivity of 93.14% and specificity of 60.00%). Feature selection by the Boruta algorithm was used to decrease the number of variables from 18 to 5 in the condition. The specificity of test samples for the SVM prediction model increased from 44.1% to 89.4%, and the AUC increased from 0.700 to 0.927.Conclusions: SVM algorithms can be used to establish a prediction model for dehydration‐associated ND, with good classification results.

AMPK activation by Tanshinone IIA protects neuronal cells from oxygen-glucose deprivation AMPK activation by Tanshinone IIA protects neuronal cells from oxygen-glucose deprivation