Stroke ranks as the third leading cause of death and the fourth leading cause of disability worldwide. Its high disability rate and prolonged recovery period not only severely impact patients' quality of life but also impose a significant burden on families and society. Primary prevention is the cornerstone of stroke control, as early intervention on risk factors can effectively reduce its incidence. Therefore, the development of predictive models for first-ever stroke risk holds substantial clinical value. In recent years, advancements in big data and artificial intelligence technologies have opened new avenues for stroke risk prediction. This article reviews the current research status of traditional methods and machine learning models in predicting first-ever stroke risk and outlines future development trends from three perspectives: First, emphasis should be placed on technological innovation by incorporating advanced algorithms such as deep learning and large models to further enhance the accuracy of predictive models. Second, there is a need to diversify data types and optimize model architectures to construct more comprehensive and precise predictive models. Lastly, particular attention should be given to the clinical validation of models in real-world settings. This not only enhances the robustness and generalizability of the models but also promotes physicians' understanding of predictive models, which is crucial for their application and dissemination.

Introduction: Dental caries is a preventable chronic disease whereby identification of risk factors will facilitate preventive measures. This study aims to determine the level of oral health literacy (OHL), self-care practices (SCP), salivary parameters and ascertain its assocation with caries status amongst the undergraduates in IMU University. Methods: Levels of OHL (Knowledge-OHL, dental services utilisation, and label reading habit) and SCP were assessed through a self-administered questionnaire. Chairside saliva kits were used to measure the salivary parameters whilst clinical examination was performed to assess caries status. Independent T-test and Analysis of Variance (ANOVA) was used to compare differences between sex and courses respectively for measures of interest (OHL, SCP, salivary parameters, and caries status) whereas bivariate correlation with Pearsons’s coefficient was performed to examine their association with caries status. Results: The participants (n=132) had a mean Knowledge-OHL score of 23.75±8.09 with no significant difference between sex (females, 24.01±8.51; males, 23.48±7.69; p=0.15). Dentistry students had significantly higher Knowledge-OHL score than students of all other courses (p=0.01). The mean SCP score was 20.19±3.16 whereas mean DMFT was 2.32 ±3.14. All participants had healthy saliva parameters. Caries status was significantly correlated with Knowledge-OHL score (p=0.02, r=-0.18), dental services utilisation (p=0.04, r=-0.15) but not with label reading habit (p=0.78, r=0.03), SCP (p=0.30, r=-0.05) and all salivary parameters. Conclusion Knowledge-OHL and oral health services utilisation are significantly associated with oral health status
Dental Caries ; Health Literacy ; Oral Health ; Saliva ; Self Care

Background: s/Aims: Non-invasive models stratifying clinically significant portal hypertension (CSPH) are limited. Herein, we developed a new non-invasive model for predicting CSPH in patients with compensated cirrhosis and investigated whether carvedilol can prevent hepatic decompensation in patients with high-risk CSPH stratified using the new model. Methods: Non-invasive risk factors of CSPH were identified via systematic review and meta-analysis of studies involving patients with hepatic venous pressure gradient (HVPG). A new non-invasive model was validated for various performance aspects in three cohorts, i.e., a multicenter HVPG cohort, a follow-up cohort, and a carvediloltreating cohort. Results: In the meta-analysis with six studies (n=819), liver stiffness measurement and platelet count were identified as independent risk factors for CSPH and were used to develop the new “CSPH risk” model. In the HVPG cohort (n=151), the new model accurately predicted CSPH with cutoff values of 0 and –0.68 for ruling in and out CSPH, respectively. In the follow-up cohort (n=1,102), the cumulative incidences of decompensation events significantly differed using the cutoff values of <–0.68 (low-risk), –0.68 to 0 (medium-risk), and >0 (high-risk). In the carvediloltreated cohort, patients with high-risk CSPH treated with carvedilol (n=81) had lower rates of decompensation events than non-selective beta-blockers untreated patients with high-risk CSPH (n=613 before propensity score matching [PSM], n=162 after PSM). Conclusions Treatment with carvedilol significantly reduces the risk of hepatic decompensation in patients with high-risk CSPH stratified by the new model.

Background: s/Aims: Non-invasive models stratifying clinically significant portal hypertension (CSPH) are limited. Herein, we developed a new non-invasive model for predicting CSPH in patients with compensated cirrhosis and investigated whether carvedilol can prevent hepatic decompensation in patients with high-risk CSPH stratified using the new model. Methods: Non-invasive risk factors of CSPH were identified via systematic review and meta-analysis of studies involving patients with hepatic venous pressure gradient (HVPG). A new non-invasive model was validated for various performance aspects in three cohorts, i.e., a multicenter HVPG cohort, a follow-up cohort, and a carvediloltreating cohort. Results: In the meta-analysis with six studies (n=819), liver stiffness measurement and platelet count were identified as independent risk factors for CSPH and were used to develop the new “CSPH risk” model. In the HVPG cohort (n=151), the new model accurately predicted CSPH with cutoff values of 0 and –0.68 for ruling in and out CSPH, respectively. In the follow-up cohort (n=1,102), the cumulative incidences of decompensation events significantly differed using the cutoff values of <–0.68 (low-risk), –0.68 to 0 (medium-risk), and >0 (high-risk). In the carvediloltreated cohort, patients with high-risk CSPH treated with carvedilol (n=81) had lower rates of decompensation events than non-selective beta-blockers untreated patients with high-risk CSPH (n=613 before propensity score matching [PSM], n=162 after PSM). Conclusions Treatment with carvedilol significantly reduces the risk of hepatic decompensation in patients with high-risk CSPH stratified by the new model.

Background: s/Aims: Non-invasive models stratifying clinically significant portal hypertension (CSPH) are limited. Herein, we developed a new non-invasive model for predicting CSPH in patients with compensated cirrhosis and investigated whether carvedilol can prevent hepatic decompensation in patients with high-risk CSPH stratified using the new model. Methods: Non-invasive risk factors of CSPH were identified via systematic review and meta-analysis of studies involving patients with hepatic venous pressure gradient (HVPG). A new non-invasive model was validated for various performance aspects in three cohorts, i.e., a multicenter HVPG cohort, a follow-up cohort, and a carvediloltreating cohort. Results: In the meta-analysis with six studies (n=819), liver stiffness measurement and platelet count were identified as independent risk factors for CSPH and were used to develop the new “CSPH risk” model. In the HVPG cohort (n=151), the new model accurately predicted CSPH with cutoff values of 0 and –0.68 for ruling in and out CSPH, respectively. In the follow-up cohort (n=1,102), the cumulative incidences of decompensation events significantly differed using the cutoff values of <–0.68 (low-risk), –0.68 to 0 (medium-risk), and >0 (high-risk). In the carvediloltreated cohort, patients with high-risk CSPH treated with carvedilol (n=81) had lower rates of decompensation events than non-selective beta-blockers untreated patients with high-risk CSPH (n=613 before propensity score matching [PSM], n=162 after PSM). Conclusions Treatment with carvedilol significantly reduces the risk of hepatic decompensation in patients with high-risk CSPH stratified by the new model.

This study aims to explore the synergistic effects of the main components in salvianolic acids for Injection(SAFI) on key pathological events in cerebral ischemia, elucidating the pharmacological characteristics of SAFI in neuroprotection. Two major pathological gene modules related to endothelial injury and neuroinflammation in cerebral ischemia were mined from single-cell data. According to the topological distance calculated in network medicine, potential synergistic component combinations of SAFI were screened out. The results showed that the combination of caffeic acid and salvianolic acid B scored the highest in addressing both endothelial injury and neuroinflammation, demonstrating potential synergistic effects. The cell experiments confirmed that the combination of these two components at a ratio of 1∶1 significantly protected brain microvascular endothelial cells(bEnd.3) from oxygen-glucose deprivation/reoxygenation(OGD/R)-induced reperfusion injury and effectively suppressed lipopolysaccharide(LPS)-induced neuroinflammatory responses in microglial cells(BV-2). This study provides a new method for uncovering synergistic effects among active components in traditional Chinese medicine(TCM) and offers novel insights into the multi-component, multi-target acting mechanisms of TCM.
Brain Ischemia/metabolism* ; Neuroprotective Agents/pharmacology* ; Animals ; Drugs, Chinese Herbal/administration & dosage* ; Benzofurans/pharmacology* ; Mice ; Drug Synergism ; Caffeic Acids/pharmacology* ; Polyphenols/pharmacology* ; Humans ; Alkenes/pharmacology* ; Endothelial Cells/drug effects* ; Depsides

Based on ultra-performance liquid chromatography-quadrupole-electrostatic field Orbitrap high-resolution mass spectrometry(UPLC-Q-Orbitrap HRMS) technology and molecular docking, the bitter-tasting substances(hereafter referred to as "bitter substances") in Cistanche deserticola extract were investigated, and the bitter taste and efficacy relationship was explored to lay the foundation for future research on de-bittering and taste correction. Firstly, UPLC-Q-Orbitrap HRMS was used for the qualitative analysis of the constituents of C. deserticola, and 69 chemical components were identified. These chemical components were then subjected to molecular docking with the bitter taste receptor, leading to the screening of 20 bitter substances, including 6 phenylethanol glycosides, 5 flavonoids, 3 phenolic acids, 2 cycloalkenyl ether terpenes, 2 alkaloids, and 2 other components. Nine batches of fresh C. deserticola samples were collected from the same origin but harvested at different months. These samples were divided into groups based on harvest month and plant part. The bitterness was quantified using an electronic tongue, and the content of six potential bitter-active compounds(pineconotyloside, trichothecene glycoside, tubulin A, iso-trichothecene glycoside, jinshihuaoside, and jingnipinoside) was determined by high-performance liquid chromatography(HPLC). The total content of phenylethanol glycosides, polysaccharides, alkaloids, flavonoids, and phenolic acids was determined using UV-visible spectrophotometry. Chemometric analyses were then conducted, including Pearson's correlation analysis, gray correlation analysis, and orthogonal partial least squares discriminant analysis(OPLS-DA), to identify the bitter components in C. deserticola. The results were consistent with the molecular docking findings, and the two methods mutually supported each other. Finally, network pharmacological predictions and analyses were performed to explore the relationship between the targets of bitter substances and their efficacy. The results indicated that key targets of the bitter substances included EGFR, PIK3CB, and PTK2. These substances may exert their bitter effects by acting on relevant disease targets, confirming that the bitter substances in C. deserticola are the material basis of its bitter taste efficacy. In conclusion, this study suggests that the phenylethanol glycosides, primarily pineconotyloside, mauritiana glycoside, and gibberellin, are the material basis for the "bitter taste" of C. deserticola. The molecular docking technique plays a guiding role in the screening of bitter substances in traditional Chinese medicine(TCM). The bitter substances in C. deserticola not only contribute to its bitter taste but also support the concept of the "taste-efficacy" relationship in TCM, providing valuable insights and references for future research in this area.
Molecular Docking Simulation ; Taste ; Chromatography, High Pressure Liquid ; Cistanche/chemistry* ; Drugs, Chinese Herbal/chemistry* ; Humans ; Mass Spectrometry