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.

Objective: To explore the relationship between parenting styles and depressive symptoms in adolescents with nightmare disorder, so as to provide a scientific basis for formulating effective family intervention measures and psychological counseling. Methods: From January 2023 to August 2024, 90 adolescents diagnosed with nightmare disorder and admitted to Hangzhou Seventh Peoples Hospital, along with 176 healthy controls from the urban areas of Hangzhou, were recruited as participants in the study. All participants were assessed using the Nightmare Experience Questionnaire (NEQ), Family Relationship Questionnaire (FRQ), and Plutchik-van Praag Selfreport Depression Scale (PVP). The ttest and Chisquare test were conducted to compare two groups. Pearson correlation and stepwise multiple linear regression were employed to explore the correlations between PVP and NEQ or FRQ. The Process model was used to testing the mediating effects among NEQ/FRQ/PVP. Results: The nightmare disorder group had higher scores in nightmare frequency, the four factors of NEQ (physical effect, negative emotion, meaning interpretation, horrible stimulation), and PVP than the healthy control group (24.86±18.89, 10.12±3.67, 19.01±3.51, 17.02±3.31, 15.14±3.26, 14.02±4.38; 2.34±1.04, 6.49±2.18, 17.63±4.76, 13.91±4.24, 12.40±4.49, 9.39±3.28)(t=15.79, 10.11, 2.43, 6.09, 5.14, 27.46, P<0.05). The nightmare disorder group reported significantly lower scores in FRQ general attachment and maternal encouragement than the healthy control group (7.22±2.81, 16.39±3.28) (t=-5.53, -4.95). In contrast, they exhibited significantly higher scores in maternal abuse, maternal dominance, paternal freedom release, and paternal dominance than the healthy control group (8.23±1.80, 13.11±3.73, 18.36±3.37, 12.04±3.29; 6.07±1.85, 8.48±3.80, 15.15±2.51, 9.47±3.03) (t=6.70, 8.96, 5.90, 7.04, P<0.01). The results of Pearson correlation analysis showed that, in the nightmare disorder group, the PVP score was positively correlated with negative emotion, nightmare frequency, maternal abuse, and maternal dominance score (r=0.14, 0.63, 0.26, 0.51, P<0.05). The results of multiple linear regression analysis showed that when using FRQ score to predict NEQ score, the adjusted R2 in the nightmare disorder group was 0.01-0.59. Mother abuse could prediced physical effect (β=0.33); maternal dominance significantly predicted negative emotion, horrible stimulation, and nightmare frequency (β=0.29, 0.30, 0.79); paternal freedom release could predict negative emotion (β=0.26), paternal dominance predicted both negative emotion and nightmare frequency (β=0.22, 0.45) (P<0.05). Mediation analysis further revealed that, in the nightmare disorder group, PVP scores served as a mediating variable between FRQ and NEQ. Conclusion Abusive, controlling, and neglectful family upbringing styles as well as depression maybe are key factors that may contribute to the development of nightmare disorder among adolescents.

ObjectiveAs a second messenger in intracellular signal transduction, Ca²⁺ plays an important role in cell migration. Previous studies have demonstrated that extracellular Ca²⁺ influx can promote electric field-guided cell migration, known as electrotaxis. However, the effect of intracellular Ca²⁺ flow on electrotaxis is unclear. Therefore, in this study, we investigate the effect of Ca²⁺ flux on the electrotaxis of Dictyostelium discoideum. MethodsThe electrotaxis of Dictyostelium discoideum was investigated by applying a direct current (DC) electric field. Cell migration was recorded using a real-time imaging system. Calcium channel inhibitors, the extracellular Ca²⁺ chelator EGTA, Ca²⁺-free DB buffer, and caffeine were applied to investigate the impact of intra- and extracellular Ca²⁺ flow on electrotaxis. The involvement of G proteins and ERK2 in directed cell migration mediated by endoplasmic reticulum Ca²⁺ release was explored using mutants. ResultsDictyostelium discoideum migrated toward the cathode in the electric field in a voltage-dependent manner. The intracellular Ca²⁺ concentration of the cells was significantly increased in the electric field. Inhibition of both extracellular Ca²⁺ influx and intracellular Ca²⁺ release suppressed cell electrotaxis migration. Inhibition of endoplasmic reticulum Ca²⁺ release induced by caffeine significantly impaired the electrotaxis of Dictyostelium discoideum. Deletion of Gα2, Gβ, Gγ, and Erk2 notably reduced the electrotaxis of the cells. Enhancing Ca²⁺ release mediated by caffeine restored the electrotaxis of the Gα2^-, Gβ ^-, and Erk2^- mutant cells partially or completely, but did not restore electrotaxis in the Gγ^- mutant cells. ConclusionCa²⁺ release from the endoplasmic reticulum regulates electrotaxis migration in Dictyostelium discoideum and is involved in the regulation of cell electrotaxis by G proteins and ERK2.

ObjectiveTo investigate the effects of Pinelliae Rhizoma Praeparatum （PRP） on lung tissue， gut microbiota， and short-chain fatty acid （SCFA） metabolism in a model of mice with cold fluid retention in the lung and explore its mechanism of action in resolving dampness and phlegm based on the interconnection between the lung and large intestine. MethodsFifty female ICR mice were randomly divided into a normal group， model group， positive control group （Xiaoqinglong granules， 6.5 g·kg^-1）， and high-dose and low-dose PRP decoction groups （3.0， 1.5 g·kg^-1）， with 10 mice in each group. A model of mice with cold fluid retention in the lung was established using ovalbumin （OVA） sensitization combined with cold-water immersion. Drug interventions were conducted from day 18 to day 33 for 15 consecutive days. The airway resistance value of the mice was measured using a non-invasive pulmonary function analyzer. Phlegm-resolving effects were evaluated via a microplate reader. Eosinophil and neutrophil counts in bronchoalveolar lavage fluid （BALF） were analyzed using an automated hematology analyzer. Serum levels of total immunoglobulin E （IgE）， interferon-γ （IFN-γ）， interleukin-4 （IL-4）， and BALF levels of interleukin-6 （IL-6） and interleukin-8 （IL-8） were quantified by enzyme-linked immunosorbent assay （ELISA）. Lung histopathology was assessed using hematoxylin-eosin （HE） staining. Immunohistochemistry （IHC） was employed to detect mucin 5AC （MUC5AC） and aquaporin 5 （AQP5） protein expression in lung tissue. Gut microbiota composition was analyzed via agarose gel electrophoresis， and fecal SCFA levels were measured by gas chromatography-mass spectrometry （GC-MS）. ResultsCompared with the normal group， the model group exhibited significantly increased airway resistance value （RI） （P<0.05）， elevated eosinophil and neutrophil counts and IL-6 and IL-8 levels in BALF （P<0.05）， increased serum IgE and IL-4 levels （P<0.05）， with reduced IFN-γ levels （P<0.05）. It also showed thickened bronchial walls， widened alveolar septa， narrowed lumens， and mucus plugs in lung tissue， upregulated MUC5AC protein expression and downregulated AQP5 protein expression （P<0.05）， decreased relative abundance of beneficial gut microbiota （Firmicutes， Clostridia， Clostridiales， Lactobacillaceae， and Lactobacillus）， and increased abundance of harmful microbiota （Bacteroidetes， Bacteroidia， Bacteroidales， Muribaculaceae， and Muribaculum）. In addition， the model group presented reduced fecal SCFA levels （acetate， propionate， and butyrate） （P<0.05）. After the intervention of PRP decoction， compared to the model group， all drug administration groups showed decreased RI （P<0.05）， increased phenol red excretion， declined eosinophil and neutrophil counts and IL-6， IL-8， IgE， and IL-4 levels （P<0.05）， and improved IFN-γ levels （P<0.05） and lung pathology improved. The MUC5AC protein expression decreased （P<0.05）， and the AQP5 protein expression increased （P<0.05）. The disorder of gut microbiota was improved， and the diversity of gut microbiota was restored， with a significantly increased relative abundance ratio of beneficial microbiota （P<0.05） and a significantly reduced relative abundance ratio of harmful microbiota （P<0.05）. The SCFA levels （acetate， propionate， and butyrate） increased （P<0.05）. The efficacy indicators of serum inflammatory factors （IgE， IL-4， and IFN-γ）， phlegm-resolving effect， airway resistance， total pathological score， and the protein expression of MUC5AC and AQP5 were correlated with gut microbiota and SCFAs. ConclusionPRP decoction alleviates cold-phlegm syndrome by modulating the gut-lung axis， promoting beneficial gut microbiota， enhancing SCFA production， restoring the balance of gut microbiota， and suppressing respiratory inflammation. This study provides novel insights into the TCM theory of interconnection between the lung and large intestine.

ObjectiveTo investigate the effects of Pinelliae Rhizoma Praeparatum （PRP） on lung tissue， gut microbiota， and short-chain fatty acid （SCFA） metabolism in a model of mice with cold fluid retention in the lung and explore its mechanism of action in resolving dampness and phlegm based on the interconnection between the lung and large intestine. MethodsFifty female ICR mice were randomly divided into a normal group， model group， positive control group （Xiaoqinglong granules， 6.5 g·kg^-1）， and high-dose and low-dose PRP decoction groups （3.0， 1.5 g·kg^-1）， with 10 mice in each group. A model of mice with cold fluid retention in the lung was established using ovalbumin （OVA） sensitization combined with cold-water immersion. Drug interventions were conducted from day 18 to day 33 for 15 consecutive days. The airway resistance value of the mice was measured using a non-invasive pulmonary function analyzer. Phlegm-resolving effects were evaluated via a microplate reader. Eosinophil and neutrophil counts in bronchoalveolar lavage fluid （BALF） were analyzed using an automated hematology analyzer. Serum levels of total immunoglobulin E （IgE）， interferon-γ （IFN-γ）， interleukin-4 （IL-4）， and BALF levels of interleukin-6 （IL-6） and interleukin-8 （IL-8） were quantified by enzyme-linked immunosorbent assay （ELISA）. Lung histopathology was assessed using hematoxylin-eosin （HE） staining. Immunohistochemistry （IHC） was employed to detect mucin 5AC （MUC5AC） and aquaporin 5 （AQP5） protein expression in lung tissue. Gut microbiota composition was analyzed via agarose gel electrophoresis， and fecal SCFA levels were measured by gas chromatography-mass spectrometry （GC-MS）. ResultsCompared with the normal group， the model group exhibited significantly increased airway resistance value （RI） （P<0.05）， elevated eosinophil and neutrophil counts and IL-6 and IL-8 levels in BALF （P<0.05）， increased serum IgE and IL-4 levels （P<0.05）， with reduced IFN-γ levels （P<0.05）. It also showed thickened bronchial walls， widened alveolar septa， narrowed lumens， and mucus plugs in lung tissue， upregulated MUC5AC protein expression and downregulated AQP5 protein expression （P<0.05）， decreased relative abundance of beneficial gut microbiota （Firmicutes， Clostridia， Clostridiales， Lactobacillaceae， and Lactobacillus）， and increased abundance of harmful microbiota （Bacteroidetes， Bacteroidia， Bacteroidales， Muribaculaceae， and Muribaculum）. In addition， the model group presented reduced fecal SCFA levels （acetate， propionate， and butyrate） （P<0.05）. After the intervention of PRP decoction， compared to the model group， all drug administration groups showed decreased RI （P<0.05）， increased phenol red excretion， declined eosinophil and neutrophil counts and IL-6， IL-8， IgE， and IL-4 levels （P<0.05）， and improved IFN-γ levels （P<0.05） and lung pathology improved. The MUC5AC protein expression decreased （P<0.05）， and the AQP5 protein expression increased （P<0.05）. The disorder of gut microbiota was improved， and the diversity of gut microbiota was restored， with a significantly increased relative abundance ratio of beneficial microbiota （P<0.05） and a significantly reduced relative abundance ratio of harmful microbiota （P<0.05）. The SCFA levels （acetate， propionate， and butyrate） increased （P<0.05）. The efficacy indicators of serum inflammatory factors （IgE， IL-4， and IFN-γ）， phlegm-resolving effect， airway resistance， total pathological score， and the protein expression of MUC5AC and AQP5 were correlated with gut microbiota and SCFAs. ConclusionPRP decoction alleviates cold-phlegm syndrome by modulating the gut-lung axis， promoting beneficial gut microbiota， enhancing SCFA production， restoring the balance of gut microbiota， and suppressing respiratory inflammation. This study provides novel insights into the TCM theory of interconnection between the lung and large intestine.

Objective: To explore the feasibility of synthetic magnetic resonance imaging (syMRI) combined with clinicopathological characteristics for the pre-treatment prediction of chemoradiotherapy (CRT) response in advanced nasopharyngeal carcinoma (ANPC). Materials and Methods: Patients with ANPC treated with CRT between September 2020 and June 2022 were retrospectively enrolled and categorized into response group (RG, n = 95) and non RGs (NRG, n = 32) based on the Response Evaluation Criteria in Solid Tumors (RECIST) 1.1. The quantitative parameters from pre-treatment syMRI (longitudinal [T1] and transverse [T2] relaxation times and proton density [PD]), diffusion-weighted imaging (apparent diffusion coefficient [ADC]), and clinicopathological characteristics were compared between RG and NRG. Logistic regression analysis was applied to identify parameters independently associated with CRT response and to construct a multivariable model. The areas under the receiveroperating characteristic curve (AUC) for various diagnostic approaches were compared using the DeLong test. Results: The T1, T2, and PD values in the NRG were significantly lower than those in the RG (all P < 0.05), whereas no significant difference was observed in the ADC values between these two groups. Clinicopathological characteristics (Epstein–Barr virus [EBV]-DNA level, lymph node extranodal extension, clinical stage, and Ki-67 expression) exhibited significant differences between the two groups. Logistic regression analysis showed that T1, PD, EBV-DNA level, clinical stage, and Ki-67 expression had significant independent relationships with CRT response (all P < 0.05). The multivariable model incorporating these five variables yielded AUC, sensitivity, and specificity values of 0.974, 93.8% (30/32), and 91.6% (87/95), respectively. Conclusion SyMRI may be used for the pretreatment prediction of CRT response in ANPC. The multivariable model incorporating syMRI quantitative parameters and clinicopathological characteristics, which were independently associated with CRT response, may be a new tool for the pretreatment prediction of CRT response.

Objective: This study investigates the potential of radiomics to predict postoperative progression of ossification of the posterior longitudinal ligament (OPLL) after posterior cervical spine surgery. Methods: This retrospective study included 473 patients diagnosed with OPLL at Peking University Third Hospital between October 2006 and September 2022. Patients underwent posterior spinal surgery and had at least 2 computed tomography (CT) examinations spaced at least 1 year apart. OPLL progression was defined as an annual growth rate exceeding 7.5%. Radiomic features were extracted from preoperative CT images of the OPLL lesions, followed by feature selection using correlation coefficient analysis and least absolute shrinkage and selection operator, and dimensionality reduction using principal component analysis. Univariable analysis identified significant clinical variables for constructing the clinical model. Logistic regression models, including the Rad-score model, clinical model, and combined model, were developed to predict OPLL progression. Results: Of the 473 patients, 191 (40.4%) experienced OPLL progression. On the testing set, the combined model, which incorporated the Rad-score and clinical variables (area under the receiver operating characteristic curve [AUC] = 0.751), outperformed both the radiomics-only model (AUC = 0.693) and the clinical model (AUC = 0.620). Calibration curves demonstrated good agreement between predicted probabilities and observed outcomes, and decision curve analysis confirmed the clinical utility of the combined model. SHAP (SHapley Additive exPlanations) analysis indicated that the Rad-score and age were key contributors to the model’s predictions, enhancing clinical interpretability. Conclusion Radiomics, combined with clinical variables, provides a valuable predictive tool for assessing the risk of postoperative progression in cervical OPLL, supporting more personalized treatment strategies. Prospective, multicenter validation is needed to confirm the utility of the model in broader clinical settings.

Objective: To explore the feasibility of synthetic magnetic resonance imaging (syMRI) combined with clinicopathological characteristics for the pre-treatment prediction of chemoradiotherapy (CRT) response in advanced nasopharyngeal carcinoma (ANPC). Materials and Methods: Patients with ANPC treated with CRT between September 2020 and June 2022 were retrospectively enrolled and categorized into response group (RG, n = 95) and non RGs (NRG, n = 32) based on the Response Evaluation Criteria in Solid Tumors (RECIST) 1.1. The quantitative parameters from pre-treatment syMRI (longitudinal [T1] and transverse [T2] relaxation times and proton density [PD]), diffusion-weighted imaging (apparent diffusion coefficient [ADC]), and clinicopathological characteristics were compared between RG and NRG. Logistic regression analysis was applied to identify parameters independently associated with CRT response and to construct a multivariable model. The areas under the receiveroperating characteristic curve (AUC) for various diagnostic approaches were compared using the DeLong test. Results: The T1, T2, and PD values in the NRG were significantly lower than those in the RG (all P < 0.05), whereas no significant difference was observed in the ADC values between these two groups. Clinicopathological characteristics (Epstein–Barr virus [EBV]-DNA level, lymph node extranodal extension, clinical stage, and Ki-67 expression) exhibited significant differences between the two groups. Logistic regression analysis showed that T1, PD, EBV-DNA level, clinical stage, and Ki-67 expression had significant independent relationships with CRT response (all P < 0.05). The multivariable model incorporating these five variables yielded AUC, sensitivity, and specificity values of 0.974, 93.8% (30/32), and 91.6% (87/95), respectively. Conclusion SyMRI may be used for the pretreatment prediction of CRT response in ANPC. The multivariable model incorporating syMRI quantitative parameters and clinicopathological characteristics, which were independently associated with CRT response, may be a new tool for the pretreatment prediction of CRT response.

Objective: This study investigates the potential of radiomics to predict postoperative progression of ossification of the posterior longitudinal ligament (OPLL) after posterior cervical spine surgery. Methods: This retrospective study included 473 patients diagnosed with OPLL at Peking University Third Hospital between October 2006 and September 2022. Patients underwent posterior spinal surgery and had at least 2 computed tomography (CT) examinations spaced at least 1 year apart. OPLL progression was defined as an annual growth rate exceeding 7.5%. Radiomic features were extracted from preoperative CT images of the OPLL lesions, followed by feature selection using correlation coefficient analysis and least absolute shrinkage and selection operator, and dimensionality reduction using principal component analysis. Univariable analysis identified significant clinical variables for constructing the clinical model. Logistic regression models, including the Rad-score model, clinical model, and combined model, were developed to predict OPLL progression. Results: Of the 473 patients, 191 (40.4%) experienced OPLL progression. On the testing set, the combined model, which incorporated the Rad-score and clinical variables (area under the receiver operating characteristic curve [AUC] = 0.751), outperformed both the radiomics-only model (AUC = 0.693) and the clinical model (AUC = 0.620). Calibration curves demonstrated good agreement between predicted probabilities and observed outcomes, and decision curve analysis confirmed the clinical utility of the combined model. SHAP (SHapley Additive exPlanations) analysis indicated that the Rad-score and age were key contributors to the model’s predictions, enhancing clinical interpretability. Conclusion Radiomics, combined with clinical variables, provides a valuable predictive tool for assessing the risk of postoperative progression in cervical OPLL, supporting more personalized treatment strategies. Prospective, multicenter validation is needed to confirm the utility of the model in broader clinical settings.

Objective: To explore the feasibility of synthetic magnetic resonance imaging (syMRI) combined with clinicopathological characteristics for the pre-treatment prediction of chemoradiotherapy (CRT) response in advanced nasopharyngeal carcinoma (ANPC). Materials and Methods: Patients with ANPC treated with CRT between September 2020 and June 2022 were retrospectively enrolled and categorized into response group (RG, n = 95) and non RGs (NRG, n = 32) based on the Response Evaluation Criteria in Solid Tumors (RECIST) 1.1. The quantitative parameters from pre-treatment syMRI (longitudinal [T1] and transverse [T2] relaxation times and proton density [PD]), diffusion-weighted imaging (apparent diffusion coefficient [ADC]), and clinicopathological characteristics were compared between RG and NRG. Logistic regression analysis was applied to identify parameters independently associated with CRT response and to construct a multivariable model. The areas under the receiveroperating characteristic curve (AUC) for various diagnostic approaches were compared using the DeLong test. Results: The T1, T2, and PD values in the NRG were significantly lower than those in the RG (all P < 0.05), whereas no significant difference was observed in the ADC values between these two groups. Clinicopathological characteristics (Epstein–Barr virus [EBV]-DNA level, lymph node extranodal extension, clinical stage, and Ki-67 expression) exhibited significant differences between the two groups. Logistic regression analysis showed that T1, PD, EBV-DNA level, clinical stage, and Ki-67 expression had significant independent relationships with CRT response (all P < 0.05). The multivariable model incorporating these five variables yielded AUC, sensitivity, and specificity values of 0.974, 93.8% (30/32), and 91.6% (87/95), respectively. Conclusion SyMRI may be used for the pretreatment prediction of CRT response in ANPC. The multivariable model incorporating syMRI quantitative parameters and clinicopathological characteristics, which were independently associated with CRT response, may be a new tool for the pretreatment prediction of CRT response.