Ischemic stroke accounts for approximately 80% of all stroke cases worldwide and remains a leading cause of long‑term neurological disability and mortality. Intravenous thrombolysis and endovascular thrombectomy constitute the mainstay of acute reperfusion therapies, yet their clinical utility is severely constrained by narrow therapeutic time windows. Even when large‑vessel recanalization is successfully achieved, a considerable proportion of patients still suffer poor functional outcomes due to futile reperfusion and ischemia‑reperfusion injury. Historically, preclinical studies have largely focused on direct neuronal protection against calcium overload, oxidative stress and ferroptosis. Unfortunately, most promising neuroprotective agents that exhibited robust benefits in rodent models have failed to demonstrate satisfactory efficacy in phase‑Ⅱ and phase‑Ⅲ clinical trials. One critical contributor to such translational failure is the disregard for the pathological disturbance within the cerebral vascular microenvironment. Instead of merely regarding neuronal death as an autonomous cellular event, accumulating evidence indicates that sequential biochemical disruption of perivascular microenvironment acts as an initiating amplifier that propagates damage toward brain parenchyma after ischemic insult. The cerebral vascular microenvironment centers on brain microvessels and comprises endothelial cells, endothelial glycocalyx, basement membrane, pericytes, astrocytic end‑feet, perivascular microglia, as well as interstitial ions, inflammatory mediators, reactive oxygen species and extracellular matrix components. Distinct from the concept of neurovascular unit which emphasizes functional coupling between neurons, glia and vessels, the vascular microenvironment highlights dynamic biochemical homeostasis in perivascular interstitial space. Following cerebral ischemia, injured brain tissue can be spatially divided into the irreversibly damaged infarct core, metabolically‑stressed ischemic penumbra and mildly‑affected peripheral regions. These three zones lack rigid anatomical boundaries and evolve dynamically depending on reperfusion timing, presenting divergent features in blood perfusion, blood‑brain barrier integrity, cellular viability and immune‑inflammatory profiles. Four interrelated pathological cascades jointly disrupt microenvironmental homeostasis: calcium‑overload‑triggered cytotoxic cascades; blood‑brain barrier breakdown resulting from glycocalyx shedding, tight‑junction dissociation and matrix metalloproteinase‑driven basement‑membrane degradation; inflammatory‑immune dysregulation dominated by microglial phenotypic reprogramming; and mutual crosstalk between coagulation disturbance and neuroinflammation that exacerbates secondary brain injury. Multiple molecular layers orchestrate these pathological alterations. Excessive activation of HIF‑1α, NF‑κB and MAPK/ERK signaling pathways promotes barrier leakage, pro‑inflammatory factor secretion and extracellular‑matrix remodeling. Epigenetic modulation mediated by non‑coding RNAs including miRNAs and lncRNAs reshapes vascular microenvironment via three major patterns: modulating endothelial‑pericyte barrier integrity, switching microglia‑astrocyte inflammatory phenotypes, and driving metabolic reprogramming among multiple cell populations within microdomains. In addition, metabolic shift toward glycolysis among endothelial cells, microglia, astrocytes and compromised neurons establishes self‑amplifying vicious cycles of energy depletion and oxidative stress in perivascular niches. Targeted theranostic approaches oriented toward vascular microenvironment bring new opportunities for precision medicine of ischemic stroke. Magnetic nanoprobes and near‑infrared fluorescent probes respond to elevated reactive oxygen species, pH shift and inflammatory signals, enabling in situ visualization of pathological signatures of microenvironment and risk evaluation of hemorrhagic transformation. Function‑tailored nanodelivery systems exert multi‑modal protective effects: reactive oxygen species‑responsive polymeric micelles deliver calcium chelators to block calcium‑overload‑initiated injury cascades; nanozymes scavenge reactive oxygen/nitrogen species and revert microglial pro‑inflammatory polarization; biomimetic membrane‑coated nanoparticles accumulate at micro‑thrombosis sites to break thrombo‑inflammatory loops and mitigate no‑reflow phenomenon; ferroptosis‑inhibitor‑loaded nanocarriers eliminate excessive labile iron within perivascular toxic microdomains. During subacute and recovery phases, spatiotemporally controlled release of angiogenic factors by nanocarriers facilitates maturation of newly‑formed microvessels rather than blind vascular sprouting, which helps reconstruct intact blood‑brain barrier and restore microenvironmental homeostasis. Nevertheless, substantial translational bottlenecks still impede clinical transformation of these nanomedicines, including potential biosafety risks caused by in vivo material accumulation, protein‑corona‑induced impairment of targeting capacity, intrinsic blood brain barrier transport obstacles, lack of unified quality‑control criteria for large‑scale manufacture, reliance on high‑end clinical imaging instruments, and prominent interspecies discrepancies between experimental animals and human patients. On the basis of summarizing spatiotemporal pathological characteristics, multi‑level regulatory mechanisms and cutting‑edge microenvironment‑oriented diagnostic and interventional strategies, the present review advocates a conceptual shift from isolated neuronal protection to comprehensive reconstruction of perivascular vascular microenvironment homeostasis. Such perspective provides theoretical foundations for expanding therapeutic time windows and inspires future development of novel therapeutic modalities for ischemic stroke.

Objective:To summarize the clinical characteristics of patients with acute pancreatitis (AP) in plateau areas, and screen potential biomarkers for the pathogenesis of AP at high altitudes by metabolomics.Methods:A total of 42 patients with AP admitted to the Department of Gastroenterology in Lhasa People's Hospital from December 2023 to May 2024 were prospectively enrolled (AP group), and 33 healthy controls (Control group) were included during the same period. Demographic and clinical data were collected, and serum non-targeted metabolomics was performed based on ultra-performance liquid chromatography-mass spectrometry techniques. The serum metabolites map was evaluated by using principal component analysis, and a regression model with orthogonal partial least squares-discriminant analysis (OPLS-DA) was constructed to evaluate the explanatory power ( R2) and predictive power ( Q2) of these metabolites. The OPLS-DA-based dimensionality reduction was applied to compute variable importance in projection (VIP), while fold change (FC) values were used to assess the difference of metabolites between groups. A bidirectional clustering heat map was generated for samples and differential metabolites to evaluate sample similarity within groups. Additionally, a volcano plot was created to visualize differential metabolites, and the top five up-regulated and down-regulated metabolites distinguishing AP from healthy controls were selected. The receiver operating characteristic curve (ROC) was drawn, and the area under the curve (AUC), sensitivity and specificity based on ROC analysis were calculated to evaluate the differential power of differential metabolites. Results:The majority of participants were Tibetans in both the AP group (37 cases, 88.1%) and the control group (27 cases, 81.8%). The average age of AP patients was (50.45±17.85) years old, and the male to female ratio was 1.1∶1.0. The leading etiology was biliary disease (33 cases, 78.6%), and most patients encountered moderately severe disease (26 cases, 61.9%). The incidence of local complications was 83.3%, mainly thoracoabdominal effusion and peripancreatic effusion (30 cases, 71.4%). The incidence of systemic complications was 59.5%, mainly systemic inflammatory response syndrome (22 cases, 52.4%) and respiratory failure (15 cases, 35.7%). Principal component analysis showed significant differences in serum metabolites between groups. OPLS-DA showed that these metabolites effectively distinguished AP patients from healthy controls: R2=0.992 and Q2=0.913 in the positive ion mode, R2=0.983 and Q2=0.914 in the negative ion mode. There were 450 up-regulated and 366 down-regulated differential metabolites in AP group, respectively. Among them, gamma-glutamylleucine, cortisone, 4(15)-Copaen-11-ol, mytiloxanthin, and indole-3-glycol aldehyde were the top five up-regulated metabolites, while N-Acetyltryptophan, kynurenic acid, deoxyuridine monophosphate, pseudouridine, and farnesyl acetate were the top five down-regulated metabolites. ROC analysis of these markers showed that all AUC values were >0.8, with all P values <0.001, with both sensitivity and specificity exceeding 80%. Among them, N-Acetyltryptophan and farnesyl acetate possessed the best differential performance. Conclusions:Biliary causes are most frequent among AP patients in plateau area. The disease severity is mainly moderately severe, accompanied by high incidences of local and systemic complications. Some amino acids and prenol lipids could significantly distinguish AP patients from healthy controls, and might be involved in the pathogenesis of AP at high altitudes.

Objective:To explore the application value of machine learning algorithms in constructing a predictive model for cardiovascular toxicity in breast cancer patients receiving anthracycline-based chemotherapy.Methods:This study was a retrospective cohort study. The female patients with breast cancer who received anthracyclines in the Affiliated Cancer Hospital of Xinjiang Medical University from January 2020 to December 2023 were enrolled. The endpoint event was abnormal electrocardiogram (ECG). According to whether the patients had ECG abnormalities during chemotherapy, they were divided into the ECG abnormal group and the ECG normal group. The dataset was divided into the training set and the test set at a ratio of 8∶2, and logistic regression, random forest, extreme gradient boosting (XGBoost), support vector machine (SVM) and multilayer perceptron (MLP) were used to construct a risk prediction model for cardiovascular toxicity in breast cancer patients, and the receiver operating characteristic curve, calibration curve and clinical decision curve were used to evaluate the model.Results:A total of 731 female patients with breast cancer, aged (51.6±9.4) years, were enrolled. The follow-up time was (130.3±37.1) days. There were 333 cases in the ECG abnormal group and 398 cases in the ECG normal group. Seven factors influencing cardiovascular toxicity were identified, including age, menstrual history, diabetes, combination therapy with trastuzumab, combination therapy with dexrazoxane, creatine kinase isoenzymes, and α-hydroxybutyrate dehydrogenase. In the training set, the area under the curve ( AUC) for the logistic regression, random forest, XGBoost, SVM, and MLP models was 0.712, 0.863, 0.774, 0.813, and 0.733, respectively. In the test set, the AUC was 0.671, 0.778, 0.746, 0.771, and 0.705, respectively. Calibration curves and clinical decision curves showed that the random forest model performed the best. Conclusion:Models constructed with machine learning algorithms show promise in predicting cardiovascular toxicity in breast cancer patients receiving anthracycline-based chemotherapy, with the random forest prediction model performing the best.

Objective:To summarize the clinical characteristics of patients with acute pancreatitis (AP) in plateau areas, and screen potential biomarkers for the pathogenesis of AP at high altitudes by metabolomics.Methods:A total of 42 patients with AP admitted to the Department of Gastroenterology in Lhasa People's Hospital from December 2023 to May 2024 were prospectively enrolled (AP group), and 33 healthy controls (Control group) were included during the same period. Demographic and clinical data were collected, and serum non-targeted metabolomics was performed based on ultra-performance liquid chromatography-mass spectrometry techniques. The serum metabolites map was evaluated by using principal component analysis, and a regression model with orthogonal partial least squares-discriminant analysis (OPLS-DA) was constructed to evaluate the explanatory power ( R2) and predictive power ( Q2) of these metabolites. The OPLS-DA-based dimensionality reduction was applied to compute variable importance in projection (VIP), while fold change (FC) values were used to assess the difference of metabolites between groups. A bidirectional clustering heat map was generated for samples and differential metabolites to evaluate sample similarity within groups. Additionally, a volcano plot was created to visualize differential metabolites, and the top five up-regulated and down-regulated metabolites distinguishing AP from healthy controls were selected. The receiver operating characteristic curve (ROC) was drawn, and the area under the curve (AUC), sensitivity and specificity based on ROC analysis were calculated to evaluate the differential power of differential metabolites. Results:The majority of participants were Tibetans in both the AP group (37 cases, 88.1%) and the control group (27 cases, 81.8%). The average age of AP patients was (50.45±17.85) years old, and the male to female ratio was 1.1∶1.0. The leading etiology was biliary disease (33 cases, 78.6%), and most patients encountered moderately severe disease (26 cases, 61.9%). The incidence of local complications was 83.3%, mainly thoracoabdominal effusion and peripancreatic effusion (30 cases, 71.4%). The incidence of systemic complications was 59.5%, mainly systemic inflammatory response syndrome (22 cases, 52.4%) and respiratory failure (15 cases, 35.7%). Principal component analysis showed significant differences in serum metabolites between groups. OPLS-DA showed that these metabolites effectively distinguished AP patients from healthy controls: R2=0.992 and Q2=0.913 in the positive ion mode, R2=0.983 and Q2=0.914 in the negative ion mode. There were 450 up-regulated and 366 down-regulated differential metabolites in AP group, respectively. Among them, gamma-glutamylleucine, cortisone, 4(15)-Copaen-11-ol, mytiloxanthin, and indole-3-glycol aldehyde were the top five up-regulated metabolites, while N-Acetyltryptophan, kynurenic acid, deoxyuridine monophosphate, pseudouridine, and farnesyl acetate were the top five down-regulated metabolites. ROC analysis of these markers showed that all AUC values were >0.8, with all P values <0.001, with both sensitivity and specificity exceeding 80%. Among them, N-Acetyltryptophan and farnesyl acetate possessed the best differential performance. Conclusions:Biliary causes are most frequent among AP patients in plateau area. The disease severity is mainly moderately severe, accompanied by high incidences of local and systemic complications. Some amino acids and prenol lipids could significantly distinguish AP patients from healthy controls, and might be involved in the pathogenesis of AP at high altitudes.

Objective:To explore the application value of machine learning algorithms in constructing a predictive model for cardiovascular toxicity in breast cancer patients receiving anthracycline-based chemotherapy.Methods:This study was a retrospective cohort study. The female patients with breast cancer who received anthracyclines in the Affiliated Cancer Hospital of Xinjiang Medical University from January 2020 to December 2023 were enrolled. The endpoint event was abnormal electrocardiogram (ECG). According to whether the patients had ECG abnormalities during chemotherapy, they were divided into the ECG abnormal group and the ECG normal group. The dataset was divided into the training set and the test set at a ratio of 8∶2, and logistic regression, random forest, extreme gradient boosting (XGBoost), support vector machine (SVM) and multilayer perceptron (MLP) were used to construct a risk prediction model for cardiovascular toxicity in breast cancer patients, and the receiver operating characteristic curve, calibration curve and clinical decision curve were used to evaluate the model.Results:A total of 731 female patients with breast cancer, aged (51.6±9.4) years, were enrolled. The follow-up time was (130.3±37.1) days. There were 333 cases in the ECG abnormal group and 398 cases in the ECG normal group. Seven factors influencing cardiovascular toxicity were identified, including age, menstrual history, diabetes, combination therapy with trastuzumab, combination therapy with dexrazoxane, creatine kinase isoenzymes, and α-hydroxybutyrate dehydrogenase. In the training set, the area under the curve ( AUC) for the logistic regression, random forest, XGBoost, SVM, and MLP models was 0.712, 0.863, 0.774, 0.813, and 0.733, respectively. In the test set, the AUC was 0.671, 0.778, 0.746, 0.771, and 0.705, respectively. Calibration curves and clinical decision curves showed that the random forest model performed the best. Conclusion:Models constructed with machine learning algorithms show promise in predicting cardiovascular toxicity in breast cancer patients receiving anthracycline-based chemotherapy, with the random forest prediction model performing the best.

Objective To summarize the clinical characteristics,treatment outcomes,and prognostic factors of children with non-metastatic Ewing's sarcoma(ES).Methods A retrospective analysis was conducted on the clinical data of 41 children with non-metastatic ES diagnosed and treated at the Shanghai Children's Medical Center,Shanghai Jiao Tong University School of Medicine from January 2010 to December 2018.All patients underwent chemotherapy based on the RMS-2009 protocol of the center,and local treatment such as surgery and/or radiotherapy was performed according to risk grouping.The Kaplan-Meier method was used to calculate the overall survival(OS)and event-free survival(EFS)rates.Univariate prognostic analysis was performed using the log-rank test,and multivariate analysis was conducted with Cox regression.Results Of the 41 children,21 were male and 20 were female.The median age at diagnosis was 7.7 years(range:1.2-14.6 years).The median follow-up time for patients with event-free survival was 68.1 months(range:8.1-151.7 months).As of the last follow-up,33 patients were in complete remission,and the overall 5-year EFS and OS rates were(78±6)％and(82±6)％,respectively.Univariate analysis by the log-rank test showed that a tumor diameter ≥8 cm,time from diagnosis to start of local treatment ≥16 weeks,and incomplete surgical resection were associated with poor prognosis(P＜0.05).Multivariate Cox regression analysis indicated that incomplete surgical resection(HR=8.381,95％CI:1.681-41.801,P=0.010)was an independent risk factor for poor prognosis in children with ES.Secondary tumors occurred in 2 cases.Conclusions A comprehensive treatment strategy incorporating chemotherapy,surgery,and radiotherapy can improve the prognosis of children with ES.Poor prognosis is associated with an initial tumor diameter ≥8 cm,while complete surgical resection and early initiation of local treatment can improve outcomes.[Chinese Journal of Contemporary Pediatrics,2024,26(4):365-370]

Acupuncture and moxibustion has certain advantages in the treatment of post-stroke spastic paralysis，but the treatment methods and diagnosis and treatment ideas are complicated. This paper sortes out the representative contemporary acupuncture and moxibustion schools in the treatment of post-stroke spastic paralysis, analyzes their academic origins，summarizes and compares the theory，acupoint selection and technique characteristics of different schools in the diagnosis and treatment of this disease，so as to provide some references for guiding optimal treatment schemes selection in clinic.
Humans ; Moxibustion ; Muscle Spasticity/therapy* ; Acupuncture Therapy ; Schools ; Acupuncture Points ; Stroke/therapy*

Hyperuricemia is closely related to metabolic diseases.With the development of metabolic syndrome(MS),it can change the mechanical load,hormone and biochemical characteristics of patients,thus perform a complex impact on bone health.Although the increased mechanical load and the production of some hormones can prevent os-teoporosis,it can promote inflammation and oxidation in vivo,which may be harmful to bone health,and uric acid may have antioxidant effect to protect bone.Therefore,the influence of increased uric acid on bone in MS patients is still unclear.The present article will further explore this issue.

Objective Current data are insufficient for comparisons of effectiveness between percutaneous coronary intervention(PCI)and coronary artery bypass grafting(CABG)among patients with coronary artery disease(CAD)and left ventricular dysfunction.Methods A total of 905 CAD patients with reduced left ventricular ejection fraction(LVEF≤35％)in single center of China who underwent either PCI or CABG were enrolled in a real-world cohort study.Clinical outcomes included short-and long-term all-cause mortality,rates of heart failure(HF)hospitalization and repeat revascularization.Propensity score matching was used to balance the 2 cohorts.Results PCI was associated with lower 30-day mortality rate(HR 0.29,95％CI 0.09-0.88,P=0.029).At a mean follow-up of 4.5 years,PCI and CABG had similar all-cause death(HR 1.00,95％CI 0.67-1.50,P=0.990)and HF hospitalization(HR 0.81,95％CI 0.40-1.64,P=0.561),but PCI had higher risk of repeat revascularization(HR 14.46,95％CI 3.43-60.98,P＜0.001).PCI was associated with more significant LVEF improvement than CABG(P=0.031 for interaction).Conclusions CAD patients with reduced LVEF who underwent PCI had lower short-term mortality rate and more LVEF improvement but higher risk of repeat revascularization during follow-up than patients who underwent CABG.PCI showed comparable long-term survival and HF hospitalization risk.

Objective To explore the factors that affect the excretion rate of methotrexate and the occurrence of adverse reactions in high-dose methotrexate chemotherapy for osteosarcoma patients.Methods Retrospectively analyzed methotrexate excretion,liver injury,kidney injury,bone marrow suppression and other adverse drug reactions in 97 high-dose methotrexate chemotherapy cycles of 28 patients with osteosarcoma.The concentration of methotrexate in the blood at 0,24,48,72 h and the level of white protein in the blood were also analyzed.Results When the peak concentration of methotrexate(0h,Cmax)≥700 μmol·L-1 the risk of excretion delay increases:the incidence was 23.21％in group with Cmax ≥ 700 μmol·L-1,and it was 5.00％in group with Cmax＜700 μmol·L-1,(P＜0.05),but when the peak concentration was≥1 000 μmol·L-1,the risk of delayed excretion did not increase further:the incidence was 16.00％in group with Cmax 1 000 μmol·L-1,and it was 15.49％in group with Cmax＜1 000 μmol·L-1,(P＞0.05).Methotrexate blood Cmax has no significant correlation with the occurrence of important adverse reactions such as liver injury and bone marrow suppression.There was significant correlation between low serum albumin level and bone marrow suppression in patients.The average albumin level in group with bone marrow suppression was(39.1±3.4)g·L-1,which in without bone marrow suppression group was(41.2±4.0)g·L-1(P＜0.05).Conclusion During high-dose methotrexate chemotherapy in patients with osteosarcoma,delayed excretion and adverse reactions should not be prevented by lowering the peak concentration.The albumin level of patients is an important factor affecting the occurrence of bone marrow suppression.