OBJECTIVE:To investigate the relationship between serum G protein-coupled receptor kinase 2 (GRK2) level and the occurrence of sepsis-associated liver injury (SALI). METHODS:A prospective observational study was conducted. Patients with sepsis admitted to the department of intensive care medicine of Northern Jiangsu People's Hospital Affiliated to Yangzhou University from January to December 2024 were enrolled. Baseline data were collected. Peripheral venous blood samples were obtained within 24 hours to measure laboratory indicators and detect serum GRK2 level using enzyme linked immunosorbent assay (ELISA). Patients were divided into SALI and non-SALI groups based on whether SALI occurred at enrollment. Logistic regression analysis was used to identify independent risk factors for SALI, and the identify value of serum GRK2 for SALI was evaluated using receiver operator characteristic curve (ROC curve). Internal validation was performed via Bootstrap resampling with 1 000 repetitions. RESULTS:A total of 144 patients with sepsis were finally enrolled, including 82 in the non-SALI group and 62 in the SALI group. The level of serum GRK2 was significantly higher in the SALI group than that in the non-SALI group [ng/L: 808.78 (766.70, 832.14) vs. 586.40 (553.43, 614.19), P<0.05]. Additionally, compared with the non-SALI group, the SALI group showed significantly higher body mass index (BMI), proportion of smoking history, 28-day mortality, lactate, alanine aminotransferase, aspartate aminotransferase, total bilirubin, indirect bilirubin, and direct bilirubin, prothrombin time and international normalized ratio (all P<0.05). The indicators with statistically significant differences in univariate Logistic regression analysis, such as BMI, smoking history, GRK2, and lactate, were included in the multivariate Logistic regression analysis. The results showed that, elevated GRK2 [odds ratio (OR)=1.024, 95% confidence interval (95%CI) was 1.016-1.033, P<0.001], elevated lactate (OR=1.674, 95%CI was 1.160-2.418, P=0.006), and high BMI (OR=1.243, 95%CI was 1.032-1.498, P=0.022) were independent risk factors for SALI. ROC curve analysis showed that the area under the curve (AUC) of GRK2 for identifying SALI was 0.940, with a 95%CI of 0.893-0.988; the sensitivity was 0.96 and the specificity was 0.89 when the optimal cut-off value of 692.52 ng/L. The AUC of GRK2 combined with lactate was 0.946, with a 95%CI of 0.903-0.989. After internal validation, the AUC of GRK2 for identifying SALI was 0.940, with a 95%CI of 0.893-0.988. CONCLUSIONS:Elevated serum GRK2 was positively correlated with the occurrence of SALI and may serve as a potential biomarker for assisting in the diagnosis of SALI.

ObjectiveTo investigate the current status and influencing factors of digital health technology anxiety in elderly stroke patients,and to provide a basis for developing intervention strategies.MethodsFrom January to July 2025,a convenience sampling method was used to select 291 hospitalized elderly stroke patients from the neurology and neurosurgery departments of a tertiary hospital in Yangzhou city.Surveys were conducted using a general information questionnaire,the Technology Anxiety Scale,the Elderly Digital Health Literacy Assessment Scale,the Lubben Social Network Scale,the Self⁃Perception of Aging Scale,and the Active Aging Scale.A random forest model and linear regression analysis were employed to rank and identify the influencing factors of digital health technology anxiety.ResultsThe total scores for digital health technology anxiety,elderly digital health literacy,social isolation,self⁃perceived aging, and active aging were 38.66±9.75,37.31±11.85,19.51±6.11,51.86±11.28,and 104.85±16.95,respectively.According to the random forest ranking and linear regression selection,the influencing factors of digital health technology anxiety in elderly stroke patients, in order of importance,were self⁃perceived aging,active aging,social isolation,and internet use frequency.ConclusionsThe level of digital health technology anxiety in elderly stroke patients is moderate.Medical staff can implement targeted interventions based on the identified influencing factors to reduce their anxiety.

Abstract Background and aims Postischaemic neuroinflammation drives cerebral injury after stroke. Meprinβ, a zinc metalloprotease abundantly expressed in the brain parenchyma, activates proinflammatory mediators and amplifies blood–brain barrier disruption. We hypothesised that serum meprinβ reflects acute ischaemic stroke (AIS) early progression, and long-term outcomes. Methods In this prospective cohort, serum levels of meprinβ were measured at admission in AIS patients and nonstroke controls. Stroke severity was graded with the National Institute of Health Stroke Scale , and three-month functional outcomes were assessed with the modified Rankin Scale . The association between serum meprinβ level and neurological progression and clinical prognosis was assessed using multivariable-adjusted binary logistic regression and restricted cubic spline models. The incremental predictive utility of adding meprinβ to baseline models was evaluated using the net reclassification index , integrated discrimination improvement , and confusion matrices. Results Compared with controls, AIS patients presented significantly higher serum meprinβ levels. After adjustment for conventional confounders, elevated meprinβ emerged as an independent risk factor for progression and unfavourable three-month outcomes. An elevated serum meprinβ level was associated with an increased risk of both neurological progression and an unfavourable outcome. This relationship was linear for disease progression but nonlinear for poor prognosis. The addition of meprinβ to conventional risk factors significantly improved the risk reclassification and predictive accuracy for both endpoints. Conclusions Serum meprinβ serves as an independent risk factor for both early neurological progression and unfavourable outcomes. Its incorporation significantly enhances the predictive performance of conventional models, supporting its utility as a practical blood-based biomarker for risk stratification of AIS patients. Conflict of interest Hailong Yu , Aipeng Hu,Luhang Tao, Jing Hang, Xin Chen，Xiaoyun Huang, Ran Zhang, Li Dong,Xuetingwen Zhu, nothing to disclose

OBJECTIVE:This study aims to systematically evaluate the methodological quality, predictive performance, associated risk factors, and clinical applicability of prediction models for recurrence and mortality in patients with Clostridioides difficile infection (CDI). METHODS:We systematically searched relevant literature from the inception of PubMed, Web of Science, and Cochrane Library up to May 5, 2025.Two researchers independently conducted literature screening and data extraction, and the PROBAST tool was used to assess the risk of bias.A meta-analysis was performed on eligible risk factors. RESULTS:The study evaluated 15 predictive models for CDI recurrence and 10 models for CDI mortality, encompassing a total of 112,640 CDI patients. A meta-analysis of risk factors for CDI recurrence identified several significant associations: Age [MD = 2.56, 95% CI = 0.75-4.36, P = 0.005], antibiotic use [OR = 2.26, 95% CI = 1.46-3.48, P = 0.0002], proton pump inhibitors (PPIs) [OR = 2.03, 95% CI = 1.36-3.04, P < 0.00001], inflammatory bowel disease (IBD) [OR = 1.69, 95% CI = 1.31-2.21, P < 0.0001], among other factors. The meta-analysis of risk factors for CDI mortality revealed associations with: immunosuppression [OR = 1.83, 95% CI = 1.28-2.63, P = 0.001], white blood cell count (WBC) [MD = 2.55, 95% CI = 0.29-4.8, P = 0.03], the Charlson Comorbidity Index (CCI) [MD = 2.01, 95% CI = 0.24-3.79, P = 0.03], blood urea nitrogen (BUN) [MD = 2.49, 95% CI = 1.47-3.51, P < 0.00001], and creatinine levels [MD = 0.68, 95% CI = 0.19-1.17, P = 0.007], among other factors. Using PROBAST+AI, we evaluated 25 prediction models in two dimensions: model development quality and validation risk of bias. While 12 models demonstrated high development quality, 13 models exhibited high risk of bias in validation, primarily due to inadequate external validation, incomplete performance reporting, and methodological limitations in analysis. The discriminatory performance of most models for CDI recurrence was suboptimal, with area under the curve (AUC) values typically below 0.7. In contrast, CDI mortality prediction models exhibited better overall performance, with some demonstrating superior discriminatory ability (AUC up to 0.969). CONCLUSION:Current CDI prediction models exhibit limited clinical utility due to methodological flaws; future efforts must prioritize methodological rigor, standardized definitions, and robust external validation.

Metal-organic frameworks(MOFs)are a class of crystalline porous materials composed of organic ligands and metal ions/metal clusters.Due to their unique physical,chemical,and biological characteristics,they have become excellent platforms for drug delivery and play a significant role in the treatment of glioma.This article reviews the latest progress in MOFs for glioma treatment,including research advancements in drug penetration through the blood-brain barrier(BBB),targeted delivery of anti-glioma drugs,controlled drug release,and multiple-drug combination therapy.The aim is to provide a reference basis for the application and promotion of MOFs in glioma treatment.

Objective: The aim of this study was to investigate the value of serum zinc-alpha2-glycoprotein (ZAG) levels in predicting the progression and prognosis of acute ischemic stroke (AIS). Methods: A total of 210 patients with AIS who were hospitalized for 72 hours were included in the case group, and 52 patients undergoing health check-ups at the hospital in the same period were included in the control group. Serum ZAG levels were measured early in the morning on the second day after admission via enzyme-linked immunosorbent assay (ELISA). For patients with AIS, those whose National Institutes of Health Stroke Scale (NIHSS) score progression was greater than 2 were regarded as the progression group, while those whose NIHSS score was less than 2 were regarded as the nonprogression group. Prognosis was assessed via the modified Rankin scale (mRS) score after 90 days: an mRS score > 2 was considered a poor prognosis. Logistic regression was used to analyze whether the serum ZAG level was an independent factor affecting the risk of disease progression and the long-term prognosis of AIS. Nomogram models were developed to predict the progression and prognosis of AIS. Results: The serum ZAG level was significantly lower in AIS patients than in controls. The binary logistic regression analysis revealed that the serum ZAG level [odds ratio (OR) 0.963, 95% confidence interval (CI): 0.948-0.979, P < 0.01] may be an independent factor for the risk of AIS onset. Subsequent single-factor analysis revealed that the serum ZAG level in the AIS progression group was lower than that in the nonprogression group. Binary logistic regression analysis also revealed that the serum ZAG level was an independent factor (OR 0.968, 95% CI: 0.947-0.991, P = 0.005) for the risk of AIS progression. Consistently, the serum ZAG level in the poor AIS prognosis group was lower than that in the good prognosis group, and binary logistic regression analysis revealed that the serum ZAG level was an independent risk factor for poor prognosis of acute cerebral infarction (OR 0.937, 95% CI: 0.905-0.969; P < 0.01). Nomogram models including the serum ZAG level to predict the progression and prognosis of AIS showed good prediction ability. Conclusion: There is a close association between serum ZAG levels and the onset of AIS. A lower serum ZAG level may predict AIS progression and long-term poor prognosis.

BACKGROUND:The white blood cell-to-hemoglobin ratio (WHR) is a composite biomarker of inflammation and nutrition, but its prognostic role in critically ill ischemic stroke (IS) patients is unclear. METHODS:A cohort of 3,112 patients from MIMIC-IV was analyzed. WHR was calculated from first 24-hour ICU lab values. Its association with 28-day all-cause mortality(ACM) was assessed via survival analysis, Cox regression, restricted cubic splines (RCS), and subgroup analysis. Machine learning (ML) models were developed and evaluated using Receiver Operating Characteristic curve(AUC), calibration, and decision curve analysis. RESULTS:Mortality differed significantly across WHR quartiles (Log-rank p < 0.0001). A higher WHR was independently associated with increased 28-day ACM (adjusted HR per SD = 1.42; 95% CI: 1.22-1.65, p < 0.001), showing a near-linear dose-response. WHR (AUC = 0.644) outperformed its components (white blood cells, hemoglobin). The association was stronger in non-ventilated patients (interaction p = 0.003). Among ML models, the Gradient Boosting Machine (GBM) performed best (test-set AUC = 0.814) and WHR was a key predictive feature. CONCLUSION:WHR is an independent predictor of short-term mortality in critically ill IS patients. Integrating WHR into ML models like GBM improves risk stratification, offering a valuable tool for clinical prognosis.

Abstract Background and aims Given the role of oxidative stress in acute ischaemic stroke (AIS), the aim of this study was to investigate the association between serum glutathione s-transferase alpha 4 (Gsta4), a brain-abundant detoxification enzyme, and AIS progression and prognosis. Methods This prospective cohort study measured serum Gsta4 expression at admission in 188 consecutive patients with acute ischaemic stroke (AIS) and 63 community-based non-stroke controls using an enzyme-linked immunosorbent assay (ELISA). Associations of Gsta4 with stroke progression (assessed using the National Institutes of Health Stroke Scale [NIHSS]), three-month functional outcomes (evaluated by the modified Rankin Scale [mRS]), and prognosis were analysed using multivariable-adjusted logistic regression and restricted cubic spline models. The incremental prognostic value of Gsta4 expression was further quantified using the net reclassification index (NRI), integrated discrimination improvement (IDI), and receiver operating characteristic (ROC) curves. Results Serum expression of Gsta4 was significantly elevated in AIS patients compared to controls. After adjusting for conventional confounders, a higher serum Gsta4 level was significantly and independently associated with an increased risk of neurological progression and an unfavourable three-month outcome. This association was linear for neurological progression and poor prognosis, respectively. Furthermore, integrating Gsta4 expression into existing risk models greatly improved risk reclassification and predictive precision for both endpoints. Conclusions Serum Gsta4 expression serves as an independent predictor for AIS and provides incremental value compared to conventional risk factors for enhancing the prediction of disease progression and clinical outcomes. Conflict of interest Hailong Yu , Yongxin Yuan,Aipeng Hu,Luhang Tao, Jing Hang, Xin Chen，Xiaoyun Huang, Ran Zhang, Li Dong,nothing to disclose

Sepsis-associated liver injury (SALI) is an independent risk factor for multiple organ dysfunction and high mortality in septic patients, which is often associated with a poor prognosis. Currently, there is still a lack of early diagnostic biomarkers of Sepsis-Associated Liver Injury in clinical practice. YAP1 (Yes1 Associated Transcriptional Regulator) has been demonstrated to correlate with hepatic inflammation, nevertheless, its exact function and mechanism in sepsis-associated liver injury have not been conclusively determined. Clinical data of septic patients in the ICU of Northern Jiangsu People’s Hospital (Oct. 2023 - Dec. 2025) were reviewed. Patients were classified into sepsis non-liver injury (SNLI) and SALI groups according to the presence or absence of liver injury at admission. Logistic regression was used to identify independent risk factors for SALI. Receiver operating characteristic (ROC) analysis assessed predictive performance. Patients were stratified by plasma YAP1 quartiles to evaluate its association with disease progression, and significant variables were further analyzed by logistic regression. Correlations were examined using Spearman analysis. A Cox proportional hazards model was applied to assess the association between YAP1 and 28-day mortality in SALI patients. A total of 199 patients were included (SNLI, n = 121; SALI, n = 78). Plasma YAP1 was an independent protective factor for SALI (OR = 0.97, P < 0.001), while BMI (OR = 1.35, P = 0.015) and day 1 lactate (OR = 1.48, P = 0.002) were independent risk factors. YAP1 showed good predictive performance (AUC = 0.86). Higher YAP1 levels were independently associated with 72-hour SOFA score reduction (OR = 7.55, P = 0.009), indicating improved early organ function. No significant association was found between YAP1 and 28-day mortality. Plasma YAP1 is inversely associated with SALI occurrence and demonstrates good predictive performance. Higher YAP1 levels are associated with early organ function improvement but not with 28-day mortality.

Abstract Background and aims Inflammatory response is involved in the pathogenesis and prognosis of acute ischemic stroke (AIS). Yet, effective biomarkers to predict its occurrence risk, disease progress, and clinical outcomes are scarce. Serum tumor necrosis factor ligand - related molecule 1A (TL1A), an inflammation mediator, has prognostic or predictive value in various inflammatory disorders. However, the correlation between TL1A and AIS is not clear. Methods This study enrolled 228 AIS patients (≤48h from onset) and 63 healthy controls. Serum TL1A was measured by ELISA. Baseline data, NIHSS scores (admission and 48h), and 90-day mRS (poor prognosis: mRS>2) were collected. Logistic regression identified independent factors for AIS occurrence, progression, and poor prognosis. Nomogram models were built and validated. Results Comparison of baseline characteristics showed serum TL1A levels were significantly higher in the AIS group than the control group. Increased TL1A levels were also independent risk factors for both disease progression and poor short - term prognosis. The nomogram prediction models for AIS occurrence, progression, and prognosis, developed based on TL1A and other variables, had AUC values of 0.84, 0.87, and 0.82 respectively in the training set, and AUC values in the validation set were all > 0.71. Both the Hosmer - Lemeshow test and DCA indicated good calibration of the models, suggesting clinical applicability and predictive efficacy. Conclusions Serum TL1A is an independent risk factor for AIS occurrence, progression, and poor short-term prognosis. The prediction model incorporating TL1A can effectively predict the related risks, indicating its potential as a novel biomarker. Conflict of interest Hailong Yu,Luhang Tao,Jinyue Wang,Yuping Li,Xin Chen,Jing Hang,Li Dong,Aipeng Hu,Yingzhu Chen,nothing to disclose

Despite extensive research on prediction models for outcomes in aneurysmal subarachnoid hemorrhage (aSAH) patients, the distinction between models for short- and long-term outcomes remains insufficiently explored. This study aims to compare these models, identify the risk factors of poor outcomes, summarize the predictors of outcomes, and assess the performance of the prediction models for short- and long-term outcomes in aSAH patients. PubMed, Web of Science, the Cochrane Library, and Embase were searched to identify studies investigating risk factors for developed and/or validated prediction models for short-term (< 12 months) and long-term (≥ 12 months) outcomes in aSAH patients. The main outcome was neurological function, defined as poor if the Glasgow Outcome Scale (GOS) score was ≤ 3, or if the modified Rankin Scale (mRS) score was ≥ 3. Fifty-six studies reporting 61 models with 36,879 aSAH patients were included. A total of 93 predictors were examined and categorized into six domains including demographic factors, scoring systems, clinical factors, aneurysm characteristics, laboratory examinations, and imaging features. Among these, laboratory examinations were included in 57.45

PURPOSE:This systematic review and meta-analysis aimed to identify and quantify the risk factors associated with acute kidney injury (AKI) in patients with traumatic brain injury (TBI). METHODS:PubMed, Embase, and Web of Science were systematically searched for articles published up to October 2024. Observational studies published in English that reported on risk factors for AKI in TBI patients were included. Data on AKI incidence and risk factors were extracted. A meta-analysis was conducted using a random-effects model when heterogeneity I2 > 50 % and a fixed-effects model when I2 < 50 %. Risk of bias for studies was assessed using the Newcastle-Ottawa Scale (NOS). Certainty of evidence was evaluated using the GRADE approach. RESULTS:Twenty studies involving 13,115 TBI patients were included in the meta-analysis. The pooled incidence of AKI after TBI was 19 % (95 % CI 16-23). Male gender (odds ratio (OR) 1.43, 95 % CI 1.21-1.70; I2 0 %), diabetes (OR 3.59, 95 % CI 1.74-7.42; I2 0 %), Glasgow Coma Scale (GCS) (mean difference (MD) -0.48, 95 % CI -0.74,-0.23; I2 38 %), GCS ≤ 8 at admission (OR 1.56, 95 % CI 1.28-1.90; I2 0 %), Simplified Acute Physiology Score II (SAPS II) score (MD 4.65, 2.69-6.61; I2 56 %), serum creatinine level at admission (MD 18.17, 95 % CI 1.82-34.51; I2 93 %), hemoglobin (MD -6.82, 95 % CI -12.72, -0.92; I2 79 %), glucose (MD 1.42, 95 % CI 0.64-2.20; I2 0 %), the use of mannitol (OR 2.14, 95 % CI 1.08-4.25; I2 74 %), vancomycin (OR 1.75, 95 % CI 1.35-2.27; I2 0 %) and red blood cell transfusion (OR 3.35, 95 % CI 1.86-6.04; I2 59 %) increased the risk for AKI. CONCLUSION:These findings highlighted the critical need for proactive surveillance of these risk factors in clinical practice, enabling the development of prediction models to identify TBI patients at high risk of AKI.

ABSTRACT Hospital-acquired pneumonia (HAP) is prevalent in the neuro-intensive care unit (NICU), significantly increasing susceptibility to infections with multidrug-resistant organisms (MDROs), which result in high mortality rates and substantial healthcare burdens. Recognition and intervention are paramount. This study aimed to build a prediction model for MDRO infections among NICU patients with HAP. Clinical and laboratory data were collected from the NICU of a grade-A tertiary hospital. Five machine learning models (logistic regression, classification tree, support vector machine, random forest, and K-nearest neighbor) were evaluated based on sensitivity, specificity, accuracy, and receiver operating characteristic curves. A nomogram was developed using the model that performed best in MDRO infection prediction. The performance and clinical utility were assessed using the calibration curve, Brier score, and decision curve analysis. Among 791 neurocritical care patients with HAP, 172 (21.7%) were diagnosed with MDRO infections. The prediction model established by logistic regression exhibited the best performance, with an area under the curve of 0.805. Length of NICU stay (odds ratio [OR] 1.078; 95% confidence interval [CI], 1.070–1.141; P < 0.000), number of antibiotics used (OR 1.391; 95% CI, 1.138–1.700; P = 0.001), diabetes (OR 1.775; 95% CI, 1.006–3.133; P = 0.048), and carbamide (OR 1.038; 95% CI, 1.003–1.074; P = 0.035) were significantly correlated with MDRO infections and incorporated into the nomogram. The model demonstrated good calibration (Brier score 0.137). This model can provide clinicians with a tool for prevention and management of MDRO infections in NICU patients with HAP.IMPORTANCEPatients with hospital-acquired pneumonia (HAP) in the neuro-intensive care unit (NICU) are at a high risk of developing multidrug-resistant organism (MDRO) infections owing to complex conditions, critical illness, and frequent invasive procedures. However, studies focused on constructing prediction models for assessing the risk of MDRO infections in neurocritically ill patients with HAP are lacking at present. Therefore, this study aims to establish a reliable and easy-to-use nomogram for predicting the risk of MDRO infections in patients with HAP admitted to the NICU. Four easily accessed variables were included in the model, including length of NICU stay, number of antibiotics used, diabetes, and carbamide. This nomogram might help in the prediction and implementation of targeted interventions against infections with MDRO among patients with HAP in the NICU.

BACKGROUND:Sepsis-associated liver injury (SALI) refers to secondary liver function impairment caused by sepsis, patients with SALI often have worse clinical outcomes. The early identification and assessment of the occurrence and progression of SALI are pressing issues that urgently need to be resolved. AIM:To investigate the relationship between iron metabolism and SALI. METHODS:In this prospective study, 139 patients were recruited, with 53 assigned to the SALI group. The relationships between SALI and various iron metabolism-related biomarkers were examined. These biomarkers included serum iron (SI), total iron-binding capacity (TIBC), serum ferritin, transferrin, and transferrin saturation. To identify independent risk factors for SALI, both univariate and multivariate logistic regression analyses were performed. Additionally, receiver operating characteristic curve analysis was utilized to assess the predictive value of these biomarkers for the occurrence of SALI. RESULTS:There were no statistically significant differences in age, sex, body mass index, Sequential Organ Failure Assessment scores (excluding liver function), or APACHE II scores between the two groups of patients. Compared with the sepsis group, the SALI group presented significantly higher SI (P < 0.001), TIBC (P < 0.001), serum ferritin (P = 0.001), transferrin (P = 0.005), and transferrin saturation levels (P < 0.001). Multivariate logistic regression analysis revealed that SI (odds ratio = 1.24, 95% confidence interval: 1.11-1.40, P < 0.001) and TIBC levels (odds ratio = 1.13, 95% confidence interval: 1.05-1.21, P < 0.001) were independent predictors of SALI. Receiver operating characteristic curve analysis revealed that SI and TIBC had areas under the curve of 0.816 and 0.757, respectively, indicating moderate predictive accuracy for SALI. CONCLUSION:Iron metabolism disorders are closely associated with the development of SALI, and SI and TIBC may serve as potential predictive biomarkers. The combined use of SI and TIBC has superior diagnostic efficacy for SALI. These findings provide valuable insights for the early identification and management of SALI among patients with sepsis.

Acute respiratory distress syndrome (ARDS) is a common complication after type A aortic dissection surgery and often leads to worsened clinical outcomes for patients. The early prediction of postoperative ARDS is a crucial challenge in clinical practice; however, there have been few reports on related studies based on the 2023 global new definition. A retrospective analysis was conducted on the clinical data of 423 patients who were diagnosed with type A aortic dissection and who underwent surgery at Northern Jiangsu People’s Hospital in Jiangsu Province from November 2019 to April 2025. A 7:3 random division was applied to the patients, resulting in a training set n = 296 and a validation set n = 127. Risk factors were identified via LASSO analysis, and a comprehensive risk prediction model was subsequently constructed by integrating five machine learning algorithms. The receiver operating characteristic (ROC) curve was utilised, and the model with the best predictive performance was selected based on the area under the curve (AUC). Among the 423 included patients, 192 developed ARDS, with an incidence rate of 45.39

Neuroinflammation is a significant factor that exacerbates secondary damage following cerebral ischaemia/reperfusion (CI/R) injury. α-Cyperone (CYP), the principal active constituent of the traditional Chinese medicine Cyperus rotundus L., decreases inflammation. Nonetheless, its impact on CI/R injury remains unknown. Here, we examined the potential involvement of CYP in regulating CI/R injury-induced oxidative stress and apoptosis. Middle cerebral artery occlusion was used to establish a model of CI/R injury in male C57BL/6J mice. CYP (5 or 10 mg/kg) was administered by intraperitoneal injection 30 min, 24 h, and 48 h after model establishment. CYP markedly diminished the lesion volume, enhanced neuronal function and reduced apoptosis and oxidative stress. Moreover, CYP increased Nrf2, HO-1, NQO1, and SOD-1 expression in vivo, protecting neurons against hemin stimulation by facilitating Nrf2 nuclear translocation. ML385 (an Nrf2 inhibitor) fully abolished the protective effects of CYP in vivo following CI/R injury. Our data indicate that CYP mitigates CI/R injury-induced apoptosis and oxidative stress through Nrf2 signalling pathway activation, suggesting the possible therapeutic effect of CYP on CI/R injury.

OBJECTIVE: Early prediction of the onset, progression and prognosis of acute ischemic stroke (AIS) is helpful for treatment decision-making and proactive management. Although several biomarkers have been found to predict the progression and prognosis of AIS, these biomarkers have not been widely used in routine clinical practice. Xanthine oxidase (XO) is a form of xanthine oxidoreductase (XOR), which is widespread in various organs of the human body and plays an important role in redox reactions and ischemia -reperfusion injury. Our previous studies have shown that serum XO levels on admission have certain clinical predictive value for AIS. The purpose of this study was to utilize serum XO levels and clinical data to establish machine learning models for predicting the onset, progression, and prognosis of AIS. METHODS: We enrolled 328 consecutive patients with AIS and 107 healthy controls from October 2020 to September 2021. Serum XO levels and stroke-related clinical data were collected. We established 5 machine learning models-the logistic regression (LR), support vector machine (SVM), decision tree, random forest, and Knearest neighbor (KNN) models-to predict the onset, progression, and prognosis of AIS. The area under the receiver operating characteristic curve (AUROC), accuracy, sensitivity, specificity, negative predictive value, and positive predictive value were used to evaluate the predictive performance of each model. RESULTS: Among the 5 machine learning models predicting AIS onset, the AUROC values of 4 prediction models were over 0.7, while that of the KNN model was lower (AUROC = 0.6708, 95% CI 0.576-0.765). The LR model showed the best AUROC value (AUROC = 0.9586, 95% CI 0.927-0.991). Although the 5 machine learning models showed relatively poor predictive value for the progression of AIS (all AUROCs <0.7), the LR model still showed the highest AUROC value (AUROC = 0.6543, 95% CI 0.453 - 0.856). We compared the value of 5 machine learning models in predicting the prognosis of AIS, and the LR model showed the best predictive value (AUROC = 0.8124, 95% CI 0.715 - 0.910). CONCLUSIONS: The tested machine learning models based on serum levels of XO could predict the onset and prognosis of AIS. Among the 5 machine learning models, we found that the LR model showed the best predictive performance. Machine learning algorithms improve accuracy in the early diagnosis of AIS and can be used to make treatment decisions.