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: 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

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

Purpose To establish a predictive model for the sonication energy required for focused ultrasound surgery (FUS) of breast fibroadenomas.Methods This study retrospectively enrolled 87 patients with 154 benign breast tumors treated by FUS in our hospital. Radiomic analysis included 124 tumors from 69 patients, randomly split into a 3:1 ratio for training (96 cases) and validation (28 cases). Three machine learning algorithms were applied for feature selection. Then, all the selected features were used for the construction of the prediction model via four machine learning algorithms. Residual analysis and Intraclass Correlation Coefficient (ICC) analysis were performed to evaluate the performances of these four models. The importance of each feature is demonstrated by the Root Mean Square Error (RMSE) loss obtained through permutation importance measurement.Results This study collected 11 clinical features and 68 ultrasound radiomics features, totaling 79 independent variables. The Bagging Tree Model, characterized by lower and stable RMSE values and high R2 stability with increasing features, demonstrated superior predictive accuracy and explanatory power compared to other models. At the optimal feature count, identified by the minimum RMSE, 33 features were selected for further modeling. The bagging tree model has the highest ICC value among the four models, at 0.56, with a confidence interval of (0.23, 0.77).Conclusions This study established an interpretable machine learning model that integrates clinical and ultrasound radiomics features to estimate the sonication energy in FUS treatment of breast fibroadenomas.

AIMS:To investigate the effects of different subtypes of lipohypertrophy (LH) on insulin total daily dose (TDD) requirements in patients with type 1 diabetes mellitus (T1DM), and to propose subtype-specific insulin dosage adjustment strategies after avoiding injection at sites of LH. METHODS:This prospective observational study enrolled hospitalised T1DM patients with a disease duration ≥1 year. Point-of-care ultrasound was performed immediately after their admission to determine the presence and the specific type of LH. An insulin pump was installed away from the LH sites of each patient. Continuous subcutaneous insulin was infused to control their blood glucose, and the insulin dose was titrated gradually until their blood glucose stabilised to the target. RESULTS:A total of 288 patients were included. According to ultrasound screening, 104 patients (36.11%) were LH free (LH-0) and 184 patients (63.89%) were found to have LH (LH+), of which 114 had nodular hyperechoic LH (LH-1), 62 had diffuse hyperechoic LH (LH-2) and 8 had hypoechoic LH (LH-3). Paired-sample t-test showed that all LH+ groups had a decrease in TDD and TDD/body weight after avoiding injections at the lesion sites (all p < 0.05). Compared with the LH-0 group, the TDD and TDD/body weight of the LH-2 and LH-3 groups were significantly reduced (all p < 0.05), whereas there was no significant change in the LH-1 group. Among the TDD reduced in the LH-2 and LH-3 groups, the bolus insulin dosage reduced accounted for the vast majority, with 88.05% (8.55/9.71 international unit [IU]/day) and 74.78% (18.12/24.23 IU/day), respectively. CONCLUSIONS:TDD reductions vary among patients with different subtypes of LH. Nodular hyperechoic LH may not require immediate dose adjustments. Patients with diffuse hyperechoic and hypoechoic LH necessitate bolus-focused dose reductions.

AbstractObjectiveThis study aims to construct a clinical risk profile nomogram model for predicting early shoulder joint dysfunction (SJD) after breast cancer surgery.MethodsUsing a convenience sampling method, the clinical data of 161 breast cancer patients between February 2022 and July 2023 at Affiliated Cancer Hospital of Nanjing Medical University were selected and analyzed retrospectively. Risk factors were identified using univariate and multivariate logistic regression analyses. The R software was used to construct the risk prediction model and to plot the nomogram for early SJD post‐breast cancer surgery. The model's predictive performance was evaluated using the receiver operating characteristic (ROC) curve and the Hosmer–Lemeshow test.ResultsAmong the 161 patients, 104 (64.6%) experienced SJD. Multivariate logistic regression analysis finally included the functional exercise compliance scale for postoperative breast cancer patients (FECSPBCP), the compliance scale of physical exercise, the body mass index of patients, involved side to‐hand dominance, and the operation mode of breast and lymph nodes into the model. The area under the ROC curve (AUC) was 0.785 (95% confidence interval: 0.711–0.860), indicating a good model fit as confirmed by the Hosmer–Lemeshow test (X2 = 3.2891, p = .9149). Internal validation using the Bootstrap resampling method (n = 1000) yielded an AUC of 0.731.ConclusionsThe incidence of early SJD was high among postoperative breast cancer patients. The constructed risk prediction model can assist medical professionals in the early identification of high‐risk individuals and provide targeted interventions to prevent long‐term disabilities.

Aims: To investigate the multimodal ultrasonography (US) features of extramedullary plasmacytomas (EMPs) in patients with multiple myeloma (MM).Material and methods: Nine patients with MM who were diagnosed EMPs and underwent multimodal ultrasonography were enrolled in this study. Clinical information and multimodal US results were retrospectively analyzed. The multimodal US with a standardized sonographic protocol including US, color Doppler flow imaging (CDFI), microvascular US, ultrasound elastography (UE) and contrast-enhanced ultrasound (CEUS) was performed on the enrolled patients.Results: This study included both males (4 of 9) and females (5 of 9), with ages ranging from 48 to 72 years. The nine lesions were located in the skin/muscle (7 of 9) and pleura (2 of 9). On US images, all lesions showed inhomogeneous solid hypoechogenicity without calcification. Vascularity was abundant (hypervascularization). CEUS images confirmed this vascular pattern (9 of 9) and presented hyperenhancement with enlarged range. On elastographic images, the lesions presented with high elastic score (3-4).Conclusions: An inhomogeneous hypoechoic soft tissue or visceral mass occurring in MM with increased vascularity, low elasticity, and hyperenhancement along with enlarged range can indicate the diagnosis of EMP on multimodal US images.

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.

Dear Editor,Treating psoriatic arthritis（PsA） is always difficult.Systemic treatments can be administered either orally or through intramuscular and intra-articular injection,including conventional synthetics, biologics and targeted synthetic disease-modifying antirheumatic drugs [1] . The alternatives, topical external therapies,

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.

ObjectiveThis study aimed to explore the correlation between the serum level of indole-3-propionic acid (IPA) and the progression and prognosis of acute cerebral infarction (ACI).MethodsThis study enrolled 197 patients with ACI, and 53 participants from a community-based stroke screening program during the same period were included as the control group. The patients with ACI were divided into quartiles of serum IPA. A logistic regression model was used for comparison. Receiver operating characteristic (ROC) curves were drawn to evaluate the predictive value of the IPA.ResultsCompared with the healthy control group, the ACI group had lower serum IPA (P < 0.05). The serum IPA was an independent factor for acute ischemic stroke (OR=0.992, 95% CI: 0.984-0.999, P=0.035). The serum IPA was lower in patients with progressive stroke or poor prognosis than in patients with stable stroke or good prognosis (P < 0.05). Patients with ACI with low serum IPA are prone to progression and poor prognosis. The best cutoff value for predicting progression was 193.62 pg/mL (sensitivity, 67.5%; specificity 83.7%), and that for poor prognosis was 193.77 pg/mL (sensitivity, 71.1%; specificity, 72.5%).ConclusionThe serum level of IPA was an independent predictor of ACI and had certain clinical value for predicting stroke progression and prognosis in patients with ACI.

Objective: This study aimed to validate the iScore, ASTRAL score, DRAGON score, and THRIVE score for assessing large vessel occlusion-acute ischemic stroke (AIS-LVO) and establish a predictive model for AIS-LVO patients that has better performance to guide clinical practice. Methods: We retrospectively included 439 patients with AIS-LVO and collected baseline data from all of them. External validation of the iScore, ASTRAL score, DRAGON score, and THRIVE score was performed. All variables were compared between groups via univariate analysis, and the results are expressed as ORs and 95 % CIs. Independent variables with P < 0.25 were included in the multivariate logistic analysis, and statistically significant differences (P < 0.05) were identified as risk factors for prognosis in AIS-LVO patients. Receiver operating characteristic (ROC) curve analysis and decision curve analysis (DCA) were used to evaluate the predictive value of our model. Results: Our external validation resulted in an iScore under the curve (AUC) of 0.8475, an ASTRAL AUC of 0.8324, a DRAGON AUC of 0.8196, and a THRIVE AUC of 0.8039. In our research, multivariate Cox regression revealed 8 independent predictors. We used a nomogram to visualize the results of the data analysis. The AUC for the training cohort was 0.8855 (95 % CI, 0.8487-0.9222), and that in the validation cohort was 0.8992 (95% CI, 0.8496-0. 9488). Conclusions: In this study, we verified that the above scores have excellent efficacy in predicting the prognosis of AIS-LVO patients. The nomogram we developed was able to predict the prognosis of AIS-LVO more accurately and may contribute to personalized clinical decision-making and treatment for future clinical work.

Lung diseases are commonly diagnosed based on clinical pathological indications criteria and radiological imaging tools (e.g., X-rays and CT). During a pandemic like COVID-19, the use of ultrasound imaging devices has broadened for emergency examinations by taking their unique advantages such as portability, real-time detection, easy operation and no radiation. This provides a rapid, safe, and cost-effective imaging modality for screening lung diseases. However, the current pulmonary ultrasound diagnosis mainly relies on the subjective assessments of sonographers, which has high requirements for the operator’s professional ability and clinical experience. In this study, we proposed an objective and quantifiable algorithm for the diagnosis of lung diseases that utilizes two-dimensional (2D) spectral features of ultrasound radiofrequency (RF) signals. The ultrasound data samples consisted of a set of RF signal frames, which were collected by professional sonographers. In each case, a region of interest of uniform size was delineated along the pleural line. The standard deviation curve of the 2D spatial spectrum was calculated and smoothed. A linear fit was applied to the high-frequency segment of the processed data curve, and the slope of the fitted line was defined as the frequency spectrum standard deviation slope (FSSDS). Based on the current data, the method exhibited a superior diagnostic sensitivity of 98% and an accuracy of 91% for the identification of lung diseases. The area under the curve obtained by the current method exceeded the results obtained that interpreted by professional sonographers, which indicated that the current method could provide strong support for the clinical ultrasound diagnosis of lung diseases.

Objective: This study was developed to explore the incidence of multi-drug resistant organism (MDRO) infections among ruptured intracranial aneurysms(RIA) patient with hospital-acquired pneumonia(HAP) in the neurological intensive care unit (NICU), and to establish risk factors related to the development of these infections. Methods: We collected clinical and laboratory data from 328 eligible patients from January 2018 to December 2022. Bacterial culture results were used to assess MDRO strain distributions, and risk factors related to MDRO infection incidence were identified through logistic regression analyses. These risk factors were further used to establish a predictive model for the incidence of MDRO infections, after which this model underwent internal validation. Results: In this study cohort, 26.5 % of RIA patients with HAP developed MDRO infections (87/328). The most common MDRO pathogens in these patients included Multidrug-resistant Klebsiella pneumoniae (34.31 %) and Multidrug-resistant Acinetobacter baumannii (27.45 %). Six MDRO risk factors, namely, diabetes (P = 0.032), tracheotomy (P = 0.004), history of mechanical ventilation (P = 0.033), lower albumin levels (P < 0.001), hydrocephalus (P < 0.001) and Glasgow Coma Scale (GCS) score <= 8 (P = 0.032) were all independently correlated with MDRO infection incidence. The prediction model exhibited satisfactory discrimination (area under the curve [AUC], 0.842) and calibration (slope, 1.000), with a decision curve analysis further supporting the clinical utility of this model. Conclusions: In summary, risk factors and bacterial distributions associated with MDRO infections among RIA patients with HAP in the NICU were herein assessed. The developed predictive model can aid clinicians to identify and screen high-risk patients for preventing MDRO infections.

The teaching model based on case-based learning（CBL） is one of the main directions of the reform of standardized training and education for residents in China. Flipped classroom is a way to achieve efficient learning in the age of information technology. The organic combination of the two teaching methods is applied to the standardized training practice of graduate residents in the ultrasonic medicine major and comprehensively standardized teaching management. It is helpful to clarify the dominant role of graduate students in the process of standardized training, and to organically integrate theoretical teaching with simulation operation training, which has important practical significance for improving graduates’ post competency and clinical thinking ability in ultrasonic practice courses.