The purpose of this study was to develop and validate a deep learning framework for simultaneous automated quantification of diffusion-weighted imaging-the Alberta Stroke Programme early CT score (DWI-ASPECTS) and infarct core volume in middle cerebral artery acute ischaemic stroke (MCA-AIS) and to evaluate its clinical utility for severity stratification in multi-centre settings. A cohort of 738 patients diagnosed with MCA-AIS from four centres was divided into a train set (n = 408), a validation set (n = 116), an internal test set (n = 60) and an external test set (n = 154). A 3D U-Net architecture was trained for simultaneous infarct segmentation and the ASPECTS region analysis. Model performance was compared against expert neuroradiologists using intraclass correlation coefficients and the Spearman correlation coefficient. Furthermore, we investigated the correlation between ASPECTS deduction frequency, ASPECTS score, core volume and the severity of MCA-AIS. The 3D U-Net model showed a high correlation with manual segmentation, achieving a Dice coefficient of 0.801 and a Spearman correlation coefficient of 0.988 for volume measurements in the external test set. In both the internal and external test sets, the automated ASPECTS by the deep learning (DL) model (Automated), manual ASPECTS by raters (Raters) and manual ASPECTS by raters on DWI images registered with the template (Raters_template) exhibited a strong correlation and excellent agreement. The cortical regions (M1-M6) were particularly relevant in patients classified into the moderate-severe group. The threshold values for the mild group and moderate-severe group on receiver operating characteristic curve analysis for DWI-ASPECTS and Core volume, were 6 and 27.86 mL, respectively. The DL model demonstrated comparable performance to neuroradiologists' evaluation, potentially serving as an ancillary tool for physicians in making urgent clinical decisions. The severity of MCA-AIS was significantly associated with the specific ASPETCS regions and core volume, which may aid in identifying moderate-severe MCA-AIS.

Understanding the risk factors for hematoma expansion (HE) in different regions of intracerebral hemorrhage (ICH) can help in the development of more accurate HE prediction tools and in implementing more effective clinical treatment interventions. This study aims to investigate the risk factors for HE in patients with lobar and deep ICH. A retrospective analysis was conducted on 558 cases of primary supratentorial ICH from Tongji Hospital Affiliated to Tongji University. Patients were categorized into lobar ICH and deep ICH groups. Differential analysis of ICH characteristics at different locations was performed, followed by subgroup analysis based on HE occurrence. Binary logistic regression was used to identify independent risk factors for HE in each group. Among the 404 patients with ICH who underwent follow-up noncontrast computed tomography (NCCT) scans, the proportion with HE was similar in the deep ICH group (23.2

Purpose: Current prognostic scoring systems for newly diagnosed primary central nervous system lymphoma (PCNSL), such as IELSG prognostic score and MSKCC prognostic score, are widely used but have limitations in clinical practice. This study aimed to develop a novel prognostic model based on real clinical data and compare it with existing systems. Patients and Methods: A total of 288 patients newly diagnosed with PCNSL were recruited. Patients were randomly allocated to the development and validation cohorts. The least absolute shrinkage and selection operator (LASSO) regression and multivariate Cox regression analysis were used to identify the risk factors for overall survival (OS) and construct a nomogram. Additionally, KaplanMeier survival curves were plotted to show the stratification ability of the risk groups. Results: Eastern Cooperative Oncology Group performance status (ECOG-PS), albumin, and two inflammatory biomarkers D-Dimer, and neutrophil-to-lymphocyte ratio (NLR)-were independent predictors of inferior OS. The prognostic model demonstrated concordance Index (C-index) of 0.731 and 0.679 in the development and validation cohorts, respectively. In terms of the time dependent area under the curve (AUC) values for OS, the development cohort exhibited values of 0.765, 0.762, and 0.812 for 1-year, 3-year, and 5-year OS, respectively. The corresponding AUC values in the validation cohort were 0.711, 0.731, and 0.840, respectively. The calibration curves showed excellent concordance. The novel prognostic model also provided superior risk stratification for patients with PCNSL compared with existing scoring systems. Conclusion: This study presents a novel prognostic model for predicting the OS of patients with newly diagnosed PCNSL. The model accurately and effectively stratifies the prognosis of patients with PCNSL and offers valuable clinical guidance for decision making.

This study aimed to develop novel nomogram models capable of predicting early neurological deterioration (END) and 90-day prognosis outcomes in acute ischemic stroke (AIS) patients treated with IV thrombolysis. A retrospective derivation cohort comprising 298 AIS patients was enrolled from January 2021 to March 2024. A separate validation cohort of 94 patients was retrospectively enrolled between April 2024 and December 2024. END was defined as a sustained NIHSS increase of ≥ 2 points within 24 h, and poor prognosis outcome a mRS score of 3–6 at 90 days. Multivariate logistic regression analysis was employed to construct nomogram models for the prediction of outcomes. In the derivation cohort, 23.83

Purpose This study aimed to evaluate the influence of the triglyceride-glucose index (TyG index) on clinical outcomes and to develop nomogram models for predicting early neurological deterioration (END) and long-term prognosis in acute ischemic stroke (AIS) patients following intravenous thrombolytic (IVT) therapy. Methods We conducted a multi-center retrospective cohort study involving 333 AIS patients treated with IVT. The short-term and long-term outcomes were defined as the occurrence of END and 90-day prognosis. Multivariate logistic regression was used to develop nomogram models for forecasting these clinical outcomes. Results Patients in the high-TyG group exhibited significantly higher risks of END ( P = 0.0010) and poor 90-day outcomes ( P = 0.0012). Independent risk factors for END included a lower baseline NIHSS score, delayed door-to-needle time (DNT), reduced ASPECTS score, elevated TyG index, higher potassium (K+) levels, and incomplete Willis artery. Additionally, a higher initial NIHSS, increased TyG levels, presence of END, and a history of hypertension were predictors of poor prognosis. Based on the identified risk factors, two nomogram models yielded AUC values of 0.746 and 0.849 for predicting END and poor prognosis, respectively. NIHSS scores, TyG index, and admission glucose levels (Glu) emerged as prognostic indicators across all patients, while higher mean platelet volume (MPV) and history of stroke were identified as novel risk factors for poor prognosis in NO-END group. Conclusion A higher TyG index correlates with poor clinical outcomes in AIS patients post-IVT. The nomograms combining the TyG index with various factors enhanced risk prediction for END and poor prognosis.

The incidence of acute kidney injury (AKI) due to ischemia–reperfusion (IR) injury is increasing. There is no effective treatment for AKI, and because of this clinical challenge, AKI often progresses to chronic kidney disease, which is closely associated with poor patient outcomes and high mortality rates. Small extracellular vesicles from human umbilical cord mesenchymal stem cells (hUCMSC-sEVs) play increasingly vital roles in protecting tissue function from the effects of various harmful stimuli owing to their specific biological features. In this study, we found that miR-100-5p was enriched in hUCMSC-sEVs, and miR-100-5p targeted FKBP5 and inhibited HK-2 cell apoptosis by activating the AKT pathway. HK-2 cells that were exposed to IR injury were cocultured with hUCMSC-sEVs, leading to an increase in miR-100-5p levels, a decrease in FKBP5 levels, and an increase in AKT phosphorylation at Ser 473 (AKT-473 phosphorylation). Notably, these effects were significantly reversed by transfecting hUCMSCs with an miR-100-5p inhibitor. Moreover, miR-100-5p targeted FKBP5, as confirmed by a dual luciferase reporter assay. In vivo, intravenous infusion of hUCMSC-sEVs into mice suffering from IR injury resulted in significant apoptosis inhibition, functional maintenance and renal histological protection, which in turn decreased FKBP5 expression levels. Overall, this study revealed an effect of hUCMSC-sEVs on inhibiting apoptosis; hUCMSC-sEVs reduced renal IR injury by delivering miR-100-5p to HK-2 cells, targeting FKBP5 and thereby promoting AKT-473 phosphorylation to activate the AKT pathway. This study provides novel insights into the role of hUCMSC-sEVs in the treatment of AKI.

Ferroptosis is a predominant contributor to graft kidney ischemia-reperfusion injury (IRI), resulting in delayed graft function (DGF). However, much less is known about the early predicting biomarkers and therapeutic targets of DGF, especially aiming at ferroptosis. Here, we propose a precise predicting model for DGF, relying on the Akirin1 level in extracellular vesicles (EVs) derived from recipient urine 48 h after kidney transplant. In addition, we decipher a new molecular mechanism whereby Akirin1 induces ferroptosis by strengthening TP53-mediated suppression of SLC7A11 during the graft kidney IRI process, that is, Akirin1 activates the EGR1/TP53 axis and inhibits MDM2-mediated TP53 ubiquitination, accordingly upregulating TP53 in two ways. Meanwhile, we present the first evidence that miR-136-5p enriched in EVs secreted by human umbilical cord mesenchymal stem cells (UM-EVs) confers robust protection against ferroptosis and graft kidney IRI by targeted inhibition of Akirin1 but knockout of miR-136-5p in UM sharply mitigates the protection of UM-EVs. The functional and mechanistic regulation of Akirin1 is further corroborated in an allograft kidney transplant model in wild-type and Akirin1-knockout mice. In summary, these findings suggest that Akirin1, which prominently induces ferroptosis, is a pivotal biomarker and target for early diagnosis and treatment of graft kidney IRI and DGF after kidney transplant. Proposed model for how UM-EVs mitigate ferroptosis and protect against graft kidney IRI. Akirin1, which is prominently elevated in the kidney IRI process, promotes EGR1-mediated TP53 expression and suppresses MDM2-mediated TP53 ubiquitination and degradation, accordingly upregulating TP53 in two ways. This further enhances TP53-mediated suppression of SLC7A11 and thus facilitates ferroptosis. MiR-136-5p enriched in UM-EVs protects against ferroptosis and graft kidney IRI by inhibiting Akirin1. image

The outcome prediction of acute anterior circulation non-lacunar infarction (AACNLI) is important for the precise clinical treatment of this disease. However, the accuracy of prognosis prediction is still limited. This study aims to develop and compare machine learning models based on MRI radiomics of multiple ischaemic-related areas for prognostic prediction in AACNLI. This retrospective multicentre study consecutively included 372 AACNLI patients receiving MRI examinations and conventional therapy between October 2020 and February 2023. These were grouped into training set, internal test set and external test set. MRI radiomics features were extracted from the mask diffusion-weighted imaging, mask apparent diffusion coefficient (ADC) and mask ADC620 by AACNLI segmentations. Grid search parameter tuning was performed on 12 feature selection and 9 machine learning algorithms, and algorithm combinations with the smallest rank-sum of area under the curve (AUC) was selected for model construction. The performances of all models were evaluated in the internal and external test sets. The AUC of radiomics model was larger than that of non-radiomics model with the same machine learning algorithm in the three mask types. The radiomics model using least absolute shrinkage and selection operator-random forest algorithm combination gained the smallest AUC rank-sum among all the algorithm combinations. The AUC of the model with ADC620 was 0.98 in the internal test set and 0.91 in the external test set, and the weighted average AUC in the three sets was 0.96, the largest among three mask types. The Shapley additive explanations values of the maximum of National Institute of Health Stroke Scale score within 7 days from onset (7-d NIHSSmax), stroke-associated pneumonia and admission Glasgow coma scale score ranked top three among the features in AACNLI outcome prediction. In conclusion, the random forest model with mask ADC620 can accurately predict the AACNLI outcome and reveal the risk factors leading to the poor prognosis.

Background: The aim of the study is to demonstrate that radiomics of preoperative multi-sequence magnetic resonance imaging (MRI) can indeed improve the predictive performance of microvascular invasion (MVI) in hepatocellular carcinoma (HCC). Methods: A total of 206 patients with pathologically confirmed HCC who underwent preoperative enhanced MRI were retrospectively recruited. Univariate and multivariate logistic regression analysis identified the independent clinicoradiologic predictors of MVI present and constituted the clinicoradiologic model. Recursive feature elimination (RFE) was applied to select radiomics features (extracted from six sequence images) and constructed the radiomics model. Clinicoradiologic model plus radiomics model formed the clinicoradiomics model. Five-fold cross-validation was used to validate the three models. Discrimination, calibration, and clinical utility were used to evaluate the performance. Net reclassification improvement (NRI) and integrated discrimination improvement (IDI) were used to compare the prediction accuracy between models. Results: The clinicoradiologic model contained alpha-fetoprotein (AFP)_lg10, radiological capsule enhancement, enhancement pattern and arterial peritumoral enhancement, which were independent risk factors of MVI. There were 18 radiomics features related to MVI constructed the radiomics model. The mean area under the receiver operating curve (AUC) of clinicoradiologic, radiomics and clinicoradiomics model were 0.849, 0.925 and 0.950 in the training cohort and 0.846, 0.907 and 0.933 in the validation cohort, respectively. The three models' calibration curves fitted well, and decision curve analysis (DCA) confirmed the clinical usefulness. Compared with the clinicoradiologic model, the NRI of radiomics and clinicoradiomics model increased significantly by 0.575 and 0.825, respectively, and the IDI increased significantly by 0.280 and 0.398, respectively. Conclusions: Radiomics of preoperative multi-sequence MRI can improve the predictive performance of MVI in HCC.

Renal ischemia-reperfusion (I/R) injury is a leading cause of acute kidney injury (AKI), with high mortality. Recent studies have reported that human umbilical cord mesenchymal stem cells (HucMSCs) play an important role in repairing organ and tissue injuries because of their unique characteristics. However, the potential of HucMSC extracellular vesicles (HucMSC-EVs) to promote the repair of renal tubular cells remains to be explored. This study found that HucMSC-EVs derived from HucMSCs played a protective role and were associated with kidney I/R injury. We found that miR-148b-3p in HucMSC-EVs had a protective effect against kidney I/R injury. HK-2 cells overexpressing miR-148b-3p were protected against I/R injury by inhibiting apoptosis. Next, the target mRNA of miR-148b-3p was predicted online, and the target mRNA, pyruvate dehydrogenase kinase 4 (PDK4), was identified and verified using dual luciferase. We discovered that I/R injury significantly increased endoplasmic reticulum (ER) stress, whereas siR-PDK4 inhibited these effects and protected against I/R injury. Interestingly, after administrating HucMSC-EVs to HK-2 cells, PDK4 expression and ER stress induced by I/R injury were significantly inhibited. HK-2 ingested miR-148b-3p from HucMSC-EVs, and its ER induced by I/R injury was significantly deregulated. This study suggests that HucMSC-EVs protect kidneys from I/R injury during the early I/R stage. These results suggest a new mechanism for HucMSC-EVs in treating AKI and provide a new treatment strategy for I/R injury.

This study aims to explore the value of a machine learning (ML) model based on radiomics features and clinical features in predicting the outcome of spontaneous supratentorial intracerebral hemorrhage (sICH) 90 days after surgery. A total of 348 patients with sICH underwent craniotomy evacuation of hematoma from three medical centers. One hundred and eight radiomics features were extracted from sICH lesions on baseline CT. Radiomics features were screened using 12 feature selection algorithms. Clinical features included age, gender, admission Glasgow Coma Scale (GCS), intraventricular hemorrhage (IVH), midline shift (MLS), and deep ICH. Nine ML models were constructed based on clinical feature, and clinical features + radiomics features, respectively. Grid search was performed on different combinations of feature selection and ML model for parameter tuning. The averaged receiver operating characteristics (ROC) area under curve (AUC) was calculated and the model with the largest AUC was selected. It was then tested using multicenter data. The combination of lasso regression feature selection and logistic regression model based on clinical features + radiomics features had the best performance (AUC: 0.87). The best model predicted an AUC of 0.85 (95%CI, 0.75-0.94) on the internal test set and 0.81 (95%CI, 0.64-0.99) and 0.83 (95%CI, 0.68-0.97) on the two external test sets, respectively. Twenty-two radiomics features were selected by lasso regression. The second-order feature gray level non-uniformity normalized was the most important radiomics feature. Age is the feature with the greatest contribution to prediction. The combination of clinical features and radiomics features using logistic regression models can improve the outcome prediction of patients with sICH 90 days after surgery.

Ferroptosis is a predominant contributor to renal ischemia reperfusion injury (IRI) after kidney transplant, evoking delayed graft function and poorer long-term outcomes. The wide propagation of ferroptosis among cell populations in a wave-like manner, developing the "wave of ferroptosis" causes a larger area of tubular necrosis and accordingly aggravates renal allograft IRI. In this study, we decipher a whole new metabolic mechanism underlying ferroptosis and propose a novel spreading pathway of the "wave of ferroptosis" in the renal tissue microenvironment, in which renal IRI cell-secreted small extracellular vesicles (IRI-sEVs) delivering lncRNA WAC-AS1 reprogram glucose metabolism in adjacent renal tubular epithelial cell populations by inducing GFPT1 expression and increasing hexosamine biosynthesis pathway (HBP) flux, and consequently enhances O-GlcNAcylation. Additionally, BACH2 O-GlcNAcylation at threonine 389 in renal tubular epithelial cells prominently inhibits its degradation by ubiquitination and promotes importin α5-mediated nuclear translocation. We present the first evidence that intranuclear BACH2 suppresses SLC7A11 and GPX4 transcription by binding to their proximal promoters and decreases cellular anti-peroxidation capability, accordingly facilitating ferroptosis. Inhibition of sEV biogenesis and secretion by GW4869 and knockout of lncRNA WAC-AS1 in IRI-sEVs both unequivocally diminished the "wave of ferroptosis" propagation and protected against renal allograft IRI. The functional and mechanistic regulation of IRI-sEVs was further corroborated in an allograft kidney transplant model and an in situ renal IRI model. In summary, these findings suggest that inhibiting sEV-mediated lncRNA WAC-AS1 secretion and targeting HBP metabolism-induced BACH2 O-GlcNAcylation in renal tubular epithelial cells may serve as new strategies for protecting against graft IRI after kidney transplant.

Purpose: Automated postprocessing packages have been developed for managing acute ischemic stroke (AIS). These packages identify ischemic core and penumbra using either computed tomographic perfusion imaging (CTP) data or magnetic resonance imaging (MRI) data. Measurements of abnormal tissues and treatment decisions derived from different vendors can vary. The purpose of this study is to investigate the agreement of volumetric and decision-making outcomes derived from two software packages. Methods: A total of 594 AIS patients (174 underwent CTP and 420 underwent MRI) were included. Imaging data were accordingly postprocessed by two software packages: RAPID and RealNow. Volumetric outputs were compared between packages by performing intraclass correlation coefficient (ICC), Wilcoxon paired test and Bland–Altman analysis. Concordance of selecting patients eligible for mechanical thrombectomy (MT) was assessed based on neuroimaging criteria proposed in DEFUSE3. Results: In the group with CTP data, mean ischemic core volume (ICV)/penumbral volume (PV) was 14.9/81.1 mL via RAPID and 12.6/83.2 mL via RealNow. Meanwhile, in the MRI group, mean ICV/PV were 52.4/68.4 mL and 48.9/61.6 mL via RAPID and RealNow, respectively. Reliability, which was measured by ICC of ICV and PV in CTP and MRI groups, ranged from 0.87 to 0.99. The bias remained small between measurements (CTP ICV: 0.89 mL, CTP PV: −2 mL, MRI ICV: 3.5 mL and MRI PV: 6.8 mL). In comparison with CTP ICV with follow-up DWI, the ICC was 0.92 and 0.94 for RAPID and Realnow, respectively. The bias remained small between CTP ICV and follow-up DWI measurements (Rapid: −4.65 mL, RealNow: −3.65 mL). Wilcoxon paired test showed no significant difference between measurements. The results of patient triage were concordant in 159/174 cases (91%, ICC: 0.90) for CTP and 400/420 cases (95%, ICC: 0.93) for MRI. Conclusion: The CTP ICV derived from RealNow was more accurate than RAPID. The similarity in volumetric measurement between packages did not necessarily relate to equivalent patient triage. In this study, RealNow showed excellent agreement with RAPID in measuring ICV and PV as well as patient triage.

Abstract Purpose To investigate the predictive performance of machine learning-based CT radiomics for differentiating between low- and high-nuclear grade of clear cell renal cell carcinomas (CCRCCs). Methods This retrospective study enrolled 406 patients with pathologically confirmed low- and high-nuclear grade of CCRCCs according to the WHO/ISUP grading system, which were divided into the training and testing cohorts. Radiomics features were extracted from nephrographic-phase CT images using PyRadiomics. A support vector machine (SVM) combined with three feature selection algorithms such as least absolute shrinkage and selection operator (LASSO), recursive feature elimination (RFE), and ReliefF was performed to determine the most suitable classification model, respectively. Clinicoradiological, radiomics, and combined models were constructed using the radiological and clinical characteristics with significant differences between the groups, selected radiomics features, and a combination of both, respectively. Model performance was evaluated by receiver operating characteristic (ROC) curve, calibration curve, and decision curve analyses. Results SVM-ReliefF algorithm outperformed SVM-LASSO and SVM-RFE in distinguishing low- from high-grade CCRCCs. The combined model showed better prediction performance than the clinicoradiological and radiomics models (p < 0.05, DeLong test), which achieved the highest efficacy, with an area under the ROC curve (AUC) value of 0.887 (95% confidence interval [CI] 0.798–0.952), 0.859 (95% CI 0.748–0.935), and 0.828 (95% CI 0.731–0.929) in the training, validation, and testing cohorts, respectively. The calibration and decision curves also indicated the favorable performance of the combined model. Conclusion A combined model incorporating the radiomics features and clinicoradiological characteristics can better predict the WHO/ISUP nuclear grade of CCRCC preoperatively, thus providing effective and noninvasive assessment.

This study aimed to explore the factors affecting the outcomes of spontaneous supratentorial cerebral hemorrhage 90 days after surgery. A total of 256 patients with spontaneous supratentorial intracerebral hemorrhage underwent craniotomy evacuation of hematoma. The control group included 120 patients who received conservative treatment. The patients were divided into two subgroups based on a bifurcation of the modified Rankin Scale (mRS) 90 days after clinical therapeutics: good outcome (mRS score 0–3) and poor outcome (mRS score 4–6). The differences in clinical and imaging data between the two subgroups were analyzed. Based on difference analysis results, a binary logistic regression model was constructed to analyze the influencing factors related to poor outcomes. The difference analysis results in the surgery group showed statistically significant differences in age, sex, Glasgow Coma Score (GCS) on admission, coronary atherosclerosis, smoking, stroke history, blood glucose, D-dimer, hematoma size, deep cerebral hemorrhage, midline shift, hematoma burst into the ventricle, vortex sign, island sign, and black hole sign. Binary logistic regression analysis showed that deep cerebral hemorrhage, midline shift, and age > 58 years independently correlated with the poor outcomes of patients after surgery. The binary logistic regression results of the control group showed that age > 58 years and GCS ≤ 8 independently correlated with the poor outcomes of patients. Deep cerebral hemorrhage, midline shift, and age > 58 years significantly increased the risk of adverse prognosis in patients after surgery. The findings might help select the clinical treatment plan and evaluate the postoperative prognosis of patients.

Introduction:The recent publication of a trial failed to prove the efficacy of minimally invasive surgery (MIS) in patients with intracerebral hemorrhage. The aim of this study was to answer the question: Do we need more trials to compare MIS vs. conservative treatment in these patients? Methods:Databases were searched for relevant randomized trials on MIS (endoscopic surgery or stereotactic evacuation) vs. conservative treatment. The primary outcome was significant neurological debilitation or death at the follow-up, and the secondary outcome was death. Both conventional meta-analysis and trial sequential analysis (TSA) were performed. Results:Twelve trials with 2,049 patients were included. In the conventional meta-analysis, the risk ratios of MIS vs. conservative treatment were 0.82 [95% confidence interval (CI), 0.72-0.94] and 0.74 (95% CI, 0.62-0.88) for the primary and secondary outcomes, respectively. In TSA, the cumulativezcurve crossed the superiority boundary, which confirmed an 18.8% relative risk reduction of MIS vs. conservative treatment for the primary outcome. It was also highly likely that MIS would reduce mortality by 24.3%. Several sensitivity analyses suggested the robustness of our results, including different prior settings, including only trials with blind outcome assessment, and the assumption of future trials to be futile. Conclusions:Minimally invasive surgery seems to be more effective than conservative treatment in patients with intracerebral hemorrhage in reducing both morbidity and mortality. Repeating a clinical trial with similar devices, design, and outcomes is unlikely to change the current evidence.