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Aims: Coagulation abnormality is one of the primary concerns for patients with spontaneous intracerebral hemorrhage admitted to ER. Conventional laboratory indicators require hours for coagulopathy diagnosis, which brings difficulties for appropriate intervention within the optimal window. This study evaluates the possibility of building efficient coagulopathy prediction models using data mining and machine learning algorithms. Methods: A retrospective cohort enrolled 1668 cases with acute spontaneous intracerebral hemorrhage from three medical centers, excluding those under antithrombotic therapies. Coagulopathy‐related clinical parameters were initially screened by univariate analysis. Two machine learning algorithms, the random forest and the support vector machine, were deployed via an approach of four‐fold cross‐validation to screen out the most important parameters contributing to the occurrence of coagulopathy. Model discrimination was assessed using metrics, including accuracy, precision, recall, and F1 score. Results: Albumin/globulin ratio, neutrophil count, lymphocyte percentage, aspartate transaminase, alanine transaminase, hemoglobin, platelet count, white blood cell count, neutrophil percentage, systolic and diastolic pressure were identified as major predictors to the occurrence of acute coagulopathy. Compared to support vector machine, the model based on the random forest algorithm showed better accuracy (93.1%, 95% confidence interval [CI]: 0.913‐0.950), precision (92.4%, 95% CI: 0.897‐0.951), F1 score (91.5%, 95% CI: 0.889‐0.964), and recall score (93.6%, 95% CI: 0.909‐0.964), and yielded higher area under the receiver operating characteristic curve (AU‐ROC) (0.962, 95% CI: 0.942‐0.982). Conclusion: The constructed models exhibit good prediction accuracy and efficiency. It might be used in clinical practice to facilitate target intervention for acute coagulopathy in patients with spontaneous intracerebral hemorrhage. [ABSTRACT FROM AUTHOR]

Radiologists must utilize medical images of multiple modalities for tumor segmentation and diagnosis due to the limitations of medical imaging technology and the diversity of tumor signals. This has led to the development of multimodal learning in medical image segmentation. However, the redundancy among modalities creates challenges for existing subtraction-based joint learning methods, such as misjudging the importance of modalities, ignoring specific modal information, and increasing cognitive load. These thorny issues ultimately decrease segmentation accuracy and increase the risk of overfitting. This paper presents the complementary information mutual learning (CIML) framework, which can mathematically model and address the negative impact of inter-modal redundant information. CIML adopts the idea of addition and removes inter-modal redundant information through inductive bias-driven task decomposition and message passing-based redundancy filtering. CIML first decomposes the multimodal segmentation task into multiple subtasks based on expert prior knowledge, minimizing the information dependence between modalities. Furthermore, CIML introduces a scheme in which each modality can extract information from other modalities additively through message passing. To achieve non-redundancy of extracted information, the redundant filtering is transformed into complementary information learning inspired by the variational information bottleneck. The complementary information learning procedure can be efficiently solved by variational inference and cross-modal spatial attention. Numerical results from the verification task and standard benchmarks indicate that CIML efficiently removes redundant information between modalities, outperforming SOTA methods regarding validation accuracy and segmentation effect. To emphasize, message-passing-based redundancy filtering allows neural network visualization techniques to visualize the knowledge relationship among different modalities, which reflects interpretability., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Bo Jin reports was provided by National Natural Science Foundation of China and Sichuan Province Science and Technology support Program. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Background: Primary central nervous system lymphoma (PCNSL) carries a poor prognosis. Radiomics may hold potential value in prognostic assessment., Purpose: To develop and validate an MRI-based radiomics model and combine it with clinical factors to assess progression-free survival (PFS) and overall survival (OS) of patients with PCNSL., Study Type: Retrospective and prospective., Population: Three hundred seventy-nine patients (179 female, 53 ± 7 years) from 2014 to 2022., Field Strength/sequence: T2/fluid-attenuated inversion recovery, contrast-enhanced T1WI and diffusion-weighted echo-planar imaging sequences on 3.0 T., Assessment: Radiomics features were extracted from enhanced tumor regions on preoperative multi-sequence MRI. Using a least absolute shrinkage and selection operator (LASSO) Cox regression model to select radiomic signatures in training cohort (N = 169). Cox proportional hazards models were constructed for clinical, radiomics, and combined models, with internal (N = 72) and external (N = 32) cohorts validating model performance., Statistical Tests: Chi-squared, Mann-Whitney, Kaplan-Meier, log-rank, LASSO, Cox, decision curve analysis, time-dependent Receiver Operating Characteristic, area under the curve (AUC), and likelihood ratio test. P-value <0.05 was considered significant., Results: Follow-up duration was 28.79 ± 22.59 months (median: 25). High-risk patients, determined by the median radiomics score, showed significantly lower survival rates than low-risk patients. Compared with NCCN-IPI, conventional imaging and clinical models, the combined model achieved the highest C-index for both PFS (0.660 internal, 0.802 external) and OS (0.733 internal, 0.781 external) in validation. Net benefit was greater with radiomics than with clinical alone. The combined model exhibited performance with AUCs of 0.680, 0.752, and 0.830 for predicting 1-year, 3-year, and 5-year PFS, and 0.770, 0.789, and 0.863 for OS in internal validation, with PFS AUCs of 0.860 and 0.826 and OS AUCs of 0.859 and 0.748 for 1-year and 3-year survival in external validation., Data Conclusion: Incorporating a multi-sequence MR-based radiomics model into clinical models enhances the assess accuracy for the prognosis of PCNSL., Evidence Level: 4 TECHNICAL EFFICACY: Stage 2., (© 2024 International Society for Magnetic Resonance in Medicine.)

Objective: Speech arrest is a common but crucial negative motor response (NMR) recorded during intraoperative brain mapping. However, recent studies have reported nonspeech-specific NMR sites in the ventral precentral gyrus (vPrCG), where stimulation halts both speech and ongoing hand movement. The aim of this study was to investigate the spatial relationship between speech-specific NMR sites and nonspeech-specific NMR sites in the lateral frontal cortex., Methods: In this prospective cohort study, an intraoperative mapping strategy was designed to identify positive motor response (PMR) sites and NMR sites in 33 consecutive patients undergoing awake craniotomy for the treatment of left-sided gliomas. Patients were asked to count, flex their hands, and simultaneously perform these two tasks to map NMRs. Each site was plotted onto a standard atlas and further analyzed. The speech and hand motor arrest sites in the supplementary motor area of 2 patients were resected. The 1- and 3-month postoperative language and motor functions of all patients were assessed., Results: A total of 91 PMR sites and 72 NMR sites were identified. NMR and PMR sites were anteroinferiorly and posterosuperiorly distributed in the precentral gyrus, respectively. Three distinct NMR sites were identified: 24 pure speech arrest (speech-specific NMR) sites (33.33%), 7 pure hand motor arrest sites (9.72%), and 41 speech and hand motor arrest (nonspeech-specific NMR) sites (56.94%). Nonspeech-specific NMR sites and speech-specific NMR sites were dorsoventrally distributed in the vPrCG. For language function, 1 of 2 patients in the NMA resection group had language dysfunction at the 1-month follow-up but had recovered by the 3-month follow-up. All patients in the NMA resection group had fine motor dysfunction at the 1- and 3-month follow-ups., Conclusions: The study results demonstrated a functional segmentation of speech-related NMRs in the lateral frontal cortex and that most of the stimulation-induced speech arrest sites are not specific to speech. A better understanding of the spatial distribution of speech-related NMR sites will be helpful in surgical planning and intraoperative mapping and provide in-depth insight into the motor control of speech production.

Nanoparticles show great potential for drug delivery. However, suitable nanostructures capable of loading a range of drugs together with the co-delivery of siRNAs, which avoid the problem of cation-associated cytotoxicity, are lacking. Herein, we report an small interfering RNA (siRNA)-based vesicle (siRNAsome), which consists of a hydrophilic siRNA shell, a thermal- and intracellular-reduction-sensitive hydrophobic median layer, and an empty aqueous interior that meets this need. The siRNAsome can serve as a versatile nanostructure to load drug agents with divergent chemical properties, therapeutic proteins as well as co-delivering immobilized siRNAs without transfection agents. Importantly, the inherent thermal/reduction-responsiveness enables controlled drug loading and release. When siRNAsomes are loaded with the hydrophilic drug doxorubicin hydrochloride and anti-P-glycoprotein siRNA, synergistic therapeutic activity is achieved in multidrug resistant cancer cells and a tumor model., (© 2019 The Authors. Published by Wiley-VCH Verlag GmbH & Co. KGaA.)

Objective: Insular lobe gliomas continue to challenge neurosurgeons due to their complex anatomical position. Transcortical and transsylvian corridors remain the primary approaches for reaching the insula, but the adoption of one technique over the other remains controversial. The authors analyzed the transcortical approach of resecting insular gliomas in the context of patient tumor location based on the Berger-Sinai classification, achievable extents of resection (EORs), overall survival (OS), and postsurgical neurological outcome., Methods: The authors studied 255 consecutive cases of insular gliomas that underwent transcortical tumor resection in their division. Tumor molecular pathology, location, EOR, postoperative neurological outcome for each insular zone, and the accompanying OS were incorporated into the analysis to determine the value of this surgical approach., Results: Lower-grade insular gliomas (LGGs) were more prevalent (63.14%). Regarding location, giant tumors (involving all insular zones) were most prevalent (58.82%) followed by zone I+IV (anterior) tumors (20.39%). In LGGs, tumor location was an independent predictor of survival (p = 0.003), with giant tumors demonstrating shortest patient survival (p = 0.003). Isocitrate dehydrogenase 1 (IDH1) mutation was more likely to be associated with giant tumors (p < 0.001) than focal tumors located in a regional zone. EOR correlated with survival in both LGG (p = 0.001) and higher-grade glioma (HGG) patients (p = 0.008). The highest EORs were achieved in anterior-zone LGGs (p = 0.024). In terms of developing postoperative neurological deficits, patients with giant tumors were more susceptible (p = 0.038). Postoperative transient neurological deficit was recorded in 12.79%, and permanent deficit in 15.70% of patients. Patients who developed either transient or permanent postsurgical neurological deficits exhibited poorer survival (p < 0.001)., Conclusions: The transcortical surgical approach can achieve maximal tumor resection in all insular zones. In addition, the incorporation of adjunct technologies such as multimodal brain imaging and mapping of cortical and subcortical eloquent brain regions into the transcortical approach favors postoperative neurological outcomes, and prolongs patient survival.