Approximately 30%-40% of epilepsy patients do not respond well to adequate anti-seizure medications (ASMs), a condition known as pharmacoresistant epilepsy. The management of pharmacoresistant epilepsy remains an intractable issue in the clinic. Its early prediction is important for prevention and diagnosis. However, it still lacks effective predictors and approaches. Here, a classical model of pharmacoresistant temporal lobe epilepsy (TLE) was established to screen pharmacoresistant and pharmaco-responsive individuals by applying phenytoin to amygdaloid-kindled rats. Ictal electroencephalograms (EEGs) recorded before phenytoin treatment were analyzed. Based on ictal EEGs from pharmacoresistant and pharmaco-responsive rats, a convolutional neural network predictive model was constructed to predict pharmacoresistance, and achieved 78% prediction accuracy. We further found the ictal EEGs from pharmacoresistant rats have a lower gamma-band power, which was verified in seizure EEGs from pharmacoresistant TLE patients. Prospectively, therapies targeting the subiculum in those predicted as "pharmacoresistant" individual rats significantly reduced the subsequent occurrence of pharmacoresistance. These results demonstrate a new methodology to predict whether TLE individuals become resistant to ASMs in a classic pharmacoresistant TLE model. This may be of translational importance for the precise management of pharmacoresistant TLE.
Epilepsy, Temporal Lobe/diagnosis* ; Animals ; Drug Resistant Epilepsy/drug therapy* ; Electroencephalography/methods* ; Rats ; Anticonvulsants/pharmacology* ; Neural Networks, Computer ; Male ; Humans ; Phenytoin/pharmacology* ; Adult ; Disease Models, Animal ; Female ; Rats, Sprague-Dawley ; Young Adult ; Convolutional Neural Networks

The accurate prediction of drug absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties represents a crucial step in early drug development for reducing failure risk. Current deep learning approaches face challenges with data sparsity and information loss due to single-molecule representation limitations and isolated predictive tasks. This research proposes molecular properties prediction with parallel-view and collaborative learning (MolP-PC), a multi-view fusion and multi-task deep learning framework that integrates 1D molecular fingerprints (MFs), 2D molecular graphs, and 3D geometric representations, incorporating an attention-gated fusion mechanism and multi-task adaptive learning strategy for precise ADMET property predictions. Experimental results demonstrate that MolP-PC achieves optimal performance in 27 of 54 tasks, with its multi-task learning (MTL) mechanism significantly enhancing predictive performance on small-scale datasets and surpassing single-task models in 41 of 54 tasks. Additional ablation studies and interpretability analyses confirm the significance of multi-view fusion in capturing multi-dimensional molecular information and enhancing model generalization. A case study examining the anticancer compound Oroxylin A demonstrates MolP-PC's effective generalization in predicting key pharmacokinetic parameters such as half-life (T0.5) and clearance (CL), indicating its practical utility in drug modeling. However, the model exhibits a tendency to underestimate volume of distribution (VD), indicating potential for improvement in analyzing compounds with high tissue distribution. This study presents an efficient and interpretable approach for ADMET property prediction, establishing a novel framework for molecular optimization and risk assessment in drug development.
Deep Learning

Paraplegia caused by spinal cord injury is a serious neurological complication, for which surgery is currently the main treatment method. Due to different surgical approaches, patients are usually expected to maintain a passive prone position for a long time or switch between the supine and prone positions. Affected by multiple factors such as neurogenic sensory disorders, pathological changes in muscle tone and operative duration, the risk of intraoperative acquired pressure injury (IAPI) is significantly increased. Current clinical prevention strategies for IAPI in these patients predominantly focus on localized pressure relief during positioning, lacking systematic, standardized comprehensive prevention protocols or evidence-based guidelines. To address it, Department of Nursing, Orthopedics Branch, China International Exchange and Promotive Association for Medical and Health Care, Spinal Trauma Professional Committee, Orthopedics Branch, Chinese Medical Doctor Association, Nursing Group of Spine and Spinal Cord Professional Committee of Chinese Association of Rehabilitation Medicine organized experts in relevant fields to formulate Guideline for the prevention of intraoperative acquired pressure injury in paraplegic patients with spinal cord injury ( version 2025), based on evidence-based medical evidence and latest research results and clinical practice at home and abroad. Eleven recommendations were put forward from the aspects of preoperative risk assessment, intraoperative prevention strategies, postoperative handover and monitoring, and supportive mechanisms for IAPI prevention, aiming to standardize the prevention measures and management strategies of IAPI in paraplegic patients with spinal cord injury and accelerate the recovery of patients and improve the therapeutic effect.

Panax ginseng, a perennial herbaceous plant and a representative of the Panax genus, is renowned for its exceptional medicinal value and economic benefits, often referred to as the “King of Herbs.” With the increasing market demand and the limited availability of suitable cultivation land, the issue of continuous cropping obstacles for P. ginseng has become increasingly prominent, directly hindering the sustainable development of the ginseng industry. This article summarizes the concept and hazards of continuous cropping obstacles and, drawing on the latest research, provides an in-depth analysis of the causes and response mechanisms. This work aims to establish a solid foundation for future research into the mechanisms of continuous cropping obstacles in P. ginseng.

Panax ginseng, a perennial herbaceous plant and a representative of the Panax genus, is renowned for its exceptional medicinal value and economic benefits, often referred to as the “King of Herbs.” With the increasing market demand and the limited availability of suitable cultivation land, the issue of continuous cropping obstacles for P. ginseng has become increasingly prominent, directly hindering the sustainable development of the ginseng industry. This article summarizes the concept and hazards of continuous cropping obstacles and, drawing on the latest research, provides an in-depth analysis of the causes and response mechanisms. This work aims to establish a solid foundation for future research into the mechanisms of continuous cropping obstacles in P. ginseng.

Panax ginseng, a perennial herbaceous plant and a representative of the Panax genus, is renowned for its exceptional medicinal value and economic benefits, often referred to as the “King of Herbs.” With the increasing market demand and the limited availability of suitable cultivation land, the issue of continuous cropping obstacles for P. ginseng has become increasingly prominent, directly hindering the sustainable development of the ginseng industry. This article summarizes the concept and hazards of continuous cropping obstacles and, drawing on the latest research, provides an in-depth analysis of the causes and response mechanisms. This work aims to establish a solid foundation for future research into the mechanisms of continuous cropping obstacles in P. ginseng.

Paraplegia caused by spinal cord injury is a serious neurological complication, for which surgery is currently the main treatment method. Due to different surgical approaches, patients are usually expected to maintain a passive prone position for a long time or switch between the supine and prone positions. Affected by multiple factors such as neurogenic sensory disorders, pathological changes in muscle tone and operative duration, the risk of intraoperative acquired pressure injury (IAPI) is significantly increased. Current clinical prevention strategies for IAPI in these patients predominantly focus on localized pressure relief during positioning, lacking systematic, standardized comprehensive prevention protocols or evidence-based guidelines. To address it, Department of Nursing, Orthopedics Branch, China International Exchange and Promotive Association for Medical and Health Care, Spinal Trauma Professional Committee, Orthopedics Branch, Chinese Medical Doctor Association, Nursing Group of Spine and Spinal Cord Professional Committee of Chinese Association of Rehabilitation Medicine organized experts in relevant fields to formulate Guideline for the prevention of intraoperative acquired pressure injury in paraplegic patients with spinal cord injury ( version 2025), based on evidence-based medical evidence and latest research results and clinical practice at home and abroad. Eleven recommendations were put forward from the aspects of preoperative risk assessment, intraoperative prevention strategies, postoperative handover and monitoring, and supportive mechanisms for IAPI prevention, aiming to standardize the prevention measures and management strategies of IAPI in paraplegic patients with spinal cord injury and accelerate the recovery of patients and improve the therapeutic effect.

OBJECTIVE To explore and evaluate the machine learning model for prediction of bacterial bloodstream infections established based on routine test data.METHODS By means of retrospective survey,a total of 5 421 pa-tients who were hospitalized in 3 medical institutions from Jan.2015 to Dec.2022 were recruited as the research subjects,1 914 of whom were assigned as the bloodstream infection group,and 3 507 were assigned as the non-bloodstream infection group.The baseline data including gender and age and the results of routine laboratory tests were collected from the enrolled patients.The 3 types of machine learning algorithms,logistic regression,support vector machine and random forest,were respectively used for the screening of the optimal prediction model;the contribution of feature variables to the predictive capability of the model was interpreted through SHAP.The fea-ture variables of the model were optimized by using recursive feature elimination method,and the predictive effi-ciency of the model was evaluated by the area under the curve(AUC)of receiver operating characteristic(ROC)curves.RESULTS Totally 26 variables involving age,gender and blood routine test indexes were included.The random forest was chosen as the optimal machine learning algorithm for the establishment of prediction model for bloodstream infections,and the accuracy of the model was 0.709,with the AUC 0.706.The result of SHAP ex-planation indicated that the age,hematokrit and erythrocyte volume distribution width-CV had remarkable effect on the model's making right decisions.17 variables of the prediction model showed more remarkable effect than 26 variable on distinguishing from the gram-positive bacteria bloodstream infections from the gram-negative bacteria bloodstream infections,with the AUC 0.715,the sensitivity 0.701,the specificity 0.632.CONCLUSIONS The prediction model that is established based on the blood routine test indexes by machine learning algorithm can pre-dict the bacterial bloodstream infection.Meanwhile,the feature selection strategy can further improve the predic-tive efficiency of the model on basis of lowering the dimensionality.

OBJECTIVE To explore and evaluate the machine learning model for prediction of bacterial bloodstream infections established based on routine test data.METHODS By means of retrospective survey,a total of 5 421 pa-tients who were hospitalized in 3 medical institutions from Jan.2015 to Dec.2022 were recruited as the research subjects,1 914 of whom were assigned as the bloodstream infection group,and 3 507 were assigned as the non-bloodstream infection group.The baseline data including gender and age and the results of routine laboratory tests were collected from the enrolled patients.The 3 types of machine learning algorithms,logistic regression,support vector machine and random forest,were respectively used for the screening of the optimal prediction model;the contribution of feature variables to the predictive capability of the model was interpreted through SHAP.The fea-ture variables of the model were optimized by using recursive feature elimination method,and the predictive effi-ciency of the model was evaluated by the area under the curve(AUC)of receiver operating characteristic(ROC)curves.RESULTS Totally 26 variables involving age,gender and blood routine test indexes were included.The random forest was chosen as the optimal machine learning algorithm for the establishment of prediction model for bloodstream infections,and the accuracy of the model was 0.709,with the AUC 0.706.The result of SHAP ex-planation indicated that the age,hematokrit and erythrocyte volume distribution width-CV had remarkable effect on the model's making right decisions.17 variables of the prediction model showed more remarkable effect than 26 variable on distinguishing from the gram-positive bacteria bloodstream infections from the gram-negative bacteria bloodstream infections,with the AUC 0.715,the sensitivity 0.701,the specificity 0.632.CONCLUSIONS The prediction model that is established based on the blood routine test indexes by machine learning algorithm can pre-dict the bacterial bloodstream infection.Meanwhile,the feature selection strategy can further improve the predic-tive efficiency of the model on basis of lowering the dimensionality.

Objective To investigate the relationship between serum high mobility group protein B1(HMGB1),C1q/TNF-associated protein 3(CTRP3)and pain degree and lumbar function.Methods A total of 145 patients with lumbar spinal stenosis treated in the hospital from September 2021 to August 2023 were retrospectively selected as the study objects,and were divided into single-segment group(89 cases)and multi-segment group(56 cases)according to the number of lumbar spinal stenosis segments.Serum levels of HMGB1 and CTRP3 were detected by enzyme-linked immunosorbent assay.The clinicopathological features such as pain degree,lumbar function and serum levels of HMGB1 and CTRP3 were compared between the two groups.The correlation of serum HMGB1 and CTRP3 levels with pain degree and lumbar function were ana-lyzed,and the factors influencing the number of segments of lumbar stenosis were analyzed by multivariate Logistic regression.The diagnostic value of serum HMGB1 and CTRP3 levels on lumbar function of patients was analyzed by receiver operating characteristic(ROC)curve.Results Visual analogue scale(VAS)score and serum HMGB1 level in multi-segment group were higher than those in single-segment group(P＜0.05),Japanese Orthopaedic Association(JOA)score and serum CTRP3 level were lower than those in single-seg-ment group(P＜0.05).Serum HMGB1 level was negatively correlated with CTRP3 and JOA score in pa-tients with lumbar spinal stenosis(r=-0.544,-0.616,P＜0.001),and positively correlated with VAS score(r=0.453,P＜0.001).The serum CTRP3 level was negatively correlated with VAS score(r=-0.550,P＜0.001),and positively correlated with JOA score(r=0.619,P＜0.001).JOA score was nega-tively correlated with VAS score(r=-0.485,P＜0.001).Multivariate Logistic regression analysis showed that JOA score and serum CTRP3 level were protective factors for multi-segmental stenosis(P＜0.05),VAS score and serum HMGB1 level were risk factors for multi-segmental stenosis(P＜0.05).The analysis of ROC curve showed that serum HMGB1 and CTRP3 levels could be used as diagnostic indexes of lumbar spinal function in patients with lumbar spinal stenosis,and the combined diagnosis effect was better(P＜0.05).Conclusion Serum levels of HMGB1 and CTRP3 are closely related to pain degree and lumbar function in pa-tients with lumbar spinal stenosis.The combination of the two indexes can be used for the clinical diagnosis of lumbar function in patients with lumbar spinal stenosis.