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

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 the predictive value of troponin Ⅰ(TnⅠ)peak value upon admission for predicting left ventricular ejection fraction(LVEF)＜50％in patients with acute myocardial infarction(AMI)during the recovery period.Methods A retrospec-tive analysis was carried out on 220 AMI patients admitted to Beijing Friendship Hospital from 2018 to the present.The patients were di-vided into three groups based on the peak value of TnⅠ during their stay in hospital,and the baseline data were compared.Subsequently,three progressively complex regression models were constructed to evaluate the relationship between TnⅠ and LVEF＜50％.The optimal cutoff value was determined through restricted cubic spline and smooth curve analysis.Additionally,subgroup analysis was carried out to explore differences in the predictive value of TnⅠ in different populations.Results TnⅠ peak value was significantly associated(P＜0.05)with the ratio of emergency percutaneous coronary intervention(PCI),neutrophil-to-lymphocyte ratio,white blood cell count,neutro-phils,intra-aortic balloon pump usage,N-terminal pro-brain natriuretic peptide peak value,and so on.All three models showed a sig-nificant increase in the risk of LVEF＜50％with higher TnⅠ peak value(P＜0.05).Restricted cubic spline and smooth curve analysis re-vealed a linear relationship between TnⅠ peak value and LVEF values,with the optimal cutoff value for TnⅠ peak value consistently at 29.80ng/ml across the three models.Subgroup analysis showed that the predictive value of peak TnⅠ for LVEF＜50％demonstrated statisti-cally significant differences across the following subgroups:male patients,those with high BMI,hypertension,acute interventional treat-ment,as well as different age groups,and whether patients had diabetes,smoked,or consumed alcohol.Conclusion An admission TnⅠpeak value exceeding 29.80ng/ml is an independent risk factor for predicting LVEF＜50％during the recovery period in AMI patients.It can be used to identify high-risk individuals and provide a basis for early aggressive intervention.

Objective To investigate if Shikonin(SKI)can induce ferroptosis via the ROS/JNK pathway to inhibit the malignant behavior of pancreatic cancer.Methods Human pancreatic cancer PANC-1 or BxPC-3 cells were selected.Drug efficacy experiments were established with a blank control group(Con group)and low,medium,and high dose SKI groups(2,4,8 μmol/L).JNK-related mechanism experiments were categorized into a blank control group(Con group),SKI group,and SKI+JNK inhibitor group(SKI+SP600125 group).ROS-related mechanism experiments were divided into a blank control group(Con group),SKI group,and SKI+ROS scavenger group(SKI+NAC group).Cell viability was assessed using the CCK-8 method to calculate IC50;Transwell experiments evaluated cell migration and invasion capabilities;the C11 BODIPY 581/591 probe was utilized for flow cytometry to detect lipid peroxidation levels,while the FerroOrange fluorescent probe measured ferrous ion levels;ROS levels were determined using a ROS detection kit;the Western blot method identified ferroptosis-related key proteins(SLC7A11,GPX4),apoptosis-related proteins(Caspase3,PARP),and JNK pathway proteins(JNK,p-JNK);an in vivo xenograft tumor model was employed to assess tumor proliferation.Results SKI treatment significantly and dose-dependently inhibited PANC-1 cell viability(IC50:6.04 μmol/L,P<0.0001)and BxPC-3 cell viability(IC50:12.27 μmol/L,P<0.0001),and significantly reduced migrating and invasive cell numbers(P<0.0001),with migration cell numbers dropping to about 30%of the control group at 8 μmol/L SKI treatment(P<0.0001).Mechanistically,SKI induced increased intracellular lipid peroxidation,Fe2+accumulation,and significant ROS production(P<0.0001),significantly downregulated SLC7A11 and GPX4 protein expression(GPX4 protein expression reduced to 40%of that in the control group,P<0.0001),and activated JNK phosphorylation(p-JNK/JNK ratio increased to 2.8-fold,P<0.0001).Pretreatment with the JNK-specific inhibitor SP600125 or ROS scavenger NAC effectively reversed SKI's inhibition of cell viability and downregulation of SLC7A11/GPX4 protein(all P<0.01).SKI also inhibited pancreatic cancer tumor cell proliferation in vivo(P<0.0001).Conclusion SKI induces ferroptosis by activating the ROS/JNK pathway,thereby inhibiting pancreatic cancer proliferation,migration,and invasion.

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

Objective To investigate the relationship between the peak levels of N-terminal pro-B-type natriuretic peptide(NT-proBNP)during hospitalization and the risk of left ventricular ejection fraction(LVEF)＜50％during the recovery period in pa-tients with acute myocardial infarction(AMI).Methods This retrospective cohort study included 220 AMI patients treated at Beijing Friendship Hospital,Capital Medical University from January 2018 to December 2023.The patients were divided into three groups accord-ing to the tripartite value of peak levels of NT-proBNP during hospitalization.Baseline clinical characteristics,laboratory test results,and peak levels of NT-proBNP were collected.The primary outcome was LVEF＜50％during the recovery period.By incorporating con-founding factors,three stepwise complex Logistic regression model and linear regression model were used to analyze the relationship be-tween peak levels of NT-proBNP and the risk of LVEF＜50％.Restricted cubic spline(RCS)was used to explore the nonlinear relation-ship between them.Results The peak levels of NT-proBNP were positively correlated with an increased risk of LVEF＜50％during the recovery period,even after adjusting for confounding factors.RCS analysis revealed the nonlinear relationships between them.When the peak levels of NT-proBNP was 1822.991pg/ml,the sensitivity and specificity of predicting LVEF＜50％were 57.14％and 70.97％.Conclusion The NT-proBNP peak levels of 1822.991 pg/ml is a useful indicator for predicting LVEF＜50％in AMI pa-tients during the recovery period,and it can be used to identify high-risk AMI groups and provide basis for early active intervention.

Objective To explore the predictive value of troponin Ⅰ(TnⅠ)peak value upon admission for predicting left ventricular ejection fraction(LVEF)＜50％in patients with acute myocardial infarction(AMI)during the recovery period.Methods A retrospec-tive analysis was carried out on 220 AMI patients admitted to Beijing Friendship Hospital from 2018 to the present.The patients were di-vided into three groups based on the peak value of TnⅠ during their stay in hospital,and the baseline data were compared.Subsequently,three progressively complex regression models were constructed to evaluate the relationship between TnⅠ and LVEF＜50％.The optimal cutoff value was determined through restricted cubic spline and smooth curve analysis.Additionally,subgroup analysis was carried out to explore differences in the predictive value of TnⅠ in different populations.Results TnⅠ peak value was significantly associated(P＜0.05)with the ratio of emergency percutaneous coronary intervention(PCI),neutrophil-to-lymphocyte ratio,white blood cell count,neutro-phils,intra-aortic balloon pump usage,N-terminal pro-brain natriuretic peptide peak value,and so on.All three models showed a sig-nificant increase in the risk of LVEF＜50％with higher TnⅠ peak value(P＜0.05).Restricted cubic spline and smooth curve analysis re-vealed a linear relationship between TnⅠ peak value and LVEF values,with the optimal cutoff value for TnⅠ peak value consistently at 29.80ng/ml across the three models.Subgroup analysis showed that the predictive value of peak TnⅠ for LVEF＜50％demonstrated statisti-cally significant differences across the following subgroups:male patients,those with high BMI,hypertension,acute interventional treat-ment,as well as different age groups,and whether patients had diabetes,smoked,or consumed alcohol.Conclusion An admission TnⅠpeak value exceeding 29.80ng/ml is an independent risk factor for predicting LVEF＜50％during the recovery period in AMI patients.It can be used to identify high-risk individuals and provide a basis for early aggressive intervention.

Objective To investigate the relationship between the peak levels of N-terminal pro-B-type natriuretic peptide(NT-proBNP)during hospitalization and the risk of left ventricular ejection fraction(LVEF)＜50％during the recovery period in pa-tients with acute myocardial infarction(AMI).Methods This retrospective cohort study included 220 AMI patients treated at Beijing Friendship Hospital,Capital Medical University from January 2018 to December 2023.The patients were divided into three groups accord-ing to the tripartite value of peak levels of NT-proBNP during hospitalization.Baseline clinical characteristics,laboratory test results,and peak levels of NT-proBNP were collected.The primary outcome was LVEF＜50％during the recovery period.By incorporating con-founding factors,three stepwise complex Logistic regression model and linear regression model were used to analyze the relationship be-tween peak levels of NT-proBNP and the risk of LVEF＜50％.Restricted cubic spline(RCS)was used to explore the nonlinear relation-ship between them.Results The peak levels of NT-proBNP were positively correlated with an increased risk of LVEF＜50％during the recovery period,even after adjusting for confounding factors.RCS analysis revealed the nonlinear relationships between them.When the peak levels of NT-proBNP was 1822.991pg/ml,the sensitivity and specificity of predicting LVEF＜50％were 57.14％and 70.97％.Conclusion The NT-proBNP peak levels of 1822.991 pg/ml is a useful indicator for predicting LVEF＜50％in AMI pa-tients during the recovery period,and it can be used to identify high-risk AMI groups and provide basis for early active intervention.

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 establish of a newmethod:for the rapid detection of H5N1-Avian Influenza virus by combining reverse transcription (RT), multiple cross displacement amplification (MCDA) and nanoparticle-based lateral flow biosensor (LFB).Methods:MCDA primers were designed based on gene sequences specific to Hemagglutinin (HA) and Neuraminidase (NA) in H5N1 avian influenza virus. The target genes HA and NA were amplified through reverse transcription and MCDA in one reaction system. Results were displayed by LFB. The assay was named as H5N1-mRT-MCDA-LFB. The reaction conditions of the H5N1-RT-MCDA-LFB method were optimized, and the sensitivity and specificity were also assessed.Results:The H5N1-RT-MCDA-LFB assay could achieve good amplification effect at a constant temperature of 65 ℃ for 40 minutes. The method had a lower limit of detection of 100 fg per reaction with 100-fold higher sensitivity than that of the RT-qPCR (lower limit of detection 10 pg per reaction). The assay was negative in detecting 28 common viruses, mycoplasmas, chlamydias, bacterias and funguses, except for H5N1. In addition, the H5N1-RT-MCDA-LFB method showed better validation in simulated clinical samples with a lower limit of detection at 1×10 2 copies/ml. Conclusion:The H5N1-RT-MCDA-LFB assay is a valuable molecular diagnostic technique for detecting H5N1 avian influenza virus due to its simplicity, rapidity, sensitivity and specificity.