In general, bioassay-guided fractionation and isolation of bioactive constituents from botanical materials frequently ended up with the reward of a single compound. However, botanical materials typically exert their therapeutic actions through multi-pathway effects due to the intrinsic complex nature of chemical constituents. In addition, the content of bioactive compounds in botanical materials is largely dependent on humidity, temperature, soil, especially geographical origins, from which rapid and accurate identification of plant materials is pressingly needed. These long-standing obstacles collectively impede the deep exploitation and application of these versatile natural sources. To address the challenges, a new paradigm integrating biogravimetric analyses and machine learning-driven origin classification (BAMLOC) was developed. The biogravimetric analyses are based on absolute qHNMR quantification and in vivo zebrafish model-assisted activity index calculation, by which bioactive substance groups jointly responsible for the bioactivities in all fractions are pinpointed before any isolation effort. To differentiate origin-different botanical materials varying in the content of bioactive substance groups, principal component analysis, linear discriminant analysis, and hierarchical cluster analysis in conjunction with supervised support vector machine are employed to classify and predict production areas based on the detection of volatile organic compounds by E-nose and GC-MS. Expanding BAMLOC to Codonopsis Radix enables the identification of polyacetylenes and pyrrolidine alkaloids as the bioactive substance group for immune restoration effect and accurately determines the origins of plants. This study advances the toolbox for the discovery of bioactive compounds from complex mixtures and lays a more definitive foundation for the in-depth utilization of botanical materials.

Current epilepsy prediction methods are not effective in characterizing the multi-domain features of complex long-term electroencephalogram (EEG) data, leading to suboptimal prediction performance. Therefore, this paper proposes a novel multi-scale sparse adaptive convolutional network based on multi-head attention mechanism (MS-SACN-MM) model to effectively characterize the multi-domain features. The model first preprocesses the EEG data, constructs multiple convolutional layers to effectively avoid information overload, and uses a multi-layer perceptron and multi-head attention mechanism to focus the network on critical pre-seizure features. Then, it adopts a focal loss training strategy to alleviate class imbalance and enhance the model's robustness. Experimental results show that on the publicly created dataset (CHB-MIT) by MIT and Boston Children's Hospital, the MS-SACN-MM model achieves a maximum accuracy of 0.999 for seizure prediction 10 ~ 15 minutes in advance. This demonstrates good predictive performance and holds significant importance for early intervention and intelligent clinical management of epilepsy patients.
Humans ; Electroencephalography/methods* ; Epilepsy/physiopathology* ; Neural Networks, Computer ; Seizures/physiopathology* ; Signal Processing, Computer-Assisted ; Algorithms

Non-small cell lung cancer(NSCLC)is the most common form of lung cancer,and patients with advanced disease generally have a poor prognosis.In recent years,immune checkpoint inhibitors(ICIs)have been approved as first-line therapy for patients with NSCLC lacking actionable driver mutations.Emerging evidence indicates that ICIs reduce tumor burden in some patients and may affect pulmonary ventila-tion and diffusion capacity,which are associated with clinical outcomes.This review summarizes baseline pulmonary function characteristics in advanced NSCLC,outlines in the impact of ICI therapy on lung function,and discusses potential mechanisms through which immunother-apy can influence pulmonary function,with the aim of guiding lung function monitoring and individualized management during ICI treat-ment.

Non-small cell lung cancer(NSCLC)is the most common form of lung cancer,and patients with advanced disease generally have a poor prognosis.In recent years,immune checkpoint inhibitors(ICIs)have been approved as first-line therapy for patients with NSCLC lacking actionable driver mutations.Emerging evidence indicates that ICIs reduce tumor burden in some patients and may affect pulmonary ventila-tion and diffusion capacity,which are associated with clinical outcomes.This review summarizes baseline pulmonary function characteristics in advanced NSCLC,outlines in the impact of ICI therapy on lung function,and discusses potential mechanisms through which immunother-apy can influence pulmonary function,with the aim of guiding lung function monitoring and individualized management during ICI treat-ment.

The problem of drug resistance resulting from the long-term use of antimicrobials is a serious threat to global health,and the emergence of multidrug-resistant bacteria,in particular,has greatly limited therapeutic op-tions.Therefore,the development of new or alternative antimicrobial agents has become an urgent need.Antimi-crobial peptides(AMPs)have been regarded as good alternatives to antibacterial drugs due to their strong antibac-terial activity and unique mechanism of action.At present,some AMPs have completed preclinical studies on drug-resistant bacterial infections and entered the clinical trial stage,while their stability and targeting have been signifi-cantly improved through the optimisation of amino acid modification,nano-delivery system and other technolo-gies,which have gradually become a research hotspot in this field.Therefore,this paper discusses the importance of AMPs in bacterial drug resistance from the biological properties of AMPs,the mechanism of regulating bacteri-al drug resistance and the application of AMPs in the treatment of drug-resistant bacterial infections,with a view to providing a reference for the development of drugs against drug-resistant bacteria and clinical application.

The problem of drug resistance resulting from the long-term use of antimicrobials is a serious threat to global health,and the emergence of multidrug-resistant bacteria,in particular,has greatly limited therapeutic op-tions.Therefore,the development of new or alternative antimicrobial agents has become an urgent need.Antimi-crobial peptides(AMPs)have been regarded as good alternatives to antibacterial drugs due to their strong antibac-terial activity and unique mechanism of action.At present,some AMPs have completed preclinical studies on drug-resistant bacterial infections and entered the clinical trial stage,while their stability and targeting have been signifi-cantly improved through the optimisation of amino acid modification,nano-delivery system and other technolo-gies,which have gradually become a research hotspot in this field.Therefore,this paper discusses the importance of AMPs in bacterial drug resistance from the biological properties of AMPs,the mechanism of regulating bacteri-al drug resistance and the application of AMPs in the treatment of drug-resistant bacterial infections,with a view to providing a reference for the development of drugs against drug-resistant bacteria and clinical application.

Objective To establish a diagnostic model for depression in insomnia patients by mining polysomnography (PSG) data of insomnia patients with machine learning algorithms,and to provide a scientific basis for the diagnosis of depression in insomnia patients. Methods According to the inclusion and exclusion criteria,2162 insomnia inpatients and outpatients who attended the Inner Mongolia Autonomous Region Mental Health Center from January to December 2023 and underwent polysomnographic monitoring were included,and depression was diagnosed using the International Statistical Classification of Diseases and Related Health Problems,10th version (ICD-10). The general condition and PSG data of the patients were collected. Six algorithms—logistic regression (LR),Support vector machines (SVM),Random forest (RF),Adaptive Boosting (AdaBoost),Extreme Gradient Boosting (XGBoost) and Naive Bayes (NB)—were used to build the diagnostic model of depression in insomnia patients after the patients' general condition and PSG data were gathered. Results Among the enrolled patients with insomnia,40.1％ had comorbid depression. Among the six models,LR and RF exhibited the highest values of area under the curve (AUC) of receiver operating characteristic (ROC),at 0.825 and 0.823,respectively,indicating superior overall classification performance. Conclusion Logistic regression and random forest modeling have good diagnostic efficacy in the population of insomniacs with depression.

Objective To establish a diagnostic model for depression in insomnia patients by mining polysomnography (PSG) data of insomnia patients with machine learning algorithms,and to provide a scientific basis for the diagnosis of depression in insomnia patients. Methods According to the inclusion and exclusion criteria,2162 insomnia inpatients and outpatients who attended the Inner Mongolia Autonomous Region Mental Health Center from January to December 2023 and underwent polysomnographic monitoring were included,and depression was diagnosed using the International Statistical Classification of Diseases and Related Health Problems,10th version (ICD-10). The general condition and PSG data of the patients were collected. Six algorithms—logistic regression (LR),Support vector machines (SVM),Random forest (RF),Adaptive Boosting (AdaBoost),Extreme Gradient Boosting (XGBoost) and Naive Bayes (NB)—were used to build the diagnostic model of depression in insomnia patients after the patients' general condition and PSG data were gathered. Results Among the enrolled patients with insomnia,40.1％ had comorbid depression. Among the six models,LR and RF exhibited the highest values of area under the curve (AUC) of receiver operating characteristic (ROC),at 0.825 and 0.823,respectively,indicating superior overall classification performance. Conclusion Logistic regression and random forest modeling have good diagnostic efficacy in the population of insomniacs with depression.

Objective:To explore the influencing factors of accidental falls outside the hospital in elderly patients with accidental injury in the Emergency Department, so as to provide a basis for preventing accidental injuries.Methods:Convenience sampling was used to select 210 emergency elderly patients who visited the hospital due to accidental injuries from November 2017 to May 2020 in the Second Medical Center, People's Liberation Army General Hospital as the research object. Patients were investigated with the self-designed Out-of-hospital Accidental Injury Consultation Questionnaire. Single factor analysis and binomial Logistic regression were used to analyze the influencing factors of falls in elderly patients. A total of 210 questionnaires were distributed in this survey, and 180 valid questionnaires were returned with the valid response rate of 85.71%.Results:A total of 147 falls occurred among 180 elderly patients in the Emergency Department, and the incidence of falls was 81.7%. Binomial Logistic regression analysis showed that old age, outdoor, chronic diseases and no use of walking aids were independent risk factors for falls ( P<0.05) . Conclusions:The main types of accidental injuries in elderly patients are falls. Strengthening the nursing education of fall prevention measures, improving compliance with medications, choosing suitable walking aids or trying other new fall prevention technologies, and establishing a multi-party participation model can effectively prevent and reduce the occurrence of falls in elderly patients.

Objective:To analyze the prognostic factors of breast cancer patients with isolated chest wall recurrence (ICWR) after mastectomy, and investigate the optimal treatment.Methods:A total of 201 breast cancer patients with ICWR after mastectomy who were treated in Cancer Hospital, Chinese Academy of Medical Sciences and the Fifth Medical Center Chinese PLA General Hospital from October 1998 to April 2018 were retrospectively analyzed. The median follow-up was 92.8 months and survival data were obtained.Results:Among 201 patients with ICWR, 103 patients developed subsequent locoregional recurrence (sLRR) and 5-year cumulative sLRR rate was 49.1%; 134 patients developed distant metastasis (DM) and 5-year DM rate was 64.4%; 103 patients died, the median progression-free survival (PFS) was 17.4 months and the 5-year PFS rate was 23.2%; the median overall survival (OS) was 62.5 months and the 5-year OS rate was 52.1%. Multivariate analysis showed that the recurrence interval ( HR=2.17, 95% CI: 1.26-3.73) and the locoregional treatment ( HR=1.59, 95% CI: 1.05-2.40) were the independent prognostic factors for sLRR. The initial HER2 status ( HR=1.60, 95% CI: 1.03-2.48) was the independent prognostic factor for DM. The recurrence interval ( HR=1.99, 95% CI: 1.30-3.04), the locoregional treatment ( HR=1.99, 95% CI: 1.43-2.76) and the treatment modalities after recurrence ( HR=1.70, 95% CI: 1.18-2.46) were the independent prognostic factors for PFS. The initial HER2 status ( HR=1.69, 95% CI: 1.02-2.81), the recurrence interval ( HR=1.85, 95% CI: 1.15-2.98) and the treatment modalities after recurrence ( HR=2.48, 95% CI: 1.56-3.96) were the independent prognostic factors for OS. Conclusions:Breast cancer patients after ICWR have an optimistic OS until now, but the risk of sLRR and DM is high. Comprehensive treatment modalities including surgery, radiotherapy and systemic therapy improve the outcome of breast cancer patients with ICWR after mastectomy.