The traditional Chinese medicine(TCM) industry is a crucial part of China's pharmaceutical sector and plays a strategic role in ensuring public health and promoting economic and social development. In response to the practical demand for high-quality development of the TCM industry, this paper focused on the bottlenecks encountered during the digital and intelligent transformation of TCM production systems. Specifically, it explored technical strategies and methodologies for constructing the best TCM production mode. An innovative artificial intelligence(AI)-centered technical architecture for TCM production was proposed, focusing on key aspects of production management including process modeling, state evaluation, and decision optimization. Furthermore, a series of critical technologies were developed to realize the best TCM production mode. Finally, a novel AI-driven TCM production mode characterized by a closed-loop system of "measurement-modeling-decision-execution" was presented through engineering case studies. This study is expected to provide a technological pathway for developing new quality productive forces within the TCM industry.
Artificial Intelligence ; Drugs, Chinese Herbal ; Medicine, Chinese Traditional/methods* ; Humans

The study investigated the intrinsic changes in material basis of Angelicae Sinensis Radix during wine processing by headspace-gas chromatography-ion mobility spectrometry(HS-GC-IMS), headspace-solid phase microextraction-gas chromatography-mass spectrometry(HS-SPME-GC-MS), and ultra-high performance liquid chromatography-quadrupole-orbitrap mass spectrometry(UPLC-Q-Orbitrap-MS) combined with chemometrics. HS-GC-IMS fingerprints of Angelicae Sinensis Radix before and after wine processing were established to analyze the variation trends of volatile components and characterize volatile small-molecule substances before and after processing. Principal component analysis(PCA) and orthogonal partial least squares-discriminant analysis(OPLS-DA) were employed for differentiation and difference analysis. A total of 89 volatile components in Angelicae Sinensis Radix were identified by HS-GC-IMS, including 14 unsaturated hydrocarbons, 16 aldehydes, 13 ketones, 9 alcohols, 16 esters, 6 organic acids, and 15 other compounds. HS-SPME-GC-MS detected 118 volatile components, comprising 42 unsaturated hydrocarbons, 11 aromatic compounds, 30 alcohols, 8 alkanes, 6 organic acids, 4 ketones, 7 aldehydes, 5 esters, and 5 other volatile compounds. UPLC-Q-Orbitrap-MS identified 76 non-volatile compounds. PCA revealed distinct clusters of raw and wine-processed Angelicae Sinensis Radix samples across the three detection methods. Both PCA and OPLS-DA effectively discriminated between the two groups, and 145 compounds(VIP>1) were identified as critical markers for evaluating processing quality, including 4-methyl-3-penten-2-one, ethyl 2-methylpentanoate, and 2,4-dimethyl-1,3-dioxolane detected by HS-GC-IMS, angelic acid, β-pinene, and germacrene B detected by HS-SPME-GC-MS, and L-tryptophan, licoricone, and angenomalin detected by UPLC-Q-Orbitrap-MS. In conclusion, the integration of the three detection methods with chemometrics elucidates the differences in the chemical material basis between raw and wine-processed Angelicae Sinensis Radix, providing a scientific foundation for understanding the processing mechanisms and clinical applications of wine-processed Angelicae Sinensis Radix.
Wine/analysis* ; Gas Chromatography-Mass Spectrometry/methods* ; Chromatography, High Pressure Liquid/methods* ; Angelica sinensis/chemistry* ; Solid Phase Microextraction/methods* ; Drugs, Chinese Herbal/isolation & purification* ; Chemometrics ; Volatile Organic Compounds/chemistry* ; Principal Component Analysis ; Ion Mobility Spectrometry/methods*

According to the World Health Organization (WHO) manual, sperm concentration should be measured using an improved Neubauer hemocytometer, while sperm motility should be measured by manual assessment. However, in China, thousands of laboratories do not use the improved Neubauer hemocytometer or method; instead, the Makler counting chamber is one of the most widely used chambers. To study sources of error that could impact the measurement of the apparent concentration and motility of sperm using the Makler counting chamber and to verify its accuracy for clinical application, 67 semen samples from patients attending the Department of Andrology, West China Second University Hospital, Sichuan University (Chengdu, China) between 13 September 2023 and 27 September 2023, were included. Compared with applying the cover glass immediately, delaying the application of the cover glass for 5 s, 10 s, and 30 s resulted in average increases in the sperm concentration of 30.3%, 74.1%, and 107.5%, respectively (all P < 0.0001) and in the progressive motility (PR) of 17.7%, 30.8%, and 39.6%, respectively (all P < 0.0001). However, when the semen specimens were fixed with formaldehyde, a delay in the application of the cover glass for 5 s, 10 s, and 30 s resulted in an average increase in the sperm concentration of 6.7%, 10.8%, and 14.6%, respectively, compared with immediate application of the cover glass. The accumulation of motile sperm due to delays in the application of the cover glass is a significant source of error with the Makler counting chamber and should be avoided.
Humans ; Male ; Sperm Motility/physiology* ; Sperm Count ; Semen Analysis/methods* ; Spermatozoa/physiology* ; Time Factors

OBJECTIVE: To evaluate the safety and effectiveness of robot-assisted percutaneous coronary intervention (R-PCI) compared to traditional manual percutaneous coronary intervention (M-PCI). METHODS: This prospective, multicenter, randomized controlled, non-inferior clinical trial enrolled patients with coronary heart disease who met the inclusion criteria and had indications for elective percutaneous coronary intervention. Participants were randomly assigned to either the R-PCI group or the M-PCI group. Primary endpoints were clinical and technical success rates. Clinical success was defined as visually estimated residual post-percutaneous coronary intervention stenosis < 30% with no 30-day major adverse cardiac events. Technical success in the R-PCI group was defined as successful completion of percutaneous coronary intervention using the ETcath200 robot-assisted system, without conversion to M-PCI in the event of a guidewire or balloon/stent catheter that was unable to cross the vessel or was poorly supported by the catheter. Secondary endpoints included total procedure time, percutaneous coronary intervention procedure time, fluoroscopy time, contrast volume, operator radiation exposure, air kerma, and dose-area product. RESULTS: The trial enrolled 152 patients (R-PCI: 73 patients, M-PCI: 79 patients). Lesions were predominantly B2/C type (73.6%). Both groups achieved 100% clinical success rate. No major adverse cardiac events occurred during the 30-day follow-up. The R-PCI group had a technical success rate of 100%. The R-PCI group had longer total procedure and fluoroscopy times, but lower operator radiation exposure. The percutaneous coronary intervention procedure time, contrast volume, air kerma, and dose-area product were similar between the two groups. CONCLUSIONS For certain complex lesions, performing percutaneous coronary intervention using the ETcath200 robot-assisted system is safe and effective and does not result in conversion to M-PCI.

BACKGROUND: The beneficial effects of sodium-glucose co-transporter-2 inhibitors (SGLT2i) on adverse cardiac outcomes in diabetic patients are well-established. However, the effects of SGLT2i against cancer therapy-related cardiotoxicity remain understudied. We investigated the association between SGLT2i and cardiac outcomes in cancer patients. METHODS: PubMed, Embase, and the Cochrane Library were searched from their inception until September 30, 2024 for studies evaluating the effects of SGLT2i in patients with cancer. The primary outcomes included incident heart failure (HF), HF exacerbation, HF hospitalization, atrial fibrillation/atrial flutter (AF/AFL), myocardial infarction, and all-cause mortality. The secondary outcomes included acute kidney injury and sepsis. Odds ratio (OR) with 95% CI was pooled. RESULTS: Thirteen studies with 85,596 patients were included. Compared to non-SGLT2i use, SGLT2i treatment was associated with lower risks of incident HF (OR = 0.51, 95% CI: 0.32-0.79, P = 0.003), HF exacerbation (OR = 0.74, 95% CI: 0.63-0.87, P < 0.001), AF/AFL (OR = 0.67, 95% CI: 0.55-0.82, P < 0.001), myocardial infarction (OR = 0.61, 95% CI: 0.41-0.90, P = 0.01), and all-cause mortality (OR = 0.44, 95% CI: 0.28-0.69, P < 0.001), but not for HF hospitalization (OR = 0.58, 95% CI: 0.22-1.55, P = 0.28). As for safety outcomes, SGLT2i use was associated with lower risks of acute kidney injury (OR = 0.68, 95% CI: 0.57-0.81, P < 0.001) and sepsis (OR = 0.32, 95% CI: 0.23-0.44, P < 0.001). CONCLUSIONS SGLT2i were associated with lower risks of incident HF, HF exacerbation, AF/AFL, myocardial infarction, all-cause mortality, acute kidney injury, and sepsis in cancer patients.

Extracellular vesicles (EVs) are crucial for facilitating intercellular communication, promoting cell migration, and orchestrating the immune response. Recently, EVs can diagnose and treat tumors. EVs can be measured as biomarkers to provide information about the type of disease and therapeutic efficacy. Furthermore, EVs with lower immunogenicity and better biocompatibility are natural carriers of chemicals and gene drugs. Herein, we review the molecular composition, biogenesis, and separation methods of EVs. We also highlight the important role of EVs from different origins as biomarkers and drug delivery systems in tumor therapy. Finally, we provide deep insights into how EVs play a role in reversing the immunosuppressive microenvironment.

Objective:To explore the interrelationship between brain functional networks and features in functional magnetic resonance imaging(fMRI)of patients with Alzheimer's disease(AD),and to construct mixed-function networks(MFN),and apply them in machine learning classification models to improve the accuracy of AD classification.Methods:102 AD patients and 227 healthy subjects in the Alzheimer's Neuroimaging Initiative(ADNI)dataset were retrospectively analyzed.The partial correlation brain network of the blood oxygen level dependent(BOLD)signal was calculated and fused with low-frequency wave amplitude(ALFF),fractional low-frequency wave amplitude(fALFF)and local consistency(ReHo)features to construct MFN.Network topology parameters were extracted,and a variety of machine learning classification models were constructed based on MFN topological parameters,accuracy,precision,recall and area under the curve(AUC)were used to evaluate the predictive efficiency of the models.Results:By constructed MFN and calculated intra group to inter group ratio(IIGR),35 features could be obtained from ALFF,fALFF and ReHo feature topological parameter analysis,after rank sum test and FDR correction,there were statistical differences among 28 features(P＜0.05).The classification results show that,all the five classifiers have high classification performance on the test data set.The accuracy,precision and recall rates of random forest(RF),adaptive lifting algorithm(AdaBoost),guided aggregation algorithm(Bagging)and support vector machine(SVM)were all 99.7％,and the AUC values were up to 100％,99.5％,99.1％and 99.5％,respectively.The accuracy(98.5％),precision(98.5％),recall(98.5％),and AUC(99.1％)of the multi-layer perceptron(MLP)were slightly lower than other models,but remained excellent.It was worth noting that RF has the highest AUC value of all models at 100.0％,while Bagging has the lowest AUC value(99.1％)in the integrated approach.The results of performance comparison show that,MFN classification model can significantly improve the recognition and classification of AD disease,and greatly improve the performance of various indicators of the classifier.The results showed that,MFN classification model was superior to intelligent classification based fusion,DBN-based multitask learning,PVT-TSVM,unsupervised learning and clustering,SVM and SVM of degree 3 polynomial kernel function in key indicators such as accuracy(99.13％),AUC(99.42％),recall rate(99.46％)and specificity(99.42％)with plasma proteins,machine learning algorithms.It was further proved that MFN classification model has good generalization ability and robustness in AD disease classification.Conclusion:The AD classification model constructed based on brain mixed function network topology parameters and machine learning can improve the accuracy of AD classification.

Objective To investigate the distribution and antimicrobial resistance profiles of common pathogens isolated from cerebrospinal fluid(CSF)in CHINET program from 2015 to 2021.Methods The bacterial strains isolated from CSF were identified in accordance with clinical microbiology practice standards.Antimicrobial susceptibility test was conducted using Kirby-Bauer method and automated systems per the unified CHINET protocol.Results A total of 14 014 bacterial strains were isolated from CSF samples from 2015 to 2021,including the strains isolated from inpatients(95.3％)and from outpatient and emergency care patients(4.7％).Overall,19.6％of the isolates were from children and 80.4％were from adults.Gram-positive and Gram-negative bacteria accounted for 68.0％and 32.0％,respectively.Coagulase negative Staphylococcus accounted for 73.0％of the total Gram-positive bacterial isolates.The prevalence of MRSA was 38.2％in children and 45.6％in adults.The prevalence of MRCNS was 67.6％in adults and 69.5％in children.A small number of vancomycin-resistant Enterococcus faecium(2.2％)and linezolid-resistant Enterococcus faecalis(3.1％)were isolated from adult patients.The resistance rates of Escherichia coli and Klebsiella pneumoniae to ceftriaxone were 52.2％and 76.4％in children,70.5％and 63.5％in adults.The prevalence of carbapenem-resistant E.coli and K.pneumoniae(CRKP)was 1.3％and 47.7％in children,6.4％and 47.9％in adults.The prevalence of carbapenem-resistant Acinetobacter baumannii(CRAB)and Pseudomonas aeruginosa(CRPA)was 74.0％and 37.1％in children,81.7％and 39.9％in adults.Conclusions The data derived from antimicrobial resistance surveillance are crucial for clinicians to make evidence-based decisions regarding antibiotic therapy.Attention should be paid to the Gram-negative bacteria,especially CRKP and CRAB in central nervous system(CNS)infections.Ongoing antimicrobial resistance surveillance is helpful for optimizing antibiotic use in CNS infections.