ObjectiveNeuroinflammation plays a crucial role in both the onset and progression of ischemic stroke, exerting a significant impact on the recovery of the central nervous system. Excessive neuroinflammation can lead to secondary neuronal damage, further exacerbating brain injury and impairing functional recovery. As a result, effectively modulating and reducing neuroinflammation in the brain has become a key therapeutic strategy for improving outcomes in ischemic stroke patients. Among various approaches, targeting immune regulation to control inflammation has gained increasing attention. This study aims to investigate the role of in vitro induced regulatory T cells (Treg cells) in suppressing neuroinflammation after ischemic stroke, as well as their potential therapeutic effects. By exploring the mechanisms through which Tregs exert their immunomodulatory functions, this research is expected to provide new insights into stroke treatment strategies. MethodsNaive CD4⁺ T cells were isolated from mouse spleens using a negative selection method to ensure high purity, and then they were induced in vitro to differentiate into Treg cells by adding specific cytokines. The anti-inflammatory effects and therapeutic potential of Treg cells transplantation in a mouse model of ischemic stroke was evaluated. In the middle cerebral artery occlusion (MCAO) model, after Treg cells transplantation, their ability to successfully migrate to the infarcted brain region and their impact on neuroinflammation levels were examined. To further investigate the role of Treg cells in stroke recovery, the changes in cytokine expression and their effects on immune cell interactions was analyzed. Additionally, infarct size and behavioral scores were measured to assess the neuroprotective effects of Treg cells. By integrating multiple indicators, the comprehensive evaluation of potential benefits of Treg cells in the treatment of ischemic stroke was performed. ResultsTreg cells significantly regulated the expression levels of both pro-inflammatory and anti-inflammatory cytokines in vitro and in vivo, effectively balancing the immune response and suppressing excessive inflammation. Additionally, Treg cells inhibited the activation and activity of inflammatory cells, thereby reducing neuroinflammation. In the MCAO mouse model, Treg cells were observed to accumulate in the infarcted brain region, where they significantly reduced the infarct size, demonstrating their neuroprotective effects. Furthermore, Treg cell therapy notably improved behavioral scores, suggesting its role in promoting functional recovery, and increased the survival rate of ischemic stroke mice, highlighting its potential as a promising therapeutic strategy for stroke treatment. ConclusionIn vitro induced Treg cells can effectively suppress neuroinflammation caused by ischemic stroke, demonstrating promising clinical application potential. By regulating the balance between pro-inflammatory and anti-inflammatory cytokines, Treg cells can inhibit immune responses in the nervous system, thereby reducing neuronal damage. Additionally, they can modulate the immune microenvironment, suppress the activation of inflammatory cells, and promote tissue repair. The therapeutic effects of Treg cells also include enhancing post-stroke recovery, improving behavioral outcomes, and increasing the survival rate of ischemic stroke mice. With their ability to suppress neuroinflammation, Treg cell therapy provides a novel and effective strategy for the treatment of ischemic stroke, offering broad application prospects in clinical immunotherapy and regenerative medicine.

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

Objective： The aim of this study is to explore the predictive value of biparametric magnetic resonance imaging(bpMRI)for pelvic lymph node metastasis in prostate cancer patients with PSA≤20 μg/L and establish a nomogram. Methods： The imaging data and clinical data of 363 patients undergoing radical prostatectomy and pelvic lymph node dissection in the First Affiliated Hospital of Nanjing Medical University from July 2018 to December 2023 were retrospectively analyzed. Univariate analysis and multivariate logistic regression were used to screen independent risk factors for pelvic lymph node metastasis in prostate cancer, and a nomogram of the clinical prediction model was established. Calibration curves were drawn to evaluate the accuracy of the model. Results： Multivariate logistic regression analysis showed extrocapusular extension (OR=8.08,95%CI=2.62－24.97, P<0.01), enlargement of pelvic lymph nodes (OR=4.45,95%CI=1.16－17.11,P=0.030), and biopsy ISUP grade(OR=1.97,95%CI=1.12－3.46, P=0.018)were independent risk factors for pelvic lymph node metastasis. The C-index of the prediction model was 0.834, which indicated that the model had a good prediction ability. The actual value of the model calibration curve and the prediction probability of the model fitted well, indicating that the model had a good accuracy. Further analysis of DCA curve showed that the model had good clinical application value when the risk threshold ranged from 0.05 to 0.70.Conclusion： For prostate cancer patients with PSA≤20 μg/L, bpMRI has a good predictive value for the pelvic lymph node metastasis of prostate cancer with extrocapusular extension, enlargement of pelvic lymph nodes and ISUP grade≥4.
Humans ; Male ; Prostatic Neoplasms/diagnostic imaging* ; Lymphatic Metastasis ; Retrospective Studies ; Nomograms ; Prostate-Specific Antigen/blood* ; Lymph Nodes/pathology* ; Pelvis ; Predictive Value of Tests ; Prostatectomy ; Lymph Node Excision ; Risk Factors ; Magnetic Resonance Imaging ; Logistic Models ; Middle Aged ; Aged

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

Pelvic organ prolapse (POP), whose etiology is influenced by genetic and clinical risk factors, considerably impacts women's quality of life. However, the genetic underpinnings in non-European populations and comprehensive risk models integrating genetic and clinical factors remain underexplored. This study constructed the first polygenic risk score (PRS) for POP in the Chinese population by utilizing 20 disease-associated variants from the largest existing genome-wide association study. We analyzed a discovery cohort of 576 cases and 623 controls and a validation cohort of 264 cases and 200 controls. Results showed that the case group exhibited a significantly higher PRS than the control group. Moreover, the odds ratio of the top 10% risk group was 2.6 times higher than that of the bottom 10%. A high PRS was significantly correlated with POP occurrence in women older than 50 years old and in those with one or no childbirths. As far as we know, the integrated prediction model, which combined PRS and clinical risk factors, demonstrated better predictive accuracy than other existing PRS models. This combined risk assessment model serves as a robust tool for POP risk prediction and stratification, thereby offering insights into individualized preventive measures and treatment strategies in future clinical practice.
Humans ; Female ; Pelvic Organ Prolapse/epidemiology* ; Middle Aged ; Risk Assessment/methods* ; China/epidemiology* ; Multifactorial Inheritance ; Aged ; Risk Factors ; Genome-Wide Association Study ; Genetic Predisposition to Disease ; Case-Control Studies ; Adult ; Polymorphism, Single Nucleotide ; Genetic Risk Score ; East Asian People

Objective:To investigate the epidemiological and clinical characteristics of RSV among patients aged ≥60 years in Beijing from 2015 to 2023.Methods:Based on the respiratory pathogen surveillance system, samples of upper respiratory tract infections (URTI), non-severe community-acquired pneumonia (nsCAP) and severe community-acquired pneumonia (sCAP) among people aged ≥60 years were collected from 28 sentinel hospitals in 16 districts of Beijing from January 2015 to December 2023. Swab samples were collected from URTI within one week, and lower respiratory tract samples from nsCAP and sCAP were collected. Demographic and epidemiological data were also collected. Various respiratory pathogens including RSV were detected.Results:From January 2015 to December 2023, a total of 20 349 cases of acute respiratory infections aged ≥60 years were included, with the RSV-positive rate of 1.54% (313/20 349, 95% CI: 1.39%-1.68%). Among them, the total RSV-positive rates of older people during the pre-pandemic, pandemic, and post-pandemic periods of COVID-19 were 1.59% (207/13 006, 95% CI: 1.38%-1.81%), 0.82% (38/4 650, 95% CI: 0.56%-1.08%) and 2.53% (68/2 693, 95% CI: 1.93%-3.12%), respectively. The difference in RSV-positive rate was statistically significant ( P<0.001). Based on the sampling time of cases, the RSV epidemic season for older people in Beijing was from October to March of the following year, with a peak period in December or January of the following year. In the post COVID-19 pandemic, there were very few RSV-positive cases detected in the elderly from April to June 2023, with only one positive case detected in May and one in June. The RSV-positive rate of older people increased significantly from October to December, reaching 11.75% (51/383) in December. Among 263 RSV-positive cases in the elderly, RSV-A, RSV-B and unclassified type accounted for 43.35% (114/263), 29.28% (77/263) and 27.38% (72/263), respectively. Since 2020, there has been a subtype conversion, with RSV-B being the main focus. Among 197 elderly cases that have complete clinical data, the main symptoms were cough (86.8%, 171/197), sputum (80.2%, 158/197) and fever (73.60%, 145/197). About 24.87% (49/197) of elderly cases experienced complications. The hospitalization mortality rate was 4.57% (9/197), and the hospitalization rate was 78.68% (155/197). The ICU occupancy rate was 1.99% (36/197). The mechanical ventilation usage rate was 13.32% (33/197), and the length of hospital stay [ M ( Q1, Q3)] was 12 (9, 16) days. Conclusion:In Beijing, the RSV infection rate is relatively low during the COVID-19 pandemic, and the prevalence of COVID-19 is relatively high. In 2023, there was no out-of-season outbreak of RSV infection among the elderly. Elderly RSV infection cases have multiple complications, severe diseases, and poor prognosis.

Objective To establish a dynamic prediction model of fatal massive hemorrhage in trauma based on the vital signs time series data and machine learning algorithms.Methods Retrospectively analyze the vital signs time series data of 7522 patients with trauma in the Medical Information Mart for Intensive Care-Ⅳ(MIMIC-Ⅳ)database from 2008 to 2019.According to the occurrence of posttraumatic fatal massive hemorrhage,the patients were divided into two groups:fatal massive hemorrhage group(n=283)and non-fatal massive hemorrhage group(n=7239).Six machine learning algorithms,including logistic regression(LR),support vector machine(SVM),random forests(RF),adaptive boosting(AdaBoost),gated recurrent unit(GRU),and GRU-D were used to develop a dynamic prediction models of fatal massive hemorrhage in trauma.The probability of fatal massive hemorrhage in the following 1,2,and 3 h was dynamically predicted.The performance of the models was evaluated by accuracy,sensitivity,specificity,positive predictive value,negative predictive value,Youden index,and area under receiver operating characteristic curve(AUC).The models were externally validated based on the trauma database of the Chinese PLA General Hospital.Results In the MIMIC-Ⅳ database,the set of dynamic prediction models based on the GRU-D algorithm was the best.The AUC for predicting fatal major bleeding in the next 1,2,and 3 h were 0.946±0.029,0.940±0.032,and 0.943±0.034,respectively,and there was no significant difference(P=0.905).In the trauma dataset,GRU-D model achieved the best external validation effect.The AUC for predicting fatal major bleeding in the next 1,2,and 3 h were 0.779±0.013,0.780±0.008,and 0.778±0.009,respectively,and there was no significant difference(P=0.181).This set of models was deployed in a public web calculator and hospital emergency department information system,which is convenient for the public and medical staff to use and validate the model.Conclusion A set of dynamic prediction models has been successfully developed and validated,which is greatly significant for the early diagnosis and dynamic prediction of fatal massive hemorrhage in trauma.

Objective:To investigate the epidemiological and clinical characteristics of RSV among patients aged ≥60 years in Beijing from 2015 to 2023.Methods:Based on the respiratory pathogen surveillance system, samples of upper respiratory tract infections (URTI), non-severe community-acquired pneumonia (nsCAP) and severe community-acquired pneumonia (sCAP) among people aged ≥60 years were collected from 28 sentinel hospitals in 16 districts of Beijing from January 2015 to December 2023. Swab samples were collected from URTI within one week, and lower respiratory tract samples from nsCAP and sCAP were collected. Demographic and epidemiological data were also collected. Various respiratory pathogens including RSV were detected.Results:From January 2015 to December 2023, a total of 20 349 cases of acute respiratory infections aged ≥60 years were included, with the RSV-positive rate of 1.54% (313/20 349, 95% CI: 1.39%-1.68%). Among them, the total RSV-positive rates of older people during the pre-pandemic, pandemic, and post-pandemic periods of COVID-19 were 1.59% (207/13 006, 95% CI: 1.38%-1.81%), 0.82% (38/4 650, 95% CI: 0.56%-1.08%) and 2.53% (68/2 693, 95% CI: 1.93%-3.12%), respectively. The difference in RSV-positive rate was statistically significant ( P<0.001). Based on the sampling time of cases, the RSV epidemic season for older people in Beijing was from October to March of the following year, with a peak period in December or January of the following year. In the post COVID-19 pandemic, there were very few RSV-positive cases detected in the elderly from April to June 2023, with only one positive case detected in May and one in June. The RSV-positive rate of older people increased significantly from October to December, reaching 11.75% (51/383) in December. Among 263 RSV-positive cases in the elderly, RSV-A, RSV-B and unclassified type accounted for 43.35% (114/263), 29.28% (77/263) and 27.38% (72/263), respectively. Since 2020, there has been a subtype conversion, with RSV-B being the main focus. Among 197 elderly cases that have complete clinical data, the main symptoms were cough (86.8%, 171/197), sputum (80.2%, 158/197) and fever (73.60%, 145/197). About 24.87% (49/197) of elderly cases experienced complications. The hospitalization mortality rate was 4.57% (9/197), and the hospitalization rate was 78.68% (155/197). The ICU occupancy rate was 1.99% (36/197). The mechanical ventilation usage rate was 13.32% (33/197), and the length of hospital stay [ M ( Q1, Q3)] was 12 (9, 16) days. Conclusion:In Beijing, the RSV infection rate is relatively low during the COVID-19 pandemic, and the prevalence of COVID-19 is relatively high. In 2023, there was no out-of-season outbreak of RSV infection among the elderly. Elderly RSV infection cases have multiple complications, severe diseases, and poor prognosis.

This paper provides a comprehensive analysis of the current state of intelligent ophthalmology in China, including technological advancements, academic exchange platforms, policy support, future challenges, and potential solutions. Technologically, remarkable progress have been made in various areas of intelligent ophthalmology in China, including diabetic retinopathy, fundus image analysis, and crucial aspects such as quality assessment of medical artificial intelligence products, clinical research methods, technological evaluation, and industrial standards. Researchers are constantly improving the safety and standardization of intelligent ophthalmology technology by formulating clinical application guidelines and standards. Academic exchange platforms have been established to provide extensive collaboration opportunities for professionals across diverse fields, and various academic journals serve as publication platforms for intelligent ophthalmology research. Regarding public policy, the Chinese government has not only established a supportive policy environment for the advancement of intelligent ophthalmology through various documents and regulations, but provided a legal basis and management framework. However, there are still challenges to overcome, such as technological innovation, data privacy and security, outdated regulations, and talent shortages. To tackle these issues, there is a requirement for increased technological research and development, the establishment of regulatory frameworks, talent cultivation, and greater awareness and acceptance of new technologies among patients. By comprehensively addressing these challenges, intelligent ophthalmology in China is expected to continue leading the industry's global development, bringing more innovation and convenience to the field of ophthalmic healthcare.