Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

The rapid development of artificial intelligence (AI), especially generative AI (GenAI), has already brought, and will continue to bring, revolutionary changes to our daily production and life, as well as create new opportunities and challenges for diagnostic and therapeutic practices in the medical field. Haihe Hospital of Tianjin University collaborates with the National Supercomputer Center in Tianjin, Tianjin University, and other institutions to carry out research in areas such as smart healthcare, smart services, and smart management. We have conducted research and development of a full-process disease diagnosis and treatment assistance system based on GenAI in the field of smart healthcare. The development of this project is of great significance. The first goal is to upgrade and transform the hospital's information center, organically integrate it with existing information systems, and provide the necessary computing power storage support for intelligent services within the hospital. We have implemented the localized deployment of three models: Tianhe "Tianyuan", WiNGPT, and DeepSeek. The second is to create a digital avatar of the chief physician/chief physician's voice and image by integrating multimodal intelligent interaction technology. With generative intelligence as the core, this solution provides patients with a visual medical interaction solution. The third is to achieve deep adaptation between generative intelligence and the entire process of patient medical treatment. In this project, we have developed assistant tools such as intelligent inquiry, intelligent diagnosis and recognition, intelligent treatment plan generation, and intelligent assisted medical record generation to improve the safety, quality, and efficiency of the diagnosis and treatment process. This study introduces the content of a full-process disease diagnosis and treatment assistance system, aiming to provide references and insights for the digital transformation of the healthcare industry.
Artificial Intelligence ; Humans ; Delivery of Health Care ; Generative Artificial Intelligence

OBJECTIVE: This study reports the first imported case of Lassa fever (LF) in China. Laboratory detection and molecular epidemiological analysis of the Lassa virus (LASV) from this case offer valuable insights for the prevention and control of LF. METHODS: Samples of cerebrospinal fluid (CSF), blood, urine, saliva, and environmental materials were collected from the patient and their close contacts for LASV nucleotide detection. Whole-genome sequencing was performed on positive samples to analyze the genetic characteristics of the virus. RESULTS: LASV was detected in the patient's CSF, blood, and urine, while all samples from close contacts and the environment tested negative. The virus belongs to the lineage IV strain and shares the highest homology with strains from Sierra Leone. The variability in the glycoprotein complex (GPC) among different strains ranged from 3.9% to 15.1%, higher than previously reported for the seven known lineages. Amino acid mutation analysis revealed multiple mutations within the GPC immunogenic epitopes, increasing strain diversity and potentially impacting immune response. CONCLUSION The case was confirmed through nucleotide detection, with no evidence of secondary transmission or viral spread. The LASV strain identified belongs to lineage IV, with broader GPC variability than previously reported. Mutations in the immune-related sites of GPC may affect immune responses, necessitating heightened vigilance regarding the virus.
Humans ; China/epidemiology* ; Genome, Viral ; Lassa Fever/virology* ; Lassa virus/classification* ; Molecular Epidemiology ; Phylogeny

OBJECTIVE: To investigate the association between long-term glycemic control and cerebral infarction risk in patients with diabetes through a large-scale cohort study. METHODS: This prospective, community-based cohort study included 12,054 patients with diabetes. From 2006 to 2012, 38,272 fasting blood glucose (FBG) measurements were obtained from these participants. FBG trajectory patterns were generated using latent mixture modelling. Cox proportional hazards models were applied to assess the subsequent risk of cerebral infarction associated with different FBG trajectory patterns. RESULTS: At baseline, the mean age of the participants was 55.2 years. Four distinct FBG trajectories were identified based on FBG concentrations and their changes over the 6-year follow-up period. After a median follow-up of 6.9 years, 786 cerebral infarction events were recorded. Different trajectory patterns were associated with significantly varied outcome risks (Log-Rank P < 0.001). Compared with the low-stability group, Hazard Ratio ( HR) adjusted for potential confounders were 1.37 for the moderate-increasing group, 1.23 for the elevated-decreasing group, and 2.08 for the elevated-stable group. CONCLUSION Sustained high FBG levels were found to play a critical role in the development of ischemic stroke among patients with diabetes. Controlling FBG levels may reduce the risk of cerebral infarction.
Humans ; Cerebral Infarction/blood* ; Middle Aged ; Male ; Female ; Blood Glucose/analysis* ; Fasting/blood* ; Aged ; Prospective Studies ; Risk Factors ; Diabetes Mellitus/blood* ; Adult ; Proportional Hazards Models

OBJECTIVE: This study aimed to reexplore minimum iodine excretion and to build a dietary iodine recommendation for Chinese adults using the obligatory iodine loss hypothesis. METHODS: Data from 171 Chinese adults (19-21 years old) were collected and analyzed based on three balance studies in Shenzhen, Yinchuan, and Changzhi. The single exponential equation was accordingly used to simulate the trajectory of 24 h urinary iodine excretion as the low iodine experimental diets offered (iodine intake: 11-26 μg/day) and to further deduce the dietary reference intakes (DRIs) for iodine, including estimated average requirement (EAR) and recommended nutrient intake (RNI). RESULTS: The minimum iodine excretion was estimated as 57, 58, and 51 μg/day in three balance studies, respectively. Moreover, it was further suggested as 57, 58, and 51 μg/day for iodine EAR, and 80, 81, and 71 μg/day for iodine RNI or expressed as 1.42, 1.41, and 1.20 μg/(day·kg) of body weight. CONCLUSION The iodine DRIs for Chinese adults were established based on the obligatory iodine loss hypothesis, which provides scientific support for the amendment of nutrient requirements.
Humans ; Iodine/administration & dosage* ; Male ; Female ; China ; Young Adult ; Diet ; Adult ; Nutritional Requirements ; East Asian People

Nuclear medicine plays a pivotal role in diagnosis and treatment of tumors.The development of radionuclide probes with high affinity and specificity is an effective approach to achieve precise diagnosis and treatment of tumors.With high binding affinity,rapid tissue penetration and excellent pharmacokinetic properties,bicyclic peptide-based radionuclide probes demonstrate remarkable advantages in diagnosis and targeted therapy of tumors.The structural characteristics and design strategies of bicyclic peptide-based radionuclide probes were reviewed in this article,mainly focused on research progresses in targeting tumor-associated receptors.

Body weight abnormalities, including overweight, obesity, and underweight, have become a dual public health challenge in Chinese adults: overweight and obesity lead to a variety of chronic complications, while underweight increases the risks of malnutrition, sarcopenia, and organ dysfunction. To systematically address these issues, multidisciplinary experts in endocrinology, sports science, nutrition, and psychiatry from various regions have held multiple weight management seminars. Based on the latest epidemiological data and clinical evidence, they expanded the guideline to include assessment and intervention strategies for underweight, in addition to the core content of obesity management. This guideline outlines the etiological mechanisms, evaluation methods, and multidimensional management strategies for overweight and obesity, covering key areas such as diagnosis and assessment, medical nutrition therapy, exercise prescription, pharmacological intervention, and psychological support. It is intended to provide a scientific and standardized approach to weight management across the adult population, aiming to curb the rising prevalence of obesity, mitigate complications associated with abnormal body weight, and improve nutritional status and overall quality of life.

Objective:To investigate the clinical efficacy of programmed Retzius-sparing robot-assisted radical prostatectomy and " Sandwich" technique of total urethral reconstruction.Methods:The clinical data of 120 consecutive patients who underwent programmed Retzius-sparing robot-assisted radical prostatectomy (RS-RARP) by the same operator at the Cancer Hospital of the Chinese Academy of Medical Sciences from January 2023 to August 2024 were retrospectively analyzed to explore the efficacy and summarize the experience. Baseline characteristics included: mean age (67.2±7.5) years, BMI (25.3±3.1)kg/m 2, prostate volume (32.3±15.8) ml, and PSA (16.6±19.7) ng/ml (57 cases 4-10 ng/ml; 41 >10-20 ng/ml; 22 >20 ng/ml). 28 patients underwent radical prostatectomy without prostate biopsy, while 92 had biopsy-proven cancer (Gleason: 6/7/8/9/10: 18/35/17/17/5). Clinical stages were cT 1(8), cT 2(73), cT 3(39). The surgical method was programmed Retzius-sparing robot-assisted radical prostatectomy and " Sandwich" technique of total urethral reconstruction. The peritoneum was incised slightly above the Douglas pouch and denonvilliers' fascia was dissected closely along the dorsal aspect of prostate, extending to the prostatic apex. Both vas deferens were transected, and the seminal vesicles were isolated. A combination of blunt and sharp dissection was employed to expose the prostatic fascia. The bladder neck was precisely visualized and transected. The urethra was precisely dissected, exposed and divided. Complete urethral reconstruction was performed using the "Sandwich" technique of total urethral reconstruction. The operation time, intraoperative bleeding, catheter preservation time, pathological staging and positive margin rate, and recovery of urinary control immediately after postoperative catheter removal were recorded. Results:In this study, all 120 surgeries were successfully completed, with no cases converted to anterior approach radical surgery or open surgery, and no serious intraoperative complications such as post-shamus hemorrhage or ureteric/rectal injury. The median postoperative follow-up was 16.0(10.0, 20.0)months, and there were no cases of readmission for surgical complications. The average duration of surgery was (93.6±35.9) min, and the average bleeding volume was (85.3±32.1) ml. The mean duration of catheter after surgery was (7.3±1.2)d. Immediate urinary control was achieved in 98 cases when the catheter was removed, and the rate of immediate urinary control was 81.7%. Postoperative urinary control rate was 88.3% at 1 month after surgery, 94.2% at 3 month after surgery, 98.3% at 6 month after surgery. There were 70 cases with pT 2 and 50 cases with pT 3 after postoperative pathological stage. There were 18 cases (15.0%) with positive margins, including 6 cases (8.6%) with positive margins in T 2 and 12 cases (24.0%) with positive margins in T 3 stage. There were no serious complications after surgery, and urinary retention occurred in 3 cases after urinary catheter removal, and the urinary catheter was removed after 1 week. 93.3% (112/120), 90.8% (109/120), and 89.2% (107/120) of patients with PSA < 0.2 ng/ml at 1, 3, and 6 months after surgery, respectively. For postoperative erectile function, we selected patients younger than 60 years of age, who had surgery to preserve unilateral or bilateral vascular nerve bundles, and who were followed for more than 6 months. A total of 18 patients met the above conditions and were followed up for erectile function, among which 4 of the 11 patients (36.4%) who retained unilateral vascular nerve bundles regained erectile function. Among the 7 patients with bilateral vascular nerve bundle preservation, 3 patients (42.9%) regained erectile function. Conclusions:The programmed RS-RARP combined with the " Sandwich" urethral reconstruction technique is technically feasible for patients with localized prostate cancer. Recent follow-up data indicate satisfactory postoperative urinary continence and oncological control outcomes.