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.

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: To explore the application value and clinical effect of 3D printing combined with customized bone plate in the treatment of acetabular fracture. METHODS: From June 2020 to June 2022, 11 patients with acetabular fractures underwent preoperative planning using 3D printing technology and were treated with customized bone plates including 8 males and 3 females, aged 25 to 66 years old. The fractures were classified according to Letournel-Judet:4 posterior wall fractures, 2 T-type fractures, 2 transverse posterior wall fractures, 2 double column fractures, and 1 anterior column with posterior semi-transverse fractures. The operative time, intraoperative blood loss, intraoperative fluoroscopy times, postoperative drainage volume, postoperative fracture healing time, and hip function score were recorded and analyzed. RESULTS: The operation time of 11 patients was 80 to 150 min, intraoperative blood volume was 150 to 700 ml, fluoroscopy frequency was 2 to 6, postoperative drainage flow was 60 to 195 ml, and the fracture healing time was 2.5 to 6.0 months. Fracture reduction was evaluated according to Matta score:anatomical reduction in 3 cases and satisfactory reduction in 8 cases. Eleven patients were followed up for 7 to 18 months. The hip Merle d'Aubigne function scores were excellent in 6 cases, good in 3 cases, fair in 1 case and poor in 1 case. Incision fat liquefaction occurred in 1 case and obturator nerve traction in 1 case. CONCLUSION The application of 3D printing technology combined with customized bone plates in the treatment of acetabular fracture is effective. In addition, the printed model can provide the operator with the results of the three-dimensional shape of the fracture, which is convenient for surgical reduction and effectively improves the efficiency of surgery.
Humans ; Female ; Male ; Middle Aged ; Acetabulum/surgery* ; Printing, Three-Dimensional ; Adult ; Aged ; Bone Plates ; Fractures, Bone/surgery* ; Fracture Fixation, Internal/methods*

OBJECTIVE: To explore the effectiveness and feasibility of two osteotomy guides in medial open wedge high tibial osteotomy (MOWHTO). METHODS: Clinical data of 103 patients who underwent routine MOWHTO surgery between January 2020 and December 2022 were collected for retrospective analysis. The patients were divided into two groups based on the method of osteotomy guide plate. The control group of 51 patients received traditional osteotomy guide plate technique, including 17 males and 34 females, aged from 48 to 68 years old with an average of(57.93±4.82) years old, with a disease duration ranged from 1 to 8 years with an average of (4.89±1.49) years. The observation group of 52 patients received personalized osteotomy guide plate technique, including 23 males and 29 females, aged from 48 to 69 with an average of (58.22±5.10) years, with a disease duration ranged from 1 to 9 years with an average of(5.10±1.55) years. The perioperative indicators, complications, and knee joint recovery rate were statistically analyzed for both groups, as well as the preoperative and postoperative coagulation function, fibrinogen (FIB), D-dimer (D-D), gait parameters (step frequency, step length, step speed), biomechanical indicators, weight bearing line (WBL), medial proximal tibial angle (MPTA), joint line conergence angle (JLCA), and anterior cruciate ligament (ACL) function (body width, tibial anterior displacement). RESULTS: All patients were followed up for 6 months. The intraoperative blood loss, operation time, and number of fluoroscopic views in the observation group were (358.58±93.76) ml, (84.42±8.17) min, and (2.00±0.44) times, respectively, which were all less than those in the control group (465.55±105.38) ml, (96.53±10.51) min, and (6.31±0.58) times (P<0.05). Three days after surgery, the FIB and D-D levels in the observation group were (4.21±0.48) g·L^-1 and (204.47±35.59) μg·L^-1, respectively, which were both lower than those in the control group (5.56±0.57) g·L^-1 and (311.12±42.23) μg·L^-1 (P<0.05). Three months after surgery, the step frequency, step length, and step speed in the observation group were (1.89±0.23) steps·s^-1, (0.57±0.15) m, and (0.99±0.11) m·s^-1, respectively, which were all higher than those in the control group (1.80±0.18) steps·s^-1, (0.50±0.14) m, and (0.95±0.09) m·s^-1 (P<0.05). Three months after surgery, the WBL and MPTA in the observation group were (45.53±4.41)% and (87.03±8.15)°, respectively, which were both higher than those in the control group (38.38±4.36)% and (83.68±8.50)°, and the JLCA was (2.36±0.24)°, which was lower than that in the control group (2.61±0.33)° (P<0.05). The ACL body width during internal fixation removal was (5.60±0.51) mm, which was greater than that in the control group (5.08±0.56) mm, and the tibial migration was (5.70±0.42) mm, which was less than that in the control group (6.33±0.48) mm (P<0.05). There was no significant difference in the incidence of complications between the two groups (P>0.05). Six months after surgery, there was no significant difference in the recovery rate of knee joint between the two groups (P>0.05). CONCLUSION The application of personalized osteotomy guide technique in MOWHTO can help improve knee biomechanics and ACL function, and has less effect on coagulation function and no increase in complications.
Humans ; Male ; Female ; Osteotomy/methods* ; Middle Aged ; Tibia/physiopathology* ; Aged ; Biomechanical Phenomena ; Retrospective Studies ; Osteoarthritis, Knee/physiopathology*

OBJECTIVE: To evaluate the efficacy and safety of Shexiang Tongxin Dropping Pill (STDP) in treating stable angina patients with phlegm-heat and blood-stasis syndrome by exercise duration and metabolic equivalents. METHODS: This multicenter, randomized, double-blind, placebo-controlled clinical trial enrolled stable angina patients with phlegm-heat and blood-stasis syndrome from 22 hospitals. They were randomized 1:1 to STDP (35 mg/pill, 6 pills per day) or placebo for 56 days. The primary outcome was the exercise duration and metabolic equivalents (METs) assessed by the standard Bruce exercise treadmill test after 56 days of treatment. The secondary outcomes included the total angina symptom score, Chinese medicine (CM) symptom scores, Seattle Angina Questionnaire (SAQ) scores, changes in ST-T on electrocardiogram and adverse events (AEs). RESULTS: This trial enrolled 309 patients, including 155 and 154 in the STDP and placebo groups, respectively. STDP significantly prolonged exercise duration with an increase of 51.0 s, compared to a decrease of 12.0 s with placebo (change rate: -11.1% vs. 3.2%, P<0.01). The increase in METs was significantly greater in the STDP group than in the placebo group (change: -0.4 vs. 0.0, change rate: -5.0% vs. 0.0%, P<0.01). The improvement of total angina symptom scores (25.0% vs. 0.0%), CM symptom scores (38.7% vs. 11.8%), reduction of nitroglycerin consumption (100.0% vs. 11.3%), and all domains of SAQ, were significantly greater with STDP than placebo (all P<0.01). The changes in Q-T intervals at 28 and 56 days from baseline were similar between the two groups (both P>0.05). Twenty-five participants (16.3%) with STDP and 16 (10.5%) with placebo experienced AEs (P=0.131), with no serious AEs observed. CONCLUSION STDP could improve exercise tolerance in patients with stable angina and phlegm-heat and blood stasis syndrome, with a favorable safety profile. (Registration No. ChiCTR-IPR-15006020).
Humans ; Double-Blind Method ; Drugs, Chinese Herbal/adverse effects* ; Male ; Female ; Middle Aged ; Angina, Stable/physiopathology* ; Aged ; Syndrome ; Treatment Outcome ; Placebos ; Tablets

Ischemic heart disease (IHD) is associated with high morbidity and mortality rates. Reperfusion therapy is the best treatment option for this condition. However, reperfusion can aggravate myocardial damage through a phenomenon known as myocardial ischemia/reperfusion (I/R) injury, which has recently gained the attention of researchers. Several studies have shown that Chinese herbal medicines and their natural monomeric components exert therapeutic effects against I/R injury. This review outlines the current knowledge on the pathological mechanisms through which mitochondria participate in I/R injury, focusing on the issues related to energy metabolism, mitochondrial quality control disorders, oxidative stress, and calcium. The mechanisms by which mitochondria mediate cell death have also been discussed. To develop a resource for the prevention and management of clinical myocardial I/R damage, we compiled the most recent research on the effects of Chinese herbal remedies and their monomer components.

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