Objective To clarify the risk factors of diaphragmatic dysfunction (DD) after cardiac surgery with extracorporeal circulation. Methods A retrospective analysis was conducted on the data of patients who underwent cardiac surgery with extracorporeal circulation in the Department of Cardiovascular Surgery of Peking University People's Hospital from January 2023 to March 2024. Patients were divided into two groups according to the results of bedside diaphragm ultrasound: a DD group and a control group. The preoperative, intraoperative, and postoperative indicators of the patients were compared and analyzed, and independent risk factors for DD were screened using multivariate logistic regression analysis. Results A total of 281 patients were included, with 32 patients in the DD group, including 23 males and 9 females, with an average age of (64.0±13.5) years. There were 249 patients in the control group, including 189 males and 60 females, with an average age of (58.0±11.2) years. The body mass index of the DD group was lower than that of the control group [(18.4±1.5) kg/m2 vs. (21.9±1.8) kg/m2, P=0.004], and the prevalence of hypertension, chronic obstructive pulmonary disease, heart failure, and renal insufficiency was higher in the DD group (P<0.05). There was no statistical difference in intraoperative indicators (operation method, extracorporeal circulation time, aortic clamping time, and intraoperative nasopharyngeal temperature) between the two groups (P>0.05). In terms of postoperative aspects, the peak postoperative blood glucose in the DD group was significantly higher than that in the control group (P=0.001), and the proportion of patients requiring continuous renal replacement therapy was significantly higher than that in the control group (P=0.001). The postoperative reintubation rate, tracheotomy rate, mechanical ventilation time, and intensive care unit stay time in the DD group were higher or longer than those in the control group (P<0.05). Multivariate logistic regression analysis showed that low body mass index [OR=0.72, 95%CI (0.41, 0.88), P=0.011], preoperative dialysis [OR=2.51, 95%CI (1.89, 4.14), P=0.027], low left ventricular ejection fraction [OR=0.88, 95%CI (0.71, 0.93), P=0.046], and postoperative hyperglycemia [OR=3.27, 95%CI (2.58, 5.32), P=0.009] were independent risk factors for DD. Conclusion The incidence of DD is relatively high after cardiac surgery, and low body mass index, preoperative renal insufficiency requiring dialysis, low left ventricular ejection fraction, and postoperative hyperglycemia are risk factors for DD.

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.

Aim To investigate the effect of quercetin on the aging model of bone marrow mesenchymal stem cells established under microgravity. Methods Using 3D gyroscope, a aging model of bone marrow mesenchymal stem cells was constructed, and after receiving quercetin and microgravity treatment, the anti-aging effect of the quercetin was evaluated by detecting related proteins and oxidation indexes. Results Compared to the control group, the expressions of age-related proteins p21, pi6, p53 and RB in the microgravity group significantly increased, while the expressions of cyclin D1 and lamin B1 significantly decreased, with statistical significance (P<0.05). In the microgravity group, mitochondrial membrane potential significantly decreased (P<0.05), ROS accumulation significantly increased (P <0.05), SOD content significantly decreased and MDA content significantly increased (P<0.05). Compared to the microgravity group, the expressions of age-related proteins p21, pi6, p53 and RB in the quercetin group significantly decreased, while the expressions of cyclin D1 and lamin B1 significantly increased, with statistical significance (P<0.05). In the quercetin group, mitochondrial membrane potential significantly increased (P<0.05), ROS accumulation significantly decreased (P<0.05), SOD content significantly increased and MDA content significantly decreased (P<0.05). Conclusions Quercetin can resist oxidation, protect mitochondrial function and normal cell cycle, thus delaying the aging of bone marrow mesenchymal stem cells induced by microgravity.

Objective To investigate the morphological typing and clinical significance of the distal tibiofibular syndesmosis fibular notch based on CT images. Methods According to the inclusion and exclusion ceiteria‚ the imaging data of patients undergoing ankle joint CT examination were analyzed‚ and the inferior tibiofibular joint fibula notch was classified according to the morphological characteristics. The measurements included 8 distances. There were 123 males and 102 females‚ all of whom were Han nationality‚ aged 18-60 years old. Results Retrospectively analyzed the result of 225 patients from December 2013 to December 2022. The distal tibiofibular syndesmosis fibular notch was divided into four types according to morphological characteristics‚ C-shaped (50. 67%)‚ V-shaped (26. 67%)‚ flat-shaped (15. 11%) and L-shaped (7. 56%). The angle between the anterior and posterior facets of the flat shape (145. 56 ± 9. 25)° was the largest and the angle between the anterior and posterior facets of the L shape (125. 07 ± 13. 54)° was the smallest(P< 0. 05); the depth of the notch in the flat shape (3. 11 ± 0. 83) mm was the smallest and in the L shape (4. 47±1. 11) mm was the largest(P<0. 05);The posterior facet length (13. 06 ± 3. 56) mm and anterior tibiofibular gap (3. 83±1. 49) mm on left were larger than on the right side (P<0. 05); The posterior facet length (13. 36 ± 3. 46) mm‚ fibular notch depth (3. 93 ± 1. 10) mm and vertical distance of tibiofibular overlap (9. 10 ± 2. 55) mm larger in men than in women (P<0. 05). Conclusion In this study‚ the data related to the inferior tibiofibular syndesmosis notch were measured and divided into four types according to the shape. The flat inferior tibiofibular syndesmosis notch is more likely to have chronic ankle instability‚ and the fibula is more likely to move forward during anatomical reduction. The inferior tibiofibular syndesmosis of L-shaped and C-shaped notches is more prone to posterior displacement of fibula or poor rotation reduction during anatomical reduction.

Cryoablation therapy with explicit anti-tumor mechanisms and histopathological manifestations has a long history.A large number of clinical practice has shown that cryoablation therapy is safe and effective,making it an ideal tumor treatment method in theory.Previously,its efficacy and clinical application were constrained by the limitations of refrigerants and refrigeration equipment.With the development of the new generation of cryoablation equipment represented by argon helium knives,significant progress has been made in refrigeration efficien-cy,ablation range,and precise temperature measurement,greatly promoting the progression of tumor cryoablation technology.This consensus systematically summarizes the mechanism of cryoablation technology,indications for oral mucosal melanoma(OMM)cryotherapy,clinical treatment process,adverse reactions and management,cryotherapy combination therapy,etc.,aiming to provide reference for carrying out the standardized cryoablation therapy of OMM.

Purpose To establish and evaluate a high-speed fragment-induced penetrating liver injury model in pigs assisted by portable ultrasound.Materials and Methods With the aid of portable ultrasound,the lower edge of the liver at the end of expiration and the lower edge of the right chest at the end of inspiration of 10 Landrace pigs were positioned on the body surface.Then the sighting line was traced to determine the direction of projection and the sighting point.High-speed(about 627 m/s)fragments were projected through an experimental ballistic gun to induce penetrating liver injury.Blood pressure,heart rate,respiratory rate,pulse oxygen saturation and other physiological indexes were measured 15 minutes before shooting and 20 minutes after shooting.20 minutes after injury,the liver injury and the degree of injury were examined by ultrasound.After injury,the liver injury and abdominal fluid accumulation were observed by on-site portable ultrasound,and the size of liver trauma,liver injury grade,abdominal fluid accumulation location and maximum depth were recorded.The degree of liver injury was evaluated by comparison with the gross pathological results.Results Nine out of ten pigs were successfully modeled.The success rate of penetrating liver injury induced by fragments was 90%(9/10),other organ injury in abdominal cavity was 22.22%(2/9),and diaphragm penetrating injury was 22.22%(2/9),which did not occur obvious hemopneumothorax.After injury,the systolic blood pressure,diastolic blood pressure,and pulse oxygen saturation of the pigs decreased[(132.44±12.65)mmHg vs.(103.33±33.43)mmHg,(96.44±12.27)mmHg vs.(70.89±24.21)mmHg,(89.44±8.49)%vs.(76.00±13.41)%;t=2.440,2.651,4.084,all P<0.05],and the heart rate increased[(94.00±17.39)times/min vs.(139.89±37.21)times/min;t=3.534,P<0.05].Within 20 minutes after modeling,portable ultrasound images showed that the liver injury was a patchy,heterogeneous,slightly strong echo area with clear and irregular boundary,and the continuity of the local liver capsule was interrupted.The ascites appeared in the abdominal cavity with the maximum depth of(4.16±1.35)cm.The American association for the surgery of trauma(AAST)liver injury grading of gross pathology after the animals were killed showed that there were 6 cases of grade Ⅱ and 3 cases of grade Ⅲ.Along the fragment projection direction,the short diameter measured by ultrasound was positively correlated with the depth of gross pathological laceration(r=0.945,P<0.001).Compared with the gross specimen,the accuracy rate of ultrasonic AAST grading of liver injury was 88.89%(8/9).Conclusion The model of high-speed fragment-induced liver injury in pigs assisted by portable ultrasound is accurate and stable,and portable ultrasound can effectively evaluate the penetrating liver injury,which provides a basis for the treatment of liver firearm injury.