BACKGROUND:Flap transplantation technique is a commonly used surgical procedure for the treatment of severe tissue defects,but postoperative flap necrosis is easily triggered by ischemia-reperfusion injury.Therefore,it is still an important research topic to improve the survival rate of transplanted flaps. OBJECTIVE:To review the pathogenesis and latest treatment progress of flap ischemia-reperfusion injury. METHODS:CNKI,WanFang Database and PubMed database were searched for relevant literature published from 2014 to 2024.The search terms used were"flap,ischemia-reperfusion injury,inflammatory response,oxidative stress,Ca2+overload,apoptosis,mesenchymal stem cells,platelet-rich plasma,signaling pathways,shock wave,pretreatment"in Chinese and English.After elimination of irrelevant literature,poor quality and obsolete literature,77 documents were finally included for review. RESULTS AND CONCLUSION:Flap ischemia/reperfusion injury may be related to pathological factors such as inflammatory response,oxidative stress response,Ca2+overload,and apoptosis,which can cause apoptosis of vascular endothelial cells,vascular damage and microcirculation disorders in the flap,and eventually lead to flap necrosis.Studies have found that mesenchymal stem cell transplantation,platelet-rich plasma,signaling pathway modulators,shock waves,and pretreatment can alleviate flap ischemia/reperfusion injuries from different aspects and to varying degrees,and reduce the necrosis rate and necrosis area of the grafted flap.Although there are many therapeutic methods for skin flap ischemia/reperfusion injury,a unified and effective therapeutic method has not yet been developed in the clinic,and the advantages and disadvantages of various therapeutic methods have not yet been compared.Most of the studies remain in the stage of animal experiments,rarely involving clinical observations.Therefore,a lot of research is required in the future to gradually move from animal experiments to the clinic in order to better serve the clinic.

AIM: To investigate the correlation of serum Silent mating-type information regulation 2 homolog 1(SIRT1)and Vasostatin-2 content with pathological changes in diabetic retinopathy(DR)patients.METHODS: A total of 104 DR patients(104 eyes)admitted to our hospital from April 2021 to April 2024 were included as the DR group. According to different disease stages, they were assigned into a non-proliferative DR(NPDR)group of 44 cases(44 eyes)and a proliferative DR(PDR)group of 60 cases(60 eyes). Meantime, 104 patients(104 eyes)with simple diabetes were treated as non-DR group. ELISA was applied to detect the levels of SIRT1 and Vasostatin-2 in serum. The diagnostic value of serum SIRT1 and Vasostatin 2 in DR was analyzed by ROC curve. Multivariate Logistic regression was applied to analyze the factors that affected the occurrence of DR. Pearson correlation was applied to analyze the relationship between the levels of SIRT1 and Vasostatin-2 in the serum of DR patients and angiogenesis indicators(VEGF, Ang-2).RESULTS: Compared with the non-DR group, the levels of SIRT1 and Vasostatin-2 in the serum of the DR group were significantly decreased(P<0.05). Compared with the NPDR group, the levels of SIRT1 and Vasostatin-2 in the serum of the PDR group were significantly decreased(P<0.05). Compared with the non-DR group, the levels of VEGF and Ang-2 in the serum of the DR group were obviously higher(P<0.05). Compared with the single detection of serum SIRT1 and Vasostatin-2 levels, combined detection significantly increased the AUC in the diagnosis of DR(Z=4.180, 5.128, all P<0.05). Multivariate Logistic regression analysis showed that HOMA-IR(OR=3.455), fasting blood glucose(OR=1.467), SIRT1(OR=0.836), Vasostatin-2(OR=0.767), VEGF(OR=2.564), and Ang-2(OR=1.834)levels were the influencing factors on the occurrence of DR(all P<0.05). Pearson correlation analysis showed that the levels of SIRT1 and Vasostatin-2 in the serum of DR patients were negatively correlated with VEGF and Ang-2(rSIRT1 vs VEGF=-0.395, rSIRT1 vs Ang-2=-0.474, rVasostatin-2 vs VEGF=-0.323, rVasostatin-2 vs Ang-2=-0.583, all P<0.001).CONCLUSION: The abnormal decrease of serum SIRT1 and Vasostatin 2 levels in DR patients is closely related to the stage of DR lesions and angiogenesis.

Objective: Previous studies have discovered a correlation between cigarette smoking and cortical thickness and surface area, but the causal relationship remains unclear. The objective of this investigation is to scrutinize the causal association between them. Methods: To derive summary statistics from a genome-wide association study (GWAS) on cortical thickness, surface area, and four smoking behaviors: 1) age of initiation of regular smoking (AgeSmk); 2) smoking initiation (SmkInit); 3) smoking cessation (SmkCes); 4) cigarettes per day (CigDay). Linkage disequilibrium score regression (LDSC) was employed to examine genetic association analysis. Furthermore, for traits with significant genetic associations, Mendelian randomization (MR) analyses were conducted. Results: The LDSC analysis revealed nominal genetic correlations between AgeSmk and right precentral surface area, left caudal anterior cingulate surface area, left cuneus surface area, left inferior parietal surface area, and right caudal anterior cingulate thickness, as well as between CigDay and left caudal middle frontal surface area, between SmkCes and left entorhinal thickness, and between SmkInit and left rostral anterior cingulate surface area, right rostral anterior cingulate thickness, and right superior frontal thickness (rg=-0.36–0.29, p<0.05). MR analysis showed a unidirectional causal association between left caudal middle frontal surface area and CigDay (βIVW=0.056, pBonferroni=2×10-4). Conclusion Left caudal middle frontal surface area has the potential to serve as a significant predictor of smoking behavior.

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: Previous studies have discovered a correlation between cigarette smoking and cortical thickness and surface area, but the causal relationship remains unclear. The objective of this investigation is to scrutinize the causal association between them. Methods: To derive summary statistics from a genome-wide association study (GWAS) on cortical thickness, surface area, and four smoking behaviors: 1) age of initiation of regular smoking (AgeSmk); 2) smoking initiation (SmkInit); 3) smoking cessation (SmkCes); 4) cigarettes per day (CigDay). Linkage disequilibrium score regression (LDSC) was employed to examine genetic association analysis. Furthermore, for traits with significant genetic associations, Mendelian randomization (MR) analyses were conducted. Results: The LDSC analysis revealed nominal genetic correlations between AgeSmk and right precentral surface area, left caudal anterior cingulate surface area, left cuneus surface area, left inferior parietal surface area, and right caudal anterior cingulate thickness, as well as between CigDay and left caudal middle frontal surface area, between SmkCes and left entorhinal thickness, and between SmkInit and left rostral anterior cingulate surface area, right rostral anterior cingulate thickness, and right superior frontal thickness (rg=-0.36–0.29, p<0.05). MR analysis showed a unidirectional causal association between left caudal middle frontal surface area and CigDay (βIVW=0.056, pBonferroni=2×10-4). Conclusion Left caudal middle frontal surface area has the potential to serve as a significant predictor of smoking behavior.

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: Previous studies have discovered a correlation between cigarette smoking and cortical thickness and surface area, but the causal relationship remains unclear. The objective of this investigation is to scrutinize the causal association between them. Methods: To derive summary statistics from a genome-wide association study (GWAS) on cortical thickness, surface area, and four smoking behaviors: 1) age of initiation of regular smoking (AgeSmk); 2) smoking initiation (SmkInit); 3) smoking cessation (SmkCes); 4) cigarettes per day (CigDay). Linkage disequilibrium score regression (LDSC) was employed to examine genetic association analysis. Furthermore, for traits with significant genetic associations, Mendelian randomization (MR) analyses were conducted. Results: The LDSC analysis revealed nominal genetic correlations between AgeSmk and right precentral surface area, left caudal anterior cingulate surface area, left cuneus surface area, left inferior parietal surface area, and right caudal anterior cingulate thickness, as well as between CigDay and left caudal middle frontal surface area, between SmkCes and left entorhinal thickness, and between SmkInit and left rostral anterior cingulate surface area, right rostral anterior cingulate thickness, and right superior frontal thickness (rg=-0.36–0.29, p<0.05). MR analysis showed a unidirectional causal association between left caudal middle frontal surface area and CigDay (βIVW=0.056, pBonferroni=2×10-4). Conclusion Left caudal middle frontal surface area has the potential to serve as a significant predictor of smoking behavior.

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:The main clinical manifestations of knee osteoarthritis are pain,swelling,stiffness,and limited activity,which have a serious impact on the life of patients.Exercise therapy can effectively improve the related symptoms of patients with knee osteoarthritis.This paper uses the method of network meta-analysis to compare the efficacy of different exercise types in the treatment of knee osteoarthritis. METHODS:CNKI,WanFang,PubMed,Embase,Cochrane Library,Web of Science,Scopus,Ebsco,SinoMed,and UpToDate were searched with Chinese search terms"knee osteoarthritis,exercise therapy"and English search terms"knee osteoarthritis,exercise".Randomized controlled trials on the application of different exercise types in patients with knee osteoarthritis from October 2013 to October 2023 were collected.The outcome measures included visual analog scale,Western Ontario and McMaster Universities Osteoarthritis Index score,Timed Up and Go test,and 36-item short form health survey.Literature quality analysis was performed using the Cochrane Manual recommended tool for risk assessment of bias in randomized controlled trials.Two researchers independently completed the data collection,collation,extraction and analysis.RevMan 5.4 and Stata 18.0 software were used to analyze and plot the obtained data. RESULTS:A total of 29 articles with acceptable quality were included,involving 1 633 patients with knee osteoarthritis.The studies involved four types of exercise:aerobic training,strength training,flexibility/skill training,and mindfulness relaxation training.(1)The results of network meta-analysis showed that compared with routine care/health education,aerobic training could significantly improve pain symptoms(SMD=-3.26,95%CI:-6.33 to-0.19,P<0.05);strength training(SMD=-0.79,95%CI:-1.34 to-0.23,P<0.05)and mindfulness relaxation training(SMD=-0.79,95%CI:-1.23 to-0.34,P<0.05)could significantly improve the function of patients.Aerobic training(SMD=-1.37,95%CI:-2.24 to-0.51,P<0.05)and mindfulness relaxation training(SMD=-0.41,95%CI:-0.80 to-0.02,P<0.05)could significantly improve the functional mobility of patients.Mindfulness relaxation training(SMD=0.70,95%CI:0.21-1.18,P<0.05)and strength training(SMD=0.42,95%CI:0.03-0.81,P<0.05)could significantly improve the quality of life of patients.(2)The cumulative probability ranking results were as follows:pain:aerobic training(86.6%)>flexibility/skill training(60.1%)>strength training(56.8%)>mindfulness relaxation training(34.7%)>routine care/health education(11.7%);Knee function:strength training(73.7%)>mindfulness relaxation training(73.1%)>flexibility/skill training(56.1%)>aerobic training(39.9%)>usual care/health education(7.6%);Functional mobility:aerobic training(94.7%)>mindfulness relaxation training(65.5%)>strength training(45.1%)>flexibility/skill training(41.6%)>routine care/health education(3.2%);Quality of life:mindfulness relaxation training(91.3%)>strength training(68.0%)>flexibility/skill training(44.3%)>aerobic training(34.0%)>usual care/health education(12.3%). CONCLUSION:(1)Exercise therapy is effective in the treatment of knee osteoarthritis,among which aerobic training has the best effect on relieving pain and improving functional mobility.Strength training and mindfulness relaxation training has the best effect on improving patients'function.Mindfulness relaxation training has the best effect on improving the quality of life of patients.(2)Limited by the quality and quantity of the included literature,more high-quality studies are needed to verify it.

Objective To compare the incidence of complications after removal of chest drainage tube in the early and late stages after sublobectomy for non-small cell lung cancer (NSCLC), and to analyze the factors affecting postoperative pleural drainage volume (PDV), so as to explore the countermeasures and achieve rapid postoperative rehabilitation. Methods The patients with NSCLC who underwent minimally invasive sublobectomy in our hospital from January to October 2021 were enrolled. According to the median time of extubation, the patients were divided into an early extubation group (time with tube≤3 days) and a late extubation group (time with tube>3 days). The patients were matched via propensity score matching with a ratio of 1:1 and a caliper value of 0.02. The incidence of complications and perioperative parameters after removal of the thoracic drainage tube were analyzed and compared between the two groups, and univariate and multiple linear regression analyses were performed. Results A total of 157 patients were enrolled, including 79 males and 78 females, with an average age of (58.22±11.06) years. There were 76 patients in the early extubation group, 81 patients in the late extubation group, and 56 patients were in each group after propensity score matching. Compared with late extubation group, there was no significant difference in the incidence of infection after extubation (10.7% vs. 16.1%, P=0.405) or pleural effusion after extubation (5.4% vs. 3.6%, P=0.647) in early extubation group, and there was no second operation in both groups. Univariate analysis showed that smoking history (P=0.001), postoperative serum albumin reduction value (P=0.017), surgical approach (P=0.014), lesion location (P=0.027), differentiation degree (P=0.041), TNM stage (P=0.043), number of dissected lymph nodes (P=0.016), and intraoperative blood loss (P=0.016) were infuencing factors for increased postoperative PDV. Multiple linear regression analysis showed that smoking history (P=0.002), postoperative serum albumin reduction value (P=0.041), and the number of dissected lymph nodes (P=0.023) were independent risk factors for increased postoperative PDV. Conclusion There is no significant difference in the incidence of complications after extubation between early and late extubations. Preoperative smoking history, excessive postoperative serum albumin decreases, and excessive number of dissected lymph nodes during the surgery are independent risk factors for increased postoperative PDV.