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.

ObjectiveMagnetoencephalography (MEG), a non-invasive neuroimaging technique, meticulously captures the magnetic fields emanating from brain electrical activity. Compared with MEG based on superconducting quantum interference devices (SQUID), MEG based on optically pump magnetometer (OPM) has the advantages of higher sensitivity, better spatial resolution and lower cost. However, most of the current studies are clinical studies, and there is a lack of animal studies on MEG based on OPM technology. Pain, a multifaceted sensory and emotional phenomenon, induces intricate alterations in brain activity, exhibiting notable sex differences. Despite clinical revelations of pain-related neuronal activity through MEG, specific properties remain elusive, and comprehensive laboratory studies on pain-associated brain activity alterations are lacking. The aim of this study was to investigate the effects of inflammatory pain (induced by Complete Freund’s Adjuvant (CFA)) on brain activity in a rat model using the MEG technique, to analysis changes in brain activity during pain perception, and to explore sex differences in pain-related MEG signaling. MethodsThis study utilized adult male and female Sprague-Dawley rats. Inflammatory pain was induced via intraplantar injection of CFA (100 μl, 50% in saline) in the left hind paw, with control groups receiving saline. Pain behavior was assessed using von Frey filaments at baseline and 1 h post-injection. For MEG recording, anesthetized rats had an OPM positioned on their head within a magnetic shield, undergoing two 15-minute sessions: a 5-minute baseline followed by a 10-minute mechanical stimulation phase. Data analysis included artifact removal and time-frequency analysis of spontaneous brain activity using accumulated spectrograms, generating spectrograms focused on the 4-30 Hz frequency range. ResultsMEG recordings in anesthetized rats during resting states and hind paw mechanical stimulation were compared, before and after saline/CFA injections. Mechanical stimulation elevated alpha activity in both male and female rats pre- and post-saline/CFA injections. Saline/CFA injections augmented average power in both sexes compared to pre-injection states. Remarkably, female rats exhibited higher average spectral power 1 h after CFA injection than after saline injection during resting states. Furthermore, despite comparable pain thresholds measured by classical pain behavioral tests post-CFA treatment, female rats displayed higher average power than males in the resting state after CFA injection. ConclusionThese results imply an enhanced perception of inflammatory pain in female rats compared to their male counterparts. Our study exhibits sex differences in alpha activities following CFA injection, highlighting heightened brain alpha activity in female rats during acute inflammatory pain in the resting state. Our study provides a method for OPM-based MEG recordings to be used to study brain activity in anaesthetized animals. In addition, the findings of this study contribute to a deeper understanding of pain-related neural activity and pain sex differences.

ObjectiveTo observe the clinical effectiveness and safety of Xiaozhong Zhitong Mixture （消肿止痛合剂） combined with antibiotic bone cement in the treatment of diabetic foot ulcer （DFU） with damp-heat obstructing syndrome. MethodsA total of 72 DFU patients with damp-heat obstructing syndrome were randomly assigned to treatment group （36 cases） and the control group （36 cases）. Both groups received standard treatment and topical antibiotic bone cement for ulcer wounds， while the treatment group received oral Xiaozhong Zhitong Mixture （50 ml per time， three times daily） in additionally. Both groups underwent daily wound dressing changes for 21 consecutive days. Ulcer healing rate， serum levels of tumor necrosis factor-alpha （TNF-α）， interleukin-1 beta （IL-1β）， malondialdehyde （MDA）， superoxide dismutase （SOD）， C-reactive protein （CRP）， and white blood cell （WBC） count were observed before and after treatment， and visual analog scale （VAS） scores for wound pain， traditional Chinese medicine （TCM） syndrome scores， and the DFU Healing Scale （DMIST scale） were also compared. Liver and kidney function were evaluated before and after treatment， and adverse events such as allergic reactions， worsening ulcer pain were recorded. ResultsTotally 35 patients in the treatment group and 33 in the control group were included in the final analysis. The ulcer healing rate in the treatment group was （87.93±9.34）%， significantly higher than （81.82±12.02）% in the control group （P = 0.035）. Compared to pre-treatment levels， both groups showed significant reductions in serum CRP， WBC， MDA， IL-1β， and TNF-α levels， with an increase in SOD level （P＜0.05）. TCM syndrome scores， VAS， and DMIST scores also significantly decreased in both groups （P＜0.05）， with greater improvements in the treatment group （P＜0.05）. No significant adverse reactions were observed in either group during treatment. ConclusionXiaozhong Zhitong Mixture combined with antibiotic bone cement has significant advantages in promoting DFU healing， reducing inflammatory response， and alleviating oxidative stress in DFU patients with damp-heat obstructing syndrome， with good safety for DFU patients with damp-heat obstructing syndrome.

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 compare the effectiveness of TiRobot assisted F screw technique and inverted triangle parallel nail internal fixation in the treatment of unstable femoral neck fractures.Methods A retrospective analysis was conducted on 72 patients with unstable femoral neck fractures who were treated with percutaneous cannulated screw fixation assisted with TiRobot Orthopaedic robot from December 2019 to April 2021.Among them,37 patients were treated with F screw internal fixa-tion,including 16 males and 21 females,aged47 to 64years old with an average of(53.87±5.28)years old;According to Pauwels classification,there were 1 case of type Ⅰ,19 cases of type Ⅱ,17 cases of type Ⅲ;8 cases of combined medical diseases;17 cases of falling,8 cases of traffic accident and 12 cases of falling from height;The time from injury to operation was 29 to 49 hours with average of(35.00±7.34)hours.Another 35 cases used internal fixation with an inverted triangle parallel nail,including 13 males and 22 females with an average age of 46 to 63 years old(52.36±5.05)years old;According to the Pauwels injury classifi-cation:there were 2 cases of type Ⅰ,21 cases of type Ⅱ,12 cases of type Ⅲ;6 cases of medical diseases,15 cases of falling in-jury,9 cases of traffic accident,11 cases of falling injury;The time from injury to operation was 30 to 45 hours with an average of(33.00±6.83)h.The intraoperative blood loss,operation time,intraoperative fluoroscopy times,follow-up time,fracture healing time,postoperative complications were observed and compared between the two groups.The hip joint function was e-valuated by Harris score at 6 months and 12 months after operation.Results There was no significant difference in operation time,intraoperative blood loss,intraoperative fluoroscopy times and other intraoperative data between two groups(P＞0.05).Both groups were followed up regularly,and the follow-up time was 12 to 16 months.The fracture healing time and Harris score of the F screw internal fixation group were better than those of the inverted triangle parallel nail internal fixation group(P＜0.05).There was 1 case of femoral neck shortening in the F screw internal fixation group,1 case of nonunion,1 case of nail withdrawal,and 1 case of lower extremity deep vein thrombosis in the inverted triangle internal fixation group.The incidence of complications in the F screw internal fixation group was lower than that in the inverted triangle parallel nail internal fixation group(P＜0.05).Conclusion Percutaneous cannulated F screw technique using Tirobot navigation positioning system is a safe and effective treatment for patients with unstable femoral neck fractures.It can significantly shorten the fracture healing time,reduce the incidence of postoperative complications,significantly improve hip joint function,and improve the quality of life.

Primary myelofibrosis(PMF)is a myeloproliferative neoplasm with splenomegaly as the major clinical manifestation,which is commonly considered to be linked to splenic extramedullary hematopoiesis.Alteration of CXCL12/CXCR4 pathway can lead to the migration of hematopoietic stem cells and hematopoietic progenitor cells from bone marrow to spleen which results in splenic extramedullary hematopoiesis.In addition,low GATA1 expression and the abnormal secretion of cytokines were found to be significantly associated with splenomegaly.With the application of JAK1/2 inhibitors in clinical,the symptoms of splenomegaly have been significantly improved in PMF patients.This article will review the pathogenesis and targeted treatment progress of splenomegaly in PMF.

Objective:To investigate the clinical characteristics and influence of co-mutated gene on acute myeloid leukemia patients(AML)with FMS-like tyrosine kinase-3(FLT3)mutations.Methods:A total of 273 FLT3+AML patients were enrolled,and the co-mutation gene data of the patients were collected to further analyze the prognosis of the patients.FLT3 and other common mutations were quantified by PCR amplification products direct sequencing and second-generation sequencing(NGS).Results:When patients were divided into FLT3 ITD+,FLT3 TKD+,FLT3 ITD++TKD+and FLT3 ITD-+TKD-group according to the type of FLT3 mutations,it was found that the frequencies of TET2,GATA2,NRAS and ASXL1 mutation were significantly different among the 4 groups(all P＜0.05).When patients were divided into allelic ratio(AR)≥0.5 and＜0.5 group,it was found that the frequencies of FLT3 ITD+,FLT3 ITD-+TKD-,NPM1,NRAS and C-kit were significantly different between the two groups(all P＜0.05).When patients were divided into normal and abnormal karyotype group,it was found that the frequencies of FLT3 ITD+,FLT3 TKD+,NPM1,GATA2 and C-kit were significantly different between the two groups(all P＜0.05).The median overall survival(OS)of AML patients with FLT3 TKD+(including FLT3 ITD++TKD+)was longer than that of patients with FLT3 ITD+alone(P＜0.05).The OS and relapse-free survival(RFS)of AML patients with FLT3++TET2+were both shorter than those of patients with FLT3++TET2-(both P＜0.05).Conclusion:The mutation frequencies of co-mutated genes are correlated with subtypes of FLT3,karyotype and AR.AML patients with FLT3 TKD+have longer OS than patients with FLT3 ITD+alone,and patients with co-mutation of TET2 have shorter median OS and RFS.