Objective: To explore the distribution of traditional Chinese medicine (TCM) syndromes in children with hepatolenticular degeneration (Wilson disease, WD) based on factor analysis and cluster analysis. Methods: From November 2018 to November 2023, general information (gender, age of admission, age of onset, course of disease, clinical staging, Western medicine clinical symptoms, and family history) and TCM four-examination informations (symptoms and signs) were retrospectively collected from 757 cases of children with WD at the First Affiliated Hospital of Anhui University of Chinese Medicine, and factor analysis and cluster analysis were used to investigate TCM syndromes in children with WD. Results: A total of 757 children with WD were included, of which 483 were male and 274 were female; the median age at admission was 12.58 years, the median age at onset was 8.33 years, and the median course of disease was 24.37 months; clinical typing result indicated 506 cases of hepatic type, 133 cases of brain type, 99 cases of mixed-type, and 19 cases of other type; 36.46% of the children had no clinical symptoms (elevated aminotransferases or abnormalities in copper biochemistry); a total of 177 cases had a definite family history, and 10 cases had a suspected family history. Forty-three TCM four-examination information were obtained, with the top 10 in descending order being feeling listless and weak, brown urine, slow action, inappetence, dim complexion, slurred speech, angular salivation, body weight loss, hand and foot tremors, and abdominal fullness. In children with WD, the syndrome element of disease location was primarily characterized by the liver, involving the spleen and kidney, and the syndrome elements of disease nature were characterized by dampness, heat, and yin deficiency. Based on factor analysis and cluster analysis, five TCM syndromes were derived, which were, in order, syndrome of dampness-heat accumulation (265 cases, 35.01%), syndrome of yin deficiency of the liver and kidney (202 cases, 26.68%), syndrome of liver hyperactivity with spleen deficiency (185 cases, 24.44%), syndrome of qi and blood deficiency (79 cases, 10.44%), and syndrome of yang deficiency of the spleen and kidney (26 cases, 3.43%). Conclusion The TCM syndromes of children with WD were primarily syndromes of dampness-heat accumulation, yin deficiency of the liver and kidney, and liver hyperactivity with spleen deficiency. The liver was the main disease location, and the disease nature was characterized by deficiency in origin and excess in superficiality, excess and deficiency mixed. These findings suggest that treating children with WD should be based on the liver while also considering the spleen and kidney.

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.

ObjectiveTo study the correlations between traditional Chinese medicine （TCM） syndromes and lipid metabolism in children with Wilson disease （WD）. MethodsClinical data and lipid metabolism indicators ［total cholesterol （TC）， triglycerides （TG）， low-density lipoprotein cholesterol （LDL-C）， high-density lipoprotein cholesterol （HDL-C）， apolipoprotein A1 （ApoA1）， apolipoprotein B （ApoB）， and lipoprotein a （Lpa）］ were retrospectively collected from 341 children with WD. The clinical data were compared among WD children with different syndromes， and the correlations between TCM syndromes and lipid metabolism in children with WD were analyzed. Least absolute shrinkage and selection operator （LASSO） regression was used for variable screening， and unordered multinomial Logistic regression was employed to analyze the effects of lipid metabolism indicators on TCM syndromes. ResultsThe 341 children with WD included 121 （35.5%） children with the dampness-heat accumulation syndrome， 103 （30.2%） children with the liver-kidney Yin deficiency syndrome， 68 children with the combined phlegm and stasis syndrome， 29 children with the spleen-kidney Yang deficiency syndrome， and 20 children with the liver qi stagnation syndrome. The liver-kidney Yin deficiency syndrome， combined phlegm and stasis syndrome， and spleen-kidney Yang deficiency syndrome had correlations with the levels of lipid metabolism indicators （P<0.05）. Lipid metabolism abnormalities occurred in 232 （68.0%） children， including hypertriglyceridemia （108）， hypercholesterolemia （23）， mixed hyperlipidemia （67）， lipoprotein a-hyperlipoproteinemia （12）， and hypo-HDL-cholesterolemia （22）. The percentages of hypertriglyceridemia and hypo-HDL-cholesterolemia varied among children with different TCM syndromes （P<0.05）. Correlations existed for the liver-kidney Yin deficiency syndrome with TG， TC， and HDL-C， the combined phlegm and stasis syndrome with TG， the spleen-kidney Yang deficiency syndrome with TG， TC， and LDL-C， and the liver Qi stagnation syndrome with TC and LDL-C （P<0.05， P<0.01）. ConclusionThe TCM syndromes of children with WD are dominated by the dampness-heat accumulation syndrome and the liver-kidney Yin deficiency syndrome， and dyslipidemia in the children with WD is dominated by hypertriglyceridemia and mixed hyperlipidemia. There are different correlations between TCM syndromes and lipid metabolism indicators， among which TG， TC， LDL-C， and HDL-C could assist in identifying TCM syndromes in children with WD.

OBJECTIVE To analyze the prevalence of potentially inappropriate medication （PIM） in elderly patients with femoral neck fractures at admission and compare the concordance of 3 evaluation criteria. METHODS A retrospective study was conducted to review the data of elderly patients with femoral neck fractures admitted to the Department of Orthopedics in Northern Jiangsu People’s Hospital from July 2022 to June 2023. The PIMs were identified according to the Criteria of Potentially Inappropriate Medications for Older Adults in China：2017 edition （hereinafter referred to as Chinese criteria）， American Geriatrics Society 2023 Updated AGS Beers Criteria® for Potentially Inappropriate Medication in Older Adults （hereinafter referred to as 2023 Beers criteria）， third version criteria for screening tool of older people’s prescriptions for potentially inappropriate medication （hereinafter referred to as STOPP criteria version 3）. The concordance of the 3 evaluation criteria was compared by using Kappa statistics. RESULTS A total of 246 patients were included in this study； 49 patients （19.92%） with 77 PIMs were detected by the Chinese criteria， 64 patients （26.02%） with 118 PIMs were detected by the 2023 Beers criteria， and 41 patients （16.67%） with 67 PIMs were detected by the STOPP criteria version 3； 22 patients met all three criteria simultaneously. The concordance among the three criteria showed moderate agreement （0.417≤Kappa≤0.486） when compared in pairs. CONCLUSIONS There are certain differences in the PIM evaluated by the three criteria， but the prevalence of PIMs is below 30% according to the different H202134） criteria. Benzodiazepines， antipsychotics， antidepressants， and other drugs may increase the risk of patients falling again.

Aim To study the effect of menthol on hypobaric hypoxia-induced pulmonary arterial hypertension and explore the underlying mechanism in mice. Methods 10 to 12 weeks old wild type (WT) mice and TRPM8 gene knockout (TRPM8

AIM: To investigate the efficacy of lacrimal duct laser dacryoplasty combined with intubation and postoperative meibomian gland treatment in patients with chronic dacryocystitis complicated by meibomian gland dysfunction.METHODS: Data were collected from 128 patients with chronic dacryocystitis complicated by meibomian gland dysfunction treated at Xi'an No.1 Hospital from March 2021 to December 2022. All patients underwent lacrimal duct laser dacryoplasty combined with intubation. Postoperatively, those patients were randomly divided into two groups: group A(64 cases, without meibomian gland treatment)and group B(64 cases, with meibomian gland treatment). The lacrimal intubation was removed at 3 mo after surgery to evaluate the patency rate of lacrimal irrigation. Additionally, changes in the ocular surface disease index(OSDI)score, non-invasive tear film break-up time, tear meniscus height, conjunctival hyperemia analysis, meibomian gland analysis, tear lipid layer thickness, tear ferning test, and conjunctival impression cytology were compared between the two groups.RESULTS: The lacrimal irrigation patency rates in the group A and group B were 78.1% and 81.2% respectively, with no statistically significant difference between the two groups(P>0.05); compared with the group A, group B showed a significant extension in non-invasive tear breakup time at 3 mo after surgery, and the OSDI score, conjunctival hyperemia analysis, tear ferning test and conjunctival impression cytology grading were all significantly decreased(all P<0.05), while there was no significant difference in tear meniscus height, tear lipid layer thickness and meibomian gland loss score between the two groups(all P>0.05).CONCLUSION: Comprehensive treatment for patients with chronic dacryocystitis combined with meibomian gland dysfunction have improved patients' comfort, tear film stability, and reduces local inflammatory response. It is important to simultaneously address ocular surface microenvironment abnormalities during surgical treatment to achieve satisfactory efficacy.