AIM: To evaluate the surgical outcomes and changes in the ocular surface microenvironment following radiofrequency minimally invasive treatment for conjunctivochalasis-induced epiphora.METHODS: Patients with epiphora primarily caused by conjunctivochalasis were enrolled. All patients had conjunctivochalasis of ≥grade II, and their symptoms showed no significant improvement after previous pharmacological treatment. All patients underwent radiofrequency minimally invasive correction of conjunctivochalasis, supplemented with artificial tears, anti-inflammatory therapy, and ocular surface repair treatment postoperatively. At 8 wk post-surgery, the ocular surface disease index(OSDI), eye redness, tear secretion, non-invasive tear break-up time, lipid layer thickness, tear ferning test, and conjunctival impression cytology were assessed to compare treatment efficacy and observe changes in the ocular surface microenvironment.RESULTS: A total of 43 cases(43 eyes)of conjunctivochalasis and with a main complaint of epiphora were included, including 23 males and 20 males, with a mean age of 64.69±3.36 years. The total effective rate of surgery was 91% at 8 wk postoperatively. Compared with preoperative values, the OSDI scores significantly decreased and the non-invasive tear break-up time was prolonged at 8 wk post-surgery(all P<0.05). No statistically significant differences were observed in lipid layer thickness or tear secretion at 8 wk postoperatively(all P>0.05). The normal rate of chloramphenicol taste test increased from 21% preoperatively to 63% postoperatively; the normal rate of eye redness increased from 40% to 70%; normal rate of tear ferning grading improved from 30% to 63%; and normal conjunctival impression cytology grading increased from 21% to 74%.CONCLUSION: Radiofrequency minimally invasive treatment is effective for conjunctivochalasis and is straightforward to perform. Patients with conjunctivochalasis often present with other ocular surface issues beyond conjunctivochalasis itself, such as insufficient tear secretion, reduced lipid layer thickness, and other dry eye-related problems. Therefore, a comprehensive approach emphasizing tear dynamics should be adopted during treatment.

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 By observing the clinical efficacy of Zhinao capsule in the treatment of Parkinson's disease with mild cognitive impairment(PD-MCI)patients,we also explored the potential mechanism using network pharmacology and molecular docking technology,with the aim of providing a new idea for the treatment of PD-MCI with traditional Chinese medicine.Methods Random number table method was applied to divide the control group and test group into 35 cases each.Parkinson's disease basic treatment plan was used in the control group,and Zhinao capsule was added in the test group.The observation course was determined to be 8 weeks.The Montreal Cognitive Assessment(MoCA)score,MDS-Unified Parkinson's Disease Rating Scale(MDS-UPDRS)Ⅰ,Ⅱ and Ⅲ,PD Traditional Chinese Medicine(PD-TCM),and safety-related indexes were completed before treatment and at the end of the 8th week of treatment.The network pharmacology method was used to obtain the targets related to Zhinao capsules and PD-MCI.Constructed and analyzed the"drug-component-target"network.Analysis of"drug-disease"intersecting targets and enrichment of Gene Ontology(GO)and Kyoto Encyclopedia of Genes and Genomes(KEGG)pathways was obtained using R language.Protein-protein interaction(PPI)networks were constructed using Cytoscape 3.9.1.Finally,molecular docking was conducted.Results The MoCA score of the test group was remarkably greater than that of the control group after treatment(P<0.05);and the TCM syndrome score were noticeably below that of the control group(P<0.05).The test group showed a notable increase in the mean value of MoCA scale scores,and a clear decrease in the MDS-UPDRS Ⅰand PD-TCM before and after treatment(P<0.05).The MDS-UPDRS Ⅱ and Ⅲ scale scores of the test and control groups decreased to different degrees after treatment,but there was no significant difference(P>0.05).Zhinao capsules for PD-MCI treatment has calycosin,quercetin,kaempferol,etc.as core active ingredients,and TNF,IL-1β,IL-6,etc.as core targets.Molecular docking results also showed better binding of the core target to the active ingredient.Zhinao capsules regulates the expression of PPAR,cGMP-PKG,Oxytocin and other signaling pathway-related genes.Conclusion The Zhinao capsules can improve the cognitive function of PD-MCI patients,and the mechanism may be related to the fact that the components such as calycosin,quercetin,and kaempferol act on the targets such as TNF,IL-1β,and IL-6,and mediate the signaling pathways such as PPAR,cGMP-PKG,and Oxytocin.

Objective By observing the clinical efficacy of Zhinao capsule in the treatment of Parkinson's disease with mild cognitive impairment(PD-MCI)patients,we also explored the potential mechanism using network pharmacology and molecular docking technology,with the aim of providing a new idea for the treatment of PD-MCI with traditional Chinese medicine.Methods Random number table method was applied to divide the control group and test group into 35 cases each.Parkinson's disease basic treatment plan was used in the control group,and Zhinao capsule was added in the test group.The observation course was determined to be 8 weeks.The Montreal Cognitive Assessment(MoCA)score,MDS-Unified Parkinson's Disease Rating Scale(MDS-UPDRS)Ⅰ,Ⅱ and Ⅲ,PD Traditional Chinese Medicine(PD-TCM),and safety-related indexes were completed before treatment and at the end of the 8th week of treatment.The network pharmacology method was used to obtain the targets related to Zhinao capsules and PD-MCI.Constructed and analyzed the"drug-component-target"network.Analysis of"drug-disease"intersecting targets and enrichment of Gene Ontology(GO)and Kyoto Encyclopedia of Genes and Genomes(KEGG)pathways was obtained using R language.Protein-protein interaction(PPI)networks were constructed using Cytoscape 3.9.1.Finally,molecular docking was conducted.Results The MoCA score of the test group was remarkably greater than that of the control group after treatment(P<0.05);and the TCM syndrome score were noticeably below that of the control group(P<0.05).The test group showed a notable increase in the mean value of MoCA scale scores,and a clear decrease in the MDS-UPDRS Ⅰand PD-TCM before and after treatment(P<0.05).The MDS-UPDRS Ⅱ and Ⅲ scale scores of the test and control groups decreased to different degrees after treatment,but there was no significant difference(P>0.05).Zhinao capsules for PD-MCI treatment has calycosin,quercetin,kaempferol,etc.as core active ingredients,and TNF,IL-1β,IL-6,etc.as core targets.Molecular docking results also showed better binding of the core target to the active ingredient.Zhinao capsules regulates the expression of PPAR,cGMP-PKG,Oxytocin and other signaling pathway-related genes.Conclusion The Zhinao capsules can improve the cognitive function of PD-MCI patients,and the mechanism may be related to the fact that the components such as calycosin,quercetin,and kaempferol act on the targets such as TNF,IL-1β,and IL-6,and mediate the signaling pathways such as PPAR,cGMP-PKG,and Oxytocin.

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 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: To investigate the association between long-term glycemic control and cerebral infarction risk in patients with diabetes through a large-scale cohort study. METHODS: This prospective, community-based cohort study included 12,054 patients with diabetes. From 2006 to 2012, 38,272 fasting blood glucose (FBG) measurements were obtained from these participants. FBG trajectory patterns were generated using latent mixture modelling. Cox proportional hazards models were applied to assess the subsequent risk of cerebral infarction associated with different FBG trajectory patterns. RESULTS: At baseline, the mean age of the participants was 55.2 years. Four distinct FBG trajectories were identified based on FBG concentrations and their changes over the 6-year follow-up period. After a median follow-up of 6.9 years, 786 cerebral infarction events were recorded. Different trajectory patterns were associated with significantly varied outcome risks (Log-Rank P < 0.001). Compared with the low-stability group, Hazard Ratio ( HR) adjusted for potential confounders were 1.37 for the moderate-increasing group, 1.23 for the elevated-decreasing group, and 2.08 for the elevated-stable group. CONCLUSION Sustained high FBG levels were found to play a critical role in the development of ischemic stroke among patients with diabetes. Controlling FBG levels may reduce the risk of cerebral infarction.
Humans ; Cerebral Infarction/blood* ; Middle Aged ; Male ; Female ; Blood Glucose/analysis* ; Fasting/blood* ; Aged ; Prospective Studies ; Risk Factors ; Diabetes Mellitus/blood* ; Adult ; Proportional Hazards Models

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.

OBJECTIVES: To study the clinical characteristics, efficacy, and prognosis of pediatric Langerhans cell histiocytosis (LCH). METHODS: A retrospective analysis was conducted on 72 children with newly diagnosed LCH. RESULTS: The median age of the 72 children was 5 years (range: 0-14 years), with skull involvement being the most common (56 cases, 77.8%). The BRAF-V600E mutation was not associated with clinical characteristics, efficacy, or prognosis (P>0.05). The 5-year overall survival rate was 91.6%±4.2%, and the 5-year event-free survival (EFS) rate was 67.5%±5.8%. The 6-week chemotherapy response rate and 5-year EFS rate were lower in the risk organ involvement group compared to the no risk organ involvement group (P<0.05). The five-year overall survival rates for the group with multi-system involvement and the group with platelet count ≥450×10⁹/L were respectively lower than those for the single-system involvement group and the group with platelet count <450×10⁹/L (P<0.05). Risk organ involvement is an independent risk factor for 5-year EFS (P<0.05). CONCLUSIONS Skull is the most commonly affected site in pediatric LCH. The BRAF-V600E mutation is not related to clinical characteristics, efficacy, or prognosis. Elevated platelet count, risk organ involvement, and multisystem involvement are associated with poor prognosis, with risk organ involvement being an independent risk factor for 5-year EFS.
Humans ; Histiocytosis, Langerhans-Cell/therapy* ; Child, Preschool ; Child ; Male ; Infant ; Female ; Adolescent ; Retrospective Studies ; Proto-Oncogene Proteins B-raf/genetics* ; Prognosis ; Infant, Newborn ; Mutation