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 investigate the antibacterial activity of the antifungal peptide Mt6-21DLeu derived from Musca domestica against Acinetobacter baumannii (AB) and unravel its underlying mechanisms, so as to provide insights into development of novel agents against AB. Methods The minimum inhibitory concentrations (MICs) of Mt6-21DLeu, M. domestica-derived antifungal peptide-1 (MAF-1A), and polymyxin B were determined against Staphylococcus aureus, Bacillus subtilis, Escherichia coli, Klebsiella pneumoniae, Pseudomonas aeruginosa and AB using the broth microdilution assay, and the antibacterial activity of Mt6-21DLeu and polymyxin B was dynamically assessed against AB over 24 hours with time-kill curves. The inhibitory effects of Mt6-21DLeu and polymyxin B on biofilm formation in AB at concentrations of 1/4 × MIC, 1/2 × MIC and MIC, and the eradication effects of Mt6-21DLeu and polymyxin B on mature biofilms in AB at concentrations of MIC, 2 × MIC, and 4 × MIC were evaluated using crystal violet staining. Structural changes in the cell membrane of AB were observed 3 hours post-exposure to Mt6-21DLeu at concentrations of MIC and 2 × MIC using scanning electron microscopy, and alterations in the cell membrane permeability of AB were analyzed 3 hours post-treatment with Mt6-21DLeu at concentrations of MIC and 2 × MIC by means of fluorescence microscopy and propidium iodide (PI) staining. Intracellular reactive oxygen species (ROS) levels in AB were measured 3 hours post-treatment with Mt6-21DLeu at concentrations of MIC, 2 × MIC, and 4 × MIC using flow cytometry. The survival of Caenorhabditis elegans exposed to Mt6-21DLeu at concentrations of MIC, 2 × MIC, and 4 × MIC was monitored for 7 consecutive days, and survival curves were plotted to evaluate the in vivo toxicity of Mt6-21DLeu. In addition, C. elegans infected with AB and treated with Mt6-21DLeu at a concentration of 4 × MIC served as the treatment group, and uninfected C. elegans served as the control group, while infected but untreated C. elegans served as the infection group. The in vivo antibacterial efficacy of Mt6-21DLeu at a concentration of 4 × MIC was evaluated by comparing the survival curves and bacterial load among the three groups. Results The MICs of MAF-1A were all >128 μg/mL against S. aureus, B. subtilis, E. coli, K. pneumoniae, P. aeruginosa, and AB. In contrast, the MICs of Mt6-21DLeu were >128, 32, 8, 8, 16, and 4 μg/mL against these strains, respectively, and the MIC of Mt6-21DLeu against AB was close to that of polymyxin B (2 μg/mL). Time-kill curve analysis showed that both Mt6-21DLeu at concentrations of MIC and 2 × MIC and polymyxin B at a concentration of MIC inhibited AB growth over the 24-hour study period. The biofilm biomass in AB was (52.38 ± 6.92)%, (40.88 ± 9.17)% and (14.77 ± 6.00)% post-exposure with Mt6-21DLeu at concentrations of 1/4 × MIC, 1/2 × MIC and MIC, (61.58 ± 7.35)%, (47.42 ± 5.51)% and (20.85 ± 10.48)% post-treatment with polymyxin B at concentrations of 1/4 × MIC, 1/2 × MIC and MIC, and (100.00 ± 15.92)% in the control group (only bacterial suspension), respectively (F = 68.38, P < 0.001), and pairwise comparisons indicated that Mt6-21DLeu and polymyxin B at all concentrations significantly inhibited biofilm formation as compared to the control group (all P values < 0.001). The mature biofilm biomass in AB was (73.44 ± 11.41)%, (72.56 ± 13.08)% and (49.65 ± 9.23)% post-exposure to Mt6-21DLeu at concentrations of MIC, 2 × MIC, and 4 × MIC, (84.38 ± 8.60)%, (72.31 ± 9.63)% and (58.85 ± 4.96)% post-treatment with polymyxin B at concentrations of MIC, 2 × MIC, and 4 × MIC, and (100.00 ± 6.36)% in the control group (F = 35.63, P < 0.001), and pairwise comparisons revealed that Mt6-21DLeu at all concentrations significantly eradicated biofilm biomass (all P values < 0.05); however, polymyxin B showed no clear-cut eradication effect at a concentration of MIC (P > 0.05). Scanning electron microscopy revealed pore formation and content leakage in the cell membrane of AB 3 hours post-treatment with Mt6-21DLeu at concentrations of MIC and 2 × MIC. Fluorescence microscopy showed that the proportions of PI-stained AB were (24.79 ± 11.51)% and (68.44 ± 15.80)% post-treatment with Mt6-21DLeu at concentrations of MIC and 2 × MIC, and (0.96 ± 0.94)% in the phosphate-buffered saline (PBS) treatment group (F = 105.90, P < 0.001), with the highest proportion of PI-stained AB seen post-treatment with Mt6-21DLeu at a concentration of 2 × MIC (P < 0.05). Flow cytometry revealed that the relative intracellular ROS levels in AB were (652.00 ± 141.90), (694.33 ± 14.19), and (974.33 ± 160.02) 3 hours post-treatment with Mt6-21DLeu at concentrations of MIC, 2 × MIC and 4 × MIC, and (403.67 ± 86.56) in the PBS treatment group, respectively (F = 12.27, P < 0.05), with the highest intracellular ROS level measured following treatment with Mt6-21DLeu at a concentration of 4 × MIC (P < 0.05). Survival curve analysis revealed that Mt6-21DLeu posed no impact on C. elegans survival at concentrations of MIC (χ² = 0.02, P > 0.05), 2 × MIC (χ² = 0.06, P > 0.05) or 4 × MIC (χ² = 0.16, P > 0.05), and there was a significant difference in the survival period of C. elegans among the control group, the infection group, and the treatment group (χ² = 82.66, P < 0.05), with a significantly longer survival period in the treatment group than in the infection group (χ² = 45.00, P < 0.05). In addition, the log-transformed bacterial colony counts in C. elegans were (0.00 ± 0.00), (5.46 ± 0.03), and (3.91 ± 0.47) CFU/mL in the control group, the infection group, and the treatment group, respectively (F = 324.80, P < 0.001), and the log-transformed bacterial colony counts in C. elegans were significantly lower in the treatment group than in the infection group (P < 0.05). Conclusions Mt6-21DLeu exerts potent antibacterial effects through disrupting the cell membrane integrity of AB and promoting intracellular ROS accumulation in AB, and exhibits promising potential for treatment of AB infections both in vivo and in vitro, which may serve as a candidate drug molecule against multidrug-resistant AB infections.

OBJECTIVE: To analyze the disease patterns and acupoint selection characteristics of acupuncture for epilepsy in ancient acupuncture texts, providing references and ideas for clinical acupuncture treatment of epilepsy. METHODS: Texts from the Chinese Medical Classics (5th edition) regarding acupuncture for epilepsy are reviewed. The frequency of acupoints, meridian association, distribution, specific points, corresponding epilepsy subtypes, and needling techniques are statistically analyzed. The Apriori algorithm is used for association rule analysis, and a complex network analysis is conducted for high-frequency acupoints and their corresponding subtypes and treatments. RESULTS: A total of 205 acupuncture prescriptions are identified. Ancient texts favored differentiation-based treatments for epilepsy, primarily classified into epilepsy, wind epilepsy, and five epilepsy. Commonly used acupoints include Baihui (GV20), Jiuwei (CV15), Shenmen (HT7), Shenting (GV24), and Xinshu (BL15), with a focus on the acupoints of the governor vessel, the bladder meridian, and the conception vessel. The acupoints on the head, face are combined with the acupoints on the limbs, with skillful use of the five-shu points and intersection acupoints. The most frequent combinations are Shenmen (HT7)-Baihui (GV20), Shenting (GV24)-Baihui (GV20), and Xinshu (BL15)-Shenmen (HT7). Visual network analysis revealed that Baihui (GV20)-Shenting (GV24), Baihui (GV20)-Shenmen (HT7), and Baihui (GV20)-Zhaohai (KI6) are core acupoint combinations. Treatment mainly involved moxibustion or combined acupuncture and moxibustion. CONCLUSION The acupoint selection for epilepsy treatment in ancient texts is precise, frequently using Baihui (GV20), Jiuwei (CV15), Shenmen (HT7), Shenting (GV24), and Xinshu (BL15), etc., with emphasis on calming epilepsy, awakening the spirit, relaxing tendons, and nourishing the heart.
Acupuncture Points ; Humans ; Epilepsy/history* ; History, Ancient ; Acupuncture Therapy/history* ; Medicine in Literature/history* ; Meridians ; China

Definitive treatment of lung cancer with radiotherapy is challenging, as respiratory motion and anatomical changes can increase the risk of severe off-target effects during radiotherapy. Online adaptive radiotherapy (ART) is an evolving approach that enables timely modification of a treatment plan during the interfraction of radiotherapy, in response to physiologic or anatomic variations, aiming to improve the dose distribution for precise targeting and delivery in lung cancer patients. The effectiveness of online ART depends on the seamless integration of multiple components: sufficient quality of linear accelerator-integrated imaging guidance, deformable image registration, automatic recontouring, and efficient quality assurance and workflow. This review summarizes the present status of online ART for lung cancer, including key technologies, as well as the challenges and areas of active research in this field.
Humans ; Lung Neoplasms/radiotherapy* ; Radiotherapy Planning, Computer-Assisted/methods*