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.

Qualitative and quantitative analysis methods for chemical components of different processed products of Corni Fructus were established to systematically characterize and identify these components, and the content of the main differential components was determined. The chemical components of different processed products of Corni Fructus were collected using ultra-high performance liquid chromatography-quadrupole time-of-flight tandem mass spectrometry(UPLC-Q-TOF-MS). Through analysis of self-built databases, literature, and reference standards, a total of 93 components were obtained, including 19 iridoids, 15 flavonoids, 16 organic acids, eight triterpenoids, eight tannins, four amino acids, two polysaccharides, five olefins, and 16 other compounds. Additionally, by using multivariate statistical methods, the differential components between different processed products of Corni Fructus were screened under the conditions of VIP>1.0 and FC<0.5 or FC>2.0 and P<0.05. The PCA and OPLS-DA results showed differences in the chemical components between different processed products of Corni Fructus. A total of 21 differential components were screened, including tartaric acid, morroniside, and rutin. On this basis, ultra-high performance liquid chromatography-triple quadrupole tandem mass spectrometry(UPLC-QqQ-MS/MS) was used to determine the content of 10 main common differential components, including gallic acid, morroniside, ursolic acid, loganin, swertiamarin, rutin, 5-hydroxymethylfurfural, cornuside Ⅰ, quercetin, and oleanolic acid. The above 10 components showed a good linear relationship within the determined concentration range, with the precision, stability, repeatability, and sample recovery rate all meeting the requirements. Compared with that in Corni Fructus, the content of iridoid glycosides in wine-prepared Corni Fructus and wine-and honey-prepared Corni Fructus decreased, while the content of gallic acid, rutin, quercetin, 5-hydroxymethylfurfural, ursolic acid, and oleanolic acid increased. Compared with wine-prepared Corni Fructus, wine-and honey-prepared Corni Fructus showed varying degrees of increase in all other components, except for a slight decrease in gallic acid content. In summary, this study clarified the influence of different processing methods on the chemical components of Corni Fructus, providing a theoretical basis for the scientific connotation, overall quality evaluation, and clinically rational application of Corni Fructus processing in the future.
Tandem Mass Spectrometry/methods* ; Chromatography, High Pressure Liquid/methods* ; Cornus/chemistry* ; Drugs, Chinese Herbal/chemistry* ; Fruit/chemistry*

Extensive studies have identified potential adverse effects on semen quality of obesity, based on body mass index, but the association between body fat distribution, a more relevant indicator for obesity, and semen quality remains less clear. We conducted a longitudinal study of 4304 sperm donors from the Guangdong Provincial Human Sperm Bank (Guangzhou, China) during 2017-2021. A body composition analyzer was used to measure total and local body fat percentage for each participant. Generalized estimating equations were employed to assess the association between body fat percentage and sperm count, motility, and morphology. We estimated that each 10% increase in total body fat percentage (estimated change [95% confidence interval, 95% CI]) was significantly associated with a 0.18 × 10 6 (0.09 × 10 6 -0.27 × 10 6 ) ml and 12.21 × 10 6 (4.52 × 10 6 -19.91 × 10 6 ) reduction in semen volume and total sperm count, respectively. Categorical analyses and exposure-response curves showed that the association of body fat distribution with semen volume and total sperm count was stronger at higher body fat percentages. In addition, the association still held among normal weight and overweight participants. We observed similar associations for upper limb, trunk, and lower limb body fact distributions. In conclusion, we found that a higher body fat distribution was significantly associated with lower semen quality (especially semen volume) even in men with a normal weight. These findings provide useful clues in exploring body fat as a risk factor for semen quality decline and add to evidence for improving semen quality for those who are expected to conceive.
Humans ; Male ; Adult ; Semen Analysis ; China ; Body Fat Distribution ; Longitudinal Studies ; Sperm Count ; Sperm Motility ; Body Mass Index ; Tissue Donors ; Obesity/complications* ; Spermatozoa ; Young Adult ; Middle Aged ; East Asian People

A 10-year-old girl was admitted with a 38-hour history of widespread subcutaneous petechiae and hematuria and a 6-hour history of jaundice and oliguria. Physical examination revealed widespread subcutaneous petechiae and jaundice of the skin and sclera. Laboratory tests showed anemia, thrombocytopenia, acute kidney injury, and markedly elevated lactate dehydrogenase. Thrombotic microangiopathy was initially diagnosed, with a high suspicion of atypical hemolytic uremic syndrome (aHUS). Eculizumab was initiated within 9 hours of admission (within 48 hours of onset). After the first infusion, hemolysis rapidly ceased, and the platelet count and renal function gradually returned to normal. Whole-exome sequencing identified homozygous deletions of CFHR1 exon 2 and CFHR4 exon 1. aHUS typically has abrupt onset and rapid progression. Clinicians should maintain high suspicion for aHUS when the triad of thrombocytopenia, microangiopathic hemolytic anemia, and acute kidney injury is present. Ultra-early eculizumab (within 48 hours of onset) rapidly blocks complement-mediated thrombotic microangiopathy, reverses organ injury, and improves long-term prognosis. Additionally, complement-related genetic testing is important for etiological clarification and individualized determination of eculizumab treatment duration.
Humans ; Antibodies, Monoclonal, Humanized/administration & dosage* ; Female ; Atypical Hemolytic Uremic Syndrome/drug therapy* ; Child ; Complement C3b Inactivator Proteins

Transcranial temporal interference stimulation (tTIS) is a novel non-invasive neuromodulation technique with the potential to precisely target deep brain structures. This study explores the neural and behavioral effects of tTIS on the superior colliculus (SC), a region involved in eye movement control, in mice. Computational modeling revealed that tTIS delivers more focused stimulation to the SC than traditional transcranial alternating current stimulation. In vivo experiments, including Ca²⁺ signal recordings and eye movement tracking, showed that tTIS effectively modulates SC neural activity and induces eye movements. A significant correlation was found between stimulation frequency and saccade frequency, suggesting direct tTIS-induced modulation of SC activity. These results demonstrate the precision of tTIS in targeting deep brain regions and regulating eye movements, highlighting its potential for neuroscientific research and therapeutic applications.
Animals ; Superior Colliculi/physiology* ; Transcranial Direct Current Stimulation/methods* ; Eye Movements/physiology* ; Male ; Mice ; Mice, Inbred C57BL

[This corrects the article DOI: 10.1016/j.jpha.2023.08.006.].