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.

AIM:To explore the fundus vascular characteristics of healthy individuals based on deep learning techniques, with a view to discovering the range of normal values of the fundus arteries and veins, as well as the relationship between physiological factors, such as gender, age, body mass index(BMI), blood pressure, and fundus vasculature characteristics.METHODS:Fundus images of healthy people were taken from a professional fundus camera, and the subject's blood pressure and laboratory test was collected. Additionally, the fundus arteries and veins were segmented by the improved U-Net model, and the color, morphology and Haralick texture features of the vessels were extracted from computer vision technology.RESULTS:A total of 4 487 cases fundus images were taken and 326 cases with healthy and clear fundus images were screened, including 200 males and 126 females. There were differences in the morphology, color, and textural characteristics of the left and right eyes, as well as of the fundus arterioles and veins, with a mean vessel width(width)of 1.146 in the arteries and 1.430 in the veins, and an arteriovenous ratio about 4:5. Fundus artery and vein characteristics in healthy individuals of different ages(21-30, 31-40, 41-50): compared with the healthy population aged 21-30 and 31-40 years, arterial and venous inverse difference moment(idm), f12 and venous angular second moment(asm)values increased, and arterial and venous contrast(con), entropy(ent), difference entropy(den), and venous sum entropy(sen)values decreased in 41-50 years. Compared with the 21-30 years age group, arterial f12 values increased and venous con values decreased in 31-40 years(all P<0.05). Fundus vascular characteristics of healthy individuals of different sexes: compared with male, fundus arterial and venous sum average(sav), sum variance(sva)values, arterial curved values, and venous b mean, bsd, variance(var), sen, ent values increased in female, while venous area value of female decreased(all P<0.05). There were no statistically significant differences in fundus arteriosus and venous features in healthy subjects with different levels of BMI(all P>0.05). Fundus characteristics of healthy people with different degrees of blood pressure: there were statistically significant differences in fundus arteriosus area, width, and venous con, idm, dva, and den values between the normal blood pressure and high blood pressure groups(all P<0.05).CONCLUSION: The characteristics of the left and right eyes as well as the fundus arteries and veins differ in healthy individuals and correlate with physiological factors such as gender, age and blood pressure, which have the value of a potential microcirculation marker.

Sulforaphane is a naturally occurring active substance derived from cruciferous vegetables with potent antioxidant and anticancer properties. Researches have shown that sulforaphane has good bioavailability and can be absorbed by the small intestine through passive transport, followed by excretion in the form of urine via the hydrophobic acid pathway. In addition, since sulforaphane is easy to be absorbed and metabolized, wrapping sulforaphane with nanomaterials can improve its bioavailability and stability, prolong its action time in human body, and better utilize its therapeutic effect. In terms of mechanism of action, sulforaphane can activate Nrf2 and HSF1 signaling pathways, induce the expression of phase II detoxification enzymes HO-1, NADPH, GST and HSP, thus regulating the concentration of oxidative stress ROS in vivo; inhibit NF-κB signaling pathway, thus suppressing the expression of inflammatory factors TNF-α, IL-1 and IL-6; regulate epigenetic modifications, thus inhibiting HDAC and DNMT, and increasing the concentration of histone H3 and H4. By regulating the expression levels of the above factors, sulforaphane can affect the occurrence and development of cancer, neurodegenerative diseases and other diseases. In recent years, several phase I/II clinical trials have shown that sulforaphane has good drug-generating properties. For example, researchers have found that patients with skin cancer have not shown any health problems and their corresponding functional problems have improved greatly after long-term use of sulforaphane. This suggests that in the future sulforaphane has a very high medicinal potential for the treatment of cancer and neurodegenerative diseases. In this paper, we review the pharmacokinetics, target of action and safety of sulforaphane and its research progress in tumor and neurodegenerative diseases to provide a reference for the future application of sulforaphane in the treatment of tumor and neurodegenerative diseases.

Objective:To explore the clinical and electrophysiological characteristics of peripheral neuropathy in prediabetic patients.Methods:Subjects aged 20-65 years with high-risk factors of impaired glycemia enrolled in Beijing Tiantan Hospital, Capital Medical University from 2019 to 2022 were recruited to conduct oral glucose tolerance test, after excluding other causes of neuropathy or radiculopathy. Patients with impaired fasting glucose or impaired glucose tolerance were defined by American Diabetes Association criteria. These patients were divided into clinical polyneuropathy (PN) and clinical non-PN groups, according to the 2010 Toronto consensus criteria and the presence of PN symptoms and signs or not. Nerve conduction studies (NCS), F wave, sympathetic skin response (SSR), R-R interval variation (RRIV) and current perception thresholds (CPT) were performed and the abnormal rate was compared between different electrodiagnostic methods and between clinical subgroups.Results:Among the 73 prediabetic patients ultimately enrolled, only 20 (27.4%) can be diagnosed as clinical PN according to the Toronto consensus criteria. The abnormal rate of CPT (68.5%, 50/73) was significantly higher than those of F wave (2.7%, 2/73), lower limb NCS (0, 0/73), upper limb NCS changes of carpal tunnel syndrome (26.0%, 19/73), SSR (6.8%, 5/73) and RRIV (5.5%, 4/73; McNemar test, all P<0.001). With sinusoid-waveform current stimuli at frequencies of 2 000 Hz, 250 Hz and 5 Hz, the CPT device was used to measure cutaneous sensory thresholds of large myelinated, small myelinated and small unmyelinated sensory fibers respectively. CPT revealed a 21.9% (16/73) abnormal rate of unmyelinated C fiber in the hands of prediabetic patients, significantly higher than that of large myelinated Aβ fibers [8.2% (6/73), χ2=5.352, P=0.021]. Both abnormal rates of small myelinated Aδ [42.5% (31/73)] and unmyelinated C fibers [39.7% (29/73)] in the feet of prediabetic patients were significantly higher than that of large myelinated Aβ fibers [11.0% (8/73), χ2=18.508, 15.965, both P<0.001]. Compared with the clinical non-PN group, the abnormal rates of CPT [90.0% (18/20) vs 60.4% (32/53), χ2=5.904, P=0.015] and SSR [20.0% (4/20) vs 1.9% (1/53), P=0.016) were significantly higher in the clinical PN group. Conclusions:Peripheral neuropathies in prediabetic patients are usually asymptomatic or subclinical, and predispose to affect unmyelinated and small myelinated sensory fibers. Selective electrodiagnostic measurements of small fibers help to detect prediabetic neuropathies in the earliest stages of the disease.

Sensory conflict impacts postural control, yet its effect on cortico-muscular interaction remains underexplored. We aimed to investigate sensory conflict's influence on the cortico-muscular network and postural stability. We used a rotating platform and virtual reality to present subjects with congruent and incongruent sensory input, recorded EEG (electroencephalogram) and EMG (electromyogram) data, and constructed a directed connectivity network. The results suggest that, compared to sensory congruence, during sensory conflict: (1) connectivity among the sensorimotor, visual, and posterior parietal cortex generally decreases, (2) cortical control over the muscles is weakened, (3) feedback from muscles to the cortex is strengthened, and (4) the range of body sway increases and its complexity decreases. These results underline the intricate effects of sensory conflict on cortico-muscular networks. During the sensory conflict, the brain adaptively decreases the integration of conflicting information. Without this integrated information, cortical control over muscles may be lessened, whereas the muscle feedback may be enhanced in compensation.
Humans ; Muscle, Skeletal ; Electromyography/methods* ; Electroencephalography/methods* ; Brain ; Brain Mapping

Abstract On August 11, 2022, Tongxiang Center for Disease Control detected a case of brucellosis, and the case was not engaged in related work. Case finding and risk factor investigation were immediately conducted to trace the source of infection. It was revealed that the case sold aquatic products at a farmer's market and frequently picked up goods at a seafood warehouse adjacent to a lamb slaughtering site. There was a potential risk of infection due to indirect contact with slaughtered lambs or contaminants. Serological tests were conducted on 9 employees of the slaughtering site and 48 residents nearby, and the brucellosis cases diagnosed in hospitals in the same area were searched. A total of 11 brucellosis cases were identified, including 9 confirmed cases and 2 asymptomatic infections. There were 2 cases of slaughtering workers and 9 cases of non-occupational individuals from the surrounding area of the slaughtering site. Brucella melitensis biovar 3 were isolated from a slaughtering worker and a non-occupational individual. The slaughtered lambs primarily came from northern regions such as Inner Mongolia and Heilongjiang. It was concluded that it was a cluster caused by Brucella melitensis biovar 3 and spread through direct or indirect contact with imported infected lambs or contaminated environments from a lamb slaughtering site. It is suggested to strengthen the quarantine of imported sheep, legally shut down non-compliant lamb slaughtering sites, implement designated slaughtering and enhance occupational protection.