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 investigate the effect of quercetin on the aging model of bone marrow mesenchymal stem cells established under microgravity. Methods Using 3D gyroscope, a aging model of bone marrow mesenchymal stem cells was constructed, and after receiving quercetin and microgravity treatment, the anti-aging effect of the quercetin was evaluated by detecting related proteins and oxidation indexes. Results Compared to the control group, the expressions of age-related proteins p21, pi6, p53 and RB in the microgravity group significantly increased, while the expressions of cyclin D1 and lamin B1 significantly decreased, with statistical significance (P<0.05). In the microgravity group, mitochondrial membrane potential significantly decreased (P<0.05), ROS accumulation significantly increased (P <0.05), SOD content significantly decreased and MDA content significantly increased (P<0.05). Compared to the microgravity group, the expressions of age-related proteins p21, pi6, p53 and RB in the quercetin group significantly decreased, while the expressions of cyclin D1 and lamin B1 significantly increased, with statistical significance (P<0.05). In the quercetin group, mitochondrial membrane potential significantly increased (P<0.05), ROS accumulation significantly decreased (P<0.05), SOD content significantly increased and MDA content significantly decreased (P<0.05). Conclusions Quercetin can resist oxidation, protect mitochondrial function and normal cell cycle, thus delaying the aging of bone marrow mesenchymal stem cells induced by microgravity.

Objective To explore the etiology and pathogenesis of dry eye by studying the distribution pattern of gender,age and traditional Chinese medicine(TCM)syndrome type in dry eye patients and by analyzing their correlation.Methods A total of 244 patients with dry eye who met the inclusion criteria were selected.The distribution of gender,age and TCM syndrome types was statistically analyzed,and then the correlation of TCM syndrome types with gender and age of dry eye patients was explored.Results(1)Of the 244 dry eye patients,96(39.34%)were male and 148(60.66%)were female,the incidence of the female being higher than that of the male.There were 124(50.82%)patients younger than 45 years old,81(33.20%)patients aged 45-60 years old,and 39(15.98%)patients older than 60 years old.The proportion of the patients younger than 45 years old was higher than that of other age groups.(2)Among the 244 patients with dry eyes,89 cases(36.47%)were differentiated as liver and kidney deficiency syndrome,75 cases(30.74%)were differentiated as qi stagnation and blood stasis syndrome,69 cases(28.28%)were differentiated as spleen and kidney deficiency,and 11 cases(4.51%)were differentiated as yin deficiency and damp-heat syndrome.And the occurrence frequency of the above four syndrome types was in descending order.(3)In the dry eye patients of various age groups,patients aged＜45 years old predominantly suffered from qistagnation and blood stasis syndrome,accounting for 41.94%(52/124);patients aged 45-60 years old and those aged＞60 years old predominantly suffered from liver and kidney deficiency syndrome,accounting for 46.91%(38/81)and 53.85%(21/39),respectively.The distribution of TCM syndrome types varied in the patients with different age groups,and the difference was statistically significant(χ2 = 22.128,P＜0.01).(4)In male dry eye patients,qi stagnation and blood stasis syndrome was predominant,accounting for 39.58%(38/96);among female dry eye patients,liver and kidney deficiency syndrome and spleen and kidney deficiency syndrome were prevalent,accounting for 41.89%(62/148)and 31.08%(46/148),respectively.The distribution of TCM syndrome types varied in the patients with different genders,and the difference was statistically significant(χ2 = 82.610,P＜0.01).Conclusion The TCM syndromes of patients with dry eyes are frequently differentiated as liver and kidney deficiency syndrome,followed by the qi stagnation and blood stasis syndrome.The prevalence of dry eyes is related to the gender and age,and gender and age are correlated with the TCM syndrome types to certain extent.

ObjectiveExisting artificial vision devices can be divided into two types: implanted devices and extracorporeal devices, both of which have some disadvantages. The former requires surgical implantation, which may lead to irreversible trauma, while the latter has some defects such as relatively simple instructions, limited application scenarios and relying too much on the judgment of artificial intelligence (AI) to provide enough security. Here we propose a system that has voice interaction and can convert surrounding environment information into tactile commands on head and neck. Compared with existing extracorporeal devices, our device can provide a larger capacity of information and has advantages such as lower cost, lower risk, suitable for a variety of life and work scenarios. MethodsWith the latest remote wireless communication and chip technologies, microelectronic devices, cameras and sensors worn by the user, as well as the huge database and computing power in the cloud, the backend staff can get a full insight into the scenario, environmental parameters and status of the user remotely (for example, across the city) in real time. In the meanwhile, by comparing the cloud database and in-memory database and with the help of AI-assisted recognition and manual analysis, they can quickly develop the most reasonable action plan and send instructions to the user. In addition, the backend staff can provide humanistic care and emotional sustenance through voice dialogs. ResultsThis study originally proposes the concept of “remote virtual companion” and demonstrates the related hardware and software as well as test results. The system can not only achieve basic guide functions, for example, helping a person with visual impairment to shop in supermarkets, find seats at cafes, walk on the streets, construct complex puzzles, and play cards, but also can meet the demand for fast-paced daily tasks such as cycling. ConclusionExperimental results show that this “remote virtual companion” is applicable for various scenarios and demands. It can help blind people with their travels, shopping and entertainment, or accompany the elderlies with their trips, wilderness explorations, and travels.

Purpose To evaluate the feasibility of cardiac magnetic resonance fractal analysis in evaluating left ventricular trabecular complexity in hypertrophic cardiomyopathy(HCM),and to study the degree of left ventricular trabecular complexity in HCM and the relationship between excessive trabecular complexity and cardiac function.Materials and Methods From August 2020 to December 2022,a total of 80 patients with HCM from the Second Affiliated Hospital of Nanchang University were retrospectively analyzed.Additionally,80 healthy volunteers were recruited as the control group.Left ventricular functional parameters and fractal dimension(FD)of left ventricular trabecular myocardium were measured.The differences of mean global FD,max basal FD and max apical FD were compared between the HCM group and the control group,the correlation between FDs and cardiac function parameters was evaluated.The diagnostic efficiency of mean global FD,max apical FD and max basal FD was analyzed via receiver operating characteristic curve.Results The mean global FD of HCM group was significantly higher than that of normal group,and the difference was statistically significant(1.303±0.047 vs.1.229±0.026;t=-12.387,P<0.001).Mean global FD showed the best performance in differentiating HCM from normal control group.The optimal cut-off value for the diagnosis of HCM was 1.251,with the area under curve of 0.933(95%CI 0.896-0.969).Mean global FD was positively correlated with maximum wall thickness and left ventricular mass index(r=0.686,0.687,P<0.001),and max apical FD was positively correlated with left ventricular ejection fraction(r=0.520,P<0.001).Conclusion The FD obtained by cardiac magnetic resonance fractal analysis technique is reproducible and has definite value in the diagnosis of HCM,with association with the structure and function of left heart.

Plant natural products have a wide range of pharmacological properties, not only can they be used as plant dietary supplements to meet the nutritional needs of the human body in the accelerated pace of life, but also occupy an important position in the research and development of therapeutic drugs for the treatment of tumors, inflammation and other diseases, and have been widely accepted by the public due to their good safety. However, despite the above advantages of plant natural products, limiting factors such as low solubility, poor stability, lack of targeting, high toxicity and side effects, and unacceptable odor have greatly impeded their conversion to clinical applications. Therefore, the development of new avenues for the application of new natural products has become an urgent problem to be solved at present. In recent years, with the continuous development of research, various strategies have been developed to improve the bioavailability of natural products. Among them, nanocarrier delivery system is one of the most attractive strategies at present. In past studies, a large number of nanomaterials (organic, inorganic, etc.) have been developed to encapsulate plant-derived natural products for their efficient delivery to specific organs and cells. Up to now, nanotechnology has not only been limited to pharmaceutical applications, but is also competing in the fields of nanofood processing technology and nanoemulsions. Among the various nanocarriers, liposomes are the largest nanocarriers with the largest market share at present. Liposomes are bilayer nanovesicles synthesized from amphiphilic substances, which have advantages such as high drug loading capacity and stability. Attractively, the flexible surface of liposomes can be modified with various functional elements. Functionalized modification of liposomes with different functional elements such as antibodies, nucleic acids, peptides, and stimuli-responsive moieties can bring out the excellent drug delivery function of liposomes to a greater extent. For example, the modification of functional elements with targeting function such as nucleic acids and antibodies on the surface of liposomes can deliver natural products to the target location and improve the bioavailability of drugs; the modification of stimulus-responsive groups such as photosensitizers, magnetic nanoparticles, pH-responsive groups, and temperature sensitizers on the surface of liposomes can achieve controlled release of drugs, localized targeting, and synergistic thermotherapy. In addition to the above properties, by using functionalized liposomes to encapsulate natural products with irritating properties can also effectively mask the irritating properties of natural products, improve public acceptance, and increase the possibility of application of irritating natural products. There are various strategies for modifying liposomes with functional elements, and the properties of functionalized liposomes constructed by different construction strategies differ. The commonly used construction strategies for functionalized liposomes include covalent modification and non-covalent modification. These two types of construction strategies have their own advantages and disadvantages. Covalent modification has better stability than non-covalent modification, but its operation is cumbersome. With the above background, this review focuses on the three typical problems faced by plant natural products at present, and summarizes the specific applications of functionalized liposomes in them. In addition, this paper summarizes the construction strategies for building different types of functionalized liposomes. Finally, this paper will also review the opportunities and challenges faced by functionalized liposomes to enter clinical therapy, and explore the opportunities to overcome these problems, with a view to better realizing the precise control of plant nanomedicines, and providing ideas and inspirations for researchers in related fields as well as relevant industrial staff.