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.

Glaucoma is a group of optic nerve disorders characterized by progressive optic nerve atrophy and visual field defects, which can lead to irreversible blindness. Early diagnosis of glaucoma is essential for preventing visual loss. However, due to the absence of obvious early symptoms, the diagnosis of glaucoma remains challenging. Visual electrophysiological examinations, an objective approach for evaluating visual function, have the potential to be used in the early diagnosis of glaucoma. This review integrates the latest publications to introduce visual electrophysiological examination techniques, including electroretinography(ERG)and visual evoked potential(VEP). It also explores the mechanisms underlying these techniques and their application value in the early diagnosis of glaucoma. In addition, this review summarizes the advantages, limitations, and applicable scenarios of different visual electrophysiological techniques. Finally, the review provides an outlook on the development prospects of visual electrophysiological techniques in the early diagnosis of glaucoma. The findings of this review can assist clinicians in selecting appropriate diagnostic methods, promote the innovation and development of early visual electrophysiological diagnostic techniques for glaucoma, and contribute to reducing the risk of blindness caused by glaucoma.

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.

Optical imaging is highly valued for its superior temporal and spatial resolution. This is particularly important in near-infrared II (NIR-II, 1 000-3 000 nm) imaging, which offers advantages such as reduced tissue absorption, minimal scattering, and low autofluorescence. These characteristics make NIR-II imaging especially suitable for deep tissue visualization, where high contrast and minimal background interference are critical for accurate diagnosis and monitoring. Currently, inorganic fluorescent probes—such as carbon nanotubes, rare earth nanoparticles, and quantum dots—offer high brightness and stability. However, they are hindered by ambiguous structures, larger sizes, and potential accumulation toxicity in vivo. In contrast, organic fluorescent probes, including small molecules and polymers, demonstrate higher biocompatibility but are limited by shorter emission wavelengths, lower quantum yields, and reduced stability. Recently, gold clusters have emerged as a promising class of nanomaterials with potential applications in biocatalysis, fluorescence sensing, biological imaging, and more. Water-soluble gold clusters are particularly attractive as fluorescent probes due to their remarkable optical properties, including strong photoluminescence, large Stokes shifts, and excellent photostability. Furthermore, their outstanding biocompatibility—attributed to good aqueous stability, ultra-small hydrodynamic size, and high renal clearance efficiency—makes them especially suitable for biomedical applications. Gold clusters hold significant potential for NIR-II fluorescence imaging. Atomic-precision gold clusters, typically composed of tens to hundreds of gold atoms and measuring only a few nanometers in diameter, possess well-defined three-dimensional structures and clear spatial coordination. This atomic-level precision enables fine-tuned structural regulation, further enhancing their fluorescence properties. Variations in cluster size, surface ligands, and alloying elements can result in distinct physicochemical characteristics. The incorporation of different atoms can modulate the atomic and electronic structures of gold clusters, while diverse ligands can influence surface polarity and steric hindrance. As such, strategies like alloying and ligand engineering are effective in enhancing both fluorescence and catalytic performance, thereby meeting a broader range of clinical needs. In recent years, gold clusters have attracted growing attention in the biomedical field. Their application in NIR-II imaging has led to significant progress in vascular, organ, and tumor imaging. The resulting high-resolution, high signal-to-noise imaging provides powerful tools for clinical diagnostics. Moreover, biologically active gold clusters can aid in drug delivery and disease diagnosis and treatment, offering new opportunities for clinical therapeutics. Despite the notable achievements in fundamental research and clinical translation, further studies are required to address challenges related to the standardized synthesis and complex metabolic behavior of gold clusters. Resolving these issues will help accelerate their clinical adoption and broaden their biomedical applications.

Objective To study the pharmacokinetic characteristics of lamotrigine tablets in Chinese healthy subjects under fasting and fed conditions,and to evaluate the bioequivalence and safety profiles between the domestic test preparation and the original reference preparation.Methods Twenty-four Chinese healthy male and female subjects were enrolled under fasting and fed conditions,18 male and 6 female subjects under fasting conditions,17 male and 7 female subjects under fed conditions.A random,open,single-dose,two preparations,two sequences and double-crossover design was used.Plasma samples were collected over a 72-hour period after give the test or reference preparations 50 mg under fasting and fed conditions.The concentration of lamotrigine in plasma was detected by liquid chromatography-tandem mass spectrometry,and the main pharmacokinetic parameters were calculated to evaluate the bioequivalence by WinNonLin 8.1 program.Results The main pharmacokinetic parameters of single-dose the tested and reference preparations were as follows:The fasting condition Cmax were(910.93±248.02)and(855.87±214.36)ng·mL-1;tmax were 0.50(0.25,4.00)and 1.00(0.25,3.50)h;t1/2 were(36.1±9.2)and(36.0±8.2)h;AUC0_72h were(27 402.40±4 752.00)and(26 933.90±4 085.80)h·ng·mL-1.The fed condition Cmax were(701.62±120.67)and(718.95±94.81)ng·mL-1;tmax were 4.00(1.00,5.00)and 4.00(0.50,5.00)h;t1/2 were(44.2±12.4)and(44.0±12.0)h;AUC0-72h were(30 253.20±7 018.00)and(30 324.60±6 147.70)h·ng·mL-1.The 90％confidence intervals of the geometric mean ratios of Cmax and AUC0-72 hfor the test preparation and reference preparation were all between 80.00％and 125.00％under fasting and fed conditions.Conclusion Two kinds of lamotrigine tablets are bioequivalent,and have similar safety in Chinese healthy male and female subjects under fasting and fed conditions.

Objective: To observe and compare the clinical effects of different electroacupuncture waveforms on primary dysmenorrhea. Methods: This was a prospective, randomized, three-group, parallel-controlled trial. Participants with primary dysmenorrhea were randomly divided into dense-sparse wave, continuous wave, and discontinuous wave groups in a 1:1:1 ratio. Two lateral Ciliao (BL 32) points were used. All three groups started treatment 3–5 days before menstruation, once a day for six sessions per course of treatment, one course of treatment per menstrual cycle, and three menstrual cycles. The primary outcome measure was the proportion with an average visual analog scale (VAS) score reduction of ≥50% from baseline for dysmenorrhea in the third menstrual cycle during treatment. The secondary outcome measures included changes in dysmenorrhea VAS scores, Cox Menstrual Symptom Scale scores and the proportion of patients taking analgesic drugs. Results: The proportion of cases where the average VAS score for dysmenorrhea decreased by ≥50% from baseline in the third menstrual cycle was not statistically significant (P > .05). Precisely 30 min after acupuncture and regarding immediate analgesia on the most severe day of dysmenorrhea, there was a statistically significant difference in the dense-sparse wave group compared with the other two groups during the third menstrual cycle (P < .05). Additionally, there was a statistically significant difference between the dense-sparse wave and discontinuous wave groups 24 h after acupuncture (P < .05). Conclusions Waveform electroacupuncture can alleviate primary dysmenorrhea and its related symptoms in patients. The three groups showed similar results in terms of short- and long-term analgesic efficacy and a reduction in the number of patients taking analgesic drugs. Regarding achieving immediate analgesia, the dense-sparse wave group was slightly better than the other two groups.

Medicinal plants are a valuable source of essential medicines and herbal products for healthcare and disease therapy. Compared with chemical synthesis and extraction, the biosynthesis of natural products is a very promising alternative for the successful conservation of medicinal plants, and its rapid development will greatly facilitate the conservation and sustainable utilization of medicinal plants. Here, we summarize the advances in strategies and methods concerning the biosynthesis and production of natural products of medicinal plants. The strategies and methods mainly include genetic engineering, plant cell culture engineering, metabolic engineering, and synthetic biology based on multiple "OMICS" technologies, with paradigms for the biosynthesis of terpenoids and alkaloids. We also highlight the biosynthetic approaches and discuss progress in the production of some valuable natural products, exemplifying compounds such as vindoline (alkaloid), artemisinin and paclitaxel (terpenoids), to illustrate the power of biotechnology in medicinal plants.