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.

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.

As China accelerates its efforts in deep space exploration and long-duration space missions, including the operationalization of the Tiangong Space Station and the development of manned lunar missions, safeguarding astronauts’ physiological and cognitive functions under extreme space conditions becomes a pressing scientific imperative. Among the multifactorial stressors of spaceflight, microgravity emerges as a particularly potent disruptor of neurobehavioral homeostasis. Dopamine (DA) plays a central role in regulating behavior under space microgravity by influencing reward processing, motivation, executive function and sensorimotor integration. Changes in gravity disrupt dopaminergic signaling at multiple levels, leading to impairments in motor coordination, cognitive flexibility, and emotional stability. Microgravity exposure induces a cascade of neurobiological changes that challenge dopaminergic stability at multiple levels: from the transcriptional regulation of DA synthesis enzymes and the excitability of DA neurons, to receptor distribution dynamics and the efficiency of downstream signaling pathways. These changes involve downregulation of tyrosine hydroxylase in the substantia nigra, reduced phosphorylation of DA receptors, and alterations in vesicular monoamine transporter expression, all of which compromise synaptic DA availability. Experimental findings from space analog studies and simulated microgravity models suggest that gravitational unloading alters striatal and mesocorticolimbic DA circuitry, resulting in diminished motor coordination, impaired vestibular compensation, and decreased cognitive flexibility. These alterations not only compromise astronauts’ operational performance but also elevate the risk of mood disturbances and motivational deficits during prolonged missions. The review systematically synthesizes current findings across multiple domains: molecular neurobiology, behavioral neuroscience, and gravitational physiology. It highlights that maintaining DA homeostasis is pivotal in preserving neuroplasticity, particularly within brain regions critical to adaptation, such as the basal ganglia, prefrontal cortex, and cerebellum. The paper also discusses the dual-edged nature of DA plasticity: while adaptive remodeling of synapses and receptor sensitivity can serve as compensatory mechanisms under stress, chronic dopaminergic imbalance may lead to maladaptive outcomes, such as cognitive rigidity and motor dysregulation. Furthermore, we propose a conceptual framework that integrates homeostatic neuroregulation with the demands of space environmental adaptation. By drawing from interdisciplinary research, the review underscores the potential of multiple intervention strategies including pharmacological treatment, nutritional support, neural stimulation techniques, and most importantly, structured physical exercise. Recent rodent studies demonstrate that treadmill exercise upregulates DA transporter expression in the dorsal striatum, enhances tyrosine hydroxylase activity, and increases DA release during cognitive tasks, indicating both protective and restorative effects on dopaminergic networks. Thus, exercise is highlighted as a key approach because of its sustained effects on DA production, receptor function, and brain plasticity, making it a strong candidate for developing effective measures to support astronauts in maintaining cognitive and emotional stability during space missions. In conclusion, the paper not only underscores the centrality of DA homeostasis in space neuroscience but also reflects the authors’ broader academic viewpoint: understanding the neurochemical substrates of behavior under microgravity is fundamental to both space health and terrestrial neuroscience. By bridging basic neurobiology with applied space medicine, this work contributes to the emerging field of gravitational neurobiology and provides a foundation for future research into individualized performance optimization in extreme environments.

Aim To explore the effects of Huannao Yicong decoction(HYD)on the learning and memory ability and brain iron metabolism in APP/PS1 mice and the correlation of HAMP knockout mice and APP/PS1 double transgenic model mice.Methods The ex-periment was divided into five groups,namely,HAMP-/-group(6-month HAMP gene knockout mice),APP/PS1 group(6-month APP/PS1-double-transgenic mice),HAMP-/-+HYD,APP/PS1+HYD,and negative control group(6-month C57BL/6J mice),with six mice in each group.The dose was ad-ministered(13.68 g·kg-1 weight),and the other groups received distilled water for gavage once a day for two months.After the administration of the drug,the mice in each group were tested for learning and memory in the Morris water maze;Biochemical detec-tion was performed to detect iron ion content in each mouse brain;Western blot and RT-qPCR were carried out to analyze hippocampal transferrin(TF),transfer-rin receptor1(TFR1),membrane iron transporter1(FPN1)divalent metal ion transporter 1(DMT1)and β-amyloid protein(Aβ)protein and mRNA expression levels in each group.Results Compared with the normal group,both HAMP-/-mice and APP/PS1 mice had reduced the learning and memory capacity,in-creased iron content in brain tissue,Aβ protein ex-pression increased in hippocampus of HAMP-/-group and APP/PS1 group mice(P＜0.01),the protein and mRNA expression of TF,TFR1 and DMT1 increased in hippocampal tissues of HAMP-/-and APP/PS1 groups(P＜0.01),and the FPN1 protein and mRNA expres-sion decreased(P＜0.01).Compared with the HAMP-and APP/PS1 groups,respectively,HAMP-/-+HYD group and APP/PS1+HYD group had improved learning and memory ability,decreased iron content,decreased Aβ protein expression(P＜0.01),decreased TF,TFR1,DMT1 protein and mR-NA expression(P＜0.01),and increased expression of FPN1 protein and mRNA(P＜0.01).Conclusions There is some association between HAMP-/-mice and APP/PS1 mice,HYD can improve the learning and memory ability of HAMP-/-and APP/PS1 mice and reduce the Aβ deposition.The mechanism may be related to the regulation of TF,TFR1,DMT1,FPN1 expression and improving brain iron overload.

Aim To investigate the impact of Cinobu-facini on the proliferation,invasion,and apoptosis of HepG2 cells and the underlying mechanism.Methods The proliferation of HepG2 cells was assessed using the CCK-8 method following treatment with Cinobufaci-ni.The invasion capability of HepG2 cells was evalua-ted through Transwell assay after exposure to Cinobufa-cini.The apoptosis rates of HepG2 cells post Cinobufa-cini intervention were measured using flow cytometry,and the expression levels of VEGF in the culture medi-um of HepG2 cells were determined using enzyme-linked immunoassay.Furthermore,qRT-PCR and Western blot analyses were conducted to assess the im-pact of Cinobufacini on mRNA and protein expression levels related to the CXCL5/FOXD1/VEGF pathway.The interaction between CXCL5 and FOXD1 was inves-tigated via co-immunoprecipitation.Results Cinobufa-cini treatment led to a gradual decrease in HepG2 cell viability in a dose-dependent manner compared to the control group(P＜0.05).Moreover,Cinobufacini sig-nificantly suppressed HepG2 cell invasion(P＜0.05)while enhancing cell apoptosis(P＜0.05).Notably,Cinobufacini exhibited inhibitory effects on the CX-CL5/FOXD1/VEGF pathway,as evidenced by re-duced expression of related mRNA and proteins(P＜0.05).FOXD1 was identified as the binding site of CXCL5.Overexpression of CXCL5 resulted in in-creased proliferation and VEGF secretion by HepG2 cells(P＜0.05),and increased expression of FOXD1 and VEGF(P＜0.05).However,Cinobufacini inter-vention effectively inhibited liver cancer cell prolifera-tion and invasion(P＜0.05),promoted apoptosis(P＜0.05),reduced VEGF secretion by HepG2 cells(P＜0.05),and downregulated the expression of CXCL5 and FOXD1 in HepG2 cells(P＜0.05);but com-pared with the unexpressed group of Cinobufacini,its ability to inhibit cell activity was weakened(P＜0.05),and its ability to inhibit the expression of CX-CL5,FOXD1,and VEGF was weakened(P＜0.05).Conclusion Cinobufacini may inhibit HepG2 cell pro-liferation and invasion and promote HepG2 cell apopto-sis by regulating the CXCL5/FOXD1/VEGF pathway.

Objective To explore the difference in the effectiveness between proximal femoral nail anti-rotation(PFNA)and proximal femoral locking compression plate(PFLCP)of intertrochanteric fracture in the elderly patients combined with knee osteoarthritis.Methods The clinical data of 65 intertrochanteric femoral fractures combined with knee osteoarthritis be-tween June 2015 and February 2021 were retrospectively analyze.They were divided into two groups according to the different surgical methods.PFNA group was composed of 36 patients,12 males and 24 females,aged from 61to 88 years old with an av-erage of(77.0±6.4)years old.There were 17 cases of left injury and 19 cases of right injury.According to modified Evans clas-sification,there were 3 cases of type Ⅱ,19 cases of type Ⅲ,10 cases of type Ⅳ,and 4 cases of type Ⅴ.PFLCP group was com-posed of 29 patients,11 males and 18 females,aged from 60 to 92 years old with an average of(78.8±6.5)years old.There were 14 cases of left injury and 15 cases of right injury.According to modified Evans classification,there were 2 cases of typeⅡ,18 cases of type Ⅲ,7 cases of type Ⅳ,and 2 cases of type Ⅴ.Comparison of operation time,intraoperation blood loss,postoperative bed time,incidence of postoperative complications,Harris score at 6 months and 1 year postoperation.Results All 65 patients were followed up ranging from 12 to 24 months with an average of(16.9±3.6)months.In the PFNA and PFLCP groups,the operation time was respectively(57.6±6.8)min and(77.4±6.5)min,the intraoperative blood loss was(128.3±50.3)ml and(156.3±23.9)ml,postoperative bed time was(4.0±2.5)days and(8.1±2.0)days,Harris score at 6 months post-operative was(45.3±8.6)points and(36.3±7.0)points.There were significant differences between two groups(P＜0.05).Inci-dence of postoperative complications was 19.4％(7/36)and 34.5％(10/29),Harris score at 1 year postoperative was(60.8±6.7)points and(59.0±8.1)points.There was no significant difference between the two groups(P＞0.05).Conclusion Compared with PFLCP,PFNA treatment of elderly patients with knee osteoarthritis between the femoral intertrochanteric fractures shorter surgical time,less intraoperative blood loss,bed rest after surgery,short-term hip function recovery better,when the affected knee joint can tolerate traction,can be used as a priority.