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.

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.

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

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.

This article discussed the evolution of the traditional preparation process of Pinelliae Rhizoma Fermentata.The production methods for Pinelliae Rhizoma Fermentata in Song Dynasty include cake-making of Pinelliae Rhizoma together with ginger juice and fermentation after cake-making,and the former method of cake-making was the mainstream.The process technology in Jin and Yuan Dynasties inherited from that in Song Dynasty,and the application of Pinelliae Rhizoma Fermentata had certain limitations.The medical practitioners of Ming Dynasty elucidated the mechanism of processing of Pinelliae Rhizoma Fermentata,and proposed the view of"sliced Pinelliae Rhizoma being potent while fermented Pinelliae Rhizoma being mild".In the Ming Dynasty,LI Shi-Zhen defined the cake-making process and fermentation process for Pinelliae Rhizoma,and HAN Mao's Han Shi Yi Tong(Han's Clear View of Medicine)contained five prescriptions for the processing of Pinelliae Rhizoma Fermentata,which had the epoch-making signficance in the expansion of prescriptions for the processing of Pinelliae Rhizoma Fermentata.In the Qing Dynasty,HAN Fei-Xia's ten methods for making Pinelliae Rhizoma Fermentata were summarized on the basis of the methods recorded in Han Shi Yi Tong,and at that time,the processing of Pinelliae Rhizoma Fermentata and the preparation of Massa Medicata Fermentata interacted with each other.After the founding of the People's Republic of China,the local experience in the preparation of Pinelliae Rhizoma Fermentata was deeply influenced by the methods in the Qing Dynasty,and the local preparation technical standards gradually became the same.Moreover,this article also explored the issues of the importance of"Pinelliae Rhizoma"and"ingredients for fermentation",the pre-treatment of Pinelliae Rhizoma,the distinction between cake-making process and fermentation process for Pinelliae Rhizoma,the amount of flour added as well as the timing of adding,the addition of Massa Medicata Fermentata powder,the role of Alum in Pinelliae Rhizoma Fermentata and so on.