Abstract To promote adolescents active participation in physical activity and improve sleep quality, the article analyzes the relationship of adolescent physical activity with subjective sleep satisfaction, sleep latency, sleep continuity, sleep efficiency, and sleep duration. It explores potential mechanisms underlying the link between physical activity and sleep quality, including physiological mechanisms (circadian rhythms, body temperature, neuroendocrine systems, and immune function), and psychological mechanisms (stress relief, improvement of negative emotions, and promotion of mental relaxation). Based on existing research, it is recommended that adolescents engage in moderate to vigorous physical activity daily to promote improved sleep quality.

ObjectiveTo investigate the mechanisms by which Mahuang Lianqiao Chixiaodoutang （MLC） improves obesity-type polycystic ovary syndrome （PCOS） through the phosphatidylinositol 3-kinase （PI3K）/protein kinase B （Akt） signaling pathway. MethodsThirty-six female Sprague-Dawley （SD） rats were randomly divided into a blank control group （Con） and an obesity-type PCOS model preparation group. The model was induced by gavage with letrozole （1 mg·kg^-1） combined with a high-fat diet （HFD）. After model establishment， the obesity-type PCOS model preparation group was further divided into the model group （Mod， normal saline）， metformin group （Met， 0.3 g·kg^-1）， low-dose MLC group （MLC-L， 4.3 g·kg^-1）， medium-dose MLC group （MLC-M， 8.6 g·kg^-1）， and high-dose MLC group （MLC-H， 17.2 g·kg^-1）. Active components of MLC and targets of obesity-type PCOS were screened from databases， a protein-protein interaction （PPI） network was constructed， and gene ontology（GO）and Kyoto Encyclopedia of Genes and Genomes （KEGG） pathway enrichment analysis was performed. The gut microbiota structure was analyzed based on 16S rRNA sequencing and correlated with network pharmacology pathways. Body weight and estrous cycle were dynamically monitored. Ovarian morphology was observed by hematoxylin-eosin （HE） staining. Cell apoptosis was detected by terminal deoxynucleotidyl transferase-mediated dUTP nick-end labeling （TUNEL）. Enzyme-linked immunosorbent assay （ELISA） was used to detect levels of follicle-stimulating hormone （FSH）， luteinizing hormone （LH）， anti-Müllerian hormone （AMH）， testosterone （T）， and estradiol （E₂）. Western blot was used to detect the protein expression levels of phosphorylated PI3K/PI3K （p-PI3K/PI3K）， phosphorylated Akt/Akt （p-Akt/Akt）， B-cell lymphoma-2 （Bcl-2）， and Bcl-2-associated X protein （Bax）. ResultsNetwork pharmacology screening identified 124 active components of MLC and 408 overlapping targets between the herbal formula and the disease. Core targets such as Akt1 and Bcl-2 were revealed. As indicated by 16S rRNA sequencing， the abundances of Lachnospiraceae， Lachnoclostridium， and Dorea were increased in the MLC groups （P<0.05）， while the abundance of Veillonella was decreased （P<0.05）. KEGG correlation analysis integrating network pharmacology and gut microbiota data showed significant enrichment of the PI3K/Akt signaling pathway. Animal experiments showed that， compared with the Mod group， body weight decreased to normal levels in the Met， MLC-M， and MLC-H groups. The estrous cycle became regular. The number of corpora lutea increased and cystic follicles decreased. Serum levels of T， FSH， and LH/FSH were reduced （P<0.05， P<0.01）， while the E₂ level was increased （P<0.01）. Ovarian cell apoptosis was reduced （P<0.01）， and the protein expression levels of p-PI3K/PI3K， p-Akt/Akt， and Bcl-2 in ovarian tissue were significantly increased， whereas Bax protein expression was significantly decreased （P<0.05， P<0.01）. ConclusionMLC can regulate gut microbiota structure， effectively improve ovarian pathology in rats with obesity-type PCOS， and inhibit ovarian granulosa cell apoptosis. The mechanism may be associated with upregulation of the PI3K/Akt signaling pathway.

BackgroundAdolescents with depressive disorder often engage in non-suicidal self-injury （NSSI） behaviors， which severely impacts their physical and mental health. Impulsivity and emotional regulation are key factors influencing NSSI behaviors. However， research on the mechanisms through which impulsivity and emotional regulation affect NSSI behaviors in adolescent depressive disorder patients with NSSI remains insufficient， limiting the development of effective intervention strategies. ObjectiveTo explore the differences in impulsivity and emotion regulation abilities between adolescent patients with depressive disorder accompanied by and without NSSI behaviors， and to analyze the association between NSSI behaviors and impulsivity and emotion regulation abilities in adolescent patients with depressive disorder accompanied by NSSI behaviors. MethodsA total of 184 adolescents hospitalized in the child and adolescent psychiatry department of Wuxi Mental Health Center from October 2023 to August 2024， who met the diagnostic criteria for depressive disorder according to the Diagnostic and Statistical Manual of Mental Disorders， fifth edition （DSM-5）， were consecutively enrolled as study subjects. Based on the diagnostic criteria for NSSI in DSM-5， patients were divided into NSSI group （n=108） and non-NSSI group （n=76）. The Barratt Impulsiveness Scale-11 （BIS-11）， the Emotion Regulation Questionnaire （ERQ）， the Adolescent Self-Harm Questionnaire， and the Positive and Negative Suicide Ideation （PANSI） were used for assessment. Spearman correlation analysis was employed to explore the correlation between the scores of the Adolescent Self-Harm Questionnaire and the scores of BIS-11 and ERQ in the NSSI group. Multiple regression analysis was conducted to examine the effects of impulsivity and emotion regulation on NSSI behaviors in the NSSI group. ResultsCompared to the non-NSSI group， the NSSI group showed significantly higher scores in BIS-11 non-planned impulsivity （Z=-4.181， P<0.05）， action impulsivity （t=4.944， P<0.05）， cognitive impulsivity （Z=-3.392， P<0.05）， and total score （t=4.763， P<0.05）， and lower scores in the cognitive reappraisal of ERQ （t=-4.094， P<0.05） and total score （Z=-2.299， P<0.05）， and higher scores in the expression inhibition of ERQ （Z=-3.019， P<0.05）. The correlation analysis results showed that the score of the adolescent self-harm questionnaire in the NSSI group was positively correlated with the behavioral impulsivity factor score in the BIS-11 （r=0.434， P<0.05）. Multiple regression analysis indicated that action impulsivity factor was a significant correlate of self-injury behaviors in the NSSI group （B=0.855， P<0.05）， explaining 22.30% of the total variance. ConclusionAdolescent patients with depressive disorder accompanied by NSSI behaviors exhibit higher levels of impulsivity and poorer emotional regulation abilities. Action impulsivity may play a significant role in the mechanism of NSSI behaviors. ［Funded by Wuxi Municipal Health Commission Research Project （number， Q202320）］

ObjectiveTo investigate the mechanisms by which Mahuang Lianqiao Chixiaodoutang （MLC） improves obesity-type polycystic ovary syndrome （PCOS） through the phosphatidylinositol 3-kinase （PI3K）/protein kinase B （Akt） signaling pathway. MethodsThirty-six female Sprague-Dawley （SD） rats were randomly divided into a blank control group （Con） and an obesity-type PCOS model preparation group. The model was induced by gavage with letrozole （1 mg·kg^-1） combined with a high-fat diet （HFD）. After model establishment， the obesity-type PCOS model preparation group was further divided into the model group （Mod， normal saline）， metformin group （Met， 0.3 g·kg^-1）， low-dose MLC group （MLC-L， 4.3 g·kg^-1）， medium-dose MLC group （MLC-M， 8.6 g·kg^-1）， and high-dose MLC group （MLC-H， 17.2 g·kg^-1）. Active components of MLC and targets of obesity-type PCOS were screened from databases， a protein-protein interaction （PPI） network was constructed， and gene ontology（GO）and Kyoto Encyclopedia of Genes and Genomes （KEGG） pathway enrichment analysis was performed. The gut microbiota structure was analyzed based on 16S rRNA sequencing and correlated with network pharmacology pathways. Body weight and estrous cycle were dynamically monitored. Ovarian morphology was observed by hematoxylin-eosin （HE） staining. Cell apoptosis was detected by terminal deoxynucleotidyl transferase-mediated dUTP nick-end labeling （TUNEL）. Enzyme-linked immunosorbent assay （ELISA） was used to detect levels of follicle-stimulating hormone （FSH）， luteinizing hormone （LH）， anti-Müllerian hormone （AMH）， testosterone （T）， and estradiol （E₂）. Western blot was used to detect the protein expression levels of phosphorylated PI3K/PI3K （p-PI3K/PI3K）， phosphorylated Akt/Akt （p-Akt/Akt）， B-cell lymphoma-2 （Bcl-2）， and Bcl-2-associated X protein （Bax）. ResultsNetwork pharmacology screening identified 124 active components of MLC and 408 overlapping targets between the herbal formula and the disease. Core targets such as Akt1 and Bcl-2 were revealed. As indicated by 16S rRNA sequencing， the abundances of Lachnospiraceae， Lachnoclostridium， and Dorea were increased in the MLC groups （P<0.05）， while the abundance of Veillonella was decreased （P<0.05）. KEGG correlation analysis integrating network pharmacology and gut microbiota data showed significant enrichment of the PI3K/Akt signaling pathway. Animal experiments showed that， compared with the Mod group， body weight decreased to normal levels in the Met， MLC-M， and MLC-H groups. The estrous cycle became regular. The number of corpora lutea increased and cystic follicles decreased. Serum levels of T， FSH， and LH/FSH were reduced （P<0.05， P<0.01）， while the E₂ level was increased （P<0.01）. Ovarian cell apoptosis was reduced （P<0.01）， and the protein expression levels of p-PI3K/PI3K， p-Akt/Akt， and Bcl-2 in ovarian tissue were significantly increased， whereas Bax protein expression was significantly decreased （P<0.05， P<0.01）. ConclusionMLC can regulate gut microbiota structure， effectively improve ovarian pathology in rats with obesity-type PCOS， and inhibit ovarian granulosa cell apoptosis. The mechanism may be associated with upregulation of the PI3K/Akt signaling pathway.

ObjectiveIn traditional Chinese medicine (TCM), the foundational doctrine that the eyes reflect the essence of the internal viscera establishes ocular observation as a cornerstone of diagnostic practice. Specifically, the morphological characteristics and coloration variations of the scleral microvasculature serve as critical clinical indicators for assessing the dynamic balance of Qi and Blood, as well as the pathological status of internal organs. Historically, however, TCM eye diagnosis has relied predominantly on the subjective clinical experience and visual acuity of individual practitioners, leading to inherent challenges in standardization and reproducibility. While automated computer-aided diagnostic systems offer a promising solution, existing vessel segmentation algorithms encounter significant domain-specific bottlenecks when applied to scleral imagery. These challenges primarily stem from the highly reflective and moist nature of the ocular surface, which generates severe reflective interference. Furthermore, the inherent low contrast of fine capillary networks against complex background textures, compounded by non-uniform illumination, frequently results in high false-positive rates, misdetections, and severe vessel fragmentation. To address these critical limitations and advance the objective quantification of TCM diagnostics, this paper proposes a novel, highly robust sclera vessel segmentation framework that innovatively integrates Frangi-Sato dual-filter adaptive enhancement with pixel-level reflection detection. MethodsThe proposed methodology systematically addresses the segmentation pipeline through three synergistic stages. First, to overcome the structural limitations of single-filter approaches, a multi-scale weighted fusion strategy is meticulously designed to harness the complementary extraction capabilities of both Frangi and Sato filters. This adaptive enhancement optimally balances the preservation of main vessel trunk continuity with the heightened sensitivity required for delineating delicate, low-contrast peripheral capillaries. Second, to tackle the persistent issue of reflective highlights, a sophisticated multi-feature synergistic reflection detection module is introduced. By jointly analyzing local information entropy, gradient field variations, and intensity statistical distributions, this module achieves precise, pixel-level identification and elimination of reflective artifacts without compromising the underlying vascular structures. Finally, a dual-level adaptive thresholding strategy, featuring an innovative “core protection” mechanism, is implemented. This critical step effectively suppresses complex background noise while rigorously preserving the structural and topological integrity of the intricate vessel network, preventing the structural breaks often seen in conventional binarization methods. ResultsThe efficacy of the proposed framework was rigorously evaluated using both self-constructed clinical datasets specifically acquired for TCM research and standardized public datasets. Extensive experimental results demonstrate that the proposed method consistently outperforms state-of-the-art traditional approaches and contemporary deep learning models. Specifically, the proposed method achieves a Dice similarity coefficient of approximately 0.71 on the private clinical dataset, and secures the best performance across the majority of quantitative metrics on both datasets. Notably, the framework exhibits exceptional robustness and generalization capabilities in highly challenging scenarios characterized by intense reflective interference, low signal-to-noise ratios, and cross-domain image variations. ConclusionThis study successfully realizes the high-integrity, automated segmentation of scleral vessel networks under complex clinical imaging conditions. By overcoming the fundamental algorithmic challenges of reflection interference and micro-vessel loss, the proposed methodology provides potential support for the digitization, objective standardization, and intelligent advancement of modern TCM eye diagnosis systems.

ObjectiveIn traditional Chinese medicine (TCM), the foundational doctrine that the eyes reflect the essence of the internal viscera establishes ocular observation as a cornerstone of diagnostic practice. Specifically, the morphological characteristics and coloration variations of the scleral microvasculature serve as critical clinical indicators for assessing the dynamic balance of Qi and Blood, as well as the pathological status of internal organs. Historically, however, TCM eye diagnosis has relied predominantly on the subjective clinical experience and visual acuity of individual practitioners, leading to inherent challenges in standardization and reproducibility. While automated computer-aided diagnostic systems offer a promising solution, existing vessel segmentation algorithms encounter significant domain-specific bottlenecks when applied to scleral imagery. These challenges primarily stem from the highly reflective and moist nature of the ocular surface, which generates severe reflective interference. Furthermore, the inherent low contrast of fine capillary networks against complex background textures, compounded by non-uniform illumination, frequently results in high false-positive rates, misdetections, and severe vessel fragmentation. To address these critical limitations and advance the objective quantification of TCM diagnostics, this paper proposes a novel, highly robust sclera vessel segmentation framework that innovatively integrates Frangi-Sato dual-filter adaptive enhancement with pixel-level reflection detection. MethodsThe proposed methodology systematically addresses the segmentation pipeline through three synergistic stages. First, to overcome the structural limitations of single-filter approaches, a multi-scale weighted fusion strategy is meticulously designed to harness the complementary extraction capabilities of both Frangi and Sato filters. This adaptive enhancement optimally balances the preservation of main vessel trunk continuity with the heightened sensitivity required for delineating delicate, low-contrast peripheral capillaries. Second, to tackle the persistent issue of reflective highlights, a sophisticated multi-feature synergistic reflection detection module is introduced. By jointly analyzing local information entropy, gradient field variations, and intensity statistical distributions, this module achieves precise, pixel-level identification and elimination of reflective artifacts without compromising the underlying vascular structures. Finally, a dual-level adaptive thresholding strategy, featuring an innovative “core protection” mechanism, is implemented. This critical step effectively suppresses complex background noise while rigorously preserving the structural and topological integrity of the intricate vessel network, preventing the structural breaks often seen in conventional binarization methods. ResultsThe efficacy of the proposed framework was rigorously evaluated using both self-constructed clinical datasets specifically acquired for TCM research and standardized public datasets. Extensive experimental results demonstrate that the proposed method consistently outperforms state-of-the-art traditional approaches and contemporary deep learning models. Specifically, the proposed method achieves a Dice similarity coefficient of approximately 0.71 on the private clinical dataset, and secures the best performance across the majority of quantitative metrics on both datasets. Notably, the framework exhibits exceptional robustness and generalization capabilities in highly challenging scenarios characterized by intense reflective interference, low signal-to-noise ratios, and cross-domain image variations. ConclusionThis study successfully realizes the high-integrity, automated segmentation of scleral vessel networks under complex clinical imaging conditions. By overcoming the fundamental algorithmic challenges of reflection interference and micro-vessel loss, the proposed methodology provides potential support for the digitization, objective standardization, and intelligent advancement of modern TCM eye diagnosis systems.

Objective: To investigate the correlation between the color parameters (L^*, a^*, b^*, Eab^* values) of Polygoni Multiflori Radix Praeparata powder prepared by different processing techniques and the contents of 2,3,5,4’-tetrahydroxystilbene-2-O-β-D-glucopyranoside, emodin, physcion, emodin-8-O-β-D-glucopyranoside, physcion-8-O-β-D-glucopyranoside.
Methods: The L^*, a^*, b^* and Eab^* values of Polygoni Multiflori Radix Praeparata powder prepared by different processing techniques were determined by spectrophotometer, and the contents of the five components were determined by high performance liquid chromatography. Secondly, SPSS 26.0 software and Simca 14.1 software were used to analyze the correlation.
Results: Through the hierarchical cluster analysis (HCA), it was found that the steamed samples and black bean steamed samples could be obviously divided into two categories: raw products and processed products. The processed products could be further divided into 2-8 h and 12-48 h. Pearson correlation analysis showed that the content of stilbene glycoside was significantly positively correlated with L^*, a^* and b^* values (P<0.01). The a^* value was significantly positively correlated with the content of emodin and physcion (P<0.01). Emodin-8-O-β-D-glucoside was positively correlated with L^* value and b^* value, while physcion-8-O-β-D-glucoside was negatively correlated with a^* value. Partial least squares discriminant analysis (PLS) showed that 2,3,5,4’-tetrahydroxystilbene-2-O-β-D-glucoside (VIP=1.69) and emodin-8-O-β-D-glucoside (VIP=1.06) were the key variables affecting chromaticity characteristics (P<0.01).
Conclusion: The three processes of steaming, black bean steaming and black bean stewing are consistent in composition transformation and chromaticity variation, and stilbene glycoside can be used as a specific index component to characterize the processed color. Chromatic parameters can effectively reflect the processing progression and serve as quality monitoring indicators during production.

As an important part of Chinese traditional culture， the processing technology of traditional Chinese medicine（TCM） carries the wisdom of TCM for thousands of years， and its process is complex and rigorous. With the popularization of modern production technology， traditional processing techiques are facing the dual pressures from technological innovation and production standardization under the perspective of intangible cultural heritage. The modernization of TCM processing technology is an inevitable trend for industrial upgrading， but it cannot be separated from the foundation of traditional skills and ignore the core concepts and cultural values it embodies. Therefore， by analyzing the core characteristics of TCM processing technology and its differences with modern production， this paper discusses the establishment of a synergistic innovation mechanism between traditional techniques and modern technologies， the promotion of joint research and development between scientific research institutes and the industry， the strengthening of standardization of processing techniques， and the enhancement of social education and industry training to improve the recognition and inheritance of processing techniques in order to achieve the goal of innovation and protection of TCM processing technologies in the context of modernization， and to promote the high-quality development of the TCM processing industry.

Currently, the academic community, industry, and governmental institutions worldwide are dedicated to developing and applying artificial intelligence and other advanced analytical tools to drive the transformation of healthcare services. However, there are still many challenges, with only a few artificial intelligence tools having achieved sufficient effectiveness in improving clinical outcomes for cardiovascular diseases and strokes to be widely used. In response, the American Heart Association has formulated related scientific statements outlining the latest research developments in artificial intelligence algorithms and data science for the diagnosis, classification, and treatment of cardiovascular diseases. These statements also summarize the current best practices, research gaps, and existing challenges of artificial intelligence tools, aiming to promote the development of this field. This article interprets this scientific statement in conjunction with the relevant research practices of the author's team.

The European Association for Cardio-Thoracic Surgery (EACTS) has recently updated and published the "2024 EACTS guidelines on perioperative medication in adult cardiac surgery". Based on the latest evidence, the guidelines have been updated in multiple aspects including underlying disease management, antithrombotic medication, arrhythmia treatment and other supportive care, etc. This paper aims to summarize and interpret the guidelines, in order to promote clinicians’ understanding and optimize perioperative medical treatment in adult cardiac surgery.