ObjectiveTo explore the changes in color， odor and chemical components during wine-processing of Chuanxiong Rhizoma（CR）， identify differential markers， and provide a basis for standardizing the process and establishing quality standards. MethodsFifteen batches of CR samples from 4 producing areas were collected. Colorimeter and electronic nose were used to detect the color changes and odor components of CR before and after wine-processing. Multivariate statistical methods including partial least squares-discriminant analysis（PLS-DA）， principal component analysis（PCA）， discriminant factor analysis（DFA） and Fisher discriminant analysis were applied to identify wine-processed CR at different processing stages and establish discriminant models， and differential components were screened out based on variable importance in the projection（VIP） value1. Then， high performance liquid chromatography（HPLC） was employed to detect the content changes of four components（ferulic acid， senkyunolide I， senkyunolide A and ligustilide） during the processing stages. ResultsThe differences of wine-processed CR at various stages were primarily reflected in color parameters L^*（brightness value）， a^*（red-green value） and b^*（yellow-blue value）. Based on chromaticity differences， the color reference ranges were established for moderately processed CR， including L^* of 46.75-48.24， a^* of 5.37-6.07 and b^* of 20.32-21.70. In odor analysis， DFA revealed significant differences among processing stages， and 11 odor markers were identified， with four differential markers（4-hydroxy-3-butylphthalide， isopropyl butyrate， L-limonene and 1-methoxyhexane） based on VIP values. HPLC results showed that there was no significant difference of the four components except for ligustilide in wine-processed CR at different stages. ConclusionThis study achieved rapid identification of wine-processed CR with different processing degrees by electronic sensory technology and differential component content detection， with discrimination accuracy rates of 92.4% and 93.272% for color and odor， respectively. This paper also established the reference ranges of main colorimetric parameters for wine-processed CR at different stages， and four differential components were screened out， providing a basis for standardizing the processing of wine-processed CR and establishing quality standards for this decoction pieces.

ObjectiveTo explore the changes in color， odor and chemical components during wine-processing of Chuanxiong Rhizoma（CR）， identify differential markers， and provide a basis for standardizing the process and establishing quality standards. MethodsFifteen batches of CR samples from 4 producing areas were collected. Colorimeter and electronic nose were used to detect the color changes and odor components of CR before and after wine-processing. Multivariate statistical methods including partial least squares-discriminant analysis（PLS-DA）， principal component analysis（PCA）， discriminant factor analysis（DFA） and Fisher discriminant analysis were applied to identify wine-processed CR at different processing stages and establish discriminant models， and differential components were screened out based on variable importance in the projection（VIP） value1. Then， high performance liquid chromatography（HPLC） was employed to detect the content changes of four components（ferulic acid， senkyunolide I， senkyunolide A and ligustilide） during the processing stages. ResultsThe differences of wine-processed CR at various stages were primarily reflected in color parameters L^*（brightness value）， a^*（red-green value） and b^*（yellow-blue value）. Based on chromaticity differences， the color reference ranges were established for moderately processed CR， including L^* of 46.75-48.24， a^* of 5.37-6.07 and b^* of 20.32-21.70. In odor analysis， DFA revealed significant differences among processing stages， and 11 odor markers were identified， with four differential markers（4-hydroxy-3-butylphthalide， isopropyl butyrate， L-limonene and 1-methoxyhexane） based on VIP values. HPLC results showed that there was no significant difference of the four components except for ligustilide in wine-processed CR at different stages. ConclusionThis study achieved rapid identification of wine-processed CR with different processing degrees by electronic sensory technology and differential component content detection， with discrimination accuracy rates of 92.4% and 93.272% for color and odor， respectively. This paper also established the reference ranges of main colorimetric parameters for wine-processed CR at different stages， and four differential components were screened out， providing a basis for standardizing the processing of wine-processed CR and establishing quality standards for this decoction pieces.

Abstract The prevention and control of myopia in Chinese children and adolescents has become a major public health issue. While maintaining increased outdoor activity as a cornerstone intervention, there is an urgent need to explore new complementary approaches that can be effectively implemented in both indoor and outdoor settings. In recent years, environmental spatial frequency has gained increasing attention as one of the key environmental factors influencing the development and progression of myopia. Both animal studies and human research have confirmed that indoor environments lacking mid to high spatial frequency components, often characterized as "visually impoverished", can promote axial elongation and myopia through mechanisms such as disruption of retinal neural signaling, impaired accommodative function, and altered expression of related molecules. Based on the scientific consensus, it is recommended that "enriching of environmental spatial frequency" should be integrated into the myopia prevention and control framework. Following the principles of schoolled organization, family cooperation, community involvement, and student participation, specific measures are put forward in three areas：optimizing school visual settings, improving home spatial environments, and promoting healthy visual behavior. The aim is to create "visually friendly" indoor environments as an important supplement to outdoor activity, thereby providing a novel perspective and strategy for comprehensively advancing myopia prevention and control among children and adolescents.

Neurocritical care involves complex pathophysiological mechanisms, and its incidence is higher, injuries are more severe, and treatment is more challenging in high-altitude environments. This consensus, based on the latest domestic and international evidence-based medical data, establishes a standardized, goal-oriented framework for neurocritical care management applicable in high-altitude regions and nationwide. The consensus was developed following international standards for evidence quality assessment and underwent two rounds of Delphi expert consultation, resulting in 32 recommendation statements covering three parts: management systems, monitoring and assessment, and core strategies. Key updates include: advocating for the establishment of independent neurocritical care units and implementing precise tiered diagnosis and treatment based on the "Five Differences in Critical Care" concept; constructing a "trinity" multimodal brain monitoring system centered on cerebral blood flow, cerebral oxygenation, and brain function, emphasizing routine bedside transcranial Doppler ultrasound, cerebral oximetry, and continuous electroencephalography monitoring; shifting management strategies from mild hypothermia therapy to targeted temperature management, and defining the "446" target management pathway for the supercritical stage; emphasizing the assessment of static and dynamic cerebrovascular autoregulation functions through multimodal methods to achieve individualized optimal mean arterial pressure management; elevating cerebrospinal fluid management goals to the level of "glymphatic system" function maintenance; implementing a multidisciplinary collaborative, whole-process management model focusing on patients' long-term neurological functional outcomes; de-escalation criteria include multidimensional indicators such as recovery of brain structure, restoration of cerebrovascular autoregulation, improvement in cerebrospinal fluid dynamics, and reduction in biomarker levels; and integrating cutting-edge technologies like artificial intelligence into post-critical care management and rehabilitation planning. This consensus systematically integrates the entire process of neurocritical care management, reflecting the modern connotation of goal-oriented, dynamic, and multimodal integration in neurocritical care medicine. It aims to adapt to new trends such as deepening understanding of pathophysiological mechanisms, the integration of medicine and engineering, and the empowerment of artificial intelligence, thereby further advancing the discipline of critical care medicine.

Despite breakthroughs in immunotherapy for solid tumors, significant variations in treatment efficacy persist. Up to 80% of cancer patients suffer from malnutrition, which leads to: lymphoid atrophy and reduced T-cell reserves; deficiency of substrates required for T-cell activation and expansion; concurrent inflammation hindering T-cell infiltration into tumors; and cachexia accelerating PD-1 antibody clearance. Clinical studies confirm that severe malnutrition significantly impairs immune responses and increases the risk of treatment toxicity. Therefore, implementing standardized nutritional therapy is crucial for optimizing the reserve, activation, expansion, and infiltration capacity of immune cells, thereby providing a sound immune system foundation for immunotherapy. Immunonutrition therapy, by enhancing immunonutrients such as arginine, omega-3 polyunsaturated fatty acids, and nucleotides, reduces the secretion of pro-inflammatory mediators and promotes T-cell activation and proliferation. This enhances anti-tumor immune responses, prolongs survival, and advances cancer treatment towards multimodal combination and precision approaches.

ObjectiveTo establish a method for the identification of Haliotidis Concha and its counterfeits， and to improve its quality evaluation method. MethodsA total of 17 batches of Haliotis discus hannai， 4 batches of H. ruber， 3 batches of H. laevigata， 3 batches of H. ovina， 3 batches of H. diversicolor， 3 batches of H. asinina， 3 batches of H. iris were collected. Ultra-high performance liquid chromatography-quadrupole/electrostatic field orbitrap high-resolution mass spectrometry（UPLC-Q-Exactive-Orbitrap-MS/MS） was used to analyze the hydrolysates of different original Haliotidis Concha and its counterfeits， and the potential characteristic ions of each species were screened by Venn diagram. UPLC-triple quadrupole tandem mass spectrometry（UPLC-QqQ-MS/MS） was used to validate the characteristic ions， and the specific detection method of the characteristic ions was established. ResultsA total of 1 182， 167， 47， 89， 104， 203， 424 potential characteristic ions were screened from H. discus hannai， H. ruber， H. laevigata， H. ovina， H. diversicolor， H. asinina and H. iris， respectively. And 9 characteristic ions were selected. The precision， stability and repeatability of the 9 characteristic ions in the established identification method met the requirements. Different original Haliotidis Concha and its counterfeits could detect their own characteristic ions， including m/z 631.83-886.48（double charge） and m/z 631.83-443.74（double charge） of H. discus hannai， m/z 699.28-232.11（double charge） and m/z 699.28-544.27（double charge） of H. ruber， m/z 535.76-752.37（double charge） and m/z 535.76-548.28（double charge） of H. laevigata， m/z 708.35-442.28（double charge） and m/z 708.35-215.14（double charge） of H. ovina， m/z 561.33-614.86（triple charge）， m/z 561.33-468.28（triple charge）， m/z 608.29-618.32（double charge） and m/z 608.29-390.21（double charge） of H. diversicolor， m/z 769.85-274.10（double charge）， m/z 769.85-532.75（double charge）， m/z 827.43-646.36（single charge）， m/z 827.43-257.12（single charge） of H. asinina， and m/z 468.24-576.29（double charge） and m/z 468.24-505.26（double charge） of H. iris. ConclusionIn this study， a total of 9 characteristic ions are screened from 6 kinds of original Haliotidis Concha and its counterfeits， and a specific identification method is established， which is helpful to solve the limitations of the existing quality evaluation methods of Haliotidis Concha， and provide a basis for the production， circulation and medication quality.

ObjectiveTo establish a method for the identification of Haliotidis Concha and its counterfeits， and to improve its quality evaluation method. MethodsA total of 17 batches of Haliotis discus hannai， 4 batches of H. ruber， 3 batches of H. laevigata， 3 batches of H. ovina， 3 batches of H. diversicolor， 3 batches of H. asinina， 3 batches of H. iris were collected. Ultra-high performance liquid chromatography-quadrupole/electrostatic field orbitrap high-resolution mass spectrometry（UPLC-Q-Exactive-Orbitrap-MS/MS） was used to analyze the hydrolysates of different original Haliotidis Concha and its counterfeits， and the potential characteristic ions of each species were screened by Venn diagram. UPLC-triple quadrupole tandem mass spectrometry（UPLC-QqQ-MS/MS） was used to validate the characteristic ions， and the specific detection method of the characteristic ions was established. ResultsA total of 1 182， 167， 47， 89， 104， 203， 424 potential characteristic ions were screened from H. discus hannai， H. ruber， H. laevigata， H. ovina， H. diversicolor， H. asinina and H. iris， respectively. And 9 characteristic ions were selected. The precision， stability and repeatability of the 9 characteristic ions in the established identification method met the requirements. Different original Haliotidis Concha and its counterfeits could detect their own characteristic ions， including m/z 631.83-886.48（double charge） and m/z 631.83-443.74（double charge） of H. discus hannai， m/z 699.28-232.11（double charge） and m/z 699.28-544.27（double charge） of H. ruber， m/z 535.76-752.37（double charge） and m/z 535.76-548.28（double charge） of H. laevigata， m/z 708.35-442.28（double charge） and m/z 708.35-215.14（double charge） of H. ovina， m/z 561.33-614.86（triple charge）， m/z 561.33-468.28（triple charge）， m/z 608.29-618.32（double charge） and m/z 608.29-390.21（double charge） of H. diversicolor， m/z 769.85-274.10（double charge）， m/z 769.85-532.75（double charge）， m/z 827.43-646.36（single charge）， m/z 827.43-257.12（single charge） of H. asinina， and m/z 468.24-576.29（double charge） and m/z 468.24-505.26（double charge） of H. iris. ConclusionIn this study， a total of 9 characteristic ions are screened from 6 kinds of original Haliotidis Concha and its counterfeits， and a specific identification method is established， which is helpful to solve the limitations of the existing quality evaluation methods of Haliotidis Concha， and provide a basis for the production， circulation and medication quality.

ObjectiveBased on ultra performance liquid chromatography-quadrupole-electrostatic field orbital trap high-resolution mass spectrometry（UPLC-Q-Orbitrap-MS）， to identify the in vivo and in vitro chemical components of total phenolic acids in Gei Herba（TPAGH）， and to clarify the pharmacological effects and potential mechanisms of the effective part in promoting acute wound healing and inhibiting scar formation. MethodsUPLC-Q-Orbitrap-MS was used to identify the chemical components of TPAGH and ingredients absorbed in vivo after topical administration. A total of 120 ICR mice were randomly divided into the model group， recombinant human epidermal growth factor（rhEGF） group（4 mg·kg^-1）， and low， medium， and high dose groups of TPAGH（3.5， 7， 14 mg·kg^-1）， with 24 mice in each group. A full-thickness skin excision model was constructed， and each administration group was coated with the drug at the wound site， and the model group was treated with an equal volume of normal saline， the treatment was continued for 30 days， during which 8 mice from each group were sacrificed on days 6， 12， and 30. The healing of the wounds in the mice was observed， and histopathological changes in the skin tissues were dynamically observed by hematoxylin-eosin（HE）， Masson， and Sirius red staining， and enzyme-linked immunosorbent assay（ELISA） was used to dynamically measure the contents of interleukin-6（IL-6）， tumor necrosis factor-α（TNF-α）， vascular endothelial growth factor A（VEGFA）， matrix metalloproteinase（MMP）-3 and MMP-9 in skin tissues. Network pharmacology was used to predict the targets related to the promotion of acute wound healing and the inhibition of scar formation by TPAGH， and molecular docking of key components and targets was performed. Gene Ontology（GO） biological process analysis and Kyoto Encyclopedia of Genes and Genomes（KEGG） pathway enrichment analysis were carried out for the related targets， so as to construct a network diagram of herbal material-compound-target-pathway-pharmacological effect-disease for further exploring its potential mechanisms. ResultsA total of 146 compounds were identified in TPAGH， including 28 phenylpropanoids， 31 tannins， 23 triterpenes， 49 flavonoids， and 15 others， and 16 prototype components were found in the serum of mice. Pharmacodynamic results showed that， compared with the model group， the TPAGH groups showed a significant increase in relative wound healing rate and relative scar inhibition rate（P<0.05）， and the number of new capillaries， number of fibroblasts， number of new skin appendages， epidermal regeneration rate， collagen deposition ratio， and Ⅲ/Ⅰ collagen ratio in the tissue were significantly improved（P<0.05， 0.01）， the levels of IL-6， TNF-α， MMP-3 and MMP-9 in the skin tissues were reduced to different degrees， while the level of VEGFA was increased. Network pharmacology analysis screened 10 core targets， including tumor protein 53（TP53）， sarcoma receptor coactivator（SRC）， protein kinase B（Akt）1， signal transducer and activator of transcription 3（STAT3）， epidermal growth factor receptor（EGFR） and so on， participating in 75 signaling pathways such as advanced glycation end-products（AGE）-receptor for AGE（AGE/RAGE） signaling pathway， phosphatidylinositol 3-kinase（PI3K）/Akt signaling pathway， mitogen-activated protein kinase（MAPK） signaling pathway. Molecular docking confirmed that the key components genistein， geraniin， and casuariin had good binding ability to TP53， SRC， Akt1， STAT3 and EGFR. ConclusionThis study comprehensively reflects the chemical composition of TPAGH and the absorbed components after topical administration through UPLC-Q-Orbitrap-MS. TPAGH significantly regulates key indicators of skin healing and tissue reconstruction， thereby clarifying its role in promoting acute wound healing and inhibiting scar formation. By combining in vitro and in vivo component identification with network pharmacology， the study explores how key components may bind to targets such as TP53， Akt1 and EGFR， exerting therapeutic effects through related pathways such as immune inflammation and vascular regeneration.

Objective: To conduct a scoping review on the types, construction methods and predictive performance of health literacy prediction models based on machine learning methods, so as to provide the reference for the improvement and application of such models. Methods: Publications on health literacy prediction models conducted using machine learning methods were retrieved from CNKI, Wanfang Data, VIP, PubMed and Web of Science from inception to May 1, 2024. The quality of literature was assessed using the Prediction Model Risk of Bias ASsessment Tool. Basic characteristics, modeling methods, data sources, missing value handling, predictors and predictive performance were reviewed. Results: A total of 524 publications were retrieved, and 22 publications between 2007 and 2024 were finally enrolled. Totally 48 health literacy prediction models were involved, and 25 had a high risk of bias (52.08%), with major issues focusing on missing value handling, predictor selection and model evaluation methods. Modeling methods included regression models, tree-based machine learning methods, support vector machines and neural network models. Predictors primarily encompassed factors at four aspects: individual, interpersonal, organizational and society/policy aspects, with age, educational level, economic status, health status and internet use appearing frequently. Internal validation was conducted in 14 publications, and external validation was conducted in 4 publications. Forty-two models reported the areas under the receiver operating characteristic curve, which ranged from 0.52 to 0.983, indicating good discrimination. Conclusion Health literacy prediction models based on machine learning methods perform well, but have deficiencies in risk of bias, data processing and validation.