ObjectiveThis paper aims to find the identified and validated clinical biomarker data building upon a clinical study of early-phase phase Ⅱ and investigate the correlation analysis of Huanglian Jiedu Wan on syndrome improvement and clinical biomarkers in the treatment of "excess heat-toxicity" based on a machine learning model. Additionally， the effective prediction of clinical biomarker values for the main symptoms of the "excess heat-toxicity" syndrome was assessed. MethodsA total of 229 patients meeting the inclusion criteria for "excess heat-toxicity" syndrome were randomly divided into the Huanglian Jiedu Wan group and the placebo group. Syndrome score transition matrices were constructed for the Huanglian Jiedu Wan group and the placebo group based on three main symptoms of "excess heat-toxicity" syndrome， such as oral ulcers， sore throat， and gum swelling and pain. Data from the patients with these three syndromes were also integrated for an overall analysis. The corresponding syndrome score transition matrices were further constructed to visualize symptom change trends of the patients in the two groups via heatmaps. Based on the identified and validated clinical biomarkers related to inflammation， oxidative stress， and energy metabolism in the early phase， Spearman correlation analysis was employed to analyze and evaluate the associations between clinical biomarkers and syndrome improvement. Key clinical biomarkers reflecting the effect of Huanglian Jiedu Wan were screened through the comparison of differences between groups. An extreme gradient boosting （XGBoost） algorithm was used to develop a prediction model for main symptom classification， with classification performance evaluated through 10-fold cross-validation. Feature importance analysis was applied to identify variables with the greatest contribution to the prediction result. ResultsThe syndrome transition matrix results indicated that the Huanglian Jiedu Wan group showed a superior effect to the placebo group in improving oral ulcers， sore throat， and overall symptoms， with significant effects observed especially in sore throat and overall symptom analyses （P<0.01）. Spearman correlation analysis revealed that several clinical biomarkers positively correlated with "excess heat-toxicity" syndrome and its main symptom improvement， were also called "heat-related biomarkers"， including succinic acid， α-ketoglutaric acid， glycine， lactic acid， adenosine monophosphate （AMP）， tumor necrosis factor-α （TNF-α）， interferon-γ （IFN-γ）， interleukin-1β （IL-1β）， interleukin-4 （IL-4）， interleukin-6 （IL-6）， interleukin-8 （IL-8）， interleukin-10 （IL-10）， and so on. Conversely， clinical biomarkers negatively correlated with symptom severity， were also called "heat-clearing related biomarkers" after administration of Huanglian Jiedu Wan， including malic acid， fumaric acid， cis-aconitic acid， adrenocorticotropic hormone （ACTH）， IL-1β， IL-4， IL-8， succinic acid， and citric acid. The XGBoost classification model using all 52 biomarkers as variables achieved an average test accuracy of 0.754 and an average F1 score of 0.777. Feature importance analysis identified the scores of glutamic acid in saliva and IL-6 were the highest in all the variables， with importance scores of 0.081 and 0.080， respectively. After screening out 14 key variables and optimizing the parameters， model performance improved to an average accuracy of 0.758 and an F1 score of 0.798. Feature importance analysis further determined that the glutamic acid in saliva and IL-6 showed obvious changes after screening the variables， confirming the good syndrome prediction ability of the model constructed by these key clinical biomarkers. ConclusionThis study systematically elucidates the correlation between syndrome improvement and clinical biomarkers of Huanglian Jiedu Wan in the treatment of "excess heat-toxicity" syndrome. An XGBoost classification model based on key clinical biomarkers is successfully established， achieving effective prediction of the symptoms related to the "excess heat-toxicity" syndrome such as oral ulcers and sore throat and providing a new insight for objective identification of traditional Chinese medicine syndromes.

ObjectiveThis paper aims to find the identified and validated clinical biomarker data building upon a clinical study of early-phase phase Ⅱ and investigate the correlation analysis of Huanglian Jiedu Wan on syndrome improvement and clinical biomarkers in the treatment of "excess heat-toxicity" based on a machine learning model. Additionally， the effective prediction of clinical biomarker values for the main symptoms of the "excess heat-toxicity" syndrome was assessed. MethodsA total of 229 patients meeting the inclusion criteria for "excess heat-toxicity" syndrome were randomly divided into the Huanglian Jiedu Wan group and the placebo group. Syndrome score transition matrices were constructed for the Huanglian Jiedu Wan group and the placebo group based on three main symptoms of "excess heat-toxicity" syndrome， such as oral ulcers， sore throat， and gum swelling and pain. Data from the patients with these three syndromes were also integrated for an overall analysis. The corresponding syndrome score transition matrices were further constructed to visualize symptom change trends of the patients in the two groups via heatmaps. Based on the identified and validated clinical biomarkers related to inflammation， oxidative stress， and energy metabolism in the early phase， Spearman correlation analysis was employed to analyze and evaluate the associations between clinical biomarkers and syndrome improvement. Key clinical biomarkers reflecting the effect of Huanglian Jiedu Wan were screened through the comparison of differences between groups. An extreme gradient boosting （XGBoost） algorithm was used to develop a prediction model for main symptom classification， with classification performance evaluated through 10-fold cross-validation. Feature importance analysis was applied to identify variables with the greatest contribution to the prediction result. ResultsThe syndrome transition matrix results indicated that the Huanglian Jiedu Wan group showed a superior effect to the placebo group in improving oral ulcers， sore throat， and overall symptoms， with significant effects observed especially in sore throat and overall symptom analyses （P<0.01）. Spearman correlation analysis revealed that several clinical biomarkers positively correlated with "excess heat-toxicity" syndrome and its main symptom improvement， were also called "heat-related biomarkers"， including succinic acid， α-ketoglutaric acid， glycine， lactic acid， adenosine monophosphate （AMP）， tumor necrosis factor-α （TNF-α）， interferon-γ （IFN-γ）， interleukin-1β （IL-1β）， interleukin-4 （IL-4）， interleukin-6 （IL-6）， interleukin-8 （IL-8）， interleukin-10 （IL-10）， and so on. Conversely， clinical biomarkers negatively correlated with symptom severity， were also called "heat-clearing related biomarkers" after administration of Huanglian Jiedu Wan， including malic acid， fumaric acid， cis-aconitic acid， adrenocorticotropic hormone （ACTH）， IL-1β， IL-4， IL-8， succinic acid， and citric acid. The XGBoost classification model using all 52 biomarkers as variables achieved an average test accuracy of 0.754 and an average F1 score of 0.777. Feature importance analysis identified the scores of glutamic acid in saliva and IL-6 were the highest in all the variables， with importance scores of 0.081 and 0.080， respectively. After screening out 14 key variables and optimizing the parameters， model performance improved to an average accuracy of 0.758 and an F1 score of 0.798. Feature importance analysis further determined that the glutamic acid in saliva and IL-6 showed obvious changes after screening the variables， confirming the good syndrome prediction ability of the model constructed by these key clinical biomarkers. ConclusionThis study systematically elucidates the correlation between syndrome improvement and clinical biomarkers of Huanglian Jiedu Wan in the treatment of "excess heat-toxicity" syndrome. An XGBoost classification model based on key clinical biomarkers is successfully established， achieving effective prediction of the symptoms related to the "excess heat-toxicity" syndrome such as oral ulcers and sore throat and providing a new insight for objective identification of traditional Chinese medicine syndromes.

ObjectiveThis paper aims to investigate the role of NDC80 kinetochore complex component （NUF2） and magnesium homeostasis in ovarian cancer cell apoptosis， as well as the regulatory mechanism of gypenoside L （Gyp-L） on NUF2 and magnesium homeostasis. MethodsOvarian cancer OVCAR3 cells were divided into a blank control group， a low-concentration Gyp-L group （50 µmol·L^-1）， a high-concentration Gyp-L group （100 µmol·L^-1）， and a cisplatin （15 µmol·L^-1） group. The migration， proliferation， and apoptosis capabilities of OVCAR3 cells were evaluated through cell scratch assays， clonal experiments， and terminal-deoxynucleotidyl transferase-mediated dUTP-biotin nick end labeling assay （TUNEL） staining. Differentially expressed genes of ovarian cancer were screened by using the Gene Expression Omnibus （GEO） database. The interaction relationships of differentially expressed genes and proteins were analyzed via the Search Tool for Recurring Instances of Neighbouring Genes （STRING） database. The prognostic survival analysis was performed by using the Tumor Immune Estimation Resource （TIMER） database， and the differential expression levels of genes were validated with the Gene Expression Profiling Interactive Analysis （GEPIA） database. The mRNA expression levels of NUF2， magnesium homeostasis-related indicators， such as magnesium transporter 1 （MAGT1）， non-imprinted in Prader-Willi/Angelman syndrome 1 （NIPA1）， NIPA-like domain containing 1 （NIPAL1）， as well as apoptosis-related indicators B cell lymphoma-2 （Bcl-2） and Bcl-2-associated X protein （Bax） in OVCAR3 cells， were detected by real-time quantitative polymerase chain reaction （Real-time PCR）. The protein expression levels of NUF2， MAGT1， NIPA1， NIPAL1， Bcl-2， and Bax in OVCAR3 cells were quantitatively analyzed by ProteinSimple WES. A model of overexpression of NUF2 was constructed， and Gyp-L intervention was performed. The molecular mechanism by which Gyp-L induces ovarian cancer cell apoptosis by regulating NUF2 and influencing magnesium homeostasis was quantitatively analyzed and detected through cell cloning， TUNEL staining， Real-time PCR， and ProteinSimple WES. Finally， the Mg²⁺ content and protein synthesis efficiency were detected by immunofluorescence. ResultsGyp-L significantly inhibited the migration and proliferation capabilities of OVCAR3 cells and promoted their apoptosis （P<0.05）. Overexpression of NUF2 markedly increased the expression levels of MAGT1， NIPA1， NIPAL1， and Bcl-2， while reducing the expression level of Bax （P<0.05）. It also significantly elevated intracellular Mg²⁺ content and protein synthesis efficiency and simultaneously inhibited apoptosis （P<0.05）. Gyp-L could reverse the magnesium homeostasis imbalance and apoptosis inhibition caused by the overexpression of NUF2， downregulating the expression levels of NUF2， MAGT1， NIPA1， NIPAL1， and Bcl-2 （P<0.05）， while upregulating the expression level of Bax （P<0.05）. ConclusionGyp-L can inhibit the occurrence of ovarian cancer， and its mechanism may involve inhibiting the expression of NUF2 to maintain magnesium homeostasis and inducing apoptosis of ovarian cancer cells.

With the accelerating trend of population aging, the number of elderly patients with lung cancer continues to rise, and the disease burden is becoming increasingly heavy. The clinical management of these patients faces severe challenges due to their decreased physiological reserve, complex comorbidities, and significant individual heterogeneity. Consequently, under traditional diagnosis and treatment models, doctors often struggle to identify the individualized risks of elderly patients in a timely and comprehensive manner, which can easily lead to decision biases such as undertreatment or overtreatment. In view of this, this study advocates for the establishment of an umbrella decision-making model specifically tailored for elderly lung cancer patients. Grounded in a multidisciplinary team (MDT) platform, this model deeply integrates oncological indicators with the comprehensive geriatric assessment (CGA) system. By holistically considering multidimensional variables including tumor burden, organ function, frailty index, cognitive status, and social support, the model establishes an operational mechanism characterized by "single entry, precise stratification, and targeted selection". Accordingly, patients can be scientifically triaged into distinct intervention tiers, such as active surveillance, minimally invasive surgery, drug therapy, radiotherapy, and best supportive care, thereby achieving real-time alignment between treatment intensity and patient fitness. This article elaborates on the construction logic and key operational procedures of this novel decision-making framework, aiming to guide clinical practice beyond the limitations of a tumor-centric perspective toward a holistic, dynamic, whole-course management strategy. This transition seeks to ensure optimal quality of life and clinical net benefit for elderly patients alongside survival prolongation.

Background Polycyclic aromatic hydrocarbons (PAHs) readily adsorb onto the surface of fine particulate matter (PM_2.5) and are representative components of organic pollutants in the atmosphere. PAHs are known for their significant carcinogenic, teratogenic, and mutagenic toxicity, and long-term exposure to PAHs has adverse effects on human health. Objective To investigate the concentration, composition, and pollution sources of 16 priority PAHs in PM_{2. 5} of Yanta District, Xi'an, and to evaluate the associated health risks. Methods PM_2.5 samples were collected from March 2023 to February 2024. The concentrations of 16 PAHs (ΣPAHs) were determined by high performance liquid chromatography (HPLC). Diagnostic ratios (DR) and positive matrix factorization (PMF) were used to identify the sources of PAHs. Pearson correlation analysis was applied to examine the relationship between PAHs concentrations and meteorological factors. Furthermore, the toxicity of individual PAHs was converted into benzo[a]-pyrene (BaP) toxicity equivalents to calculate the toxicity of total PAHs. An incremental lifetime carcinogenic risk (ILCR) model was established to evaluate the health risks of PAHs inhalation across different demographic groups. Results The average concentration of ΣPAHs in PM_2.5 of Yanta District, Xi'an followed a seasonal descending order: winter (15.71 ng·m⁻³) > spring (5.55 ng·m⁻³) > autumn (4.34 ng·m⁻³) > summer (1.47 ng·m⁻³). The relative abundances of PAHs in PM_2.5 varied significantly by ring number, 4-ring PAHs accounted for the largest proportion, followed by 5-ring, 3-ring and 6-ring PAHs, while 2-ring PAHs were the least abundant. This ring distribution pattern remained consistent across seasons. The contribution rates of different emission sources to ΣPAHs concentration exhibited seasonal variations. Source apportionment revealed that industrial and traffic emissions were major contributors in spring; traffic emissions dominated in summer and autumn; and coal, natural gas, and biomass combustion were the predominant sources in winter. Correlation analysis showed that ΣPAHs concentration was highly positively correlated with atmospheric pressure and negatively correlated with temperature, but not significantly correlated with precipitation, wind speed, solar radiation, and relative humidity. The ILCR model indicated significant seasonal differences (winter > spring > autumn > summer) in inhalation health risks between adults and children, primarily due to variations in inhalation rates and body weight. Adults faced higher health risks than children across all seasons. Conclusion PAHs pollution in PM_2.5 of Yanta District, Xi 'an is primarily driven by traffic emissions and the combustion of coal, natural gas, and biomass. PAHs concentrations are significantly influenced by meteorological factors, particularly air pressure, and temperature. Inhalation exposure poses distinct health risks to adults in winter. To effectively reduce ΣPAHs concentrations and mitigate air pollution, emission controls on winter coal combustion should be prioritized.

Abstract To consolidate existing evidence and clarify the association between physical activity (PA) and mental health among college students, through a systematic review of domestic and foreign literature, the study summarizes the dynamic bidirectional relationship between college students PA and mental health (including mood, depression, anxiety, sleep, and life satisfaction), and explores underlying mechanisms. The results indicate that PA could improve mood, alleviate depression and anxiety, promote better sleep, and improve life satisfaction, whereas mental health status can also have a reverse impact on PA motivation and behavioral patterns. In conclusion, a dynamic two way relationship exists between the two, and future research should further refine measurement, design, and methodology to provide a basis for promoting mental health in colleges.

This paper summarizes Professor LI Diangui's experience in treating vitiligo from the perspective of turbid toxins latent in the collaterals. Based on the theory of turbid-toxin， professor LI proposes that turbid toxins latent in the collaterals and qi-blood disharmony constitute the core pathogenesis of the onset and progression of the disease， and that resolving turbidity and removing toxins while harmonizing qi and blood should serve as the fundamental therapeutic principles. In clinical practice， treatment is tailored according to the evolution of the pathogenesis and syndrome differentiation. When stagnation of qi movement leads to the endogenous accumulation of turbid pathogens， treatment focuses on relieving constraint， dispersing turbidity， soothing the liver， and regulating qi， with a modified self-formulated Kaiyu Sanzhuo Decoction （开郁散浊汤）. When turbid pathogens overflow to the superficial tissues and obstruct the cutaneous collaterals， treatment aims to resolve blood stasis， disperse turbidity， regulate qi， and unblock the collaterals， using a modified self-formulated Tongluo Huazhuo Decoction （通络化浊汤）. When deficiency of healthy qi allows toxins to remain latent and the disease becomes prolonged， treatment emphasizes reinforcing healthy qi， eliminating turbidity， tonifying qi， and nourishing blood， with a modified self-formulated Fuzheng Quzhuo Decoction （扶正祛浊汤）.

Objective To analyze the prescription patterns of Professor Huang Feng,a nationally renowned traditional Chinese medicine(TCM)practitioner,in treating osteomyelitis using data mining methods.Methods Prescription data from effective medical records of osteomyelitis treated by Professor Huang Feng between January 2018 and December 2022 were collected and screened.Microsoft Excel,SPSS Modeler 18.0,and SPSS Statistics 25 were used to analyze the frequency and the distribution of properties,flavors,and meridian tropism of prescribed medications,along with association rule analysis and cluster analysis of high-frequency drugs.Results A total of 137 prescriptions involving 86 Chinese medicinals were included.Eighteen high-frequency medicinals(frequency＞30 times)were identified,namely Glycyrrhizae Radix et Rhizoma,Astragali Radix,Coicis Semen,Angelicae Sinensis Radix,Smilacis Glabrae Rhizoma,Achyranthis Bidentatae Radix,Bletillae Rhizoma,Rehmanniae Radix,Paeoniae Radix Alba,Dendrobii Caulis,Polygalae Radix,Lablab Semen Album,Corydalis Rhizoma,Angelicae Dahuricae Radix,Drynariae Rhizoma,Sanguisorbae Radix,Poria,and Mume Fructus.Most of the prescribed medicinals were neutral in nature,sweet,bitter,and pungent in flavor,and had the meridian tropism of liver,spleen,and lung meridians.Association rule analysis yielded 67 drug association rules,and the high-support combinations were the drug combinations of Astragali Radix respectively with Coicis Semen,Angelicae Sinensis Radix,Smilacis Glabrae Rhizoma and Achyranthis Bidentatae Radix,reflecting the compatibility principles of supplementing and invigorating qi-blood,activating blood circulation to resolve stasis,and draining dampness to remove toxins.Cluster analysis revealed three core clusters:Cluster 1 consisted of Glycyrrhizae Radix et Rhizoma,Astragali Radix,Coicis Semen,Smilacis Glabrae Rhizoma,Angelicae Sinensis Radix,Bletillae Rhizoma,Paeoniae Radix Alba,Angelicae Dahuricae Radix,Mume Fructus,Polygalae Radix and Sanguisorbae Radix;Cluster 2 consisted of Rehmanniae Radix and Dendrobii Caulis;Cluster 3 consisted of Achyranthis Bidentatae Radix,Lablab Semen Album,Corydalis Rhizoma and Poria.Conclusion For the treatment of osteomyelitis,Professor Huang Feng follows the principle of combining supporting healthy qi with eliminating pathogens,focuses on clearing damp-heat and pathogenic toxins accompanied by activating blood circulation to resolve stasis,and lays stress on adaptation to local condition and activating spleen-stomach to reinforce vital qi.

Objective To analyze the correlation between inflammation indicators(systemic inflammatory response syndrome and systemic immune-inflammation index)and adverse outcomes in patients with symptomatic stroke during hospitalization.Methods This study was a prospective cohort study including consecutive patients with symptomatic stroke who were hospitalized in Fuwai Hospital from January to September 2023.The past medical history,clinical symptoms and signs of the patients were collected.The neurological damage was evaluated with National Institute of Health Stroke Scale(NIHSS).Laboratory test results were recorded and the SIRI and SII in-dex were calculated.Patients were followed up for 90 days after the stroke,and their neurological outcomes were evaluated using the modified Rankin Scale.A score of 0-1 was classified as good outcome,and a score ≥2 was classified as poor outcome.The correlation between inflammatory response indicators and poor outcomes was as-sessed using multiple Logistic regression.Results A total of 97 patients with in-hospital symptomatic stroke were included with an average age of 61.8±12.7 years.Among them,there were 28 females(28.9％,28/97),9 with a history of prior stroke(9.3％,9/97),15 with atrial fibrillation(15.5％,15/97),16 with heart failure(16.5％,16/97),and 7 with myocardial infarction(7.2％,7/97).Correlation analysis showed that the NIHSS score at the time of stroke onset was significantly correlated with the patient's post-stroke SIRI(r=0.237,P＜0.05)and SII(r=0.234,P＜0.05).After 90 days of follow-up,41 cases(42.3％,41/97)had a poor outcome.Multiple Logistic regression analysis showed that post-stroke SIRI(Or=4.71,95％CI:1.24-17.90)and SII(Or=3.13,95％CI:0.88-11.06)were correlated with poor outcomes within 90 days after the stroke.Analysis using restricted cubic splines showed that as the levels of SIRI and SII increased,the risk of poor out-comes in patients with in-hospital symptomatic stroke increased.Conclusions SIRI is an independent risk factor for poor outcomes in patients with in-hospital symptomatic stroke,and the risk of poor neurological outcomes in-creases with high level of SIRI.