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.

Mucosae are the primary sites of causing infectious diseases in human beings. The high transmissibility and mortality rates of infectious diseases pose a serious threat to human health. Mucosal vaccines, which induce both mucosal and systemic immune responses, have emerged as an effective method for preventing or treating infectious diseases. This paper provides an overview of the structure and response characteristics of the mucosal immune system, as well as adjuvants and delivery vectors in vaccine design. It focuses on the multi-dimensional evaluation system of immunoprotective efficacy,covering key indicators such as antibody response, neutralization ability, antibody affinity and tissue-resident memory cells,providing reference for the future development of more effective mucosal vaccines.

Objective To investigate the post-discharge exercise behavior and factors influencing moderate to vigorous intensity physical activity (MVPA) in patients undergoing lung surgery. Methods A total of 2874 patients from the large prospective, observational perioperative lung symptom study cohort (CN-PRO-Lung 3) in the Department of Thoracic Surgery at Sichuan Cancer Hospital between April 7, 2021, and January 31, 2024, were selected as the survey subjects. A survey was conducted using the Investigation of Exercise Behavior after Lung Surgery questionnaire and the International Physical Activity Questionnaire-Short Form (IPAQ-SF) among patients who underwent lung surgery. Binary logistic regression was used to analyze the factors influencing patients’ engagement in MVPA. Results A total of 702 patients were surveyed, including 252 males and 450 females, with an average age of (52.4±10.2) years. Patients with lung cancer accounted for 85.9%. Only 36.0% of the patients had regular exercise habits, while 42.3% did not engage in any physical activity. The three main barriers for postoperative exercise were physical discomfort (pain, coughing, shortness of breath, etc, 54.7%), lack of professional guidance (41.7%), and concerns about the surgical wound (28.9%). The proportions of patients engaging in vigorous, moderate, and low-intensity physical activity were 5.7%, 28.2%, and 66.1%, respectively. Multivariate analysis showed that patients with a personal annual income ≥50000 yuan (OR=1.52, 95%CI 1.01-2.29, P=0.044), high school education or above (OR=1.92, 95%CI 1.33-2.76, P<0.001), and lobectomy (OR=1.44, 95%CI 1.02-2.03, P=0.037) engaged in more MVPA. Conclusion Patients undergoing lung surgery have inadequate physical activity after discharge, particularly lacking in MVPA. Patients with higher income, higher educational levels, and lobectomy are more frequently engaged in MVPA. Measures such as symptom control, providing exercise guidance, and enhancing education on wound care may potentially improve the inadequate physical activity in lung surgery patients after discharge.

Health economics evidence plays an important role in linking clinical value evidence with health resource allocation decisions in the development of clinical practice guidelines. It can not only effectively balance clinical effectiveness and economic feasibility but also avoid forming "idealized" recommendations that are detached from the affordability of the healthcare system or the burden-bearing capacity of patients. To promote guideline developers to use health economics evidence more standardizedly and fully, this paper conducts an in-depth analysis of the current application status, existing challenges, access channels, and application processes of health economics evidence in current guidelines, and on this basis, puts forward considerations and suggestions for strengthening and standardizing the application of health economics evidence in China's clinical practice guidelines.

Objective To analyze the expression and diagnostic value of procalcitonin(PCT),white blood cell count(WBC),and C-reactive protein(CRP)in respiratory infections in the intensive care unit(ICU),and their correlation with the clinical pulmonary infection score(CPIS).Methods A total of 105 patients with respiratory infections admitted to the ICU from March 2019 to June 2024 were selected as the observation group,while 117 patients with no respiratory infection in the ICU were selected as the control group.PCT levels in both groups were measured using the Zybio-Q7 immunoquantitative analyzer,WBC levels were measured using XT-4000i white blood cell analyzer,and CRP levels were measured using the immunoturbidimetric method.The relationship between the expression of PCT,WBC and CRP and the clinical characteristics and CPIs score of respiratory infection in ICU were analyzed.Results The proportion of patients in the observation group with ICU stay>15 days and mechanical ventilation>7 days was significantly higher than that in the control group(P<0.05).Compared to the control group,the levels of PCT,WBC,CRP and CPIS score in the observation group were elevated,with statistically significant differences(P<0.05).Multivariate logistic regression analysis showed that ICU stay>15 days(OR=4.087),PCT(OR=6.543),WBC(OR=2.652),and CRP(OR=8.964)were risk factors for the occurrence of respiratory infections in the ICU.Pearson correlation analysis indicated a positive correlation between PCT and CPIS scores(r=0.925,P=0.001),a positive correlation between WBC and CPIS scores(r=0.739,P=0.001);and a positive correlation between CRP and CPIS scores(r=0.948,P=0.001).The ROC curve demonstrated that the combined diagnostic value of the three markers for ICU respiratory infections was higher than that of PCT,WBC,or CRP alone(P=0.002).Conclusion The combination of PCT,WBC,and CRP has high diagnostic value for ICU respiratory infections and is positively correlated with CPIs scores.Therefore,the above indicators can provide a reliable basis for the early diagnosis of respiratory infection in ICU.

Objective To explore the effects and mechanism of finasteride on epithelial mesenchymal transition in bladder urothelial carcinoma.Methods T24 human bladder cancer cells were treated with different concentrations of finasteride(0.1%,0.5%,and 1%)for 24 hours,with untreated T24 cells as the control group.Some cells were subjected to hypoxic conditions(1%O2)to mimic a low-oxygen microenvironment.Cell proliferation was assessed by immunofluorescence staining for Ki-67.Apoptosis rates were evaluated using Annexin V-FITC/PI double staining followed by flow cytometry,and cell migration was analyzed via scratch wound assays.Western blot was used to detect the expression of EMT-related proteins such as HIF-1α,E-cadherin,N-cadherin,and Vimentin.To further validate the role of the HIF-1α pathway in finasteride's anti-tumor effects,a HIF-1α overexpression lentiviral vector(MOP-1)was constructed and transfected into T24 cells.For animal experiment,T24 tumor-bearing nude mice were randomly divided into four groups:control,MOP-1,1%finasteride,and 1%finasteride+MOP-1(n=4 per group).After 4 weeks of continuous oral administration,tumor growth,metastasis,and perihepatic lymph node changes were recorded,and histological analysis was performed using H&E staining.Results Finasteride affects the proliferation,apoptosis,and epithelial-to-mesenchymal transition of T24 cells in a concentration-dependent manner.Compared to the control group,finasteride-treated T24 cells showed a reduced number of cells in the growth phase,decreased migration,increased expression of epithelial markers,and decreased expression of mesenchymal markers(P<0.05).In T24 cells exposed to hypoxic conditions,the expression of HIF-1α pathway markers was significantly increased(P<0.05),while 1%finasteride treatment inhibited the HIF-1α pathway(P<0.05).The HIF-1α pathway expression level in the 1%finasteride and HIF-1α overexpression plasmid group was lower than that in the control group(P<0.05).Additionally,the tumor mass-to-body weight ratios were lower,with the number of bladder cancer metastases to the liver decreased(P<0.05).Conclusion Finasteride can effectively block the growth of bladder cancer and the transition of epithelial cells into mesenchymal cells by inhibiting HIF-1α/Snail expression.

Objective To explore the value of CT signs and quantitative parameters of artificial intelligence (AI) in predicting the efficacy of neoadjuvant chemotherapy for colorectal cancer. Methods A total of 349 colorectal cancer patients who received neoadjuvant chemotherapy at Cangzhou Hospital of Integrated Traditional Chinese and Western Medicine in Hebei Province from January 2022 to January 2025 were selected and and divided into the effective group (n = 267) and the ineffective group (n = 82) according to the evaluation criteria for the efficacy of solid tumors. Conduct a CT examination and extract AI quantitative parameters from the CT images based on the lesion. The data were analyzed using SPSS21.0 software, Logistic regression was used to screen the influencing factors of ineffective neoadjuvant chemotherapy in patients with colorectal cancer, and separate and combined models of CT signs and AI quantitative parameters were established. The predictive effect of the model was verified by using the ROC curve, calibration curve and decision curve. Results Compared with the effective group, the proportion of regular tumor morphology and the proportion of non-enlarged lymph nodesin the ineffective group were smaller. The tumor volume, peak value and entropy value were larger (P < 0.05). Multivariable analysis showed that irregular shape (OR= 4.216), presence of lymph node enlargement (OR = 8.998), larger tumor volume (OR = 1.109), higher average CT value (OR = 1.120), elevated peak value (OR = 2.528), and increased entropy value (OR = 1.390) were independent risk factors for ineffective neoadjuvant chemotherapy in colorectal cancer (P < 0.05). The areas under the ROC curves of the individual and combined models of CT signs and AI quantitative parameters were 0.777, 0.818, and 0.877, respectively(P < 0.05). The calibration curve showed a Brier score of 0.091. The decision curve showed that the threshold was between 0.10 and 0.85, and the combined model achieved a relatively high net clinical benefit. Conclusion CT signs combined with AI quantitative parameters has a predictive value for the efficacy of neoadjuvant chemotherapy in colorectal cancer. To provide evidence-based basis for clinical screening of the population benefiting from chemotherapy and optimization of treatment strategies.

Bone morphogenetic proteins (BMPs) are multifunctional growth factors of the transforming growth factor β (TGF-β) superfamily. They regulate steroid secretion from mammalian granulosa cells, promote granulosa cell survival and proliferation, and inhibit follicular atresia, luteinization, and granulosa cell apoptosis, thereby promoting the development and maturation of mammalian follicles. At the same time, BMPs play an important role in embryonic morphogenesis, induction of uterine receptivity, and blastocyst attachment. This paper describes the effects of BMPs on mammalian follicular and embryonic development and the roles of BMPs in female reproduction, focusing on the process in which BMPs promote follicular maturation by regulating steroid secretion from granulosa cells during mammalian oocyte maturation. This review aims to provide a reference for further research on mammalian oocyte culture and improvement of reproductive efficiency in female animals.
Animals ; Embryonic Development/drug effects* ; Female ; Bone Morphogenetic Proteins/pharmacology* ; Reproduction/physiology* ; Humans ; Granulosa Cells/cytology* ; Oocytes

Objective To establish a finite element model of thoracolumbar kyphosis(TLK)for young patients with idiopathic Scheuermann's disease(SD),validate its effectiveness,and perform fi-nite element analysis to identify biomechanical factors contributing to low back pain caused by SD.Methods One TLK patient(26 years old)and one healthy(30 years old)subject were recruited.Spi-nal DICOM data were reconstructed three-dimensionally using Mimics software.Surface repair and fit-ting were performed in Geomagic Studio 2017,followed by modeling of intervertebral discs and end-plates in 3-matic software.Volume meshing and ligament construction were completed in Hypermesh 2017.Static simulations of the T8-S1 spinal segments for both groups were conducted in ANSYS.Af-ter validating the model using mechanical data,stress and strain distributions across vertebral bodies,intervertebral discs,and facet joint cartilage were calculated under identical constraints and loads.Re-sults 1)Significant differences were observed between the TLK patient and healthy subject in the me-chanical distribution of the T8-S1 segments of the spine.The healthy spine exhibited a peak strain of 1.62 mm,while the TLK patient showed a peak strain of 6.97 mm,330.2%higher than the former.Moreover,the peak stress in the TLK patient(10.51 MPa)exceeded the healthy subject(9.07 MPa)by 16.0%.2)Both groups demonstrated peak vertebral stress at the L5 level(9.07 MPa in the healthy individual vs.10.51 MPa in the TLK patient,15.8%higher).Stress distribution in the TLK patient was markedly uneven,with significant concentrations at the pedicles and laminae of L2,L3,and L4 vertebrae.3)Peak disc stress in the TLK patient was consistently higher,most notably at the L5/S1 segment(0.78 MPa in the TLK patient vs.0.16 MPa in the healthy subject,387.5%higher).4)Facet joint cartilage stress distribution in the TLK patient was abnormal.The healthy subject exhib-ited peak stress at L5/S1(1.53 MPa),whereas TLK patient showed peak stress at L1/L2(7.44 MPa),14.18 times higher than the former.Conclusion This study validates the finite element model of TLK deformity,confirming its utility for TLK-related research.Moreover,significant differences in stress-strain distribution and magnitude are observed between the TLK patient and healthy subject.Meanwhile,prolonged abnormal biomechanical loading,particularly localized stress concentrations in the L1-S1 vertebral bodies,discs,and facet joints,may accelerate degenerative changes and elevate the risk of low back pain.