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.

Background Attention-deficit/hyperactivity disorder (ADHD) is one of the most common neurodevelopmental disorders in children and adolescents. Current research suggests that environmental factors, such as per- and polyfluoroalkyl substances (PFAS), may be associated with an increased risk of ADHD in offspring. However, most epidemiological evidence originates from non-Chinese populations, with limited research investigating the association between prenatal PFAS and ADHD-like symptoms in Chinese school-aged children. Objective To examine the association between prenatal PFAS exposure and ADHD-like symptoms in 7-year-old children. Methods Based on the Shanghai Birth Cohort, this study included 488 mother-child pairs. PFAS were measured in maternal serum during the second trimester using ultra-performance liquid chromatography-tandem mass spectrometry. The analysis focused on eight specific compounds from three PFAS categories [perfluorooctanoic acid (PFOA), perfluorooctanesulfonic acid (PFOS), and perfluorohexanesulfonic acid (PFHxS)]. The hyperactivity subscale of the Strengths and Difficulties Questionnaire (SDQ) was used to assess ADHD-like symptoms in children at age 7. Basic demographic information was collected via questionnaires and medical records. Negative binomial regression models were used to evaluate the relationship between individual exposures to the three typical PFAS categories (PFOA, PFOS, and PFHxS) and children's ADHD-like symptoms, while the bayesian kernel machine regression (BKMR) mixture model was employed to assess the overall effect of PFAS mixture exposure on ADHD-like symptoms. Results The detection rates of the eight target PFAS in maternal second-trimester serum were all above 85%, with PFOA exhibiting the highest median concentration (10.21 ng·mL⁻¹). Overall, 14.75% of the children exhibited ADHD-like symptoms. Results from the negative binomial regression models showed that second-trimester exposures to PFOA (IRR=1.15, 95%CI: 1.05, 1.26), n-PFOS (IRR=1.08, 95%CI: 1.01, 1.15), 6m-PFOS (IRR=1.13, 95%CI: 1.05, 1.21), and 1m-PFOS (IRR=1.11, 95%CI: 1.03, 1.18) were associated with an increased risk of ADHD-like symptoms in 7-year-old children. Stratified analyses suggested potentially statistically significant associations in girls [e.g., for PFOA, boys vs. girls: 1.09 (0.96, 1.23) vs. 1.25 (1.09, 1.44), P-int=0.31]. The BKMR mixture model indicated that the risk of ADHD-like symptoms trended upward with increasing PFAS mixture concentrations, although this association was statistically significant only among girls (P < 0.05). Conclusion Prenatal PFAS exposure may be associated with an increased risk of ADHD-like symptoms in 7-year-old children, and this adverse association appears to be more prominent in girls.

OBJECTIVE To provide guidance for the individualized use of antiepileptic drugs （AEDs） in patients with epilepsy or bipolar affective disorder.METHODS Clinical data of patients with epilepsy or bipolar affective disorder who received AEDs treatment and underwent therapeutic drug monitoring （TDM） at Xi’an International Medical Center Hospital from January 2023 to December 2025 were retrospectively collected. The baseline information， characteristics of AEDs utilization， implementation status of TDM and laboratory parameters were summarized. Spearman correlation analysis， univariate analysis and multivariate linear regression analysis were performed to identify the independent influencing factors for blood concentrations of AEDs.RESULTS A total of 1 572 patients were enrolled， including 660 males and 912 females. The majority were aged ≥60 years （30.15%）； 71.76% of patients were on monotherapy with AEDs， with valproic acid being the most commonly prescribed （51.97%）； combination therapy was predominantly a two-drug regimen （26.21%）， while three-drug regimens were rare （2.04%）. A total of 2 901 blood samples for TDM were collected， with the highest number of samples for valproic acid and levetiracetam （1 987 and 684 samples， respectively）. TDM for all five drugs was primarily conducted once； among which， the blood concentrations attainment rates exceeded 50% for patients taking valproic acid and levetiracetam， while the median blood concentrations for patients taking lamotrigine， oxcarbazepine， and carbamazepine were all below the lower limit of the therapeutic reference range. Based on the results of Spearman correlation analysis， univariate analysis， and multivariate linear regression analysis， patient gender， platelet count （PLT）， alanine transaminase （ALT）， aspartate transaminase （AST）， and total bilirubin （TBil） were found to be independent factors influencing blood concentration of valproic acid （unstandardized coefficients were －6.770 7， －0.078 6， －0.099 4， 0.083 2， －0.578 9， P ＜0.05）， while age and creatinine （CRE） were found to be independent factors influencing blood concentration of levetiracetam （unstandardized coefficients were 0.108 7， 0.047 8， P ＜0.05）.CONCLUSIONS Female gender and elevated AST levels are risk factors for increased blood concentration of valproic acid， while elevated PLT， ALT and TBil levels are associated with decreased blood concentration of valproic acid. Patients of advanced age and those with renal insufficiency bear a relatively high risk of in vivo levetiracetam accumulation.

Objective:To explore the movement patterns and factors influencing lung tumors tracked using the M6 cyberknife stereotactic radiotherapy(SRT)system and to provide a reference for the implementation of precise stereotactic radiotherapy for lung tumors.Method:A retrospective analysis was conducted on 29 patients with lung tumors who were treated using x-sight lung tracking technology and the M6 cyberknife SRT system at Tianjin Medical University Cancer Institute&Hospital,from January 2022 to August 2024.The tumor location and volume,irradiation dose,isodose line,and number of divisions were recorded.Lung tumor location and SPSS 26.0 software were used to analyze the movement amplitude of tumors in the left and right(LFT/RGT,LR)directions,the anterior-posterior(ANT/POS,AP)direction,and the superior-inferior(SUP/INF,SI)direction.The results are expressed as the mean±standard deviation((x)±s)mm,and a t-test was used for inter-group comparisons.Multiple linear regression was used to analyze the effects of factors such as age,gender,tumor location(upper and lower lungs),and tumor volume on the amplitudes of the lung tumor movements.Result:The average motion amplitudes in the LR direc-tions,AP direction,and SI direction of the tumor target areas were(3.5±1.8)mm,(5.3±1.7)mm,and(7.3±5.4)mm for the upper lung,based on 19 cases,and(3.1±1.6)mm,(4.5±2.2)mm,and(12.2±4.4)mm for the lower lung,based on 10 cases,respectively.There was a statistic-ally significant difference(P=0.015 3)in the amplitude of movements between the lower and upper lung tumors in the SI direction.The lung tumor movement amplitude in the SI direction was influenced by tumor location(P=0.035),and the movement amplitudes in the LR direc-tions and the AP direction were not related to factors such as gender,age,tumor location,and tumor volume.Conclusions:The lung tumor movement amplitudes for the different locations varied depending on the respiratory movement shown by the patient.In the SI direction,the movement amplitude of the lower lung tumors was greater than that of upper lung tumors,and this was due to tumor location effects.The movement amplitudes of the lower and upper lung tumors were similar in the LR directions and AP directions.Furthermore,movement amplitude was not affected by gender,age,tumor location,and tumor volume.

Objective:Exploring the association rules of factors influencing high hospitalization costs for cancer patients, providing references for hospitals to optimize medical cost management measures.Methods:In the inpatient case information system of a tertiary general hospital, the medical record homepages of inpatients in the DRG groups of the oncology department in 2022 were obtained. The upper four scores of hospitalization costs was used as the threshold for patient grouping. Patients with hospitalization costs≥this threshold were the high-cost group, while other patients were control group; 12 factors, including age, gender, and admission condition, etc, were considered as potential influencing factors of high hospitalization costs. FP-Growth and Apriori algorithms were used to excavate the potential association rules between the influencing factors of high hospitalization costs. Logistic regression was used to analyze the independent influencing factors of high hospitalization costs.Results:A total of 5 512 hospitalized patients were included, including 1 378 patients in the high-cost group. Thirteen validated strong association rules for factors influencing high hospitalization costs were obtained, of which the rule antecedents included age (≥70 years), number of days in hospital (≥7 days), other diagnoses (≥5), surgery, planned readmission, use of antibiotics, admission (general/critical), living admission score (61~99), level of care (level 1/level 2), non-day ward, criticality during hospitalisation. Logistic regression results showed that all nine influencing factors except gender, use of antibiotics, and readmission plans were independent influences on high hospitalization costs ( P<0.05). Conclusions:The joint application of FP-Growth and Apriori algorithm could effectively explore the association rules of high hospitalization costs for oncology patients. The early warning information mainly included the number of hospitalization days, the number of other diagnoses, surgeries, and so on. It was suggested that medical institutions can reasonably control the high hospitalization costs through clinical pathway management, diagnosis and treatment process reengineering, admission risk assessment, and multidisciplinary collaborative diagnosis and treatment strategies.

Objective:To analyze the influencing factors of China′s DRG payment system reform(DRG reform) and its hierarchical relationship, for references for the in-depth promotion of China′s medical insurance payment reform.Methods:Relevant literature on DRG reform in China from databases such as CNKI, Wanfang Database, Pubmed, etc, were obtained. Content analysis method was used to extract the influencing factors of DRG reform. The correlation between each influencing factor was determined through expert discussion. An interpretive structural model(ISM) was constructed to analyze the hierarchical relationship of factors influencing DRG reform.Results:After analysis, the influencing factors(12) of DRG reform in China were included such as medical level, hospital management, and medical staff′s cognition and behavior. Among them, the local situation was the deep-level factor affecting DRG reform, 9 factors such as data quality assurance and policy design/implementation were the middle-level factors, and patients′ interests/needs and disease grouping were the surface-level factors.Conclusions:There were many influencing factors on the reform of China′s DRG payment system. It was suggested that relevant management departments in various regions should focus on the actual situation of the locality, take data quality and policy design and implementation as the key points of reform, formulate a scientific and reasonable DRG grouping scheme, safeguard the interests of patients, so as to promote the deepening of DRG reform.

Objective To investigate the predictive value of preoperative systemic inflammatory response index(SIRI)combined with clinicopathological parameters for postoperative recurrence in cervical cancer and to construct a prognostic model in order to optimize recurrence risk assessment.Methods Patients with cervical cancer who underwent standard surgical treatment at the First Affiliated Hospital of Chongqing Medical University(training cohort,n=996)and Chongqing Maternal and Child Health Hospital(validation cohort,n=496)between January 2017 and January 2022 were retrospectively enrolled based on our strict inclusion and exclusion criteria.Univariate and multivariate Cox regression analyses were performed to identify independent prognostic factors for recurrence-free survival(RFS),and then a nomogram was constructed.Receiver operating characteristic(ROC)curve was plotted to assess the predictive performance of the model,and the area under the curve(AUC)and calibration curve were employed to evaluate the model.Kaplan-Meier survival analysis was performed to determine the clinical application.Results Cox regression analysis demonstrated that International Federation of Gynecology and Obstetrics(FIGO)stage(P<0.001),tumor size(P<0.001),pathological type(P<0.001),tumor grade(P=0.007),parametrial invasion(P<0.001),depth of myometrial invasion(P=0.019),lymphovascular space invasion(P=0.019),vaginal margin involvement(P=0.010),adjuvant therapy(P=0.012),and SIRI(P<0.001)were independent prognostic factors for RFS.Our nomogram model based on above prognostic factors exhibited superior predictive performance for 1-,3-,and 5-year RFS,with a significantly higher AUC value(0.886)than those of single-parameter models.Conclusion Our nomogram model demonstrated good accuracy in predicting RFS in cervical cancer patients,providing a potential tool for personalized clinical decision-making in recurrence risk management.

Widespread utilization of glyphosate has led to environmental residues,posing potential threats to ecological systems and human health.Traditional methods for detection of glyphosate are limited by specialized equipment and operational techniques,resulting in inefficient responses.Therefore,it is urgent to develop a convenient,sensitive and accurate detection method for detection of glyphosate.Herein,a new naphthalenesulfonamide-based"Turn-on"fluorescent probe was synthesized using 2-chloroaniline and dansyl chloride as raw materials through a one-step process,which showed a good linear relationship between the glyphosate concentration in concentration range of 0.003-70 μmol/L and the fluorescence intensity(R2=0.995),with a detection limit of 2.73 nmol/L(S/N=3).Analytical techniques such as nuclear magnetic resonance(NMR)spectroscopy and high-resolution mass spectrometry(HRMS)were used to investigate the interaction mechanism between the fluorescent probe and glyphosate.The results indicated that a nucleophilic substitution reaction occurred between the probe and the secondary amine(—NH—)of glyphosate,inducing a photoinduced electron transfer(PET)effect which enhanced the fluorescence intensity by 11.2 times.The probe showed good anti-interference ability towards coexisting metal ions,anions and pesticides in water.When applied to determination of glyphosate in the samples such as tap water,river water(Xiangxi River Reservoir),soil,soybeans,and corn,the spiking recoveries ranged from 94.7％to 109.9％,demonstrating the high accuracy and broad applicability of this detection method.A portable test strip based on this fluorescent probe was developed for rapid semi-quantitative analysis of glyphosate.The developed method was rapid,sensitive,and portable,providing theoretical and technical support for on-site measurement of environmental contaminants.

OBJECTIVE To construct three-class （insufficient， normal， excessive） and two-class （insufficient， normal） models for predicting plasma concentration of valproic acid （VPA）， and compare the performance of these two models， with the aim of providing a reference for formulating clinical medication strategies. METHODS The clinical data of 480 patients who received VPA treatment and underwent blood concentration test at the Xi’an International Medical Center Hospital were collected from November 2022 to September 2024 （a total of 695 sets of data）. In this study， predictive models were constructed for target variables of three-class and two-class models. Feature ranking and selection were carried out using XGBoost scores. Twelve different machine learning algorithms were used for training and validation， and the performance of the models was evaluated using three indexes: accuracy， F1 score， and the area under the working characteristic curve of the subject （AUC）. RESULTS XGBoost feature importance scores revealed that in the three-class model， the importance ranking of kidney disease and electrolyte disorders was higher. However， in the two-class model， the importance ranking of these features significantly decreased， suggesting a close association with the excessive blood concentration of VPA. In the three-class model， Random Forest method performed best， with F1 score of 0.704 0 and AUC of 0.519 3 on the test set； while in the two-class model， CatBoost method performed optimally， with F1 score of 0.785 7 and AUC of 0.819 5 on the test set. CONCLUSIONS The constructed three-class model has the ability to predict excessive VPA blood concentration， but its prediction and model generalization abilities are poor； the constructed two-class model can only perform classification prediction for insufficient and normal blood concentration cases， but its model performance is stronger.