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.

p21 (encoded by the CDKN1A gene) is a critical cell cycle regulatory protein endowed with versatile biological functions. In various sex hormone-related cancers, p21 exhibits a paradoxical dual role, capable of both inhibiting tumorigenesis and promoting cancer progression, exerting dual, often opposing, effects on cellular fate that are dictated by the specific context. The clinical targeting of p21 remains elusive, largely due to its functionally pleiotropic and context-dependent nature within intricate regulatory networks. During the initial, hormone-dependent phase of cancers like breast and prostate cancer, p21 expression and activity are largely governed by the transcriptional programs of estrogen or androgen receptor signaling. This hormonal regulation contributes to the control of tumor cell proliferation and underpins the initial efficacy of endocrine therapies. In contrast, as these diseases advance to late stages or evolve into non-hormone-dependent subtypes—exemplified by castration-resistant prostate cancer (CRPC) and specific forms of triple-negative breast cancer (TNBC)—these conventional hormonal control mechanisms often become dysfunctional or are entirely bypassed. This fundamental transition creates a critical therapeutic void, highlighting the urgent need to identify and exploit alternative molecular pathways to effectively target p21’s function. Promising strategies may include the precise modulation of its upstream transcriptional regulators, downstream effector proteins, or the intersecting parallel signaling networks that critically influence its activity. This review provides a systematic synthesis of the intricate and interconnected mechanisms that underpin the dual effects of p21 in sex hormone-related tumors. These mechanisms are categorized into three core, interrelated functional domains. (1) cell cycle regulation: p21 executes its canonical tumor-suppressive role by binding to and inhibiting cyclin-dependent kinases (CDKs) and by directly interacting with proliferating cell nuclear antigen (PCNA), thereby inducing cell cycle arrest, predominantly at the G1/S checkpoint; (2) apoptosis modulation: p21 exerts a highly context-dependent influence on programmed cell death, functioning either as a pro-apoptotic agent under severe genotoxic stress or as a pro-survival factor by inhibiting apoptosis through interactions with proteins like Bcl-2; (3) hormonal and signaling crosstalk: p21 is an integral node within broader cellular networks, engaging in direct physical interactions with hormone receptors(e.g., AR, ER) and participating in complex feedback loops with key oncogenic pathways, including PI3K/AKT, MAPK/ERK, and p53. Critically, the role of p21 is not static but highly dynamic. It can undergo a functional switch from tumor-suppressive to tumor-promoting in response to therapeutic pressures, metabolic alterations, or evolving tumor microenvironment cues. These adaptive shifts are frequently implicated in the development of therapy resistance and disease recurrence, particularly in advanced, hormone-resistant cancers. By synthesizing these insights, this review aims to establish a coherent theoretical framework to guide the future development of novel therapeutic strategies that target the p21 pathway. It underscores the necessity of moving beyond a simplistic, binary view of p21 and emphasizes the forthcoming challenges, such as the discovery of reliable biomarkers to predict its functional state and the rational design of context-specific pharmacological modulators to selectively harness its therapeutic potential.

Atherosclerosis （AS） is a chronic and inflammatory vascular disease. Macrophages are common immune cells and play an important role in the development of AS. In recent years， research has found that the formation of AS plaques is closely related to pathological and physiological processes such as macrophage polarization， energy metabolism， and lipid phagocytosis. This review aims to summarize the mechanism of macrophages in the development of AS， and to explore potential therapeutic methods for delaying AS by regulating macrophages， providing new ideas for the treatment and research of AS.

Objective To provide a reference for the establishment, development and application of the adverse drug reaction （ADR） automated monitoring system, through verifying and quantifying the research hotspots and advantages of the system by CiteSpace software and systematic review. Methods Literature on ADR automated monitoring up to December 2023 were retrieved and screened from CNKI and web of science databases. CiteSpace 6.4.R1 software was used to conduct co-occurrence, clustering and emergence analysis, and to visualize and comparatively analyze the research hotspots, rules and distribution in the field of automated monitoring of ADR at home and abroad. In compliance with the preferred reporting items for systematic reviews and Meta-analyses （PRISMA）, literature covering publications in English and Chinese including detection rates of ADR collected using Incident Reporting Systems （IRSs） and/or automated monitoring systems were retrieved and screened. The advantages and disadvantages of automated monitoring systems were analyzed by comparing the differences between these two systems in terms of the number of ADR reports and the types of positive signals. Results A total of 56 articles in English and 80 articles in Chinese were indexed by CiteSpace. The research hotspots in recent years included data mining, deep learning, text classification techniques, machine learning and so on. A total of seven studies compiled with the inclusion criteria for the systematic evaluation, all of which were completed between 1991 and 2021 in hospitals in four countries. 150 526 medical records were reviewed from 15 institutions. A total of 194 ADR reports were collected by IRSs. A total of 2 090 ADR reports were collected by the automated monitoring system over the same period, indicating a 977% increase in the number of ADR reports （P=0.0156） compared with the IRSs. Conclusion The ADR automatic monitoring system had significantly improved the level of drug risk identification and reduced costs, but it was necessary to optimize the algorithm, expand the data source and carry out standardization construction to overcome the current limitations.

Objectives:The aim is to analyze the cost structure and coefficient of variation for hospitalized patients with malnutrition based on Diagnosis-Related Groups(DRG),providing a reference for the further application and promotion of DRG.Method:Data were collected from patients admitted to Ningxia Hui Autonomous Region People's Hospital between March 2023 and August 2023.A diagnostic system based on artificial intelligence was used to identify malnourished patients.The composition of hospitalization costs for these individuals was described and analyzed,as was the coefficient of variation for various costs within DRG groupings.A multivariate regression analysis was conducted to identify the factors that influence patient hospitalization costs.Results:The average age of hospitalized patients with malnutrition was(68.12±16.43)years,with an average length of stay of(14.55±8.47)days,with an average hospitalization cost of(32 128.89±35 345.61)yuan.Among patients within the same DRG group,the coefficient of variation for various costs was found to be lower in the malnutrition group than in the normal group.This suggests that when assessed individually,the malnutrition group exhibited a higher degree of homogeneity in their cost structures.The factors influencing total hospitalization costs were found to be:length of hospital stay(P=0.001),nutritional monitoring fees(P=0.020),number of chronic diseases(P=0.003),and Karnofsky Performance Status(KPS)score(P=0.038).Hospitalization costs were positively correlated with both length of stay and nutritional assessment fees,but negatively correlated with the number of chronic diseases and KPS scores.Conclusions:Malnutrition has a profound impact on health outcomes,medical expenses,length of hospital stay,and disease severity.The implementation of the DRG system aims to standardize and improve the nutritional diagnosis and treatment process by categorizing different stages of malnutrition.This approach can minimize variations within DRG groups,making it easier to allocate medical resources more precisely and efficiently.Furthermore,it is a valuable reference tool for promoting DRG payment reform in different regions.

OBJECTIVE To explore the early detection of large vestibular aqueduct syndrome(LVAS)in children by applying several audiological indicators.METHODS Ninety-two children with hearing loss(aged 1-70 months)were enrolled and divided into an LVAS group(45 cases)and a control group(47 cases).Eleven audiological indicators were statistically analyzed:lateral of hearing loss,the degree of hearing loss,configuration of hearing loss;ABR air-conduction threshold;ABR air-bone gap;ASSR average threshold;ASSR thresholds at 0.5,1,2,and 4 kHz;and tympanogram type.Indicators showing significant two-group differences were used to construct a visualized multifactorial linear prediction model using the R language.RESULTS Nine indicators demonstrated statistically significant differences between groups(P<0.05):laterality,configuration of hearing loss,ABR air-conduction threshold,ASSR average threshold,ASSR thresholds at all frequencies(0.5,1,2,4 kHz),and tympanogram type.A prediction model was established.When the total model score ranged between 200 and 240 points,the predicted LVAS risk probability was 0.1 to 0.99.The consistency index(C-index)was 0.85,indicating good predictive ability of the model.CONCLUSION The identified nine audiological indicators are valuable for the early detection of LVAS in children.The developed model can estimate LVAS risk.After refinement,this model holds potential to support early clinical diagnosis and intervention.

Laccase is a type of polyphenol oxidase that can catalyze the oxidation of various substances,including phenols,aromatic amines,and catecholamines.It has been widely utilized in pollutant degradation and analytical applications.However,the high cost of preparation of natural laccase and its susceptibility to environmental factors,which can lead to denaturation and inactivation,limit its practical applications.Nanozymes,which are nanomaterials that exhibit enzyme-like properties,offer advantages such as easy preparation,adjustable activity,and exceptional stability.Currently,many types of nanozymes have been developed.Inspired by the coordination of Cu2+with amino acids in the active site of natural laccase,researchers have employed coordination synthetic strategies to prepare laccase-like nanozymes.The metal nodes in these laccase-like nanozymes include copper,manganese,and cerium,while the ligands involve a variety of chemicals like nucleotides,amino acids,polypeptides,and aromatic acids.By manipulating factors such as the metal-to-ligand ratio,reducing capacity of ligands,buffer solutions,chloride ions,bromine ions,the catalytic activity of laccase-like nanozymes can be finely tuned.In this paper,laccase-like nanozymes developed through coordination strategies were categorized and summarized,along with review of their analytical applications in detection of phenolic compounds,disease biomarkers,antibiotics,pesticides,sulfur-containing pollutants,and time-temperature indicators.Furthermore,the challenges currently faced in the research of laccase-like nanozymes and future research directions were discussed.

In this study,a sensitive lateral flow immunoassay(LFIA)platform based on carbon dots-mesoporous silica nanocomposite(CD-MSNs)fluorescent probes was constructed for high-performance detection of inflammatory marker interleukin-6(IL-6).Green fluorescent carbon dots(CDs)were prepared by hydrothermal method with 3,9-perylenic acid and 3-aminopropyltriethoxysilane(APTES)as raw materials,and highly fluorescent CD-MSNs composites were then constructed by encapsulating the prepared CDs in mesoporous silica nanoparticles(MSNs).Fluorescent probes were prepared by covalent coupling of CD-MSNs with IL-6 antibody.Fluorescent immunochromatographic test strips were constructed by spraying IL-6 capture antibody and goat anti-mouse IgG on nitrocellulose membrane as detection line(T-line)and quality control line(C-line),respectively.The fluorescence immunoassay analyzer was used to quantitatively detect the fluorescence intensity of T-line,and the experimental results showed that the LFIA platform based on this probe had a good linear relationship in IL-6 concentration range of 102-106 pg/mL,and the detection limit was 64 pg/mL,which was two orders of magnitude more sensitive than that of the traditional colloidal gold test strips.This method effectively solved the issue of insufficient sensitivity of traditional LFIA technique,and provided a rapid and highly sensitive detection method for early diagnosis of inflammatory diseases.

Objective To study the inhibitory effect of secondary metabolites of Pseudomonas aeruginosa(PA)on Candida albicans(CA)and to explore some of the mechanisms.Methods PA and CA strains were i-solated from clinical specimens from the hospital.Then,PA strains with inhibitory effects on CA were screened through cross-line test and co-incubation test,and crude extracts of PA secondary metabolites were prepared,and were tested together with pyocyanin,phenazine-1-carboxylic acid,1-hydroxyphenazine,and 3-ox-ododecyl-l-homoserine lactone(3-oxo-HSL).The inhibitory effects of various PA secondary metabolites on CA were determined through minimum inhibitory concentration test,minimum bactericidal concentration test,time-sterilization curve measurement,and XTT method activity measurement test,and some mechanisms by which PA secondary metabolites inhibited CA were explored.Results The strongest inhibitory effect on CA was 1-hydroxyphenazine,and at a concentration of 6.250 μg/mL,the relative activity of CA decreased to 0.00%.Next were pyocyanin and PA crude extract,and the relative fungal activity of CA decreased to 0.00%at concentrations of 200 and 100 μg/mL.1-hydroxyphenazine,pyocyanin,3-oxo-HSL and PA crude extract all had inhibitory effects on the formation of CA hyphae.Reactive oxygen species(ROS)were generated in CA cells treated with 1-hydroxyphenazine,phenazine 1-carboxylic acid,pyocyanin,and PA crude extract,and the highest levels of ROS were induced by pyocyanin and 1-hydroxyphenazine.Conclusion Phenazine secondary metabolites 1-hydroxyphenazine and pyocyanin have significant inhibitory effects on the growth and activity of CA,and both induce the highest amount of ROS.The quorum-sensing signal molecule 3-oxo-HSL have no in-hibitory effect on CA growth,but have a significant inhibitory effect on the formation of fungal hyphae.