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.

Owing to its inherent advantages—such as being non-destructive, rapid, highly molecule-specific, and minimally interfered with by moisture—Raman spectroscopy has been widely adopted in the fields of skin barrier function assessment, monitoring the transdermal penetration of active cosmetic ingredients, and the identification and quality control of cosmetic products. Despite these strengths, the practical application of this technique faces a significant bottleneck: the strong fluorescence background generated by endogenous skin components and exogenous cosmetic additives. Endogenous skin substances, such as structural proteins (e.g., collagen and elastin), metabolic coenzymes (e.g., nicotinamide adenine dinucleotide), and pigments (e.g., melanin), together with exogenous cosmetic constituents like organic colorants, chemical sunscreens, and fragrances, often possess strong absorption and emission characteristics. When excited by lasers, these components produce a fluorescence background that can be 10⁶ to 10⁸ times stronger than the Raman scattering signals, effectively masking the inherently weak vibrational fingerprint information. In recent years, driven by the rapid development of optoelectronic hardware and artificial intelligence algorithms, fluorescence suppression strategies have evolved from isolated, single-method approaches into comprehensive, multi-level synergistic systems. These systems are categorized into three distinct tiers: sample preparation, signal acquisition, and data processing. At the sample preparation level, techniques such as photobleaching and surface-enhanced Raman spectroscopy (SERS) are employed to eliminate or bypass the generation of fluorescence at the source. At the signal acquisition level, instrumental improvements—including the use of long-wavelength near-infrared excitation (typically 785 nm or 1 064 nm), confocal spatial filtering, and shifted excitation Raman difference spectroscopy (SERDS)— are utilized to physically isolate Raman signals from the fluorescence background. Furthermore, at the data processing level, numerical baseline correction methods such as polynomial fitting, penalized least squares (e.g., airPLS, arPLS), wavelet transform, and derivative algorithms are increasingly integrated into the analytical pipeline to extract Raman spectral features from mixed signals without increasing hardware costs or acquisition time. This review provides a systematic categorization and critical evaluation of these fluorescence suppression methods, detailing their underlying principles, technical advantages, and inherent limitations in diverse experimental setups. By focusing on critical application scenarios—including skin barrier assessment, percutaneous absorption monitoring, the routine quality control of cosmetics, and the emerging field of portable on-site detection—this paper explores the current state of technique selection and optimization. Finally, the article discusses future development trends, emphasizing the necessity of constructing adaptive, tiered suppression strategies, developing intelligent and automated data processing algorithms, and promoting the integration of portable, multi-modal diagnostic devices. The objective of this review is to provide a comprehensive technical reference to facilitate the transition of Raman spectroscopy from a specialized laboratory tool into a routine, robust analytical platform for advancements in skin science and cosmetic research.

Objective To understand the current situation of healthcare-associated infection(HAI)in China,pro-vide data support and decision-making basis for formulating scientific and effective strategies for HAI prevention and control.Methods A nationwide cross-sectional survey on HAI was conducted among various types and levels of medical institutions in China according to a unified protocol of bedside surveys and case investigations.Results In 2024,a total of 5 736 medical institutions and 2 751 765 patients were surveyed.Among them,34 889 HAI cases were identified,with a prevalence rate of 1.27％.The number of HAI episodes was 38 032,and case prevalence rate was 1.38％.The prevalence rate of HAI in medical institutions in different regions of China ranged from 0.66％to 2.35％.Among medical institutions of different scales,those with a bed capacity of ≥900 had the high-est incidence of HAI,reaching 1.65％.The most common infection site was the lower respiratory tract(44.66％),followed by the urinary tract(12.94％),surgical site(9.32％),upper respiratory tract(7.02％),and bloodstream infection(5.78％).The top 3 departments with the highest HAI rates were the general intensive care unit(10.02％),department of neurosurgery(5.51％),and department(group)of hematology(5.34％).A total of 23 238 strains of HAI pathogens were detected,with 10 714 strains(46.10％)from lower respiratory tract speci-mens.The top 5 detected strains were Klebsiella pneumoniae(14.76％),Pseudomonas aeruginosa(13.33％),Escherichia coli(12.79％),Acinetobacter baumannii(9.23％),and Staphylococcus aureus(7.88％).231 944 pa-tients underwent class Ⅰ incision surgery were monitored,with 1 647 cases experienced surgical site infection,and the prevalence rate of surgical site infection was 0.71％.The number of patients who should undergo pathogen de-tection(patients receiving therapeutic and therapeutic combined prophylactic antimicrobial agents)was 715 179,while the actual number was 480 492,with a pathogen detection rate of 67.18％.425 225 patients received patho-genic detection before treatment,with a detection rate of 59.46％.Conclusion The overall HAI prevalence in Chi-na is lower,showing disparities among medical institutions of different regions and scales.Therefore,precise imple-mentation of measures is necessary for HAI prevention and control,with a focus on high-risk institutions and high-risk departments,key areas,and critical procedures.All levels of medical institutions should continuously reduce the incidence of HAI by strengthening monitoring,standardizing the use of antimicrobial agents,and reinforcing basic HAI prevention and control measures.

Objective:To investigate the predictive value of endothelial activation and stress index(EASIX)for the prognosis of patients with mantle cell lymphoma(MCL).Methods:A retrospective analysis was conducted to assess prognosis and compare the clinical features of patients diagnosed with MCL who were admitted to the Affiliated Hospital of Xuzhou Medical University from January 2010 to June 2023,had therapeutic indications and received standard treatment.Results:A total of 66 patients were included and divided into high EASIX group and low EASIX group,according to a cutoff value of 0.97 determined by the receiver operating characteristic(ROC)curve.Multivariate Cox regression analysis showed that prealbumin＜0.2 g/L,high EASIX,and ECOG PS score ≥2 were independent risk factors influencing overall survival(OS)in MCL patients.The median OS of patients in the high and low EASIX group was 13.0 and 37.5 months,and the median progression-free survival was 8.8 and 26.0 months,respectively.The proportions of patients with ECOG PS score ≥2 and prealbumin＜0.2 g/L at onset significantly increased in the high EASIX group compared to those in the low EASIX group.Conclusion:At the time of initial diagnosis,EASIX can serve as an independent prognostic indicator impacting OS in patients with MCL.Furthermore,patients in the high EASIX group experience a poorer prognosis and shorter survival duration compared with those in the low EASIX group.

Objective To understand the current situation of healthcare-associated infection(HAI)in China,pro-vide data support and decision-making basis for formulating scientific and effective strategies for HAI prevention and control.Methods A nationwide cross-sectional survey on HAI was conducted among various types and levels of medical institutions in China according to a unified protocol of bedside surveys and case investigations.Results In 2024,a total of 5 736 medical institutions and 2 751 765 patients were surveyed.Among them,34 889 HAI cases were identified,with a prevalence rate of 1.27％.The number of HAI episodes was 38 032,and case prevalence rate was 1.38％.The prevalence rate of HAI in medical institutions in different regions of China ranged from 0.66％to 2.35％.Among medical institutions of different scales,those with a bed capacity of ≥900 had the high-est incidence of HAI,reaching 1.65％.The most common infection site was the lower respiratory tract(44.66％),followed by the urinary tract(12.94％),surgical site(9.32％),upper respiratory tract(7.02％),and bloodstream infection(5.78％).The top 3 departments with the highest HAI rates were the general intensive care unit(10.02％),department of neurosurgery(5.51％),and department(group)of hematology(5.34％).A total of 23 238 strains of HAI pathogens were detected,with 10 714 strains(46.10％)from lower respiratory tract speci-mens.The top 5 detected strains were Klebsiella pneumoniae(14.76％),Pseudomonas aeruginosa(13.33％),Escherichia coli(12.79％),Acinetobacter baumannii(9.23％),and Staphylococcus aureus(7.88％).231 944 pa-tients underwent class Ⅰ incision surgery were monitored,with 1 647 cases experienced surgical site infection,and the prevalence rate of surgical site infection was 0.71％.The number of patients who should undergo pathogen de-tection(patients receiving therapeutic and therapeutic combined prophylactic antimicrobial agents)was 715 179,while the actual number was 480 492,with a pathogen detection rate of 67.18％.425 225 patients received patho-genic detection before treatment,with a detection rate of 59.46％.Conclusion The overall HAI prevalence in Chi-na is lower,showing disparities among medical institutions of different regions and scales.Therefore,precise imple-mentation of measures is necessary for HAI prevention and control,with a focus on high-risk institutions and high-risk departments,key areas,and critical procedures.All levels of medical institutions should continuously reduce the incidence of HAI by strengthening monitoring,standardizing the use of antimicrobial agents,and reinforcing basic HAI prevention and control measures.

Objective To identify key proteins associated with sudden cardiac death(SCD)caused by lethal arrhythmia and to explore their potential molecular mechanisms through integrated proteomic analysis,data mining,and bioinformatics.Methods A lethal arrhythmia model was established in 8-week-old male Sprague-Dawley rats,which were divided into an arrhythmia group and a control group.Proteomic techniques were applied to identify and quantify proteins in left ventricular myocardial tissue,and differentially expressed proteins related to arrhythmia were screened.Key proteins were further identified through comparison with target proteins in databases combined with joint analyses.Bioinformatics methods were then used to investigate potential molecular mechanisms.Results A total of 356 differentially expressed proteins were identified,including 189 upregulated and 167 downregulated.Association analysis with target gene proteins identified 71 key proteins,and a protein-protein interaction network was constructed.GO enrichment and KEGG pathway analyses indicated that these key proteins were primarily involved in ion channel dysfunction,enhanced oxidative stress,and autonomic nervous system imbalance.Conclusion This study,through the integration of proteomics,data mining,and bioinformatics,revealed critical molecular mechanisms underlying SCD associated with lethal arrhythmia.These findings provide new perspectives and potential biomarkers for forensic identification and research on the mechanisms of death.

Objective To identify key proteins associated with sudden cardiac death(SCD)caused by lethal arrhythmia and to explore their potential molecular mechanisms through integrated proteomic analysis,data mining,and bioinformatics.Methods A lethal arrhythmia model was established in 8-week-old male Sprague-Dawley rats,which were divided into an arrhythmia group and a control group.Proteomic techniques were applied to identify and quantify proteins in left ventricular myocardial tissue,and differentially expressed proteins related to arrhythmia were screened.Key proteins were further identified through comparison with target proteins in databases combined with joint analyses.Bioinformatics methods were then used to investigate potential molecular mechanisms.Results A total of 356 differentially expressed proteins were identified,including 189 upregulated and 167 downregulated.Association analysis with target gene proteins identified 71 key proteins,and a protein-protein interaction network was constructed.GO enrichment and KEGG pathway analyses indicated that these key proteins were primarily involved in ion channel dysfunction,enhanced oxidative stress,and autonomic nervous system imbalance.Conclusion This study,through the integration of proteomics,data mining,and bioinformatics,revealed critical molecular mechanisms underlying SCD associated with lethal arrhythmia.These findings provide new perspectives and potential biomarkers for forensic identification and research on the mechanisms of death.

Objective:To explore the efficacy and adverse reactions of CAG regimen combined with venetoclax,chidamide,and azacitidine in the treatment of elderly patients with acute myeloid leukemia(AML).Methods:15 elderly AML patients aged ≥ 60 years old who were admitted to the Hematology Department of our hospital from May 2022 to October 2023 were treated with the CAG regimen combined with venetoclax,chidamide and azacitidine,and the efficacy,treatment-related adverse events,overall survival(OS)and event-free survival(EFS)were analyzed.Results:After one course of treatment,11 out of 15 patients achieved complete response(CR),3 patients achieved CR with incomplete hematologic recovery(CRi),and 1 patient died due to prior infection before efficacy evaluation,and the overall response rate(ORR)was 93.3％(14/15).The median follow-up time was 131(19-275)days,with median OS and EFS both remaining unreached.Next-generation sequencing(NGS)analysis showed that among the 15 patients,13 were detected with gene mutations,and there were 7 genes with mutation frequencies of more than 10％,including ASXL1(4 cases),RUNX1(4 cases),BCOR(3 cases),DNMT3A(3 cases),STAG2(2 cases),IDH1/2(2 cases),and TET(2 cases).Among the 13 patients with detectable mutations,12 patients achieved composite response(CR+CRi).The average recovery time of white blood cell count was 14.6 days after chemotherapy,and the average recovery time of platelets was 7.7 days after chemotherapy.The main adverse event was myelosuppression,with 10 patients accompanied by infection.Except for 1 patient who died due to septic shock during chemotherapy,no patients experienced serious complications such as heart,liver,or kidney damage during the treatment process.Conclusion:The CACAG+V regimen,which combines the CAG regimen with venetoclax,chidamide,and azacitidine,can be applied in the treatment of elderly AML patients,demonstrating good safety and induction remission rate.

OBJECTIVE: To investigate the correlation between the anterior talofibular ligament (ATFL) injury and the pathological changes of the anterior tibiotalar fat pad (ATFP) based on MRI. METHODS: The clinical and imaging data of 217 patients with ankle sprain who met the selection criteria between January 2019 and March 2024 were retrospectively analyzed. There were 113 males and 104 females with an average age of 38.2 years ranging from 18 to 60 years. Patients were divided into mild group ( n=106), moderate group ( n=63), and severe group ( n=48) according to the degree of ATFL injury. There was no significant difference in gender, side, and body mass index among the 3 groups ( P>0.05). The age of the mild group was significantly older than that of the moderate and severe groups ( P<0.05). The imaging parameters including the longest and shortest sagittal axis, the largest thickness, the longest and shortest transverse axis, the ATFP area, the area of ATFP high-signal region, and the anterior distal tibial angle (ADTA) were measured according to the MRI and X-ray films of patients. According to the morphology of ATFP, the patients were divided into type Ⅰ ( n=128), type Ⅱ ( n=73), and type Ⅲ ( n=16) based on the severity of the lesions. The distribution of ATFP types, ATFP area, area of ATFP high-signal region, and the ratio of area of ATFP high-signal region to ATFP area at the same level were statistically analyzed and compared among different ATFL injury groups. Additionally, radiographic parameters were compared across different ATFP types. Spearman rank correlation analysis was used to assess the relationships between ATFP area, area of ATFP high-signal region, and the ratio of area of ATFP high-signal region to ATFP area at the same level with patient baseline data. Through analysis of the area under curve (AUC) of ROC, optimal variables were selected for quantification to predict ATFL injury. RESULTS: There were significant differences in ATFP types among different ATFL injury groups ( P<0.05). The mild group had a higher proportion of type Ⅰ, the moderate group had a higher proportion of type Ⅱ, and the severe group had higher proportions of both typeⅡ and type Ⅲ. No significant difference was found in ATFP area among the different ATFL injury groups ( P>0.05). However, the area of ATFP high-signal region and the ratio of area of ATFP high-signal region to ATFP area at the same level were significantly lower in the mild group compared to the moderate and severe groups ( P<0.05). Except for the longest sagittal axis, maximum thickness, and longest transverse axis, which were significantly smaller in ATFP types Ⅱ and Ⅲ compared to type Ⅰ ( P<0.05), there was no significant difference in the remaining radiographic parameters among the different ATFP types ( P>0.05). Spearman rank correlation analysis revealed that ATFP area was negatively correlated with patient gender ( P<0.05), while area of ATFP high-signal region and the ratio of area of ATFP high-signal region to ATFP area at the same level were negatively correlated with patient age ( P<0.05). Through analysis of the AUC for the response variable ATFP injury, the combined diagnostic AUC of ROC for the reciprocal of the maximum thickness and the reciprocal of the area of ATFP high-signal region was 0.839 (asymptotic P<0.001). The corresponding cutoff value when the Youden index reached its maximum was 0.570 3. CONCLUSION As the severity of ATFL injury increases, the ATFP undergoes gradual morphological and functional changes. Classification based on ATFP types can assist in assessing the level of ATFL injury, thereby aiding in the prevention of post-traumatic osteoarthritis.
Humans ; Male ; Female ; Adult ; Magnetic Resonance Imaging/methods* ; Middle Aged ; Retrospective Studies ; Adipose Tissue/pathology* ; Adolescent ; Young Adult ; Lateral Ligament, Ankle/diagnostic imaging* ; Ankle Injuries/pathology*