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.

Objective: To analyze the current burden of autism spectrum disorder (ASD) among children and adolescents in China and globally, and to predict the disease burden from 2024 to 2035, providing a scientific basis for formulating relevant public health policies and intervention measures. Methods: Based on the Global Burden of Disease (GBD) database in 2023, the Joinpoint regression model was used to analyze the changing trends of the disease burden of ASD among children and adolescents in China and globally from 1990 to 2023, and the average annual percent change (AAPC) was calculated. An autoregressive integrated moving average (ARIMA) model was constructed to predict the disease burden trends of ASD among children and adolescents in China and globally from 2024 to 2035. Results: The prevalence and disability adjusted life years (DALYs) rate of ASD among children and adolescents in China increased from 452.69/100 000 and 86.67/100 000 in 1990 to 762.84/100 000 and 148.52/ 100 000 in 2023(AAPC=1.60%, 1.65%, both P <0.01). The prevalence and DALYs rate of ASD among children and adolescents globally increased from 648.49/100 000 and 123.47/100 000 to 862.44/100 000 and 167.16/100 000(AAPC=0.87%, 0.93%, both P <0.01). In 2023, the highest ASD prevalence and DALY rates occurred in children under 5 years old, with China reporting 848.14/100 000 and 166.69/100 000, both below the global averages of 928.80/100 000 and 181.34/100 000. Projections indicated that by 2035, the ASD prevalence and DALY rates in China would rise to 906.83/100 000 and 168.71/100 000, still below the global averages of 938.04/100 000 and 184.49/100 000. Conclusion The disease burden of ASD among children and adolescents in China and globally has generally increased from 1990 to 2023, with a higher risk of disease at younger ages.

Background/Aims: Metabolic dysfunction–associated steatotic liver disease (MASLD) is a chronic liver disease characterized by hepatic steatosis. Ubiquitin-specific protease 29 (USP29) plays pivotal roles in hepatic ischemiareperfusion injury and hepatocellular carcinoma, but its role in MASLD remains unexplored. Therefore, the aim of this study was to reveal the effects and underlying mechanisms of USP29 in MASLD progression. Methods: USP29 expression was assessed in liver samples from MASLD patients and mice. The role and molecular mechanism of USP29 in MASLD were assessed in high-fat diet-fed and high-fat/high-cholesterol diet-fed mice and palmitic acid and oleic acid treated hepatocytes. Results: USP29 protein levels were significantly reduced in mice and humans with MASLD. Hepatic steatosis, inflammation and fibrosis were significantly exacerbated by USP29 deletion and relieved by USP29 overexpression. Mechanistically, USP29 significantly activated the expression of genes related to fatty acid β-oxidation (FAO) under metabolic stimulation, directly interacted with long-chain acyl-CoA synthase 5 (ACSL5) and repressed ACSL5 degradation by increasing ACSL5 K48-linked deubiquitination. Moreover, the effect of USP29 on hepatocyte lipid accumulation and MASLD was dependent on ACSL5. Conclusions USP29 functions as a novel negative regulator of MASLD by stabilizing ACSL5 to promote FAO. The activation of the USP29-ACSL5 axis may represent a potential therapeutic strategy for MASLD.

Objective To establish an animal model of hand,foot,and mouth disease(HFMD)in Syrian hamsters coinfected with coxsackievirus A6(CVA6)and coxsackievirus B1(CVB1).Methods 42 Syrian hamsters were divided into a CVA6 infection group,CVB1 infection group,CVA6 and CVB1 coinfection group and control group.A HFMD model was established by nasal instillation of virus solution and phosphate-buffered saline.Clinical and physiological indicators and detoxification status were monitored and recorded for 15 d,and animals were selected on day 7(D7)after infection for histopathology and viral antigen and nucleic acid testing.Results Hamsters in the single-infection and coinfection groups showed clinical symptoms similar to human HFMD.White blood cell,neutrophil,and lymphocyte result were characteristic of viral infection.Both viral nucleic acids were detected in throat swabs,feces,blood,and tissues and both viruses were isolated from fecal samples.Pathological damage and positive co-localization of CVA6 and CVB1 viral antigen proteins and nucleic acids were found in brain and other tissues.Conclusions Nasal instillation of a CVA6 and CVB1 mixture can successfully coinfect Syrian hamsters,replicate herpes infection similar to human HFMD,and cause pathological viral myocarditis and encephalitis damage.The result showed that the coinfection group was more seriously affected than the single-infection group,with worse clinical symptoms,increased viral replication,and obvious tissue pathological damage.This study provides a reference for further basic and clinical research into human enterovirus coinfection.

Objective Explore the important genes related to occurrence and development of diabetic foot ulcer(DFU)through bioinformatics analysis.Methods Gene transcriptional profiling analysis was performed on the GSE143735 dataset of Gene Expression Omnibus(GEO).Patients in the dataset were divided into DFU group and non-DFU group based on whether they had DFU.The mRNA expression data of two groups of patients were analyzed for differentially expressed genes using GEO2R,and the volcano map was drawn.Gene ontology(GO)analysis and Kyoto Encyclopedia of Genes and Genomes(KEGG)pathway analysis were conducted through the DAVID database.The differentially expressed genes screened were placed into the String database for analysis.The protein-protein interaction network map was drawn using Cytoscape and the core genes were screened.Results A total of 706 differentially expressed genes were screened out,among which 470 were up-regulated genes and 236 were down-regulated genes.The results of GO analysis showed that the differentially expressed genes were mainly concentrated in cellular components such as nucleosomes,extracellular matrix containing collagen,and exosomes;molecular functions such as DNA binding,protein heterodimer activation,chromatin structure composition,nucleosome DNA binding,extracellular matrix structure composition,and serine inhibitor enzyme activation;biological processes such as nucleosome assembly,chromatin organization,keratinization,and telomere organization.The KEGG pathway analysis of differentially expressed genes mainly involved systemic lupus erythematosus,alcohol abuse,formation of extracellular traps of neutrophils,viral carcinogenesis,etc.Ten core genes,namely H2BC14,H3C14,H2BC17,H4C6,H2BC9,H3C1,H2BC3,H3C12,H3C13,and H2AC18,were obtained through protein interaction network screening.The expression levels of the above core genes in DFU group were significantly lower than those in non-DFU group.Conclusion The low expression of H2BC14,H3C14,H2BC17,H4C6,H2BC9,H3C1,H2BC3,H3C12,H3C13 and H2AC18 genes can be used as predictors of DFU in diabetic patients.The risk of DFU in patients can be predicted by detecting the expression levels of these genes.

To analyze the transmission characteristics of newly reported HIV-infected students aged ≥18 years in Nanjing City from 2016 to 2022 and provide evidence for AIDS publicity and intervention among young students. The pol region sequences of newly reported HIV-infected students and non-student HIV-infected individuals in Nanjing City from 2016 to 2022 were collected, and the BLAST tool was used to search the published global non-Nanjing reported HIV infection sequences in the LANL HIV database. The basic molecular transmission network and regional molecular transmission network were constructed using the HIV-TRACE in a pairwise genetic distance threshold of 1.0%. 332 sequences of infected students aged≥18 years in Nanjing City, 1 904 sequences of non-student-infected individuals in Nanjing City and 1 698 non-Nanjing-infected individuals were obtained. Among the 332 infected students, the main route of infection was homosexual (96.39%), and the subtypes were CRF01_AE (37.95%), CRF07_BC (37.65%) and CRF105_0107 (10.24%). There were 890 sequences in the regional molecular transmission network, of which 21.80% were infected students in Nanjing City, 39.89% were non-student-infected individuals in Nanjing City, and 38.31% were non-Nanjing-infected individuals. In the CRF105_0107 transmission cluster, non-student-infected individuals from Nanjing accounted for 66.95% (81/121), while in the CRF07_BC transmission cluster, non-Nanjing-infected individuals accounted for 56.66% (200/353). There were 1 644 edges connected to infected students within the regional molecular transmission network, with local transmission accounting for 64.72% and regional transmission accounting for 35.28%. Regional transmission was mainly in Guangdong Province (19.83%) and other cities in Jiangsu Province (4.50%). The HIV-1 subtypes of newly reported HIV-infected students aged≥18 years in Nanjing City are mainly CRF01_AE, CRF07_BC and CRF105_0107, with local transmission as the main transmission characteristics. There is transmission between students and non-students.

Objective To investigate the diagnostic accuracy and clinical applicability of the Linknet-VGG16 deep learning algorithm for measuring the effective optical zone(EOZ)after corneal refractive surgery.Methods This single-center retrospective cohort study included 69 patients(69 eyes)who underwent femtosecond laser-assisted in situ kerato-mileusis(FS-LASIK)(34 eyes)or small incision lenticule extraction(SMILE)(35 eyes)at the Refractive Surgery Center of Affiliated Zhengzhou Aier Eye Hospital of Henan University from June 2023 to June 2024.Data from the right eyes of all patients were selected for statistical analysis.During the surgery,patients in the FS-LASIK group adopted the VisuMax fem-tosecond laser system combined with the Amaris 750S excimer laser system,while those in the SMILE group only used the VisuMax femtosecond laser system.A total of 276 Pentacam images were re-examined postoperatively.A Linknet segmenta-tion model based on the VGG16 encoder was constructed,and image normalization techniques were applied to accelerate model convergence.Model performance was assessed using accuracy,intersection over union(IoU),and the Dice coeffi-cient.The traditional EOZ measurement method based on corneal tangential curvature served as the reference standard.Bland-Altman analysis was conducted to evaluate consistency across all images and within each group,and the time effi-ciency of both methods was compared.Results Six representative medical image segmentation architectures(U-Net,U-Net++,DeepLabv3-ResNet50,DeepLabv3+-ResNet50,Unet-Densenet169,and Linknet-VGG16)were systematically evaluated.The Linknet-VGG16 model demonstrated superior performance over the other 5 models in pixel-level accuracy,IoU and Dice coefficient,which were 99.83％,99.48％and 99.74％,respectively.Although there was no significant differ-ence in accuracy and Dice coefficient between Linknet-VGG16 and U-Net models(whose accuracy was 99.82％and Dice coefficient was 99.72％),the inference speed of the U-Net model(62.46 ms)was 31.76％slower than that of the Linknet-VGG16 model(42.62 ms).The evaluation results of a clinically applicable comprehensive scoring model(weights:accura-cy 20％,IoU 20％,Dice coefficient 20％,speed 25％,model size 15％)showed that the Linknet-VGG16 model achieved a score of 88.01,surpassing other architectures(U-Net:86.29;DeepLabv3+-ResNet50:80.41;DeepLabv3-ResNet50:73.82;U-Net++:73.22;Unet-Densenet169:66.66).Bland-Altman analysis revealed that the mean difference of the 136 images in the FS-LASIK group was 0.01 mm[95％limits of agreement(LoA):-0.36 to 0.35 mm],with 96.3％of data points falling within the LoA.The mean difference of the 140 images in the SMILE group was-0.01 mm(95％LoA:-0.36 to 0.33 mum),with 95.7％of data points falling within the LoA.The mean difference of all 276 images was 0.00 mm(95％LoA:-0.36 to 0.34 mm),with 96.4％of data points falling within the LoA.These results indicated excellent consistency.The average measurement time per image using the traditional EOZ measurement method was 13.00 minutes,whereas the deep learning model required only 3.22 seconds.Conclusion The traditional EOZ measurement method based on corne-al tangential curvature exhibits good consistency with the fully automatic EOZ measurement method based on deep learning algorithms,achieving high image recognition accuracy.Additionally,the deep learning algorithm significantly reduces measurement time,compared with the traditional method based on corneal tangential curvature.

Objective To explore the effects of sialic acid intervention during pregnancy and the lactational period on the intestinal function of autism model rats.Methods Thirty SPF-grade adult male and female Wistar rats were mated.The successfully pregnant rats were randomly assigned to a valproate-induced model(VPA)group,a high-dose sialic acid(SAH)group,a medium-dose sialic acid(SAM)group,a low-dose sialic acid(SAL)group,and a control(CON)group(n=6 per group)and were housed individually in single cages.On the 12.5th day of pregnancy(E12.5),rats in the VPA and SA intervention groups were given a single intraperitoneal injection of 600 mg/kg sodium valproate(VPA),while the pregnant rats in the CON group were given an equal amount of normal saline.The SA intervention period was from E12.5 to the 21st day after parturition.Feces of offspring rats in each group were collected.The diversity and structure of the gut microbiota were detected by 16S rRNA sequencing.The intestinal transit speed in each group was detected by intragastric administration of carmine.The levels of intestinal-related neurotransmitters(substance P,enkephalin,5-hydroxytryptamine,vasoactive intestinal peptide,and glutamate,gamma-aminobutyric acid)in the blood of rats in each group were detected by ELISA.Results High-dose SA intervention did not affect the diversity of the gut microbiota in the VPA-induced autism model rats,but it changed the structure of the gut microbiota and increased the abundance levels of Prevotella_NK3B31 group,Prevotella,Prevotella spp.,Alloprevotella,Lachnospira,Ruminococcus,and Bacteroides(P＜0.05).High-dose SA also promoted the intestinal transit speed,and increased the levels of vasoactive intestinal peptide,5-hydroxytryptamine,and gamma-aminobutyric acid in serum.Conclusions SA intervention during pregnancy and the lactational period affects the intestinal transit speed of VPA-induced autism model rats,changes the structure of the gut microbiome,and increases the levels of vasoactive intestinal peptide,5-hydroxytryptamine,and gamma-aminobutyric acid.

Objective:To study the role of cadherin 1 (CDH1) gene DNA methylation in autoimmune thyroiditis (AIT) in population from different water-iodine regions.Methods:From May to June 2019, the information of AIT cases and healthy individuals in Shandong Province were collected in three types of water-iodine regions: iodine-fortification (IF) region, iodine-adequate (IA) region and iodine-excess (IE) region. A case-control study design was applied to match 176 AIT cases (case group) with age, gender, body mass index, and place of residence in a 1 ∶ 1 ratio to 176 healthy individuals (control group). Fasting urine and whole blood samples were collected to test the contents of urinary iodine, thyroid function indicators [serum free triiodothyronine (FT 3), free thyroxine (FT 4), thyroid stimulating hormone (TSH)], and serum iodine. The DNA methylation levels of the target region of the CDH1 gene and its four CpG sites in whole blood were determined using methylation sequencing technology for target regions (MethylTarget TM). Results:The DNA methylation level of the target region of CDH1 gene in the case group was 0.832 ± 0.044, and that in the control group was 0.828 ± 0.049, there was no statistically significant difference between the two groups ( t = 0.76, P = 0.448). There was no statistically significant difference in DNA methylation levels of the four CpG sites in the target region of CDH1 gene between the case group and the control group ( P > 0.05). There was no statistically significant difference in the DNA methylation level of the CDH1 gene target region between the case group and the control group in IF, IA and IE regions ( P > 0.05). The detection results of DNA methylation levels at CpG sites in the target region of CDH1 gene in different water iodine regions showed that the DNA methylation level at site 83 in case group in IF region was higher than that in the control group ( t = 2.30, P = 0.023). However, there was no statistically significant difference in the DNA methylation levels of the four CpG sites between the case group and the control group in IA and IE regions ( P > 0.05). The DNA methylation level of CDH1 gene target region in AIT patients was not significantly correlated with urinary iodine, serum iodine, and serum FT 3, FT 4, and TSH contents ( P > 0.05), but was significantly negatively correlated with age ( r =-0.19, P = 0.014). Conclusions:The DNA methylation level at CpG site 83 of CDH1 gene in AIT patients in IF region is significantly higher than that in control population, indicating that DNA methylation at this locus may be involved in the occurrence and development of AIT after iodine fortification. The DNA methylation level of CDH1 gene is negatively correlated with age.