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.

Objective To investigate the prognostic value of neutrophil-to-lymphocyte ratio(NLR)and glycemic variability(GV)in patients with sepsis.Methods A total of 121 sepsis patients hospitalized in Jinhua Central Hospital from January 2023 to January 2024 were selected and divided into survival group(n=68)and death group(n=53)according to whether they died within 28d of confirmed sepsis.Blood glucose levels on the first 1-3 days after the diagnosis of sepsis were collected,and GV was calculated.The risk factors impacting the prognosis of patients with sepsis were analyzed through binary Logistic regression.Receiver operating characteristic(ROC)curve was used to evaluate the predictive value of relevant indicators.Results Compared with survival group,the levels of neutrophil count,acute physiology and chronic health evaluation Ⅱ(APACHE Ⅱ),procalcitonin,NLR and GV levels in death group were significantly increased(P＜0.05),while lymphocyte was decreased(P＜0.05).Logistic regression analysis indicated that NLR and GV might be independent risk factors affecting the prognosis of sepsis patients(P＜0.05).ROC curve showed area under the curve(AUC)of NLR was 0.814 and GV AUC was 0.805.Conclusion NLR and GV could potentially be independent risk factors affecting the prognosis of sepsis patients,and they hold a certain degree of predictive worth for the prognosis of sepsis patients.

Objective To investigate the value of low tube voltage and low dose contrast agents combined with artificial intelligence iterative reconstruction(AIIR)in upper limb CT angiography(CTA)for displaying arteriovenous fistula(AVF).Methods Totally 65 patients with chronic renal failure and suspected AVF stenosis who underwent upper limb CTA were prospectively enrolled and randomly divided into conventional-dose group(CD group,n=30)and low-dose group(LD group,n=35).In CD group,CTA was acquired using conventional tube voltage(100 kVp)and contrast agents dosage(1.0 ml/kg body mass),and conventional hybrid iterative reconstruction(HIR)was used to reconstruct images(group A),while in LD group,CTA was collected using 80 kVp and contrast agent dosage of 0.6 ml/kg body mass,and images were reconstructed with HIR(group B)and AIIR(group C),respectively.Then subjective and objective assessment of imaging quality were performed,respectively,the results were compared among groups,while the radiation dose and dosage of contrast agents were compared between CD and LD groups.Results No significant difference of subjective score of imaging quality nor confidence for diagnosing AVF stenosis was found between group A and group C(both P＞0.017),and all the above scores were higher than those in group B(all P＜0.017).Signal-to-noise ratio(SNR)and contrast-to-noise ratio(CNR)of subclavian artery,brachial artery and radial artery in group C were all higher than those in groups A and B(all P＜0.017),while no significant difference of SNR nor CNR was found between group A and B(all P＞0.017).The volume CT dose index,dose length product and dosage of contrast agent in LD group were all lower than those in CD group(all P＜0.001).Conclusion Low tube voltage and low dose contrast agents combined with AIIR in upper limb CTA for displaying AVF could reduce radiation dose and contrast agents dosage under the condition of ensuring imaging quality.

Objective To systematically assess the efficacy and safety of combining acupoint massage with lactulose for constipation treatment.Methods Randomized controlled trials on the effects of acupressure combined with lactulose on constipation were searched in PubMed,Web of Science,Cochrane Library,Embase,China National Knowledge Infrastructure,SinoMed,VIP and Wanfang Data Knowledge Service Platform.The search timeframe was from the establishment of the database to July 2024,and Meta-analysis was performed using RevMan5.3 and Stata15.1 software.Results A total of 8 randomized controlled trials including 675 study participants were included.Meta-analysis showed that the acupoint massage combined with lactulose group was superior to control group in terms of total effective rate,abdominal distension score,and constipation quality of life score.Furthermore,its recurrence rate was lower than that of control group.Both groups showed no statistically significant difference in adverse reactions(P＞0.05).Conclusion Acupressure combined with lactulose can effectively improve the efficiency and quality of life in the treatment of constipation,and can be safe.Due to the limitation of the quantity and quality of the included literature,more high-quality studies with long-term follow-up are still needed to supplement and validate the above conclusions in the future.

Objective To explore the application value of CT classification of lumbar spinal stenosis(LSS)in selecting endoscopic decompression surgery approach.Methods The clinical data and CT imaging materials of 300 patients with LSS,who received percutaneous endoscopic decompression surgery from March 2018 to December 2024 were selected from Hengshui Municipal People's Hospital in Hebei(Halison International Peace Hospital),were retrospectively analyzed.Preoperative CT scan was performed in all patients.Taking endoscopic classification as the golden standard,the consistency between CT classification results and endoscopic classification results was assessed by Kappa consistency analysis.The effect of surgery of different approaches for percutaneous endoscopic decompression surgery was compared.Results CT diagnostic classification of type Ⅰ LSS was obtained in 87 patients(among them 3 patients with type Ⅲ LSS were misdiagnosed as type Ⅰ LSS),type Ⅱ LSS in 141 patients and type ⅢLSS in 72 patients,the total diagnostic accuracy rate was 97.67％.Using endoscopic classification as the gold standard,the Kappa values for CT diagnosis of type Ⅰ,type Ⅱ and type Ⅲ were 0.975,1.000 and 0.973,respectively.According to the CT classification results,all 300 patients successfully selected different surgical approaches to complete the surgery,and their clinical symptoms were significantly relieved without significant complications.After surgery,the Oswestry disability index(ODI)and visual analogue scale(VAS)scores of leg/lower back in patients with different LSS types showed a decreasing trend(P＜0.05).After treatment,the total frequency of taking non-steroidal drugs was significantly lower than that before surgery(P＜0.05).Conclusion The classification of LSS is highly consistent between CT scan and endoscopic examination,which is of great value for the selection of percutaneous endoscopic decompression surgery approach.

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 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 investigate the effect of CT-derived fractional flow reserve (CT-FFR) measurement sites on the values and the diagnostic performance, and to determine the optimal measurement site for CT-FFR using invasive FFR as the reference standard.Methods:This study was part of the CT-FFR CHINA clinical trial. Patients with suspected coronary artery disease who were scheduled for invasive coronary angiography (ICA) were prospectively recruited from five clinical centers across the country from November 2018 to March 2020. Each enrolled patient underwent coronary CT angiography (CCTA), CT-FFR, ICA, and invasive pressure wire-based FFR assessments sequentially within one week. Four groups of CT-FFR values were obtained on each enrolled target vessels according to different CT-FFR measurement locations: 1, 2, 3 cm distal to the target lesion, and terminal vessel groups. Spearman and Bland-Altman analyses were used to explore the correlation and consistency of CT-FFR values and FFR values at different measurement sites. The measurement deviation of CT-FFR was also compared. Diagnostic accuracy and performance of CT-FFR, including sensitivity, specificity, positive predictive value, negative predictive value, and area under the receiver operating characteristic curve (AUC), in discriminating myocardial ischemia were analyzed across all measurement site groups on a per-vessel level, using FFR as the reference standard.Results:A total of 289 patients with 345 target lesion vessels were included. According to CCTA, there were 51 target vessels (14.8%) with<50% stenosis, 106 vessels (30.7%) with 50%-69% stenosis, and 188 vessels (54.5%) with stenosis≥70%. At per-vessel level, CT-FFR and FFR values at each measurement position group were highly positively correlated: 1 cm distal to target lesion group, r=0.734 ( P<0.001); 2 cm distal to target lesion group, r=0.732 ( P<0.001); 3 cm distal to target lesion group, r=0.737 ( P<0.001); terminal vessel group was 0.719 ( P<0.001). At per-vessel level, CT-FFR and FFR values of all measurement sites were in good agreement (Bland-Altman analysis results): 1 cm distal to target lesion group, 0.014 (95% LoA 0.002-0.026); 2 cm distal to target lesion group, 0.026 (95% LoA 0.015-0.038); 3 cm distal to target lesion group, 0.040 (95% LoA 0.039-0.051); terminal vessel group, 0.075 (95% LoA 0.064-0.087). And at per-vessel level, the accuracy of diagnosing myocardial ischemia with CT-FFR at 1 cm was highest [84.6% (95% CI 80.4%-88.3%)], and the lowest accuracy in the terminal vessel group [67.0% (95% CI 61.7%-72.0%)]. However, there was no significant difference in the diagnostic accuracy of CT-FFR at 1 cm, 2 cm [80.6% (95% CI 76.1%-84.6%)] and 3 cm [77.5% (95% CI 72.6%-81.7%)]. AUC of CT-FFR at 1 cm distal to the lesion were both highest for global level and moderately stenosis (50%-69%) lesions [0.85 (95% CI 0.81-0.89), 0.84 (95% CI 0.77-0.90)]. And the differences were statistically significant among the four measurement location groups (all P<0.05). Conclusions:The deviation of CT-FFR increases with measurement site distance distal to target lesions. One centimeter distal to the target lesion is the optimal measurement site, and the CT-FFR value here shows the highest diagnostic performance for myocardial ischemic lesions, especially for moderate stenosis.