ObjectiveTo systematically investigate the medication rules of chicken-derived medicinals for treating children enuresis in ancient Chinese medical classicals. MethodsThe China Medical Classics V5.0 database was searched to identify formulas and passages containing chicken-derived medicinals for pediatric enuresis. A database was established using Microsoft Excel 2021 to extract information including formulas， therapeutic categories， natures， flavors， channel entry， processing methods， dosage forms， and administration methods. Frequency analysis was performed， while association rule analysis and cluster analysis were conducted using R software. Gephi 0.10.1 software was used for complex network analysis. ResultsA total of 171 records were included， comprising 36 single-herb formulas and 135 compound formulas， sourced from 64 classical medical monographs. The identified chicken-derived medicinals included jichang （Intestinum Galli）， jineijin （Endothelium Corneum Gigeriae Galli）， jigan （Gallina Hepar）， jishibai （Faeces Galli Albi）， jisu （Ingluvies Galli）， jiheling （Pluma Alae Galli）， and jiweimao （Chicken tail feathers）. The top three frequently used single herbs were jichang （17 times）， jineijin （8 times）， and jiheling （5 times）. Among compound prescriptions， the three most frequently recorded formulas were Jineijin Powder （鸡内金散， 43 times）， Jichang Powder （鸡肠散， 25 times）， and Guigan Pills （桂肝丸， 24 times）， with jineijin， jichang， and jigan being the predominant chicken-derived medicinals. When converting the daily dosage of chicken-derived medicines into modern dosage units， the dose ranged from 0.53 g to 33.57 g for jineijin， with 4.48 g being the most frequently used dose， and from 0.25 g to 30.00 g for jichang， with 17.90 g being the most frequently used dose. Chicken-derived medicinals were mostly used after processing， commonly employing burning （68 times）， roasting/ stir-baking （37 times）， and calcining （24 times） to preserve properties. Powders （117 times）， pills （45 times）， and decoctions （9 times） were the principal dosage forms， and administration with wine （70 times） was the most common method. The most frequently used herbal pair was jineijin and roucongrong （Herba Cistanches）， while the core prescription combinations were Tusizi Powder （菟丝子散）， Yangshi Jichang Powder （杨氏鸡肠散）， and Rongxiang Pill （茸香丸）. ConclusionChicken-derived medicinals ， such as jichang and jineijin， are commonly processed by burning or roasting before use in the treatment of enuresis. They are often combined with longgu （Fossilia Ossis Mastodi）， muli （Concha Ostreae）， sangpiaoxiao （Oötheca Mantidis）， roucongrong and rougui （Cortex Cinnamomi） to warm and tonify the kidney qi， arrest enuresis and emission with astringents. Pills and powders are the predominant dosage forms and are commonly administered before meals with wine or salted water.

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.

AIM:The aim of our study is to investigate the metabolic profile differences during cervical lesion progression and evaluate their potential clinical value in assisting the diagnosis of cervical cancer(CC).METHODS:Ul-tra-high-performance liquid chromatography coupled with high-resolution mass spectrometry(UHPLC-HRMS)was em-ployed to conduct non-targeted metabolomic analysis of cervical swab samples from 43 CC patients,34 high-grade squa-mous intraepithelial lesion(HSIL)patients,and 43 healthy controls.Based on the distinct features among the three groups,principal component analysis(PCA)was used to identify the metabolic differences among CC,HSIL and healthy groups.MetaboAnalyst 5.0 was then employed to perform KEGG pathway enrichment analysis on the differential metabo-lites.Finally,random forest machine learning algorithm was used to construct classification prediction models for distin-guishing CC from healthy,HSIL from healthy,and CC from HSIL.The performance of these models was evaluated using receiver operating characteristic(ROC)curve analysis.RESULTS:A total of 1 543 metabolites were identified across the healthy,HSIL and CC groups after filtration,with 407 metabolites differing between the groups.The study found that metabolite PGE2 was present in all three groups,with its expression levels progressively increasing with the progression of cervical lesions.Differential metabolite enrichment analysis demonstrated that CC is associated with specific cancer-relat-ed metabolic pathways,including the tricarboxylic acid cycle,tyrosine metabolism,tryptophan metabolism,and the pen-tose phosphate pathways.Additionally,the study developed three prediction models based on metabolic products for diag-nosing HSIL and CC:the full model,the simplified model,and the PGE2 model.The results indicated that metabolites ex-hibited strong diagnostic efficiency.Both the full model and the simplified model effectively distinguished CC from HSIL,CC from healthy,and HSIL from healthy.The AUC values for the full model were 0.90,0.92 and 0.84,respectively,while those for the simplified model were 0.81,0.95 and 0.85,respectively.Furthermore,the PEG2 model achieved AUC values of 0.74 and 0.80 for distinguishing CC from healthy and HSIL from healthy,respectively.CONCLUSION:The metabolic profiles of cervical cancer exhibit significant differences during the progression of cervical cancer,and these metabolites hold potential clinical value as biomarkers for cervical lesions.

Objective:To establish an ex vivo model of button battery-induced esophageal injury in New Zealand rabbits and evaluate the interventive effects of vegetable oil through esophageal histopathological scoring.Methods:Thirty-six esophageal segments (≈5 cm length) from 10 cm specimen of 18 male rabbits were divided into model groups (15-min, 2-hr, and 6-hr exposure; n=6/group) and intervention groups (olive/peanut/soybean oil infusion; n=6/group). Button batteries were inserted to esophageal segments of model groups. Voltage drop of the battery, pH at negative electrode contact site, and histopathological injury scores were assessed. In the intervention group, button batteries were placed in the esophageal segment for 15 minutes, and olive oil, peanut oil, and soybean oil were infused. The above indicators were observed 6 hours after the button batteries were placed. One-way ANOVA was used to compare the differences of esophageal mucosal tissue damage across time points and oil types. Results:There was no significant difference in the pH value of the negative electrode contact area (9.50±0.56, 10.67±0.80, 11.17±0.40, F=1.955, P>0.05), but the discharge voltage (42.67±4.60 mV, 90.00±2.07 mV, 125.83±2.80 mV, F=156.9, P<0.001) and pathological injury scores (3.50±1.09 scores, 5.33±0.72 scores, 8.67±0.67 scores, F=9.623, P=0.002) in the model groups were significantly different. There was significant difference in pathological injury score between the 6-hour model group and the three intervention groups (8.67±0.67 scores, 7.33±0.62 scores, 6.50±0.43 scores, 6.67±0.42 scores, F=3.279, P=0.042). The difference in pathological injury score between the peanut oil intervention group and the 6-hour model group was statistically significant (mean difference=2.167, P<0.05). Conclusion:This ex vivo model effectively simulates battery-induced esophageal injury. Household peanut oil demonstrates significant protective effects, providing experimental basis for prehospital management of battery corrosion.

ObjectiveTo investigate the potential mechanism of Liangxue Tuizi Formula （凉血退紫方， LXTZF） in treating Henoch-Schönlein Purpura （HSP） by examining its regulatory effect on neutrophil extracellular trap （NETs） dysregulation via the rapidly accelerated fibrosarcoma kinase （RAF）/mitogen-activated protein kinase （MEK）/extracellular signal-regulated kinase （ERK） signaling pathway. MethodsSeventy Wistar rats were randomly allocated into a blank control group （n=14） and a modeling group （n=56）. Rats in the modelling group underwent an eight-week modelling period to establish HSP rat models with blood-heat syndrome via modified ovalbumin （OVA） induction method combined with oral administration of heat-property Chinese herbal medicine. Fifty successfully modeled rats were subsequently randomly divided into five groups （n=10 per group）， model group， compound glycyrrhizin group， LXTZF group， RAF inhibitor group， and LXTZF + RAF agonist group. Additionally， 10 rats were selected from the original blank control group for the final experiment. From the 11th week of modelling， rats in the blank control group and the model group received 1 ml/（100 g·d） ultrapure water via oral administration， in addition to 0.5 ml/（kg·d） 0.9% sodium chloride solution via intraperitoneal injection. The LXTZF group and the compound glycyrrhizin group received 7.5 g/（kg·d） LXTZF granule suspension via gavage， 13.5 mg/（kg·d） compound glycyrrhizin suspension via gavage， respectively. The RAF inhibitor group received 1 mg/（kg·d） GW5074 suspension via intraperitoneal injection and ultrapure water via oral administration； the LXTZF + RAF agonist group received 7.5 g/（kg·d） LXTZF granule suspension via gavage and 1 mg/（kg·d） paclitaxel suspension via intraperitoneal injection. All administrations were performed once daily for 4 weeks. After intervention， skin tissue histopathology was examined by hematoxylin and eosin （H&E） staining， immunoglobulin A （IgA） deposition was assessed via immunofluorescence， serum levels of neutrophil elastase （NE）， tumor necrosis factor-α （TNF-α）， and vascular cell adhesion molecule-1 （VCAM-1） were measured using enzyme-linked immunosorbent assay （ELISA）， serum myeloperoxidase （MPO） level was determined by a colorimetric assay； the mRNA expression levels of RAF， MEK， and ERK in skin tissue were detected by real-time quantitative polymerase chain reaction （RT-qPCR）； and the protein expression of RAF， MEK， ERK， as well as phosphorylated MEK （p-MEK） and phosphorylated ERK （p-ERK）， were analyzed by Western Blot. ResultsSkin tissue in the blank control group rats remained normal， whereas the model group exhibited neutrophil infiltration and haemorrhage with red blood cell rupture. In all drug intervention groups， neutrophil infiltration and haemorrhagic exudation reduced markedly， with LXTZF group demonstrating the most pronounced improvement. Compared with the blank control group， rats in the model group exhibited enhanced IgA fluorescence intensity in skin tissue， elevated serum levels of NE， MPO， TNF-α and VCAM-1， increased mRNA expression of RAF， MEK， ERK1 and ERK2， as well as heightened RAF protein levels and p-MEK/MEK and p-ERK/ERK ratios （P＜0.05）. Compared with the model group， the drug intervention groups exhibited reduced IgA fluorescence intensity in skin tissue， along with decreased serum levels of NE， MPO， TNF-α， and VCAM-1 （P＜0.05）. In LXTZF group and RAF inhibition groups， reduced mRNA expression of RAF， MEK， ERK1， and ERK2 was observed in rat skin tissue， alongside decreased RAF protein levels and reduced p-MEK/MEK and p-ERK/ERK ratios （P＜0.05）. Compared with LXTZF + RAF agonist group， the compound glycyrrhizin group， LXTZF group， and RAF inhibitior group exhibited reduced IgA fluorescence intensity in skin tissue， decreased serum NE， MPO， TNF-α， and VCAM-1 levels， and decreased MEK mRNA expression and p-MEK/MEK ratio （P＜0.05）. ConclusionThe potential mechanism by which LXTZF treats Henoch-Schönlein purpura with blood heat syndrome may involve blocking the RAF/MEK/ERK signaling pathway in skin tissue， and suppressing excessive formation of NETs， thereby reducing IgA deposition in dermal microvessels and attenuating systemic inflammatory responses.

Infantile spasm is an age-related epileptic encephalopathy，most are drug-resistant，as the chirldren grow older，it will transfer into other epileptic syndrome.Most chirldren have a bad prognosis with neurodevelomental disorders.For now，aderenocortitropic hormone，glucocorticoid and vigabatrin are first-line treatment choice.But the control rate of spasm、remission rate of EEG and recurrence rate after first-line treatment are not ideal.It's necessary to find more effective treatment plans desperately.Considering the diversity and complexity of infantile spasm's etiologies，there are a lot of studies about treatment plan based on etiology.Controlling spasm more effectively，improving prognosis，personalized therapy and targeted therapy become the new focus.This article reviews the current treatment status of infantile spasm，also upates some novel methods which is not widely applied in clinical practice.

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.

AIM:The aim of our study is to investigate the metabolic profile differences during cervical lesion progression and evaluate their potential clinical value in assisting the diagnosis of cervical cancer(CC).METHODS:Ul-tra-high-performance liquid chromatography coupled with high-resolution mass spectrometry(UHPLC-HRMS)was em-ployed to conduct non-targeted metabolomic analysis of cervical swab samples from 43 CC patients,34 high-grade squa-mous intraepithelial lesion(HSIL)patients,and 43 healthy controls.Based on the distinct features among the three groups,principal component analysis(PCA)was used to identify the metabolic differences among CC,HSIL and healthy groups.MetaboAnalyst 5.0 was then employed to perform KEGG pathway enrichment analysis on the differential metabo-lites.Finally,random forest machine learning algorithm was used to construct classification prediction models for distin-guishing CC from healthy,HSIL from healthy,and CC from HSIL.The performance of these models was evaluated using receiver operating characteristic(ROC)curve analysis.RESULTS:A total of 1 543 metabolites were identified across the healthy,HSIL and CC groups after filtration,with 407 metabolites differing between the groups.The study found that metabolite PGE2 was present in all three groups,with its expression levels progressively increasing with the progression of cervical lesions.Differential metabolite enrichment analysis demonstrated that CC is associated with specific cancer-relat-ed metabolic pathways,including the tricarboxylic acid cycle,tyrosine metabolism,tryptophan metabolism,and the pen-tose phosphate pathways.Additionally,the study developed three prediction models based on metabolic products for diag-nosing HSIL and CC:the full model,the simplified model,and the PGE2 model.The results indicated that metabolites ex-hibited strong diagnostic efficiency.Both the full model and the simplified model effectively distinguished CC from HSIL,CC from healthy,and HSIL from healthy.The AUC values for the full model were 0.90,0.92 and 0.84,respectively,while those for the simplified model were 0.81,0.95 and 0.85,respectively.Furthermore,the PEG2 model achieved AUC values of 0.74 and 0.80 for distinguishing CC from healthy and HSIL from healthy,respectively.CONCLUSION:The metabolic profiles of cervical cancer exhibit significant differences during the progression of cervical cancer,and these metabolites hold potential clinical value as biomarkers for cervical lesions.

Objective:To establish an ex vivo model of button battery-induced esophageal injury in New Zealand rabbits and evaluate the interventive effects of vegetable oil through esophageal histopathological scoring.Methods:Thirty-six esophageal segments (≈5 cm length) from 10 cm specimen of 18 male rabbits were divided into model groups (15-min, 2-hr, and 6-hr exposure; n=6/group) and intervention groups (olive/peanut/soybean oil infusion; n=6/group). Button batteries were inserted to esophageal segments of model groups. Voltage drop of the battery, pH at negative electrode contact site, and histopathological injury scores were assessed. In the intervention group, button batteries were placed in the esophageal segment for 15 minutes, and olive oil, peanut oil, and soybean oil were infused. The above indicators were observed 6 hours after the button batteries were placed. One-way ANOVA was used to compare the differences of esophageal mucosal tissue damage across time points and oil types. Results:There was no significant difference in the pH value of the negative electrode contact area (9.50±0.56, 10.67±0.80, 11.17±0.40, F=1.955, P>0.05), but the discharge voltage (42.67±4.60 mV, 90.00±2.07 mV, 125.83±2.80 mV, F=156.9, P<0.001) and pathological injury scores (3.50±1.09 scores, 5.33±0.72 scores, 8.67±0.67 scores, F=9.623, P=0.002) in the model groups were significantly different. There was significant difference in pathological injury score between the 6-hour model group and the three intervention groups (8.67±0.67 scores, 7.33±0.62 scores, 6.50±0.43 scores, 6.67±0.42 scores, F=3.279, P=0.042). The difference in pathological injury score between the peanut oil intervention group and the 6-hour model group was statistically significant (mean difference=2.167, P<0.05). Conclusion:This ex vivo model effectively simulates battery-induced esophageal injury. Household peanut oil demonstrates significant protective effects, providing experimental basis for prehospital management of battery corrosion.

Objective It aims to construct an evaluation index system for the development level of intelligent health insurance,which can serve as a reference for health insurance management departments in assessing the develop-ment level of intelligent health insurance and the implementation of health insurance informatization.Methods Key events in intelligent health insurance were identified based on event system theory and text analysis.The evaluation index system was determined through a combination of expert interviews and Delphi expert consultations.The entro-py method was used to calculate the weights of each index,followed by the assessment of the current and ideal de-velopment levels.Results A total of 16 experts were consulted.After two rounds of Delphi expert consultation,two first-level indicators and 18 second-level indicators were finally included in the system.The current development level of intelligent health insurance in China is at the intelligent development stage(2.524 points),while the ideal de-velopment level is at the intelligent improvement stage(4.073 points).The positivity coefficient of both rounds of Del-phi expert consultation was 100%,with an authority coefficient of 0.842,and the degree of expert coordination im-proved with each round.Conclusion The constructed evaluation index system exhibits high scientificity,stability,and generalizability.It can provide an effective evaluation tool for the development of intelligent health insurance in various pooled areas.