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.

AIM: To quantitatively evaluate the clinical characteristics of haze after transepithelial photorefractive keratectomy(TPRK)for astigmatism using corneal densitometry.METHODS:In this retrospective clinical study, a total of 74 patients(106 eyes)with astigmatism ≥1.25 D who underwent TPRK in our hospital from October 2022 to December 2024 were continuously collected. All of the study subjects were divided into transparent group(65 eyes)and haze group(41 eyes)based on whether haze occurred after surgery. Pentacam examination was performed before and after surgery, and corneal densitometry was recorded at the time points of preoperation, 1 mo postoperation in the transparent group and the most severe haze degree in the haze group. The collected corneal densitometry included the average densitometry of the entire corneal layer in the central 2 mm, 2-6 mm, and 6-10 mm areas, as well as the average densitometry of the entire layer of the corneal section in the center 6 mm of the astigmatism axis(astigmatism expressed in negative cylindrical form)and orthogonal axis(the axis perpendicular to the astigmatism axis), and the average densitometry of the entire layer of the corneal section in the nasal and temporal 2-6 mm areas of the astigmatism axis in the haze group of patients with regular astigmatism. The change in corneal densitometry after surgery compared with that before surgery was calculated.RESULTS:There was no statistically significant difference in baseline data such as gender, age, and spherical equivalent between the transparent group and the haze group(all P>0.05). The change in corneal densitometry in the 2-6 mm area of the haze group was greater than that in the transparent group(Z=-2.226, P=0.026), while there was no significant difference in the change of corneal densitometry in the central 2 mm and 6-10 mm areas between the two groups(both P>0.05). There was no significant difference in the change of corneal densitometry between the transparent group and haze group along the orthogonal axis(all P>0.05), while the change of corneal densitometry in the haze group along the astigmatism axis was greater than that in the transparent group(Z=-2.371, P=0.018). The temporal corneal densitometry of patients with regular astigmatism in the haze group after surgery was higher than that of the nasal side, and the change in corneal densitometry was also greater than that of the nasal side(Z=-4.288, P<0.001; Z=-4.043, P<0.001).CONCLUSION:Unlike spherical correction for myopia and hyperopia, haze after TPRK for astigmatism was mainly manifested in the peripheral cutting area of the astigmatism axis, and patients with regular astigmatism had a higher probability or severity of haze on the temporal side of the astigmatism axis than on the nasal side.

Objective: To analyze the epidemiological characteristics and changing trends of non suicidal self injury (NSSI) behaviors among middle school students in Jiading District of Shanghai, from 2015 to 2023, so as to provide a basis for the development of NSSI prevention and control measures among students. Methods: Using a stratified cluster random sampling method, a total of five times for Shanghai Adolescent Health Risk Behavior Surveys were conducted for every two years in Jiading District of Shanghai from 2015 to 2023. A total of 5 231 middle school students from junior high schools and senior high schools were selected for questionnaire surveys. Intergroup comparisons were performed using the x 2 test or the χ 2 trend test, and the JointPoint 5.0 software was used to analyze the changing trends, with the annual percent change (APC) used for evaluation. A binary Logistic regression model was employed to analyze the related factors of NSSI behavior among middle school students. Results: In 2023, the reported NSSI rate among middle school students in Jiading District was 14.2%. The rate was significantly higher among junior high school students (17.1%) than that among senior high school students (11.1%), and higher among females (19.2%) than that among males (10.0%) ( χ 2=10.04, 23.21, both P <0.01). From 2015 to 2023, the overall reported NSSI rate showed an increasing trend, rising from 8.6% in 2015 to 14.2% in 2023 ( χ 2 trend =22.25), with an APC of 6.64% ( t =3.49), and the APC for girls was 9.79 % ( t =3.20) (all P <0.05). Among students reporting NSSI, the proportion experiencing ≥6 episodes increased from 10.8% in 2015 to 19.2% in 2023 ( χ 2 trend =6.57, P <0.05). Multivariate Logistic regression analysis indicated that girls, junior high school students, those with insomnia, depressive emotion and drinkers had higher risks of NSSI, compared to boys, senior high school students, those without insomnia, non depressive emotion students and non drinkers ( OR =1.71, 1.96, 3.44, 4.76, 1.77, all P < 0.05 ). Conclusions The reported rate of NSSI among middle school students in Jiading District of Shanghai, increased annually from 2015 to 2023, and the proportion of repeated NSSI also showed an upward trend. Early intervention measures targeting middle school students, especially junior high school students and females, should be implemented to prevent and control its occurrence and development.

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.

Polypoidal choroidal vasculopathy(PCV)is one of the important subtypes of neovascular age-related macular degeneration(nARMD), which causes severe vision loss. It is necessary to distinguish PCV from other nARMD subtypes to guide the clinical treatment plans and predict disease outcomes. In recent years, artificial intelligence(AI)has been widely used in the diagnosis and research of ophthalmic diseases. By utilizing machine learning or deep learning combined with examination images in disease classification, lesion segmentation, and quantitative assessment, etc. This article reviews the recent applications of AI in the differential diagnosis of PCV through various examination images, the segmentation and quantification of biomarkers, as well as the prediction of genotype, response to anti-vascular endothelial growth factor(VEGF)therapy, and the short-term risk of vitreous hemorrhage. It summarizes the difficulties and challenges in clinical practice of AI and looks forward to the advantages and development trends of AI in PCV applications in the future. The article aims to provide more information for further research and application, thereby improving the diagnostic rate of PCV, optimizing treatment plans, and improving patients' visual prognosis.

Polypoidal choroidal vasculopathy(PCV)is one of the important subtypes of neovascular age-related macular degeneration(nARMD), which causes severe vision loss. It is necessary to distinguish PCV from other nARMD subtypes to guide the clinical treatment plans and predict disease outcomes. In recent years, artificial intelligence(AI)has been widely used in the diagnosis and research of ophthalmic diseases. By utilizing machine learning or deep learning combined with examination images in disease classification, lesion segmentation, and quantitative assessment, etc. This article reviews the recent applications of AI in the differential diagnosis of PCV through various examination images, the segmentation and quantification of biomarkers, as well as the prediction of genotype, response to anti-vascular endothelial growth factor(VEGF)therapy, and the short-term risk of vitreous hemorrhage. It summarizes the difficulties and challenges in clinical practice of AI and looks forward to the advantages and development trends of AI in PCV applications in the future. The article aims to provide more information for further research and application, thereby improving the diagnostic rate of PCV, optimizing treatment plans, and improving patients' visual prognosis.

lumbar fusion surgery is one of the commonly used surgical interventions and treatments for lumbar degenerative diseases.A small proportion of patients still experience complications such as low back pain after lumbar fusion surgery.Spinal-pelvic parameters are closely related to the occurrence of complications in patients after lumbar fusion surgery.This article reviews the commonly used lumbar fixation and fusion surgical techniques and mainstream sagittal spinal-pelvic parameters.Additionally,it summarizes the research progress and current status regarding the relationship between sagittal spinal-pelvic parameters and various postoperative complications,including low back pain.

Objective:To explore the differences in clinical efficacy between Co-Cr-Mo guided multidirectional sliding growing rod technology (CMSG) and traditional growing rod in the treatment of neurofibromatosis type 1 dysplastic early-onset scoliosis.Methods:A retrospective analysis was conducted on the data of 20 patients with neurofibromatosis type 1 dysplastic early-onset scoliosis who underwent surgical treatment in the Scoliosis Department of Zhengzhou Orthopaedic Hospital Affiliated to Henan University from January 2010 to July 2022. There were 10 patients in the traditional rod group (treated with traditional growing rod surgery) and 10 patients in the CMSG group (treated with CMSG technology). All patients were ≤10 years old and had a Cobb angle ≥45°. The number of surgeries and the occurrence of complications were recorded. The Cobb angle of the main scoliotic curve, the Cobb angle of kyphosis from T 5 to T 12, and the height from T 1 to S 1 were measured from the imaging data to evaluate the correction of deformity and spinal growth. Results:There were no significant differences in age, gender, follow - up time, preoperative Cobb angle, and preoperative Cobb angle of kyphosis from T 5-T 12 between the CMSG group and the TGR group ( P>0.05). The number of surgeries 1.3±0.67 and the total medical cost 91, 100±34, 700 yuan in the CMSG group were lower than those in the TGR group (5.3±1.77 times and 155, 800±45, 900 yuan], and the differences were statistically significant ( t=6.687, P<0.001; t=3.558, P=0.002). The Cobb angles of the main curve before surgery, after the first surgery, and at the last follow - up in the CMSG group were 69.7°±17.8°, 19.8°±9.7°, and 24.4°±9.0° respectively, while those in the TGR group were 62.0°±11.1°, 32.1°±11.4°, and 33.3°±11.6° respectively. The differences in Cobb angles after the first surgery and at the last follow-up between the two groups were statistically significant ( t=2.633, P=0.017; t=2.313, P=0.033). The Cobb angles of kyphosis from T 5 to T 12 before surgery, after the first surgery, and at the last follow - up in the CMSG group were 40.0°±24.2°, 21.0°±6.0°, and 33.6°±9.3° respectively, while those in the TGR group were 31.3°±14.5°, 26.3°±10.5°, and 32.3°±17.2° respectively. There were no significant differences in the Cobb angles of kyphosis from T 5 to T 12 after the first surgery and at the last follow-up between the two groups ( t=1.383, P=0.184; t=0.243, P=0.811). The heights from T 1 to S 1 before surgery, after the first surgery, and at the last follow-up in the CMSG group were 30.5±3.4 cm, 33.7±3.3 cm, and 37.9±4.8 cm respectively, with an annual increase of 1.18±0.39 cm. The heights from T 1 to S 1 in the TGR group were 29.1±3.0 cm, 31.4±2.9 cm, and 36.3±3.5 cm respectively, with an annual increase of 1.25±0.23 cm. There was no significant difference in the annual growth height of T 1-S 1 between the two groups ( t=1.367, P=0.189). During the follow-up period, 3 patients in the CMSG group had 3 complications: 1 case of coronal plane trunk decompensation, 1 case of rod fracture, and 1 case of distal junctional kyphosis. In the TGR group, 7 patients had 8 complications: 2 cases of wound rupture, 3 cases of screw loosening, 1 case of distal addition phenomenon, 1 case of proximal addition phenomenon, and 1 case of rod fracture. Conclusion:The Co-Cr-Mo guided multidirectional sliding growing rod technique is safe and effective in treating type 1 neurofibromatosis with malnutrition type early-onset scoliosis. It can effectively control the progression of spinal deformities,preserve the growth ability of the spine, and have a low overall incidence of complications.

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.