We report a case of adult-onset Still's disease(AOSD)presenting with fever,myalgia,and pleural effusion.The patient exhibited significantly elevated inflammatory markers and exudative pleural effusion with markedly elevated leukocyte count showing neutrophilic predominance.After comprehensive exclusion of infectious etiologies,the diagnosis of AOSD was established.Clinical symptoms resolved following anti-inflam-matory therapy with glucocorticoids and tocilizumab.This case highlights a critical clinical insight:while infec-tion is a common cause of elevated inflammatory markers,it is not an invariable etiology.In such presentations,meticulous clinical observation,thorough diagnostic screening,and systematic inference are es-sential for accurate diagnosis.

We report a case of adult-onset Still's disease(AOSD)presenting with fever,myalgia,and pleural effusion.The patient exhibited significantly elevated inflammatory markers and exudative pleural effusion with markedly elevated leukocyte count showing neutrophilic predominance.After comprehensive exclusion of infectious etiologies,the diagnosis of AOSD was established.Clinical symptoms resolved following anti-inflam-matory therapy with glucocorticoids and tocilizumab.This case highlights a critical clinical insight:while infec-tion is a common cause of elevated inflammatory markers,it is not an invariable etiology.In such presentations,meticulous clinical observation,thorough diagnostic screening,and systematic inference are es-sential for accurate diagnosis.

Objective:To develop and evaluate a deep learning-based automatic measurement model for panoramic ultrasound biomicroscopy (UBM) images.Methods:A diagnostic test study was conducted.Preoperative UBM examination results of 372 patients who underwent implantable collamer lens (ICL) implantation were collected at the Eye Center of Renmin Hospital of Wuhan University between February 2021 and March 2023.A total of 1 368 panoramic UBM images were obtained to establish an image database.The dataset was divided into a training set (760 images), a validation set (86 images) and an internal test set (522 images).An expert panel consisting of three ophthalmologists annotated the images.The UNet+ + network was used to automatically segment anterior segment tissues, such as the cornea, lens and iris.In addition, image processing techniques and geometric localization algorithms were developed to automatically identify the anatomical landmarks of pupil diameter (PD), anterior chamber depth (ACD), angle-to-angle distance (ATA) and sulcus-to-sulcus distance (STS) to complete the measurement of these parameters.Additionally, 480 panoramic UBM images of 135 patients (240 eyes) from Aier Eye Hospital of Wuhan University were used as an external test set to further evaluate the performance of the model in different centers.The consistency between the measurements from the model and expert panel, the Pentacam system was assessed.Finally, 150 images were randomly selected from the external test set for a human-machine comparison to further evaluate the model's performance.This study adhered to the Declaration of Helsinki.The study protocol was approved by the Ethics Committee of Renmin Hospital of Wuhan University (No.WDRY-2022-K109) and Aier eye Hospital of Wuhan University (No.2023IRBKY120903).Written informed consent was obtained from each subject.Results:In the internal test dataset and external test dataset, with manual labeling as the reference standard, the model achieved a mean Dice coefficient of not less than 0.882.At least 95.65% of the anatomical landmark localization results had Euclidean distance differences within 250 μm.The intraclass correlation coefficients (ICCs) for the measurements of PD, ACD, angle-to-angle ATA, and STS were at least 0.958, with mean relative errors not exceeding 2.407%.With the Pentacam measurements as the reference standard, the ICCs for PD in the internal and external test sets were 0.540 and 0.466, respectively, while the ICCs for ACD were 0.946 and 0.908, respectively.In the human-machine comparison, the ICCs between the model's measurements and those of senior experts were all not lower than 0.969.Conclusions:The deep learning-based model can automatically measure anterior segment parameters from preoperative panoramic UBM images of patients undergoing ICL surgery.The model demonstrates a consistency comparable to that of senior experts, while providing higher efficiency.In terms of ACD measurement, the model shows good agreement between the measurements obtained from the model and Pentacam system.

Objective To explore the symptom clusters in renal transplant recipients and con-struct a concurrent symptom network to identify core symptoms.Methods A total of 343 patients with followed up after renal transplantation were selected as the study subjects.A general information questionnaire and the Chinese version of the Modified Transplant Symptom Occurrence and Symptom Distress Scale were employed to analyze the occurrence of symptoms in patients.In this study,only symptoms with an incidence rate greater than 20％and Spearman correlation coefficient greater than 0.40 between symptom severity and total score were retained.Exploratory factor analysis was used to extract symptom clusters with a factor loading of ≥0.45 as the criterion.The R language was utilized to construct symptom network,based on which core symptoms and bridge symptoms were identified.Results A total of 5 symptom clusters were extracted in this study:the neuro-gastrointestinal symp-tom cluster,the mood-related symptom cluster,the hormone-related symptom cluster,the energy de-ficiency symptom cluster and the vision-related symptom cluster.The core symptoms were anxiety(rs=1.75),mood swings(rs=1.50)and muscle weakness(rs=1.27).The top three symptoms in terms of bridge strength were muscle weakness(rb=0.87),lack of vitality(rb=0.66)and fa-tigue(rb=0.65).Conclusion Multiple symptoms are presented in patients after renal transplanta-tion.Based on the results of symptom network analysis,clinicians can strengthen the assessment of core symptoms and bridge symptoms to develop precise intervention strategies and improve the effectiveness of symptom management.

Objective:To develop and evaluate a deep learning-based automatic measurement model for panoramic ultrasound biomicroscopy (UBM) images.Methods:A diagnostic test study was conducted.Preoperative UBM examination results of 372 patients who underwent implantable collamer lens (ICL) implantation were collected at the Eye Center of Renmin Hospital of Wuhan University between February 2021 and March 2023.A total of 1 368 panoramic UBM images were obtained to establish an image database.The dataset was divided into a training set (760 images), a validation set (86 images) and an internal test set (522 images).An expert panel consisting of three ophthalmologists annotated the images.The UNet+ + network was used to automatically segment anterior segment tissues, such as the cornea, lens and iris.In addition, image processing techniques and geometric localization algorithms were developed to automatically identify the anatomical landmarks of pupil diameter (PD), anterior chamber depth (ACD), angle-to-angle distance (ATA) and sulcus-to-sulcus distance (STS) to complete the measurement of these parameters.Additionally, 480 panoramic UBM images of 135 patients (240 eyes) from Aier Eye Hospital of Wuhan University were used as an external test set to further evaluate the performance of the model in different centers.The consistency between the measurements from the model and expert panel, the Pentacam system was assessed.Finally, 150 images were randomly selected from the external test set for a human-machine comparison to further evaluate the model's performance.This study adhered to the Declaration of Helsinki.The study protocol was approved by the Ethics Committee of Renmin Hospital of Wuhan University (No.WDRY-2022-K109) and Aier eye Hospital of Wuhan University (No.2023IRBKY120903).Written informed consent was obtained from each subject.Results:In the internal test dataset and external test dataset, with manual labeling as the reference standard, the model achieved a mean Dice coefficient of not less than 0.882.At least 95.65% of the anatomical landmark localization results had Euclidean distance differences within 250 μm.The intraclass correlation coefficients (ICCs) for the measurements of PD, ACD, angle-to-angle ATA, and STS were at least 0.958, with mean relative errors not exceeding 2.407%.With the Pentacam measurements as the reference standard, the ICCs for PD in the internal and external test sets were 0.540 and 0.466, respectively, while the ICCs for ACD were 0.946 and 0.908, respectively.In the human-machine comparison, the ICCs between the model's measurements and those of senior experts were all not lower than 0.969.Conclusions:The deep learning-based model can automatically measure anterior segment parameters from preoperative panoramic UBM images of patients undergoing ICL surgery.The model demonstrates a consistency comparable to that of senior experts, while providing higher efficiency.In terms of ACD measurement, the model shows good agreement between the measurements obtained from the model and Pentacam system.

Objective To analyze the co-expressed genes in blood lipid metabolism,hyperlipidemia and tacrolimus metabolism and their correlation with blood lipid levels in kidney transplant recipients.Methods Co-expressed genes were screened from Comparative Toxicogenomic Database(CTD).Baseline data of 25 kidney transplant recipients were collected.The expression levels of ATP binding cassette subfamily A member 1(ABCA1),peroxisome proliferator activated receptor γ(PPAR-γ)and glycosylphosphatidylinositol anchored high density lipoprotein binding protein 1(GPIHBP1)were measured.All recipients were followed up.The concentrations of fasting blood glucose,glycosylated hemoglobin,triglyceride,total protein,albumin,globulin,cholesterol,high-density lipoprotein,low-density lipoprotein and tacrolimus blood concentration were collected at postoperative 1,3,6 and 12 months,and the incidence of hyperlipidemia in the recipients was analyzed.The correlation between ABCA1,GPIHBP1,PPAR-γ and clinical indexes was assessed.The diagnostic effiiciency of related indexes for hyperlipidemia after kidney transplantation was evaluated.Results Three co-expressed genes including ABCA1,PPAR-γ and GPIHBP1 were screened.ABC Al was positively correlated with cholesterol level at postoperative 6 months and tacrolimus blood concentration at postoperative 3 months,whereas negatively correlated with fasting blood glucose level at postoperative 3 months(all P＜0.05).GPIHBP1 was negatively correlated with preoperative cholesterol and triglyceride levels,whereas positively correlated with tacrolimus blood concentration at postoperative 3 months(all P＜0.05).PPAR-γ was negatively correlated with preoperative globulin and low-density lipoprotein levels(both P＜0.05).ABCA1,GPIHBP1 and PPAR-γ combined with preoperative globulin and blood glucose level at postoperative 1 and 6 months after operation yielded high diagnostic effiiciency for hypertriglyceridemia after kidney transplantation(AUC=0.900).ABCA1,GPIHBP1 and PPAR-γ combined with tacrolimus blood concentrations at postoperative 1 and 6 months and blood glucose level at postoperative 6 months had high diagnostic effiiciency for hypercholesterolemia after kidney transplantation(AUC=0.931).Conclusions ABCA1,GPIHBP1 and PPAR-γ are correlated with blood lipid level and tacrolimus blood concentration after kidney transplantation to different degrees.No definite evidence has been supported for predicting hyperlipidemia after kidney transplantation.Immunity improvement and rational blood glucose management may be beneficial factors for hyperlipidemia control.

After organ transplantation, graft rejection is one of the most common complications, which will cause adverse injuries to grafts regardless of etiology. Tertiary lymphoid structures (TLS) occur in non-lymphoid tissues under pathological conditions. Identification and characterization of mechanisms regulating graft rejection can not only promote understanding of the survival and prognosis of organ grafts, but also provide ideas for treating graft rejection reaction.

Objective To investigate the influencing factors of medication adherence among renal transplant recipients taking immunosuppressive drugs. Methods In this study, 377 kidney transplant recipients who received follow-up care in the kidney transplant follow-up center of the Affiliated Hospital of Guizhou Medical University from October 2021 to July 2022 was selected by convenience sampling method. General data questionnaire and Basel Assessment Scale of immunosuppressive drug compliance were used for questionnaire analysis. Results A total of 377 renal transplant recipients were included in this study, including 236 males and 141 females. The overall adherence score was (4.90±1.55), indicating poor adherence. Multivariate Logistic regression analysis revealed that medication adherence among renal transplant recipients taking immunosuppressive drugs was correlated with marital status and postoperative time of renal transplantation. Conclusion Medication adherence among renal transplant recipients taking immunosuppressive drugs needs to be improved, with marital status and postoperative time of renal transplantation being the primary influencing factors.

Objective To investigate the influencing factors of unplanned rehospitalization within one year after surgery among adult renal transplant recipients. Methods The clinical data of 299 recipients who underwent renal transplant surgery in the Department of Organ Transplantation of the Affiliated Hospital of Guizhou Medical University from January 2020 to December 2022 were retrospectively analyzed. The recipients were divided into unplanned rehospitalization group and non-rehospitalization group based on whether they experienced unplanned rehospitalization within one year after surgery. Univariate analysis and binary Logistic regression analysis were performed to explore the influencing factors of unplanned rehospitalization within one year after renal transplantation. Results Among the 299 recipients, 102 experienced unplanned rehospitalization, with an incidence rate of 34.11%. Univariate analysis revealedstatistically significant differences were noted between the two groups in terms of gender, occupational status, preoperative underlying disease, rejection reactions, nosocomial infections, immunosuppressive medication regimens, serum creatinine, cystatin C, serum phosphorus, serum potassium, and initial hospitalization duration (P < 0.05). Binary Logistic regression analysis showed that having ≥3 kinds of preoperative underlying disease (OR=2.122, 95%CI, 1.198 to 3.759) and experiencing rejection reactions (OR=3.162, 95%CI, 1.217 to 8.218) were independent risk factors for unplanned rehospitalization within one year after renal transplantation (P < 0.05). Conclusion The rate of unplanned rehospitalization within one year after surgery is relatively high among adult renal transplant recipients. Having ≥3 preoperative underlying disease and experiencing rejection reactions are independent risk factors for unplanned rehospitalization within one year after surgery. Transplantation healthcare teams can develop corresponding strategies targeting these risk factors to reduce the rate of unplanned rehospitalization and alleviate the burden of disease.

Purpose To evaluate the changes of carotid artery elasticity in children with type 1 diabetes mellitus (T1DM) via RF-data based quantitative analysis on vessel stiffness technique (R-VQS),and to detect its correlation with the duration of diabetes mellitus and blood sugar control. Materials and Methods A total of 75 children with T1DM from the Pediatrics Department of Henan Chest Hospital,the Pediatrics Department of Henan People's Hospital,and the Endocrinology,Genetics,and Metabolism Department of Henan Children's Hospital were prospectively collected from September 2019 to November 2021. According to the T1DM duration,all patients were divided into long course group (duration ≥5 years,n=30 cases) and short course group (duration<5 years,n=45 cases). And 30 age and sex matched controls were also collected. The systolic diameter (Diam),vascular displacement (Dist),pulse wave velocity (PWV),stiffness coefficient (HC) and intima-media thickness (IMT) of common carotid artery were measured by R-VQS. Fasting blood glucose (FBG),glycosylated hemoglobin (HbA1c),triglyceride (TG),serum total cholesterol (TC),high-density lipoprotein (HDL),low-density lipoprotein cholesterol (LDL) were collected to compared the differences of parameters among the three groups. Results The FBG,HbA1c and IMT of the long and short course groups were all significantly higher than those of control group (all P<0.05). There were statistically significant differences in PWV and HC among the three groups (F=22.854,P<0.001;F=16.811,P<0.001;H=33.214,P<0.001). The course of disease was moderately correlated with PWV and HC (r=0.621,0.517,respectively,P<0.001). HbA1c was moderately correlated with PWV (r=0.562,P<0.001). Conclusion Early onset of T1DM in children may lead to decreased carotid artery elasticity,and prolonged hyperglycemia can aggravate the degree of arterial elasticity damage. R-VQS is a simple,effective,and non-invasive method for assessing arterial elasticity,providing a basis for clinical diagnosis and treatment.