Objective To systematically analyze the literature characteristics of Journal of Organ Transplantation since its inception. Methods Using the China National Knowledge Infrastructure (CNKI) academic journal full-text database as the data source, all articles published in the Journal of Organ Transplantation from January 2010 to August 2025 were retrieved. After excluding non-academic papers, a total of 1 568 research papers were included. R language 4.3.0, Bibliometrix package 3.2.1, and Citespace software were used to analyze the number of publications, publishing institutions, authors, keywords and other aspects. Results The number of publications in Journal of Organ Transplantation increased from an average of 82 articles per year in the early years after its inception to 113 articles per year in recent years, a growth of 37.8%. The geographical distribution of publishing institutions covers 32 provinces, cities and autonomous regions nationwide, mainly concentrated in the South China, East China and North China regions, and has now basically covered the central and western regions in recent years. The author collaboration network includes 45 authors distributed across 7 major collaboration clusters, forming a stable multi-level national research system centered on key university-affiliated hospitals. The high-frequency keywords are dominated by "liver transplantation" (425 times) and "kidney transplantation" (396 times). The theme evolution shows a clear three-stage characteristic: initially focusing on clinical technology application, deepening to immune mechanism exploration in the middle stage, and recently (since 2022) focusing on cutting-edge research areas such as xenotransplantation. Conclusions Journal of Organ Transplantation has witnessed the rapid development of China's organ transplantation cause, fully reflecting the research status and trends in China's organ transplantation field, and has provided an important platform for the future development and international cooperation in China's organ transplantation field.

Obstructive sleep apnea (OSA) is an increasingly widespread sleep-breathing disordered disease, and is an independent risk factor for many high-risk chronic diseases such as hypertension, coronary heart disease, stroke, arrhythmias and diabetes, which is potentially fatal. The key to the prevention and treatment of OSA is early diagnosis and treatment, so the assessment and diagnostic technologies of OSA have become a research hotspot. This paper reviews the research progresses of severity assessment parameters and diagnostic technologies of OSA, and discusses their future development trends. In terms of severity assessment parameters of OSA, apnea hypopnea index (AHI), as the gold standard, together with the percentage of duration of apnea hypopnea (AH%), lowest oxygen saturation (LSpO₂), heart rate variability (HRV), oxygen desaturation index (ODI) and the emerging biomarkers, constitute a multi-dimensional evaluation system. Specifically, the AHI, which measures the frequency of sleep respiratory events per hour, does not fully reflect the patients’ overall sleep quality or the extent of their daytime functional impairments. To address this limitation, the AH%, which measures the proportion of the entire sleep cycle affected by apneas and hypopneas, deepens our understanding of the impact on sleep quality. The LSpO₂ plays a critical role in highlighting the potential severe hypoxic episodes during sleep, while the HRV offers a different perspective by analyzing the fluctuations in heart rate thereby revealing the activity of the autonomic nervous system. The ODI provides a direct and objective measure of patients’ nocturnal oxygenation stability by calculating the number of desaturation events per hour, and the biomarkers offers novel insights into the diagnosis and management of OSA, and fosters the development of more precise and tailored OSA therapeutic strategies. In terms of diagnostic techniques of OSA, the standardized questionnaire and Epworth sleepiness scale (ESS) is a simple and effective method for preliminary screening of OSA, and the polysomnography (PSG) which is based on recording multiple physiological signals stands for gold standard, but it has limitations of complex operations, high costs and inconvenience. As a convenient alternative, the home sleep apnea testing (HSAT) allows patients to monitor their sleep with simplified equipment in the comfort of their own homes, and the cardiopulmonary coupling (CPC) offers a minimal version that simply analyzes the electrocardiogram (ECG) signals. As an emerging diagnostic technology of OSA, machine learning (ML) and artificial intelligence (AI) adeptly pinpoint respiratory incidents and expose delicate physiological changes, thus casting new light on the diagnostic approach to OSA. In addition, imaging examination utilizes detailed visual representations of the airway’s structure and assists in recognizing structural abnormalities that may result in obstructed airways, while sound monitoring technology records and analyzes snoring and breathing sounds to detect the condition subtly, and thus further expands our medical diagnostic toolkit. As for the future development directions, it can be predicted that interdisciplinary integrated researches, the construction of personalized diagnosis and treatment models, and the popularization of high-tech in clinical applications will become the development trends in the field of OSA evaluation and diagnosis.

Ursodeoxycholic acid(UDCA)is a naturally occurring,low-toxicity,and hydrophilic bile acid(BA)in the human body that is converted by intestinal flora using primary BA.Solute carrier family 7 member 11(SLC7A11)functions to uptake extracellular cystine in exchange for glutamate,and is highly expressed in a variety of human cancers.Retroperitoneal liposarcoma(RLPS)refers to liposarcoma originating from the retroperitoneal area.Lipidomics analysis revealed that UDCA was one of the most significantly down-regulated metabolites in sera of RIPS patients compared with healthy subjects.The augmentation of UDCA concentration(≥25 μg/mL)demonstrated a suppressive effect on the proliferation of liposarcoma cells.[15N2]-cystine and[13Cs]-glutamine isotope tracing revealed that UDCA impairs cystine uptake and glutathione(GSH)synthesis.Mechanistically,UDCA binds to the cystine transporter SLC7A11 to inhibit cystine uptake and impair GSH de novo synthesis,leading to reactive oxygen species(ROS)accumulation and mitochondrial oxidative damage.Furthermore,UDCA can promote the anti-cancer effects of ferroptosis inducers(Erastin,RSL3),the murine double minute 2(MDM2)inhibitors(Nutlin 3a,RG7112),cyclin dependent kinase 4(CDK4)inhibitor(Abemaciclib),and glutaminase inhibitor(CB839).Together,UDCA functions as a cystine exchange factor that binds to SLC7A11 for antitumor activity,and SLC7A11 is not only a new transporter for BA but also a clinically applicable target for UDCA.More importantly,in combination with other antitumor chemotherapy or physiotherapy treatments,UDCA may provide effective and promising treatment strategies for RLPS or other types of tumors in a ROS-dependent manner.

Objective To comparatively observe the value of 131I whole-body scanning(WBS)and 131I-SPECT/CT for evaluating residual thyroid tissue,lymph node and distant metastasis,as well as risk of recurrence of differentiated thyroid cancer(DTC)after surgical resection and initial radioactive iodine(RAI)treatment.Methods Totally 367 DTC patients who underwent initial RAI treatment after surgical resection and then 131I-WBS and 131I SPECT/CT scanning were retrospectively collected.131I-WBS and 131I SPECT/CT were compared for identifying residual thyroid,lymph node and distant metastases.According to follow-up results,the risk of DTC recurrence was evaluated based on 131I-WBS and 131I-SPECT/CT,respectively.Results Residual thyroid was detected in 353 cases and suspected in 3 cases with 131 I-WBS,which was diagnosed in 349 cases with 131I-SPECT/CT,and no significant difference was found between 2 methods(P=0.289).131I-WBS detected 36 cases with and suspected 67 cases with lymph node metastases,312 without distant metastases,while 131I-SPECT/CT diagnosed lymph node metastases in 52 cases;131I-WBS detected 20 cases with and suspected 35 cases with distant metastases,while 131I-SPECT/CT diagnosed 60 cases with distant metastases but could not diagnose in 3 cases,304 without distant metastases.The detection rate of 131I-SPECT/CT for lymph node and distant metastasis were both higher than that of 131I-WBS(P=0.018,P＜0.001).During follow-up period,there were 94 cases with low risk,155 with medium risk and 118 with high risk of DTC recurrence according to 131I-SPECT/CT,while 116 cases of low risk,137 of medium risk and 114 of high risk based on 131I-SPECT/CT,and the evaluating results were different between 2 methods in 40 cases(40/367,10.90％).Conclusion Compared with 131I-WBS,131I-SPECT/CT had better clinical value for evaluating lymph node and distant metastases and assessing recurrence risk of DTC after initial RAI treatment.

Pregnancy with chronic kidney disease (CKD), particularly in stages 4-5, carries high risks of adverse outcomes including maternal renal failure, preeclampsia/eclampsia, fetal growth restriction, and preterm birth. This report described a 26-year-old woman with congenital solitary kidney, polycystic ovaries, and uterine septum due to PAX2 gene variant, complicated by CKD stage 4. Through multidisciplinary team precision management and individualized treatment strategies, including timely initiation of dialysis, the patient successfully maintained pregnancy until 34 +1 weeks and delivered a female infant via cesarean section. This case summarizes key management experiences for end-stage renal disease in pregnancy, highlighting early risk assessment, precise nutritional management, hemodialysis protocol optimization, and the crucial role of multidisciplinary collaboration, providing valuable references for managing CKD-complicated pregnancies.

Objective:The purpose of this study is to explore the influencing factors of poor prognosis in henoch-schonlein purpura(HSP)children,and to construct a nomogram model and verify its validity through the analysis results of Logistic regression model.Methods:Collected the clinical data of 170 HSP children who were treated in Fuzhou Luoyuan County Hospital from January 2015 to May 2023 were retrospectively analyzed.They were divided into good prognosis group and poor prognosis group according to the prognosis after treatment.The clinical data and related biochemical indexes of different prognostic groups were compared.The factors of poor prognosis in HSP children were analyzed by Logistic regression model.A predictive model(nomogram)for poor prognosis in HSP children were constructed and evaluated its predictive performance.Prediction accuracy were evaluateed by receiver operating characteristic(ROC)curve.Results:170 HSP children,69 cases(40.59％)had poor prognosis and 101 cases(59.41％)had good prognosis.C-reactive protein(CRP),kidney injury at initial onset,platelet count(PLT),respiratory infection,recurrent rash,and immunoglobulin A(IgA)levels in poor prognosis group were higher than those in good prognosis group(P＜0.05).Multivariate logistic analysis showed that,recurrent rash,respiratory infection,elevated PLT,CRP and IgA levels were independent influencing factors of poor prognosis in HSP children(P＜0.05).The consistency index(C-index)of the column chart model established based on the results of multiple factor analysis was 0.798.The internal verification was carried out by Bootstrap self-sampling method,and the average absolute error of calibration curve was 0.017.ROC curve was drawn according to independent influencing factors and nomogram prediction probability,the area under the curve was 0.674(recurrent rash),0.649(respiratory infection),0.777(PLT),0.733(CRP),0.749(IgA)and 0.910(nomogram prediction probability)respectively.Conclusion:Recurrent rash,respiratory infection,PLT,CRP and IgA are independent factors affecting the prognosis of HSP children.The column chart prediction model constructed based on the above factors has certain predictive value for the poor prognosis in HSP children.

Mesenchymal stem cells(MSCs)play a crucial role in tissue repair and regeneration,and the extracellular vesicle(EV)released by them holds great promise for applications in clinical biomarkers,vaccines,and drug delivery.However,MSCs-derived EV(MSCs-EV)face challenges such as low pro-duction yield,poor retention,and targeted delivery issues.There-fore,engineering MSCs-EV to enhance their performance and en-able visual research has become a hot topic.Curcumin(CUR),an active component in traditional chinese medicine,exhibits pharmacological effects but has limited bioavailability.Using MSCs-EV as a carrier for CUR delivery can address its solubility and bioavailability challenges.This article reviews the drug loading methods,engineering strategies of MSCs-EV,and their important applications in the delivery and treatment of CUR for cartilage injury diseases.It provides a basis for the clinical ap-plication of engineered MSCs-EV in CUR delivery for cartilage repair,offering potential solutions to the challenges in cartilage tissue repair.

Objective To investigate the effects of ischemia time and storage periods on RNA quality in fresh-frozen breast cancer(BC)and esophageal cancer(EC)tissue samples in order to establish evidence-based protocols for biobank sample management.Methods The tumor(T)and paired normal(N)tissue samples from 6 cases of BC and 6 cases of EC were collected and cryopreserved in Biobank,Shantou Central Hospital.Mirror paraffin-embedded tissues were simultaneously prepared into sections for morphological analysis.The samples were divided into two groups of<15 min and 15-30 min according to ischemia time,and RNA quality was analyzed at 4 storage periods of 8-10 months(T1),14-16 months(T2),26-28 months(T3)and 38-40 months(T4).Results In 96 analyzed samples,93.8%(90/96)exhibited high quality(RIN≥6),with 89.6%(43/48)in BC and 97.9%(47/48)in EC.Significant differences in RIN were observed between BC group and EC group(8.050 vs.8.600,P=0.009).In EC group,RIN value was significantly negatively correlated with RNA yield(P<0.001).Moreover,RIN values of tumor-normal pairs exhibited markedly significant differences(7.550 vs.9.000,P<0.001).In contrast,no significant difference was detected in BC group(8.200 vs.7.700,P=0.348).Statistical analysis showed that RIN value was positively correlated with 28S/18S(P<0.001),but had no correlation with tumor content(P=0.676)and necrotic content(P=0.055).Neither ischemia time(<15 min vs.15-30 min:8.200 vs.8.300,P=0.932)nor storage periods(T1-T4:8.400,7.700,8.450,8.600,P=0.163)compromised RNA quality.Conclusion Organ origin and tissue type could influence RNA quality of fresh-frozen tissue samples.However,limited ischemia time(≤30 min)and long-term storage period(38-40 months)do not adversely affect RNA quality in fresh-frozen breast cancer and esophageal cancer tissue samples.

Oropharyngeal squamous cell carcinoma(OPSCC)is a malignant tumor originating from the squamous epithelium of the oro-pharyngeal mucosa,accounting for more than 90％of oropharyngeal malignancies.In recent years,human papillomavirus(HPV)infec-tion has become one of the primary etiological factors of oropharyngeal squamous carcinoma.The incidence of HPV-associated oropharyn-geal squamous carcinoma has been rising annually,with a noticeable trend toward younger populations,posing a significant threat to hu-man health.Due to the distinct biological behavior and clinical characteristics of HPV-associated oropharyngeal squamous carcinoma com-pared to its non-HPV-related counterpart,the diagnostic and treatment strategies for oropharyngeal squamous carcinoma have undergone substantial changes.Prevention and screening for oropharyngeal squamous carcinoma are of critical importance.The diagnostic and treat-ment process involves multi-disciplinary collaboration,including oral and maxillofacial surgery,otolaryngology,head and neck surgery,oncology,radiology and pathology.Based on evidence from clinical practice,a comprehensive,integrated diagnostic and therapeutic ap-proach has been established,centered around the concept of"prevention,screening,diagnosis,treatment,and rehabilitation",covering the entire patient lifecycle and providing a valuable reference for clinical practice.

Objective To construct 5 machine-learning models and compare their performance in predicting the associations between pre-pregnancy socio-psycho-behavioral exposures of both spouses and preconception outcomes.Methods Based on Chongqing Preconception Reproductive Health and Birth Outcome Cohort of volunteers recruited from Chongqing Health Center for Women and Children during January 2019 and March 2022,5 447 couples were recruited and surveyed through interviewer-interview for the demographic and social-psychological-behavioral data of both spouses(221 variables).According to the inclusion and exclusion criteria,4 097 couples were finally included,and randomly assigned into a training set(n=2 867 spouses)and a validation set(n=1 230 spouses)at a ratio of 7∶3.Feature analysis and collinear screening were applied to select the potential exposure factors.In consideration of difficulty to carry out semen parameters analysis in primary healthcare institutions,feature Set 1 including sperm parameters and feature Set 2 excluding semen parameters were constructed by including or excluding sperm quality simultaneously in the training set and the validation set.Five algorithms,that is,Logistic Regression,Naive Bayes,Random Forest,Gradient Boosting Machine,and Support Vector Machine,were used to construct preconception outcome prediction models,and the parameters of each model were optimized using random search combined with grid search.The predictive performance of each model was compared using precision,recall,F1 score,area under the receiver operating characteristic curve(AUC),and calibration curve.The optimal model was then selected by comparing the changes in the predictive ability of the questionnaire data for fertility outcomes with or without semen parameters.Results There were 24 variables screened out in feature Set 1,and 16 variables in feature Set 2.In feature Set 1,the gradient boosting machine performed better,with a relatively higher AUC value(0.651)and better F1 score(0.61).The logistic regression model performed stably(AUC value=0.647)and was suitable as the reference model.The random forest(AUC value=0.641),Naive Bayes(AUC value=0.641),and support vector machine(AUC value=0.634)performed second-best.By utilizing the gradient boosting machine,comparable results were found between the predictions from feature sets with or without semen parameters,as in feature Set 1,the AUC value of its validation set was 0.651(95%CI:0.629～0.681),the prediction accuracy was 0.63,the recall rate was 0.65,and the average precision value F1 was 0.61;and in feature Set 2,the AUC value of its validation set was 0.649(95%CI:0.624～0.663),and both the calibration curves were close to the ideal curve.The prediction results indicated that in feature Set 1,the features highly negatively correlated with preconception outcomes were female age,male age,and no pregnancy within 1 year without contraception,while the features highly positively correlated with preconception outcomes were female pregnancy history,total sperm vitality,and use of contraceptive measures before enrollment.Conclusion Among the 5 machine-learning algorithms performed in this cohort data,the gradient boosting machine shows slightly better performance.There are 24 factors being associated with preconception outcomes in both spouses,and the performance of the simplified model excluding semen parameters is not significantly declined.It is feasible to use machine-learning methods to predict human preconception outcomes through social-psychological-behavioral questionnaires.