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.

Although a single nucleotide polymorphism for N-acetyltransferase 10 (NAT10) has been identified in patients with early-onset stroke, the role of NAT10 in ischemic injury and the related underlying mechanisms remains elusive. Here, we provide evidence that NAT10, the only known RNA N4-acetylcytidine (ac4C) modification "writer", is increased in the damaged cortex of patients with acute ischemic stroke and the peri-infarct cortex of mice subjected to photothrombotic (PT) stroke. Pharmacological inhibition of NAT10 with remodelin on Days 3-7 post-stroke or astrocytic depletion of NAT10 via targeted virus attenuates ischemia-induced infarction and improves functional recovery in PT mice. Mechanistically, NAT10 enhances ac4C acetylation of the inflammatory cytokine tissue inhibitor of metalloproteinase 1 (Timp1) mRNA transcript, which increases TIMP1 expression and results in the accumulation of microtubule-associated protein 1 light chain 3 (LC3) and progression of astrocyte autophagy. These findings demonstrate that NAT10 regulates astrocyte autophagy by targeting Timp1 ac4C after stroke. This study highlights the critical role of ac4C in the regulation of astrocyte autophagy and proposes a promising strategy to improve post-stroke outcomes via NAT10 inhibition.

The prefrontal cortex (PFC) plays a pivotal role in orchestrating higher-order emotional and cognitive processes, a function that depends on the precise modulation of synaptic activity. Although pharmacological studies have demonstrated that dopamine signaling through dopamine D1 receptor (DRD1) in the PFC is essential for these functions, the cell-type-specific and molecular mechanisms underlying the neuromodulatory effects remain elusive. Using cell-type-specific knockout mice and patch-clamp recordings, we investigated the regulatory role of DRD1 on neurons and astrocytes in synaptic transmission and plasticity. Furthermore, we explored the mechanisms by which DRD1 on astrocytes regulate synaptic transmission and plasticity at the cellular level, as well as emotional and cognitive functions at the behavioral level, through two-photon imaging, microdialysis, high-performance liquid chromatography, transcriptome sequencing, and behavioral testing. We found that conditional knockout of the Drd1 in astrocytes (CKO^AST) increased glutamatergic synaptic transmission and long-term potentiation (LTP) in the medial prefrontal cortex (mPFC), whereas Drd1 deletion in pyramidal neurons did not affect synaptic transmission. The elevated level of d-serine in the mPFC of CKO^AST mice increased glutamatergic transmission and LTP through NMDA receptors. In addition, CKO^AST mice exhibited abnormal emotional and cognitive function. Notably, these behavioral changes in CKO^AST mice could be reversed through the administration of d-serine degrease to the mPFC. These results highlight the critical role of the astrocytic DRD1 in modulating mPFC synaptic transmission and plasticity, as well as higher brain functions through d-serine, and may shed light on the treatment of mental disorders.

Purpose To explore the feasibility and validity of radiomics combined with blind/referenceless image spatial quality evaluator(Brisque)for automated assessment of arterial-phase image quality in hepatic gadolinium ethoxybenzyl diethylenetriamine pentaacetic acid(Gd-EOB-DTPA)dynamic enhanced MRI examinations.Materials and Methods A total of 326 arterial phase images from January 2020 to March 2024 in the First Affiliated Hospital of Henan University of Chinese Medicine for liver Gd-EOB-DTPA dynamic enhancement MRI examination were retrospectively collected.A 4-level(1-4 points)Likert scale was used for subjective image quality scoring,and then binary classification was used to divide the images into clinically acceptable group(166 images with a score of 2-4)and clinically unacceptable group(160 images with a score of 1).All images were randomly divided into a training set(227 images)and a test set(99 images)in a 7∶3 ratio.Brisque images were created based on arterial-phase images,and subsequently Brisque image features,image histologic features of the original arterial-phase images and combined features of the two were extracted.The extracted data were reduced in dimension and the optimal features of radiomics were selected.Based on the Logistic regression algorithm,radiomics models,Brisque models and combined models of the two were constructed.The receiver operating characteristic curve was drawn for each subject,and the area under the curve(AUC),threshold,sensitivity,specificity,accuracy,negative predictive value and positive predictive value were calculated to analyze the diagnostic performance of each model,and the net benefit of the models was evaluated by decision curve analysis.Results The AUC values of the radiomics model,Brisque model and combined model in the training set were 0.942(95%CI 0.914-0.969),0.722(95%CI 0.655-0.788)and 0.967(95%CI 0.947-0.987),respectively.In the test set,the AUC values were 0.904(95%CI 0.848-0.960),0.684(95%CI 0.579-0.789)and 0.920(95%CI 0.866-0.972),respectively.There was statistically significant difference between two of the three models in the training set(Z=6.493,3.397,7.427;all P<0.05);in the test set,there was statistically significant difference between the combined model and the Brisque model,and between the radiomics model and the Brisque model(Z=4.137,3.740,both P<0.05),but no statistically significant difference between the combined model and the radiomics model(P>0.05).The diagnostic performance was higher in the combined model in both the training set and the test set.The decision curve analysis curve suggested that the net benefit of the combined model was greater than that of the other two models.Conclusion Radiomics combined with Brisque can be applied in the assessment of the quality of arterial-phase images in Gd-EOB-DTPA dynamic-enhanced MRI examinations of the liver to automatically detect clinically unacceptable arterial-phase images of the liver with high diagnostic efficacy,which contributes to the improvement of the quality of the images at the time of acquisition,leading to a reduction in the number of duplicate scans and an increase in the accuracy of the diagnosis.

Objective:To observe the effectiveness of combining mirror therapy with pneumatic flexible glove training in treating hand dysfunction after a stroke. Its effect on cerebral cortex activation was documented using near-infrared functional imaging of the brain.Methods:A total of 84 stroke survivors with hand dysfunction were randomly divided into a Mirror Group, a Glove Group and a Combined Group, each of 28. In addition to standard rehabilitation training, the Glove Group received 20 minutes of training with a pneumatic soft glove, 5 days per week for 6 consecutive weeks. The Mirror Group received mirror therapy (MT). The Combined Group was given both simultaneously. Before and after the treatment, everyone′s upper limb functioning was evaluated (using the Fugl-Meyer Upper Limb Assessment (FMA-U)), along with their hand motor skills (using the Arm Action Test (ARAT)) and their ability in daily living activities (using the Barthel Index (BI)). Functional near-infrared spectroscopy (fNIRS) was employed to measure any changes in oxygenated hemoglobin (HBO) concentration at 730nm and 850nm wavelengths.Results:The FMA-U, ATAT and BI scores in both the proximal and distal regions of all three groups showed significant improvement after the treatments compared to pre-treatment levels. The combined group demonstrated significantly better distal FMA-U and ARAT scores after the treatment (12.25±8.80 and 20.93±15.68 respectively), outperforming both the glove and mirror groups. The infrared spectroscopy revealed that bilateral SM1 activation, affected-side somatosensory association cortex (SAC) and supplementary motor cortex excitability in both the mirror and combined groups were significantly better than among the glove group after the experiment.Conclusions:Combined with pneumatic flexible glove training, mirror therapy can not only significantly improve the hand function of stroke survivors, but also activate the relevant brain regions of both hemispheres through bilateral motor patterns combined with multisensory stimulation, promoting the balance between hemispheres.

Objective To establish an acute rejection model of cervical heart transplantation in mice and evaluate the survival and dynamic rejection process post-transplantation. Methods Mice were randomly divided into sham operation group (n=10), syngeneic transplantation group (n=21), and allogeneic transplantation group (n=65). Sham operation, syngeneic cervical heart transplantation, and allogeneic cervical heart transplantation were performed respectively. The survival of recipient mice and grafts, histopathological changes of graft tissues, subpopulations of splenic lymphocytes, and expression of inflammatory factors in serum and grafts were observed. Results The survival rate and graft survival rate of the sham operation group and syngeneic transplantation group were 100% at 7 days after surgery. In the allogeneic transplantation group, 5 cases failed and died on the first day after surgery. The survival rate at 7 days after surgery was 86%, and all surviving mice had grafts that stopped beating at 7 days after surgery. The allogeneic transplantation group showed significant rejection at 7 days after surgery, accompanied by tissue damage and CD8⁺ T cell infiltration. The proportion of CD8⁺ T cells in the spleen continued to rise post-operation, while the proportion of CD4⁺ T cells showed a downward trend. The expression of interferon-γ in serum and grafts peaked at 5 days after surgery, while the expression of tumor necrosis factor-α showed no statistical significance. Conclusions Acute rejection following heart transplantation in mice intensifies between 5 to 7 days after surgery, which may be a critical time window for immunological intervention.

BACKGROUND:Research on carbon nanomaterials in the biomedical field is booming,and related scientific research results are increasing year by year.However,visualization analysis of the annual number of publications,the research status of countries,institutions,authors,and research hotspots and trends in this field is relatively scarce. OBJECTIVE:To present the research status of carbon nanomaterials in biomedical field,reveal the main research subjects,explore the research hotspots and development trends,and provide a reference for the future development of this field. METHODS:The core data set of Web of Science was used as the literature source to search the relevant researches on carbon nanomaterials in the biomedical field from 2012 to 2023.The knowledge map was generated by using Citespace software with countries,institutions,authors,keywords,and co-citations as nodes and for visualization analysis. RESULTS AND CONCLUSION:(1)A total of 2 932 papers were included in this study.In the medical field,carbon nanomaterials had a large number of papers and a fast growth rate.The United States has a large number of papers;China is an emerging force in this field,although the number of papers is the largest,but the level of research and influence need to be improved.The Chinese Academy of Sciences is the largest cooperative network institution,which mainly targets domestic institutions and lacks cooperation with well-known foreign institutions.(2)Keyword analysis displays that the green synthesis method and application of displaying carbon points have been the focus of research,followed by the new method of combining carbon nanomaterials with cancer phototherapy and immunotherapy,the key direction of future research.(3)The dynamic development trend of co-citations suggests that tissue engineering is a hot research topic of carbon nanomaterials in the field of biomedicine,mainly including the research of carbon nanomaterials for the repair and regeneration of heart and nerve tissue and as a bio-ink additive for 3D and 4D bioprinting.(4)In the future,with the development of the biomedical field in the direction of precision and treatment,researchers should speed up the creation of carbon-based systems formed by the combination of scientific and effective carbon nanomaterials with science and technology,new polymers or organic molecules,and new therapeutic methods,so as to give full play to the maximum effect of carbon nanomaterials.

BACKGROUND:Acupoint embedding and floating needle therapy are two methods for the treatment of knee osteoarthritis,but there are few reports on the combined treatment of the two methods. OBJECTIVE:To investigate the therapeutic effect of acupoint embedding combined with floating needle therapy on different stages of knee osteoarthritis using generalized estimating equations. METHODS:A total of 436 patients with knee osteoarthritis admitted to Dongguan Hospital of Traditional Chinese Medicine from February 2019 to February 2023 were selected as the research subjects.All patients were randomly divided into a control group with floating needle therapy(n=218)and an observation group with acupoint embedding method combined with floating needle therapy(n=218).Staging was performed according to the K-L staging method.In the control group,there were 57 cases in stage Ⅰ,62 in stage Ⅱ,49 in phase Ⅲ,and 50 in stage Ⅳ,while in the observation group,there were 48 cases in stage Ⅰ,66 in stage Ⅱ,63 in phase Ⅲ,and 41 in stage Ⅳ.The levels of indexes and clinical efficacy were compared between groups before and after treatment.Generalized estimating equation model was used to analyze the influencing factors of clinical efficacy and the interaction effect of different time points,different methods and different stages on therapeutic efficacy. RESULTS AND CONCLUSION:There was no significant difference in baseline data between the observation group and the control group,as well as between the patients of different stages(P>0.05).After treatment,the cure rate of stage Ⅰ patients was the highest after treatment,and the total improvement rate in the observation group was significantly higher than that in the control group.There were significant differences in the cure rate among different stages in each group(P<0.05).After treatment,all indicators in the two groups were significantly decreased.In the control group,there were significant differences in various indicators of patients at different stages after 4 weeks of treatment(P<0.05).In the observation group,after 2 weeks of treatment,there were significant differences in various indicators of patients at different stages(P<0.05),and all the indexes in the observation were lower than those in the control group after 4 weeks of treatment(P<0.05)and the therapeutic effect in the observation group was better than that in the control group.Generalized estimating equation model analysis showed that trauma history,interleukin-6 level,treatment method,treatment time and K-L stage all significantly affected the clinical efficacy in patients.In the interaction effect analysis,after 2 weeks of treatment,there was a significant difference in the visual analogue scale score between the two groups in stages Ⅲ and Ⅳ(P<0.05).After 4 weeks of treatment,there was a significant difference in the visual analogue scale score between the two groups in stages Ⅱ,Ⅲ,and Ⅳ(P<0.05).To conclude,acupoint embedding combined with floating needle therapy is superior to floating needle therapy alone in the treatment of different stages of knee osteoarthritis.Trauma history,interleukin-6 level,treatment method,treatment time and K-L stage significantly influence the therapeutic effect.

Objective To develop and compare the predictive performance of five machine learning models for adverse postoperative outcomes in cardiac surgery patients, and to identify key decision factors through SHapley Additive exPlanations (SHAP) interpretability analysis. Methods A retrospective collection of perioperative data (including demographic information, preoperative, intraoperative, and postoperative indicators) with 88 variables was conducted from adult cardiac surgery patients at the First Affiliated Hospital of Xinjiang Medical University in 2023. Adverse postoperative outcomes were defined as the occurrence of acute kidney injury and/or in-hospital mortality during the postoperative hospitalization period following cardiac surgery. Patients were divided into an adverse outcome group and a favorable outcome group based on the presence of adverse postoperative outcomes. After screening feature variables using the least absolute shrinkage and selection operator (LASSO) regression method, five machine learning models were constructed: eXtreme gradient boosting (XGBoost), random forest (RF), gradient boosting machine (GBM), light gradient boosting machine (LightGBM), and generalized linear model (GLM). The dataset was randomly divided into a training set and a test set at a 7 : 3 ratio using stratified sampling, with postoperative outcome as the stratification factor. Model performance was evaluated using receiver operating characteristic curves, decision curve analysis, and F1 Score. The SHAP method was applied to analyze feature contribution. Results A total of 639 patients were included, comprising 395 males and 244 females, with a median age of 62 (55, 69) years. The adverse outcome group consisted of 191 patients, while the favorable outcome group included 448 patients, resulting in an adverse postoperative outcome incidence of 29.9%. Univariate analysis showed no significant differences between the two groups for any variables (P>0.05). Using LASSO regression, 16 feature variables were selected (including cardiopulmonary bypass support time, blood glucose on postoperative day 3, creatine kinase-MB isoenzyme, systemic inflammatory response index, etc.), and five machine learning models (GLM, RF, GBM, LightGBM, XGBoost) were constructed. Evaluation results demonstrated that the XGBoost model exhibited the best predictive performance on both the training set (n=447) and test set (n=192), with area under the curve values of 0.761 [95%CI (0.719, 0.800) ] and 0.759 [95%CI (0.692, 0.818) ], respectively. It also significantly outperformed other models in positive predictive value, and balanced accuracy in the test set. Decision curve analysis further confirmed its clinical utility across various risk thresholds. SHAP analysis indicated that variables such as cardiopulmonary bypass support time, blood glucose on postoperative day 3, creatine kinase-MB isoenzyme, and inflammatory markers (SIRI, NLR, CAR) had high contributions to the prediction. Conclusion The XGBoost model effectively predicts adverse postoperative outcomes in cardiac surgery patients. Clinically, attention should be focused on cardiopulmonary bypass support time, postoperative blood glucose control, and monitoring of inflammatory levels to improve patient prognosis.

Objective:To investigate the effects of 10° head up tilt bed rest (HUT) on human cardiac function via 3D speckle tracking echocardiography (3D-STE), and to study the difference in cardiac function under the submaximal exercise load between the horizontal position and 10° HUBR.Methods:Thirty young healthy volunteers were recruited as the subjects, who were randomly divided into an 10° HUT exercise group and horizontal exercise group with 15 subjects in each. Subjects in both groups were asked to ride the bicycle ergometer in the 10° HUBR position and supine position respectively. The load started with 50 W and was increased by 25 W every 3 min until it reached the maximum of 125 W. Before the exercise (resting state), 1 min after the load was increased each time, and 3 min after exercise (recovery period), the following indices were collected: ①basic cardiac function indices: heart rate (HR), systolic blood pressure (SBP) and diastolic blood pressure (DBP), ②conventional cardiac ultrasound indices: left ventricular ejection fraction (LVEF), stroke volume (SV) and cardiac output (CO), ③left ventricular strain indices: global longitudinal strain (GLS), global circumferential strain (GCS), and global area strain (GAS) measured by 3D-SET. The changes of these indices in the 2 groups of subjects under different exercise loads were observed.Results:The differences in the major effect of the basic heart indices (HR, SBP and DBP), conventional cardiac ultrasound indices (LVEF, SV and CO) and left ventricular strain indices (GLS, GCS and GAS) in response to the exercise load were statistically significant ( F=194.90, 113.66, 17.19, P=0.017, 0.018, 0.001). With the increase of the exercise load, the basic heart indices and conventional cardiac ultrasound indices kept rising, the left ventricular strain indices reached the minimum under a moderate exercise load (75 W), HR, SBP and CO were higher than those of the resting state ( P<0.05 or 0.01). Both LVEF under exercise loads of 75, 100, 125 W and during recovery, and SV under exercise loads of 100, 125 W and during recovery were significantly higher than those of the resting state ( P<0.05 or 0.01), while GLS and GCS under exercise loads of 50, 75, 125 W ( P<0.05 or 0.01), and GAS under exercise loads of 50, 75 W ( P<0.01) were significantly lower. There were statistically significant differences not only in GCS across the groups ( F=4.60, P=0.026) but also in DBP due to the interactions between the grouping and exercise loads ( F=3.13, P=0.031). DBP was higher than that of the resting state when the exercise load was 125 W in both groups. Conclusions:During submaximal exercise, myocardial contractility shows sustained enhancement with the increase of the exercise load. The results of GLS, GCS and GAS indicate that myocardial strain reaches its lowest value under a moderate exercise load, suggesting that moderate exercise can be used to evaluate cardiac function via 3D-SET. Under a simulated lunar gravity of 10° HUT, there is less deformation in the short axis direction of the myocardium, indicating that GCS can be used as a sensitive indicator to detect changes in cardiac function under different gravities.