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.

ObjectiveTo analyze the research status and development trends of non-pharmacological therapies for post-stroke spasticity (PSS) over the past two decades. MethodsRelevant literatures on non-pharmacological rehabilitation of PSS published from January, 2004 to June, 2024 were retrieved from Web of Science Core Collection. CiteSpace 6.3.R6 and VOSviewer 1.6.18 were used for visualization analysis. ResultsA total of 780 publications were included. The annual number of publications showed an overall upward trend. China, the USA, and Italy contributed the highest number of publications. The Hong Kong Polytechnic University and researcher Noureddin Nakhostin Ansari were identified as the most influential institution and author, respectively. High-frequency keywords and cluster labels included electric stimulation, transcranial magnetic stimulation, robot and acupuncture. ConclusionOver the past 20 years, researches on non-pharmacological therapies for PSS have remained active, with hotspots focusing on diverse interventions such as electrical stimulation, magnetic stimulation and robot-assisted therapy.

ObjectiveTo construct and validate a clinical prediction model for inflammatory remission outcomes in rheumatoid arthritis （RA） patients with liver and kidney deficiency syndrome treated with Bushen Zhiwang Decoction （补肾治尪汤， BZD） based on metabolomics. MethodsA prospective cohort study was conducted， enrol-ling 60 RA patients with liver and kidney deficiency syndrome. All patients were treated with BZD and conventional-dose oral conventional synthetic disease-modifying antirheumatic drugs （csDMARDs） for 12 months. Clinical data were collected， and the change in disease activity score in 28 joints （DAS28） after treatment compared with baseline （△DAS28） was used as the primary outcome and grouping criterion. Peripheral blood samples were collected before treatment to analyze plasma metabolites. Differential analysis and least absolute shrinkage and selection operator （LASSO） regression were used to preliminarily screen differential metabolites， followed by machine learning algorithms to further identify a core metabolite combination. Based on the expression levels of the core metabolite combination， a novel metabolite index， namely the metabolomics-based inflammatory remission score （Met-IRS）， was calculated using standar-dized metabolite values， and its clinical applicability was evaluated. A clinical prediction model was constructed by integrating clinical characteristics and Met-IRS， and the model performance was assessed. ResultsAmong the 60 patients， those with △DAS28 ≥ 0.27 were assigned to the high inflammatory remission group， while those with △DAS28 ＜ 0.27 were assigned to the low inflammatory remission group， with 30 cases in each group. Compared to the low inflammatory remission group， the high inflammatory remission group showed a higher frequency of methotrexate use and a lower positive rate of rheumatoid factor （RF） （P＜0.05）. Seven core metabolites were identified as the optimal combination， including mangiferic acid， fatty acid-hydroxy fatty acid ester 40∶6， fatty acid-hydroxy fatty acid ester 18∶0， fatty acid-hydroxy fatty acid ester 36∶1， glucosylceramide， lysophosphatidylcholine 22∶5， and pregnanetriol ketone. The calculated Met-IRS comprehensively reflected the characteristics of differential metabolites and demonstrated clinical applicability. Met-IRS was significantly higher in the high inflammatory remission group than in the low inflammatory remission group， and was positively correlated with high inflammatory remission outcomes （P＜0.05）. Based on the variables Met-IRS， methotrexate use， leflunomide use， and RF positivity， a clinical prediction model for inflammatory remission in RA treatment （Cj-RTRM） was constructed. Model performance evaluation demonstrated that the model had good clinical predictive ability， with an area under the receiver operating characteristic curve （AUC） of 0.880， sensitivity 0.967， specificity 0.700 and Youden's index 0.667. ConclusionThe clinical prediction model Cj-RTRM constructed based on the metabolomics-based inflammatory remission score Met-IRS can effectively predict clinical inflammatory remission outcomes in RA patients treated with BZD and accurately identify the advantageous population for this treatment. This model provides guiding evidence for dynamic inflammation monitoring， targeted management， and identification of populations with advantages in traditional Chinese medicine.

The initiation of adaptive immune responses relies on the precise recognition and interpretation of antigenic information. In this process, the specific binding of T cell receptors (TCRs) to peptide-major histocompatibility complex (pMHC) molecules represents one of the key molecular events in the initiation of adaptive immune responses. Accordingly, the structural features of TCR-pMHC complexes provide a fundamental basis for dissecting antigen recognition mechanisms and support rational vaccine design, therapeutic target discovery in TCR-based immunotherapy, and TCR identification and optimization. However, experimental determination of TCR-pMHC structures remains costly, time-consuming, and limited in coverage, making computational approaches essential for rapidly obtaining reliable structural information. Computational methods for predicting the structures of TCR-pMHC complexes have advanced rapidly in recent years, driven by progress in deep learning-based modeling frameworks and the increasing availability of structural and sequence resources. Despite these developments, most existing tools do not adequately distinguish the key structural and biophysical differences between MHC class I (MHC-I) and MHC class II (MHC-II) complexes during model construction. As a consequence, their predictive performance differs substantially between class I and class II complexes. In general, structural predictions for class I complexes outperform those for class II complexes. This discrepancy may be related to several fundamental differences between the two systems, including the architecture of the peptide-binding groove, the distribution of peptide lengths, and the properties of peptide flanking residues (PFRs). Compared with MHC-I molecules, MHC-II molecules usually bind longer antigenic peptides, which typically range from 13 to 25 amino acids in length. PFRs at both termini of these peptides participate in regulating the overall conformation of TCR-pMHC class II complexes and exert a pronounced effect on the geometric and physicochemical characteristics of the TCR-pMHC binding interface. Furthermore, within the TCR recognition interface, the complementarity-determining regions (CDRs) consist of segments that differ markedly in conformational behavior. They commonly include regions that are relatively rigid and structurally stable, together with highly flexible segments exhibiting substantial conformational plasticity. These rigidity-flexibility features constitute an essential structural basis enabling TCRs to recognize diverse peptide-MHC ligands and to accommodate conformational heterogeneity at the interface. However, many current modeling tools, in an effort to enforce global conformational stability or reduce structural noise, tend to over-constrain intrinsically flexible regions. Such oversimplification may lead to inappropriate rigidification of flexible CDR loops, resulting in local structural distortions, compromised interface geometry, or even complete modeling failure for specific complexes. Against this background, the review approaches the field from the perspective of computational differences between MHC-I and MHC-II complexes. We first systematically organize and summarize available resources related to TCRs and pMHCs, including structural datasets, sequence databases, prediction tools, and benchmarking studies. We then focus on five representative tools capable of predicting both class I and class II complexes—AlphaFold2, AlphaFold3, TCRmodel2, tFold-TCR, and TCR-pHLA_ModellerS. After excluding structures present in the training sets of these tools, we constructed a benchmark dataset comprising 25 class I and 10 class II TCR-pMHC complexes in the bound state and conducted a systematic evaluation using this dataset. We first employ widely used general evaluation metrics, including All-Atom Root Mean Square Deviation (All-Atom RMSD), Backbone RMSD, Template Modeling score (TM-score), and DockQ, to assess the global conformational accuracy and interface modeling quality of class I and class II complexes. For class II complexes, we propose for the first time a peptide flanking residue deviation index, including the PFRs-Deviation Index (PFRs-DI), N-PFR-Deviation Index (N-PFR-DI), and C-PFR-Deviation Index (C-PFR-DI), to quantitatively characterize conformational deviations in PFRs. In addition, we propose the CDR conformational consistency index (CCC) designed to qualitatively evaluate the ability of prediction tools to capture TCR CDR conformational flexibility. These metrics collectively assess a tool’s ability to model both overall conformation and critical functional regions, thereby addressing the limitations of existing evaluation criteria that overemphasize global structure while inadequately capturing modeling quality in key functional areas. This establishes a unified analytical framework for MHC-I and MHC-II complexes to guide data resource selection, modeling strategy formulation, and evaluation system development. The framework further advances computational modeling and provides crucial support for multi-scale analysis of TCR-pMHC recognition mechanisms and their biological functions.

The initiation of adaptive immune responses relies on the precise recognition and interpretation of antigenic information. In this process, the specific binding of T cell receptors (TCRs) to peptide-major histocompatibility complex (pMHC) molecules represents one of the key molecular events in the initiation of adaptive immune responses. Accordingly, the structural features of TCR-pMHC complexes provide a fundamental basis for dissecting antigen recognition mechanisms and support rational vaccine design, therapeutic target discovery in TCR-based immunotherapy, and TCR identification and optimization. However, experimental determination of TCR-pMHC structures remains costly, time-consuming, and limited in coverage, making computational approaches essential for rapidly obtaining reliable structural information. Computational methods for predicting the structures of TCR-pMHC complexes have advanced rapidly in recent years, driven by progress in deep learning-based modeling frameworks and the increasing availability of structural and sequence resources. Despite these developments, most existing tools do not adequately distinguish the key structural and biophysical differences between MHC class I (MHC-I) and MHC class II (MHC-II) complexes during model construction. As a consequence, their predictive performance differs substantially between class I and class II complexes. In general, structural predictions for class I complexes outperform those for class II complexes. This discrepancy may be related to several fundamental differences between the two systems, including the architecture of the peptide-binding groove, the distribution of peptide lengths, and the properties of peptide flanking residues (PFRs). Compared with MHC-I molecules, MHC-II molecules usually bind longer antigenic peptides, which typically range from 13 to 25 amino acids in length. PFRs at both termini of these peptides participate in regulating the overall conformation of TCR-pMHC class II complexes and exert a pronounced effect on the geometric and physicochemical characteristics of the TCR-pMHC binding interface. Furthermore, within the TCR recognition interface, the complementarity-determining regions (CDRs) consist of segments that differ markedly in conformational behavior. They commonly include regions that are relatively rigid and structurally stable, together with highly flexible segments exhibiting substantial conformational plasticity. These rigidity-flexibility features constitute an essential structural basis enabling TCRs to recognize diverse peptide-MHC ligands and to accommodate conformational heterogeneity at the interface. However, many current modeling tools, in an effort to enforce global conformational stability or reduce structural noise, tend to over-constrain intrinsically flexible regions. Such oversimplification may lead to inappropriate rigidification of flexible CDR loops, resulting in local structural distortions, compromised interface geometry, or even complete modeling failure for specific complexes. Against this background, the review approaches the field from the perspective of computational differences between MHC-I and MHC-II complexes. We first systematically organize and summarize available resources related to TCRs and pMHCs, including structural datasets, sequence databases, prediction tools, and benchmarking studies. We then focus on five representative tools capable of predicting both class I and class II complexes—AlphaFold2, AlphaFold3, TCRmodel2, tFold-TCR, and TCR-pHLA_ModellerS. After excluding structures present in the training sets of these tools, we constructed a benchmark dataset comprising 25 class I and 10 class II TCR-pMHC complexes in the bound state and conducted a systematic evaluation using this dataset. We first employ widely used general evaluation metrics, including All-Atom Root Mean Square Deviation (All-Atom RMSD), Backbone RMSD, Template Modeling score (TM-score), and DockQ, to assess the global conformational accuracy and interface modeling quality of class I and class II complexes. For class II complexes, we propose for the first time a peptide flanking residue deviation index, including the PFRs-Deviation Index (PFRs-DI), N-PFR-Deviation Index (N-PFR-DI), and C-PFR-Deviation Index (C-PFR-DI), to quantitatively characterize conformational deviations in PFRs. In addition, we propose the CDR conformational consistency index (CCC) designed to qualitatively evaluate the ability of prediction tools to capture TCR CDR conformational flexibility. These metrics collectively assess a tool’s ability to model both overall conformation and critical functional regions, thereby addressing the limitations of existing evaluation criteria that overemphasize global structure while inadequately capturing modeling quality in key functional areas. This establishes a unified analytical framework for MHC-I and MHC-II complexes to guide data resource selection, modeling strategy formulation, and evaluation system development. The framework further advances computational modeling and provides crucial support for multi-scale analysis of TCR-pMHC recognition mechanisms and their biological functions.

Laboratory animal science is an emerging interdisciplinary field supporting life science and medical research, and a key component of talent cultivation and technological innovation in medical colleges. Currently, this field faces challenges such as a significant imbalance between talent supply and demand and a weak systematic training system. To respond to national strategies and societal needs, Shandong First Medical University established the School of Laboratory Animal through industry-education integration during the 14th Five-Year Plan period. The school is based on The Medical Laboratory Technology major, cultivates application-oriented, interdisciplinary professionals in laboratory animal science, and has constructed a three-in-one training system integrating "course learning–scientific research–industry practice". Specific measures for talent cultivation include: optimizing the general education, professional courses, and intensive practical modules in the talent training program, and establishing the "Yellow River Class" integrating industry and education; implementing an undergraduate mentor system, and conducting scientific research training based on The Model Animal Research and Development Engineering Laboratory; collaborating with bases across the entire industrial chain of laboratory animal production, research, application, and quality control, appointing industry mentors, and strengthening practical teaching. Practice has shown that this system has been remarkably effective: a total of 320 undergraduate students have been enrolled since 2020; the employment rate of undergraduate graduates for two consecutive cohorts has reached 100%, and the postgraduate enrollment rate has exceeded 50%; undergraduate students have won numerous national and provincial awards in academic competitions, and have obtained multiple patents and published papers. In the future, the school will further integrate the advantages of medicine, agriculture, and science, optimize the depth and breadth of courses, strengthen the construction of faculty and teaching materials, and improve the undergraduate-master's integrated training mechanism. This article can provide a reference for the training of professionals in laboratory animal science and related biomedical fields in medical colleges.

Objective To evaluate the diagnostic efficacy of different pathogen detection methods in active tuberculosis (ATB) of kidney transplant recipients (KTRs) and analyze their diagnostic value in different types of clinical specimens. Methods A retrospective observational study was conducted, including adult KTRs with suspected ATB from the Eighth Medical Center of Chinese People's Liberation Army General Hospital from January 2020 to January 2025. Baseline data of the recipients were collected and analyzed, and the diagnostic criteria were based on a composite reference standard (including clinical diagnosis and bacteriological confirmation). The single-detection efficacy of acid-fast staining smear, mycobacterium tuberculosis culture, Xpert test for the detection of mycobacterium tuberculosis nucleic acid and resistance to rifampicin (Xpert MTB/RIF), as well as tuberculosis or non-tuberculosis mycobacterial DNA detection was compared. The differences in sensitivity among different methods were analyzed. Results A total of 95 KTRs with suspected ATB and 136 specimens were included. And 52 cases were diagnosed as ATB, among which 48 cases were confirmed by bacteriological means (92%). The sensitivity of Xpert MTB/RIF and DNA detection (0.55 and 0.52, respectively) was significantly higher than that of acid-fast staining smear (0.30) and mycobacterium tuberculosis culture (0.35) (P < 0.01). There was a statistically significant difference in the bacteriological diagnosis rate among different specimen types (P = 0.035). The detection sensitivity of each method was the highest in pus, puncture tissue and bronchoalveolar lavage fluid, followed by sputum and pleural and abdominal effusions, and the lowest in cerebrospinal fluid. Conclusions The sensitivity of nucleic acid amplification technology for immunosuppressed KTRs is slightly reduced, but it should still be recommended as the preferred initial screening tool. The comprehensive strategy of "multi-site sampling (prior to obtaining high bacterial load specimens) combined with multiple method detection" may improve the diagnostic efficacy.

OBJECTIVE To study the safety of Shuangshu decoction in rats and its efficacy in improving functional dyspepsia （FD） in rats. METHODS In safety test， 40 rats were divided into blank control group， Shuangshu decoction low-dose， medium- dose and high-dose groups [108， 216， 324 g/（kg·d）， calculated by raw medicine， the same applies below]； they were given relevant medicine intragastrically， for continuous 14 days. The mortality and toxic reactions of rats were recorded， and the organ indexes of the liver， kidney， spleen， lung and heart of rats were calculated； the pathological morphological changes in the liver， kidney， spleen， lung， heart， stomach， duodenum， and colon were observed to evaluate the acute toxicity of Shuangshu decoction. Another 40 rats were grouped and administered in the same way for 30 consecutive days. The mortality and toxic reactions of the rats were recorded， and the corresponding organ indexes were calculated. The pathological morphological changes in the corresponding organs were observed， and blood routine and serum biochemical indicators were measured， in order to assess the subacute toxicity of Shuangshu decoction. In pharmacodynamic experiments: 50 rats were divided into blank control group， model group， and Shuangshu decoction low-， medium-， and high-dose groups （9.45， 18.9， 37.8 g/kg）， with 10 rats in each group. Except for blank control group， rats in all other groups were used to establish the FD rat model by subcutaneous injection of loperamide （3.5 mg/kg）. Rats in each group were administered the corresponding drug solution/normal saline intragastrically， once a day， for 14 consecutive days. After the last medication， fecal moisture content， intestinal propulsion rate， gastric emptying rate and serum level of motilin were all detected， and interstitial cell of Cajal （ICC） ultrastructure of rats was observed in colon tissue. RESULTS The safety experiments showed that no death occurred in each dose group， and no significant difference was found in organ coefficient， routine blood and serum biological index， compared to blank control group （P＞0.05）； no abnormality was found in organ appearance and pathological sections. The results of the pharmacodynamic experiments showed that， compared with the blank control group， the fecal moisture content， gastric emptying rate， intestinal propulsion rate， and serum motilin levels in the model group were significantly decreased （P＜0.05）； in the colonic tissue， the mitochondria in the ICC exhibited severe swelling with the disappearance of cristae， and the endoplasmic reticulum was dilated. Compared with model group， the rats in Shuangshu decoction high-dose group showed significant increases in the above quantitative indicators （P＜ 0.05）； additionally， there was a large number of mitochondria in the ICC of the colonic tissue， with clear cristae and regular arrangement. CONCLUSIONS Shuangshu decoction is safe and has a beneficial improving effect on FD rats； its mechanism of action may be related to the regulation of gastrointestinal hormone expression to promote gastric emptying and intestinal propulsion， as well as the repair of mitochondrial structure in ICCs to restore gastrointestinal function.

ObjectiveThe theory of homotherapy for heteropathy is one of the classical rules in traditional Chinese medicine. Taking this theory as a breakthrough point， this study employed gas chromatography-mass spectrometry （GC-MS） to elucidate the mechanism underlying the therapeutic effects of Zhuyuwan on both intrahepatic cholestasis （IC） and ulcerative colitis （UC） from the viewpoint of serum metabolic homeostasis. MethodsThe rat models of α-naphthylisothiocyanate （ANIT）-induced cholestasis and 2，4，6-trinitro-benzenesulfonic acid （TNBS）-induced UC were treated with low （0.6 g·kg^-1） and high （1.2 g·kg^-1） doses of Zhuyuwan by gavage. In the experiment regarding IC， 24 Sprague-Dawley （SD） rats were randomly assigned into four groups： normal， ANIT model， low-dose Zhuyuwan， and high-dose Zhuyuwan. In the experiment regarding UC， 24 SD rats were randomly allocated into four groups： normal， TNBS model， low-dose Zhuyuwan， and high-dose Zhuyuwan. Firstly， the two disease models and the intervention effects of Zhuyuwan on the two diseases were evaluated based on serum levels of biochemical indicators ［alanine aminotransferase （ALT）， aspartate transaminase （AST）， γ-glutamyltranspeptidase （γ-GT）， and total bile acid （TBA）］， colon damage score， colon weight index， disease activity index， and histopathological changes in rats. Secondly， the rat serum samples were analyzed by gas chromatography-mass spectrometry （GC-MS） to screen the common core pathways of the two disease models， and the expression of core genes in the pathways was determined by Real-time PCR， on the basis of which the biological mechanism of the treatment of the two disease models by Zhuyuwan was ultimately elucidated. ResultsThe results of the experiment regarding IC showed that the ANIT model group had higher ALT， AST， γ-GT， and TBA levels than the normal group （P<0.01）. Compared with the ANIT model group， the low-dose Zhuyuwan group showed declined ALT and TBA levels （P<0.01） and the high-dose Zhuyuwan group showed lowered ALT， TBA， AST， and γ-GT levels （P<0.01）. The results of the experiment regarding UC showed that compared with the normal group， the TNBS model group presented increases in the colonic damage score， colon weight index， and disease activity index （P<0.01）. Compared with the TNBS model group， the low-dose Zhuyuwan group showcased declines in colon weight index （P<0.01） and disease activity index （P<0.05）， and the high-dose Zhuyuwan group showed reductions in the colon damage score， colon weight index， and disease activity index （P<0.01）. GC-MS metabolomics analysis combined with qRT-PCR demonstrated that Zhuyuwan had a similar inverse regulatory effect on arginine metabolism disruption in the above two disease models. ConclusionZhuyuwan exhibited definite therapeutic effects on both IC and UC， and the regulation of arginine biosynthesis pathway is the core mechanism for the treatment of both diseases by Zhuyuwan.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.