OBJECTIVE To investigate the effect and mechanism of modified Lichong decoction （MLCD） on the apoptosis of transplanted tumor cells in nude mice. METHODS Human gastric cancer AGS cells were cultured， and a nude mice transplanted tumor model was established. The nude mice were divided into the model group and MLCD low-， medium- and high-dose groups （150， 300， 600 mg/kg）. They were given distilled water or the corresponding drug solution by gavage once daily for four consecutive weeks. The size of transplanted tumors in nude mice was measured every six days， and the tumor volume was calculated. After the medication， the nude mice were sacrificed， and the transplanted tumor tissues were isolated. The contents of lactate dehydrogenase （LDH） and reactive oxygen species （ROS） in the transplanted tumor tissues were detected， and the changes in mitochondrial membrane potential were assessed. The pathological morphological changes were observed. The enzymatic activities of caspase-3， caspase-8， and caspase-9， as well as protein expressions of Fas and FasL and mRNA expressions of caspase-3， caspase-8， caspase-9， Fas and FasL in the transplanted tumor tissues， were detected. RESULTS Compared with the model group， the volume of transplanted tumors in nude mice from all MLCD dose groups was reduced to varying degrees. The contents of LDH and ROS， as well as the enzymatic activities of caspase-3， caspase-8 and caspase-9， were significantly increased/enhanced. The mitochondrial membrane potential was significantly decreased. The protein expressions of Fas and FasL， and the mRNA expressions of caspase-3， caspase-8， caspase-9， Fas and FasL were significantly up-regulated. Most of these differences were statistically significant （ P ＜0.05 or P ＜0.01）. Pathological results showed that with increasing doses of MLCD， the cellular density in the transplanted tumor tissues gradually decreased， and typical morphological features of apoptosis， such as loosening and increasing fragmentation， became more prominent. CONCLUSIONS MLCD can induce apoptosis in transplanted tumor cells of nude mice， and its mechanism may be related to the activation of the Fas/FasL pathway and the caspase apoptotic pathway.

ObjectiveIn traditional Chinese medicine (TCM), the foundational doctrine that the eyes reflect the essence of the internal viscera establishes ocular observation as a cornerstone of diagnostic practice. Specifically, the morphological characteristics and coloration variations of the scleral microvasculature serve as critical clinical indicators for assessing the dynamic balance of Qi and Blood, as well as the pathological status of internal organs. Historically, however, TCM eye diagnosis has relied predominantly on the subjective clinical experience and visual acuity of individual practitioners, leading to inherent challenges in standardization and reproducibility. While automated computer-aided diagnostic systems offer a promising solution, existing vessel segmentation algorithms encounter significant domain-specific bottlenecks when applied to scleral imagery. These challenges primarily stem from the highly reflective and moist nature of the ocular surface, which generates severe reflective interference. Furthermore, the inherent low contrast of fine capillary networks against complex background textures, compounded by non-uniform illumination, frequently results in high false-positive rates, misdetections, and severe vessel fragmentation. To address these critical limitations and advance the objective quantification of TCM diagnostics, this paper proposes a novel, highly robust sclera vessel segmentation framework that innovatively integrates Frangi-Sato dual-filter adaptive enhancement with pixel-level reflection detection. MethodsThe proposed methodology systematically addresses the segmentation pipeline through three synergistic stages. First, to overcome the structural limitations of single-filter approaches, a multi-scale weighted fusion strategy is meticulously designed to harness the complementary extraction capabilities of both Frangi and Sato filters. This adaptive enhancement optimally balances the preservation of main vessel trunk continuity with the heightened sensitivity required for delineating delicate, low-contrast peripheral capillaries. Second, to tackle the persistent issue of reflective highlights, a sophisticated multi-feature synergistic reflection detection module is introduced. By jointly analyzing local information entropy, gradient field variations, and intensity statistical distributions, this module achieves precise, pixel-level identification and elimination of reflective artifacts without compromising the underlying vascular structures. Finally, a dual-level adaptive thresholding strategy, featuring an innovative “core protection” mechanism, is implemented. This critical step effectively suppresses complex background noise while rigorously preserving the structural and topological integrity of the intricate vessel network, preventing the structural breaks often seen in conventional binarization methods. ResultsThe efficacy of the proposed framework was rigorously evaluated using both self-constructed clinical datasets specifically acquired for TCM research and standardized public datasets. Extensive experimental results demonstrate that the proposed method consistently outperforms state-of-the-art traditional approaches and contemporary deep learning models. Specifically, the proposed method achieves a Dice similarity coefficient of approximately 0.71 on the private clinical dataset, and secures the best performance across the majority of quantitative metrics on both datasets. Notably, the framework exhibits exceptional robustness and generalization capabilities in highly challenging scenarios characterized by intense reflective interference, low signal-to-noise ratios, and cross-domain image variations. ConclusionThis study successfully realizes the high-integrity, automated segmentation of scleral vessel networks under complex clinical imaging conditions. By overcoming the fundamental algorithmic challenges of reflection interference and micro-vessel loss, the proposed methodology provides potential support for the digitization, objective standardization, and intelligent advancement of modern TCM eye diagnosis systems.

ObjectiveIn traditional Chinese medicine (TCM), the foundational doctrine that the eyes reflect the essence of the internal viscera establishes ocular observation as a cornerstone of diagnostic practice. Specifically, the morphological characteristics and coloration variations of the scleral microvasculature serve as critical clinical indicators for assessing the dynamic balance of Qi and Blood, as well as the pathological status of internal organs. Historically, however, TCM eye diagnosis has relied predominantly on the subjective clinical experience and visual acuity of individual practitioners, leading to inherent challenges in standardization and reproducibility. While automated computer-aided diagnostic systems offer a promising solution, existing vessel segmentation algorithms encounter significant domain-specific bottlenecks when applied to scleral imagery. These challenges primarily stem from the highly reflective and moist nature of the ocular surface, which generates severe reflective interference. Furthermore, the inherent low contrast of fine capillary networks against complex background textures, compounded by non-uniform illumination, frequently results in high false-positive rates, misdetections, and severe vessel fragmentation. To address these critical limitations and advance the objective quantification of TCM diagnostics, this paper proposes a novel, highly robust sclera vessel segmentation framework that innovatively integrates Frangi-Sato dual-filter adaptive enhancement with pixel-level reflection detection. MethodsThe proposed methodology systematically addresses the segmentation pipeline through three synergistic stages. First, to overcome the structural limitations of single-filter approaches, a multi-scale weighted fusion strategy is meticulously designed to harness the complementary extraction capabilities of both Frangi and Sato filters. This adaptive enhancement optimally balances the preservation of main vessel trunk continuity with the heightened sensitivity required for delineating delicate, low-contrast peripheral capillaries. Second, to tackle the persistent issue of reflective highlights, a sophisticated multi-feature synergistic reflection detection module is introduced. By jointly analyzing local information entropy, gradient field variations, and intensity statistical distributions, this module achieves precise, pixel-level identification and elimination of reflective artifacts without compromising the underlying vascular structures. Finally, a dual-level adaptive thresholding strategy, featuring an innovative “core protection” mechanism, is implemented. This critical step effectively suppresses complex background noise while rigorously preserving the structural and topological integrity of the intricate vessel network, preventing the structural breaks often seen in conventional binarization methods. ResultsThe efficacy of the proposed framework was rigorously evaluated using both self-constructed clinical datasets specifically acquired for TCM research and standardized public datasets. Extensive experimental results demonstrate that the proposed method consistently outperforms state-of-the-art traditional approaches and contemporary deep learning models. Specifically, the proposed method achieves a Dice similarity coefficient of approximately 0.71 on the private clinical dataset, and secures the best performance across the majority of quantitative metrics on both datasets. Notably, the framework exhibits exceptional robustness and generalization capabilities in highly challenging scenarios characterized by intense reflective interference, low signal-to-noise ratios, and cross-domain image variations. ConclusionThis study successfully realizes the high-integrity, automated segmentation of scleral vessel networks under complex clinical imaging conditions. By overcoming the fundamental algorithmic challenges of reflection interference and micro-vessel loss, the proposed methodology provides potential support for the digitization, objective standardization, and intelligent advancement of modern TCM eye diagnosis systems.

Objective To explore the valve regurgitation status of left heart after the implantation of left ventricular assist device (LVAD) and its effect on prognosis of patients with LVAD implantation. Methods A total of 35 patients with cardiomyopathy who underwent magnetic levitation LVAD implantation at Zhongshan Hospital, Fudan University from February 2021 to July 2024 were retrospectively selected. Clinical data during hospitalization were collected, including preoperative basic data and postoperative valve regurgitation status. Telephone follow-ups were conducted to monitor patients’ survival status and transthoracic echocardiography was used to assess left valve function. Kaplan-Meier survival curves and log-rank test were employed to compare the survival rate of patients with different levels of valve regurgitation. Results The 35 patients had a mean age of (53.9±11.1) years, with 85.7% male, and 3 patients (8.6%) died during hospitalization. Preoperatively, 17 patients (48.6%) had moderate or greater mitral regurgitation, while all 35 patients had less than moderate aortic regurgitation. One month postoperatively, thirty patients were followed up, among which 24 patients (80%) had less than moderate mitral regurgitation, including 11 cases with alleviated regurgitation compared to pre-surgery; 6 patients (20%) had moderate or greater mitral regurgitation, including 4 cases with stable regurgitation and 2 cases with progression of regurgitation compared to pre-surgery; 2 patients (6.7%) had progression of aortic regurgitation to moderate or greater. The follow-up time was 1.2 (1.0, 2.1) years, with 1-year survival rate of 91.4% and 3-year survival rate of 71.1%. Survival analysis showed that the 3-year survival rate of patients with moderate or greater mitral regurgitation one month postoperatively was significantly lower than that of patients with less than moderate regurgitation (66.7% vs 83.3%, P＝0.046). Conclusions After the implantation of magnetic levitation LVAD, most patients showed improvement in mitral regurgitation, while aortic regurgitation remained unchanged. The degree of mitral regurgitation one month postoperatively is associated with prognosis.

Objective:To investigate the value of biparametric-MRI (bpMRI) based peritumoral radiomics for preoperative prediction of extraprostatic extension (EPE) in prostate cancer (PCa).Methods:In this cross-sectional study, consecutive bpMRI of patients undergoing prostatectomy for PCa were retrospectively collected from the First Medical Center (center 1) and the Third Medical Center (center 2) of Chinese PLA General Hospital. A total of 274 patients were finally enrolled. Patients at center 1 from January 2020 to December 2022 were randomly divided into a training set (149 cases) and an internal validation set (63 cases) by stratified random sampling. Patients at center 2 from January 2023 to March 2024 were assigned to the external test set (62 cases). Patients were categorized into EPE-positive group and EPE-negative group according to pathological assessment postoperatively. In the training set, there were 49 cases in EPE-positive group and 100 cases in EPE-negative group. In the internal validation set, there were 26 cases in EPE-positive group and 37 cases in EPE-negative group. In the external test set, there were 22 cases in EPE-positive group and 40 cases in EPE-negative group. Axial T 2WI and apparent diffusion coefficient (ADC) images were manually annotated to obtain index lesion regions of interest (ROIs), with the peritumoral ROIs subsequently delineated by semi-automatic segmentation technique. Radiomics features were extracted from intra-tumoral, peri-tumoral, and intra-tumoral plus peri-tumoral ROIs. The training set data was employed to select and optimize features to build the radiomics models. The logistic regression analysis was used to develop radiomics, clinical, and integrated models. The predictive performance was assessed by the area under the receiver operating characteristic curve (AUC) in the external test set, and compared by the DeLong test. The sensitivity and specificity were compared by the exact McNemar test. Results:In the external test set, the peri-tumoral radiomics model based on bpMRI showed the highest performance in evaluating EPE, with an AUC of 0.739 (95% CI 0.611-0.842), which was identified as the optimal radiomics model. EPE grade ( OR=6.151, 95% CI 3.371-11.226, P<0.001) was incorporated into the clinical model, with an AUC of 0.780 (95% CI 0.657-0.875) in the external test set. The integrated model had an AUC of 0.817 (95% CI 0.698-0.904) in the external test set. There was no statistically significant difference in comparisons of AUCs among the three models (all P>0.05). The sensitivity of the integrated model (68.2%) showed no significant difference from those of the clinical model and the optimal radiomics model (77.3% and 86.4%, respectively; P=0.500 and P=0.289). However, the specificity of the integrated model (85.0%) was significantly higher than those of the clinical model (67.5%, P=0.016) and the optimal radiomics model (50.0%, P<0.001). Conclusion:A bpMRI-based peritumoral radiomics integrating clinical model demonstrates high performance for preoperative prediction of EPE in PCa.

BACKGROUND:Exosomes have been confirmed to be closely related to cartilage degeneration in osteoarthritis.However,the role and mechanism of exosome-derived genes in cartilage degeneration of osteoarthritis have not been fully elucidated.OBJECTIVE:Bioinformatics analyses were used to screen the genes related to cartilage degeneration in the synovial exosomes of patients with osteoarthritis,and to determine their biological functions and signaling pathways in order to provide new therapeutic targets for delaying cartilage degeneration in osteoarthritis.METHODS:Firstly,osteoarthritis-related exosome dataset GSE185059 and cartilage degeneration dataset GSE114007 were downloaded from Gene Expression Omnibus(GEO)database to screen exosome-derived cartilage degeneration related genes.GO functional and KEGG pathway enrichment analyses were performed based on the screened exosome-derived cartilage degeneration related genes.Protein-protein interaction network was drawn and Ingenuity Pathway Analysis(IPA)was conducted to screen and obtain key exosome-derived cartilage degeneration-related genes.Finally,qRT-PCR was used to verify the expression of key genes in osteoarthritis cartilage tissue and interleukin-1β stimulated chondrocyte models.RESULTS AND CONCLUSION:(1)There were 831 differentially expressed genes in the GSE185059 dataset and 5 323 differentially expressed genes in the GSE114007 dataset.A total of 94 exosome-derived cartilage degeneration related genes were screened after the intersection of these differentially expressed genes,of which 51 genes were down-regulated and 43 genes were up-regulated.(2)GO functional enrichment analysis showed that the up-regulated genes were mainly involved in the positive regulation of cell-cell adhesion,the positive regulation of T cell activation,and chronic inflammatory response,while the down-regulated genes were mainly involved in biological processes such as cell aggregation,cartilage differentiation and development,and skeletal system morphogenesis.(3)KEGG pathway enrichment analysis showed that exosome-derived cartilage degeneration-related genes were mainly involved in tryptophan enrichment metabolism,vitamin B6 metabolism,and leukocyte transendothelial migration.(4)The constructed protein-protein interaction network confirmed the existence of multiple interaction relationships among exosome-derived cartilage degeneration-related genes.Combined with five algorithms in CytoHubba software,four key exosome-derived cartilage degeneration-related genes were further screened,namely THY1,CYP1A1,NFKB2,and COL6A3.(5)The results of qRT-PCR showed that compared with normal cartilage,the expressions of THY1 and COL6A3 in osteoarthritic cartilage were increased,while the expression of CYP1A1 and NFKB2 was decreased.Similarly,compared with the unstimulated group,the expression of THY1 and COL6A3 in the interleukin-1β induced chondrocytes was upregulated,while the expression of CYP1A1 and NFKB2 was downregulated.(6)These results indicate that THY1,CYP1A1,NFKB2,and COL6A3 are genes related to cartilage degeneration in the exosomes of synovial fluid of patients with osteoarthritis,and may participate in the pathogenesis of osteoarthritis by regulating biological processes such as protein tyrosine kinase activity and lipid metabolism,as well as nuclear factor-κB signaling pathway and focal adhesion signaling pathway.However,the specific regulatory roles and molecular mechanisms of these key genes in cartilage degeneration need to be further verified by experiments.

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.

The purpose of this study was to establish a luciferase immunosorbent assay(LISA)using the Crimean-Congo hemorrhagic fever virus(CCHFV)glycoprotein C(Gc),a specific antigen,for the detection of CCHFV IgG antibodies.Three antigenic fragments based on CCHFV glycoprotein C were designed,and three recombinant plasmids were constructed by liga-tion with the NanoLuc luciferase(NLuc)expression vector pNLF1-N through molecular cloning.The accuracy of the sequences in the recombinant plasmids was confirmed through sequencing.The recombinant plasmids were transfected into eukaryotic cells to obtain fusion proteins containing specific antigens and luciferase,and the expression of the fusion proteins was verified by western blotting,thereby facilitating the establishment of the CCHFV-LISA detection technique.The assay's sensitivity,specificity,and stability were evaluated and compared with those of a commercial CCHFV IgG antibody test kit.Three recom-binant antigen fragments of CCHFV Gc—NLuc-Gc-Full,NLuc-Gc-C1,and NLuc-Gc-C2—were expressed,with molecular weights of 80.1 kDa,62.8 kDa,and 53.9 kDa,respectively.The optimal fragment for CCHFV detection was NLuc-Gc-C2.The sensitivity of the CCHFV-LISA was 90.9％,and the specificity was 100％;the findings were highly concordant with those for the commercial CCHFV enzyme-linked immunosorbent assay kit.Repeatability tests indicated no statistically significant differ-ences in inter-and intra-assay variability within the same batch.The LISA was highly specific,sensitive,and user-friendly in detecting IgG antibodies against the CCHFV.Therefore,this method may facilitate serological diagnosis and epidemiological studies in endemic regions,and provide essential technical support for disease surveillance and early warning.

Objective To screen out the indexes of medical insurance fund under China Healthcare Security Diagno-sis Related Groups(CHS-DRG)payment based on Delphi method and improve the efficiency of medical insurance fund supervision.Methods Using the Delphi method,32 experts in the medical insurance field were selected and 2 rounds of expert questionnaires were conducted to design an evaluation scale for the monitoring indicators of medi-cal insurance funds under CHS-DRG payment.The consultation results were evaluated from three aspects of motiva-tion,authority,and coordination.Results A medical insurance fund supervision index system based on three dimen-sions of medical insurance fund use quality,efficiency and safety supervision is constructed,including 3 first-level indicators,7 second-level indicators and 44 third-level indicators.Results The construction ofmedical insurance fund supervision index system under CHS-DRG payment should respect the actual clinical diagnosis and treatment,strengthen the intelligent supervision method,and timely intervene in medical insurance fund management risks.

Objective:To analyze the clinical characteristics, treatments, and prognosis of patients with ovarian juvenile granulosa cell tumor (JGCT).Methods:Clinical and pathological data, and follow-up information of 34 patients diagnosed with JGCT from 2000 to 2021 were collected from the surveillance, epidemiology, and end results (SEER) database. A retrospective analysis was conducted to summarize the patients′ clinical and pathological characteristics, treatments, and prognosis. Propensity score matching (PSM) was used to match the JGCT cases with adult granulosa cell tumor (AGCT) cases in SEER database. A total of 96 patients with ovarian granulosa cell tumor (OGCT), including 32 cases of JGCT and 64 cases of AGCT, were enrolled in a matched cohort analysis. Univariate and multivariate Cox regression analysis were performed on the matched cohort to explore the risk factors for overall survival. Kaplan-Meier curves and the log-rank test were used to compare the survival outcomes between JGCT and AGCT.Results:(1) The median age at diagnosis for the 34 JGCT patients was 19.5 years (ranged: 1-48 years), with 3 patients aged ≤10 years, 16 patients aged 11-20 years, 11 patients aged 21-30 years, and 4 patients aged >30 years. Tumors originated unilaterally in 33 patients, with only 1 case originating bilaterally. The maximum tumor diameter was recorded in 26 patients, with a median size of 12.4 cm (ranged: 3.5-40.0 cm). According to the 2014 International Federation of Gynecology and Obstetrics (FIGO) staging system, 19 patients were diagnosed with stage Ⅰ (including 10 cases with stage Ⅰa and 9 cases with stage Ⅰc), 4 patients with stage Ⅱ, 8 patients with stage Ⅲ, and 3 patients with stage Ⅳ. Two patients did not undergo surgery for the resection of lesions. Stage Ⅰ patients (15/19) underwent fertility-sparing surgery, while stage Ⅱ-Ⅲ patients underwent either fertility-sparing surgery or cytoreductive surgery (6 cases each). Stage Ⅳ patients underwent cytoreductive surgery (2 cases). Lymph node dissection was performed in 10 patients, among which only 1 patient with positive lymph nodes metastasis. None of the 34 patients received radiotherapy, while 18 patients received adjuvant chemotherapy (included neoadjuvant chemotherapy and postoperative adjuvant chemotherapy). The proportion of stage Ⅰ patients receiving adjuvant chemotherapy was relatively low, with only 4 out of 19 patients (including 2 out of 10 cases for stage Ⅰa and 2 out of 9 cases for stage Ⅰc). The proportions of patients receiving adjuvant chemotherapy for stages Ⅱ, Ⅲ and Ⅳ were 3 out of 4 cases, 8 out of 8 cases, and 3 out of 3 cases, respectively. The follow-up ended in December 2021, with 20 patients alive and 14 dead. The survival rate for ovarian JGCT patients was 59% (20/34). Among them, the survival rate for stage Ⅰ patients was 16/19, while for stage Ⅱ-Ⅳ patients, it was 4/15; there was a statistically significant difference ( P=0.002). Among stage Ⅱ-Ⅲ patients, the survival rate at the end of follow-up was 1/6 for those who underwent fertility-sparing surgery, compared to 3/6 for those who underwent cytoreductive surgery ( P=0.546). (2) For the 96 OGCT patients after matching using the PSM method, 64 ovarian AGCT patients had 5 deaths and 59 survivors during the follow-up period, the survival rate was 92% (59/64) at the end of follow-up. In contrast, among the 32 ovarian JGCT patients, 13 died and 19 survived, resulting in a survival rate of 59% (19/32) at the end of follow-up, which was statistically significant difference for the AGCT group ( P<0.001). Univariate Cox analysis revealed that histology, extent of surgery, chemotherapy, postoperative tumor residual status, and stage all significantly affected the survival outcomes of OGCT patients (all P<0.05). Multivariate Cox analysis revealed that variables with significant statistical differences were histology and stage. The median survival time for JGCT patients was 126 months, while AGCT patients median survival time was not reached with a statistically significant between the two groups ( P<0.001). Conclusions:Ovarian JGCT predominantly occur in adolescents and young women. Lymph node metastasis is relatively rare, and treatment primarily involves surgery and adjuvant chemotherapy. Most ovarian JGCT patients are diagnosed at stage Ⅰ, with a favorable prognosis. Fertility-preserving surgery is recommended, involving salpingo-oophorectomy on the affected side plus comprehensive staging surgery, or a second surgery to achieve comprehensive staging. For stage Ⅱ-Ⅳ ovarian JGCT patients, the prognosis is relatively poor, and fertility-preserving surgery should be considered with caution. The prognosis of ovarian JGCT patients is worse than that of ovarian AGCT patients.