Clinical and Experimental Medicine最新文献

Cancer has emerged as a significant threat to human health. Nucleic acid therapeutics regulate the gene expression process by introducing exogenous nucleic acid fragments, offering new possibilities for tumor remission and even cure. Their mechanism of action and high specificity demonstrate great potential in cancer treatment. However, nucleic acid drugs face challenges such as low stability and limited ability to cross physiological barriers in vivo. To address these issues, various nucleic acid delivery vectors have been developed to enhance the stability and facilitate precise targeted delivery of nucleic acid drugs within the body. In this review article, we primarily introduce the structures and principles of nucleic acid drugs commonly used in cancer therapy, as well as their cellular uptake and intracellular transportation processes. We focus on the various vectors commonly employed in nucleic acid drug delivery, highlighting their research progress and applications in recent years. Furthermore, we propose potential trends and prospects of nucleic acid drugs and their carriers in the future.

Chronic kidney disease (CKD) represents a significant global public health issue, with its incidence and prevalence escalating annually. Metabolic disorders are one of the major etiological factors of CKD. This study investigates the relationship between the emerging metabolic index triglyceride-glucose body mass index (TyG-BMI) and the onset of CKD. Our study enrolled 3,485 healthy participants (1,576 men and 1,909 women), with a follow-up period of 3 years. The primary outcome was the emergence of CKD, defined by an eGFR less than 60 mL/(min × 1.73 m²) or the onset of proteinuria. To examine the TyG-BMI and CKD onset relationship, we used univariate and multivariate logistic regression analyses, stratified analyses, and receiver operating characteristic (ROC) curves. After a three-year follow-up, CKD developed in 2% (n = 70) of the participants. Subjects were divided into three equal groups based on their TyG-BMI values, from lowest to highest. After adjusting for potential confounders, the highest TyG-BMI group exhibited a multifactor-adjusted odds ratio (OR) of 4.24 (95% CI 1.30-13.78, P = 0.016) compared to the lowest group. Stratified analyses revealed that the association between TyG-BMI and CKD onset was stronger among females, individuals younger than 60 years, and those with a BMI ≥ 24 kg/m². Furthermore, TYG-BMI was effective in predicting the incidence of CKD. Our findings indicate that TyG-BMI is an independent risk factor for the onset of CKD and that assessment of TyG-BMI may be useful for the early identification of individuals at high risk for CKD.

The application of CAR-T cells in solid tumors poses several challenges, including poor T cell homing ability, limited infiltration of T cells and an immunosuppressive tumor environment. In this study, we developed a novel approach to address these obstacles by designing GPC3-specific CAR-T cell that co-express IL-21 and CXCL9 (21 × 9 GPC3 CAR-T cells) and blocking the PD-1 expression on it. The proliferation, cell phenotype, cytokine secretion and cell migration of indicated CAR-T cells were evaluated in vitro. The cytotoxic activities of genetically engineered CAR-T cells were accessed in vitro and in vivo. Compared to conventional GPC3 CAR-T cells, the 21 × 9 GPC3 CAR-T cells demonstrated superior proliferation, cytokine secretion and chemotaxis capabilities in vitro. Furthermore, when combined with PD-1 blockade, the 21 × 9 GPC3 CAR-T cells exhibited enhanced proliferation, cytokine secretion and enrichment of effector T cells such as CTL, NKT and TEM cells. In xenograft tumor models, the PD-1 blocked 21 × 9 GPC3 CAR-T cells effectively suppressed HCC xenograft growth and increased T cell infiltration. Overall, our study successfully generated GPC3 CAR-T cells expressing both IL-21 and CXCL9, demonstrated that combining PD-1 blockade can further enhance CAR-T cell function by promoting proliferation, cytokine secretion, chemotaxis and antitumor activity. These findings present a hopeful and potentially effective strategy for GPC3-positive HCC patients.

Replication-defective viral vector vaccines have several advantages over conventional subunit vaccines, including potent antibody responses, cellular responses critical for eliminating pathogen-infected cells, and the induction of highly immunogenic and durable immune responses without adjuvants. The Human papillomavirus (HPV), a microorganism with over 200 genotypes, plays a crucial role in inducing human tumors, with the majority of HPV-related malignancies expressing HPV proteins. Tumors associated with HPV infection, most of which result from HPV16 infection, include those affecting the cervix, anus, vagina, penis, vulva, and oropharynx. In recent years, the development of therapeutic HPV vaccines utilizing viral vectors for the treatment of premalignant lesions or tumors caused by HPV infection has experienced rapid growth, with numerous research pipelines currently underway. Simultaneously, screening for optimal antigens requires more basic research and more optimized methods. In terms of preclinical research, we present the various models used to assess vaccine efficacy, highlighting their respective advantages and disadvantages. Further, we present current research status of therapeutic vaccines using HPV viral vectors, especially the indications, initial efficacy, combination drugs, etc. In general, this paper summarizes current viral vector therapeutic HPV vaccines in terms of HPV infection, antigen selection, vectors, efficacy evaluation, and progress in clinical trials.

The clinical significance of an abnormal free light chain (FLC) test, performed due to unspecific complains in the absence of a known plasma cell dyscrasia (PCD) or lymphoproliferative disease (LPD), is not fully elucidated. We investigated the importance of an abnormal FLC ratio (FLC-R) in this setting. Patients registered in the Maccabi Healthcare Services database, tested for FLC during 2007-2023 without previously documented PCD/LPD or increased total protein (TP) level, were reviewed. Demographics, co-morbidities, and laboratory tests were recorded. FLC-R was defined as normal (0.26-1.65) or slightly (slAb 0.1-0.26/1.65-4), moderately (mAbn 0.1-0.05/4-8) and significantly abnormal (sigAb- < 0.05 or > 8). Factors associated with PCD/LPD and overall survival were identified. In total, 8,661 patients, 2,215 (25.6%) with abnormal FLC-R [2,090 (24.1%)-slAb, 65 (0.75%)-mAbn and 60 (0.7%)-sigAb], were analyzed. Almost none had anemia nor acute renal failure. 14% had concomitant increased immunoglobulins. Within a median follow-up of 52 months, 943 were diagnosed with PCD (816-MGUS, 127-MM/Amyloidosis/plasmacytoma) and 48 with LPD. Median time to PCD and LPD were 19 and 28 months. Multivariate analysis found slAb (HR = 1.8, CI95%:1.53-2.12, p < 0.001), mAbn (HR = 6.3, CI95%:4.16-9.53, p < 0.001), and sigAb FLC (HR = 10.4, CI95%:7.0-15.35, p < 0.001), to be associated with PCD/LPD diagnosis. Decreased IgG, increased IgA, and concomitant comorbidities predicted PCD, whereas increased IgM predicted LPD. Older age, male gender, anemia, decreased albumin, increased IgG and concomitant comorbidities, predicted shorter survival. Our large study emphasizes the independent clinical significance of abnormal FLC-R as a predictor of PCD/LPD diagnosis even in patients with normal TP level, promoting early detection of PCD/LPD.

The Sysmex XN series haematopoietic progenitor cell (XN-HPC) is a novel tool for assessing stem cell yield before allogeneic haematopoietic stem cell transplantation. This study aimed to establish a reference interval (RI) for XN-HPC in peripheral blood allogeneic transplant donors following granulocyte colony-stimulating factor (G-CSF) stimulation and determine its clinical significance. All specimens were analysed using Sysmex XN-20. Samples were collected and analysed using non-parametric percentile methods to define the RIs. Quantile regression was used to explore the dependency of the RIs on sex and age. Samples were included in clinical decision limits for apheresis based on receiver operating characteristic curve analysis. The non-parametrically estimated RI for XN-HPC was 623.50 (90% confidence interval [CI90%] 510.00-657.00) to 4,144.28 (CI90% 3,761.00-4,547.00). The RIs for the XN-HPC were not age-dependent but were sex-dependent. The RI for males was 648.40 (CI90% 582.00-709.00)-4,502.60 (CI90% 4,046.00-5,219.00) and for females was 490.90 (CI90% 311.00-652.00)-3,096.90 (CI90% 2,749.00-3,782.00). Comparisons based on XN-HPC values between the poor and less-than-optimal groups, good and less-than-optimal groups, and good and non-good groups had areas under the curve of 0.794 (P < 0.001), 0.768 (P < 0.001), and 0.806 (P < 0.001), respectively, indicating a good predictive value for mobilisation effectiveness. XN-HPC data exceeding 3974 × 10⁶/L suggested that a sufficient number of stem cells could be collected clinically. Values > 5318 < 10⁶/L indicated 100% mobilisation effectiveness. We established an RI for XN-HPC in peripheral blood allogeneic transplant donors following G-CSF stimulation and determined clinical decision thresholds for mobilisation efficiency.

The utilization of artificial intelligence (AI) in rheumatic diseases has enhanced the diagnostic accuracy of rheumatic diseases, enabled the prediction of patient outcomes, expanded treatment options, and facilitated the provision of individualized medical solutions. The research in this field has been progressively growing in recent years. Consequently, there is a need for bibliometric analysis to elucidate the current state of advancement and predominant research foci in AI applications within rheumatic diseases. Additionally, it is crucial to identify key contributors and their interrelations in this field. This study aimed to conduct a bibliometric analysis to investigate the current research hotspots and collaborative networks in the application of AI in rheumatic disease in recent years. A comprehensive search was conducted in Web of Science for articles on artificial intelligence in rheumatic diseases, published in SSCI and SCI-EXPANDED until January 1, 2024. Utilizing software tools like VOSviewers and CiteSpace, we analyzed various parameters including publication year, journal, country, institution, and authorship. This analysis extended to examining cited authors, generating reference and citation network graphs, and creating co-citation network and keyword maps. Additionally, research hotspots and trends in this domain were evaluated. As of January 1, 2024, a total of 3508 articles have been published on the application of artificial intelligence (AI) in rheumatic disease, exhibiting a steady rise in both the annual publication frequency and rate. "Scientific Reports" emerged as the leading journal in terms of relevant publications. The United States stood out as the predominant country in terms of the volume of published papers, with the University of California, San Francisco (UCSF) being the most prolific and frequently cited institution. Among authors, Young Ho Lee and Valentina Pedoia were noted for their significant contributions, with Pedoia achieving the highest average citation count per publication. Machine learning emerged as a prominent and central keyword. The trend indicates a growing interest in AI research within rheumatologic diseases, with its role expected to become increasingly pivotal in the field. This study presents a comprehensive summary of research trends and developments in the application of artificial intelligence (AI) in rheumatic diseases. It offers insights into potential collaborations and prospects for future research, clarifying the research frontiers and emerging directions in recent years. The findings of this study serve as a valuable reference for scholars studying rheumatology and immunology.

Objectives: There is currently no evidence documenting the clinical characteristics and prognosis of non-high-risk patients with incidental stage T1 lung cancer (LC). The aim of this study was to investigate the clinical characteristics and prognosis of non-high-risk patients with incidental stage T1 LC.

Methods: This prospective cohort study included patients with incidental stage T1 LC who were diagnosed pathologically at the First Affiliated Hospital of Chongqing Medical University between 1st Jan 2019 and 31st Dec 2023. The follow-up time for all participants concluded on 31st Jan 2024, or upon death. All included patients were divided into non-high-risk (observation) and high-risk (control) groups based on the 2021 US preventative services task force recommendations. The primary outcomes were overall survival probability and LC-specific survival probability. The secondary outcomes were clinical characteristics, including demographic variables, histological types and TNM staging.

Results: We studied 1876 patients with incidental stage T1 LC. Of these, 1491 (79.48%) non-high-risk patients were included in the observation group, and the remaining 385 (20.52%) high-risk patients composed the control group. The follow-up interval was between 0 and 248 months for all participants, with a median time of 41.64 ± 23.85 months. The patients in the observation group were younger and had smaller tumors, more adenocarcinomas, and earlier disease stages than those in the control group (p ≤ 0.001). The overall survival probability (HR = 0.23, [95% CI: 0.18, 0.31], p < 0.001) and the LC-specific survival probability (HR = 0.23, [95% CI: 0.17, 0.31], p < 0.001) for the patients in the observation group were also both higher than those in the control group. The results appeared to be consistent across important subgroups.

Conclusion: In this study, non-high-risk patients with incidental stage T1 LC were younger, had smaller tumors, had more adenocarcinomas, had a lower probability of metastasis, and had longer survival than did high-risk patients.

To compare clinical characteristics and survival outcomes of patients with multiple renal cell carcinoma versus single renal cell carcinoma. Develop a prognostic model for predicting prognosis in patients with multiple tumors and analyze prognostic factors. Patients with primary multiple renal cell carcinoma were selected from the Surveillance, Epidemiology, and End Results database (2004-2015). They were divided into single-tumor and multiple-tumor groups. Survival analysis was conducted using the Kaplan-Meier method and log-rank test. A Cox regression model was used to identify potential prognostic factors. A total of 19,489 renal cell carcinoma cases were included, with 947 in the multiple-tumor group and 18,542 in the single-tumor group. The multiple-tumor group had lower cancer-specific survival (P = 0.03, HR = 1.431). Cox regression identified risk factors for the multiple-tumor group including number of tumors, gender, combined summary stage, T stage, N stage, tumor size, and type of surgery. The predicted probabilities showed acceptable agreement with the actual observations at 3-, 5-, and 8-years area under the curve values in both the training and validation cohorts (0.831 vs. 0.605; 0.775 vs. 0.672; and 0.797 vs. 0.699, respectively). Compared with single renal cell carcinoma, multiple renal cell carcinoma is associated with decreased cancer-specific survival. Additionally, we identified several prognostic factors including the number of tumors, T stage, tumor size, and type of surgery. These findings offer valuable insights for selecting appropriate treatment strategies for patients diagnosed with multiple renal cell carcinomas.