In December 2019, an outbreak of COVID-19 occurred in Wuhan, China, and spread to the whole of China and to multiple countries worldwide. Unlike SARS and MERS, where secondary transmission mostly occurred in hospital settings, COVID-19 transmission occurs in large numbers within families. Herein we report three cases of a familial cluster with one family member being a kidney transplant recipient. The initial clinical symptoms of COVID-19 in these three patients were the same, but their progression was different. Based on the severity of clinical symptoms, chest computer tomography findings and SARS-Cov-2 RNA test results, we admitted the husband to the respiratory intensive care unit (RICU) and used a treatment consisting of immunosuppressant reduction/cessation and low dose methylprednisolone-based therapy, and his wife to the respiratory isolation ward. In contrast, the son received in-home isolation and home-based care. All three family members made a full recovery.

Background: Despite the fact that 1-year graft and recipient survival rates are above 90% in most transplant centers, improving long-term graft survival remains an important challenge. Immunosuppressant nonadherence has been recognized as one of the important risk factors for long-term graft failure. Understanding the modifiable correlates and risk factors for medication non-adherence is essential to develop interventions to improve adherence and thus long-term transplantation outcomes.Methods: This study conducted a questionnaire survey on 431 renal transplant recipients who were followed up in the outpatient clinic between January 2022 and January 2023, and 409 valid questionnaires were returned. The BAASIS questionnaire was used to assess the prevalence of nonadherence to immunosuppressive therapy (implementation phase) in Chinese renal transplant recipients and to explore the multilevel correlates of immunosuppressive nonadherence. The BAASIS questionnaire was used to categorize renal transplant recipients into adherent (n = 239) and non-adherent (n = 170) groups, and a prospective cohort study with a 1-year follow-up was conducted to explore the impact of immunosuppressant non-adherence on clinical outcomes.Results: The prevalence of nonadherence to immunosuppressant therapy in renal transplant recipients in this study was as high as 41.6%. The number of years post-transplant (OR: 1.240, 95% CI: 1.136–1.353, p < 0.001) and the frequency of twice-daily dosing (OR: 5.145, 95% CI: 2.690–9.840, p < 0.001) were positively correlated with immunosuppressive nonadherence. There was a significant difference in TAC IPV (Intra-individual Variability) between the adherent and nonadherent groups (22.7 ± 8.7 vs. 25.4 ± 11.6, p = 0.010). Renal function remained stable during the follow-up period in the recipients in the adherence group and tended to decrease in the recipients in the non-adherence group (F = 4.932, p = 0.001). The rates of graft loss (7.1% vs. 1.7%, p = 0.006) and rejection (12.4% vs. 4.2%, p = 0.002) were higher in the nonadherent group than in the adherent group.Conclusion: Longer time post-transplant and higher frequency of immunosuppressive dosing were positively associated with nonadherence to immunosuppressives medication. Immunosuppressant nonadherence was associated with adverse graft outcomes.

Clear cell renal cell carcinoma (ccRCC) is an immunogenic tumor, and investigating the immunorelated genes is essential. To investigate the immunoprognostic genes of ccRCC, we analyzed the data assimilated from a public database (The Cancer Genome Atlas (TCGA) database and the gene expression omnibus (GEO) database) using bioinformatics. Then, an immunoprognosis model was constructed to identify four hub genes with moderate predictive values for the prognosis of ccRCC patients. These four genes were associated with the prognosis of ccRCC patients based on Oncomine and Gena Expression Profiling Interactive Analysis (GEPIA) databases. The correlation analysis between the immune infiltrate, immune checkpoints, and immunotherapy and this immunoprognosis model showed that immune infiltration could predict the immunotherapy effects. We also conducted a quantitative real-time polymerase chain reaction analysis and found that the expressions of three hub genes were associated with tumor progression (P<0.1). In conclusion, four genes that may serve as potential biomarkers in ccRCC were identified with respect to prognosis.

Introduction: Many challenges remain for long-term survival of renal allografts. Once-daily sirolimus (SRL) combined with low-dose extended-release tacrolimus (LER-TAC) may improve medication adherence and reduce the potential nephrotoxicity of calcineurin inhibitors (CNI) compared with standard immunosuppression regimens, thus potentially improving long-term graft survival.Methods: This retrospective, observational, single-center, propensity score matching (PSM) study compared conversion to SRL combined with low-dose ER-TAC and mycophenolic acid (MPA) combined with standard-dose TAC in kidney transplant recipients. After PSM, there were 56 patients in each group. Efficacy, safety, and medication adherence were evaluated over 12 months.Results: There was no significant difference between the two groups in terms of graft and recipient survival and incidence of biopsy-proven acute rejection (p = 1.000), and none of the recipients developed dnDSA after conversion. The mean eGFR improved in SRL + LER-TAC group after conversion compared to before conversion (51.12 ± 20.1 ml/min/1.73 m² vs. 56.97 ± 19.23 ml/min/1.73 m², p < 0.05). The medication adherence at 12 months after conversion was superior to before conversion (p = 0.002).Discussion: Our findings suggest that an immunosuppressive regimen of SRL combined with low-dose ER-TAC is no less effective and safe than standard immunosuppressive regimens for renal transplant recipients and may improve graft renal function and medication adherence.

Background. Renal ischemia‐reperfusion injury (RIRI) plays an important role in the poor prognosis of patients with renal transplants. However, the potential targets and mechanism of IRI are still unclear. Method. Differential gene expression (DEG) analysis and weighted correlation network analysis (WGCNA) were performed on the GSE27274 dataset. Pathway enrichment analysis on the DEGs was performed. To identify the hub DEGs, we constructed a protein‐protein interaction (PPI) network. Finally, the hub genes were verified, and candidate drugs were screened from the DsigDB database. Results. A hundred DEGs and four hub genes (Atf3, Psmb6, Psmb8, and Psmb10) were screened out. Pathway enrichment analysis revealed that 100 DEGs were mainly enriched in apoptosis and the TNF signaling pathway. The four hub genes were verified in animal models and another dataset (GSE148420). Thereafter, a PPI network was used to identify the four hub genes (Atf3, Psmb6, Psmb8, and Psmb10). Finally, eight candidate drugs were identified as potential drugs. Conclusion. Three hub genes (Psmb6, Psmb8, and Psmb10) were associated with RIRI and could be potential novel biomarkers for RIRI.