Identification of RNA-binding protein genes associated with renal rejection and graft survival.

Rejection is one of the major factors affecting the long-term prognosis of kidney transplantation, and timely recognition and aggressive treatment of rejection is essential to prevent disease progression. RBPs are proteins that bind to RNA to form ribonucleoprotein complexes, thereby affecting RNA stability, processing, splicing, localization, transport, and translation, which play a key role in post-transcriptional gene regulation. However, their role in renal transplant rejection and long-term graft survival is unclear. The aim of this study was to comprehensively analyze the expression of RPBs in renal rejection and use it to construct a robust prediction strategy for long-term graft survival. The microarray expression profiles used in this study were obtained from GEO database. In this study, a total of eight hub RBPs were identified, all of which were upregulated in renal rejection samples. Based on these RBPs, the renal rejection samples could be categorized into two different clusters (cluster A and cluster B). Inflammatory activation in cluster B and functional enrichment analysis showed a strong association with rejection-related pathways. The diagnostic prediction model had a high diagnostic accuracy for T cell mediated rejection (TCMR) in renal grafts (area under the curve = 0.86). The prognostic prediction model effectively predicts the prognosis and survival of renal grafts (p < .001) and applies to both rejection and non-rejection situations. Finally, we validated the expression of hub genes, and patient prognosis in clinical samples, respectively, and the results were consistent with the above analysis.

Deep learning-enabled classification of kidney allograft rejection on whole slide histopathologic images.

Background: Diagnosis of kidney transplant rejection currently relies on manual histopathological assessment, which is subjective and susceptible to inter-observer variability, leading to limited reproducibility. We aim to develop a deep learning system for automated assessment of whole-slide images (WSIs) from kidney allograft biopsies to enable detection and subtyping of rejection and to predict the prognosis of rejection. Method: We collected H&E-stained WSIs of kidney allograft biopsies at 400x magnification from January 2015 to September 2023 at two hospitals. These biopsy specimens were classified as T cell-mediated rejection, antibody-mediated rejection, and other lesions based on the consensus reached by two experienced transplant pathologists. To achieve feature extraction, feature aggregation, and global classification, we employed multi-instance learning and common convolution neural networks (CNNs). The performance of the developed models was evaluated using various metrics, including confusion matrix, receiver operating characteristic curves, the area under the curve (AUC), classification map, heat map, and pathologist-machine confrontations. Results: In total, 906 WSIs from 302 kidney allograft biopsies were included for analysis. The model based on multi-instance learning enables detection and subtyping of rejection, named renal rejection artificial intelligence model (RRAIM), with the overall 3-category AUC of 0.798 in the independent test set, which is superior to that of three transplant pathologists under nearly routine assessment conditions. Moreover, the prognosis models accurately predicted graft loss within 1 year following rejection and treatment response for rejection, achieving AUC of 0.936 and 0.756, respectively. Conclusion: We first developed deep-learning models utilizing multi-instance learning for the detection and subtyping of rejection and prediction of rejection prognosis in kidney allograft biopsies. These models performed well and may be useful in assisting the pathological diagnosis.

Case report: Application of nonsurgical method in saving transplant renal vein thrombosis caused by acute diarrhea.

Transplant renal vein thrombosis is a rare complication after kidney transplantation, which can seriously threaten graft survival. Though the measures like thrombolytic therapy or operative intervention could be taken to deal with this complication, allograft loss is the most common outcome. Thus, early finding as well as decisive intervention is crucial to saving the graft. Here we present a 46-year-old male patient who underwent kidney transplantation from a cadaveric donor who developed a transplant renal venous thrombosis induced by acute diarrhea more than 1 year after renal transplantation with an initial symptom of sudden anuria and pain in the graft area. Subsequently, serum creatinine levels increased to 810.0 µmol/L. Pelvic CT showed increased vascular density of the transplanted kidney, and contrast-enhanced ultrasound confirmed venous thrombosis. The patient was treated with heparin sodium alone and diuresis gradually resumed. After more than 1 year of follow-up, serum creatinine returned to the baseline level prior to thrombosis. Our case indicates that quick ancillary examination and treatment without hesitation would be indispensable in rescuing allografts with renal vein thrombus. Unfractionated heparin can be recommended as an effective treatment for mid-long-term renal transplantation patients with renal vein thrombosis.

Antisperm antibodies in infertile men and their effect on semen parameters: a systematic review and meta-analysis.

BACKGROUND: Antisperm antibodies (ASA) in males cause the autoimmune disease 'immune infertility'. The mechanism of ASA cause male infertility is not clear. Present studies have investigated the effect of ASA and their incidence in men with unexplained infertility, as well as to evaluate the correlation between the ASA and semen parameter alterations but have shown inconsistent results. We performed a systematic literature review and meta-analysis to assess the association between ASA and basic semen parameters in infertility men. METHODS: Systematic literature searches were conducted with PubMed, EMBASE, Science Direct/Elsevier, CNKI and the Cochrane Library up to October 2014 for case-control studies that involved the impact of ASA on semen parameters. Meta-analysis was performed with Review Manager. Standard mean differences (SMD) of semen parameters were identified with 95% CI in a random or fixed effects model. RESULTS: Eight studies were identified, including 238 cases of ASA positive infertility male and 929 ASA negative controls. Our results illustrated that the sperm concentration and sperm motility (a+b) from ASA positive patients were significantly lower than ASA negative controls (SMD (95% CI) -23.64 [-43.47, -3.81], -16.40 [-27.92, -4.88], respectively). However, semen liquefaction time in the ASA positive group was significantly longer than the control group (SMD (95% CI) 4.19 [1.72, 6.66]). There was no significant effect of ASA on the sperm volume, sperm viability, sperm progressive motility, sperm normal morphology and sperm abnormal morphology. CONCLUSIONS: The present study illustrates that there was a significant negative effect of ASA on sperm concentration, sperm motility (a+b) and sperm liquefaction.