Background Body mass index (BMI) is a convenient measure used to assess obesity and is used to select candidates for kidney donation. Glomerulomegaly is an early indicator of obesity-related kidney disease. Whether obesity assessment by BMI best reflects underlying glomerulomegaly and is predictive of adverse changes in renal function postdonation is unclear. Methods We performed a retrospective study on a cohort of 1065 living donors at the Mayo Clinic in Rochester; obesity measures by BMI and by computed tomography were compared between 20 donors with largest to 20 donors with the smallest glomerular volumes (on implantation biopsy). In addition, the change in kidney function postdonation (mean 7 months) was compared across BMI groups (<25, 25–29, 30–34, ≥35 kg/m2) in about 500 donors. Results We observed that larger glomerular volume was more strongly associated with BMI per standard deviation (SD) (odds ratio [OR] =5.0, P = 0.002) than waist circumference/height2 per SD (OR = 3.9, P = 0.02), visceral fat/height2 per SD (OR = 2.4, P = 0.02), subcutaneous fat/height2 per SD (OR = 2.0, P = 0.06), renal hilar fat/height2 per SD (OR = 1.6, P = 0.19), or peri/pararenal fat/height2 per SD (OR = 1.5, P = 0.23). Postdonation changes in glomerular filtration rate, blood pressure, and albuminuria were similar across BMI categories. Conclusions The BMI outperforms various computed tomography measures of abdominal fat in detecting obesity-related glomerulomegaly. Despite this strong association with glomerulomegaly, short-term renal function outcomes are similar across BMI categories. Long-term follow-up is required to definitively define the impact of obesity on kidney function after donation.

Significance Statement Nephron number currently can be estimated only from glomerular density on a kidney biopsy combined with cortical volume from kidney imaging. Because of measurement biases, refinement of this approach and validation across different patient populations have been needed. The prognostic importance of nephron number also has been unclear. The authors present an improved method of estimating nephron number that corrects for several biases, resulting in a 27% higher nephron number estimate for donor kidneys compared with a prior method. After accounting for comorbidities, the new nephron number estimate does not differ between kidney donors and kidney patients with tumor and shows consistent associations with clinical characteristics across these two populations. The findings also indicate that low nephron number predicts CKD independent of biopsy and clinical characteristics in both populations. Background Nephron number can be estimated from glomerular density and cortical volume. However, because of measurement biases, this approach needs refinement, comparison between disparate populations, and evaluation as a predictor of CKD outcomes. Methods We studied 3020 living kidney donors and 1354 patients who underwent radical nephrectomy for tumor. We determined cortex volume of the retained kidney from presurgical imaging and glomerular density by morphometric analysis of needle core biopsy of the donated kidney and wedge sections of the removed kidney. Glomerular density was corrected for missing glomerular tufts, absence of the kidney capsule, and then tissue shrinkage on the basis of analysis of 30 autopsy kidneys. We used logistic regression (in donors) and Cox proportional hazard models (in patients with tumor) to assess the risk of CKD outcomes associated with nephron number. Results Donors had 1.17 million nephrons per kidney; patients with tumor had 0.99 million nephrons per kidney. A lower nephron number was associated with older age, female sex, shorter height, hypertension, family history of ESKD, lower GFR, and proteinuria. After adjusting for these characteristics, nephron number did not differ between donors and patients with tumor. Low nephron number (defined by <5th or <10th percentile by age and sex in a healthy subset) in both populations predicted future risk of CKD outcomes independent of biopsy and clinical characteristics. Conclusions Compared with an older method for estimating nephron number, a new method that addresses several sources of bias results in nephron number estimates that are 27% higher in donors and 1% higher in patients with tumor and shows consistency between two populations. Low nephron number independently predicts CKD in both populations.

Background Computer‐assisted scoring is gaining prominence in the evaluation of renal histology; however, much of the focus has been on identifying larger objects such as glomeruli. Total inflammation impacts graft outcome, and its quantification requires tools to identify objects at the cellular level or smaller. The goal of the current study was to use CD45 stained slides coupled with image analysis tools to quantify the amount of non‐glomerular inflammation within the cortex. Methods Sixty renal transplant whole slide images were used for digital image analysis. Multiple thresholding methods using pixel intensity and object size were used to identify inflammation in the cortex. Additionally, convolutional neural networks were used to separate glomeruli from other objects in the cortex. This combined measure of inflammation was then correlated with rescored Banff total inflammation classification and outcomes. Results Identification of glomeruli on biopsies had high fidelity (mean pixelwise dice coefficient of .858). Continuous total inflammation scores correlated well with Banff rescoring (maximum Pearson correlation .824). A separate set of thresholds resulted in a significant correlation with alloimmune graft loss. Conclusions Automated scoring of inflammation showed a high correlation with Banff scoring. Digital image analysis provides a powerful tool for analysis of renal pathology, not only because it is reproducible and can be automated, but also because it provides much more granular data for studies.

Purpose of reviewThis review is intended to provide an up-to-date analysis of the structural and functional alterations of the kidneys that accompany healthy and unhealthy aging in humans. Macro-and micro-structural changes and glomerular filtration rate (whole kidney and single nephron) accompanying aging will be stressed. Recent findingsComparative findings concerning distribution of anatomic changes of the kidney healthy and unhealthy aging are reviewed. Challenges concerning definition of chronic kidney disease (CKD) in otherwise healthy aging patients are discussed. The complex interactions of CKD and aging are discussed. The role of podocyte dysbiosis in kidney aging is reviewed. SummaryKidney aging is a complex phenomenon often difficult to distinguish from CKD. Nonetheless, phenotypes of healthy and unhealthy aging are evident. Much more information concerning the molecular characteristics of normal kidney aging and its relevance to chronic kidney disease is needed.

The aim of this study is to investigate the use of an exponential-plateau model to determine the required training dataset size that yields the maximum medical image segmentation performance. CT and MR images of patients with renal tumors acquired between 1997 and 2017 were retrospectively collected from our nephrectomy registry. Modality-based datasets of 50, 100, 150, 200, 250, and 300 images were assembled to train models with an 80–20 training-validation split evaluated against 50 randomly held out test set images. A third experiment using the KiTS21 dataset was also used to explore the effects of different model architectures. Exponential-plateau models were used to establish the relationship of dataset size to model generalizability performance. For segmenting non-neoplastic kidney regions on CT and MR imaging, our model yielded test Dice score plateaus of $$0.93\pm 0.02$$ 0.93 ± 0.02 and $$0.92\pm 0.04$$ 0.92 ± 0.04 with the number of training-validation images needed to reach the plateaus of 54 and 122, respectively. For segmenting CT and MR tumor regions, we modeled a test Dice score plateau of $$0.85\pm 0.20$$ 0.85 ± 0.20 and $$0.76\pm 0.27$$ 0.76 ± 0.27 , with 125 and 389 training-validation images needed to reach the plateaus. For the KiTS21 dataset, the best Dice score plateaus for nn-UNet 2D and 3D architectures were $$0.67\pm 0.29$$ 0.67 ± 0.29 and $$0.84\pm 0.18$$ 0.84 ± 0.18 with number to reach performance plateau of 177 and 440. Our research validates that differing imaging modalities, target structures, and model architectures all affect the amount of training images required to reach a performance plateau. The modeling approach we developed will help future researchers determine for their experiments when additional training-validation images will likely not further improve model performance.

COVID-19 patients may experience with a wide range of cardiovascular complications during infection: obstructive and non-obstructive coronary artery disease-acute coronary syndrome (myocardial infarction type 1 and type 2), arterial or venous thromboembolic diseases, myocarditis, pericarditis, pericardial effusion, stress cardiomyopathy (Takotsubo syndrome), arrhythmias, acute heart failure, shock and sudden cardiac death (cardiac arrest). Cardiovascular complications that may occur after COVID-19 vaccination are: myocarditis, pericarditis, thromboembolic events, hypertension, acute coronary syndrome, stress cardiomyopathy, arrhythmias and cardiac arrest. Myocarditis and pericarditis occurred in 3/4 of all cases after the second dose of mRNA vaccine against SARS-COV2 virus, most often in young adults. Vaccine-induced immune thrombotic thrombocytopenia (VITT) is a rare condition that occurs after vaccination against SARS-COV2, more prevalently in young women (under 50 years of age). The incidence of acute myocardial infarction is 0.02% and 0.03% depending on the type of mRNA vaccine (Pfizer or Moderna), more common in males and the elderly, with symptoms onset the most frequently up to 24 hours after vaccine application. The most common arrhythmias that occur after COVID-19 vaccination are sinus tachycardia, atrial fibrillation, and supraventricular tachycardia. The benefit-risk ratio of COVID-19 vaccination to the occurrence of cardiovascular complications strongly prevails in favor of vaccines for all age groups (older than 12 years) and for both sexes.

T he incidence of symptomatic and asymptomatic kidney stone disease in the general population is on the rise. 1 Therefore, it is not surprising that as many as 13% of potential kidney donors have a history of symptomatic nephrolithiasis or asymptomatic kidney stones found incidentally on computed tomography (CT) scan during donor evaluation. 2 In the general population, nephrolithiasis is associated with significant long-term morbidity, including chronic kidney disease and hypertension. [3][4][5] Several risk factors have been

In search of metabolites signature as biomarkers during EAE disease, we profiled urine from B6 EAE using global untargeted metabolomics. Evaluation of metabolomic profiling of urine from EAE and healthy B6 group by using a combination of high-throughput liquid-and-gas chromatography with mass spectrometry, we found that 132 out of 322 (41%) metabolites were differentially altered (P&lt;0.05) indicating robust alteration in the urine metabolomics profile during disease. Among the perturbed metabolites, 11 were up regulated in EAE urine whereas 121 were down regulated. We conducted pathway analysis of the biochemical pathways of the KEGG and considered both concerted changes in metabolite intensity within the pathway (GlobalTest) and alterations of high impact, and found that a number of pathways were significantly altered including glyoxylate and dicarboxylate, phenylanine metabolism, porphyrin & chlorophyll metabolism, primary bile acid biosynthesis, cysteine & methione metabolism, taurine and hypotaurine metabolism, glycine, serine & threonine metabolism, and beta-alanine metabolism. Alteration in these pathways during EAE disease suggesting that perturbation of certain central metabolites could have impact on multiple metabolic pathways. While some of these metabolite changes could easily be developed as biomarkers, the key to translating metabolomics into therapeutics would require figuring out the central altered metabolic pathway(s), once studied in detail.