Endoscopic observations as a tool to define underlying pathology in kidney stone formers

Determinants of renal papillary appearance in kidney stone formers: An in-depth examination

OBJECTIVES

The aim of this study is to investi-gate the association between the urinary metabolic milieu and kidney stone recurrence with a validated papillary evaluation score (PPLA).

MATERIALS AND METHODS

We prospectively enrolled 30 stone for-mers who underwent retrograde intrarenal surgery procedures. Visual inspection of the accessible renal papillae was performed to calculate PPLA score, based on the characterization of ductal plugging, surface pitting, loss of papillary contour and Randall's plaque extension. Stone compositions, 24h urine collections and kidney stone events during follow-up were collected. Relative supersaturation ratios (RSS) for calcium oxalate (CaOx), brushite and uric acid were calculated using EQUIL-2. PPLA score > 3 was defined as high.

RESULTS

Median follow-up period was 11 months (5, 34). PPLA score was inversely correlated with BMI (OR 0.59, 95% CI 0.38, 0.91, p = 0.018), type 2 diabetes (OR 0.04, 95% CI 0.003, 0.58, p = 0.018) and history of recurrent kidney stones (OR 0.17, 95%CI 0.04, 0.75, p = 0.019). The associations between PPLA score, diabetes and BMI were not confirmed after excluding patients with uric acid stones. Higher PPLA score was associated with lower odds of new kidney stone events during follow-up (OR 0.15, 95% CI 0.02, 1.00, p = 0.05). No other significant correla-tions were found.

CONCLUSIONS

Our results confirm the lack of efficacy of PPLA score in phenotyping patients affected by kidney stone disease or in predicting the risk of stone recurrence. Larger, long-term studies need to be performed to clarify the role of PPLA on the risk of stone recurrence.

Construction and Analysis of Immune Infiltration-Related ceRNA Network for Kidney Stones

Purpose: Kidney stones is a common medical issue that mediates kidney injury and even kidney function loss. However, the exact pathogenesis still remains unclear. This study aimed to explore the potential competing endogenous RNA (ceRNA)-related pathogenesis of kidney stones and identify the corresponding immune infiltration signature. Methods: One mRNA and one long non-coding RNA (lncRNA) microarray dataset was obtained from the GEO database. Subsequently, we compared differentially expressed mRNAs (DE-mRNAs) and lncRNAs between Randall's plaques in patients with calcium oxalate (CaOx) stones and controls with normal papillary tissues. lncRNA-targeted miRNAs and miRNA-mRNA pairs were predicted using the online databases. lncRNA-related DE-mRNAs were identified using the Venn method, and GO and KEGG enrichment analyses were subsequently performed. The immune-related lncRNA-miRNA-mRNA ceRNA network was developed. The CIBERSORT algorithm was used to estimate the rate of immune cell infiltration in Randall's plaques. The ceRNA network and immune infiltration were validated in the glyoxylate-induced hyperoxaluric mouse model and oxalate-treated HK-2 cells. Results: We identified 2,340 DE-mRNAs and 929 DE-lncRNAs between Randall's plaques in patients with CaOx stones and controls with normal papillary tissues. lncRNA-related DE-mRNAs were significantly enriched in extracellular matrix organization and collagen-containing extracellular matrix, which were associated with kidney interstitial fibrosis. The immune-related ceRNA network included 10 lncRNAs, 23 miRNAs, and 20 mRNAs. Moreover, we found that M2 macrophages and resting mast cells were differentially expressed between Randall's plaques and normal tissues. Throughout kidney stone development, kidney tubular injury, crystal deposition, collagen fiber deposition, TGF-β expression, infiltration of M1 macrophages, and activation of mast cells were more frequent in glyoxylate-induced hyperoxaluric mice compared with control mice. Nevertheless, M2 macrophage infiltration increased in early stages (day 6) and decreased as kidney stones progressed (day 12). Furthermore, treatment with 0.25 and 0.5 mM of oxalate for 48 h significantly upregulated NEAT1, PVT1, CCL7, and ROBO2 expression levels and downregulated hsa-miR-23b-3p, hsa-miR-429, and hsa-miR-139-5p expression levels in the HK-2 cell line in a dose-dependent manner. Conclusion: We found that significant expressions of ceRNAs (NEAT1, PVT1, hsa-miR-23b-3p, hsa-miR-429, hsa-miR-139-5p, CCL7, and ROBO2) and infiltrating immune cells (macrophages and mast cells) may be involved in kidney stone pathogenesis. These findings provide novel potential therapeutic targets for kidney stones.

Endoscopic Papillary Abnormalities and Stone Recognition (EPSR) during Flexible Ureteroscopy: A Comprehensive Review

Introduction: The increasing efficiency of the different lasers and the improved performance of endoscopic devices have led to smaller stone fragments that impact the accuracy of microscopic evaluation (morphological and infrared). Before the stone destruction, the urologist has the opportunity to analyze the stone and the papillary abnormalities endoscopically (endoscopic papillary recognition (EPR) and endoscopic stone recognition (ESR)). Our objective was to evaluate the value for those endoscopic descriptions. Methods: The MEDLINE and EMBASE databases were searched in February 2021 for studies on endoscopic papillary recognition and endoscopic stone recognition. Results: If the ESR provided information concerning the main crystallization process, EPR provided information concerning the origin of the lithogenesis and its severity. Despite many actual limitations, those complementary descriptions could support the preventive care of the stone formers in improving the diagnosis of the lithogenesis mechanism and in identifying high-risk stone formers. Conclusion: Until the development of an Artificial Intelligence recognition, the endourologist has to learn EPSR to minimize the distortion effect of the new lasers on the stone analysis and to improve care efficiency of the stone formers patients.

Automatic detection of calcium phosphate deposit plugs at the terminal ends of kidney tubules

Kidney stones are a common urologic condition with a high amount of recurrence. Recurrence depends on a multitude of factors the incidence of precursors to kidney stones, plugs, and plaques. One method of characterising the stone precursors is endoscopic assessment, though it is manual and time-consuming. Deep learning has become a popular technique for semantic segmentation because of the high accuracy that has been demonstrated. The present Letter examined the efficacy of deep learning to segment the renal papilla, plaque, and plugs. A U-Net model with ResNet-34 encoder was tested; the Letter examined dropout (to avoid overtraining) and two different loss functions (to address the class imbalance problem. The models were then trained in 1666 images and tested on 185 images. The Jaccard-cross-entropy loss function was more effective than the focal loss function. The model with the dropout rate 0.4 was found to be more effective due to its generalisability. The model was largely successful at delineating the papilla. The model was able to correctly detect the plaques and plugs; however, small plaques were challenging. Deep learning was found to be applicable for segmentation of an endoscopic image for the papilla, plaque, and plug, with room for improvement.