Fabrication of acoustically and physically validated artificial stones to natural kidney stones under shock waves and laser lithotripsy.

To present an efficient method for fabricating artificial kidney stones with acoustic and physical properties to assess their fragmentation efficiency under shock waves and laser lithotripsy for very hard stones. The mixture ratio of super-hard plaster and water was adjusted to produce artificial kidney stones for comparison with > 95% human genuine calcium oxalate monohydrate (COM) and uric acid (UA) stones. Acoustic and physical properties, such as wave speed, stone hardness, density, compressive strength, and stone-free rates under shock-wave and laser lithotripsy, were assessed. The longitudinal wave speed of artificial stones prepared at a plaster-to-water ratio of 15:3 closely matched that of COM stones. Similarly, the transverse wave speed of artificial stones prepared at a plaster-to-water ratio of 15:3 to 15:5 aligned with that of COM stones. Stone fragmentation using shock-wave of artificial stones with mixed ratios ranging from 15:3 to 15:5 resembled that of COM stones. The Vickers hardness was similar to that of artificial stones produced with a mixing ratio of 15:3, similar to that of COM stones, while that of artificial stones produced with a mixing ratio of 15:5 was similar to that of UA stones. Density-wise, artificial stones with mixing ratios of 15:4 and 15:5 resembled COM stones. Compressive strength test results did not confirm the similarity between natural and artificial stones. The stone fragmentation using laser showed that stones produced with higher moisture content at a mixing ratio of 15:6 were similar to COM stones. This novel method for fabricating artificial kidney stones could be used to provide reliable materials for lithotripsy research.

Large-scale identification of calcium oxalate stone inhibitory proteins in normal human urine.

Recent evidence has shown that proteins in normal human urine can inhibit calcium oxalate (CaOx) kidney stone formation. Herein, we performed fast protein liquid chromatography (FPLC) to fractionate normal human urinary proteins using anion-exchange (DEAE) and size-exclusion (Superdex 200) materials. FPLC fractions (F1-F15) were examined by CaOx crystallization, growth, aggregation and crystal-cell adhesion assays. The fractions with potent inhibitory activities against CaOx crystals were then subjected to mass spectrometric protein identification. The data revealed that 13 of 15 fractions showed inhibitory activities in at least one crystal assay. Integrating CaOx inhibitory scores demonstrated that F6, F7 and F8 had the most potent inhibitory activities. NanoLC-ESI-Qq-TOF MS/MS identified 105, 93 and 53 proteins in F6, F7 and F8, respectively. Among them, 60 were found in at least two fractions and/or listed among known inhibitors with solid experimental evidence in the StoneMod database (https://www.stonemod.org). Interestingly, 10 of these 60 potential inhibitors have been reported with lower urinary levels in CaOx stone formers compared with healthy (non-stone) individuals, strengthening their roles as potent CaOx stone inhibitors. Our study provides the largest dataset of potential CaOx stone inhibitory proteins that will be useful for further elucidations of stone-forming mechanisms and ultimately for therapeutic/preventive applications.

Comparison of automated kidney stone size measurement and volumetry in photon counting CT compared to 3rd generation dual energy CT and physically measurements - an ex vivo study.

PURPOSE: This ex vivo study aimed to compare a newly developed dual-source photon-counting CT (PCCT) with a 3rd generation dual-source dual-energy CT (DECT) for the detection and measurement (stone lengths and volumetrics) of urinary stones. METHODS: 143 urinary stones with a known geometry were physically measured and defined as reference values. Next, urinary stones were placed in an anthropomorphic abdomen-model and were scanned with DECT and PCCT. Images were read by two experienced examiners and automatically evaluated using a specific software. RESULTS: DECT and PCCT showed a high sensitivity for manual stone detection of 97.9% and 94.4%, and for automatic detection of 93.0% and 87.4%, respectively. Compared to that uric acid and xanthine stones were recognized slightly worse by DECT and PCCT with manual stone detection (93.3% and 82.2%), and with automatic detection (77.8% and 60.0%). All other stone entities were completely recognized. By comparing the maximum diameter of the reference value and DECT, Pearson-correlation was 0.96 (p < 0.001) for manual and 0.97 (p < 0.001) for automatic measurement, and for PCCT it was 0.94 (p < 0.001) for manual and 0.97 (p < 0.001) for automatic measurements. DECT and PCCT can also reliably determine volume manually and automatically with a Pearson-correlation of 0.99 (p < 0.001), respectively. CONCLUSION: Both CTs showed comparable results in stone detection, length measurement and volumetry compared to the reference values. Automatic measurement tends to underestimate the maximum diameter. DECT proved to be slightly superior in the recognition of xanthine and uric acid stones.

Association between the oxidative balance score and kidney stones in adults.

OBJECTIVES: This study was to investigate the correlation between oxidative balance score (OBS) and the prevalence of kidney stones in the general adult population. MATERIALS AND METHODS: We conducted an analysis using data from the 2007-2018 National Health and Nutrition Examination Survey (NHANES) project, including 17,988 participants. The OBS was computed based on previous research, combining 16 dietary factors and 4 lifestyle factors. Multiple logistic regressions and restricted cubic spline (RCS) regressions were utilized to explore the associations between OBS and kidney stone prevalence. RESULTS: Our analysis included 1,622 adults with kidney stones and 16,366 adults without kidney stones. The average age of participants was 46.86 ± 0.27 years, with 50.72% being male. The median OBS was 22.00 (17.00, 27.00). After adjusting for all covariates, each one-unit increase in OBS was associated with a 3% decrease in kidney stone prevalence (odds ratio [OR] = 0.97 [0.96-0.98], P < 0.001). Moreover, compared to the first quartile, the fourth quartile of OBS (OR = 0.65 [0.50-0.84], P = 0.001) exhibited a negative association with kidney stone prevalence after adjusting for multiple variables. Furthermore, we observed a non-linear negative relationship between OBS and kidney stone prevalence, with inflection points at 18.2 (P for nonlinearity = 0.048). Stratified analysis did not identify any variables significantly affecting the results. CONCLUSION: Our findings indicate that a higher OBS is associated with a decreased prevalence of kidney stones in the general adult population.

Vascular calcification in kidney stone formers: the impact of age and stone composition.

An increased prevalence of vascular calcification (VC) has been reported in kidney stone formers (KSFs), along with an elevated cardiovascular risk. The aim of the current study is to assess whether VC in these patients develops at a younger age and is influenced by stone composition. This single-center, matched case-control study included KSFs with uric acid or calcium oxalate stones (diagnosed based on stone analysis) and age- and sex-matched controls without a history of nephrolithiasis. The prevalence and severity of abdominal aortic calcification (AAC) and bone mineral density (BMD) were compared between KSFs and non-KSFs. In total, 335 patients were investigated: 134 with calcium oxalate stones, 67 with uric acid stones, and 134 controls. Overall, the prevalence of AAC was significantly higher among calcium stone formers than among the controls (67.9% vs. 47%, p = 0.002). In patients under 60 years of age, those with calcium oxalate stones exhibited both a significantly elevated AAC prevalence (61.9% vs. 31.3%, p = 0.016) and severity (94.8 ± 15.4 vs. 30.3 ± 15.95, p = 0.001) compared to the controls. Within the age group of 40-49, osteoporosis was identified only in the KSFs. Multivariate analysis identified age, smoking, and the presence of calcium stones as independent predictors of AAC. This study highlights that VC and osteoporosis occur in KSFs at a younger age than in non-stone-formers, suggesting potential premature VC. Its pathogenesis is intriguing and needs to be elucidated. Early evaluation and intervention may be crucial for mitigating the cardiovascular risk in this population.

Case of drug-induced kidney stone from overuse of phenazopyridine.

Drug-induced nephrolithiasis represents only 1%-2% of stone cases. Here we focus on drugs capable of crystallizing and forming stone, specifically phenazopyridine (Pyridium/Azo). This is a case of a patient who presented with a stone conglomerate in the right proximal ureter and underwent definitive treatment. Interestingly, the stone had a purple hue with FTIR spectroscopy showing stone composition of calcium oxalate (monohydrate and dihydrate) and a material resembling phenazopyridine. We retrospectively learned that she used multiple extended courses of phenazopyridine over 3 months.

Calcium phosphate controls nucleation and growth of calcium oxalate crystal phases in kidney stones.

Kidney stone disease is a serious disease due to the severe pain it causes, high morbidity, and high recurrence rate. Notably, calcium oxalate stones are the most common type of kidney stone. Calcium oxalate appears in two forms in kidney stones: the stable phase, monohydrate (COM), and the metastable phase, dihydrate (COD). Particularly, COM stones with concentric structures are hard and difficult to treat. However, the factor determining the growth of either COM or COD crystals in the urine, which is supersaturated for both phases, remains unclear. This study shows that calcium phosphate ingredients preferentially induce COM crystal nucleation and growth, by observing and analyzing kidney stones containing both COM and COD crystals. The forms of calcium phosphate are not limited to Randall's plaques (1-2 mm size aggregates, which contain calcium phosphate nanoparticles and proteins, and form in the renal papilla). For example, aggregates of strip-shaped calcium phosphate crystals and fields of dispersed calcium phosphate microcrystals (nano to micrometer order) also promote the growth of concentric COM structures. This suggests that patients who excrete urine with a higher quantity of calcium phosphate crystals may be more prone to forming hard and troublesome COM stones.

Renal papillary tip biopsy in stone formers: a review of clinical safety and insights.

Alexander Randall first published renal papillary tip findings from stone formers in 1937, paving the way for endoscopic assessment to study stone pathogenesis. We performed a literature search to evaluate the safety of papillary tip biopsy and clinical insights gained from modern renal papillary investigations. A search on the topic of renal papillary biopsy provided an overview of Randall's plaques (RP), classification systems for renal papillary grading, and a summary of procedure type, complications, and outcomes. Within 26 identified manuscripts, 660 individuals underwent papillary tip biopsy percutaneously (n = 562), endoscopically (n = 37), or unspecified (n = 23). Post-operative hemoglobin changes were similar to controls. One individual (0.2%) reported fever > 38°, and long-term mean serum creatinine post-biopsy (n = 32) was unchanged. Biopsies during ureteroscopy or PCNL added ~20-30 min of procedure time. Compared to controls, papillary plaque-containing tissue had upregulation in pro-inflammatory genes, immune cells, and cellular apoptosis. Urinary calcium and papillary plaque coverage were found to differ between RP and non-RP stone formers, suggesting differing underlying pathophysiology for these groups. Two renal papillary scoring systems have been externally validated and are used to classify stone formers. Overall, this review shows that renal papillary biopsies have a low complication profile with high potential for further research. Systematic adaption of a papillary grading scale, newer tissue analysis techniques, and the development of animal models of Randall's plaque may allow further exploration of plaque pathogenesis and identify targets for prevention therapies in patients with nephrolithiasis.

Development of UroSAM: A Machine Learning Model to Automatically Identify Kidney Stone Composition from Endoscopic Video.

Introduction: Chemical composition analysis is important in prevention counseling for kidney stone disease. Advances in laser technology have made dusting techniques more prevalent, but this offers no consistent way to collect enough material to send for chemical analysis, leading many to forgo this test. We developed a novel machine learning (ML) model to effectively assess stone composition based on intraoperative endoscopic video data. Methods: Two endourologists performed ureteroscopy for kidney stones ≥ 10 mm. Representative videos were recorded intraoperatively. Individual frames were extracted from the videos, and the stone was outlined by human tracing. An ML model, UroSAM, was built and trained to automatically identify kidney stones in the images and predict the majority stone composition as follows: calcium oxalate monohydrate (COM), dihydrate (COD), calcium phosphate (CAP), or uric acid (UA). UroSAM was built on top of the publicly available Segment Anything Model (SAM) and incorporated a U-Net convolutional neural network (CNN). Discussion: A total of 78 ureteroscopy videos were collected; 50 were used for the model after exclusions (32 COM, 8 COD, 8 CAP, 2 UA). The ML model segmented the images with 94.77% precision. Dice coefficient (0.9135) and Intersection over Union (0.8496) confirmed good segmentation performance of the ML model. A video-wise evaluation demonstrated 60% correct classification of stone composition. Subgroup analysis showed correct classification in 84.4% of COM videos. A post hoc adaptive threshold technique was used to mitigate biasing of the model toward COM because of data imbalance; this improved the overall correct classification to 62% while improving the classification of COD, CAP, and UA videos. Conclusions: This study demonstrates the effective development of UroSAM, an ML model that precisely identifies kidney stones from natural endoscopic video data. More high-quality video data will improve the performance of the model in classifying the majority stone composition.

Kidney stone growth through the lens of Raman mapping.

Bulk composition of kidney stones, often analyzed with infrared spectroscopy, plays an essential role in determining the course of treatment for kidney stone disease. Though bulk analysis of kidney stones can hint at the general causes of stone formation, it is necessary to understand kidney stone microstructure to further advance potential treatments that rely on in vivo dissolution of stones rather than surgery. The utility of Raman microscopy is demonstrated for the purpose of studying kidney stone microstructure with chemical maps at ≤ 1 µm scales collected for calcium oxalate, calcium phosphate, uric acid, and struvite stones. Observed microstructures are discussed with respect to kidney stone growth and dissolution with emphasis placed on < 5 µm features that would be difficult to identify using alternative techniques including micro computed tomography. These features include thin concentric rings of calcium oxalate monohydrate within uric acid stones and increased frequency of calcium oxalate crystals within regions of elongated crystal growth in a brushite stone. We relate these observations to potential concerns of clinical significance including dissolution of uric acid by raising urine pH and the higher rates of brushite stone recurrence compared to other non-infectious kidney stones.

A Nuclear Magnetic Resonance (NMR)- and Mass Spectrometry (MS)-Based Saturation Kinetics Model of a Bryophyllum pinnatum Decoction as a Treatment for Kidney Stones.

Bryophyllum pinnatum (BP) is a medicinal plant used to treat many conditions when taken as a leaf juice, leaves in capsules, as an ethanolic extract, and as herbal tea. These preparations have been chemically analyzed except for decoctions derived from boiled green leaves. In preparation for a clinical trial to validate BP tea as a treatment for kidney stones, we used NMR and MS analyses to characterize the saturation kinetics of the release of metabolites. During boiling of the leaves, (a) the pH decreased to 4.8 within 14 min and then stabilized; (b) regarding organic acids, citric and malic acid were released with maximum release time (tmax) = 35 min; (c) for glycoflavonoids, quercetin 3-O-α-L-arabinopyranosyl-(1 → 2)-α-L-rhamnopyranoside (Q-3O-ArRh), myricetin 3-O-α-L-arabinopyranosyl-(1 → 2)-α-L-rhamnopyranoside (M-3O-ArRh), kappinatoside, myricitrin, and quercitrin were released with tmax = 5-10 min; and (d) the total phenolic content (TPC) and the total antioxidant capacity (TAC) reached a tmax at 55 min and 61 min, respectively. In summary, 24 g of leaves boiled in 250 mL of water for 61 min ensures a maximal release of key water-soluble metabolites, including organic acids and flavonoids. These metabolites are beneficial for treating kidney stones because they target oxidative stress and inflammation and inhibit stone formation.

Advancements in Uric Acid Stone Detection: Integrating Deep Learning with CT Imaging and Clinical Assessments in the Upper Urinary Tract.

INTRODUCTION: Among upper urinary tract stones, a significant proportion comprises uric acid stones. The aim of this study was to use machine learning techniques to analyze CT scans and blood and urine test data, with the aim of establishing multiple predictive models that can accurately identify uric acid stones. METHODS: We divided 276 patients with upper urinary tract stones into two groups: 48 with uric acid stones and 228 with other types, identified using Fourier-transform infrared spectroscopy. To distinguish the stone types, we created three types of deep learning models and extensively compared their classification performance. RESULTS: Among the three major types of models, considering accuracy, sensitivity, and recall, CLNC-LR, IMG-support vector machine (SVM), and FUS-SVM perform the best. The accuracy and F1 score for the three models were as follows: CLNC-LR (82.14%, 0.7813), IMG-SVM (89.29%, 0.89), and FUS-SVM (29.29%, 0.8818). The area under the curves for classes CLNC-LR, IMG-SVM, and FUS-SVM were 0.97, 0.96, and 0.99, respectively. CONCLUSION: This study shows the feasibility of utilizing deep learning to assess whether urinary tract stones are uric acid stones through CT scans, blood, and urine tests. It can serve as a supplementary tool for traditional stone composition analysis, offering decision support for urologists and enhancing the effectiveness of diagnosis and treatment.

Confining calcium oxalate crystal growth in a carbonated apatite-coated microfluidic channel to better understand the role of Randall's plaque in kidney stone formation.

Effective prevention of recurrent kidney stone disease requires the understanding of the mechanisms of its formation. Numerous in vivo observations have demonstrated that a large number of pathological calcium oxalate kidney stones develop on an apatitic calcium phosphate deposit, known as Randall's plaque. In an attempt to understand the role of the inorganic hydroxyapatite phase in the formation and habits of calcium oxalates, we confined their growth under dynamic physicochemical and flow conditions in a reversible microfluidic channel coated with hydroxyapatite. Using multi-scale characterization techniques including scanning electron and Raman microscopy, we showed the successful formation of carbonated hydroxyapatite as found in Randall's plaque. This was possible due to a new two-step flow seed-mediated growth strategy which allowed us to coat the channel with carbonated hydroxyapatite. Precipitation of calcium oxalates under laminar flow from supersaturated solutions of oxalate and calcium ions showed that the formation of crystals is a substrate and time dependent complex process where diffusion of oxalate ions to the surface of carbonated hydroxyapatite and the solubility of the latter are among the most important steps for the formation of calcium oxalate crystals. Indeed when an oxalate solution was flushed for 24 h, dissolution of the apatite layer and formation of calcium carbonate calcite crystals occurred which seems to promote calcium oxalate crystal formation. Such a growth route has never been observed in vivo in the context of kidney stones. Under our experimental conditions, our results do not show any direct promoting role of carbonated hydroxyapatite in the formation of calcium oxalate crystals, consolidating therefore the important role that macromolecules can play in the process of nucleation and growth of calcium oxalate crystals on Randall's plaque.

StoneMod 2.0: Database and prediction of kidney stone modulatory proteins.

Stone modulators are various kinds of molecules that play crucial roles in promoting/inhibiting kidney stone formation. Several recent studies have extensively characterized the stone modulatory proteins with the ultimate goal of preventing kidney stone formation. Herein, we introduce the StoneMod 2.0 database (https://www.stonemod.org), which has been dramatically improved from the previous version by expanding the number of the modulatory proteins in the list (from 32 in the initial version to 17,130 in this updated version). The stone modulatory proteins were recruited from solid experimental evidence (via PubMed) and/or predicted evidence (via UniProtKB, QuickGO, ProRule, STITCH and OxaBIND to retrieve calcium-binding and oxalate-binding proteins). Additionally, StoneMod 2.0 has implemented a scoring system that can be used to determine the likelihood and to classify the potential stone modulatory proteins as either "solid" (modulator score ≥ 50) or "weak" (modulator score < 50) modulators. Furthermore, the updated version has been designed with more user-friendly interfaces and advanced visualization tools. In addition to the monthly scheduled update, the users can directly submit their experimental evidence online anytime. Therefore, StoneMod 2.0 is a powerful database with prediction scores that will be very useful for many future studies on the stone modulatory proteins.

Non-Coding RNAs in Kidney Stones.

Nephrolithiasis is a major public health concern associated with high morbidity and recurrence. Despite decades of research, the pathogenesis of nephrolithiasis remains incompletely understood, and effective prevention is lacking. An increasing body of evidence suggests that non-coding RNAs, especially microRNAs (miRNAs) and long non-coding RNAs (lncRNAs), play a role in stone formation and stone-related kidney injury. MiRNAs have been studied quite extensively in nephrolithiasis, and a plethora of specific miRNAs have been implicated in the pathogenesis of nephrolithiasis, involving remarkable changes in calcium metabolism, oxalate metabolism, oxidative stress, cell-crystal adhesion, cellular autophagy, apoptosis, and macrophage (Mp) polarization and metabolism. Emerging evidence suggests a potential for miRNAs as novel diagnostic biomarkers of nephrolithiasis. LncRNAs act as competing endogenous RNAs (ceRNAs) to bind miRNAs, thereby modulating mRNA expression to participate in the regulation of physiological mechanisms in kidney stones. Small interfering RNAs (siRNAs) may provide a novel approach to kidney stone prevention and treatment by treating related metabolic conditions that cause kidney stones. Further investigation into these non-coding RNAs will generate novel insights into the mechanisms of renal stone formation and stone-related renal injury and might lead to new strategies for diagnosing and treating this disease.

The first epidemiology of urolithiasis in Northern Vietnam: Urinary stone composition, age, gender, season, and clinical features study.

OBJECTIVES: The aim of this study is to analyze the compositions of urinary stones and investigate their distributions in different ages, genders, seasons, and clinical features of Northern Vietnamese patients. METHODS: A total of 231 patients with urinary stones from Northern Vietnam were collected and analyzed composition from 1/2021-12/2022. For all patients, age, sex, stone location, stone side, urine pH, and hospitalized date (month) were collected. RESULTS: Kidney stones are more frequently found in men than women with the male: female urinary stones ratio in this study being 1.96:1. The highest stone prevalence appeared between 60 and 69 years old. The most common stone composition was calcium oxalate, followed by calcium phosphate, uric acid, struvite, and cysteine. Mix stones of CaOx and CaP were more prevalent than pure stones. Males submitted more CaOx, CaP, and UA stones, whereas females were susceptible to infectious stones. Stones were more frequently found on the left side of the upper urinary tract (51.9%) than on the right side (27.3%) and lower urinary tract (7.8%). Cultural tendency leads to a smaller number of stones during the Lunar new year (February), and Ghost month (August).

Kidney stones: natural remedies and lifestyle modifications to alleviate their burden.

BACKGROUND: Kidney stones (KSs), in fact, have been considered one of the most ancient and prevalent medical conditions that impact a significant number of human beings all around the world. Such stones can range greatly in size and can be detected in any part of the urinary system, including the kidneys, ureters, or bladder itself. The development of stones is caused by the mineral's crystallization, which then interacts with each other and adheres together. Kidney stone formation can represent a prime medical condition for which there are numerous therapies available, among them natural ones. Recurrence of stones after curing is very common, and strategies available to prevent their reoccurrence or even their development for the first time are numerous, with enhanced fluid consumption or avoiding dehydration being the most important one. OBJECTIVE: The current review article aims to draw attention to the potential of natural remedies besides lifestyle modification in the management and prevention of KSs. This is not arbitrary but based on real, documented scientific evidence. METHOD: The natural remedies mentioned in the context of this manuscript were chosen for their availability in almost all nations, or perhaps even in every home. RESULTS: The findings of the present article are very promising and exhibit the potential benefit of natural remedies in addition to shifting to a healthy lifestyle in both the treatment and prevention of KSs.

Tight junction and kidney stone disease.

Defects of tight junction (TJ) are involved in many diseases related to epithelial cell functions, including kidney stone disease (KSD), which is a common disease affecting humans for over a thousand years. This review provides brief overviews of KSD and TJ, and summarizes the knowledge on crystal-induced defects of TJ in renal tubular epithelial cells (RTECs) in KSD. Calcium oxalate (CaOx) crystals, particularly COM, disrupt TJ via p38 MAPK and ROS/Akt/p38 MAPK signaling pathways, filamentous actin (F-actin) reorganization and α-tubulin relocalization. Stabilizing p38 MAPK signaling, reactive oxygen species (ROS) production, F-actin and α-tubulin by using SB239063, N-acetyl-L-cysteine (NAC), phalloidin and docetaxel, respectively, successfully prevent the COM-induced TJ disruption and malfunction. Additionally, genetic disorders of renal TJ, including mutations and single nucleotide polymorphisms (SNPs) of CLDN2, CLDN10b, CLDN14, CLDN16 and CLDN19, also affect KSD. Finally, the role of TJ as a potential target for KSD therapeutics and prevention is also discussed.

Synergistic effect of doxycycline and aqueous extract of irradiated khella on structure of nanobacteria isolated from kidney stones: In vitro and in vivo studies.

Kidney stones have been associated with an increased risk of chronic kidney diseases, end-stage renal failure. This study is devoted to isolate nanobacteria from patients with active urolithiasis and investigate the in vitro and in vivo antinanobacterial activity of some antibiotics alone or in combination with extracts of irradiated herbs from certain medicinal plants. Nanobacteria were detected using scanning (SEM) and transmission (TEM) electron microscopy, protein electrophoresis (SDS-PAGE) and DNA profile. The antimicrobial susceptibility of some biofilm-producing nanobacterial isolates was evaluated. The effect of medicinal plant extracts on growth was tested. A combination treatment between the most potent extracts and antibiotics was tested on biofilm production, protein profile, release of 260 nm absorbing material, protein content, and ultrastructure of the strongest biofilm producers. In vivo study of nanobacteria and its treatment by the most potent agents was evaluated on male rats. Renal function was measured in serum; histological examination and oxidative stress parameters were determined in kidney tissues. Results showed that streptomycin, trimethoprim/sulfamethoxazole, doxycycline, and water extracts of irradiated khella at 6 kGy had antinanobacterial activity. Meanwhile, the synergistic effect of the aqueous extract of irradiated Khella and doxycycline showed higher inhibition activity on microbial growth and biofilm production. They affected dramatically the strength of its cell membrane and subsequently its ultrastructure. Moreover, these results are confirmed by ameliorations in renal function and histological alterations. It could be concluded that the combination of DO and an aqueous extract of irradiated khella has an antinephrotoxic effect against nanobacteria-induced renal toxicity.

Routine Urinary Biochemistry Does Not Accurately Predict Stone Type Nor Recurrence in Kidney Stone Formers: A Multicentre, Multimodel, Externally Validated Machine-Learning Study.

Objectives: Urinary biochemistry is used to detect and monitor conditions associated with recurrent kidney stones. There are no predictive machine learning (ML) tools for kidney stone type or recurrence. We therefore aimed to build and validate ML models for these outcomes using age, gender, 24-hour urine biochemistry, and stone composition. Materials and Methods: Data from three cohorts were used, Southampton, United Kingdom (n = 3013), Newcastle, United Kingdom (n = 5984), and Bern, Switzerland (n = 794). Of these 3130 had available 24-hour urine biochemistry measurements (calcium, oxalate, urate [Ur], pH, volume), and 1684 had clinical data on kidney stone recurrence. Predictive ML models were built for stone type (n = 5 models) and recurrence (n = 7 models) using the UK data, and externally validated with the Swiss data. Three sets of models were built using complete cases, multiple imputation, and oversampling techniques. Results: For kidney stone type one model (extreme gradient boosting [XGBoost] built using oversampled data) was able to effectively discriminate between calcium oxalate, calcium phosphate, and Ur on both internal and external validation. For stone recurrence, none of the models were able to discriminate between recurrent and nonrecurrent stone formers. Conclusions: Kidney stone recurrence cannot be accurately predicted using modeling tools built using specific 24-hour urinary biochemistry values alone. A single model was able to differentiate between stone types. Further studies to delineate accurate predictive tools should be undertaken using both known and novel risk factors, including radiomics and genomics.