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Abstract Renal proximal tubule epithelial cells have considerable intrinsic repair capacity following injury. However, a fraction of injured proximal tubule cells fails to undergo normal repair and assumes a proinflammatory and profibrotic phenotype that may promote fibrosis and chronic kidney disease. The healthy to failed repair change is marked by cell state-specific transcriptomic and epigenomic changes. Single nucleus joint RNA- and ATAC-seq sequencing offers an opportunity to study the gene regulatory networks underpinning these changes in order to identify key regulatory drivers. We develop a regularized regression approach to construct genome-wide parametric gene regulatory networks using multiomic datasets. We generate a single nucleus multiomic dataset from seven adult human kidney samples and apply our method to study drivers of a failed injury response associated with kidney disease. We demonstrate that our approach is a highly effective tool for predicting key cis- and trans-regulatory elements underpinning the healthy to failed repair transition and use it to identify NFAT5 as a driver of the maladaptive proximal tubule state.

Amongst the range of bioprinting technologies currently available, bioprinting by material extrusion is gaining increasing popularity due to accessibility, low cost, and the absence of energy sources, such as lasers, which may significantly damage the cells. New applications of extrusion-based bioprinting are systematically emerging in the biomedical field in relation to tissue and organ fabrication. Extrusion-based bioprinting presents a series of specific challenges in relation to achievable resolutions, accuracy and speed. Resolution and accuracy in particular are of paramount importance for the realization of microstructures (for example, vascularization) within tissues and organs. Another major theme of research is cell survival and functional preservation, as extruded bioinks have cells subjected to considerable shear stresses as they travel through the extrusion apparatus. Here, an overview of the main available extrusion-based printing technologies and related families of bioprinting materials (bioinks) is provided. The main challenges related to achieving resolution and accuracy whilst assuring cell viability and function are discussed in relation to specific application contexts in the field of tissue and organ fabrication.

IntroductionThe presence of cancer in dogs was detected by Raman spectroscopy of urine samples and chemometric analysis of spectroscopic data. The procedure created a multimolecular spectral fingerprint with hundreds of features related directly to the chemical composition of the urine specimen. These were then used to detect the broad presence of cancer in dog urine as well as the specific presence of lymphoma, urothelial carcinoma, osteosarcoma, and mast cell tumor.MethodsUrine samples were collected via voiding, cystocentesis, or catheterization from 89 dogs with no history or evidence of neoplastic disease, 100 dogs diagnosed with cancer, and 16 dogs diagnosed with non-neoplastic urinary tract or renal disease. Raman spectra were obtained of the unprocessed bulk liquid urine samples and were analyzed by ISREA, principal component analysis (PCA), and discriminant analysis of principal components (DAPC) were applied using the Rametrix®Toolbox software.Results and discussionThe procedure identified a spectral fingerprint for cancer in canine urine, resulting in a urine screening test with 92.7% overall accuracy for a cancer vs. cancer-free designation. The urine screen performed with 94.0% sensitivity, 90.5% specificity, 94.5% positive predictive value (PPV), 89.6% negative predictive value (NPV), 9.9 positive likelihood ratio (LR+), and 0.067 negative likelihood ratio (LR-). Raman bands responsible for discerning cancer were extracted from the analysis and biomolecular associations were obtained. The urine screen was more effective in distinguishing urothelial carcinoma from the other cancers mentioned above. Detection and classification of cancer in dogs using a simple, non-invasive, rapid urine screen (as compared to liquid biopsies using peripheral blood samples) is a critical advancement in case management and treatment, especially in breeds predisposed to specific types of cancer.

This study addresses market concentration among major corporations, highlighting the utility of relative entropy for understanding diversification strategies. It introduces entropic value at risk (EVaR) as a coherent risk measure, which is an upper bound to the conditional value at risk (CVaR), and explores its generalization, relativistic value at risk (RLVaR), rooted in Kaniadakis entropy. Through extensive empirical analysis on both developed (i.e., S&P 500 and Euro Stoxx 50) and developing markets (i.e., BIST 100 and Bovespa), the study evaluates entropy-based criteria in portfolio selection, investigates model behavior across different market types, and assesses the impact of cryptocurrency introduction on portfolio performance and diversification. The key finding indicates that entropy measures effectively identify optimal portfolios, particularly in scenarios of heightened risk and increased concentration, crucial for mitigating negative net performances during low returns or high turnover. Bitcoin is primarily used for diversification and performance enhancement in the BIST 100 index, while its allocation in other markets remains minimal or non-existent, confirming the extreme concentration observed in stock markets dominated by a few leading stocks.

The main objective of this work is to test whether some stochastic models typically used in financial markets could be applied to the COVID-19 pandemic. To this end, we have implemented the ARIMAX and Cox-Ingersoll-Ross (CIR) models originally designed for interest rate pricing but transformed by us into a forecasting tool. For the latter, which we denoted CIR*, both the Euler-Maruyama method and the Milstein method were used. Forecasts obtained with the maximum likelihood method have been validated with 95% confidence intervals and with statistical measures of goodness of fit, such as the root mean square error (RMSE). We demonstrate that the accuracy of the obtained results is consistent with the observations and sufficiently accurate to the point that the proposed CIR* framework could be considered a valid alternative to the classical ARIMAX for modelling pandemics.

Abstract Many processes in nature are the result of many coupled individual subsystems (like population dynamics or neurosystems). Not always such systems exhibit simple stable behaviors that in the past science has mostly focused on. Often, these systems are characterized by bursts of seemingly stochastic activity, interrupted by quieter periods. The hypothesis is that the presence of a strong deterministic ingredient is often obscured by the stochastic features. We test this by modeling classically stochastic considered real-world data from both, the stochastic as well as the deterministic approaches to find that the deterministic approach’s results level with those from the stochastic side. Moreover, the deterministic approach is shown to reveal the full dynamical systems landscape, which can be exploited for steering the dynamics into a desired regime.

This research aims to propose the so-called CIR#, which takes its cue from the well- known Cox-Ingersoll-Ross (CIR) model originally devised for pricing, as a general econometric model. To this end, we present the results on two very different time series such as Polish interest rates (subject to market sentiments) and seasonal tourism (subject to pandemic lock-down measures). For interest rates, as reference models, we consider an improved version of the CIR model (denoted CIRadj), the Hull and White model, the exponentially weighted moving average (EWMA) which is often adopted whenever no structure is assumed in the data and a popular machine learning model such as the short-term memory network (LSTM). For tourism, as a benchmark, we consider seasonal autoregressive integrated moving average (SARIMA) complemented by the generalized autoregressive conditional heteroskedasticity (GARCH) for modelling the variance, the classic Holt-Winters model and the aforementioned LSTM. Results support the claim that the CIR# performs better than the other models in all considered cases being able to deal with erratic behaviour in data.

Entropy serves as a measure of chaos in systems by representing the average rate of information loss about a phase point’s position on the attractor. When dealing with a multifractal system, a single exponent cannot fully describe its dynamics, necessitating a continuous spectrum of exponents, known as the singularity spectrum. From an investor’s point of view, a rise in entropy is a signal of abnormal and possibly negative returns. This means he has to expect the unexpected and prepare for it. To explore this, we analyse the New York Stock Exchange (NYSE) U.S. Index as well as its constituents. Through this examination, we assess their multifractal characteristics and identify market conditions (bearish/bullish markets) using entropy, an effective method for recognizing fluctuating fractal markets. Our findings challenge conventional beliefs by demonstrating that price declines lead to increased entropy, contrary to some studies in the literature that suggest that reduced entropy in market crises implies more determinism. Instead, we propose that bear markets are likely to exhibit higher entropy, indicating a greater chance of unexpected extreme events. Moreover, our study reveals a power-law behaviour and indicates the absence of variance.

Abstract We ask whether empirical finance market data (Financial Stress Index, swap and equity, emerging and developed, corporate and government, short and long maturity), with their recently observed alternations between calm periods and financial turmoil, could be described by a low-dimensional deterministic model, or whether this requests a stochastic approach. We find that a deterministic model performs at least as well as one of the best stochastic models, but may offer additional insight into the essential mechanisms that drive financial markets.

Insights into the use of cellular therapeutics, extracellular vesicles (EVs), and tissue engineering strategies for regenerative medicine applications are continually emerging with a focus on personalized, patient-specific treatments. Multiple pre-clinical and clinical trials have demonstrated the strong potential of cellular therapies, such as stem cells, immune cells, and EVs, to modulate inflammatory immune responses and promote neoangiogenic regeneration in diseased organs, damaged grafts, and inflammatory diseases, including COVID-19. Over 5,000 registered clinical trials on ClinicalTrials.gov involve stem cell therapies across various organs such as lung, kidney, heart, and liver, among other applications. A vast majority of stem cell clinical trials have been focused on these therapies’ safety and effectiveness. Advances in our understanding of stem cell heterogeneity, dosage specificity, and ex vivo manipulation of stem cell activity have shed light on the potential benefits of cellular therapies and supported expansion into clinical indications such as optimizing organ preservation before transplantation. Standardization of manufacturing protocols of tissue-engineered grafts is a critical first step towards the ultimate goal of whole organ engineering. Although various challenges and uncertainties are present in applying cellular and tissue engineering therapies, these fields’ prospect remains promising for customized patient-specific treatments. Here we will review novel regenerative medicine applications involving cellular therapies, EVs, and tissue-engineered constructs currently investigated in the clinic to mitigate diseases and possible use of cellular therapeutics for solid organ transplantation. We will discuss how these strategies may help advance the therapeutic potential of regenerative and transplant medicine.

Regenerative medicine (RM) is changing how we think and practice transplant medicine. In regenerative medicine, the aim is to develop and employ methods to regenerate, restore or replace damaged/diseased tissues or organs. Regenerative medicine investigates using tools such as novel technologies or techniques, extracellular vesicles, cell-based therapies, and tissue-engineered constructs to design effective patient-specific treatments. This review illustrates current advancements in regenerative medicine that may pertain to transplant medicine. We highlight progress made and various tools designed and employed specifically for each tissue or organ, such as the kidney, heart, liver, lung, vasculature, gastrointestinal tract, and pancreas. By combing both fields of transplant and regenerative medicine, we can harbor a successful collaboration that would be beneficial and efficacious for the repair and design of de novo engineered whole organs for transplantations.

We developed and tested a method to detect COVID-19 disease, using urine specimens. The technology is based on Raman spectroscopy and computational analysis. It does not detect SARS-CoV-2 virus or viral components, but rather a urine ‘molecular fingerprint’, representing systemic metabolic, inflammatory, and immunologic reactions to infection. We analyzed voided urine specimens from 46 symptomatic COVID-19 patients with positive real time-polymerase chain reaction (RT-PCR) tests for infection or household contact with test-positive patients. We compared their urine Raman spectra with urine Raman spectra from healthy individuals (n = 185), peritoneal dialysis patients (n = 20), and patients with active bladder cancer (n = 17), collected between 2016–2018 (i.e., pre-COVID-19). We also compared all urine Raman spectra with urine specimens collected from healthy, fully vaccinated volunteers (n = 19) from July to September 2021. Disease severity (primarily respiratory) ranged among mild (n = 25), moderate (n = 14), and severe (n = 7). Seventy percent of patients sought evaluation within 14 days of onset. One severely affected patient was hospitalized, the remainder being managed with home/ambulatory care. Twenty patients had clinical pathology profiling. Seven of 20 patients had mildly elevated serum creatinine values (>0.9 mg/dl; range 0.9–1.34 mg/dl) and 6/7 of these patients also had estimated glomerular filtration rates (eGFR)

In the field of cryptography, many algorithms rely on the computation of modular multiplicative inverses to ensure the security of their systems. In this study, we build upon our previous research by introducing a novel sequence, (zj)j≥0, that can calculate the modular inverse of a given pair of integers (a,n), i.e., a−1;mod,n. The computational complexity of this approach is O(a), which is more efficient than the traditional Euler’s phi function method, O(n,ln,n). Furthermore, we investigate the properties of the sequence (zj)j≥0 and demonstrate that all solutions of the problem belong to a specific set, I, that only contains the minimum values of (zj)j≥0. This results in a reduction of the computational complexity of our method, especially when a∼n and it also opens new opportunities for discovering closed-form solutions for the modular inverse.

Portfolio selection models based on second-order stochastic dominance (SSD) have the advantage of providing portfolios that reflect the behavior of risk-averse investors without the need to specify the utility function. Several scholars apply SSD conditions with respect to a reference distribution, typically that of the market index, to find its dominant SSD portfolio. However, since the reference distribution could strongly influence asset allocation, in this article, we compare two SSD-based portfolio selection strategies with a reshaping of the reference distribution in terms of its skewness and, consequently, its variance. Through an extensive empirical analysis based on multiasset investment universes, we empirically show that the SSD portfolios dominating the new skewed benchmark index generally perform better.

Abstract Significant progress has been made in recent years in characterizing human multipotent progenitor cells (hMPCs) of the early pancreas; however, the identity and persistence of these cells during the second trimester, after the initiation of branching morphogenesis, remain elusive. Additionally, studies on hMPCs have been hindered by few isolation methods that allow for the recovery of live cells. Here, we investigated the tip progenitor domain in the branched epithelium of human fetal pancreas between 13.5 and 17.5 gestational weeks by immunohistological staining. We also used a novel RNA‐based technology to isolate live cells followed by gene expression analyses. We identified cells co‐expressing SOX9 and PTF1A, two transcription factors known to be important for pancreatic MPCs, within the tips of the epithelium and observed a decrease in their proportions over time. Pancreatic SOX9+/PTF1A+ cells were enriched for MPC markers, including MYC and GATA6. These cells were proliferative and appeared active in branching morphogenesis and matrix remodeling, as evidenced by gene set enrichment analysis. We identified a hub of genes pertaining to the expanding tip progenitor niche, such as FOXF1, GLI3, TBX3, FGFR1, TGFBR2, ITGAV, ITGA2, and ITGB3. YAP1 of the Hippo pathway emerged as a highly enriched component within the SOX9+/PTF1A+ cells. Single‐cell RNA‐sequencing further corroborated the findings by identifying a cluster of SOX9+/PTF1A+ cells with multipotent characteristics. Based on these results, we propose that the SOX9+/PTF1A+ cells in the human pancreas are uncommitted MPC‐like cells that reside at the tips of the expanding pancreatic epithelium, directing self‐renewal and inducing pancreatic organogenesis. Stem Cells Translational Medicine 2019;8:1249&1264

Bladder cancer (BCA) is relatively common and potentially recurrent/progressive disease. It is also costly to detect, treat, and control. Definitive diagnosis is made by examination of urine sediment, imaging, direct visualization (cystoscopy), and invasive biopsy of suspect bladder lesions. There are currently no widely-used BCA-specific biomarker urine screening tests for early BCA or for following patients during/after therapy. Urine metabolomic screening for biomarkers is costly and generally unavailable for clinical use. In response, we developed Raman spectroscopy-based chemometric urinalysis (Rametrix™) as a direct liquid urine screening method for detecting complex molecular signatures in urine associated with BCA and other genitourinary tract pathologies. In particular, the RametrixTM screen used principal components (PCs) of urine Raman spectra to build discriminant analysis models that indicate the presence/absence of disease. The number of PCs included was varied, and all models were cross-validated by leave-one-out analysis. In Study 1 reported here, we tested the Rametrix™ screen using urine specimens from 56 consented patients from a urology clinic. This proof-of-concept study contained 17 urine specimens with active BCA (BCA-positive), 32 urine specimens from patients with other genitourinary tract pathologies, seven specimens from healthy patients, and the urinalysis control SurineTM. Using a model built with 22 PCs, BCA was detected with 80.4% accuracy, 82.4% sensitivity, 79.5% specificity, 63.6% positive predictive value (PPV), and 91.2% negative predictive value (NPV). Based on the number of PCs included, we found the RametrixTM screen could be fine-tuned for either high sensitivity or specificity. In other studies reported here, RametrixTM was also able to differentiate between urine specimens from patients with BCA and other genitourinary pathologies and those obtained from patients with end-stage kidney disease (ESKD). While larger studies are needed to improve RametrixTM models and demonstrate clinical relevance, this study demonstrates the ability of the RametrixTM screen to differentiate urine of BCA-positive patients. Molecular signature variances in the urine metabolome of BCA patients included changes in: phosphatidylinositol, nucleic acids, protein (particularly collagen), aromatic amino acids, and carotenoids.

Raman Chemometric Urinalysis (RametrixTM) was used to discern differences in Raman spectra from (i) 362 urine specimens from patients receiving peritoneal dialysis (PD) therapy for end-stage kidney disease (ESKD), (ii) 395 spent dialysate specimens from those PD therapies, and (iii) 235 urine specimens from healthy human volunteers. RametrixTM analysis includes spectral processing (e.g., truncation, baselining, and vector normalization); principal component analysis (PCA); statistical analyses (ANOVA and pairwise comparisons); discriminant analysis of principal components (DAPC); and testing DAPC models using a leave-one-out build/test validation procedure. Results showed distinct and statistically significant differences between the three types of specimens mentioned above. Further, when introducing "unknown" specimens, RametrixTM was able to identify the type of specimen (as PD patient urine or spent dialysate) with better than 98% accuracy, sensitivity, and specificity. RametrixTM was able to identify "unknown" urine specimens as from PD patients or healthy human volunteers with better than 96% accuracy (with better than 97% sensitivity and 94% specificity). This demonstrates that an entire Raman spectrum of a urine or spent dialysate specimen can be used to determine its identity or the presence of ESKD by the donor.

Raman chemometric urinalysis (Rametrix™) was used to analyze 235 urine specimens from healthy individuals. The purpose of this study was to establish the "range of normal" for Raman spectra of urine specimens from healthy individuals. Ultimately, spectra falling outside of this range will be correlated with kidney and urinary tract disease. Rametrix™ analysis includes direct comparisons of Raman spectra but also principal component analysis (PCA), discriminant analysis of principal components (DAPC) models, multivariate statistics, and it is available through GitHub as the Rametrix™ LITE Toolbox for MATLAB®. Results showed consistently overlapping Raman spectra of urine specimens with significantly larger variances in Raman shifts, found by PCA, corresponding to urea, creatinine, and glucose concentrations. A 2-way ANOVA test found that age of the urine specimen donor was statistically significant (p < 0.001) and donor sex (female or male identification) was less so (p = 0.0526). With DAPC models and blind leave-one-out build/test routines using the Rametrix™ PRO Toolbox (also available through GitHub), an accuracy of 71% (sensitivity = 72%; specificity = 70%) was obtained when predicting whether a urine specimen from a healthy unknown individual was from a female or male donor. Finally, from female and male donors (n = 4) who contributed first morning void urine specimens each day for 30 days, the co-occurrence of menstruation was found statistically insignificant to Rametrix™ results (p = 0.695). In addition, Rametrix™ PRO was able to link urine specimens with the individual donor with an average of 78% accuracy. Taken together, this study established the range of Raman spectra that could be expected when obtaining urine specimens from healthy individuals and analyzed by Rametrix™ and provides the methodology for linking results with donor characteristics.

In literature, there are a number of cryptographic algorithms (RSA, ElGamal, NTRU, etc.) that require multiple computations of modulo multiplicative inverses. In this paper, we describe the modulo operation and we recollect the main approaches to computing the modulus. Then, given a and n positive integers, we present the sequence (zj)j≥0, where zj=zj−1+aβj−n, a

The financial system in the Kingdom of Saudi Arabia (KSA) has a history of relative soundness, particularly in banking, due to comparatively strict and enforced domestic supervision, and supported by what has been for the most part a reasonably robust economy. However, the sector is facing challenges of a sluggish economy, need for reform and negative effects of the COVID-19 pandemic. This paper strives to assess how well the government thus far has responded to the challenges in the financial sector. The working hypothesis is that the insurance industry has improved its position, resulting in higher efficiency and profitability and lower risk. This is an industry historically plagued by too many players, financial issues and less than adequate controls. The analysis undertaken bears out the hypothesis, as revealed by the enhanced contribution made by the industry since the pandemic. Analysis suggests that strides have been made in that industry in terms of helping to diversify the economy with the onset of the pandemic. Moreover, evidence is provided that the sensitivity to changes in oil volumes (rather than to changes in oil prices) is a key risk factor for the financial sector in the KSA. These findings have implications for policy makers in terms of leveraging the pandemic conditions as an opportunity to drive further reform and diversify the economy with lower risk.

The aim of this work was to test how returns are distributed across multiple asset classes, markets and sampling frequency. We examine returns of swaps, equity and bond indices as well as the rescaling by their volatilities over different horizons (since inception to Q2-2020). Contrarily to some literature, we find that the realized distributions of logarithmic returns, scaled or not by the standard deviations, are skewed and that they may be better fitted by t-skew distributions. Our finding holds true across asset classes, maturity and developed and developing markets. This may explain why models based on dynamic conditional score (DCS) have superior performance when the underlying distribution belongs to the t-skew family. Finally, we show how sampling and distribution of returns are strictly connected. This is of great importance as, for example, extrapolating yearly scenarios from daily performances may prove not to be correct.

This paper has the objective to empirically test Harrod’s explanations of economic dynamics addressing both growth and business cycles. In particular we test Harrod’s speculation that opening the economy to foreign trade could lead to a reduction of cyclical instability. The main variables determining the dynamic behaviour are wealth, savings, competitiveness and, therefore, balance of trade. The behaviour of the model under scrutiny is highly non-linear and the conclusion is that stable cycles are possible only under particular conditions. In fact, this empirical test shows a stable long-run pattern for those specific parameters.

β-Cell replacement through transplantation is the only curative treatment to establish a long-term stable euglycemia in diabetic patients. Owing to the shortage of donor tissue, attempts are being made to develop alternative sources of insulin-secreting cells. Stem cells differentiation and reprograming as well as isolating pancreatic progenitors from different sources are some examples; however, no approach has yet yielded a clinically relevant solution. Dissociated islet cells that are cultured in cell numbers by in vitro proliferation provide a promising platform for redifferentiation towards β-cells phenotype. In this study, we cultured islet-derived cells in vitro and examined the expression of β-cell genes during the proliferation. Islets were isolated from porcine pancreases and enzymatically digested to dissociate the component cells. The cells proliferated well in tissue culture plates and were subcultured for no more than 5 passages. Only 10% of insulin expression, as measured by PCR, was preserved in each passage. High glucose media enhanced insulin expression by about 4–18 fold, suggesting a glucose-dependent effect in the proliferated islet-derived cells. The islet-derived cells also expressed other pancreatic genes such as Pdx1, NeuroD, glucagon, and somatostatin. Taken together, these results indicate that pancreatic islet-derived cells, proliferated in vitro, retained the expression capacity for key pancreatic genes, thus suggesting that the cells may be redifferentiated into insulin-secreting β-like cells.

We report the rare occurrence of donor-derived myeloid sarcoma in two kidney transplant patients who received organs from a single deceased donor. There was no evidence of preexisting hematologic malignancy in the donor at the time of organ recovery. Both recipients developed leukemic involvement that appeared to be limited to the transplanted organ. Fluorescence in situ hybridization (FISH) and molecular genotyping analyses confirmed that the malignant cells were of donor origin in each patient. Allograft nephrectomy and immediate withdrawal of immunosuppression were performed in both cases; systemic chemotherapy was subsequently administered to one patient. Both recipients were in remission at least one year following the diagnosis of donor-derived myeloid sarcoma. These cases suggest that restoration of the immune system after withdrawal of immunosuppressive therapy and allograft nephrectomy may be sufficient to control HLA-mismatched donor-derived myeloid sarcoma without systemic involvement.

Aim of the Study: To assess how virtual reality (VR) patient-specific simulations can support decision-making processes and improve care in pediatric urology, ultimately improving patient outcomes. Patients and Methods: Children diagnosed with urological conditions necessitating complex procedures were retrospectively reviewed and enrolled in the study. Patient-specific VR simulations were developed with medical imaging specialists and VR technology experts. Routine CT images were utilized to create a VR environment using advanced software platforms. The accuracy and fidelity of the VR simulations was validated through a multi-step process. This involved comparing the virtual anatomical models to the original medical imaging data and conducting feedback sessions with pediatric urology experts to assess VR simulations' realism and clinical relevance. Results: A total of six pediatric patients were reviewed. The median age of the participants was 5.5 years (IQR: 3.5–8.5 years), with an equal distribution of males and females across both groups. A minimally invasive laparoscopic approach was performed for adrenal lesions (n = 3), Wilms' tumor (n = 1), bilateral nephroblastomatosis (n = 1), and abdominal trauma in complex vascular and renal malformation (ptotic and hypoplastic kidney) (n = 1). Key benefits included enhanced visualization of the segmental arteries and the deep vascularization of the kidney and adrenal glands in all cases. The high depth perception and precision in the orientation of the arteries and veins to the parenchyma changed the intraoperative decision-making process in five patients. Preoperative VR patient-specific simulation did not offer accuracy in studying the pelvic and calyceal anatomy. Conclusions: VR patient-specific simulations represent an empowering tool in pediatric urology. By leveraging the immersive capabilities of VR technology, preoperative planning and intraoperative navigation can greatly impact surgical decision-making. As we continue to advance in medical simulation, VR holds promise in educational programs to include even surgical treatment of more complex urogenital malformations. [ABSTRACT FROM AUTHOR]

Aim: To review our 10 years of experience with the endoscopic treatment of vesicoureteral reflux (VUR) in children, emphasizing the long-term efficacy of the "combined STING-HIT" technique. Materials and Methods: A retrospective study was performed including all children with symptomatic VUR undergoing the cystoscopic injection of bulking agents from January 2013 to December 2022 in our pediatric tertiary referral center. Three different endoscopic techniques were adopted: the "combined STING-HIT" technique, the STING technique, and the HIT technique. Treatment success was defined as symptom remission and VUR resolution on the voiding cystourethrogram (VCUG) performed at the 3-month follow-up. Results: In the study period, 140 (F:M = 64:76) patients and 228 ureters were treated at a median patient age of 3 (2.0– 6.0) years. After a single endoscopic treatment, VUR resolved in 203 (88%) ureters. The VUR resolution rate after a single endoscopic treatment was 95% (n=70/74) in case of I–II VUR, 88% (n=87/99) in case of III VUR; 83% (n=38/46) in case of IV VUR; 89% (n=8/9) in case of V VUR (p-value: 0.174). Overall, one or two endoscopic treatments succeeded in 219 (96%) ureters. The overall VUR resolution rate following one or two endoscopic treatments was 100% (74/74) in case of I–II VUR, 93% (n=92/99) in case of III VUR; 96% (n=44/46) in case of IV VUR; 100% (n=9/9) in case of V VUR (p-value: 0.083). Despite not being statistically significant, the VUR resolution rate was higher for the "combined STING-HIT" technique, both after one (92%: n=110/119; versus 85%; n=62/73 versus 86%; n=31/36; p-value: 0.225) or two (98%: n=116/119; versus 95%; n=69/73 versus 94%; n=34/36; p-value: 0.469) endoscopic treatments. Conclusion: The endoscopic approaches were highly successful for the treatment of VUR in children. The "combined STING-HIT" technique was a safe and effective procedure, being associated with the higher resolution rate. [ABSTRACT FROM AUTHOR]