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Only 60-75% of conventional kidney stone surgeries achieve complete stone-free status. Up to 30% of patients with residual fragments <2 mm in size experience subsequent stone-related complications. Here we demonstrate a stone retrieval technology in which fragments are rendered magnetizable with a magnetic hydrogel so that they can be easily retrieved with a simple magnetic tool. The magnetic hydrogel facilitates robust in vitro capture of stone fragments of clinically relevant sizes and compositions. The hydrogel components exhibit no cytotoxicity in cell culture and only superficial effects on ex vivo human urothelium and in vivo mouse bladders. Furthermore, the hydrogel demonstrates antimicrobial activity against common uropathogens on par with that of common antibiotics. By enabling the efficient retrieval of kidney stone fragments, our method can lead to improved stone-free rates and patient outcomes. The success of surgical kidney stone removal is limited by the ability to efficiently retrieve stone fragments, resulting in incomplete stone clearance and subsequent morbidity. Here, the authors show the efficacy and biocompatibility of a magnetic hydrogel that selectively coats human kidney stone fragments in vitro allowing their total extraction using a magnetic wire.

IntroductionThough Wilms tumor (WT) is one of the most common malignancies in children, there is a paucity of epidemiologic studies exploring sociodemographic disparities in treatment and survival. Here, we leveraged a national cancer registry to examine sociodemographic factors associated with receipt of adjuvant therapy, either chemotherapy or radiation, as well as overall survival among pediatric patients with WT.Materials and methodsWithin the Surveillance Epidemiology and End Results database (2000–2016), we identified 2043 patients (≤ 20 years of age) with unilateral WT. Multivariable logistic regression and Cox proportional hazard models were constructed to examine the association of sociodemographic factors with, respectively, adjuvant chemotherapy/radiotherapy and overall survival (OS).ResultsPatients in the lowest SES quintile (OR 0.56, 95% CI 0.33–0.93, p = 0.03) were less likely to receive chemotherapy as compared to those in the highest SES quintile, though this association did not persist in sensitivity analyses including only patients at least 2 years of age and patients with regional/distant disease. In addition, female patients were more likely to receive chemotherapy (OR 1.46, 95% CI 1.08–1.97, p = 0.02) than male patients. Age, race, year of diagnosis, insurance status, and tumor laterality were not associated with receipt of chemotherapy. No sociodemographic variables were associated with receipt of radiotherapy. Lastly, as compared to Non-Hispanic-White patients, Hispanic patients had worse OS (HR 1.59, 95% CI 1.08–2.35, p = 0.02); no other sociodemographic variables were associated with OS.ConclusionsThis study suggests multilevel sociodemographic disparities involving ethnicity and SES in WT treatment and survival.

Antenatal hydronephrosis (HN) impacts up to 5% of pregnancies and requires close, frequent follow-up monitoring to determine who may benefit from surgical intervention. To create an automated HN Severity Index (HSI) that helps guide clinical decision-making directly from renal ultrasound images. We applied a deep learning model to paediatric renal ultrasound images to predict the need for surgical intervention based on the HSI. The model was developed and studied at four large quaternary free-standing paediatric hospitals in North America. We evaluated the degree to which HSI corresponded with surgical intervention at each hospital using area under the receiver-operator curve, area under the precision-recall curve, sensitivity, and specificity. HSI predicted subsequent surgical intervention with > 90% AUROC, > 90% sensitivity, and > 70% specificity in a test set of 202 patients from the same institution. At three external institutions, HSI corresponded with AUROCs ≥ 90%, sensitivities ≥ 80%, and specificities > 50%. It is possible to automatically and reliably assess HN severity directly from a single ultrasound. The HSI stratifies low- and high-risk HN patients thus helping to triage low-risk patients while maintaining very high sensitivity to surgical cases. HN severity can be predicted from a single patient ultrasound using a novel image-based artificial intelligence system.

Objective: Investigate whether residential prenatal exposure to heavy metal hazardous air pollutants (HMHAPs) is associated with an increased risk of hypospadias. Methods: Data on non-syndromic hypospadias cases (n = 8981) and control patients delivered in Texas were obtained from the Texas Birth Defects Registry and matched 1:10 by birth year. Average exposure concentrations of HMHAPs were obtained from the 2005 U.S. Environmental Protection Agency National-Scale Air Toxics Assessment and categorized into quintiles. Odds ratios and 95% confidence intervals were estimated. STROBE reporting guidelines were followed. Results: We observed associations between hypospadias and prenatal HMHAP exposure. Manganese demonstrated significant increased risk of hypospadias at the medium, medium-high and high exposure quintiles; lead in the medium-high and high exposure quintiles. Cadmium, mercury and nickel demonstrated a significant inverted “U-shaped” association for exposures with significant associations in the medium and medium-high quintiles but not in the medium-low and high quintiles. Arsenic and chromium demonstrated a significant bivalent association for risk of hypospadias in a lower quintile as well as a higher quintile with non-significant intermediate quintiles. Conclusions: Using data from one of the world’s largest active surveillance birth defects registries, we identified significant associations between hypospadias and HMHAP exposures. These results should be used in counseling for maternal demographic risk factors as well as avoidance of heavy metals and their sources.

Purpose The use of postoperative prophylactic antibiotics in pediatric upper urinary tract reconstruction remains controversial. In this study, we examined whether low dose antibiotics administered following pediatric pyeloplasty reduce the incidence of febrile urinary tract infections at our institution. As a secondary outcome, in those patients with infection, additional analysis was performed to better quantify which patient population benefits the most from low dose prophylactic antibiotics. Methods Institutional review board approval (IRB) was obtained. All methods were carried out in accordance with relevant guidelines and regulations. A retrospective study was performed in patients who underwent pyeloplasty (2011–2017) at our institution. Surgical approach (laparoscopic versus robotic assisted versus open, with or without internal JJ ureteral stent) were based on surgeon preference. Patients of 8 fellowship trained pediatric urologists were included in the study period. Patients with prior history of urologic interventions or other congenital genitourinary tract abnormalities were excluded. Demographics (age, gender, ethnicity, insurance status), prior history of culture proven urinary tract infection, surgical details (administration of perioperative antibiotics), and postoperative outcomes including; 1) re-admission 30 days post-surgery, 2) any urine cultures collected due to suspected urinary tract infection. Results A total of 209 patients (149 boys, 60 girls) met our inclusion criteria with 55/209 (26%) receiving postoperative prophylactic antibiotics. The average age was 6 years (range: 2 months-18 years). Indwelling ureteral stent was used in 176 (84%) patients. Eleven patients (5%) had a culture-proven urinary tract infection within 30-days postoperatively. No significant differences were seen in postoperative complications or incidence of urinary tract infection when comparing surgical approaches, +/- ureteral stent, or the use of antibiotics. Secondary analysis noted statistically significant increase in post-operative urinary tract infection in younger children (2.8 v. 6.2 years, p  = 0.02), those patients who had a positive preoperative urine culture (8/11, p  = 0.01) and those with public health insurance ( p  = 0.038). Conclusion The incidence of postoperative urinary tract infection following pyeloplasty in our cohort was relatively low. There was a higher incidence of urinary tract infection in patients less than 3 years old. The use of antibiotics in patients post pyeloplasty did not appear to affect the incidence of post-operative urinary tract infection, however, they may have a role in children who have not yet potty trained and in patients with positive preoperative urine culture.

The potential of deep learning to predict renal obstruction using kidney ultrasound images has been demonstrated. However, these image-based classifiers have incorporated information using only single-visit ultrasounds. Here, we developed machine learning (ML) models incorporating ultrasounds from multiple clinic visits for hydronephrosis to generate a hydronephrosis severity index score to discriminate patients into high versus low risk for needing pyeloplasty and compare these against models trained with single clinic visit data. We included patients followed for hydronephrosis from three institutions. The outcome of interest was low risk versus high risk of obstructive hydronephrosis requiring pyeloplasty. The model was trained on data from Toronto, ON and validated on an internal holdout set, and tested on an internal prospective set and two external institutions. We developed models trained with single ultrasound (single-visit) and multi-visit models using average prediction, convolutional pooling, long-short term memory and temporal shift models. We compared model performance by area under the receiver-operator-characteristic (AUROC) and area under the precision-recall-curve (AUPRC). A total of 794 patients were included (603 SickKids, 102 Stanford, and 89 CHOP) with a pyeloplasty rate of 12%, 5%, and 67%, respectively. There was no significant difference in developing single-visit US models using the first ultrasound vs. the latest ultrasound. Comparing single-visit vs. multi-visit models, all multi-visit models fail to produce AUROC or AUPRC significantly greater than single-visit models. We developed ML models for hydronephrosis that incorporate multi-visit inference across multiple institutions but did not demonstrate superiority over single-visit inference. These results imply that the single-visit models would be sufficient in aiding accurate risk stratification from single, early ultrasound images.

In design-oriented biomedical engineering courses, some instructors teach need-driven methods for health technology innovation that use a “need statement” to reflect a student team’s hypothesis about the most fruitful direction for their project. While need statements are of the utmost importance to the projects, we were not aware of any comprehensive rubric for helping instructors evaluate them. Leveraging resources such as the Biodesign textbook along with input from faculty teaching health technology design at our university, we created a rubric for evaluating the construction of need statements. We then introduced the rubric to undergraduate students in a 3-week intersession course in fall 2023. Afterward, we used the rubric to compare the de-identified final need statements from 2023 to the de-identified final need statements from students in the course in 2022 and 2021. Our assumption that need statements from 2023 would score better against the rubric than those from previous years proved not to be the case. However, we gleaned valuable lessons about the role of rubrics in supporting student learning and increasing alignment among faculty, as well as insights about rubric development and areas for future study. In this article, we also share the initial version of the rubric so that other instructors can adapt and improve upon it for their own courses.

Previous work has shown the potential of deep learning to predict renal obstruction using kidney ultrasound images. However, these image-based classifiers have been trained with the goal of single-visit inference in mind. We compare methods from video action recognition (i.e. convolutional pooling, LSTM, TSM) to adapt single-visit convolutional models to handle multiple visit inference. We demonstrate that incorporating images from a patient's past hospital visits provides only a small benefit for the prediction of obstructive hydronephrosis. Therefore, inclusion of prior ultrasounds is beneficial, but prediction based on the latest ultrasound is sufficient for patient risk stratification.