Endosurgical treatment of newborns and young children with posterior urethral valves

Objective: Posterior urethral valves are a rare but the most common cause of lower urinary tract obstruction in male newborns and infants. Patients are at high risk of recurrent urinary tract infections, acute kidney injury and chronic kidney disease. The aim of this study was to present our experience of transurethral incision of posterior urethral valves in newborns as a gold standard of treatment. Materials and methods: We conducted a case-series of patients with posterior urethral valves who underwent transurethral incision during 2017–2021. We analyzed clinical characteristics of patients, recurrence, and complications during a follow-up of 3-12 months. Results: Of 26 patients who underwent transurethral incision of posterior urethra valves 14 had concomitant vesicoureteral reflux of II-V degrees and 12 – obstructive megaureter. Eight children received endoscopic injections (7 ureters) and ureteral stenting (5 ureters). Six infants (9 ureters) had Cohen's operation of which 4 patients had ureteric reimplantation using laparoscopic pneumovesicum and 2 patients underwent open procedure. In 4 cases we performed laparoscopic nephroureterectomy. In 12 children a spontaneous regression of VUR and megaureter within 3 months after TUI were observed. Four children developed chronic kidney disease of 1-2 stages. Conclusions: Early diagnosis and surgery treatment of posterior urethra valves in newborns and infants provide excellent outcomes in most patients. Cystourethroscopy with the transurethral incision is highly recommended to be performed in the neonatal period as the spontaneous recovery of concomitant vesicoureteral reflux and megaureter are quite often

Machine Learning Outperforms Regression Analysis to Predict Next-Season Major League Baseball Player Injuries: Epidemiology and Validation of 13,982 Player-Years From Performance and Injury Profile Trends, 2000-2017

Background: Machine learning (ML) allows for the development of a predictive algorithm capable of imbibing historical data on a Major League Baseball (MLB) player to accurately project the player\u27s future availability. Purpose: To determine the validity of an ML model in predicting the next-season injury risk and anatomic injury location for both position players and pitchers in the MLB. Study Design: Descriptive epidemiology study. Methods: Using 4 online baseball databases, we compiled MLB player data, including age, performance metrics, and injury history. A total of 84 ML algorithms were developed. The output of each algorithm reported whether the player would sustain an injury the following season as well as the injury\u27s anatomic site. The area under the receiver operating characteristic curve (AUC) primarily determined validation. Results: Player data were generated from 1931 position players and 1245 pitchers, with a mean follow-up of 4.40 years (13,982 player-years) between the years of 2000 and 2017. Injured players spent a total of 108,656 days on the disabled list, with a mean of 34.21 total days per player. The mean AUC for predicting next-season injuries was 0.76 among position players and 0.65 among pitchers using the top 3 ensemble classification. Back injuries had the highest AUC among both position players and pitchers, at 0.73. Advanced ML models outperformed logistic regression in 13 of 14 cases. Conclusion: Advanced ML models generally outperformed logistic regression and demonstrated fair capability in predicting publicly reportable next-season injuries, including the anatomic region for position players, although not for pitchers

Discovery of Age-Related Protein Folding Stability Differences in the Mouse Brain Proteome

Described here is the application of thermodynamic stability measurements to study age-related differences in the folding and stability of proteins in a rodent model of aging. Thermodynamic stability profiles were generated for 809 proteins in brain cell lysates from mice, aged 6 (<i>n</i> = 7) and 18 months (<i>n</i> = 9) using the Stability of Proteins from Rates of Oxidation (SPROX) technique. The biological variability of the protein stability measurements was low and within the experimental error of SPROX. A total of 83 protein hits were detected with age-related stability differences in the brain samples. Remarkably, the large majority of the brain protein hits were destabilized in the old mice, and the hits were enriched in proteins that have slow turnover rates (<i>p</i> < 0.07). Furthermore, 70% of the hits have been previously linked to aging or age-related diseases. These results help validate the use of thermodynamic stability measurements to capture relevant age-related proteomic changes and establish a new biophysical link between these proteins and aging

Somatostatin analog (octreotide) and sirolimus immunosuppressive therapy in the treatment of chyloperitoneum and chylothorax in newborns and infants

Background: Chyloperitoneum (CP) and chylothorax (CT) are rare conditions that have a high mortality rate and unclear treatment options. Their incidence in neonates ranges from 1 in 20000 to 1 in 187000 live births. This study aims to evaluate the effectiveness of synthetic somatostatin analog (octreotide) and sirolimus therapy in treating chylous pleural and peritoneal collections in newborns and infants. Methods: We conducted a retrospective analysis of 10 children with either chylothorax or chyloperitoneum, treated in our department between 2018 and 2023. The study was approved by the Local Independent Ethics Committee of The National Medical Research Center of Children's Health, under Protocol №7, dated 11 May 2023. The parents voluntarily signed an informed consent form for the off-label use of the drug. We reviewed the medical records for demographic information, clinical presentation, management, and outcome. Results: Our study looked at patients aged between 0 and 5.5 months, with seven cases of chyloperitoneum and three cases of chylothorax. We initially used octreotide, which was then switched to sirolimus if there was no improvement. Octreotide was effective in five children after 10-18 days of treatment, while the effect of sirolimus was observed 8-14 days after starting treatment. One patient, who had a history of a giant omphalocele with primary closure, experienced complications after 8 weeks of sirolimus therapy, including bilateral knee arthritis, leukopenia, and lymphopenia. Fortunately, there were no fatal outcomes. Conclusion: Sirolimus therapy is effective in treating newborns with chylothorax or chyloperitoneum, with a low risk of complications even in those cases not responding to octreotide therapy. It is recommended that octreotide therapy should not exceed 10 days, after which sirolimus can be prescribed

Machine Learning Outperforms Logistic Regression Analysis to Predict Next-Season NHL Player Injury: An Analysis of 2322 Players From 2007 to 2017

© The Author(s) 2020. Background: The opportunity to quantitatively predict next-season injury risk in the National Hockey League (NHL) has become a reality with the advent of advanced computational processors and machine learning (ML) architecture. Unlike static regression analyses that provide a momentary prediction, ML algorithms are dynamic in that they are readily capable of imbibing historical data to build a framework that improves with additive data. Purpose: To (1) characterize the epidemiology of publicly reported NHL injuries from 2007 to 2017, (2) determine the validity of a machine learning model in predicting next-season injury risk for both goalies and position players, and (3) compare the performance of modern ML algorithms versus logistic regression (LR) analyses. Study Design: Descriptive epidemiology study. Methods: Professional NHL player data were compiled for the years 2007 to 2017 from 2 publicly reported databases in the absence of an official NHL-approved database. Attributes acquired from each NHL player from each professional year included age, 85 performance metrics, and injury history. A total of 5 ML algorithms were created for both position player and goalie data: random forest, K Nearest Neighbors, Naïve Bayes, XGBoost, and Top 3 Ensemble. LR was also performed for both position player and goalie data. Area under the receiver operating characteristic curve (AUC) primarily determined validation. Results: Player data were generated from 2109 position players and 213 goalies. For models predicting next-season injury risk for position players, XGBoost performed the best with an AUC of 0.948, compared with an AUC of 0.937 for LR (P \u3c.0001). For models predicting next-season injury risk for goalies, XGBoost had the highest AUC with 0.956, compared with an AUC of 0.947 for LR (P \u3c.0001). Conclusion: Advanced ML models such as XGBoost outperformed LR and demonstrated good to excellent capability of predicting whether a publicly reportable injury is likely to occur the next season