Background/aims: Astronauts exposed to microgravity during the course of spaceflight undergo physiologic changes that alter the urinary environment so as to increase the risk of renal stone formation. This study was undertaken to identify a simple method with which to evaluate the potential risk of renal stone development during spaceflight.
Method: We used a large database of urinary risk factors obtained from 323 astronauts before and after spaceflight to generate a mathematical model with which to predict the urinary supersaturation of calcium stone forming salts.
Result: This model, which involves the fewest possible analytical variables (urinary calcium, citrate, oxalate, phosphorus, and total volume), reliably and accurately predicted the urinary supersaturation of the calcium stone forming salts when compared to results obtained from a group of 6 astronauts who collected urine during flight.
Conclusions: The use of this model will simplify both routine medical monitoring during spaceflight as well as the evaluation of countermeasures designed to minimize renal stone development. This model also can be used for Earth-based applications in which access to analytical resources is limited.
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http://dx.doi.org/10.1159/000057445 | DOI Listing |
World J Gastroenterol
January 2025
Université de Bourgogne, Institut Agro-INRAe, Dijon 21000, France.
The recent study exploring the bidirectional associations between gallstone disease, non-alcoholic fatty liver disease, and kidney stone disease highlights a critical concern in chronic disease management. Given the rising global prevalence of these conditions, understanding their interconnections is essential. The study emphasizes the importance of shared risk factors, such as obesity, type 2 diabetes, dyslipidemia, and oxidative stress, and calls for multidisciplinary screening strategies.
View Article and Find Full Text PDFObjective: To determine whether the position of the bolster affects the access tract (supracostal/infracostal) for a superior calyceal puncture during prone PCNL and its effect on pleural complications.
Materials And Methods: It was a randomized clinical trial. Patients in whom superior calyceal puncture was done were divided into two groups by systematic sampling method, group 1 (horizontal bolster) and group 2 (vertical bolster), 50 patients in each group.
BMC Urol
January 2025
Urology and Nephrology Research Center (UNRC), Research Institute for Urology and Nephrology, Center of Excellence in Urology, Shahid Labbafinejad Hospital, Shahid Beheshti University of Medical Sciences (SBMU), Tehran, Iran.
Background: Medical Expulsive Therapy (MET) has been recommended as an established modality for the treatment of distal ureteral stones due to its clearance rate, pain control, and patient satisfaction while having minimal morbidity in comparison to other urologic interventions. In some studies, a combination of medications has been used, which we assessed in this network meta-analysis (NMA).
Methods: We conducted systematic searches in PubMed, Scopus, and Web of Science to identify relevant trials published between 2001 and 2024.
World J Urol
January 2025
Department of Urology, Ruby Hall Clinic, Pune, India.
Background: We aimed to evaluate and compare the rise in the temperature for the safety of the kidney parenchyma on firing the Holmium: Yttrium Aluminium Garnet laser and the Thulium Fiber Laser during laser lithotripsy in humans.
Method: We included 30 pre-stented patients with renal calculi undergoing Retrograde intra-renal surgery. They were randomized into two groups - 15 patients underwent holmium laser lithotripsy and 15 patients underwent TFL laser lithotripsy.
Sci Rep
January 2025
Department of Urology, Vanderbilt University Medical Center, Nashville, USA.
Recent advancements of large language models (LLMs) like generative pre-trained transformer 4 (GPT-4) have generated significant interest among the scientific community. Yet, the potential of these models to be utilized in clinical settings remains largely unexplored. In this study, we investigated the abilities of multiple LLMs and traditional machine learning models to analyze emergency department (ED) reports and determine if the corresponding visits were due to symptomatic kidney stones.
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