PurposeTo evaluate the feasibility of the interferon-gamma release assay (IGRA) as a supplementary diagnostic tool for the diagnosis of genitourinary tuberculosis (GUTB).Materials and MethodsFifty-seven patients who were tested with the IGRA to diagnose GUTB were included. All patients had clinical or radiologic features suspicious for GUTB. Signs and symptoms included chronic dysuria with long-standing sterile pyuria, renal calcification with distorted renal calyces and contracted renal pelvis, and chronic epididymitis. Patients who had a history of tuberculosis in other organs were excluded. Tests including IGRA, urine acid-fast bacilli (AFB) stain and culture, urine tuberculosis polymerase chain reaction (UT-PCR), and radiological examinations were performed to confirm GUTB. The medical records of the patients were reviewed retrospectively.ResultsThe IGRA result was positive in 30 patients (52.6%). The results of the urine AFB stain and culture were positive in 5 patients (8.8%) and 7 patients (12.2%), respectively. The results of UT-PCR were positive in 9 patients (15.8%). The 7 patients who showed positive results in the urine AFB stain and culture also had positive results on the IGRA. A UT-PCR-negative patient was diagnosed with GUTB by positive results on both the IGRA and AFB stain and culture.ConclusionsThe IGRA might feasibly be used as a supplementary or screening tool for the diagnosis of GUTB in addition to urine AFB stain and culture. Further studies for statistical evaluation of its sensitivity, specificity, and efficacy are needed.
Purpose To investigate the risk of symptomatic urolithiasis requiring surgical treatment according to obesity and metabolic health status using a nationwide dataset of the Korean population. Materials and Methods Of the 5,300,646 persons who underwent health examinations between the year 2009 and 2016, within one year after the health examination, 35,137 patients who underwent surgical treatment for urolithiasis were enrolled. Participants were classified as “obese” or “non-obese” using a body mass index (BMI) cutoff of 25 kg/m 2 . People who developed ≥1 metabolic disease component in the index year were considered “metabolically unhealthy”, while those with none were considered “metabolically healthy”. Results Out of 34,330 participants excluding 843 missing, 16,509 (48.1%), 4,320 (12.6%), 6,456 (18.8%), and 7,045 (20.5%) subjects were classified into the metabolically healthy non-obese (MHNO), metabolically unhealthy non-obese (MUNO), metabolically healthy obese (MHO), and metabolically unhealthy obese (MUO) group, respectively. Mean BMI was 22.1±1.9 kg/m 2 , 22.9±1.6 kg/m 2 , 26.9±1.8 kg/m 2 , and 27.9±2.4 kg/m 2 respectively. After adjusting the age and sex, the subjects in the MUNO group had an HR (95% CI) of 1.192 (1.120–1.268), those in the MHO group, 1.242 (1.183–1.305), and those in the MUO group, 1.341 (1.278–1.407) for either extracorporeal shockwave lithotripsy or surgery, compared to those in the MHNO group. Conclusions Metabolically healthy, obese individuals have a higher risk of developing symptomatic urolithiasis than non-obese, unhealthy, but have a lower risk than obese, unhealthy. It suggests that metabolic health and obesity have collaborative effects, independently affecting the development of symptomatic urinary stone diseases.
Purpose This prospective, randomized, controlled study investigated the use of tamsulosin, a selective alpha-blocker, as a prophylactic medication to prevent postoperative urinary retention (POUR) following lower limb arthroplasty. Materials and Methods The criterion for diagnosing POUR was used a postoperative bladder volume over 400 mL with incomplete emptying. Patients who underwent primary total hip or knee arthroplasty were randomly assigned at a 1:1 ratio to tamsulosin treatment and non-treatment groups at a single center from September 2018 to November 2018. The treatment group received 0.2 mg of tamsulosin orally once at night for 3 days starting on postoperative day 1. During this 3-day period, an indwelling Foley catheter was maintained. The incidence of POUR according to tamsulosin treatment following lower limb arthroplasty was the primary outcome. Results In total, 100 patients were enrolled, of whom 5 discontinued participation. POUR was diagnosed in 20 of the remaining 95 patients (21.1%). The treatment group contained 48 patients, of whom 6 (12.5%) developed POUR, whereas POUR occurred in the 14 of the 47 patients (29.8%) in the non-treatment group. Tamsulosin treatment reduced the risk of POUR by two-thirds (odds ratio [OR], 0.337; 95% confidence interval [CI], 0.117–0.971; p=0.044). The risk reduction associated with tamsulosin treatment remained robust post-adjustment for potential covariates (OR, 0.250; 95% CI, 0.069–0.905; p=0.038). Conclusions Tamsulosin administration immediately after lower limb arthroplasty reduced the incidence of urinary retention and diminished the need for long-term catheterization.
Purpose This study aimed to predict the composition of urolithiasis using deep learning from urinary stone images. Materials and Methods We classified 1,332 stones into 31 classes according to the stone composition. The top 4 classes with a frequency of 110 or more (class 1: calcium oxalate monohydrate [COM] 100%, class 2: COM 80%+struvite 20%, class 3: COM 60%+calcium oxalate dihydrate [COD] 40%, class 4: uric acid 100%) were selected. With the 965 stone images of the top 4 classes, we used the seven convolutional neural networks (CNN) to classify urinary stones and compared their classification performances. Results Among the seven models, Xception_Ir0.001 showed the highest accuracy, precision, and recall and was selected as the CNN model to predict the stone composition. The sensitivity and specificity for the 4 classes by Xception_Ir0.001 were as follows: class 1 (94.24%, 91.73%), class 2 (85.42%, 96.14%), class 3 (86.86%, 99.59%), and class 4 (94.96%, 98.82%). The sensitivity and specificity of the individual components of the stones were as follows. COM (98.82%, 94.96%), COD (86.86%, 99.64%), struvite (85.42%, 95.59%), and uric acid (94.96%, 98.82%). The area under the curves for class 1, 2, 3, and 4 were 0.98, 0.97, 1.00, and 1.00, respectively. Conclusions This study showed the feasibility of deep learning for the diagnostic ability to assess urinary stone composition from images. It can be an alternative tool for conventional stone analysis and provide decision support to urologists, improving the effectiveness of diagnosis and treatment.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.