Vesicoureteral reflux (VUR), a common congenital urinary tract anomaly, refers to retrograde flow of urine from the bladder into the upper urinary tract. The main goal in the treatment of pediatric VUR is to preserve renal function by preventing pyelonephritis. Many surgical management options are available for pediatric VUR. Open ureteral reimplantation has a high success rate but is invasive and is associated with postoperative pain and morbidity. Endoscopic therapy is minimally invasive but has the disadvantages of decreased short-term success and recurrence of reflux over the long term. Laparoscopic or robotic ureteral reimplantation has become increasingly popular owing to its effectiveness and minimal invasiveness, but long-term outcomes have yet to be documented. Urologists should make an effort to select the appropriate surgical strategy by taking into consideration the individual characteristics of the patient such as age, gender, grade of reflux at presentation, status of renal parenchyma, combined bladder and ureteral circumstances, functional status of the bladder and bowel, and preferences of the patients' family.
Humans ; Pain, Postoperative ; Pediatrics ; Pyelonephritis ; Recurrence ; Replantation ; Ureter ; Urinary Bladder ; Urinary Tract ; Urinary Tract Infections ; Vesico-Ureteral Reflux*

Background: and Purpose It is challenging to detect Parkinson’s disease (PD) in its early stages, which has prompted researchers to develop techniques based on machine learning methods for detecting PD. However, previous studies did not fully incorporate the slow progression of PD over a long period of time nor consider that its symptoms occur in a time-sequential manner. Contributing to the literature on PD, which has relied heavily on cross-sectional data, this study aimed to develop a method for detecting PD early that can process time-series information using the long short-term memory (LSTM) algorithm. Methods: We sampled 926 patients with PD and 9,260 subjects without PD using medicalclaims data. The LSTM algorithm was tested using diagnostic histories, which contained the diagnostic codes and their respective time information. We compared the prediction power of the 12-month diagnostic codes under two different settings over the 4 years prior to the first PD diagnosis. Results: The model that was trained using the most-recent 12-month diagnostic codes had the best performance, with an accuracy of 94.25%, a sensitivity of 82.91%, and a specificity of 95.26%. The other three models (12-month codes from 2, 3, and 4 years prior) were found to have comparable performances, with accuracies of 92.27%, 91.86%, and 91.81%, respectively.The areas under the curve from our data settings ranged from 0.839 to 0.923. Conclusions We explored the possibility that PD specialists could benefit from our proposed machine learning method as an early detection method for PD.

Background: and Purpose Parkinson’s disease (PD) is characterized by various prodromal symptoms, and these symptoms are mostly investigated retrospectively. While some symptoms such as rapid eye movement sleep behavior disorder are highly specific, others are common. This makes it challenging to predict those at risk of PD based solely on less-specific prodromal symptoms. The prediction accuracy when using only less-specific symptoms can be improved by analyzing the vast amount of information available using sophisticated deep-learning techniques. This study aimed to improve the performance of deep-learning-based screening in detecting prodromal PD using medical-claims data, including prescription information. Methods: We sampled 820 PD patients and 8,200 age- and sex-matched non-PD controls from Korean National Health Insurance cohort data. A deep-learning algorithm was developed using various combinations of diagnostic codes, medication codes, and prodromal periods. Results: During the prodromal period from year -3 to year 0, predicting PD using only diagnostic codes yielded a high accuracy of 0.937. Adding medication codes for the same period did not increase the accuracy (0.931–0.935). For the earlier prodromal period (year -6 to year -3), the accuracy of PD prediction decreased to 0.890 when using only diagnostic codes. The inclusion of all medication-codes data increased that accuracy markedly to 0.922. Conclusions A deep-learning algorithm using both prodromal diagnostic and medication codes was effective in screening PD. Developing a surveillance system with automatically collected medical-claims data for those at risk of developing PD could be cost-effective. This approach could streamline the process of developing disease-modifying drugs by focusing on the most-appropriate candidates for inclusion in accurate diagnostic tests.

Background: and Purpose Parkinson’s disease (PD) is characterized by various prodromal symptoms, and these symptoms are mostly investigated retrospectively. While some symptoms such as rapid eye movement sleep behavior disorder are highly specific, others are common. This makes it challenging to predict those at risk of PD based solely on less-specific prodromal symptoms. The prediction accuracy when using only less-specific symptoms can be improved by analyzing the vast amount of information available using sophisticated deep-learning techniques. This study aimed to improve the performance of deep-learning-based screening in detecting prodromal PD using medical-claims data, including prescription information. Methods: We sampled 820 PD patients and 8,200 age- and sex-matched non-PD controls from Korean National Health Insurance cohort data. A deep-learning algorithm was developed using various combinations of diagnostic codes, medication codes, and prodromal periods. Results: During the prodromal period from year -3 to year 0, predicting PD using only diagnostic codes yielded a high accuracy of 0.937. Adding medication codes for the same period did not increase the accuracy (0.931–0.935). For the earlier prodromal period (year -6 to year -3), the accuracy of PD prediction decreased to 0.890 when using only diagnostic codes. The inclusion of all medication-codes data increased that accuracy markedly to 0.922. Conclusions A deep-learning algorithm using both prodromal diagnostic and medication codes was effective in screening PD. Developing a surveillance system with automatically collected medical-claims data for those at risk of developing PD could be cost-effective. This approach could streamline the process of developing disease-modifying drugs by focusing on the most-appropriate candidates for inclusion in accurate diagnostic tests.

Background: and Purpose Parkinson’s disease (PD) is characterized by various prodromal symptoms, and these symptoms are mostly investigated retrospectively. While some symptoms such as rapid eye movement sleep behavior disorder are highly specific, others are common. This makes it challenging to predict those at risk of PD based solely on less-specific prodromal symptoms. The prediction accuracy when using only less-specific symptoms can be improved by analyzing the vast amount of information available using sophisticated deep-learning techniques. This study aimed to improve the performance of deep-learning-based screening in detecting prodromal PD using medical-claims data, including prescription information. Methods: We sampled 820 PD patients and 8,200 age- and sex-matched non-PD controls from Korean National Health Insurance cohort data. A deep-learning algorithm was developed using various combinations of diagnostic codes, medication codes, and prodromal periods. Results: During the prodromal period from year -3 to year 0, predicting PD using only diagnostic codes yielded a high accuracy of 0.937. Adding medication codes for the same period did not increase the accuracy (0.931–0.935). For the earlier prodromal period (year -6 to year -3), the accuracy of PD prediction decreased to 0.890 when using only diagnostic codes. The inclusion of all medication-codes data increased that accuracy markedly to 0.922. Conclusions A deep-learning algorithm using both prodromal diagnostic and medication codes was effective in screening PD. Developing a surveillance system with automatically collected medical-claims data for those at risk of developing PD could be cost-effective. This approach could streamline the process of developing disease-modifying drugs by focusing on the most-appropriate candidates for inclusion in accurate diagnostic tests.

BACKGROUND: Oligonucleotide chip technology has proven to be a very useful tool in the rapid diagnosis of infectious disease. Rifampin resistance is considered as a useful marker of multidrug-resistance in tuberculosis. Mutations in the rpoB gene coding β subunit of RNA polymerase represent the main mechanism of rifampin resistance. The purpose of this study was to develop a diagnosis kit using oligonucleotide chip for the rapid and accurate detection of rifampin-resistance in Mycobacterium tuberculosis. METHOD: Tle sequence specific probes for mutations in the rpoB gene were designed and spotted onto the glass slide, oligonucleotide chip. 38 clinical isolates of Mycobacterium were tested. A part of rpoB was amplified, labelled, and hybridized on the oligonucleotide chip with probes. Results were analyzed with a laser scanner. Direct sequencing was done to verify the results. RESULT: The low-density oligonucleotide chip designed to determine the specific mutations in the rpoB gene of M. tuberculosis accurately detected rifampin resistance associated with mutations in 28 clinical isolates. Mutations at codons 531, 526, and 513 were confirmed by direct sequencing analysis. CONCLUSION: Mutant detection using oligonucleotide chip technology is a reliable and useful diagnostic tool for the detection of multidrug-resistance in M. tuberculosis.
Clinical Coding ; Codon ; Communicable Diseases ; Diagnosis ; DNA-Directed RNA Polymerases ; Glass ; Mycobacterium ; Mycobacterium tuberculosis ; Rifampin ; Tuberculosis*

Botryoid Wilms tumor, a very rare variant of Wilms tumor, arises from the pelvocalyceal system, and its occurrence in the fetal or neonatal period has never been reported in the literature. Herein, we report an exceedingly rare and challenging case of botryoid Wilms tumor in a neonate who initially presented with fetal hydronephrosis. Postnatal ultrasonography revealed multiple lobulating hypoechoic masses with varying degrees of intralesional vascularity within the dilated pelvocalyceal system. To our knowedge, this is a case report of botryoid Wilms tumor of the youngest child in English literature.

PURPOSE: The exact role of angiogenesis in prostate cancer is unknown. We investigated whether endocrine therapy inhibits angiogenesis, and influences the expression of thrombospondin-1 (TSP-1), a potent inhibitor of angiogenesis, and vascular endothelial growth factor (VEGF) in prostate cancer. MATERIALS AND METHODS: Employing immunohistochemistry, we assessed the expression of VEGF and TSP-1 in archival tissues from 46 patients with metastatic prostate cancer (30 before androgen deprivation therapy and 16 after at least 6-months' duration of androgen deprivation therapy). For each tumour, microvascular density (MVD) counts were determined using immunohistochemical staining for factor VIII. The relationship between MVD and the expression of VEGF and TSP-1, the tumour grade was assessed in metastatic prostate cancer. RESULTS: The mean MVD counts (71.1 vessels per 200x high-power field) in 16 patients with metastatic cancer after androgen deprivation therapy was significantly higher than that (51.7) in 30 patients before androgen deprivation therapy (p<0.05). The immunohistochemical analysis demonstrated a higher TSP-1 expression (p<0.01), and a lower VEGF expression (p<0.01), in androgen deprivation group. There was no significant correlation between VEGF or TSP-1 expression and the mean MVD counts. The MVD counts had no correlation with Gleason scores or initial PSA levels. CONCLUSIONS: Endocrine therapy in metastatic prostate cancer significantly decreased MVD counts, the expression of VEGF and significantly increased the expression of TSP-1. The present study shows that decreased angiogenesis including changes in the expressions of angiogenic factors, might have an important role in the therapeutic effect of androgen deprivation in metastatic prostate cancer.
Angiogenesis Inducing Agents ; Factor VIII ; Humans ; Immunohistochemistry ; Prostate* ; Prostatic Neoplasms* ; Thrombospondin 1 ; Vascular Endothelial Growth Factor A*

Genetically engineered mouse models are commonly preferred for studying the human disease due to genetic and pathophysiological similarities between mice and humans. In particular, Cre-loxP system is widely used as an integral experimental tool for generating the conditional. This system has enabled researchers to investigate genes of interest in a tissue/cell (spatial control) and/or time (temporal control) specific manner. A various tissue-specific Cre-driver mouse lines have been generated to date, and new Cre lines are still being developed. This review provides a brief overview of Cre-loxP system and a few commonly used promoters for expression of tissue-specific Cre recombinase. Also, we finally introduce some available links to the Web sites that provides detailed information about Cre mouse lines including their characterization.
Animals ; Humans ; Mice* ; Recombinases

Although prostatic calculi are common, complications are fortunately rare. Here, we report a case of prostatic calculi causing urethral obstruction. A 66-year-old man presented with severe voiding difficulty and urge incontinence. He was found to have multiple large prostatic calculi obstructing the prostatic urethra as well as several bladder calculi. Attempts at endoscopic removal were unsuccessful, which resulted in an iatrogenic urethral diverticulum due to fragmented calculi. The residual calculi and diverticulum were removed successfully by open surgery.
Aged ; Calculi ; Diverticulum ; Humans ; Prostate ; Urethra ; Urethral Obstruction ; Urinary Bladder ; Urinary Bladder Calculi ; Urinary Incontinence, Urge