Reappraisal of the effectiveness of 99mTc-dimercaptosuccinic acid scans for selective voiding cystourethrography in children with a first febrile urinary tract infection

AbstractRecent studies have yielded conflicting results regarding the ability of technetium-99m dimercaptosuccinic acid (99mTc-DMSA) renal scans for identifying high-grade vesicoureteral reflux (VUR) in children with a first febrile urinary tract infection (UTI). This study aimed to reevaluate the effectiveness of 99mTc-DMSA renal scans for selective voiding cystourethrography (VCUG) in children with a first febrile UTI. The medical records of children aged ≤ 5 years who were admitted with a first febrile UTI were retrospectively reviewed. Ultrasonography (US) and DMSA renal scans were performed within 3–5 days after admission, and VCUG was performed 7–10 days after antibiotics treatment. A total of 653 children were enrolled for analysis, including 579 patients aged < 2 years (Group A) and 74 patients aged 2–5 years (Group B). In Group A, DMSA scans were abnormal for 346 patients (59.8%), and normal for 233 patients (40.2%). High-grade VUR was present in 99 of 346 patients (28.9%) with abnormal DMSA scans, but present in only 16 of 233 patients (6.9%) with normal DMSA scans (p < 0.001). Regarding the prediction of high-grade VUR, the sensitivity and negative predictive value (NPV) for the DMSA scans were 86.1% and 93.1%, respectively. In Group B, DMSA scans were abnormal for 36 patients (48.6%), and normal for 38 patients (51.4%). High-grade VUR was present in 12 of 36 patients (33.3%) with abnormal DMSA scans, whereas none of the 38 patients with normal DMSA scans had high-grade VUR (p < 0.001). The sensitivity and NPV of the DMSA scans were both 100%. Using the selective VCUG strategy, approximately 40% of Group A patients and 50% of Group B patients could be spared an unnecessary VCUG, respectively. Our study results suggest that 99mTc-DMSA renal scans are effective in identifying children with a first febrile UTI for selective VCUG

Federated learning enables big data for rare cancer boundary detection.

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Author Correction: Federated learning enables big data for rare cancer boundary detection.

10.1038/s41467-023-36188-7NATURE COMMUNICATIONS14

Federated Learning Enables Big Data for Rare Cancer Boundary Detection

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

CFD Investigating the Changes of FAC Wear Sites Due to the Power Uprate of Nuclear Power Plant

Abstract Changes of flow-accelerated corrosion (FAC) wear sites on the piping due to the power uprate of nuclear power plant are investigated by way of computational fluid dynamics (CFD) models. These models proposed in this paper include the threedimensional two-phase flow models and appropriate FAC models. The computations are performed using commercial code Fluent 6.2 which is control-volume-based. A boiling water reactor (BWR), located at Taiwan, is selected in the present analytical works. Simulation results clearly reveal that the present model can precisely capture the two-phase phenomena within the piping system. Coupled with the calculated two-phase flow characteristics, the appropriate FAC indictors can predict the local distributions of severe FAC sites. These predicted results show reasonable agreement with the plant measurements. Therefore, the impacts of power uprate on the changes of wear sites can be confidently investigated by the present CFD model. Through the comparisons of predictions for the selected BWRs under 100%, 105%, and 110% power levels, the simulation results clearly reveal that the power uprate does not significantly change the characteristics of FAC wear sites

FDG-PET in thyroid papillary carcinoma: a case report

The most common form of thyroid cancer is papillary thyroid cancer, accounting for 70-90% of well differentiated thyroid malignancies. The incidence of malignant thyroid tumours is approximately 4/100,000 in women and 1.5/100,000 in men. In recent years, there has been an increase in the incidence of papillary carcinoma; whether this is due to dietary changes over time remains unclear. Although papillary tumours tend to metastasise via lymphatics, they have a good prognosis, with a 90% 10-year survival rate. Positron emission tomography (PET) with the glucose analogue fluorodeoxyglucose (FDG-PET) has a special place in the investigation of papillary thyroid malignancies. These tumours are generally poorly differentiated and have impaired Na+/I- pumps, resulting in poor iodine uptake. However, these tumour cells have an increased expression of GLUT-1 and GLUT-3 glucose transporters. FDG is a glucose analogue that does not enter glycolysis, and so papillary tumour cells accumulate FDG, leading to positive findings on PET. Therefore, FDG-PET is especially important in cases where an iodine 131 (131I) scan is negative but serum thyroglobulin levels remain high. In this scenario, FDG-PET sensitivity has been shown to be greater than 90%.</p