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Background: The eradication rates of sequential therapy are high in clinical trials; however, the adherence for follow‐up or the patient population in a real‐world setting might be different from those in trails. This study investigates the effectiveness of sequential therapy in a real‐world setting and the factors that lead to treatment failure. Materials and Methods: In this retrospective study, patients receiving sequential therapy as a first‐line anti‐Helicobacter pylori (H. pylori) treatment in a real‐world setting were reviewed. The age adjusted Charlson Comorbidity Index (age‐CCI) and baseline variety of medications were reviewed to determine factors correlated with nonadherence for post‐treatment testing and H. pylori eradication failure. Results: A total of 1053 patients were reviewed. A total of 579 patients receiving sequential therapy were included in the analyses. Among them, 462 received post‐treatment testing and were placed into the follow‐up group. Thus, the post‐treatment testing rate was 79.8%. Stroke was an independent factor of nonadherence for post‐treatment testing. In the follow‐up group, the eradication failure rate was 8.2%. Female sex (odds ratio [OR] 2.41 [95% CI 1.16–5.03], p = 0.02) and age‐CCI ≥2 (OR 3.16 [1.05–9.48], p = 0.04) were independent factors of H. pylori eradication failure. The eradication failure rates were 14.4%, 7.8%, 7.1%, and 3.1% for the females with age‐CCI ≥2, females with age‐CCI <2, males with age‐CCI ≥2, and males with age‐CCI <2 subgroups, respectively (p = 0.027). Conclusions: In a real‐world setting, the adherence rate of post‐treatment testing for sequential therapy as a first‐line anti‐H. pylori treatment was found to be suboptimal. Female sex and age‐CCI ≥2 were independent factors of eradication failure. [ABSTRACT FROM AUTHOR]

This paper describes the establishment by the Institute of Nuclear Energy Research (INER, Taiwan) of the reference air kerma rate (RAKR) calibration standard for measurement of high-dose rate (HDR) 192Ir brachytherapy source strength. A bilateral comparison has been made in the RAKR standards for HDR 192Ir brachytherapy sources at the INER and Physikalisch-Technische Bundesanstalt (PTB, Germany) and the measurement difference was within the overall standard uncertainty and showed good agreement between the two calibration standard systems established at the INER and the PTB. Besides, INER also worked with 20 domestic hospitals to organise an on-site measurement comparison programme to explore the status of HDR 192Ir brachytherapy source strength determination in Taiwan. The comparison results presented the ratios of RAKR with vendor values, as determined by INER and hospitals from the programme. The ratios fall in all cases within the ±3 % guaranteed by the vendors for a coverage factor of k=2 or at 95 % confidence level. [ABSTRACT FROM PUBLISHER]

Objective: Common bile duct (CBD) stones caused diseases are life-threatening. Because CBD stones locate in the distal part of the CBD and have relatively small sizes, detecting CBD stones from CT scans is a challenging issue in the medical domain., Methods and Procedures: We propose a deep learning based weakly-supervised method called multiple field-of-view based attention driven network (MFADNet) to detect CBD stones from CT scans based on image-level labels. Three dominant modules including a multiple field-of-view encoder, an attention driven decoder and a classification network are collaborated in the network. The encoder learns the feature of multi-scale contextual information while the decoder with the classification network is applied to locate the CBD stones based on spatial-channel attentions. To drive the learning of the whole network in a weakly-supervised and end-to-end trainable manner, four losses including the foreground loss, background loss, consistency loss and classification loss are proposed., Results: Compared with state-of-the-art weakly-supervised methods in the experiments, the proposed method can accurately classify and locate CBD stones based on the quantitative and qualitative results., Conclusion: We propose a novel multiple field-of-view based attention driven network for a new medical application of CBD stone detection from CT scans while only image-levels are required to reduce the burdens of labeling and help physicians automatically diagnose CBD stones. The source code is available at https://github.com/nchucvml/MFADNet after acceptance., Clinical Impact: Our deep learning method can help physicians localize relatively small CBD stones for effectively diagnosing CBD stone caused diseases., (This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/.)