Taipei Veterans General Hospital

About: Taipei Veterans General Hospital is a healthcare organization based out in Taipei, Taiwan. It is known for research contribution in the topics: Population & Cancer. The organization has 11878 authors who have published 16478 publications receiving 363424 citations. The organization is also known as: Táiběi Róngmín Zǒngyī Yuàn.

Trial of Satralizumab in Neuromyelitis Optica Spectrum Disorder

Abstract: Background Neuromyelitis optica spectrum disorder (NMOSD) is an autoimmune disease of the central nervous system and is associated with autoantibodies to anti–aquaporin-4 (AQP4-IgG) in app...

Artificial intelligence for diagnosis and grading of prostate cancer in biopsies: a population-based, diagnostic study.

Abstract: Summary Background An increasing volume of prostate biopsies and a worldwide shortage of urological pathologists puts a strain on pathology departments. Additionally, the high intra-observer and inter-observer variability in grading can result in overtreatment and undertreatment of prostate cancer. To alleviate these problems, we aimed to develop an artificial intelligence (AI) system with clinically acceptable accuracy for prostate cancer detection, localisation, and Gleason grading. Methods We digitised 6682 slides from needle core biopsies from 976 randomly selected participants aged 50–69 in the Swedish prospective and population-based STHLM3 diagnostic study done between May 28, 2012, and Dec 30, 2014 (ISRCTN84445406), and another 271 from 93 men from outside the study. The resulting images were used to train deep neural networks for assessment of prostate biopsies. The networks were evaluated by predicting the presence, extent, and Gleason grade of malignant tissue for an independent test dataset comprising 1631 biopsies from 246 men from STHLM3 and an external validation dataset of 330 biopsies from 73 men. We also evaluated grading performance on 87 biopsies individually graded by 23 experienced urological pathologists from the International Society of Urological Pathology. We assessed discriminatory performance by receiver operating characteristics and tumour extent predictions by correlating predicted cancer length against measurements by the reporting pathologist. We quantified the concordance between grades assigned by the AI system and the expert urological pathologists using Cohen's kappa. Findings The AI achieved an area under the receiver operating characteristics curve of 0·997 (95% CI 0·994–0·999) for distinguishing between benign (n=910) and malignant (n=721) biopsy cores on the independent test dataset and 0·986 (0·972–0·996) on the external validation dataset (benign n=108, malignant n=222). The correlation between cancer length predicted by the AI and assigned by the reporting pathologist was 0·96 (95% CI 0·95–0·97) for the independent test dataset and 0·87 (0·84–0·90) for the external validation dataset. For assigning Gleason grades, the AI achieved a mean pairwise kappa of 0·62, which was within the range of the corresponding values for the expert pathologists (0·60–0·73). Interpretation An AI system can be trained to detect and grade cancer in prostate needle biopsy samples at a ranking comparable to that of international experts in prostate pathology. Clinical application could reduce pathology workload by reducing the assessment of benign biopsies and by automating the task of measuring cancer length in positive biopsy cores. An AI system with expert-level grading performance might contribute a second opinion, aid in standardising grading, and provide pathology expertise in parts of the world where it does not exist. Funding Swedish Research Council, Swedish Cancer Society, Swedish eScience Research Center, EIT Health.

Association between rmpA and magA Genes and Clinical Syndromes Caused by Klebsiella pneumoniae in Taiwan

Abstract: Background. The association of the magA gene with the hypermucoviscosity phenotype relevant to the pathogenesis of Klebsiella pneumoniae liver abscess has been reported in Taiwan. Similarly, the rmpA gene, known as a positive regulator of extracapsular polysaccharide synthesis that confers a mucoid phenotype, may be another candidate gene causing hypermucoviscosity. However, the association of rmpA with K. pneumoniae clinical syndromes is unreported. We aimed to investigate the clinical correlation between rmpA and primary Klebsiella abscess, focusing on sites other than the liver. Methods. From July 2003 through December 2004, a total of 151 K. pneumoniae isolates recovered from 151 patients with bacteremia were collected from 2 large medical centers in southern Taiwan. Clinical data were collected from medical records. The genes rmpA and magA were amplified by polymerase chain reaction using specific primers. Results. The prevalences of hypermucoviscosity, rmpA, and magA were 38%, 48%, and 17%, respectively. As determined by statistical multivariate analysis, strains carrying rmpA were significantly associated with the hypermucoviscosity phenotype, and there was a significant correlation with purulent tissue infections, such as liver abscess and lung, neck, psoas muscle, or other focal abscess. Conclusion. Our data support a statistical correlation between the rmpA gene and virulence in terms of abscess formation for these hypermucoviscous K. pneumoniae strains. Hypermucoviscosity associated with rmpA, together with a thorough physical examination, may be helpful as a guide to carry out appropriate diagnostic tests on patients with an initially unknown source of K. pneumoniae bacteremia, particularly when looking for the occurrence of an underlying abscess.