Septicemia of Vibrio spp. such as non-O1 Vibrio cholerae presented with diarrhea, fasciitis, cellulitis or otitis media are common in cirrhotic patients (Lin, C.-J., Chiu, C.-T., Lin, D.-Y., et al., Am. J. Gastroenterol., 91, 336-340, 1996). It may result from a lower C3/C4 level, a lower serum ferritin level or opsonophagocytosis dysfunction. High mortality in such cases has been noted. However, endophthalmitis is rare in such patients, and has never been reported. We present a cirrhotic patient of non-O1 and non-O139 V. cholerae septicemia complicated with endophthalmitis.

Men having sex with men (MSM) accounts for 33.6% of all reported cases of HIV-1 infection in Taiwan. The aim of this study was to investigate the epidemiology of HIV-1 infection among MSM in gay saunas in Taiwan.

Flavimonas oryzihabitans has emerged as a potential nosocomial pathogen in recent years. The typing method for characterization of this species has never been reported before. Pulsed-field gel electrophoresis (PFGE) and enterobacterial repetitive intergenic consensus (ERIC)-based PCR were used to generate DNA fingerprints for 14F. oryzihabitans isolates obtained from eight episodes of nosocomial infections during a 2-year period. Both techniques successfully classified these clinical isolates into eight distinct genotypes, thus indicating that all of these episodes of infections were independent. In contrast, repeated isolates from the same patient were assigned to identical genotypes. The reproducibility of both techniques was good. Therefore, we conclude that both PFGE and ERIC-PCR have comparable reproducible and discriminatory powers for the typing of F. oryzihabitans and may be useful for clarifying the epidemiology of this species; however, ERIC-PCR has the advantages of both speed and simplicity.

Persons who inject drugs (PWID) and their risk-related behaviors (e.g., unprotected sex and sharing needles/syringes/other injection equipment) have caused severe public health problems, especially in the rapid spread of HIV-1 and HCV. Here, we reconstructed the epidemic history of HIV-1 circulating recombinant form (CRF) 01_AE, CRF07_BC, and HCV subtype-6w among Taiwanese PWID. The timescales were estimated using phylogenetic and Bayesian coalescent analyses. The results revealed that CRF01_AE started to circulate in the Taiwanese PWID population in central Taiwan at 1992.5 (95% credible region: 1988.8–1995.9) and spread to other regions of Taiwan, while CRF07_BC was first identified in southern Taiwan at 2000.0 (95% CR: 1997.8–2002.2) and then spread northward to central-northern Taiwan. All HCV-6 strains were from Asia (that is, China, Myanmar, Taiwan, and Vietnam) and originated in 1928.1 (95% CR: 1890.2–1966.0). Furthermore, subtype-6w isolates from different regions of Taiwan appeared to share a common source that existed in the mid-1990s (95% CR: 1985.0–2001.8) or thereabouts. The routes of drug trafficking and the resulting high prevalence of HIV-1/HCV co-infections among PWID might have contributed to the virus transmission and promoted its spread worldwide. Long-term monitoring and policy implementation in at-risk populations would be useful for disease control.

This paper presents a convolutional neural network (CNN) model, underpinned by deep learning principles, tailored to predict endovascular intervention outcomes in patients with acute anterior circulation ischemic stroke. Recognizing the crucial role of predictive models in guiding patient prognosis and treatment selection, we partitioned our dataset into training and testing subsets. The proposed CNN model exhibited outstanding performance during its training phase, reaching a stellar accuracy rate of 100%. This performance was maintained during the testing phase, with an accuracy surpassing 94.12%. Such minimal deviation between training and testing accuracies underscores the robustness of our model and suggests a reduced likelihood of overfitting, enhancing its relevance for real-world clinical scenarios. These promising results signify a leap forward in harnessing the power of deep learning for medical prognostication, particularly in stroke intervention outcomes.