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Liver injury with marked elevation of aspartate aminotransferase enzyme (AST) is commonly observed in dengue infection. To understand the pathogenesis of this liver damage, we compared the plasma levels of hepatic specific, centrilobular predominant enzymes (glutamate dehydrogenase, GLDH; glutathione S transferase-α, αGST), periportal enriched 4-hydroxyphenylpyruvate dioxygenase (HPPD), periportal predominant arginase-1 (ARG-1), and other non-specific biomarkers (paraoxonase-1, PON-1) in patients with different outcomes of dengue infection. This hospital-based study enrolled 87 adult dengue patients, stratified into three groups based on plasma AST levels (< 80, 80-400, > 400 U/L) in a 1:1:1 ratio (n = 40, n = 40, n = 40, respectively. The new liver enzymes in the blood samples from the 4th to 6th days of their illness were measured by commercial enzyme-linked immunosorbent assay (ELISA) or colorimetric kits. Based on the diagnosis at discharge days, our patients were classified as 40 (46%) dengue without warning signs (D), 35 (40.2%) dengue with warning signs (DWS), and 11 (12.6%) severe dengue (SD) with either shock (two patients) or AST level over 1000 U/L (nine patients), using the 2009 WHO classification. The group of high AST (> 400 U/L) also had higher ALT, GLDH, ARG-1, and HPPD than the other groups, while the high (> 400 U/L) and moderate (80-400 U/L) AST groups had higher ALT, αGST, ARG-1, and HPPD than the low AST group (< 80 U/L). There was a good correlation between AST, alanine aminotransferase enzyme (ALT), and the new liver biomarkers such as GLDH, αGST, ARG-1, and HPPD. Our findings suggest that dengue-induced liver damage initiates predominantly in the centrilobular area toward the portal area during the dengue progression. Moreover, these new biomarkers should be investigated further to explain the pathogenesis of dengue and to validate their prognostic utility., (© 2024. The Author(s).)

Background: Diagnosis of tuberculous meningitis (TBM) is hampered by the lack of a gold standard. Current microbiological tests lack sensitivity and clinical diagnostic approaches are subjective. We therefore built a diagnostic model that can be used before microbiological test results are known., Methods: We included 659 individuals aged [Formula: see text] years with suspected brain infections from a prospective observational study conducted in Vietnam. We fitted a logistic regression diagnostic model for TBM status, with unknown values estimated via a latent class model on three mycobacterial tests: Ziehl-Neelsen smear, Mycobacterial culture, and GeneXpert. We additionally re-evaluated mycobacterial test performance, estimated individual mycobacillary burden, and quantified the reduction in TBM risk after confirmatory tests were negative. We also fitted a simplified model and developed a scoring table for early screening. All models were compared and validated internally., Results: Participants with HIV, miliary TB, long symptom duration, and high cerebrospinal fluid (CSF) lymphocyte count were more likely to have TBM. HIV and higher CSF protein were associated with higher mycobacillary burden. In the simplified model, HIV infection, clinical symptoms with long duration, and clinical or radiological evidence of extra-neural TB were associated with TBM At the cutpoints based on Youden's Index, the sensitivity and specificity in diagnosing TBM for our full and simplified models were 86.0% and 79.0%, and 88.0% and 75.0% respectively., Conclusion: Our diagnostic model shows reliable performance and can be developed as a decision assistant for clinicians to detect patients at high risk of TBM. Diagnosis of tuberculous meningitis is hampered by the lack of gold standard. We developed a diagnostic model using latent class analysis, combining confirmatory test results and risk factors. Models were accurate, well-calibrated, and can support both clinical practice and research., (© 2024. The Author(s).)

Background: Wearable devices have been used extensively both inside and outside of the hospital setting. During the COVID-19 pandemic, in some contexts, there was an increased need to remotely monitor pulse and saturated oxygen for patients due to the lack of staff and bedside monitors., Objective: A prototype of a remote monitoring system using wearable pulse oximeter devices was implemented at the Hospital for Tropical Diseases in Ho Chi Minh City, Vietnam, from August to December 2021. The aim of this work was to support the ongoing implementation of the remote monitoring system., Methods: We used an action learning approach with rapid pragmatic methods, including informal discussions and observations as well as a feedback survey form designed based on the technology acceptance model to assess the use and acceptability of the system. Based on these results, we facilitated a meeting using user-centered design principles to explore user needs and ideas about its development in more detail., Results: In total, 21 users filled in the feedback form. The mean technology acceptance model scores ranged from 3.5 (for perceived ease of use) to 4.4 (for attitude) with behavioral intention (3.8) and perceived usefulness (4.2) scoring in between. Those working as nurses scored higher on perceived usefulness, attitude, and behavioral intention than did physicians. Based on informal discussions, we realized there was a mismatch between how we (ie, the research team) and the ward teams perceived the use and wider purpose of the technology., Conclusions: Designing and implementing the devices to be more nurse-centric from their introduction could have helped to increase their efficiency and use during the complex pandemic period., (©An Phuoc Luu, Truong Thanh Nguyen, Van Thi Cam Cao, Trinh Hoang Diem Ha, Lien Thi Thu Chung, Trung Ngoc Truong, Tung Nguyen Le Nhu, Khoa Bach Dao, Hao Van Nguyen, Phan Nguyen Quoc Khanh, Khanh Thuy Thuy Le, Luu Hoai Bao Tran, Phung Tran Huy Nhat, Duc Minh Tran, Yen Minh Lam, Catherine Louise Thwaites, Jacob Mcknight, Nguyen Van Vinh Chau, Jennifer Ilo Van Nuil, Vietnam ICU Translational Applications Laboratory (VITAL). Originally published in JMIR Human Factors (https://humanfactors.jmir.org), 05.01.2024.)