Search

Abstract The integration of artificial intelligence (AI) into healthcare systems within low-middle income countries (LMICs) has emerged as a central focus for various initiatives aiming to improve healthcare access and delivery quality. In contrast to high-income countries (HICs), which often possess the resources and infrastructure to adopt innovative healthcare technologies, LMICs confront resource limitations such as insufficient funding, outdated infrastructure, limited digital data, and a shortage of technical expertise. Consequently, many algorithms initially trained on data from non-LMIC settings are now being employed in LMIC contexts. However, the effectiveness of these systems in LMICs can be compromised when the unique local contexts and requirements are not adequately considered. In this study, we evaluate the feasibility of utilizing models developed in the United Kingdom (a HIC) within hospitals in Vietnam (a LMIC). Consequently, we present and discuss practical methodologies aimed at improving model performance, emphasizing the critical importance of tailoring solutions to the distinct healthcare systems found in LMICs. Our findings emphasize the necessity for collaborative initiatives and solutions that are sensitive to the local context in order to effectively tackle the healthcare challenges that are unique to these regions.

Abstract Muscle ultrasound has been shown to be a valid and safe imaging modality to assess muscle wasting in critically ill patients in the intensive care unit (ICU). This typically involves manual delineation to measure the rectus femoris cross-sectional area (RFCSA), which is a subjective, time-consuming, and laborious task that requires significant expertise. We aimed to develop and evaluate an AI tool that performs automated recognition and measurement of RFCSA to support non-expert operators in measurement of the RFCSA using muscle ultrasound. Twenty patients were recruited between Feb 2023 and July 2023 and were randomized sequentially to operators using AI (n = 10) or non-AI (n = 10). Muscle loss during ICU stay was similar for both methods: 26 ± 15% for AI and 23 ± 11% for the non-AI, respectively (p = 0.13). In total 59 ultrasound examinations were carried out (30 without AI and 29 with AI). When assisted by our AI tool, the operators showed less variability between measurements with higher intraclass correlation coefficients (ICCs 0.999 95% CI 0.998–0.999 vs. 0.982 95% CI 0.962–0.993) and lower Bland Altman limits of agreement (± 1.9% vs. ± 6.6%) compared to not using the AI tool. The time spent on scans reduced significantly from a median of 19.6 min (IQR 16.9–21.7) to 9.4 min (IQR 7.2–11.7) compared to when using the AI tool (p

Abstract Collaborative efforts in artificial intelligence (AI) are increasingly common between high-income countries (HICs) and low- to middle-income countries (LMICs). Given the resource limitations often encountered by LMICs, collaboration becomes crucial for pooling resources, expertise, and knowledge. Despite the apparent advantages, ensuring the fairness and equity of these collaborative models is essential, especially considering the distinct differences between LMIC and HIC hospitals. In this study, we show that collaborative AI approaches can lead to divergent performance outcomes across HIC and LMIC settings, particularly in the presence of data imbalances. Through a real-world COVID-19 screening case study, we demonstrate that implementing algorithmic-level bias mitigation methods significantly improves outcome fairness between HIC and LMIC sites while maintaining high diagnostic sensitivity. We compare our results against previous benchmarks, utilizing datasets from four independent United Kingdom Hospitals and one Vietnamese hospital, representing HIC and LMIC settings, respectively.

Objectives: We studied the immunogenicity after primary and booster vaccinations of the Abdala COVID-19 vaccine, a receptor-binding domain protein subunit vaccine, in Vietnamese people by determining the level of neutralization and cross-neutralization activities against the ancestral SARS-CoV-2 and its variants and SARS-CoV-1. Methods: We performed a prospective observational study, enrolling adults aged 19-59 years in Dong Thap province, southern Vietnam, and collected blood samples from baseline until 4 weeks after the booster dose. We measured anti-nucleocapsid, anti-spike, and neutralizing antibodies against SARS-CoV-2 and assessed the cross-neutralization against 14 SARS-CoV-2 variants and SARS-CoV-1. Complementary antibody data came from Vietnamese health care workers fully vaccinated with ChAdOx1-S. Results: After primary vaccination, anti-spike antibody and neutralizing antibodies were detectable in 98.4% and 87% of 251 study participants, respectively, with neutralizing antibody titers similar to that induced by ChAdOx1-S vaccine. Antibody responses after a homologous (Abdala COVID-19) or heterologous (messenger RNA BNT162b2) booster could neutralize 14 SARS-CoV-2 variants (including Omicron) and SARS-CoV-1. Conclusions: Abdala COVID-19 vaccine is immunogenic in Vietnamese people. Enhanced antibody response after a booster dose could cross-neutralize 14 SARS-CoV-2 variants and SARS-CoV-1. Our results have added to the growing body of knowledge about the contribution of protein subunit vaccine platforms to pandemic control.

This study analyses the square diaphragm structure of a micro-electro-mechanical system (MEMS) pressure sensor using the finite element method (FEM). The research investigates an enhancement in stress distribution achieved by coating a silicon (Si) diaphragm with a thin layer of silicon carbide (3C-SiC). Applying a thin layer of 3C-SiC material onto a Si diaphragm enhances the stress distribution of MEMS pressure sensors. The design structure of the square diaphragm of a MEMS pressure sensor is simulated and analysed using 3D FEM in COMSOL multiphysics. Four piezo-resistors are configured in a Wheatstone bridge to translate resistance variations into an output voltage. Then, the stress distributions are calculated by solving the 3D structures of MEMS pressure sensors in the stationary solver of the solid mechanics module in COMSOL multiphysics. The findings indicate a significantly higher stress distribution in the 3C-SiC layer compared to a solely silicon diaphragm. Consequently, it is posited that the stress distribution in MEMS pressure sensors could be considerably augmented with increasing pressure, increasing diaphragm length, and decreasing diaphragm thickness. Also, the output voltage of more than 20 mV can be achievable with this design for using the thin layer of 3C-SiC material on a Si diaphragm in designing MEMS piezoresistive pressure sensors.

Dengue shock (DS) is the most severe complication of dengue infection; endothelial hyperpermeability leads to profound plasma leakage, hypovolaemia and extravascular fluid accumulation. At present, the only treatment is supportive with intravenous fluid, but targeted endothelial stabilising therapies and host immune modulators are needed. With the aim of prioritising potential therapeutics, we conducted a prospective observational study of adults (≥16 years) with DS in Vietnam from 2019-2022, comparing the pathophysiology underlying circulatory failure with patients with septic shock (SS), and investigating the association of biomarkers with clinical severity (SOFA score, ICU admission, mortality) and pulmonary vascular leak (daily lung ultrasound for interstitial and pleural fluid). Plasma was collected at enrolment, 48 hours later and hospital discharge. We measured biomarkers of inflammation (IL-6, ferritin), endothelial activation (Ang-1, Ang-2, sTie-2, VCAM-1) and endothelial glycocalyx breakdown (hyaluronan, heparan sulfate, endocan, syndecan-1). We enrolled 135 patients with DS (median age 26, median SOFA score 7, 34 required ICU admission, 5 deaths), together with 37 patients with SS and 25 healthy controls. Within the DS group, IL-6 and ferritin were associated with admission SOFA score (IL-6: βeta0.70, p

At atmospheric pressure (p=101325 Pa), the effects of humidity and temperature on moist air become important when discussing the quality factor of micro-cantilever and micro-bridge resonators. The squeeze film damping (SFD) problem, the dominant damping source for micro-beam resonators, is modelled using the modified molecular gas lubrication (MMGL) equation with finite element modelling (FEM) in the eigenvalue problem. The MMGL equation is modified with the effective viscosity of moist air (µeff) to account for the effects of humidity and temperature. Other damping sources, such as thermoelastic damping (TED) and the support loss of micro-beam resonators, are also calculated. The quality factor of micro-beam resonators is then discussed over a wide range of temperatures and relative humidity levels at atmospheric pressure and gas rarefaction. The results show that the quality factor of micro-cantilever and micro-bridge resonators increases as both humidity and temperature rise in atmospheric pressure and gas rarefaction. Furthermore, the quality factor of a micro-bridge resonator with changes in humidity and temperature is significantly higher than that of a micro-cantilever resonator in atmospheric pressure and gas rarefaction.

By bestowing honours, states produce subjects. But what kind of subjects are created if those honoured do not seek state honours, because they find the honours not worth the responsibilities they have to shoulder, or because they do not internalise the state's values that the honours embody? Building on ethnographic materials from Vietnam, this article explores how some who receive honours are "grateful coerced subjects" on whom state honours are imposed through social pressure, persistent persuasion, or as a matter of fait accompli they cannot refuse. The bestowal of honours exposes them to pressure from the wider society to comply with state's goals and values to prove themselves worthy of the honours, in ways they find physically burdensome and morally problematic. Yet the coerced subjects do not hold a grudge against the state, and even feel grateful to the state for conferring honours on them. [ABSTRACT FROM AUTHOR]

The cleavage of viral surface proteins by furin is associated with some viruses' high virulence and infectivity. The human papillomavirus (HPV) requires the proteolytic processing of its capsid proteins for activation before entry. Variability in reactivity with furin and other proprotein convertases (PCs) among HPV types was investigated. HPV16, the most prevalent and carcinogenic HPV type, reacted with PCs with the broadest selectivity compared to other types in reactions of pseudoviral particles with the recombinant PCs, furin, PC4, PC5, PACE4, and PC7. Proteolytic preactivation was assessed using a well-established entry assay into PC-inhibited cells based on the green fluorescent protein as a reporter. The inhibition of the target cell PC activity with serpin-based PC-selective inhibitors also showed a diversity of PC selectivity among HPV types. HPV16 reacted with furin at the highest rate compared to the other types in time-dependent preactivation reactions and produced the highest entry values standardized to pseudoviral particle concentration. The predominant expression of furin in keratinocytes and the high reactivity of HPV16 with this enzyme highlight the importance of selectively targeting furin as a potential antiviral therapeutic approach. [ABSTRACT FROM AUTHOR]

Background Methods Results Conclusions Small cell lung cancer (SCLC) is the most aggressive neuroendocrine lung cancer, with a dismal 5‐year survival rate. No reliable biomarkers or imaging are available for early SCLC detection. In a search for a specific marker of SCLC, this study identified that hepatocyte cell adhesion molecule 2 (HEPACAM2), a member of the immunoglobulin‐like superfamily, is highly and specifically expressed in SCLC.This study investigated HEPACAM2 expression in patients with SCLC via RNA sequencing and evaluated its relationship to progression‐free survival (PFS) and overall survival (OS). Immunofluorescence microscopy was used to assess the cellular location of HEPACAM2 and to conduct in vitro and in vivo studies to understand its expression and functional significance. These findings were integrated with databases of patients with SCLC.HEPACAM2 is highly expressed and specific to SCLC. HEPACAM2 levels are inversely correlated with PFS and OS in patients with SCLC and are expressed at all stages. Moreover, HEPACAM2 messenger RNA and its peptides can be detected in the secretomes in cell lines. Positively correlated with ASCL1 expression in SCLC tumors, HEPACAM2 is localized primarily to the plasma membrane and linked to extracellular matrix signaling and cellular migration. A loss of HEPACAM2 in SCLC cells attenuated ASCL1 and MYC expression. Consistent with clinical data, in vitro and in vivo studies suggested that HEPACAM2 promotes cancer cell growth.With its remarkable specificity, high expression, presence in early disease, and extracellular secretion, HEPACAM2 could be a potential diagnostic cell surface biomarker for early SCLC detection. These findings warrant further investigation into its role in the pathobiology of SCLC. [ABSTRACT FROM AUTHOR]

The integration of artificial intelligence (AI) into healthcare systems within low-middle income countries (LMICs) has emerged as a central focus for various initiatives aiming to improve healthcare access and delivery quality. In contrast to high-income countries (HICs), which often possess the resources and infrastructure to adopt innovative healthcare technologies, LMICs confront resource limitations such as insufficient funding, outdated infrastructure, limited digital data, and a shortage of technical expertise. Consequently, many algorithms initially trained on data from non-LMIC settings are now being employed in LMIC contexts. However, the effectiveness of these systems in LMICs can be compromised when the unique local contexts and requirements are not adequately considered. In this study, we evaluate the feasibility of utilizing models developed in the United Kingdom (a HIC) within hospitals in Vietnam (a LMIC). Consequently, we present and discuss practical methodologies aimed at improving model performance, emphasizing the critical importance of tailoring solutions to the distinct healthcare systems found in LMICs. Our findings emphasize the necessity for collaborative initiatives and solutions that are sensitive to the local context in order to effectively tackle the healthcare challenges that are unique to these regions., (© 2024. The Author(s).)

We studied a community cluster of 25 mpox cases in Vietnam caused by emerging monkeypox virus sublineage C.1 and imported into Vietnam through 2 independent events; 1 major cluster carried a novel APOBEC3-like mutation. Three patients died; all had advanced HIV co-infection. Viral evolution and its potential consequences should be closely monitored.

Mucormycosis is an emerging and deadly invasive fungal infection caused by fungi belonging to the Mucorales order. We investigated the myosin superfamily, which encompasses diverse actin-based motor proteins with various cellular functions. Specifically, the role of the Myo5B (ID 179665) protein from the myosin class V family in Mucor lusitanicus was explored by generating silencing phenotypes and null mutants corresponding to the myo5B gene. Silencing fungal transformants exhibited a markedly reduced growth rate and a nearly complete absence of sporulation compared to the wild-type strain. The myo5BΔ null mutant strain displayed atypical characteristics, including abnormally short septa and inflated hyphae. Notably, there were a majority of small yeast-like cells instead of filamentous hyphae in the mutant. These yeast-like cells cannot germinate normally, resulting in a loss of polarity. In vivo virulence assays conducted in the Galleria mellonella invertebrate model revealed that the myo5BΔ mutant strain was avirulent. These findings shed light on the crucial contributions of the Myo5B protein to the dimorphism and pathogenicity of M. lusitanicus . Therefore, the myosin V family is a potential target for future therapeutic interventions aimed at treating mucormycosis.

Muscle ultrasound has been shown to be a valid and safe imaging modality to assess muscle wasting in critically ill patients in the intensive care unit (ICU). This typically involves manual delineation to measure the rectus femoris cross-sectional area (RFCSA), which is a subjective, time-consuming, and laborious task that requires significant expertise. We aimed to develop and evaluate an AI tool that performs automated recognition and measurement of RFCSA to support non-expert operators in measurement of the RFCSA using muscle ultrasound. Twenty patients were recruited between Feb 2023 and July 2023 and were randomized sequentially to operators using AI (n = 10) or non-AI (n = 10). Muscle loss during ICU stay was similar for both methods: 26 ± 15% for AI and 23 ± 11% for the non-AI, respectively (p = 0.13). In total 59 ultrasound examinations were carried out (30 without AI and 29 with AI). When assisted by our AI tool, the operators showed less variability between measurements with higher intraclass correlation coefficients (ICCs 0.999 95% CI 0.998-0.999 vs. 0.982 95% CI 0.962-0.993) and lower Bland Altman limits of agreement (± 1.9% vs. ± 6.6%) compared to not using the AI tool. The time spent on scans reduced significantly from a median of 19.6 min (IQR 16.9-21.7) to 9.4 min (IQR 7.2-11.7) compared to when using the AI tool (p < 0.001). AI-assisted muscle ultrasound removes the need for manual tracing, increases reproducibility and saves time. This system may aid monitoring muscle size in ICU patients assisting rehabilitation programmes., (© 2024. The Author(s).)