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BackgroundDengue is one of the most common diseases in the tropics and subtropics. Whilst mortality is a rare event when adequate supportive care can be provided, a large number of patients get hospitalised with dengue every year that places a heavy burden on local health systems. A better understanding of the support required at the time of hospitalisation is therefore of critical importance for healthcare planning, especially when resources are limited during major outbreaks.MethodsHere we performed a retrospective analysis of clinical data from over 1500 individuals hospitalised with dengue in Vietnam between 2017 and 2019. Using a broad panel of potential biomarkers, we sought to evaluate robust predictors of prolonged hospitalisation periods.ResultsOur analyses revealed a lead-time bias, whereby early admission to hospital correlates with longer hospital stays - irrespective of disease severity. Importantly, taking into account the symptom duration prior to hospitalisation significantly affects observed associations between hospitalisation length and previously reported risk markers of prolonged stays, which themselves showed marked inter-annual variations. Once corrected for symptom duration, age, temperature at admission and elevated neutrophil-to-lymphocyte ratio were found predictive of longer hospitalisation periods.ConclusionThis study demonstrates that the time since dengue symptom onset is one of the most significant predictors for the length of hospital stays, independent of the assigned severity score. Pre-hospital symptom durations need to be accounted for to evaluate clinically relevant biomarkers of dengue hospitalisation trajectories.

We are now witnessing the rapid growth of advanced technologies and their application, leading to Smart Manufacturing (SM). The Internet of Things (IoT) is one of the main technologies used to enable smart factories, which is connecting all industrial assets, including machines and control systems, with the information systems and the business processes. Industrial Control Systems of smart IoT-based factories are one of the top industries attacked by numerous threats, especially unknown and novel attacks. As a result, with the distributed structure of plenty of IoT front-end sensing devices in SM, an effectively distributed anomaly detection (AD) architecture for IoT-based ICSs should: achieve high detection performance, train and learn new data patterns in a fast time scale, and have lightweight to be deployed on resource-constrained edge devices. To date, most solutions for anomaly detection have not fulfilled all of these requirements. In addition, the interpretability of why an instance is predicted to be abnormal is hardly concerned. In this paper, we propose the so- called FedeX architecture to address those challenges. The experiments show that FedeX outperforms 14 other existing anomaly detection solutions on all detection metrics with the liquid storage data set. And with Recall of 1 and F1-score of 0.9857, it also outperforms those solutions on the SWAT data set. FedeX is also proven to be fast in terms of training time of about 7.5 minutes and lightweight in terms of hardware requirement with memory consumption of 14%, allowing us to deploy anomaly detection tasks on top of edge computing infrastructure and in real-time. Besides, FedeX is considered as one of the frameworks at the forefront of interpreting the predicted anomalies by using XAI, which enables experts to make quick decisions and trust the model more.

We studied sandfly (Diptera: Psychodidae) populations in six provinces of Vietnam. This work explores the diversity of sandfly species according to the province, as well as environment, and updated information on public health since leishmaniasis cases were reported in two provinces. Sandflies were collected using 428 CDC light traps from May 30 to October 13, 2016 and identified based on the morphology of the cibarium, pharynx and/or male genitalia or female spermathecae. A total of 2585 sandflies belonging to five genera and 13 identified species were collected. The main species were: the Sergentomyia barraudi group (12.53%), Se. sylvatica (9.63%) and Phlebotomus stantoni (3.95%). In all, 294 Sergentomyia specimens classified as Se. sp2 and Se. sp3 and a heterogeneous group, herein called Se. und_sp., showed unknown morphological characteristics requiring further studies. We provide detailed comments about morphological description and taxonomical identification in order to help standardization of sandfly classification in Southeast Asia. We observed differentiation according to the provinces in terms of density and species richness, with Lang Son having the highest density and Ninh Binh having the highest species richness. The majority of specimens were collected in rock caves and outdoors, suggesting mainly cavernicolous and exophilic characters of sandfly species in Northern Vietnam. However, specimens were also collected in intra- and peri-domiciliary sites. It is worth noting that Ph. stantoni was the main species found in dog sheds and indoors, and in particular in a leishmaniasis patient’s house.

The present study investigates the potential effects and risks of polyethylene microbeads (PEMBs) and PEMBs associated with PCB-153 (PEMB-PCB) on Daphnia magna using a chronic exposure study approach. Daphnia magna were exposed to PEMBs, PCB-153, and PEMB-PCB for 21 days. Accumulation of PEMBs and PCB-153 in D. magna and effects on survival and reproduction of the organisms were measured. Overall, D. magna ingested and accumulated PEMBs and PCB-153. The accumulation of PEMBs increased with exposure time and was higher for PEMB exposure than for PEMB-PCB exposure. However, PCB-153 accumulation did not appear to increase over time. Exposure to PCB-153 in water resulted in higher PCB-153 accumulation than did PEMB-PCB exposure. Polyethylene microbeads and PCB-153 appeared to interact, resulting in a lower accumulation of PEMBs and PCB-153 and causing less effect on reproduction of D. magna. Survival of D. magna was affected by PEMBs, PCB-153, and PEMB-PCB. Results of the present study reveal potential risks of MPs to living organisms and ecosystems, especially when considering long-term exposure, which is relevant for microplastic pollution because of the persistence and continuous increase in MP abundance in the environment. More research should be conducted to understand the interactive effect mechanisms of MP and pollutant mixture exposure on organisms that would help explain the decreased accumulation of pollutants in organisms when MPs are present. [ABSTRACT FROM AUTHOR]