Climate change and human health in Vietnam: a systematic review and additional analyses on current impacts, future risk, and adaptation.

This study aims to investigate climate change's impact on health and adaptation in Vietnam through a systematic review and additional analyses of heat exposure, heat vulnerability, awareness and engagement, and projected health costs. Out of 127 reviewed studies, findings indicated the wider spread of infectious diseases, and increased mortality and hospitalisation risks associated with extreme heat, droughts, and floods. However, there are few studies addressing health cost, awareness, engagement, adaptation, and policy. Additional analyses showed rising heatwave exposure across Vietnam and global above-average vulnerability to heat. By 2050, climate change is projected to cost up to USD1-3B in healthcare costs, USD3-20B in premature deaths, and USD6-23B in work loss. Despite increased media focus on climate and health, a gap between public and government publications highlighted the need for more governmental engagement. Vietnam's climate policies have faced implementation challenges, including top-down approaches, lack of cooperation, low adaptive capacity, and limited resources.

Antibiotic resistance of Streptococcus pneumoniae in Vietnamese children with severe pneumonia: a cross-sectional study.

Background: Streptococcus pneumoniae is the most common bacterium that causes community-acquired pneumonia (CAP) in children. The rate of S. pneumoniae resistance to antibiotics is increasing, particularly in patients with severe CAP. Therefore, the level of antibiotic resistance of S. pneumoniae causing severe CAP in Vietnamese children requires regular monitoring. Methods: This was a cross-sectional descriptive study. Nasopharyngeal aspiration specimens from children were cultured, isolated, and examined for S. pneumoniae. Bacterial strains were assessed for antimicrobial susceptibility, and the minimum inhibitory concentration (MIC) was determined. Results: Eighty-nine strains of S. pneumoniae were isolated from 239 children with severe CAP. The majority of isolates were completely non-susceptible to penicillin (1.1% intermediate, 98.9% resistant) and highly resistant to erythromycin (96.6%) and clarithromycin (88.8%); the rate of resistance to ceftriaxone was 16.9%, with the proportion of intermediate resistance at 46.0%; 100% of strains were susceptible to vancomycin and linezolid. For most antibiotics, MIC50 and MIC90 were equal to the resistance threshold according to the Clinical and Laboratory Standards Institute 2021; penicillin had an eight-fold increase in MIC90 (64 mg/L) and ceftriaxone had a 1.5-fold increase in MIC90 (6 mg/L). Conclusion: Streptococcus pneumoniae isolates described in this study were resistant to many antibiotics. Penicillin should not be the first-line antibiotic of choice, and ceftriaxone at an enhanced dose should be used instead.

Sparse Optical Flow Implementation Using a Neural Network for Low-Resolution Thermal Aerial Imaging.

This study is inspired by the widely used algorithm for real-time optical flow, the sparse Lucas-Kanade, by applying a feature extractor to decrease the computational requirement of optical flow based neural networks from real-world thermal aerial imagery. Although deep-learning-based algorithms have achieved state-of-the-art accuracy and have outperformed most traditional techniques, most of them cannot be implemented on a small multi-rotor UAV due to size and weight constraints on the platform. This challenge comes from the high computational cost of these techniques, with implementations requiring an integrated graphics processing unit with a powerful on-board computer to run in real time, resulting in a larger payload and consequently shorter flight time. For navigation applications that only require a 2D optical flow vector, a dense flow field computed from a deep learning neural network contains redundant information. A feature extractor based on the Shi-Tomasi technique was used to extract only appropriate features from thermal images to compute optical flow. The state-of-the-art RAFT-s model was trained with a full image and with our proposed alternative input, showing a substantial increase in speed while maintain its accuracy in the presence of high thermal contrast where features could be detected.

A Comparison of Dense and Sparse Optical Flow Techniques for Low-Resolution Aerial Thermal Imagery.

It is necessary to establish the relative performance of established optical flow approaches in airborne scenarios with thermal cameras. This study investigated the performance of a dense optical flow algorithm on 14 bit radiometric images of the ground. While sparse techniques that rely on feature matching techniques perform very well with airborne thermal data in high-contrast thermal conditions, these techniques suffer in low-contrast scenes, where there are fewer detectable and distinct features in the image. On the other hand, some dense optical flow algorithms are highly amenable to parallel processing approaches compared to those that rely on tracking and feature detection. A Long-Wave Infrared (LWIR) micro-sensor and a PX4Flow optical sensor were mounted looking downwards on a drone. We compared the optical flow signals of a representative dense optical flow technique, the Image Interpolation Algorithm (I2A), to the Lucas-Kanade (LK) algorithm in OpenCV and the visible light optical flow results from the PX4Flow in both X and Y displacements. The I2A to LK was found to be generally comparable in performance and better in cold-soaked environments while suffering from the aperture problem in some scenes.

Study on the co-infection of children with severe community-acquired pneumonia.

BACKGROUD: Pneumonia is one of the leading causes of death in children under 5 years old. Viruses have historically been the most common cause of community-acquired pneumonia in children. Co-infections in severe pneumonia are more concern by clinicians. METHOD: It was a perspective and descriptive study. Real-time polymerase chain reaction (RT-PCR) is a modern test that was used to detect many new pathogens, including microbiological co-infections. RT-PCR technique was used in this study to investigate the causes of severe pneumonia. RESULTS: Through the analysis of nasopharyngeal aspiration samples from 95 children with severe community-acquired pneumonia, the positive RT-PCR rate was 90.5%. Viral-bacterial co-infection accounted for the highest proportion (43.1%), followed by bacterial co-infection (33.7%), viral infection (7.4%), bacterial infection (6.3%) and the remaining 9.5% was unknown. In the co-infections groups, the five main bacteria species detected by PCR were Streptococcus pneumoniae, Haemophilus influenzae, MRSA, Moraxella catarrhalis and Mycoplasma pneumoniae. CONCLUSION: Antibiotic treatment should focus on detected microbes in cases of severe pneumonia for having a good result.

A Review of Modern Thermal Imaging Sensor Technology and Applications for Autonomous Aerial Navigation.

Limited navigation capabilities of many current robots and UAVs restricts their applications in GPS denied areas. Large aircraft with complex navigation systems rely on a variety of sensors including radio frequency aids and high performance inertial systems rendering them somewhat resistant to GPS denial. The rapid development of computer vision has seen cameras incorporated into small drones. Vision-based systems, consisting of one or more cameras, could arguably satisfy both size and weight constraints faced by UAVs. A new generation of thermal sensors is available that are lighter, smaller and widely available. Thermal sensors are a solution to enable navigation in difficult environments, including in low-light, dust or smoke. The purpose of this paper is to present a comprehensive literature review of thermal sensors integrated into navigation systems. Furthermore, the physics and characteristics of thermal sensors will also be presented to provide insight into challenges when integrating thermal sensors in place of conventional visual spectrum sensors.

Noncontact Sensing of Contagion.

The World Health Organization (WHO) has declared COVID-19 a pandemic. We review and reduce the clinical literature on diagnosis of COVID-19 through symptoms that might be remotely detected as of early May 2020. Vital signs associated with respiratory distress and fever, coughing, and visible infections have been reported. Fever screening by temperature monitoring is currently popular. However, improved noncontact detection is sought. Vital signs including heart rate and respiratory rate are affected by the condition. Cough, fatigue, and visible infections are also reported as common symptoms. There are non-contact methods for measuring vital signs remotely that have been shown to have acceptable accuracy, reliability, and practicality in some settings. Each has its pros and cons and may perform well in some challenges but be inadequate in others. Our review shows that visible spectrum and thermal spectrum cameras offer the best options for truly noncontact sensing of those studied to date, thermal cameras due to their potential to measure all likely symptoms on a single camera, especially temperature, and video cameras due to their availability, cost, adaptability, and compatibility. Substantial supply chain disruptions during the pandemic and the widespread nature of the problem means that cost-effectiveness and availability are important considerations.