Brain thermal kinetics at brain-eyelid thermal tunnels overcoming COVID-19 thermometry limitations for automated asymptomatic infection detection in concert with physical and biological principles

For centuries, temperature measurement deficiencies attributable to biological barriers and low thermo-conductivity (k) have precluded accurate surface-based fever assessment. At this stage of the pandemic, infection detection in children (who due to immature immune system may not effectively respond to vaccines) is critical because children can be readily infected and also become a large mutation reservoir. We reveal hitherto-unrecognized worldwide body temperature measurements (T°), in children and adults, over tissue typified by low-k similar to wood that may reach 6.8°C in thermal variability, hampering thereby COVID-19 control. Brain-eyelid thermal tunnels’ (BTT) integration of low-k and high-k regions creating a thermal pathway for undisturbed heat transmission from hypothalamus to high-k skin eliminates current shortcomings and makes the brain indispensable for defeating COVID-19 given that brain thermoregulatory signals are not limited by mutations. Anatomo-histologic, emissive, physiologic, and thermometric bench-to-bedside studies characterized and overcome biophysical limitations of thermometry through high-k eyelid-enabled brain temperature measurements in children and adults. BTT eyelid features fat-free skin (~900 µm) and unique light emission through a blood/fat configuration in the underlying tunnel. Contrarily, forehead features variable and thick dermis (2000–2500 µm) and variable fat layers (1100–2800 µm) resulting in variable low-k as well as temperatures 1.97 °C lower than BTT temperature (BTT°). Highest emission present in only ~3.1% of forehead averaged 1.08±0.49 °C (mean±SD) less than BTT° (p=0.008). Environmental and biological impacts during fanning revealed thermal imaging limitations for COVID-19 screening. Comparison of paired measurements for 100 pediatric patients showed that in the children subgroup above 37°C, BTT° exceeded body core temperature (Core°) in 60/72 patients; the average difference in the 72 patients was 0.62±0.7°C (p<0.001 by unpaired t-test); and in the subgroup beyond 37.5°C, BTT° exceeded Core° in 30/32 patients. Delineating hypothalamic activity in children facilitates early infection detection, which is essential because children’s immunogenicity prevents effective vaccination and causes accelerated viral evolution. Capturing hypothalamic thermal signals from BTT was further supported by brain thermal kinetics via BTT using wearables during anesthesia, sedation, sleep, brain injury, exercise, and asymptomatic infection, which revealed brain/core discordance and enabled automated noninvasive afebrile infection detection for interrupting asymptomatic human-to-human transmission. BTT-based spot-check thermometry can be harmlessly implemented for children worldwide without undue burden and costs; meanwhile, continuous brain-eyelid T° in concert with biological and physical principles affords a new dimension for combating pandemics. The “detection–vaccination” pair solution presented is required to mitigate COVID-19 from spreading indefinitely through mutations and vaccine evasion while opening a viable path for eradicating COVID-19.

Sistem Deteksi Infeksi COVID-19 Pada Hasil X-Ray Rontgen menggunakan Algoritma Convolutional Neural Network (CNN)

The development of the world's technology is growing rapidly, especially in the field of health in the form of detection tools of various objects, including disease objects. The technology in point is part of artificial intelligence that is able to recognize a set of imagery and classify automatically with deep learning techniques. One of the deep learning networks widely used is convolutional neural network with computer vision technology. One of the problems with computer vision that is still developing is object detection as a useful technology to recognize objects in the image as if humans knew the object of the image. In this case, a computer machine is trained in learning using artificial neural networks. One of the sub types of artificial neural networks that are able to handle computer vision problems is by using deep learning techniques with convolutional neural network algorithms. The purpose of this research is to find out how to design the system, the network architecture used for COVID-19 infection detection. The system cannot perform detection of other objects. The results of COVID-19 infection detection with convolutional neural network algorithm show unlimited accuracy value that ranges from 60-99%.

COVID-19 mitigation by digital contact tracing and contact prevention (app-based social exposure warnings)

A plethora of measures are being combined in the attempt to reduce SARS-CoV-2 spread. Due to its sustainability, contact tracing is one of the most frequently applied interventions worldwide, albeit with mixed results. We evaluate the performance of digital contact tracing for different infection detection rates and response time delays. We also introduce and analyze a novel strategy we call contact prevention, which emits high exposure warnings to smartphone users according to Bluetooth-based contact counting. We model the effect of both strategies on transmission dynamics in SERIA, an agent-based simulation platform that implements population-dependent statistical distributions. Results show that contact prevention remains effective in scenarios with high diagnostic/response time delays and low infection detection rates, which greatly impair the effect of traditional contact tracing strategies. Contact prevention could play a significant role in pandemic mitigation, especially in developing countries where diagnostic and tracing capabilities are inadequate. Contact prevention could thus sustainably reduce the propagation of respiratory viruses while relying on available technology, respecting data privacy, and most importantly, promoting community-based awareness and social responsibility. Depending on infection detection and app adoption rates, applying a combination of digital contact tracing and contact prevention could reduce pandemic-related mortality by 20–56%.