Electrochemical Biosensing and Deep Learning-Based Approaches in the Diagnosis of COVID-19: A Review

COVID-19 caused by the transmission of SARS-CoV-2 virus taking a huge toll on global health and caused life-threatening medical complications and elevated mortality rates, especially among older adults and people with existing morbidity. Current evidence suggests that the virus spreads primarily through respiratory droplets emitted by infected persons when breathing, coughing, sneezing, or speaking. These droplets can reach another person through their mouth, nose, or eyes, resulting in infection. The gold standard\u27\u27 for clinical diagnosis of SARS-CoV-2 is the laboratory-based nucleic acid amplification test, which includes the reverse transcription-polymerase chain reaction (RT-PCR) test on nasopharyngeal swab samples. The main concerns with this type of test are the relatively high cost, long processing time, and considerable false-positive or false-negative results. Alternative approaches have been suggested to detect the SARS-CoV-2 virus so that those infected and the people they have been in contact with can be quickly isolated to break the transmission chains and hopefully, control the pandemic. These alternative approaches include electrochemical biosensing and deep learning. In this review, we discuss the current state-of-the-art technology used in both fields for public health surveillance of SARS-CoV-2 and present a comparison of both methods in terms of cost, sampling, timing, accuracy, instrument complexity, global accessibility, feasibility, and adaptability to mutations. Finally, we discuss the issues and potential future research approaches for detecting the SARS-CoV-2 virus utilizing electrochemical biosensing and deep learning

Skin cancers in albinos in a teaching Hospital in eastern Nigeria - presentation and challenges of care

<p>Abstract</p> <p>Background</p> <p>Albinism is a genetic disorder characterized by lack of skin pigmentation. It has a worldwide distribution but is commoner in areas close to the equator like Nigeria. Skin cancers are a major risk associated with albinism and are thought to be a major cause of death in African albinos. Challenges faced in the care of these patients need to be highlighted in order to develop a holistic management approach with a significant public health impact. The aim of the study was to determine the pattern of skin cancers seen in Albinos, and to highlight problems encountered in their management.</p> <p>Method</p> <p>Case records of albinos managed in Imo state University teaching Hospital from June 2007 to May 2009 were reviewed. The data obtained was analyzed using descriptive statistics.</p> <p>Results and discussion</p> <p>In the period under review, albinos accounted for 67% of patients managed for primary skin cancers. There were twenty patients with thirty eight (38) lesions. Sixty one percent of the patients were below 40 years. Average duration of symptoms at presentation was 26 months. The commonest reason for late presentation was the lack of funds. Squamous cell carcinoma was the commonest histologic variant. Most patients were unable to complete treatment due to lack of funds.</p> <p>Conclusion</p> <p>Albinism appears to be the most important risk factor in the development of skin cancers in our environment. Late presentation and poor rate of completion of treatment due to poverty are major challenges.</p

Accurate and reliable quantification of total microalgal fuel potential as fatty acid methyl esters by in situ transesterification

In the context of algal biofuels, lipids, or better aliphatic chains of the fatty acids, are perhaps the most important constituents of algal biomass. Accurate quantification of lipids and their respective fuel yield is crucial for comparison of algal strains and growth conditions and for process monitoring. As an alternative to traditional solvent-based lipid extraction procedures, we have developed a robust whole-biomass in situ transesterification procedure for quantification of algal lipids (as fatty acid methyl esters, FAMEs) that (a) can be carried out on a small scale (using 4–7 mg of biomass), (b) is applicable to a range of different species, (c) consists of a single-step reaction, (d) is robust over a range of different temperature and time combinations, and (e) tolerant to at least 50% water in the biomass. Unlike gravimetric lipid quantification, which can over- or underestimate the lipid content, whole biomass transesterification reflects the true potential fuel yield of algal biomass. We report here on the comparison of the yield of FAMEs by using different catalysts and catalyst combinations, with the acid catalyst HCl providing a consistently high level of conversion of fatty acids with a precision of 1.9% relative standard deviation. We investigate the influence of reaction time, temperature, and biomass water content on the measured FAME content and profile for 4 different samples of algae (replete and deplete Chlorella vulgaris, replete Phaeodactylum tricornutum, and replete Nannochloropsis sp.). We conclude by demonstrating a full mass balance closure of all fatty acids around a traditional lipid extraction process

Deep Neural Networks for Energy and Position Reconstruction in EXO-200

We apply deep neural networks (DNN) to data from the EXO-200 experiment. In the studied cases, the DNN is able to reconstruct the relevant parameters - total energy and position - directly from raw digitized waveforms, with minimal exceptions. For the first time, the developed algorithms are evaluated on real detector calibration data. The accuracy of reconstruction either reaches or exceeds what was achieved by the conventional approaches developed by EXO-200 over the course of the experiment. Most existing DNN approaches to event reconstruction and classification in particle physics are trained on Monte Carlo simulated events. Such algorithms are inherently limited by the accuracy of the simulation. We describe a unique approach that, in an experiment such as EXO-200, allows to successfully perform certain reconstruction and analysis tasks by training the network on waveforms from experimental data, either reducing or eliminating the reliance on the Monte Carlo.Comment: Accepted version. 33 pages, 28 figure

Measurement of the Spectral Shape of the beta-decay of 137Xe to the Ground State of 137Cs in EXO-200 and Comparison with Theory

We report on a comparison between the theoretically predicted and experimentally measured spectra of the first-forbidden non-unique $\beta$-decay transition ^{137}\textrm{Xe}(7/2^-)\to\,^{137}\textrm{Cs}(7/2^+). The experimental data were acquired by the EXO-200 experiment during a deployment of an AmBe neutron source. The ultra-low background environment of EXO-200, together with dedicated source deployment and analysis procedures, allowed for collection of a pure sample of the decays, with an estimated signal-to-background ratio of more than 99-to-1 in the energy range from 1075 to 4175 keV. In addition to providing a rare and accurate measurement of the first-forbidden non-unique $\beta$-decay shape, this work constitutes a novel test of the calculated electron spectral shapes in the context of the reactor antineutrino anomaly and spectral bump.Comment: Version as accepted by PR

Techniques of the FLASH Thin Target Experiment

The fluorescence yield in air is reported for wavelength and pressure ranges of interest to ultra-high energy cosmic ray detectors. A 28.5 GeV electron beam was used to excite the fluorescence. Central to the approach was the system calibration, using Rayleigh scattering of a nitrogen laser beam. In atmospheric pressure dry air, at 304 K the yield is 20.8 {+-} 1.6 photons per MeV