A comparison of antigen-based rapid test and RT-PCR test to diagnose COVID-19 and its infectivity

Background. Medical screening and diagnostic cost and equipment availability has been a major obstacle to supposed-to-be extensive tracing, and overall, to the end of COVID-19 pandemic. Even though RT-PCR is the gold diagnostic standard, it is costly, lengthy, and may be unavailable in remote areas. Therefore, antigen-based COVID-19 rapid tests may be a solution to quickly detect and screen communities suspected of contracting COVID-19. Objective. This paper aims to observe how reliable antigen-based COVID-19 rapid tests are compared to RT-PCR testing. Material and methods. An observational cross-sectional study was performed on 101 samples to find the specificity, sensitivity, and accuracy of antigen-based rapid testing compared to RT-PCR testing performed on every individual. Then, a pattern between CT values and duration between onset of symptoms and testing to antigen-based rapid test result was observed to find a cut-off value such that the person may be deemed safe to exit isolation. Outcomes. A cut-off CT value of above 30.04 (p < 0.01) with a sensitivity of 66.7% and specificity of 77.8% (moderate accuracy) obtained from ROC analysis showed negative results on antigen-based rapid tests. The tests showed an overall accuracy of 67.3%, where results between the two tests were consistent. Conclusion. Therefore, an estimated CT value of 30 was moderately proved to be used as a criterion to end isolation and presume the person no longer sheds SARS-CoV-2.

Swab-Yourself Trial With Economic Monitoring and Testing for Infections Collectively (SYSTEMATIC): Part 2. A Diagnostic Accuracy and Cost-effectiveness Study Comparing Rectal, Pharyngeal, and Urogenital Samples Analyzed Individually, Versus as a Pooled Specimen, for the Diagnosis of Gonorrhea and Chlamydia

Background Sexual history does not accurately identify those with extragenital Neisseria gonorrhoeae (NG) and Chlamydia trachomatis (CT), so universal extragenital sampling is recommended. Nucleic acid amplification tests (NAATs) are expensive. If urogenital, plus rectal and pharyngeal, samples are analyzed, the diagnostic cost is trebled. Pooling samples into 1 NAAT container would cost the same as urogenital samples alone. We compared clinician triple samples analyzed individually with self-taken pooled samples for diagnostic accuracy, and cost, in men who have sex with men (MSM) and females. Methods This was a prospective, convenience sample in United Kingdom sexual health clinic. Randomized order of clinician and self-samples from pharynx, rectum, plus first-catch urine (FCU) in MSM and vulvovaginal swabs (VVS) in females, for NG and CT detection. Results Of 1793 participants (1284 females, 509 MSM), 116 had NG detected (75 urogenital, 83 rectum, 72 pharynx); 276 had CT detected (217 urogenital, 249 rectum, 63 pharynx). There was no difference in sensitivities between clinician triple samples and self-pooled specimens for NG (99.1% and 98.3%), but clinician samples analyzed individually identified 3% more chlamydia infections than pooled (99.3% and 96.0%; P = .027). However, pooled specimens identified more infections than VVS/FCU alone. Pooled specimens missed 2 NG and 11 CT infections, whereas VVS/FCU missed 41 NG and 58 CT infections. Self-taken pooled specimens were the most cost-effective. Conclusions FCU/VVS testing alone missed many infections. Self-taken pooled samples were as sensitive as clinician triple samples for identifying NG, but clinician samples analyzed individually identified 3% more CT infections than pooled. The extragenital sampling was achievable at no additional diagnostic cost to the FCU/VVS. Clinical Trials Registration NCT02371109.

Recent Advances of Fluid Manipulation Technologies in Microfluidic Paper-Based Analytical Devices (μPADs) toward Multi-Step Assays

Microfluidic paper-based analytical devices (μPADs) have been suggested as alternatives for developing countries with suboptimal medical conditions because of their low diagnostic cost, high portability, and disposable characteristics. Recently, paper-based diagnostic devices enabling multi-step assays have been drawing attention, as they allow complicated tests, such as enzyme-linked immunosorbent assay (ELISA) and polymerase chain reaction (PCR), which were previously only conducted in the laboratory, to be performed on-site. In addition, user convenience and price of paper-based diagnostic devices are other competitive points over other point-of-care testing (POCT) devices, which are more critical in developing countries. Fluid manipulation technologies in paper play a key role in realizing multi-step assays via μPADs, and the expansion of biochemical applications will provide developing countries with more medical benefits. Therefore, we herein aimed to investigate recent fluid manipulation technologies utilized in paper-based devices and to introduce various approaches adopting several principles to control fluids on papers. Fluid manipulation technologies are classified into passive and active methods. While passive valves are structurally simple and easy to fabricate, they are difficult to control in terms of flow at a specific spatiotemporal condition. On the contrary, active valves are more complicated and mostly require external systems, but they provide much freedom of fluid manipulation and programmable operation. Both technologies have been revolutionized in the way to compensate for their limitations, and their advances will lead to improved performance of μPADs, increasing the level of healthcare around the world.

Multiplexed detection of biomarkers in lateral-flow immunoassays

Multiplexed detection of biomarkers, i.e., simultaneous detection of multiple biomarkers in a single assay, can enhance diagnostic precision, improve diagnostic efficiency, reduce diagnostic cost, and alleviate pain of patients.

Fault Diagnosis Method of Check Valve Based on Multikernel Cost-Sensitive Extreme Learning Machine

Check valve is one of the most important components and most easily damaged parts in high pressure diaphragm pump, which is a typical representative of reciprocating machinery. In order to ensure the normal operation of the pump, it is necessary to monitor its running state and diagnose fault. However, in the fault diagnosis of check valve, the classification models with single kernel function can not fully interpret the classification decision function, and meanwhile unreasonable assumption of diagnostic cost equalization has a significant impact on classification results. Therefore, the multikernel function and cost-sensitive mechanism are introduced to construct the fault diagnosis model of check valve based on the multikernel cost-sensitive extreme learning machine (MKL-CS-ELM) in this paper. The comparative test results of check valve for high pressure diaphragm pump show that MKL-CS-ELM can obtain fairly or slightly better performance than ELM, CS-ELM, MKL-ELM, and multikernel cost-sensitive support vector learning machine (MKL-CS-SVM). At the same time, the presented method can obtain very high accuracy under imbalance datasets condition and effectively overcome the weakness of diagnostic cost equalization and improve the interpretability and reliability of the decision function of classification model. It, therefore, is more suitable for the practical application.

Fishing diseased abalone to promote yield and conservation

Past theoretical models suggest fishing disease-impacted stocks can reduce parasite transmission, but this is a good management strategy only when the exploitation required to reduce transmission does not overfish the stock. We applied this concept to a red abalone fishery so impacted by an infectious disease (withering syndrome) that stock densities plummeted and managers closed the fishery. In addition to the non-selective fishing strategy considered by past disease-fishing models, we modelled targeting (culling) infected individuals, which is plausible in red abalone because modern diagnostic tools can determine infection without harming landed abalone and the diagnostic cost is minor relative to the catch value. The non-selective abalone fishing required to eradicate parasites exceeded thresholds for abalone sustainability, but targeting infected abalone allowed the fishery to generate yield and reduce parasite prevalence while maintaining stock densities at or above the densities attainable if the population was closed to fishing. The effect was strong enough that stock and yield increased even when the catch was one-third uninfected abalone. These results could apply to other fisheries as the diagnostic costs decline relative to catch value.

Sequential Diagnosis by Abstraction

When a system behaves abnormally, sequential diagnosis takes a sequence of measurements of the system until the faults causing the abnormality are identified, and the goal is to reduce the diagnostic cost, defined here as the number of measurements. To propose measurement points, previous work employs a heuristic based on reducing the entropy over a computed set of diagnoses. This approach generally has good performance in terms of diagnostic cost, but can fail to diagnose large systems when the set of diagnoses is too large. Focusing on a smaller set of probable diagnoses scales the approach but generally leads to increased average diagnostic costs. In this paper, we propose a new diagnostic framework employing four new techniques, which scales to much larger systems with good performance in terms of diagnostic cost. First, we propose a new heuristic for measurement point selection that can be computed efficiently, without requiring the set of diagnoses, once the system is modeled as a Bayesian network and compiled into a logical form known as d-DNNF. Second, we extend hierarchical diagnosis, a technique based on system abstraction from our previous work, to handle probabilities so that it can be applied to sequential diagnosis to allow larger systems to be diagnosed. Third, for the largest systems where even hierarchical diagnosis fails, we propose a novel method that converts the system into one that has a smaller abstraction and whose diagnoses form a superset of those of the original system; the new system can then be diagnosed and the result mapped back to the original system. Finally, we propose a novel cost estimation function which can be used to choose an abstraction of the system that is more likely to provide optimal average cost. Experiments with ISCAS-85 benchmark circuits indicate that our approach scales to all circuits in the suite except one that has a flat structure not susceptible to useful abstraction.