Evaluation of the efficacy of LAMP-based SARS-CoV-2 detection with simple RNA extraction from nasopharyngeal swabs: A prospective observational study

The Loopamp SARS-CoV-2 Detection Kit is used for the detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Loop-mediated isothermal amplification (LAMP) is based on a measurement principle that can be used with a relatively simple device. Detection using this kit requires viral RNA extraction from samples with the QIAGEN QIAamp Viral Mini Kit (QIAGEN extraction) or the Loopamp Viral RNA Extraction Kit (Eiken extraction), which are recommended by the manufacturer. However, the efficacy of LAMP-based SARS-CoV-2 detection using these extraction methods has not been compared. In this study, we aimed to compare the results of genome extraction and detection from nasopharyngeal swab samples using the QIAGEN and Eiken extraction kits. The present study involved patients who presented to the Rinku General Medical Center with suspected COVID-19 (25 positive and 26 negative cases). A comparison of the results obtained using each extraction method with those obtained via PCR showed that the positive, negative, and overall concordance rates between QIAGEN extraction and PCR were 96.0% (24/25 samples), 100% (26/26), and 98.0% (50/51; κ = 0.96, 95% CI = 0.69–1.00), respectively. Results with Eiken extraction were also favorable, with positive, negative, and overall concordance rates of 88.0% (22/25), 100% (26/26), and 94.1% (48/51; κ = 0.88, 95% CI = 0.61–1.00), respectively. Favorable results were obtained using both QIAGEN and Eiken extraction kits. Since Eiken extraction can be completed in a few minutes, it enables prompt and reliable testing for SARS-CoV-2 detection.

Protein Biomarkers in Glaucoma: A Review

Glaucoma is a multifactorial disease. Early diagnosis of this disease can support treatment and reduce the effects of pathophysiological processes. A significant problem in the diagnosis of glaucoma is limited access to the tested material. Therefore, intensive research is underway to develop biomarkers for fast, noninvasive, and reliable testing. Biomarkers indicated in the formation of glaucoma include chemical compounds from different chemical groups, such as proteins, sugars, and lipids. This review summarizes our knowledge about protein and/or their protein-like derived biomarkers used for glaucoma diagnosis since 2000. The described possibilities resulting from a biomarker search may contribute to identifying a group of compounds strongly correlated with glaucoma development. Such a find would be of great importance in the diagnosis and treatment of this disorder, as current screening techniques have low sensitivity and are unable to diagnose early primary open-angle glaucoma.

Bees can be trained to identify SARS-CoV-2 infected samples.

The COVID19 pandemic has illustrated the need for the development of fast and reliable testing methods for novel, zoonotic, viral diseases in both humans and animals. Pathologies lead to detectable changes in the Volatile Organic Compound (VOC) profile of animals, which can be monitored, thus allowing the development of a rapid VOC-based test. In the current study, we successfully trained honeybees (Apis mellifera) to identify SARSCoV2 infected minks (Neovison vison) thanks to Pavlovian conditioning protocols. The bees can be quickly conditioned to respond specifically to infected mink odours and could therefore be part of a wider SARSCoV2 diagnostic system. We tested two different training protocols to evaluate their performance in terms of learning rate, accuracy and memory retention. We designed a non-invasive rapid test in which multiple bees are tested in parallel on the same samples. This provided reliable results regarding a subjects health status. Using the data from the training experiments, we simulated a diagnostic evaluation trial to predict the potential efficacy of our diagnostic test, which yielded a diagnostic sensitivity of 92% and specificity of 86%. We suggest that a honeybee-based diagnostics can offer a reliable and rapid test that provides a readily available, low-input addition to the currently available testing methods. A honeybee based diagnostic test might be particularly relevant for remote and developing communities that lack the resources and infrastructure required for mainstream testing methods.

Automated ELISA On-Chip for the Detection of Anti-SARS-CoV-2 Antibodies

The COVID-19 pandemic has been the most critical public health issue in modern history due to its highly infectious and deathly potential, and the limited access to massive, low-cost, and reliable testing has significantly worsened the crisis. The recovery and the vaccination of millions of people against COVID-19 have made serological tests highly relevant to identify the presence and levels of SARS-CoV-2 antibodies. Due to its advantages, microfluidic-based technologies represent an attractive alternative to the conventional testing methodologies used for these purposes. In this work, we described the development of an automated ELISA on-chip capable of detecting anti-SARS-CoV-2 antibodies in serum samples from COVID-19 patients and vaccinated individuals. The colorimetric reactions were analyzed with a microplate reader. No statistically significant differences were observed when comparing the results of our automated ELISA on-chip against the ones obtained from a traditional ELISA on a microplate. Moreover, we demonstrated that it is possible to carry out the analysis of the colorimetric reaction by performing basic image analysis of photos taken with a smartphone, which constitutes a useful alternative when lacking specialized equipment or a laboratory setting. Our automated ELISA on-chip has the potential to be used in a clinical setting and mitigates some of the burden caused by testing deficiencies.

An Approach to the Definition of the Aerodynamic Comfort of Motorcycle Helmets

The aim of this work is to obtain a reliable testing methodology for the characterization of the perceived aerodynamic comfort of motorcycle helmets. Attention was paid to the rider’s perception of annoying vibrations induced by wind. In this optic, an experimental comparative campaign was performed in the wind tunnel, testing 16 helmets in two different configurations of neck stiffness. The dataset was collected within a convolutional neural network (CNN or ConvNet) of images, creating a ranking by identifying the best and the worst helmets. The results revealed that each helmet has unique aerodynamic characteristics. Depending on the ranking scale previously created, the aerodynamic comfort of each helmets can be classified within the scale.

Development of a Fitness Test Battery for Special Weapons and Tactics (SWAT) Operators—A Pilot Study

This investigation examined relationships between a Special Weapons and Tactics-specific fitness test (SORT) and an obstacle course (OC) used for qualification in fourteen male SWAT members from three local, regional police departments. The SORT included: squat, pushup, and lunge in 60 s; pullup hold; sled drag; and Yo-Yo Intermittent Recovery Test L1. The obstacle course included: 25 m sprint (repeated); window ascent; scale under a wall; 25 m serpentine run (repeated), body drag (20 m, repeated). Pearson coefficients examined SORT and OC relationships (p ≤ 0.05); intraclass correlation coefficients (ICC2,1) assessed agreement of SORT trials. Repeated measures ANOVA evaluated differences in SORT metrics across time. Coefficients of variation (COV) examined SORT scoring consistency. The YoYo test was related to all SORT assessments (r = −0.803–0.894), except sled drag. The remaining SORT metrics were related to ≥two tests. SORT COVs ranged from 0.77–13.26% for trials 1–2 but decreased between trials 2–3 (0.95–8.97%). The OC was associated with YoYo, lunges, squats and sled drag (r = −0.790, −0.730, −0.766, and 0.802, respectively). No differences (p > 0.05) existed across SORT trials for event scores. The SORT battery appears to be a valid and reliable testing measure to assess SWAT occupational specific fitness.

Prediction of individual COVID-19 diagnosis using baseline demographics and lab data

The global surge in COVID-19 cases underscores the need for fast, scalable, and reliable testing. Current COVID-19 diagnostic tests are limited by turnaround time, limited availability, or occasional false findings. Here, we developed a machine learning-based framework for predicting individual COVID-19 positive diagnosis relying only on readily-available baseline data, including patient demographics, comorbidities, and common lab values. Leveraging a cohort of 31,739 adults within an academic health system, we trained and tested multiple types of machine learning models, achieving an area under the curve of 0.75. Feature importance analyses highlighted serum calcium levels, temperature, age, lymphocyte count, smoking, hemoglobin levels, aspartate aminotransferase levels, and oxygen saturation as key predictors. Additionally, we developed a single decision tree model that provided an operable method for stratifying sub-populations. Overall, this study provides a proof-of-concept that COVID-19 diagnosis prediction models can be developed using only baseline data. The resulting prediction can complement existing tests to enhance screening and pandemic containment workflows.

Investigation into the Lithium-Ion Battery Fire Resistance Testing Procedure for Commercial Use

Lithium-ion batteries (LIBs) have many advantages (e.g., high voltage and long-life cycle) in comparison to other energy storage technologies (e.g., lead acid), resulting in their applicability in a wide variety of structures. Simultaneously, the thermal stability of LIBs is relatively poor and can be damaged by exposure to fire. This paper presents an investigation into a fire resistance safety test for LIBs and the use of thermal sensors to evaluate exposure conditions and estimate the temperatures to which cells are subjected. Temperature distribution data and statistical analysis show significant differences of over 200 ∘C, indicating the stochastic nature of the heating curve despite following the testing procedure requirements. We concluded that the current testing procedure is inadequate for the reliable testing of LIBs, leaving an alarming loophole in the fire safety evaluation. The observed instability is mostly related to wind speed and direction, and fire source size.

Phenotypic characterization of Macrolide-Lincosamide-Streptogramin B resistance in Staphylococcus aureus

Staphylococcus aureus (S.aureus) is a prevalent organism causing infections in the community and hospital. A variety of antibiotics are used, including the Macrolide-Lincosamide-StreptograminB (MLSB) family of antibiotics in which clindamycin is the preferred agent. Widespread use of these antibiotics leads to resistance to these MLSB antibiotics; a D-test can characterize the different MLSB phenotypes. This study was taken up with an objective to perform a double disc diffusion test for detecting different phenotypes in S.aureus with particular reference to inducible clindamycin resistance. Out of a total of 174(100%) strains of S.aureus, 98(56.32%) were MRSA, and 76(43.68%) were MSSA. All isolates were tested by D-test. A total of 47(27.01%) were of cMLSB phenotype, 31(17.82%) were of iMLSB phenotype, and 96(55.17%) were of MS phenotype. The majority of MRSA strains were cMLSB phenotype(76.60%) and iMLSB phenotype (64.52%) in comparison to MSSA isolates. Although iMLSB phenotypes are present in both MRSA and MSSA, iMLSB was more in MRSA isolates. Appropriate susceptibility data is essential for a clinician to start clindamycin therapy to prevent therapeutic failures with inducible MLSB resistance in S.aureus isolates. It will be appropriate for all the clinical laboratories to report inducible Clindamycin resistance in S.aureus strains (both MRSA and MSSA), for which D-test is a reliable testing method.

Toward Commercialization of Stable Devices: An Overview on Encapsulation of Hybrid Organic-Inorganic Perovskite Solar Cells

Perovskite solar cells (PSCs) represent a promising technology for energy harvesting due to high power conversion efficiencies up to 26%, easy manufacturing, and convenient deposition techniques, leading to added advantages over other contemporary competitors. In order to promote this technology toward commercialization though, stability issues need to be addressed. Lately, many researchers have explored several techniques to improve the stability of the environmentally-sensitive perovskite solar devices. Challenges posed by environmental factors like moisture, oxygen, temperature, and UV-light exposure, could be overcome by device encapsulation. This review focuses the attention on the different materials, methods, and requirements for suitable encapsulated perovskite solar cells. A depth analysis on the current stability tests is also included, since accurate and reliable testing conditions are needed in order to reduce mismatching involved in reporting the efficiencies of PSC.