Human Sensory Neuron-like Cells and Glycated Collagen Matrix as a Model for the Screening of Analgesic Compounds

Increased collagen-derived advanced glycation end-products (AGEs) are consistently related to painful diseases, including osteoarthritis, diabetic neuropathy, and neurodegenerative disorders. We have recently developed a model combining a two-dimensional glycated extracellular matrix (ECM-GC) and primary dorsal root ganglion (DRG) that mimicked a pro-nociceptive microenvironment. However, culturing primary cells is still a challenge for large-scale screening studies. Here, we characterized a new model using ECM-GC as a stimulus for human sensory-like neurons differentiated from SH-SY5Y cell lines to screen for analgesic compounds. First, we confirmed that the differentiation process induces the expression of neuron markers (MAP2, RBFOX3 (NeuN), and TUBB3 (β-III tubulin), as well as sensory neuron markers critical for pain sensation (TRPV1, SCN9A (Nav1.7), SCN10A (Nav1.8), and SCN11A (Nav1.9). Next, we showed that ECM-GC increased c-Fos expression in human sensory-like neurons, which is suggestive of neuronal activation. In addition, ECM-GC upregulated the expression of critical genes involved in pain, including SCN9A and TACR1. Of interest, ECM-GC induced substance P release, a neuropeptide widely involved in neuroinflammation and pain. Finally, morphine, the prototype opiate, decreased ECM-GC-induced substance P release. Together, our results suggest that we established a functional model that can be useful as a platform for screening candidates for the management of painful conditions.

Contactless facial video recording with deep learning models for the detection of atrial fibrillation

Atrial fibrillation (AF) is often asymptomatic and paroxysmal. Screening and monitoring are needed especially for people at high risk. This study sought to use camera-based remote photoplethysmography (rPPG) with a deep convolutional neural network (DCNN) learning model for AF detection. All participants were classified into groups of AF, normal sinus rhythm (NSR) and other abnormality based on 12-lead ECG. They then underwent facial video recording for 10 min with rPPG signals extracted and segmented into 30-s clips as inputs of the training of DCNN models. Using voting algorithm, the participant would be predicted as AF if > 50% of their rPPG segments were determined as AF rhythm by the model. Of the 453 participants (mean age, 69.3 ± 13.0 years, women, 46%), a total of 7320 segments (1969 AF, 1604 NSR & 3747others) were analyzed by DCNN models. The accuracy rate of rPPG with deep learning model for discriminating AF from NSR and other abnormalities was 90.0% and 97.1% in 30-s and 10-min recording, respectively. This contactless, camera-based rPPG technique with a deep-learning model achieved significantly high accuracy to discriminate AF from non-AF and may enable a feasible way for a large-scale screening or monitoring in the future.

RT-PCR Detection of SARS-CoV-2 Among Individuals from the Upper Silesian Region—Analysis of 108 516 Tests.

Background: The COVID-19 pandemic triggered by the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has left a huge mark on everyday lives, introducing restrictions and plunging the global economy. This study aimed to analyze the available epidemiological data from the register of one of the largest laboratories testing for SARS-CoV-2 in the Silesian voivodship of Poland. Methods: This analysis is based upon the epidemiological records collected between 30 March 2020, and 30 April 2021, by the Silesian Park of Medical Technology Kardio-Med Silesia (Zabrze, Poland). In addition, we performed SARS-CoV-2 variant detection in samples from patients reinfected with SARS-CoV-2. Results: Our results confirm that SARS-CoV-2 infections are more common in urban areas. Laboratoryconfirmed COVID19 cases represent 13.21% of all RT-PCR test results during the 13 months of our laboratory diagnostics for SARS-CoV-2 infections. Detection of SARS-CoV-2 variants in samples of potentially reinfected patients showed discrepancies in the results. Conclusions: Due to the higher risk of SARS-CoV-2 infection among the Upper Silesian population, the region is at greater risk of deteriorating economic situation and healthcare as compared to other areas of Poland. RT-PCR methods are inexpensive and suitable for large-scale screening, but they can be untrustworthy so detection of SARS-CoV-2 variants in samples should be confirmed by sequencing.

Evaluation of an Antigen Detection Rapid Diagnostic Test for Detection of SARS-CoV-2 in Clinical Samples

Antigen detection rapid diagnostic tests have been developed for first-line large-scale screening given their rapidity, simplicity, and accuracy. This study evaluates the diagnostic performance of an antigen detection rapid diagnostic test (BLOK BioScience, London, UK) detecting SARS-CoV-2 nucleocapsid protein. Serially diluted SARS-CoV-2 isolate and 110 NPS from COVID-19 patients were tested to determine the test’s sensitivity, and other viral isolates and 20 NPS from non-infected individuals were, for specificity, also tested. Ten clinical samples from COVID-19 patients with SARS-CoV-2 variants, including alpha, beta, gamma, delta, and eta variants, were collected to evaluate the test’s potential application in detecting emerging variants. Overall sensitivity was 92%, and stratifying into viral loads yielded 100% for Ct < 25 samples including SARS-CoV-2 variants, but 11.11% for Ct ≥ 30 samples. The analytical sensitivity of log10 TCID50/mL 2.0 was identified for SARS-CoV-2. Ninety-seven percent specificity with only SARS-CoV cross-reactivity lead to the Youden index of 0.89. The rapid diagnostic test has a high sensitivity for detecting SARS-CoV-2 in high viral load samples, possibly including emerging SARS-CoV-2 variants, but reduced sensitivity in low viral load samples suggests its optimized usage as a complementary testing method to other tests, including RT-PCR or a point-of-care test for large-scale screening, particularly for pandemic areas or airport border infection control.