Correctly performed nasal swabs.

Infection

Head Office of Vienna Hospital Association, Thomas Klestil Platz 7, 1030, Vienna, Austria.

Published: August 2021

Background: As the incidence of new cases of coronavirus disease increased exponentially, the use of viral swabs to collect nasopharyngeal specimens are increasing drastically. Therefore, healthcare workers military staff and uneducated nonprofessional's were ordered to make this swabs. Subsequently case reports reported about basal skull perforation, cerebrospinal fluid fistula and injury due to an incorrect technique.

Methods: Search of the literature.

Results: Only in 44% of the videos (Youtube) nasal swabs were correctly performed. Due to an false technique biological sampling resulted in false-negative COVID-19 tests.

Conclusion: Although professional societies started to report about this unacceptable situation, no publication reported about this health endangerment. In this time of overwhelming information and diversity of opinions, it is necessary to report about this in the hope, all media and TV reports will follow this article to show correctly performed nasal swabs to reduce false-negative COVID-19 tests and injury.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7994961PMC
http://dx.doi.org/10.1007/s15010-021-01607-8DOI Listing

Publication Analysis

Top Keywords

correctly performed
12
nasal swabs
12
performed nasal
8
false-negative covid-19
8
swabs
5
swabs background
4
background incidence
4
incidence cases
4
cases coronavirus
4
coronavirus disease
4

Similar Publications

We test here the prediction capabilities of the new generation of deep learning predictors in the more challenging situation of multistate multidomain proteins by using as a case study a coiled-coil family of Nucleotide-binding Oligomerization Domain-like (NOD-like) receptors from and a few extra examples for reference. Results reveal a truly remarkable ability of these platforms to correctly predict the 3D structure of modules that fold in well-established topologies. A lower performance is noticed in modeling morphing regions of these proteins, such as the coiled coils.

View Article and Find Full Text PDF

Background: Uveal melanoma (UM) is the most common primary intraocular malignancy in adults. The median overall survival time for patients who develop metastasis is approximately one year. In this study, we aim to leverage deep learning (DL) techniques to analyze digital cytopathology images and directly predict the 48 month survival status on a patient level.

View Article and Find Full Text PDF

Accuracy of Artificial Intelligence Based Chatbots in Analyzing Orthopedic Pathologies: An Experimental Multi-Observer Analysis.

Diagnostics (Basel)

January 2025

Center for Musculoskeletal Surgery, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, 13353 Berlin, Germany.

The rapid development of artificial intelligence (AI) is impacting the medical sector by offering new possibilities for faster and more accurate diagnoses. Symptom checker apps show potential for supporting patient decision-making in this regard. Whether the AI-based decision-making of symptom checker apps shows better performance in diagnostic accuracy and urgency assessment compared to physicians remains unclear.

View Article and Find Full Text PDF

We aimed to analyze potential predictors for the development of metachronous fractures (MFs) after osteoporotic vertebral fractures (OVFs), with particular focus on radiological variables obtained at initial X-rays and computed tomography (CT) examinations, treatment applied (conservative management [CM] versus percutaneous vertebroplasty [PV]), and fractures located at the thoracolumbar junction (T11-L2). We conducted a two-center, observational retrospective study, including patients with single-level OVFs treated with CM or VP. We collected socio-demographic, radiological and treatment-related variables.

View Article and Find Full Text PDF

: Alzheimer's disease is a progressive neurological condition marked by a decline in cognitive abilities. Early diagnosis is crucial but challenging due to overlapping symptoms among impairment stages, necessitating non-invasive, reliable diagnostic tools. : We applied information geometry and manifold learning to analyze grayscale MRI scans classified into No Impairment, Very Mild, Mild, and Moderate Impairment.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!