The gold standard for the diagnosis of coronavirus disease 2019 (COVID-19) is a nucleic acid detection test for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which may occasionally reveal false-positive or false-negative results. Herein, we describe a case of a patient infected with human coronavirus NL63 (HCoV-NL63) who was falsely diagnosed with COVID-19 using the Ampdirect™ 2019-nCoV detection kit (Shimadzu Corporation, Japan) and SARS-CoV-2 Detection Kit (TOYOBO co., ltd.), and was admitted to a COVID-19 hospital ward. We suspected a cross-reaction between HCoV-NL63 and SARS-CoV-2; however, the reported genome sequences of HCoV-NL63 and N1/N2 primers for SARS-CoV-2 do not correspond. Thus, the PCR result was supposed to be a false positive possibly due to contamination or human error. Although the issue of a false-negative result has been the focus of much attention to prevent the spread of the disease, a false positive is fraught with problems as well. Physicians should recognize that unnecessary isolation violates human rights and a careful diagnosis is indispensable when the results of laboratory testing for COVID-19 are unclear. Generally, in cases such as a duplicate PCR test was partially positive, either N1 or N2 alone was positive, PCR testing for two or more target regions resulted in a positive only for single region, a high cycle threshold >35 was obtained, a false positive should be suspected. Especially, when these conditions coincide, we should recognize the high likelihood of a false positive.
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http://dx.doi.org/10.1016/j.jiac.2021.05.001 | DOI Listing |
Otol Neurotol
February 2025
Department of Radiology, Columbia University Irving Medical Center, New York, NY, USA.
Objective: To compare the diagnostic capability of Pöschl reformations created from temporal bone CT (TBCT) and high-resolution noncontrast CT head exams (HR-NECTH) to detect and classify superior semicircular canal (SSC) abnormalities.
Study Design: Retrospective case review.
Setting: Tertiary referral center.
J Magn Reson Imaging
January 2025
Department of Radiology, Ålesund Hospital, Møre og Romsdal Hospital Trust, Ålesund, Norway.
Background: Deep learning-based segmentation of brain metastases relies on large amounts of fully annotated data by domain experts. Semi-supervised learning offers potential efficient methods to improve model performance without excessive annotation burden.
Purpose: This work tests the viability of semi-supervision for brain metastases segmentation.
Ophthalmol Ther
January 2025
International Health Policy Program (IHPP), Ministry of Public Health, Nonthaburi, Thailand.
Introduction: Screening diabetic retinopathy (DR) for timely management can reduce global blindness. Many existing DR screening programs worldwide are non-digital, standalone, and deployed with grading retinal photographs by trained personnel. To integrate the screening programs, with or without artificial intelligence (AI), into hospital information systems to improve their effectiveness, the non-digital workflow must be transformed into digital.
View Article and Find Full Text PDFProc Natl Acad Sci U S A
January 2025
Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford OX3 7BN, United Kingdom.
A value is conventionally interpreted either as a) the probability by chance of obtaining more extreme results than those observed or b) a tool for declaring significance at a prespecified level. Both approaches carry difficulties: b) does not allow users to make inferences based on the data in hand, and is not rigorously followed by researchers in practice, while (a) is not meaningful as an error rate. Although values retain an important role, these shortcomings are likely to have contributed significantly to the scientific reproducibility crisis.
View Article and Find Full Text PDFIntroduction Mucormycosis is an uncommon fungal infection caused by filamentous fungi of the Mucorales order, namely Rhizopus, Lichthemia, andMucor species. The incidence and prevalence of mucormycosis reached an all-time high during the COVID-19 pandemic due to excessive steroid use and other factors, leading to the coining of the term CAM (COVID Associated Mucormycosis). The diagnosis of mucormycosis is by a combination of histopathology and microbiological techniques, such as KOH mount and culture.
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