Background: Anxiety symptoms have been reported in many populations during the COVID-19 pandemic, but not in adolescents with a hearing loss. This study aimed to investigate the presence of symptoms of anxiety disorders (ADs) in adolescents with hearing loss (HL) during the COVID-19 pandemic, 2020.
Methods: In this cross sectional study, 56 adolescents with HL (aged 12 to 18 years) including 23 deaf, and 33 hard of hearing (HH) were selected from four counties located in western Iran using a census method. Adolescents with HL filled out the self-report of the Screen for Child Anxiety Related Emotional Disorders (SCARED).
Results: The results showed that the presence of symptoms of ADs in adolescents with HL was 37.5%, and higher in deaf than in HH adolescents (60.9% in deaf vs. 21.2% in HH, p = 0.003). Among the subscales, only the Social Anxiety Disorder (39.1% in deaf vs. 90.1 in HH, p = 0.009) and the School Avoidance (52.2% in deaf vs. 24.2% in HH, p = 0.031) significantly differed. The mean score of Panic Disorder, Social Anxiety Disorder, and Anxiety Disorders in the deaf adolescents were higher than in HH ones.
Conclusions: Our study showed the presence of significant symptoms of ADs in a sample of Iranian adolescents with HL, especially in deaf adolescents, during the COVID-19 pandemic.
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http://dx.doi.org/10.1186/s12888-021-03118-0 | DOI Listing |
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University of Nebraska Medical Center, Omaha, NE, USA.
The meat processing industry was significantly impacted by the COVID-19 pandemic. Deemed essential, the meat processing workforce faced the risk of exposure to the SARS-CoV-2 virus. Along with other essential workforces, meat processing workers were prioritized in the national approach to receive COVID-19 vaccines by the Centers for Disease Control and Prevention Advisory Committee on Immunization Practices.
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Sens Diagn
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Department of Bioengineering, Rice University Houston TX 77030 USA
CRISPR-Cas-based lateral flow assays (LFAs) have emerged as a promising diagnostic tool for ultrasensitive detection of nucleic acids, offering improved speed, simplicity and cost-effectiveness compared to polymerase chain reaction (PCR)-based assays. However, visual interpretation of CRISPR-Cas-based LFA test results is prone to human error, potentially leading to false-positive or false-negative outcomes when analyzing test/control lines. To address this limitation, we have developed two neural network models: one based on a fully convolutional neural network and the other on a lightweight mobile-optimized neural network for automated interpretation of CRISPR-Cas-based LFA test results.
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Department of Physics, School of Science and Engineering, Ateneo de Manila University, Quezon City, Philippines.
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