ABSTRACT
Purpose: This study aimed to identify the factors affecting the self-care of older adults living alone in a community during the COVID-19 pandemic. Methods: Data were collected through a questionnaire survey of 160 older adults living alone in Seoul, Gyeong-gi, and Chung-cheong province using self-care, health conservation, psychological well-being, and self-esteem scales. Data were analyzed using the Pearson cor-relation coefficients, t-test, analysis of variance ANOVA, and Hierarchial stepwise multiple regression with the SPSS/Windows version 25.0 program. Results: The greatest variables affecting the self-care for older adults living alone were compliance with COVID-19 quarantine rules (β=-.04, p=.054), interactions with the neighborhood (β=-.06, p=.029), economic status (β=.10, p=.008), health conservation (β=.29, p<.001), psychological well-being (β=.18, p=.008), and self-esteem (β=.35, p<.001). The explanatory power of these variables was 70.0% (F=18.45, p<.001). Conclusion: Regarding the COVID-19 pandemic, these factors were highly mutually related to older adults living alone, so it is necessary for older adults living alone to actively participate in self-care programs using direct and indirect community resources. © 2023 Korean Gerontological Nursing Society.
ABSTRACT
Background SARS-CoV-2 can spread both from symptomatic and asymptomatic individuals. Ocular manifestations due to SARS-CoV-2 have been described, being conjunctival inflammation the most common affectation. Evidence shows that conjunctivitis could be the first and/or only manifestation of COVID-19. This study aimed to develop and validate a COVID-19 screening method based on eyes photographs and artificial intelligence. Methods In this multicentre study, 1,200 participants were enrolled from Shanghai Public Health Clinical Center (SPHCC) Fudan University, AIMOMICS LAB and La Fe University and Polytechnic Hospital (LFUPH) of Valencia (Spain). Pictures of participants' ocular surface were taken in four different positions with mobile phone cameras, and a Deep Learning System (DLS) was developed through machine learning to identify characteristic conjunctival inflammation patterns. The study was conducted in accordance with the Declaration of Helsinki and was approved by the Ethics Committees of SPHCC and LFUPH. Results The area under the receiver-operating-characteristic curve (AUC), sensitivity, specificity, and accuracy were calculated according to the results of our binary classification network. Bootstrapping with 1,000 replicates was used to estimate 95% confidence intervals of the performance metrics, with photography as the resampling unit. On the subject-level classification, the network achieved the AUC of 0.976 (95% CI 0.965-0.988) among Asian population and 0.892 (95% CI 0-763-1.000) among Caucasian population. Conclusions Preliminary results show that this DLS performed well in identifying probable asymptomatic COVID-19 cases through the analysis of participants' eyes pictures. This method could be an innocuous, accessible, low cost and quick COVID-19 screening method. Eventually, it could potentially contribute to pandemic control. Key messages In the context of the COVID-19 pandemic it would be useful to have a screening method to easily and quickly detect asymptomatic individuals, in addition to using temperature control. Preliminary results show that this Deep Learning System (DLS) based on eyes pictures taken with mobile phone cameras could be an innocuous, accessible, low cost and quick COVID-19 screening method.
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Background: COVID-19 patients showed certain characteristic features of multiple signs in bilateral lungs. Some patients only had a single pulmonary lobe lesion, which has not been reported previously. Single pulmonary lobe lesions are easily missed or misdiagnosed if they do not receive enough attention. Objective: To study the imaging manifestations, clinical features and outcomes of patients with COVID-19 with only one single pulmonary lobe lesion. Methods: Patient clinical data were collected only from patients with confirmed SARS-CoV-2 infection by RT-PCR, which was confined to only single lobe lesions on chest CT imaging findings at the onset. Which lobe was frequently involved, the imaging manifestations, clinical features and outcomes were also analyzed. Result: From January 1, 2020, to March 14, 2020, a total of 367 inpatients were diagnosed with COVID-19, in which 50 (13.6%) patients were confirmed with only one single pulmonary lobe lesion. The most frequently involved lobe was the right lower lobe (18 patients, 36%, highest). Lesions in the lower lobe easily spread to all lobes of the bilateral lungs (P<0.001, χ2=10.264), especially the left lower lobe, and were less frequent in the right upper lobe. During hospitalization, 2 (4%) patients were admitted to the ICU, 2 (4%) patients died, and 28 (56%) patients developed lesions in other lobes within 6.32±3.71 days. Conclusions: The general pattern of COVID-19 imaging with localized nodules may also cause severe respiratory symptoms of bilateral lung disease, serious complications, or even death in patients with multiple lobe lesions or bilateral lung lesions, which should not be underestimated.
ABSTRACT
Serum amyloid A(SAA) is a novel marker widely used in the acute infection disease, especially viral infection. SAA has shown a cerntain value in assisting the clinical diagnosis, discrimination of severity and monitoring of progress and outcome of COVID-19. This paper introduces the application of SAA structural, function andits dynamic detection in the diagnosis of COVID-19, and the significance of combined detection with COVID-19 antibodies, nucleic acid and other diagnostic indicators.