Coronavirus Disease 2019 Screening Research Articles

Desorption Separation Ionization Mass Spectrometry (DSI-MS) for Rapid Analysis of COVID-19.

During the coronavirus disease 2019 (COVID-19) pandemic, which has witnessed over 772 million confirmed cases and over 6 million deaths globally, the outbreak of COVID-19 has emerged as a significant medical challenge affecting both affluent and impoverished nations. Therefore, there is an urgent need to explore the disease mechanism and to implement rapid detection methods. To address this, we employed the desorption separation ionization (DSI) device in conjunction with a mass spectrometer for the efficient detection and screening of COVID-19 urine samples. The study encompassed patients with COVID-19, healthy controls (HC), and patients with other types of pneumonia (OP) to evaluate their urine metabolomic profiles. Subsequently, we identified the differentially expressed metabolites in the COVID-19 patients and recognized amino acid metabolism as the predominant metabolic pathway involved. Furthermore, multiple established machine learning algorithms validated the exceptional performance of the metabolites in discriminating the COVID-19 group from healthy subjects, with an area under the curve of 0.932 in the blind test set. This study collectively suggests that the small-molecule metabolites detected from urine using the DSI device allow for rapid screening of COVID-19, taking just three minutes per sample. This approach has the potential to expand our understanding of the pathophysiological mechanisms of COVID-19 and offers a way to rapidly screen patients with COVID-19 through the utilization of machine learning algorithms.

Analysis of Vocal Signatures of COVID-19 in Cough Sounds: A Newer Diagnostic Approach Using Artificial Intelligence.

Artificial intelligence (AI) based models are explored increasingly in the medical field. The highly contagious pandemic of coronavirus disease 2019 (COVID-19) affected the world and availability of diagnostic tools high resolution computed tomography (HRCT) and/or real-time reverse transcriptase-polymerase chain reaction (RTPCR) was very limited, costly and time consuming. Therefore, the use of AI in COVID-19 for diagnosis using cough sounds can be efficacious and cost effective for screening in clinic or hospital and help in early diagnosis and further management of patients. To develop an accurate and fast voice-processing AI software to determine voice-based signatures in discriminating COVID-19 and non-COVID-19 cough sounds for screening of COVID-19. A prospective study involving 117 patients was performed based on online and/or offline voice data collection of cough sounds of COVID-19 patients in isolation ward of a tertiary care teaching hospital and non-COVID-19 participants using a smart phone. A website-based AI software was developed to identify the cough sounds as COVID-19 or non-COVID-19. The data were divided into three segments including training set, validation set and test set. A pre-processing algorithmwas utilized and combined with Short Time Fourier Transform feature representation and Logistic regression model. A precise software was used to identify vocal signatures and K-fold cross validation was carried out. A total of 117 audio recordings of cough sounds were collected through the developed website after inclusion-exclusion criteria out of which 52 have been marked belonging to COVID-19 positive, while 65 were marked as COVID-19 negative/unsure /never had COVID-19, which were assumed to be COVID-19 negative based on RT-PCR test results. The mean and standard error values for the accuracies attained at the end of each experiment in training, validation and testing set were found to be 67.34%±0.22, 58.57%±1.11 and 64.60%±1.79 respectively. The weight values were found to be positive which were contributing towards predicting the samples as COVID-19 positive with large spikes around 7.5 kHz, 7.8 kHz, 8.6 kHz and 11 kHz which can be used for classification. The proposed AI based approach can be a helpful screening tool for COVID-19 using vocal sounds of cough. It can help the health system by reducing the cost burden and improving overall diagnosis and management of the disease.

Evolution of illness severity in hospital admissions due to COVID-19, Québec, Canada, January to April 2022.

The coronavirus disease 2019 (COVID-19) severity is influenced by multiple factors, such as age, underlying medical conditions, individual immunity, infecting variant, and clinical practice. The highly transmissible Omicron variants resulted in decreased COVID-19 screening capacity, which limited disease severity surveillance. To report on the temporal evolution of disease severity among patients admitted to Québec hospitals due to COVID-19 between January 2, 2022, and April 23, 2022, which corresponded to the peak period of hospitalizations due to Omicron. Retrospective population-based cohort study of all hospital admissions due to COVID-19 in Québec, between January 2, 2022, and April 23, 2022. Study period was divided into four-week periods, corresponding roughly to January, February, March and April. Regression using Cox and Poisson generalized estimating equations (GEEs) was used to quantify temporal variations in length of stay and risk of complications (intensive care admission or in-hospital death) through time, using the Omicron peak (January 2022) as reference. Measures were adjusted for age, sex, vaccination status, presence of chronic diseases, and clustering by hospital. During the study period, 9,178 of all 18,272 (50.2%) patients hospitalized with a COVID-19 diagnosis were admitted due to COVID-19. Of these, 1,026 (11.2%) were admitted to intensive care and 1,523 (16.6%) died. Compared to January, the risk of intensive care admission was 25% and 31% lower in March and April respectively, while in-hospital fatality continuously decreased by 45% lower in April. The average length of stay was temporarily lower in March (9%). Severity of admissions due to COVID-19 decreased in the first months of 2022, when predominant circulating variants were considered to be of similar severity. Monitoring hospital admissions due to COVID-19 can contribute to disease severity surveillance.

Impact of COVID-19 infection during the postoperative period in patients who underwent gastrointestinal surgery: a retrospective study.

The coronavirus disease 2019 (COVID-19) pandemic has led to significant global casualties. This study examines the postoperative impact of COVID-19 on patients who underwent gastrointestinal surgery, considering their heightened vulnerability to infections and increased morbidity and mortality risk. This retrospective observational study was conducted at a tertiary center and patients who underwent gastrointestinal surgery between January 2022 and February 2023 were included. Postoperative COVID-19 infection was defined as the detection of severe acute respiratory syndrome coronavirus 2 RNA by RT-PCR within 14 days after surgery. Propensity score matching was performed including age, sex, American Society of Anesthesiology physical status classification, and emergency operation between the COVID-19-negative (-) and -positive (+) groups. Following 1:2 propensity score matching, 21 COVID-19(+) and 42 COVID-19(-) patients were included in the study. In the COVID-19(+) group, the postoperative complication rate was significantly higher (52.4% vs. 23.8%, P = 0.023). Mechanical ventilator requirement, intensive care unit (ICU) admission, and readmission rate did not significantly differ between the 2 groups. The median length of ICU (19 days vs. 4 days, P < 0.001) and hospital stay (18 vs. 8 days, P = 0.015) were significantly longer in the COVID-19(+) group. Patients with COVID-19 had a 2.4 times higher relative risk (RR) of major complications than patients without COVID-19 (RR, 2.37; 95% confidence interval, 1.254-4.467; P = 0.015). COVID-19 infection during the postoperative period in gastrointestinal surgery may have adverse outcomes which may increase the risk of major complications. Preoperative COVID-19 screening and protocols for COVID-19 prevention in surgical patients should be maintained.

MiniSeg: An Extremely Minimum Network Based on Lightweight Multiscale Learning for Efficient COVID-19 Segmentation.

The rapid spread of the new pandemic, i.e., coronavirus disease 2019 (COVID-19), has severely threatened global health. Deep-learning-based computer-aided screening, e.g., COVID-19 infected area segmentation from computed tomography (CT) image, has attracted much attention by serving as an adjunct to increase the accuracy of COVID-19 screening and clinical diagnosis. Although lesion segmentation is a hot topic, traditional deep learning methods are usually data-hungry with millions of parameters, easy to overfit under limited available COVID-19 training data. On the other hand, fast training/testing and low computational cost are also necessary for quick deployment and development of COVID-19 screening systems, but traditional methods are usually computationally intensive. To address the above two problems, we propose MiniSeg, a lightweight model for efficient COVID-19 segmentation from CT images. Our efforts start with the design of an attentive hierarchical spatial pyramid (AHSP) module for lightweight, efficient, effective multiscale learning that is essential for image segmentation. Then, we build a two-path (TP) encoder for deep feature extraction, where one path uses AHSP modules for learning multiscale contextual features and the other is a shallow convolutional path for capturing fine details. The two paths interact with each other for learning effective representations. Based on the extracted features, a simple decoder is added for COVID-19 segmentation. For comparing MiniSeg to previous methods, we build a comprehensive COVID-19 segmentation benchmark. Extensive experiments demonstrate that the proposed MiniSeg achieves better accuracy because its only 83k parameters make it less prone to overfitting. Its high efficiency also makes it easy to deploy and develop. The code has been released at https://github.com/yun-liu/MiniSeg.

A Case of Retiform Purpura associated with Coronavirus Disease 2019 Infection

Since the emergence of coronavirus disease 2019 (COVID-19), several reports have described various skin manifestations associated with COVID-19. Retiform purpura is a cutaneous manifestation that is likely associated with coagulopathy in patients with severe COVID-19. Here, we present the case of a 62-year-old man with hypertension and multiple myeloma who presented with retiform purpuric patches on both legs two days after a chemotherapy session with carfilzomib and dexamethasone. He had no other respiratory symptoms or fever; however, he was hospitalized for supportive care and underwent COVID-19 screening with reverse transcriptase polymerase chain reaction, which was positive. Histopathologic evaluation of the two punch biopsy samples obtained from the affected skin revealed perivascular lymphocytic lesions with fibrinoid degeneration, extravasated red blood cells, and fibrin deposition in the deep dermis. Based on these findings, the patient was diagnosed with COVID-19 and retiform purpura. He received intensive care after developing fever, cough, sputum production, and dyspnea. However, the patient developed multifocal cerebral infarctions and eventually died. Dermatologists should recognize retiform purpura, which may appear before other COVID-19 symptoms, as an alarm sign which can aid in the prompt implementation of appropriate treatment.

A semi-quantitative upconversion nanoparticle-based immunochromatographic assay for SARS-CoV-2 antigen detection.

The unprecedented public health and economic impact of the coronavirus disease 2019 (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection has been met with an equally unprecedented scientific response. Sensitive point-of-care methods to detect SARS-CoV-2 antigens in clinical specimens are urgently required for the rapid screening of individuals with viral infection. Here, we developed an upconversion nanoparticle-based lateral flow immunochromatographic assay (UCNP-LFIA) for the high-sensitivity detection of SARS-CoV-2 nucleocapsid (N) protein. A pair of rabbit SARS-CoV-2 N-specific monoclonal antibodies was conjugated to UCNPs, and the prepared UCNPs were then deposited into the LFIA test strips for detecting and capturing the N protein. Under the test conditions, the limit of detection (LOD) of UCNP-LFIA for the N protein was 3.59 pg/mL, with a linear range of 0.01-100 ng/mL. Compared with that of the current colloidal gold-based LFIA strips, the LOD of the UCNP-LFIA-based method was increased by 100-fold. The antigen recovery rate of the developed method in the simulated pharyngeal swab samples ranged from 91.1 to 117.3%. Furthermore, compared with the reverse transcription-polymerase chain reaction, the developed UCNP-LFIA method showed a sensitivity of 94.73% for 19 patients with COVID-19. Thus, the newly established platform could serve as a promising and convenient fluorescent immunological sensing approach for the efficient screening and diagnosis of COVID-19.

Gas chromatography-flame ionization detector for sweat based COVID-19 screening

Simple approach for rapid screening of corona virus disease 2019 (COVID-19) has been developed. This applied gas chromatography-flame ionization detector (GC-FID) analyzing the potential compound marker in sweat samples obtained from COVID-19 positive and negative volunteers in Bangkok, Thailand. The samples were collected by using cotton rods for 15 min, heated at 90 °C for 5 min, and the volatile compounds in the headspace (HS) were injected (5.00 mL) at 150 °C and separated within 13.7 min. The marker peak was tentatively identified as p-cymene by the authentic standard injection and comparison with the GC-mass spectrometry (GC-MS) and comprehensive two-dimensional GC (GC × GC)-MS analysis. Possible mechanisms for the presence of p-cymene were proposed. The marker peak area thresholds were then varied and optimized via construction of the receiver operating characteristic (ROC) curve. With the optimum threshold, the established method offered the accuracy, sensitivity and specificity of 96 %. This method was insignificantly affected (p-value >0.05) by genders, body mass indices, ages, and use of deodorants as well as the p-cymene containing food. However, the performance could be affected by the population with personal hygiene or experiencing the microbiomes producing p-cymene.

Identifying facilitators and barriers to culturally responsive communication for racial, ethnic, sexual, and gender minoritized patients when screened for COVID-19 vaccinations: A scoping review protocol.

Racial, ethnic, sexual, and gender minoritized groups are considered historically excluded groups and have been disproportionately affected by the coronavirus disease 2019 (COVID-19) pandemic. The influence of social determinants of health (SDOH), including access to screening and treatment, and other systemic and structural factors are largely responsible for these disparities. Primary care practitioner (PCP) competence in culturally responsive screening practices will be critical to reducing the impact of systemic and structural factors serving as barriers to screening and treatment. Correspondingly, improving the capacity of PCPs to communicate with patients in a culturally responsive manner may influence improved screening and treatment outcomes for minoritized groups related to COVID-19. This scoping literature review aims to determine the current breadth of literature on culturally responsive communication (CRC) in regard to COVID-19 vaccination screening for historically excluded, or minoritized groups. Results from this review will inform the development of a training series and social marketing campaign to improve PCPs capacity in CRC. This manuscript provides details on our study protocol. This scoping literature review aims to analyze existing literature on culturally responsive COVID-19 vaccinations between PCPs and patients in the U.S., specifically for racial, ethnic, sexual, and gender minoritized groups. Results of this scoping review will inform the development of a training series and social marketing campaign to improve capacity of PCPs in this area. Additionally, the review will inform recommendations for future research. This scoping review will be performed following the framework of Arksey and O'Malley and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for scoping reviews (PRISMA-ScR). Relevant studies between the years 2019-2022 were identified using a rigorous search strategy across four databases: MEDLINE (via PubMed), Scopus, Cochrane (CENTRAL; via Wiley), and CINAHL (via EBSCO), using Boolean and Medical Subject Headings (MeSH) search terms. Studies will be uploaded to the data extraction tool, Covidence, to remove duplicates and perform a title/abstract screening, followed by a full-text screening. The data extraction and analysis phases of the scoping review are in progress. Data will be analyzed for themes related to culturally responsive COVID-19 screening practices in clinical encounters with the identified study populations. Results will be reported by theme and align to PRISMA-ScR guidelines. To our knowledge, this is the first study to use scoping methods to investigate the barriers and facilitators to CRC of COVID-19 vaccine screening for historically excluded communities in the U.S. The work and results from this research will be directly utilized for the development of nationally-accessible, continuing medical education materials to teach PCPs about CRC, as well as other materials to influence relevant policy changes within the healthcare landscape.

Bidirectionality between Cardiometabolic Diseases and COVID-19: Role of Humoral Immunity.

Cardiometabolic diseases and abnormalities have recently emerged as independent risk factors of coronavirus disease 2019 (COVID-19) severity, including hospitalizations, invasive mechanical ventilation, and mortality. Determining whether and how this observation translates to more effective long-term pandemic mitigation strategies remains a challenge due to key research gaps. Specific pathways by which cardiometabolic pathophysiology affects humoral immunity against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), and vice versa, remain unclear. This review summarizes current evidence of the bidirectional influences between cardiometabolic diseases (diabetes, adiposity, hypertension, CVDs) and SARS-CoV-2 antibodies induced from infection and vaccination based on human studies. Ninety-two studies among >408,000 participants in 37 countries on 5 continents (Europe, Asia, Africa, and North and South America) were included in this review. Obesity was associated with higher neutralizing antibody titers following SARS-CoV-2 infection. Most studies conducted prior to vaccinations found positive or null associations between binding antibodies (levels, seropositivity) and diabetes; after vaccinations, antibody responses did not differ by diabetes. Hypertension and CVDs were not associated with SARS-CoV-2 antibodies. Findings underscore the importance of elucidating the extent that tailored recommendations for COVID-19 prevention, vaccination effectiveness, screening, and diagnoses among people with obesity could reduce disease burden caused by SARS-CoV-2.

Enhanced real-time mass spectrometry breath analysis for the diagnosis of COVID-19.

Although rapid screening for and diagnosis of coronavirus disease 2019 (COVID-19) are still urgently needed, most current testing methods are long, costly or poorly specific. The objective of the present study was to determine whether or not artificial-intelligence-enhanced real-time mass spectrometry breath analysis is a reliable, safe, rapid means of screening ambulatory patients for COVID-19. In two prospective, open, interventional studies in a single university hospital, we used real-time, proton transfer reaction time-of-flight mass spectrometry to perform a metabolomic analysis of exhaled breath from adults requiring screening for COVID-19. Artificial intelligence and machine learning techniques were used to build mathematical models based on breath analysis data either alone or combined with patient metadata. We obtained breath samples from 173 participants, of whom 67 had proven COVID-19. After using machine learning algorithms to process breath analysis data and further enhancing the model using patient metadata, our method was able to differentiate between COVID-19-positive and -negative participants with a sensitivity of 98%, a specificity of 74%, a negative predictive value of 98%, a positive predictive value of 72% and an area under the receiver operating characteristic curve of 0.961. The predictive performance was similar for asymptomatic, weakly symptomatic and symptomatic participants and was not biased by COVID-19 vaccination status. Real-time, noninvasive, artificial-intelligence-enhanced mass spectrometry breath analysis might be a reliable, safe, rapid, cost-effective, high-throughput method for COVID-19 screening.

COVID-19 Screening Score for Patients without Acute Respiratory Symptoms Undergoing Emergency Medical Procedures in Indonesia.

To rule out coronavirus disease-2019 (COVID-19) in patients scheduled to undergo emergency medical procedures, SARS-CoV-2 reverse transcription polymerase chain reaction (RT-PCR) must be performed. In developing countries, the use of SARS-CoV-2 RT-PCR has been limited by its unavailability and long processing time. Hence, a quick screening score to predict COVID-19 may help healthcare practitioners determine which patients without acute respiratory symptoms can safely undergo an emergency medical procedure. We conducted a cross-sectional study of adult patients without acute respiratory symptoms who were admitted to the emergency department and underwent an emergency medical procedure within 24 hours after admittance. We collected baseline demographic data, COVID-19 screening variables, and SARS-CoV-2 RT-PCR as the gold standard for COVID-19 diagnosis. Bivariate and multivariate analyses were performed, and a scoring system was developed using statistically significant variables from the multivariate analysis. With data from 357 patients, multivariate backward stepwise logistic regression analysis resulted in two significant COVID-19 predictors: the presence of SARS-CoV-2-IgM antibody (adjusted odds ratio [aOR]: 7.02 [95% CI: 1.49-32.96]) and typical chest x-ray (aOR: 23.21 [95% CI: 10.01-53.78]). A scoring system was developed using these predictors with an area under the receiver operating characteristic curve of 0.71 (95% CI: 0.64-0.78). For a cutoff point of ≥ 2, the scoring system showed 42.5% sensitivity and 97.1% specificity but had poor calibration (Hosmer-Lemeshow test P value < 0.001). We believe that the development of this COVID-19 quick screening score may be helpful in a resource-limited clinical setting, but its moderate discrimination and poor calibration hinder its use as a replacement for the SARS-CoV-2 RT-PCR test for COVID-19 screening.

Burnout Prevalence and its Associated Factors among Healthcare Workers during COVID-19 Pandemic

Coronavirus disease 2019 (COVID-19) has become a global health threat and has placed an extraordinary demand on healthcare workers around the world. More than half of healthcare workers in this phase experienced burnout. The main causes of personal, professional, and patient-related burnout were found to be direct participation in COVID-19 screening or treatment, having a medical condition, and less psychological support at work. Burnout was cited by healthcare employees as being caused by their workloads, the pandemic's uncertainties, difficult work-family balance, and strained working relationships. Burnout syndrome is more directly serving the people working in professions that are seen in important of human relationships. Nursing is one of the human service professions and is specifically vital in the medical and mental health care delivery. In the health professional heavy workload and non-compliance among persons are important risk factors for burnout. Exhaustion appeared to be the major symptom, and many participants utilized problem-focused coping to deal with the adversities experienced during the pandemic. Health workers reported physical, occupational, psychological, and social-related negative impacts resulting from burnout. Burnout is generally associated with higher rates of illness, increased use of alcohol and drugs, lower career satisfaction, high “staff turnover” but also reduced the quality of service, resulting in the poor patient outcome. As the pandemic trajectory is yet unknown, these findings provide early insight and guidance for possible interventions.

Clinical Profile, Disease Severity and Perinatal Outcomes of Laboratory-confirmed COVID-19 among Pregnant Women Delivering in a Tertiary Care Centre.

Pregnant women and their fetuses represent a high-risk population during coronavirus disease 2019 (COVID-19) pandemic. During pregnancy, body undergoes significant physiologic, mechanical, and immunologic alterations which increases susceptibility to infections. Thus pregnant women are at an increased risk for severe illness from COVID-19. This study explored the clinical profile, disease severity, and perinatal outcomes of pregnant women with COVID-19. A retro-prospective cross-sectional study was conducted at KIST Medical College after ethical approval. All women (1227) who delivered from 15 April 2020 to 15 October 2021 and underwent the COVID-19 screening by Reverse Transcriptase Polymerase Chain Reaction were studied. COVID-19 positive women (44) were further analyzed and classified as asymptomatic, mild, moderate, severe, and critical diseases. Data was collected in proforma by reviewing patients' records. The data analysis were done in SPSS version 26. A descriptive statistical test and chi-square test were done. The prevalence of COVID-19 among delivering pregnant women was 3.6% (44/1227). Majority were asymptomatic 79.5% (35). About 16% (7) had mild illness and 4.5% (2) had moderate illness. The caesarean rate was 34.1% (15/44) among COVID-19 positive women. Stillbirth and preterm birth was observed in 2.3% (1) and 4.5% (2) respectively. The COVID-19 infection rate in a neonate was 4.5%. The severity of COVID-19 among delivering pregnant women was less. The perinatal outcome was also favourable. The overall prognosis of COVID-19 among mothers and newborns was good. Further research is needed to understand the true magnitude of risks and management, more so with the emergence of new variants.

Direct impact of COVID-19 by estimating disability-adjusted life years at national level in France in 2020.

The World Health Organization declared a pandemic of coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), on March 11, 2020. The standardized approach of disability-adjusted life years (DALYs) allows for quantifying the combined impact of morbidity and mortality of diseases and injuries. The main objective of this study was to estimate the direct impact of COVID-19 in France in 2020, using DALYs to combine the population health impact of infection fatalities, acute symptomatic infections and their post-acute consequences, in 28 days (baseline) up to 140 days, following the initial infection. National mortality, COVID-19 screening, and hospital admission data were used to calculate DALYs based on the European Burden of Disease Network consensus disease model. Scenario analyses were performed by varying the number of symptomatic cases and duration of symptoms up to a maximum of 140 days, defining COVID-19 deaths using the underlying, and associated, cause of death. In 2020, the estimated DALYs due to COVID-19 in France were 990 710 (1472 per 100 000), with 99% of burden due to mortality (982 531 years of life lost, YLL) and 1% due to morbidity (8179 years lived with disability, YLD), following the initial infection. The contribution of YLD reached 375%, assuming the duration of 140 days of post-acute consequences of COVID-19. Post-acute consequences contributed to 49% of the total morbidity burden. The contribution of YLD due to acute symptomatic infections among people younger than 70 years was higher (67%) than among people aged 70 years and above (33%). YLL among people aged 70 years and above, contributed to 74% of the total YLL. COVID-19 had a substantial impact on population health in France in 2020. The majority of population health loss was due to mortality. Men had higher population health loss due to COVID-19 than women. Post-acute consequences of COVID-19 had a large contribution to the YLD component of the disease burden, even when we assume the shortest duration of 28 days, long COVID burden is large. Further research is recommended to assess the impact of health inequalities associated with these estimates.

Rapid and Sensitive Diagnosis of COVID-19 Using an Electricity-Free Self-Testing System.

Rapid and sensitive detection of coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is essential for early diagnosis and effective treatment. Nucleic acid testing has been considered the gold standard method for the diagnosis of COVID-19 for its high sensitivity and specificity. However, the polymerase chain reaction (PCR)-based method in the central lab requires expensive equipment and well-trained personnel, which makes it difficult to be used in resource-limited settings. It highlights the need for a sensitive and simple assay that allows potential patients to detect SARS-CoV-2 by themselves. Here, we developed an electricity-free self-testing system based on reverse transcription loop-mediated isothermal amplification (RT-LAMP) that allows for rapid and accurate detection of SARS-CoV-2. Our system employs a heating bag as the heat source, and a 3D-printed box filled with phase change material (PCM) that successfully regulates the temperature for the RT-LAMP. The colorimetric method could be completed in 40 min and the results could be read out by the naked eye. A ratiometric measurement for exact readout was also incorporated to improve the detection accuracy of the system. This self-testing system is a promising tool for point-of-care testing (POCT) that enables rapid and sensitive diagnosis of SARS-CoV-2 in the real world and will improve the current COVID-19 screening efforts for control and mitigation of the pandemic.

A fluorescence-based sweat test sensor in a proof-of-concept clinical study for COVID-19 screening diagnosis.

During the corona virus disease 2019 (COVID-19) pandemic period, rapid screening of covid-19 patients has been of great interest by developing a fluorescent sensor for complexation with nonanal, which is a marker for Covid-19 detection in sweat. Solid phase micro-extraction gas chromatography-mass spectrometry (SPME GC-MS) was initially used to quantify nonanal in armpit sweat samples based on an external calibration curve. A sample containing a nonanal content above the threshold of 1.04 μL is expected to be COVID-19 positive with a sensitivity and specificity of 87% and 89%, respectively, validated by comparison with RT-PCR results. For more practical applications, helicene dye-encapsulated ethyl cellulose, namely EC@dyeNH, was applied to screen 140 sweat samples collected from the foreheads of volunteers. The mixed sensor and sweat solution droplets were then visualized and imaged under blacklight. The COVID-19 positive droplets exhibited yellow fluorescence emission, the brightness of which could be measured by using ImageJ in the grey scale. With the optimum color intensity of >73 for positive results, the screening performance was observed with a sensitivity and specificity of 96% and 93%, respectively. The overall test time of this method is approximately less than 15 min. This alternative method offers a promising practical screening approach for the diagnosis of COVID-19 in sweat.

Testing the Acceptability and Usability of an AI-Enabled COVID-19 Diagnostic Tool Among Diverse Adult Populations in the United States.

Although at-home coronavirus disease-2019 (COVID-19) testing offers several benefits in a relatively cost-effective and less risky manner, evidence suggests that at-home COVID-19 test kits have a high rate of false negatives. One way to improve the accuracy and acceptance of COVID-19 screening is to combine existing at-home physical test kits with an easily accessible, electronic, self-diagnostic tool. The objective of the current study was to test the acceptability and usability of an artificial intelligence (AI)-enabled COVID-19 testing tool that combines a web-based symptom diagnostic screening survey and a physical at-home test kit to test differences across adults from varying races, ages, genders, educational, and income levels in the United States. A total of 822 people from Richmond, Virginia, were included in the study. Data were collected from employees and patients of Virginia Commonwealth University Health Center as well as the surrounding community in June through October 2021. Data were weighted to reflect the demographic distribution of patients in United States. Descriptive statistics and repeated independent t tests were run to evaluate the differences in the acceptability and usability of an AI-enabled COVID-19 testing tool. Across all participants, there was a reasonable degree of acceptability and usability of the AI-enabled COVID-19 testing tool that included a physical test kit and symptom screening website. The AI-enabled COVID-19 testing tool demonstrated overall good acceptability and usability across race, age, gender, and educational background. Notably, participants preferred both components of the AI-enabled COVID-19 testing tool to the in-clinic testing. Overall, these findings suggest that our AI-enabled COVID-19 testing approach has great potential to improve the quality of remote COVID testing at low cost and high accessibility for diverse demographic populations in the United States.

Sinonasal Lobular Capillary Hemangioma After Continuous Self-tests for COVID-19: A Case Report and Review of the Literature

Sinonasal lobular capillary hemangiomas (LCH) are rare benign vascular lesions commonly arising from the nasal septum. Nasopharyngeal, nasal mid-turbinate, and anterior nasal swabbing is the preferred method of screening for coronavirus disease-2019 (COVID-19). Herein, we present a case of a sinonasal LCH in a child after continuous self-tests for COVID-19, with an anterior nasal swab. The child presented with a well-defined red mass in the anterior part of the nasal septum, which was removed endoscopically. Histopathology revealed a LCH. This is the first report of a complication other than epistaxis with the use of an anterior nasal swab. Our literature review identified 32 studies reporting complications of COVID-19 screening. Cerebrospinal fluid leaks and foreign body retention are the most common ones. A proper specimen collection technique and a quick patient history with an emphasis on risk factors are the best practices to prevent complications from COVID-19 screening.

Viral shedding patterns of symptomatic SARS-CoV-2 infections by periods of variant predominance and vaccination status in Gyeonggi Province, Korea.

We compared the viral cycle threshold (Ct) values of infected patients to better understand viral kinetics by vaccination status during different periods of variant predominance in Gyeonggi Province, Korea. We obtained case-specific data from the coronavirus disease 2019 (COVID-19) surveillance system, Gyeonggi in-depth epidemiological report system, and Health Insurance Review & Assessment Service from January 2020 to January 2022. We defined periods of variant predominance and explored Ct values by analyzing viral sequencing test results. Using a generalized additive model, we performed a nonlinear regression analysis to determine viral kinetics over time. Cases in the Delta variant's period of predominance had higher viral shedding patterns than cases in other periods. The temporal change of viral shedding did not vary by vaccination status in the Omicron-predominant period, but viral shedding decreased in patients who had completed their third vaccination in the Delta-predominant period. During the Delta-predominant and Omicron-predominant periods, the time from symptom onset to peak viral shedding based on the E gene was approximately 2.4 days (95% confidence interval [CI], 2.2 to 2.5) and 2.1 days (95% CI, 2.0 to 2.1), respectively. In one-time tests conducted to diagnose COVID-19 in a large population, although no adjustment for individual characteristics was conducted, it was confirmed that viral shedding differed by the predominant strain and vaccination history. These results show the value of utilizing hundreds of thousands of test data produced at COVID-19 screening test centers.