SwabExpress: An End-to-End Protocol for Extraction-Free COVID-19 Testing.

BACKGROUND: The urgent need for massively scaled clinical testing for SARS-CoV-2, along with global shortages of critical reagents and supplies, has necessitated development of streamlined laboratory testing protocols. Conventional nucleic acid testing for SARS-CoV-2 involves collection of a clinical specimen with a nasopharyngeal swab in transport medium, nucleic acid extraction, and quantitative reverse-transcription PCR (RT-qPCR). As testing has scaled across the world, the global supply chain has buckled, rendering testing reagents and materials scarce. To address shortages, we developed SwabExpress, an end-to-end protocol developed to employ mass produced anterior nares swabs and bypass the requirement for transport media and nucleic acid extraction. METHODS: We evaluated anterior nares swabs, transported dry and eluted in low-TE buffer as a direct-to-RT-qPCR alternative to extraction-dependent viral transport media. We validated our protocol of using heat treatment for viral inactivation and added a proteinase K digestion step to reduce amplification interference. We tested this protocol across archived and prospectively collected swab specimens to fine-tune test performance. RESULTS: After optimization, SwabExpress has a low limit of detection at 2-4 molecules/µL, 100% sensitivity, and 99.4% specificity when compared side by side with a traditional RT-qPCR protocol employing extraction. On real-world specimens, SwabExpress outperforms an automated extraction system while simultaneously reducing cost and hands-on time. CONCLUSION: SwabExpress is a simplified workflow that facilitates scaled testing for COVID-19 without sacrificing test performance. It may serve as a template for the simplification of PCR-based clinical laboratory tests, particularly in times of critical shortages during pandemics.

COVID-19 and the liver: overview.

On 12 March 2020, the WHO declared that the coronavirus disease 2019 (COVID-19) constitutes a pandemic. Cases of liver damage or dysfunction (mainly characterized by moderately elevated serum aspartate aminotransferase levels) have been reported among patients with COVID-19. However, it is currently uncertain whether the COVID-19 related liver damage/dysfunction is due mainly to the viral infection by itself or other coexisting conditions, such as the use of potentially hepatotoxic medications and the coexistence of systemic inflammatory response, respiratory distress syndrome-induced hypoxia, and multiple organ dysfunction. Individuals at high risk for severe COVID-19 are typical of older age and/or present with comorbid conditions such as diabetes, cardiovascular disease, and hypertension. This is also the same profile for those at increased risk for unrecognized underlying liver disease, especially nonalcoholic fatty liver disease. This could make them more susceptible to liver injury from the virus, medications used in supportive management, or hypoxia. So the aim of this review was to illustrate the clinical implications of COVID-19 on the liver in healthy and diseased states as well as the implications of common liver disorders on the outcome of COVID-19.

SARS-CoV-2 RNA detection on environmental surfaces in COVID-19 wards.

This study monitored the presence of SARS-Cov-2 RNA on environmental surfaces in hospital wards housing patients with mild, severe, and convalescent Coronavirus Disease 2019 (COVID-19), respectively. From 29 October to 4 December 2021, a total of 787 surface samples were randomly collected from a General Ward, Intensive Care Unit, and Convalescent Ward at a designated hospital for COVID-19 patients in China. All of the samples were used for SARS-Cov-2 detection. Descriptive statistics were generated and differences in the positivity rates between the wards were analyzed using Fisher's exact tests, Yates chi-squared tests, and Pearson's chi-squared tests. During the study period, 787 surface samples were collected, among which, 46 were positive for SARS-Cov-2 RNA (5.8%). The positivity rate of the contaminated area in the Intensive Care Unit was higher than that of the General Ward (23.5% vs. 10.4%, P<0.05). The positivity rate of the semi-contaminated area in the Intensive Care Unit (4.5%) was higher than that of the General Ward (1.5%), but this difference was not statistically significant (P>0.05). In the clean area, only one sample was positive in the Intensive Care Unit (0.5%). None of the samples were positive in the Convalescent Ward. These findings reveal that the SARS-Cov-2 RNA environmental pollution in the Intensive Care Unit was more serious than that in the General Ward, while the pollution in the Convalescent Ward was the lowest. Strict disinfection measures, personal protection, and hand hygiene are necessary to limit the spread of SARS-Cov-2.

A transformer-based representation-learning model with unified processing of multimodal input for clinical diagnostics.

During the diagnostic process, clinicians leverage multimodal information, such as the chief complaint, medical images and laboratory test results. Deep-learning models for aiding diagnosis have yet to meet this requirement of leveraging multimodal information. Here we report a transformer-based representation-learning model as a clinical diagnostic aid that processes multimodal input in a unified manner. Rather than learning modality-specific features, the model leverages embedding layers to convert images and unstructured and structured text into visual tokens and text tokens, and uses bidirectional blocks with intramodal and intermodal attention to learn holistic representations of radiographs, the unstructured chief complaint and clinical history, and structured clinical information such as laboratory test results and patient demographic information. The unified model outperformed an image-only model and non-unified multimodal diagnosis models in the identification of pulmonary disease (by 12% and 9%, respectively) and in the prediction of adverse clinical outcomes in patients with COVID-19 (by 29% and 7%, respectively). Unified multimodal transformer-based models may help streamline the triaging of patients and facilitate the clinical decision-making process.

Deduced Respiratory Scores on COVID-19 Patients Learning from Exertion-Induced Dyspnea.

Dyspnea is one of the most common symptoms of many respiratory diseases, including COVID-19. Clinical assessment of dyspnea relies mainly on self-reporting, which contains subjective biases and is problematic for frequent inquiries. This study aims to determine if a respiratory score in COVID-19 patients can be assessed using a wearable sensor and if this score can be deduced from a learning model based on physiologically induced dyspnea in healthy subjects. Noninvasive wearable respiratory sensors were employed to retrieve continuous respiratory characteristics with user comfort and convenience. Overnight respiratory waveforms were collected on 12 COVID-19 patients, and a benchmark on 13 healthy subjects with exertion-induced dyspnea was also performed for blind comparison. The learning model was built from the self-reported respiratory features of 32 healthy subjects under exertion and airway blockage. A high similarity between respiratory features in COVID-19 patients and physiologically induced dyspnea in healthy subjects was observed. Learning from our previous dyspnea model of healthy subjects, we deduced that COVID-19 patients have consistently highly correlated respiratory scores in comparison with normal breathing of healthy subjects. We also performed a continuous assessment of the patient's respiratory scores for 12-16 h. This study offers a useful system for the symptomatic evaluation of patients with active or chronic respiratory disorders, especially the patient population that refuses to cooperate or cannot communicate due to deterioration or loss of cognitive functions. The proposed system can help identify dyspneic exacerbation, leading to early intervention and possible outcome improvement. Our approach can be potentially applied to other pulmonary disorders, such as asthma, emphysema, and other types of pneumonia.

Identification of 3 key genes as novel diagnostic and therapeutic targets for OA and COVID-19.

Background: Corona Virus Disease 2019 (COVID-19) and Osteoarthritis (OA) are diseases that seriously affect the physical and mental health and life quality of patients, particularly elderly patients. However, the association between COVID-19 and osteoarthritis at the genetic level has not been investigated. This study is intended to analyze the pathogenesis shared by OA and COVID-19 and to identify drugs that could be used to treat SARS-CoV-2-infected OA patients. Methods: The four datasets of OA and COVID-19 (GSE114007, GSE55235, GSE147507, and GSE17111) used for the analysis in this paper were obtained from the GEO database. Common genes of OA and COVID-19 were identified through Weighted Gene Co-Expression Network Analysis (WGCNA) and differential gene expression analysis. The least absolute shrinkage and selection operator (LASSO) algorithm was used to screen key genes, which were analyzed for expression patterns by single-cell analysis. Finally, drug prediction and molecular docking were carried out using the Drug Signatures Database (DSigDB) and AutoDockTools. Results: Firstly, WGCNA identified a total of 26 genes common between OA and COVID-19, and functional analysis of the common genes revealed the common pathological processes and molecular changes between OA and COVID-19 are mainly related to immune dysfunction. In addition, we screened 3 key genes, DDIT3, MAFF, and PNRC1, and uncovered that key genes are possibly involved in the pathogenesis of OA and COVID-19 through high expression in neutrophils. Finally, we established a regulatory network of common genes between OA and COVID-19, and the free energy of binding estimation was used to identify suitable medicines for the treatment of OA patients infected with SARS-CoV-2. Conclusion: In the present study, we succeeded in identifying 3 key genes, DDIT3, MAFF, and PNRC1, which are possibly involved in the development of both OA and COVID-19 and have high diagnostic value for OA and COVID-19. In addition, niclosamide, ciclopirox, and ticlopidine were found to be potentially useful for the treatment of OA patients infected with SARS-CoV-2.

Diagnostic value and characteristic analysis of serum nucleocapsid antigen in COVID-19 patients.

Background: To date, several types of laboratory tests for coronavirus disease 2019 (COVID-19) diagnosis have been developed. However, the clinical importance of serum severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) nucleocapsid antigen (N-Ag) remains to be fully elucidated. In this study, we sought to investigate the value of serum SARS-CoV-2 N-Ag for COVID-19 diagnosis and to analyze N-Ag characteristics in COVID-19 individuals. Methods: Serum samples collected from 215 COVID-19 patients and 65 non-COVID-19 individuals were used to quantitatively detect N-Ag via chemiluminescent immunoassay according to the manufacturer's instructions. Results: The sensitivity and specificity of the N-Ag assay were 64.75% (95% confidence interval (95% CI) [55.94-72.66%]) and 100% (95% CI [93.05-100.00%]), respectively, according to the cut-off value recommended by the manufacturer. The receiver operating characteristic (ROC) curve showed a sensitivity of 100.00% (95% CI [94.42-100.00%]) and a specificity of 71.31% (95% CI [62.73-78.59%]). The positive rates and levels of serum SARS-CoV-2 N-Ag were not related to sex, comorbidity status or disease severity of COVID-19 (all P < 0.001). Compared with RT‒PCR, there was a lower positive rate of serum N-Ag for acute COVID-19 patients (P < 0.001). The positive rate and levels of serum SARS-CoV-2 N-Ag in acute patients were significantly higher than those in convalescent patients (all P < 0.001). In addition, the positive rate of serum SARS-CoV-2 N-Ag in acute COVID-19 patients was higher than that of serum antibodies (IgM, IgG, IgA and neutralizing antibodies (Nab)) against SARS-CoV-2 (all P < 0.001). However, the positive rate of serum SARS-CoV-2 N-Ag in convalescent COVID-19 patients was significantly lower than that of antibodies (all P < 0.001). Conclusion: Serum N-Ag can be used as a biomarker for early COVID-19 diagnosis based on appropriate cut-off values. In addition, our study also demonstrated the relationship between serum N-Ag and clinical characteristics.

Discovery of Peptidic Ligands against the SARS-CoV-2 Spike Protein and Their Use in the Development of a Highly Sensitive Personal Use Colorimetric COVID-19 Biosensor.

In addition to efficacious vaccines and antiviral therapeutics, reliable and flexible in-home personal use diagnostics for the detection of viral antigens are needed for effective control of the COVID-19 pandemic. Despite the approval of several PCR-based and affinity-based in-home COVID-19 testing kits, many of them suffer from problems such as a high false-negative rate, long waiting time, and short storage period. Using the enabling one-bead-one-compound (OBOC) combinatorial technology, several peptidic ligands with a nanomolar binding affinity toward the SARS-CoV-2 spike protein (S-protein) were successfully discovered. Taking advantage of the high surface area of porous nanofibers, immobilization of these ligands on nanofibrous membranes allows the development of personal use sensors that can achieve low nanomolar sensitivity in the detection of the S-protein in saliva. This simple biosensor employing naked-eye reading exhibits detection sensitivity comparable to some of the current FDA-approved home detection kits. Furthermore, the ligand used in the biosensor was found to detect the S-protein derived from both the original strain and the Delta variant. The workflow reported here may enable us to rapidly respond to the development of home-based biosensors against future viral outbreaks.

Sazgar IoT: A Device-Centric IoT Framework and Approximation Technique for Efficient and Scalable IoT Data Processing.

The Internet of Things (IoT) plays a fundamental role in monitoring applications; however, existing approaches relying on cloud and edge-based IoT data analysis encounter issues such as network delays and high costs, which can adversely impact time-sensitive applications. To address these challenges, this paper proposes an IoT framework called Sazgar IoT. Unlike existing solutions, Sazgar IoT leverages only IoT devices and IoT data analysis approximation techniques to meet the time-bounds of time-sensitive IoT applications. In this framework, the computing resources onboard the IoT devices are utilised to process the data analysis tasks of each time-sensitive IoT application. This eliminates the network delays associated with transferring large volumes of high-velocity IoT data to cloud or edge computers. To ensure that each task meets its application-specific time-bound and accuracy requirements, we employ approximation techniques for the data analysis tasks of time-sensitive IoT applications. These techniques take into account the available computing resources and optimise the processing accordingly. To evaluate the effectiveness of Sazgar IoT, experimental validation has been conducted. The results demonstrate that the framework successfully meets the time-bound and accuracy requirements of the COVID-19 citizen compliance monitoring application by effectively utilising the available IoT devices. The experimental validation further confirms that Sazgar IoT is an efficient and scalable solution for IoT data processing, addressing existing network delay issues for time-sensitive applications and significantly reducing the cost related to cloud and edge computing devices procurement, deployment, and maintenance.

Screening Hesitancy of a Universal Voluntary-based Rapid Antigen Test for coronavirus disease 2019 (COVID-19) During Omicron Wave in Hong Kong.

BACKGROUND: The Hong Kong government distributed rapid antigen test (RAT) kits to households across the city and called for a universal voluntary testing exercise for three consecutive days during the Omicron wave to identify infected persons early for quarantine and disrupt transmission chains in the community. We conducted a survey to evaluate the participation rates and explore the determinants of voluntary RAT adoption and hesitancy. METHODS: This cross-sectional survey was conducted through computer-assisted telephone interviews from 19 May to 16 June 2022 using an overlapping dual-frame telephone number sampling design. Information on willingness to adopt voluntary RAT, four themes of personal qualities, attitudes toward the government's health policies, incentives to motivate RAT adoption, and personal sociodemographic factors were collected. Logistic regression analysis was used to examine the factors associated with RAT adoption. RESULTS: Of the 1010 participants, 490 successfully responded to the fixed-line and 520 to the mobile phone survey, with response rates of 1.42% and 1.63% and screen hesitancy rates of 36.1% and 39.3%, respectively. Participants of adoption RAT were those aged 30-49 years, with high perceived COVID-19 infection severity, ≥ 3 doses of COVID-19 vaccination, and more agreement with the health policies on material resources and quarantine orders. Individuals who were less risk seeking and more altruistic reported a higher adoption of voluntary RAT. CONCLUSIONS: Understanding the willingness to participate in a voluntary universal testing programme might shed light on effective ways to minimise screening hesitancy in future public health strategies and campaigns.

Measuring symptom burden in patients with cancer during a pandemic: the MD Anderson symptom inventory for COVID-19 (MDASI-COVID).

BACKGROUND: Symptom expression in SARS-CoV-2 infection (COVID-19) may affect patients already symptomatic with cancer. Patient-reported outcomes (PROs) can describe symptom burden during the acute and postacute stages of COVID-19 and support risk stratification for levels of care. At the start of the COVID-19 pandemic, our purpose was to rapidly develop, launch through an electronic patient portal, and provide initial validation for a PRO measure of COVID-19 symptom burden in patients with cancer. METHODS: We conducted a CDC/WHO web-based scan for COVID-19 symptoms and a relevance review of symptoms by an expert panel of clinicians treating cancer patients with COVID-19 to create a provisional MD Anderson Symptom Inventory for COVID-19 (MDASI-COVID). English-speaking adults with cancer who tested positive for COVID-19 participated in the psychometric testing phase. Patients completed longitudinal assessments of the MDASI-COVID and the EuroQOL 5 Dimensions 5 Levels (EQ-5D-5L) utility index and visual analog scale, which were presented through an electronic health record patient portal. To test the validity of the MDASI-COVID to distinguish between known groups of patients, we hypothesized that patients hospitalized, including having a hospitalization extended, for COVID-19 versus those not hospitalized would experience higher symptom burden. Correlation of mean symptom severity and interference scores with relevant EQ-5D-5L scores tested concurrent validity. The reliability of the MDASI-COVID was evaluated by calculating Cronbach alpha coefficients and test-retest reliability was evaluated by calculating Pearson correlation coefficients between the initial assessment and a second assessment no more than 14 days later. RESULTS: The web-based scan found 31 COVID-19-related symptoms; rankings of a 14-clinician expert panel reduced this list to 11 COVID-specific items to be added to the core MDASI. Time from literature scan start in March 2020 to instrument launch in May 2020 was 2 months. Psychometric analysis established the MDASI-COVID's reliability, known-group validity, and concurrent validity. CONCLUSIONS: We were able to rapidly develop and electronically launch a PRO measure of COVID-19 symptom burden in patients with cancer. Additional research is needed to confirm the content domain and predictive validity of the MDASI-COVID and define the symptom burden trajectory of COVID-19.

Recent COVID-19 infection is associated with increased mortality in the ambulatory surgery population.

BACKGROUND: The effect of COVID-19 infection on post-operative mortality and the optimal timing to perform ambulatory surgery from diagnosis date remains unclear in this population. Our study was to determine whether a history of COVID-19 diagnosis leads to a higher risk of all-cause mortality following ambulatory surgery. METHODS: This cohort constitutes retrospective data obtained from the Optum dataset containing 44,976 US adults who were tested for COVID-19 up to 6 months before surgery and underwent ambulatory surgery between March 2020 to March 2021. The primary outcome was the risk of all-cause mortality between the COVID-19 positive and negative patients grouped according to the time interval from COVID-19 testing to ambulatory surgery, called the Testing to Surgery Interval Mortality (TSIM) of up to 6 months. Secondary outcome included determining all-cause mortality (TSIM) in time intervals of 0-15 days, 16-30 days, 31-45 days, and 46-180 days in COVID-19 positive and negative patients. RESULTS: 44,934 patients (4297 COVID-19 positive, 40,637 COVID-19 negative) were included in our analysis. COVID-19 positive patients undergoing ambulatory surgery had higher risk of all-cause mortality compared to COVID-19 negative patients (OR = 2.51, p < 0.001). The increased risk of mortality in COVID-19 positive patients remained high amongst patients who had surgery 0-45 days from date of COVID-19 testing. In addition, COVID-19 positive patients who underwent colonoscopy (OR = 0.21, p = 0.01) and plastic and orthopedic surgery (OR = 0.27, p = 0.01) had lower mortality than those underwent other surgeries. CONCLUSIONS: A COVID-19 positive diagnosis is associated with significantly higher risk of all-cause mortality following ambulatory surgery. This mortality risk is greatest in patients that undergo ambulatory surgery within 45 days of testing positive for COVID-19. Postponing elective ambulatory surgeries in patients that test positive for COVID-19 infection within 45 days of surgery date should be considered, although prospective studies are needed to assess this.

Interpretation of continuously measured vital signs data of COVID-19 patients by nurses and physicians at the general ward: A mixed methods study.

BACKGROUND: Continuous monitoring of vital signs is introduced at general hospital wards to detect patient deterioration. Interpretation and response currently rely on experience and expert opinion. This study aims to determine whether consensus exist among hospital professionals regarding the interpretation of vital signs of COVID-19 patients. In addition, we assessed the ability to recognise respiratory insufficiency and evaluated the interpretation process. METHODS: We performed a mixed methods study including 24 hospital professionals (6 nurses, 6 junior physicians, 6 internal medicine specialists, 6 ICU nurses). Each participant was presented with 20 cases of COVID-19 patients, including 4 or 8 hours of continuously measured vital signs data. Participants estimated the patient's situation ('improving', 'stable', or 'deteriorating') and the possibility of developing respiratory insufficiency. Subsequently, a semi-structured interview was held focussing on the interpretation process. Consensus was assessed using Krippendorff's alpha. For the estimation of respiratory insufficiency, we calculated the mean positive/negative predictive value. Interviews were analysed using inductive thematic analysis. RESULTS: We found no consensus regarding the patient's situation (α 0.41, 95%CI 0.29-0.52). The mean positive predictive value for respiratory insufficiency was high (0.91, 95%CI 0.86-0.97), but the negative predictive value was 0.66 (95%CI 0.44-0.88). In the interviews, two themes regarding the interpretation process emerged. "Interpretation of deviations" included the strategies participants use to determine stability, focused on finding deviations in data. "Inability to see the patient" entailed the need of hospital professionals to perform a patient evaluation when estimating a patient's situation. CONCLUSION: The interpretation of continuously measured vital signs by hospital professionals, and recognition of respiratory insufficiency using these data, is variable, which might be the result of different interpretation strategies, uncertainty regarding deviations, and not being able to see the patient. Protocols and training could help to uniform interpretation, but decision support systems might be necessary to find signs of deterioration that might otherwise go unnoticed.

Clinical and inflammation marker features of cancer patients with COVID-19: data of Istanbul, Turkey multicenter cancer patients (2020-2022).

OBJECTIVE: We aimed to identify a rapid, accurate, and accessible biomarker in the early stages of COVID-19 that can determine the prognosis of the disease in cancer patients. METHODS: A total number of 241 patients with solid cancers who had a COVID-19 diagnosis between March 2020 and February 2022 were included in the study. Factors and ten different markers of inflammation were analyzed by year of diagnosis of COVID-19 and grouped by severity of infection. RESULTS: Hospitalization, referral to the intensive care unit (ICU), mechanical ventilation, and death were more frequent in 2020 than in 2021 and 2022 (mortality rates, respectively, were 18.8%, 3.8%, and 2.5%). Bilateral lung involvement and chronic lung disease were independent risk factors for severe disease in 2020. In 2021-2022, only bilateral lung involvement was found as an independent risk factor for severe disease. The neutrophil-to-lymphocyte platelet ratio (NLPR) with the highest area under the curve (AUC) value in 2020 had a sensitivity of 71.4% and specificity of 73.3% in detecting severe disease (cut-off > 0.0241, Area Under the Curve (AUC) = 0.842, p <.001). In 2021-2022, the sensitivity of the C-reactive protein-to-lymphocyte ratio (CRP/L) with the highest AUC value was 70.0%, and the specificity was 73.3% (cut-off > 36.7, AUC = 0.829, p = .001). CONCLUSIONS: This is the first study to investigate the distribution and characteristics of cancer patients, with a focus on the years of their COVID-19 diagnosis. Based on the data from our study, bilateral lung involvement is an independent factor for severe disease, and the CRP/L inflammation index appears to be the most reliable prognostic marker.

Machine learning and artificial intelligence for the diagnosis of infectious diseases in immunocompromised patients.

PURPOSE OF REVIEW: Immunocompromised patients are at high risk for infection. During the coronavirus disease (COVID-19) pandemic, immunocompromised patients exhibited increased odds of intensive care unit admission and death. Early pathogen identification is essential to mitigating infection related risk in immunocompromised patients. Artificial intelligence (AI) and machine learning (ML) have tremendous appeal to address unmet diagnostic needs. These AI/ML tools often rely on the wealth of data found in healthcare to enhance our ability to identify clinically significant patterns of disease. To this end, our review provides an overview of the current AI/ML landscape as it applies to infectious disease testing with emphasis on immunocompromised patients. RECENT FINDINGS: Examples include AI/ML for predicting sepsis in high risk burn patients. Likewise, ML is utilized to analyze complex host-response proteomic data to predict respiratory infections including COVID-19. These same approaches have also been applied for pathogen identification of bacteria, viruses, and hard to detect fungal microbes. Future uses of AI/ML may include integration of predictive analytics in point-of-care (POC) testing and data fusion applications. SUMMARY: Immunocompromised patients are at high risk for infections. AI/ML is transforming infectious disease testing and has great potential to address challenges encountered in the immune compromised population.

Impact of COVID-19-related health care disruptions on pathologic cancer staging during the first pandemic year: a retrospective cohort study from March 2018 to March 2021.

BACKGROUND: The COVID-19 pandemic has created major disruptions in cancer care, with reductions in diagnostic tests and treatments. We evaluated the impact of these health care-related changes on cancer staging by comparing cancers staged before and during the pandemic. METHODS: We performed a retrospective cohort study at London Health Sciences Centre and St. Joseph's Health Care London, London, Ontario, Canada. We evaluated all pathologically staged breast, colorectal, prostate, endometrial and lung cancers (the 5 most common cancers by site, excluding nonmelanoma skin cancer) over a 3-year period (Mar. 15, 2018-Mar. 14, 2021). The pre-COVID-19 group included procedures performed between Mar. 15, 2018, and Mar. 14, 2020, and the COVID-19 group included procedures performed between Mar. 15, 2020, and Mar. 14, 2021. The primary outcome was cancer stage group, based on the pathologic tumour, lymph node, metastasis system. We performed univariate analyses to compare demographic characteristics, pathologic features and cancer stage between the 2 groups. We performed multivariable ordinal regression analyses using the proportional odds model to evaluate the association between stage and timing of staging (before v. during the pandemic). RESULTS: There were 4055 cases across the 5 cancer sites. The average number of breast cancer staging procedures per 30 days increased during the pandemic compared to the yearly average in the pre-COVID-19 period (41.3 v. 39.6), whereas decreases were observed for endometrial cancer (15.9 v. 16.4), colorectal cancer (21.8 v. 24.3), prostate cancer (13.6 v. 18.5) and lung cancer (11.5 v. 15.9). For all cancer sites, there were no statistically significant differences in demographic characteristics, pathologic features or cancer stage between the 2 groups (p > 0.05). In multivariable regression analysis, for all cancer sites, cases staged during the pandemic were not associated with higher stage (breast: odds ratio [OR] 1.071, 95% confidence interval [CI] 0.826-1.388; colorectal: OR 1.201, 95% CI 0.869-1.661; endometrium: OR 0.792, 95% CI 0.495-1.252; prostate: OR 1.171, 95% CI 0.765-1.794; and lung: OR 0.826, 95% CI 0.535-1.262). INTERPRETATION: Cancer cases staged during the first year of the COVID-19 pandemic were not associated with higher stage; this likely reflects the prioritization of cancer procedures during times of reduced capacity. The impact of the pandemic period on staging procedures varied between cancer sites, which may reflect differences in clinical presentation, detection and treatment.

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.

Thematic analysis of national online narratives on regular asymptomatic testing for Covid-19 in schools in England.

OBJECTIVE: To understand the public perceptions of the schools Covid-19 testing programme in England. DESIGN: Qualitative social media analysis. SETTING: Online users of parenting forums (Mumsnet and Netmums), Facebook newspaper pages and Daily Mail online readers, who responded to posts or articles about the schools testing programme in England, between 1 and 31 March, 2021. RESULTS: Overall, seven main themes were identified, these were divided into barriers and facilitators to engaging in testing for Covid-19. Barriers were: uncertainty around testing in the absence of symptoms; concerns about testing; implications about testing positive; mistrust in the Government. Facilitators were: desire to protect others; desire to return to normality; and hearing others' positive experiences. CONCLUSIONS: Our analysis highlighted that alongside well-established barriers to engaging in asymptomatic testing, parents were having to negotiate additional complex decisions around balancing their child's anxiety over testing alongside acknowledgement of the implications of regular testing, such as return to normality and protecting others. Parents and children would benefit from additional practical and social support to facilitate engagement with the schools testing programme.

SARS-Cov-2 Spike-S1 Antigen Test Strip with High Sensitivity Endowed by High-Affinity Antibodies and Brightly Fluorescent QDs/Silica Nanospheres.

The extensive research into developing novel strategies for detecting respiratory syndrome coronavirus 2 (SARS-CoV-2) antigens in clinical specimens, especially the sensitive point-of-care testing method, is still urgently needed to reach rapid screening of viral infections. Herein, a new lateral flow immunoassay (LFIA) platform was reported for the detection of SARS-CoV-2 spike-S1 protein antigens, in which four sensitive and specific SARS-CoV-2 mouse monoclonal antibodies (MmAbs) were tailored by using quantum dot (QD)-loaded dendritic mesoporous silica nanoparticles modified further for achieving the -COOH group surface coating (named Q/S-COOH nanospheres). Importantly, compact QD adsorption was achieved in mesoporous channels of silica nanoparticles on account of highly accessible central-radial pores and electrostatic interactions, leading to significant signal amplification. As such, a limit of detection for SARS-CoV-2 spike-S1 testing was found to be 0.03 ng/mL, which is lower compared with those of AuNPs-LFIA (traditional colloidal gold nanoparticles, Au NPs) and enzyme-linked immunosorbent assay methods. These results show that optimizing the affinity of antibody and the intensity of fluorescent nanospheres simultaneously is of great significance to improve the sensitivity of LFIA.