Artificial Neural Network Prediction of COVID-19 Daily Infection Count.

This study addresses COVID-19 testing as a nonlinear sampling problem, aiming to uncover the dependence of the true infection count in the population on COVID-19 testing metrics such as testing volume and positivity rates. Employing an artificial neural network, we explore the relationship among daily confirmed case counts, testing data, population statistics, and the actual daily case count. The trained artificial neural network undergoes testing in in-sample, out-of-sample, and several hypothetical scenarios. A substantial focus of this paper lies in the estimation of the daily true case count, which serves as the output set of our training process. To achieve this, we implement a regularized backcasting technique that utilize death counts and the infection fatality ratio (IFR), as the death statistics and serological surveys (providing the IFR) as more reliable COVID-19 data sources. Addressing the impact of factors such as age distribution, vaccination, and emerging variants on the IFR time series is a pivotal aspect of our analysis. We expect our study to enhance our understanding of the genuine implications of the COVID-19 pandemic, subsequently benefiting mitigation strategies.

Can the ADO Index Be Used as a Predictor of Mortality from COVID-19 in Patients with COPD?

Background: Several studies have shown that the risk of mortality due to COVID-19 is high in patients with COPD. However, evidence on factors predicting mortality is limited. Research Question: Are there any useful markers to predict mortality in COVID-19 patients with COPD?. Study Design and Methods: A total of 689 patients were included in this study from the COPET study, a national multicenter observational study investigating COPD phenotypes consisting of patients who were followed up with a spirometry-confirmed COPD diagnosis. Patients were also retrospectively examined in terms of COVID-19 and their outcomes. Results: Among the study patients, 105 were diagnosed with PCR-positive COVID-19, and 19 of them died. Body mass index (p= 0.01) and ADO (age, dyspnoea, airflow obstruction) index (p= 0.01) were higher, whereas predicted FEV1 (p< 0.001) and eosinophil count (p= 0.003) were lower in patients who died of COVID-19. Each 0.755 unit increase in the ADO index increased the risk of death by 2.12 times, and each 0.007 unit increase in the eosinophil count decreased the risk of death by 1.007 times. The optimum cut-off ADO score of 3.5 was diagnostic with 94% sensitivity and 40% specificity in predicting mortality. Interpretation: Our study suggested that the ADO index recorded in the stable period in patients with COPD makes a modest contribution to the prediction of mortality due to COVID-19. Further studies are needed to validate the use of the ADO index in estimating mortality in both COVID-19 and other viral respiratory infections in patients with COPD.

Prevalence and duration of SARS-CoV-2 fecal shedding in breastfeeding dyads following maternal COVID-19 diagnosis.

Background: There is a paucity of data on the presence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in feces of lactating women with coronavirus disease 2019 (COVID-19) and their breastfed infants as well as associations between fecal shedding and symptomatology. Objective: We examined whether and to what extent SARS-CoV-2 is detectable in the feces of lactating women and their breastfed infants following maternal COVID-19 diagnosis. Methods: This was a longitudinal study carried out from April 2020 to December 2021 involving 57 breastfeeding maternal-infant dyads: 33 dyads were enrolled within 7 d of maternal COVID-19 diagnosis, and 24 healthy dyads served as controls. Maternal/infant fecal samples were collected by participants, and surveys were administered via telephone over an 8-wk period. Feces were analyzed for SARS-CoV-2 RNA. Results: Signs/symptoms related to ears, eyes, nose, and throat (EENT); general fatigue/malaise; and cardiopulmonary signs/symptoms were commonly reported among mothers with COVID-19. In infants of mothers with COVID-19, EENT, immunologic, and cardiopulmonary signs/symptoms were most common, but prevalence did not differ from that of infants of control mothers. SARS-CoV-2 RNA was detected in feces of 7 (25%) women with COVID-19 and 10 (30%) of their infants. Duration of fecal shedding ranged from 1-4 wk for both mothers and infants. SARS-CoV-2 RNA was sparsely detected in feces of healthy dyads, with only one mother's and two infants' fecal samples testing positive. There was no relationship between frequencies of maternal and infant SARS-CoV-2 fecal shedding (P=0.36), although presence of maternal or infant fever was related to increased likelihood (7-9 times greater, P≤0.04) of fecal shedding in infants of mothers with COVID-19.

Plasmonic Annular Nanotrenches with 1 nm Nanogaps for Detection of SARS-CoV-2 Using SERS-Based Immunoassay.

This study represents the synthesis of a novel class of nanoparticles denoted as annular Au nanotrenches (AANTs). AANTs are engineered to possess embedded, narrow circular nanogaps with dimensions of approximately 1 nm, facilitating near-field focusing for detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) via a surface-enhanced Raman scattering (SERS)-based immunoassay. Notably, AANTs exhibited an exceedingly low limit of detection (LOD) of 1 fg/mL for SARS-CoV-2 spike glycoproteins, surpassing the commercially available enzyme-linked immunosorbent assay (ELISA) by 6 orders of magnitude (1 ng/mL from ELISA). To assess the real-world applicability, a study was conducted on 50 clinical samples using an SERS-based immunoassay with AANTs. The results revealed a sensitivity of 96% and a selectivity of 100%, demonstrating the significantly enhanced sensing capabilities of the proposed approach in comparison to ELISA and commercial lateral flow assay kits.

Accuracy of automated computer-aided risk scoring systems to estimate the risk of COVID-19: a retrospective cohort study.

BACKGROUND: In the UK National Health Service (NHS), the patient's vital signs are monitored and summarised into a National Early Warning Score (NEWS) score. A set of computer-aided risk scoring systems (CARSS) was developed and validated for predicting in-hospital mortality and sepsis in unplanned admission to hospital using NEWS and routine blood tests results. We sought to assess the accuracy of these models to predict the risk of COVID-19 in unplanned admissions during the first phase of the pandemic. METHODS: Adult ( > = 18 years) non-elective admissions discharged (alive/deceased) between 11-March-2020 to 13-June-2020 from two acute hospitals with an index NEWS electronically recorded within ± 24 h of admission. We identified COVID-19 admission based on ICD-10 code 'U071' which was determined by COVID-19 swab test results (hospital or community). We assessed the performance of CARSS (CARS_N, CARS_NB, CARM_N, CARM_NB) for predicting the risk of COVID-19 in terms of discrimination (c-statistic) and calibration (graphically). RESULTS: The risk of in-hospital mortality following emergency medical admission was 8.4% (500/6444) and 9.6% (620/6444) had a diagnosis of COVID-19. For predicting COVID-19 admissions, the CARS_N model had the highest discrimination 0.73 (0.71 to 0.75) and calibration slope 0.81 (0.72 to 0.89) compared to other CARSS models: CARM_N (discrimination:0.68 (0.66 to 0.70) and calibration slope 0.47 (0.41 to 0.54)), CARM_NB (discrimination:0.68 (0.65 to 0.70) and calibration slope 0.37 (0.31 to 0.43)), and CARS_NB (discrimination:0.68 (0.66 to 0.70) and calibration slope 0.56 (0.47 to 0.64)). CONCLUSIONS: The CARS_N model is reasonably accurate for predicting the risk of COVID-19. It may be clinically useful as an early warning system at the time of admission especially to triage large numbers of unplanned admissions because it requires no additional data collection and is readily automated.

Prognostic value of the neutrophil-to-lymphocyte ratio and C-reactive-protein-to-prealbumin ratio in hospitalized older patients with coronavirus disease 2019.

The neutrophil-to-lymphocyte ratio (NLR) and C-reactive protein-to-prealbumin ratio (CPAR) are novel markers of inflammation. The CPAR is an indicator of inflammation and malnutrition. We evaluated NLR and CPAR in combination as indicators of disease severity and prognosis in hospitalized older patients with coronavirus disease 2019 (COVID-19). A total of 222 hospitalized patients with COVID-19 (aged > 60 years) were divided into non-severe and severe groups. The severe group was subdivided into the surviving and deceased subgroups. We retrospectively assessed the predictive power of NLR and CPAR in combination (NLR + CPAR) to determine the prognosis of hospitalized older patients with COVID-19. The NLR and CPAR were significantly higher in the severe group than in the non-severe group (P < .001). Furthermore, the NLR and CPAR were higher in the deceased subgroup than in the surviving subgroup (P < .001). Pearson correlation analysis showed a highly significant positive correlation between NLR and CPAR (P < .001, r = 0.530). NLR + CPAR showed an area under the curve of 0.827 and sensitivity of 83.9% in the severe group; the area under the curve was larger (0.925) and sensitivity was higher (87.1%) in the deceased subgroup. The receiver operating characteristic curve of NLR + CPAR was significantly different from the receiver operating characteristic curves of either biomarker alone (P < .001). Kaplan-Meier analysis showed that patients in the severe group with elevated NLR + CPAR had a significantly lower 90-day survival rate than patients who lacked this finding (odds ratio 7.87, P < .001). NLR + CPAR may enable early diagnosis and assessment of disease severity in hospitalized older patients with COVID-19. This may also enable the identification of high-risk older patients with COVID-19 at the time of admission.

Economic Evaluation of COVID-19 Screening Tests and Surveillance Strategies in Low-Income, Middle-Income, and High-Income Countries: A Systematic Review.

BACKGROUND Economic evaluation of the testing strategies to control transmission and monitor the severity of COVID-19 after the pandemic is essential. This study aimed to review the economic evaluation of COVID-19 tests and to construct a model with outcomes in terms of cost and test acceptability for surveillance in the post-pandemic period in low-income, middle-income, and high-income countries. MATERIAL AND METHODS We performed the systematic review following PRISMA guidelines through MEDLINE and EMBASE databases. We included the relevant studies that reported the economic evaluation of COVID-19 tests for surveillance. Also, we input current probability, sensitivity, and specificity for COVID-19 surveillance in the post-pandemic period. RESULTS A total of 104 articles met the eligibility criteria, and 8 articles were reviewed and assessed for quality. The specificity and sensitivity of COVID-19 screening tests were reported as 80% to 90% and 40% to 90%, respectively. The target population presented a mortality rate between 0.2% and 19.2% in the post-pandemic period. The implementation model of COVID-19 screening tests for surveillance with a cost mean for molecular and antigen tests was US$ 46.64 (min-max US $0.25-$105.39) and US $6.15 (min-max US $2-$10), respectively. CONCLUSIONS For the allocation budget for the COVID-19 surveillance test, it is essential to consider the incidence and mortality of the post-pandemic period in low-income, middle-income, and high-income countries. A robust method to evaluate outcomes is needed to prevent increasing COVID-19 incidents earlier.

Green Synthesis of Ceria Nanoparticles from Cassava Tubers for Electrochemical Aptasensor Detection of SARS-CoV-2 on a Screen-Printed Carbon Electrode.

Green synthesis approaches for making nanosized ceria using starch from cassava as template molecules to control the particle size are reported. The results of the green synthesis of ceria with an optimum calcination temperature of 800 °C shows a size distribution of each particle of less than 30 nm with an average size of 9.68 nm, while the ratio of Ce3+ to Ce4+ was 25.6%. The green-synthesized nanoceria are applied to increase the sensitivity and attach biomolecules to the electrode surface of the electrochemical aptasensor system for coronavirus disease (COVID-19). The response of the aptasensor to the receptor binding domain of the virus was determined with the potassium ferricyanide redox system. The screen-printed carbon electrode that has been modified with green-synthesized nanoceria shows 1.43 times higher conductivity than the bare electrode, while those modified with commercial ceria increase only 1.18 times. Using an optimized parameter for preparing the aptasensors, the detection and quantification limits were 1.94 and 5.87 ng·mL-1, and the accuracy and precision values were 98.5 and 89.1%. These results show that green-synthesized ceria could be a promising approach for fabricating an electrochemical aptasensor.

Detection of SARS-CoV-2 using machine learning-enabled paper-assisted ratiometric fluorescent sensors based on target-induced magnetic DNAzyme.

The development of an advanced analytical platform with regard to SARS-CoV-2 is crucial for public health. Herein, we present a machine learning platform based on paper-assisted ratiometric fluorescent sensors for highly sensitive detection of the SARS-CoV-2 RdRp gene. The assay involves target-induced rolling circle amplification to generate magnetic DNAzyme, which is then detectable using the paper-assisted ratiometric fluorescent sensor. This sensor detects the SARS-CoV-2 RdRp gene with a visible-fluorescence color response. Moreover, leveraging different fluorescence responses, the ResNet algorithm of machine learning assists in accurately identifying fluorescence images and differentiating the concentration of the SARS-CoV-2 RdRp gene with over 99% recognition accuracy. The machine learning platform exhibits exceptional sensitivity and color responsiveness, achieving a limit of detection of 30 fM for the SARS-CoV-2 RdRp gene. The integration of intelligent artificial vision with the paper-assisted ratiometric fluorescent sensor presents a novel approach for the on-site detection of COVID-19 and holds potential for broader use in disease diagnostics in the future.

Correlation of SARS-CoV-2 in Wastewater and Individual Testing Results in a Jail, Atlanta, Georgia, USA.

Institution-level wastewater-based surveillance was implemented during the COVID-19 pandemic, including in carceral facilities. We examined the relationship between COVID-19 diagnostic test results of residents in a jail in Atlanta, Georgia, USA (average population ≈2,700), and quantitative reverse transcription PCR signal for SARS-CoV-2 in weekly wastewater samples collected during October 2021‒May 2022. The jail offered residents rapid antigen testing at entry and periodic mass screenings by reverse transcription PCR of self-collected nasal swab specimens. We aggregated individual test data, calculated the Spearman correlation coefficient, and performed logistic regression to examine the relationship between strength of SARS-CoV-2 PCR signal (cycle threshold value) in wastewater and percentage of jail population that tested positive for COVID-19. Of 13,745 nasal specimens collected, 3.9% were COVID-positive (range 0%-29.5% per week). We observed a strong inverse correlation between diagnostic test positivity and cycle threshold value (r = -0.67; p<0.01). Wastewater-based surveillance represents an effective strategy for jailwide surveillance of COVID-19.

Rapid identification of SARS-CoV-2 strains via isothermal enzymatic recombinase amplification and nanopore sequencing.

Surveillance of the SARS-CoV-2 genome has become a crucial technique in the management of COVID-19, aiding the pandemic response and supporting effective public health interventions. Typically, whole-genomic sequencing is used along with PCR-based target enrichment techniques to identify SARS-CoV-2 variants, which is a complicated and time-consuming process that requires central laboratory facilities. Thus, there is an urgent need to develop rapid and cost-effective tools for precise on-site detection and identification of SARS-CoV-2 strains. In this study, we demonstrate the rapid diagnosis of COVID-19 and identification of SARS-CoV-2 variants by amplification and sequencing of the entire SARS-CoV-2 S gene using isothermal enzymatic recombinase amplification combined with the advanced Oxford nanopore sequencing technique. The entire procedure, from sampling to sequencing, takes less than 8 hours and can be performed with limited resources. The newly developed method has noteworthy implications for examining the transmission dynamics of the virus, detecting novel genetic variants, and assessing the effect of mutations on diagnostic approaches, antiviral treatments, and vaccines.

Comparison of geographical and individual deprivation index to assess the risk of Sars-CoV-2 infection and disease severity: a retrospective cohort study.

BACKGROUND: It has been shown that COVID-19 affects people at socioeconomic disadvantage more strongly. Previous studies investigating the association between geographical deprivation and COVID-19 outcomes in Italy reported no differences in case-hospitalisation and case-fatality. The objective of this research was to compare the usefulness of the geographic and individual deprivation index (DI) in assessing the associations between individuals' deprivation and risk of Sars-CoV-2 infection and disease severity in the Apulia region from February to December 2020. METHODS: This was a retrospective cohort study. Participants included individuals tested for SARS-CoV-2 infection during the study period. The individual DI was calculated employing polychoric principal component analysis on four census variables. Multilevel logistic models were used to test associations between COVID-19 outcomes and individual DI, geographical DI, and their interaction. RESULTS: In the study period, 139,807 individuals were tested for COVID-19 and 56,475 (43.5%) tested positive. Among those positive, 7902 (14.0%) have been hospitalised and 2215 (4.2%) died. During the first epidemic wave, according the analysis done with the individual DI, there was a significant inversely proportional trend between the DI and the risk of testing positive. No associations were found between COVID-19 outcomes and geographic DI. During the second wave, associations were found between COVID-19 outcomes and individual DI. No associations were found between the geographic DI and the risk of hospitalisation and death. During both waves, there were no association between COVID-19 outcomes and the interaction between individual and geographical DI. CONCLUSIONS: Evidence from this study shows that COVID-19 pandemic has been experienced unequally with a greater burden among the most disadvantaged communities. The results of this study remind us to be cautious about using geographical DI as a proxy of individual social disadvantage because may lead to inaccurate assessments. The geographical DI is often used due to a lack of individual data. However, on the determinants of health and health inequalities, monitoring has to have a central focus. Health inequalities monitoring provides evidence on who is being left behind and informs equity-oriented policies, programmes and practices. Future research and data collection should focus on improving surveillance systems by integrating individual measures of inequalities into national health information systems.

Performance Evaluation of SARS-CoV-2 Rapid Antigen Test Among Symptomatic and Asymptomatic Patients in the Emergency Department of a Tertiary Care Hospital.

The aim of this study was to evaluate the diagnostic performance of the SARS-CoV-2 rapid antigen test (RAT) for the diagnosis of COVID-19 among symptomatic and asymptomatic patients. This retrospective study was done from 15th November 2021 to 15th December 2021, at National Medical Centre, Karachi, Pakistan. Two parallel nasopharyngeal swabs were collected from each patient, and SARS-CoV-2 RAT and SARS-CoV-2 real-time polymerase chain reaction (RT-PCR) were done. A total of 719 patients were included, mean age was 46.03+17.74 years with 378 (52.6%) males. The sensitivity was higher in symptomatic patients i.e. 95.18%, while RAT was found to be more specific in asymptomatic patients with a specificity of 99.83%. High diagnostic accuracy of 91.81% and 96.29% were noted in symptomatic and asymptomatic patients, respectively. SARS-CoV-2 RAT (Roche) can be used for early diagnosis of SARS-CoV-2 patients in busy emergency departments. Key Words: COVID-19, Diagnosis, Rapid antigen test, RT-PCR, Emergency service.

Rapid Nucleic Acid Diagnostic Technology for Pandemic Diseases.

The recent global pandemic of coronavirus disease 2019 (COVID-19) has enormously promoted the development of diagnostic technology. To control the spread of pandemic diseases and achieve rapid screening of the population, ensuring that patients receive timely treatment, rapid diagnosis has become the top priority in the development of clinical technology. This review article aims to summarize the current rapid nucleic acid diagnostic technologies applied to pandemic disease diagnosis, from rapid extraction and rapid amplification to rapid detection. We also discuss future prospects in the development of rapid nucleic acid diagnostic technologies.

Trajectory of cardiac troponin T following moderate-to-severe COVID-19 and the association with cardiac abnormalities.

BACKGROUND: COVID-19 has been associated with cardiac troponin T (cTnT) elevations and changes in cardiac structure and function, but the link between cardiac dysfunction and high-sensitive cardiac troponin T (hs-cTnT) in the acute and convalescent phase is unclear. OBJECTIVE: To assess whether hs-cTnT concentrations are associated with cardiac dysfunction and structural abnormalities after hospitalization for COVID-19, and to evaluate the performance of hs-cTnT to rule out cardiac pathology. METHODS: Patients hospitalized with COVID-19 had hs-cTnT measured during the index hospitalization and after 3-and 12 months, when they also underwent an echocardiographic study. A subset also underwent cardiovascular magnetic resonance imaging (CMR) after 6 months. Cardiac abnormalities were defined as left ventricular hypertrophy or dysfunction, right ventricular dysfunction, or CMR late gadolinium. RESULTS: We included 189 patients with hs-cTnT concentrations measured during hospitalization for COVID-19, and after 3-and 12 months: Geometric mean (95%CI) 13 (11-15) ng/L, 7 (6-8) ng/L and 7 (6-8) ng/L, respectively. Cardiac abnormalities after 3 months were present in 45 (30%) and 3 (8%) of patients with hs-cTnT ≥ and < 5 ng/L at 3 months, respectively (negative predictive value 92.3% [95%CI 88.5-96.1%]). The performance was similar in patients with and without dyspnea. Hs-cTnT decreased from hospitalization to 3 months (more pronounced in intensive care unit-treated patients) and remained unchanged from 3 to 12 months, regardless of the presence of cardiac abnormalities. CONCLUSION: Higher hs-cTnT concentrations in the convalescent phase of COVID-19 are associated with the presence of cardiac pathology and low concentrations (< 5 ng/L) may support in ruling out cardiac pathology following the infection.

Predicting COVID-19 outcomes with the Edmonton Obesity Staging System.

BACKGROUND: Multiple studies have demonstrated a correlation between a high body mass index and discriminatory COVID-19 outcomes. Studies appear to indicate that there is a correlation between obesity-related comorbidities and less favorable outcomes. OBJECTIVES: The primary aim of the current investigation is to conduct a thorough assessment of the correlation between BMI and comorbidities associated with obesity, and their potential impact on the severity and consequences of COVID-19 infection among patients receiving care in a tertiary healthcare setting. DESIGN: Retrospective cohort. SETTINGS: Tertiary rehabilitation center, Riyadh, Saudi Arabia. PATIENTS AND METHODS: The study included all individuals who received medical treatment and tested positive for COVID-19 by means of RT-PCR during the period from March to September 2020. COVID-19 patients were classified using Edmonton Obesity Staging System (EOSS). MAIN OUTCOME MEASURES: COVID-19-related complications, including pneumonia and cytokine release syndrome, as well as the time length to COVID-19 negativization. SAMPLE SIZE: 315 patients. RESULTS: The median (25th-75th percentiles) age of the patients was 38 (31.5-49) years old. Males outnumbered females, and 66% of patients were non-Saudis. Forty-eight patients (15.2%) had obesity class I, whereas 13 patients (4.1%) had class II. Thirty-two patients (10.2%) were classified as EOSS stage 1, 105 patients (33.3%) were classified as EOSS stage 2, and 25 patients (7.9%) were assigned to EOSS stage 3. Males predominated in EOSS stages 1 and 2, whereas females predominated in stage 3. In EOSS stage 3, 52% of cases had moderate severity and 48% had severe illness. CONCLUSIONS: EOSS distinguishes the COVID-19 risks of poor outcomes beyond BMI. Patients who were overweight or obese but remained in the stage 1 of the EOSS had a lower risk of a poor COVID-19 outome than normal-weight patients. The health status of obese patients is a more precise indicator of the progression of COVID-19 during hospitalization than BMI alone. LIMITATIONS: Given the limited capacity of urgent care facilities to conduct a comprehensive evaluation of comorbidities and other relevant outcomes in all patients, it is plausible that certain patients may have been erroneously classified with an EOSS stage 2 diagnosis, when in fact they ought to have been assigned a stage 3 diagnosis.

Effectiveness of tailored talks between a cancer screening specialist and general practitioners to improve the uptake of colorectal cancer screening in Ancona (Italy) during the pandemic period.

BACKGROUND: Colorectal cancer (CRC) screening uptake in many countries has been low and further impacted by the COVID-19 pandemic. General Practitioners (GPs) are key facilitators, however research on their impact on organised CRC screening is still limited. OBJECTIVES: To evaluate the effectiveness of tailored talks with GPs to increase population uptake of the long-established CRC screening programme in Ancona province, Italy. METHODS: In this prospective cohort study, one-to-one tailored talks were organised in January 2020 between the GPs of one county of the province (with GPs from other counties as controls) and the screening programme physician-in-chief to discuss the deployment and effectiveness of organised screening. Data was extracted from the National Healthcare System datasets and linear regression was used to assess the potential predictors of CRC screening uptake. RESULTS: The mean CRC screening uptake remained stable from 39.9% in 2018-19 to 40.8% in 2020-21 in the 22 GPs of the intervention county, whereas it statistically significantly decreased from 38.7% to 34.7% in the 232 control GPs. In multivariate analyses, belonging to the intervention county was associated with an improved uptake compared to the control counties (+5.1%; 95% Confidence Intervals - CI: 2.0%; 8.1%). CONCLUSION: Persons cared for by GPs who received a tailored talk with a cancer screening specialist avoided a drop in CRC screening adherence, which characterised all other Italian screening programmes during the COVID-19 emergency. If future randomised trials confirm the impact of tailored talks, they may be incorporated into existing strategies to improve population CRC screening uptake.

Tailored talks on CRC screening were conducted between one cancer screening specialist and GPs.Even during the pandemic, CRC screening uptake was stable among persons cared for by GPs targeted by tailored talks.If confirmed by randomised trials, tailored talks may be employed to improve CRC screening uptake.

Automatic ultrasensitive lateral flow immunoassay based on a color-enhanced signal amplification strategy.

Lateral flow immunoassays (LFIAs) are an essential and widely used point-of-care test for medical diagnoses. However, commercial LFIAs still have low sensitivity and specificity. Therefore, we developed an automatic ultrasensitive dual-color enhanced LFIA (DCE-LFIA) by applying an enzyme-induced tyramide signal amplification method to a double-antibody sandwich LFIA for antigen detection. The DCE-LFIA first specifically captured horseradish peroxidase (HRP)-labeled colored microspheres at the Test line, and then deposited a large amount of tyramide-modified signals under the catalytic action of HRP to achieve the color superposition. A limit of detection (LOD) of 3.9 pg/mL and a naked-eye cut-off limit of 7.8 pg/mL were achieved for detecting severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) nucleoprotein. Additionally, in the inactivated virus detections, LOD equivalent to chemiluminescence (0.018 TCID50/mL) was obtained, and it had excellent specificity under the interference of other respiratory viruses. High sensitivity has also been achieved for detection of influenza A, influenza B, cardiac troponin I, and human chorionic gonadotrophin using this DCE-LFIA, suggesting the assay is universally applicable. To ensure the convenience and stability in practical applications, we created an automatic device. It provides a new practical option for point-of-care test immunoassays, especially ultra trace detection and at-home testing.

Impact of the COVID-19 pandemic on the real-world diagnostic infrastructure for tuberculosis-An ESGMYC collaborative study.

We determined the impact of the COVID-19 pandemic on mycobacterial diagnostic services. 40 laboratories from 22 countries completed an online questionnaire covering the redeployment of the laboratory infrastructure and/or staff for SARS-CoV-2 testing, staff shortages and supply chain disruptions. 28 laboratories reported monthly numbers of samples processed for mycobacterial investigations and monthly numbers of M. tuberculosis complex (MTBC) PCRs performed between October 1st 2018 and October 31st 2020. More than half (23/40) of the participating TB laboratories reported having performed COVID-19 diagnostics in the early phase of the pandemic, in part with negative impact on the mycobacterial service activities. All participating laboratories reported shortages of consumables and laboratory equipment due to supply chain issues. Average monthly sample numbers decreased by 24% between January 2020 and October 2020 compared to pre-pandemic averages. At the end of the study period, most participating laboratories had not returned to pre-pandemic average MTBC PCR throughput.

Towards classification and comprehensive analysis of AI-based COVID-19 diagnostic techniques: A survey.

The unpredictable pandemic came to light at the end of December 2019, known as the novel coronavirus, also termed COVID-19, identified by the World Health Organization (WHO). The virus first originated in Wuhan (China) and rapidly affected most of the world's population. This outbreak's impact is experienced worldwide because it causes high mortality risk, many cases, and economic falls. Around the globe, the total number of cases and deaths reported till November 12, 2022, were >600 million and 6.6 million, respectively. During the period of COVID-19, several diverse diagnostic techniques have been proposed. This work presents a systematic review of COVID-19 diagnostic techniques in response to such acts. Initially, these techniques are classified into different categories based on their working principle and detection modalities, i.e. chest X-ray imaging, cough sound or respiratory patterns, RT-PCR, antigen testing, and antibody testing. After that, a comparative analysis is performed to evaluate these techniques' efficacy which may help to determine an optimum solution for a particular scenario. The findings of the proposed work show that Artificial Intelligence plays a vital role in developing COVID-19 diagnostic techniques which support the healthcare system. The related work can be a footprint for all the researchers, available under a single umbrella. Additionally, all the techniques are long-lasting and can be used for future pandemics.