A rapid and affordable point of care test for antibodies against SARS-CoV-2 based on hemagglutination and artificial intelligence interpretation.
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ABSTRACT:
SUBMITTER: Redecke V
PROVIDER: S-EPMC8718524 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Diagnostic tests that detect antibodies (AB) against SARS-CoV-2 for evaluation of seroprevalence and guidance of health care measures are important tools for managing the COVID-19 pandemic. Current tests have certain limitations with regard to turnaround time, costs and availability, particularly in point-of-care (POC) settings. We established a hemagglutination-based AB test (HAT) that is based on bi-specific proteins which contain a dromedary-derived antibody (nanobody) binding red blood cells (RBD) and a SARS-CoV-2-derived antigen, such as the receptor-binding domain of the Spike protein (Spike-RBD). While the nanobody mediates swift binding to RBC, the antigen moiety directs instantaneous, visually apparent hemagglutination in the presence of SARS-CoV-2-specific AB generated in COVID-19 patients or vaccinated individuals. Method comparison studies with assays cleared by emergency use authorization (EUA) demonstrate high specificity and sensitivity. To further increase objectivity of test interpretation, we developed an image analysis tool based on digital image acquisition (via a cell phone) and a machine learning algorithm based on defined sample-training and -validation datasets. Preliminary data, including a small clinical study, provides proof of principle for test performance in a POC setting. Together, the data support the interpretation that this AB test format, which we refer to as 'NanoSpot.ai', is suitable for POC testing, can be manufactured at very low costs and, based on its generic mode of action, can likely be adapted to a variety of other pathogens.
| S-EPMC8312898 | biostudies-literature
Project description:Background: Several serological tests for SARS-CoV-2 have been developed or use, but most have only been validated on few samples, and none provide medical practitioners with an easy-to-use, self-contained, bedside test with high accuracy. Material and methods: Two-hundred fifty-six sera from 101 patients hospitalized with SARS-CoV-2 infection (positive RT-PCR) and 50 control sera were tested for IgM/IgG using the NG-Test IgM-IgG COVID all-in-one assay. The seroconversion dynamic was assessed by symptom onset and day of RT-PCR diagnosis. Results: Among the SARS-CoV-2 infected patients, positive IgG and/or IgM result was observed for 67.3% of patients (68/101), including 17 (16.8%) already positive at the day of RT-PCR, and 51 (50.5%) with observable seroconversion, and 32.7% (33/101) remained negative as subsequent sampling was not possible (patient discharge or death). The sensitivity increased with the delay between onset of symptoms and sampling, going from 29.1%, 78.2% and 86.5% for the time periods of 0-9-, 10-14- and >14-days after the onset of symptoms, respectively. Cumulative sensitivity, specificity, Positive Predictive Value and Negative Predictive Value were 97.0%, 100%, 100% and 96.2%, respectively 15-days after the onset of symptoms. No difference in seroconversion delay was observed regardless of whether patients received ventilation. Conclusions: The NG-test is a bedside serological assay that could serve as a complementary source of diagnostic information to RT-PCR and chest imaging. It may also be useful to monitor immunological status of medical and non-medical workers during the ongoing pandemic, and the general population after social distancing measures have eased.
| S-EPMC7580567 | biostudies-literature
Project description:BackgroundAfter receiving a COVID-19 vaccine, most recipients want to know if they are protected from infection and for how long. Since neutralizing antibodies are a correlate of protection, we developed a lateral flow assay (LFA) that measures levels of neutralizing antibodies from a drop of blood. The LFA is based on the principle that neutralizing antibodies block binding of the receptor-binding domain (RBD) to angiotensin-converting enzyme 2 (ACE2).MethodsThe ability of the LFA was assessed to correctly measure neutralization of sera, plasma or whole blood from patients with COVID-19 using SARS-CoV-2 microneutralization assays. We also determined if the LFA distinguished patients with seasonal respiratory viruses from patients with COVID-19. To demonstrate the usefulness of the LFA, we tested previously infected and non-infected COVID-19 vaccine recipients at baseline and after first and second vaccine doses.ResultsThe LFA compared favorably with SARS-CoV-2 microneutralization assays with an area under the ROC curve of 98%. Sera obtained from patients with seasonal coronaviruses did not show neutralizing activity in the LFA. After a single mRNA vaccine dose, 87% of previously infected individuals demonstrated high levels of neutralizing antibodies. However, if individuals were not previously infected, only 24% demonstrated high levels of neutralizing antibodies after one vaccine dose. A second dose boosted neutralizing antibody levels just 8% higher in previously infected individuals, but over 63% higher in non-infected individuals.ConclusionsA rapid, semi-quantitative, highly portable and inexpensive neutralizing antibody test might be useful for monitoring rise and fall in vaccine-induced neutralizing antibodies to COVID-19.
| S-EPMC8567411 | biostudies-literature
Project description:BackgroundAutomated computerized electrocardiogram (ECG) interpretation algorithms are designed to enhance physician ECG interpretation, minimize medical error, and expedite clinical workflow. However, the performance of current computer algorithms is notoriously inconsistent. We aimed to develop and validate an artificial intelligence-enabled ECG (AI-ECG) algorithm capable of comprehensive 12-lead ECG interpretation with accuracy comparable to practicing cardiologists.MethodsWe developed an AI-ECG algorithm using a convolutional neural network as a multilabel classifier capable of assessing 66 discrete, structured diagnostic ECG codes using the cardiologist's final annotation as the gold-standard interpretation. We included 2,499,522 ECGs from 720,978 patients ≥18 years of age with a standard 12-lead ECG obtained at the Mayo Clinic ECG laboratory between 1993 and 2017. The total sample was randomly divided into training (n = 1,749,654), validation (n = 249,951), and testing (n = 499,917) datasets with a similar distribution of codes. We compared the AI-ECG algorithm's performance to the cardiologist's interpretation in the testing dataset using receiver operating characteristic (ROC) and precision recall (PR) curves.ResultsThe model performed well for various rhythm, conduction, ischemia, waveform morphology, and secondary diagnoses codes with an area under the ROC curve of ≥0.98 for 62 of the 66 codes. PR metrics were used to assess model performance accounting for category imbalance and demonstrated a sensitivity ≥95% for all codes.ConclusionsAn AI-ECG algorithm demonstrates high diagnostic performance in comparison to reference cardiologist interpretation of a standard 12-lead ECG. The use of AI-ECG reading tools may permit scalability as ECG acquisition becomes more ubiquitous.
| S-EPMC8890098 | biostudies-literature
Project description:Point-of-care (POC) tests to detect SARS-CoV-2 antibodies offer quick assessment of serostatus after natural infection or vaccination. We compared the field performance of the BioMedomics COVID-19 IgM/IgG Rapid Antibody Test against an ELISA in 303 participants enrolled in a SARS-CoV-2 household cohort study. The rapid antibody test was easily implemented with consistent interpretation across 14 users in a variety of field settings. Compared with ELISA, detection of seroconversion lagged by 5 to 10 days. However, it retained a sensitivity of 90% (160/177, 95% confidence interval [CI] 85-94%) and specificity of 100% (43/43, 95% CI 92-100%) for those tested 3 to 5 weeks after symptom onset. Sensitivity was diminished among those with asymptomatic infection (74% [14/19], 95% CI 49-91%) and early in infection (45% [29/64], 95% CI 33-58%). When used appropriately, rapid antibody tests offer a convenient way to detect symptomatic infections during convalescence.
| S-EPMC8733539 | biostudies-literature
Project description:ObjectivesTo evaluate and compare the diagnostic performances of a commercialized artificial intelligence (AI) algorithm for diagnosing pulmonary embolism (PE) on CT pulmonary angiogram (CTPA) with those of emergency radiologists in routine clinical practice.MethodsThis was an IRB-approved retrospective multicentric study including patients with suspected PE from September to December 2019 (i.e., during a preliminary evaluation period of an approved AI algorithm). CTPA quality and conclusions by emergency radiologists were retrieved from radiological reports. The gold standard was a retrospective review of CTPA, radiological and clinical reports, AI outputs, and patient outcomes. Diagnostic performance metrics for AI and radiologists were assessed in the entire cohort and depending on CTPA quality.ResultsOverall, 1202 patients were included (median age: 66.2 years). PE prevalence was 15.8% (190/1202). The AI algorithm detected 219 suspicious PEs, of which 176 were true PEs, including 19 true PEs missed by radiologists. In the cohort, the highest sensitivity and negative predictive values (NPVs) were obtained with AI (92.6% versus 90% and 98.6% versus 98.1%, respectively), while the highest specificity and positive predictive value (PPV) were found with radiologists (99.1% versus 95.8% and 95% versus 80.4%, respectively). Accuracy, specificity, and PPV were significantly higher for radiologists except in subcohorts with poor-to-average injection quality. Radiologists positively evaluated the AI algorithm to improve their diagnostic comfort (55/79 [69.6%]).ConclusionInstead of replacing radiologists, AI for PE detection appears to be a safety net in emergency radiology practice due to high sensitivity and NPV, thereby increasing the self-confidence of radiologists.Key points• Both the AI algorithm and emergency radiologists showed excellent performance in diagnosing PE on CTPA (sensitivity and specificity ≥ 90%; accuracy ≥ 95%). • The AI algorithm for PE detection can help increase the sensitivity and NPV of emergency radiologists in clinical practice, especially in cases of poor-to-moderate injection quality. • Emergency radiologists recommended the use of AI for PE detection in satisfaction surveys to increase their confidence and comfort in their final diagnosis.
| S-EPMC8938594 | biostudies-literature
Project description:Serological detection of antibodies to SARS-CoV-2 is essential for establishing rates of seroconversion in populations, and for seeking evidence for a level of antibody that may be protective against COVID-19 disease. Several high-performance commercial tests have been described, but these require centralised laboratory facilities that are comparatively expensive, and therefore not available universally. Red cell agglutination tests do not require special equipment, are read by eye, have short development times, low cost and can be applied at the Point of Care. Here we describe a quantitative Haemagglutination test (HAT) for the detection of antibodies to the receptor binding domain of the SARS-CoV-2 spike protein. The HAT has a sensitivity of 90% and specificity of 99% for detection of antibodies after a PCR diagnosed infection. We will supply aliquots of the test reagent sufficient for ten thousand test wells free of charge to qualified research groups anywhere in the world.
| S-EPMC8007702 | biostudies-literature
Project description:ImportanceMultimodal generative artificial intelligence (AI) methodologies have the potential to optimize emergency department care by producing draft radiology reports from input images.ObjectiveTo evaluate the accuracy and quality of AI-generated chest radiograph interpretations in the emergency department setting.Design, setting, and participantsThis was a retrospective diagnostic study of 500 randomly sampled emergency department encounters at a tertiary care institution including chest radiographs interpreted by both a teleradiology service and on-site attending radiologist from January 2022 to January 2023. An AI interpretation was generated for each radiograph. The 3 radiograph interpretations were each rated in duplicate by 6 emergency department physicians using a 5-point Likert scale.Main outcomes and measuresThe primary outcome was any difference in Likert scores between radiologist, AI, and teleradiology reports, using a cumulative link mixed model. Secondary analyses compared the probability of each report type containing no clinically significant discrepancy with further stratification by finding presence, using a logistic mixed-effects model. Physician comments on discrepancies were recorded.ResultsA total of 500 ED studies were included from 500 unique patients with a mean (SD) age of 53.3 (21.6) years; 282 patients (56.4%) were female. There was a significant association of report type with ratings, with post hoc tests revealing significantly greater scores for AI (mean [SE] score, 3.22 [0.34]; P < .001) and radiologist (mean [SE] score, 3.34 [0.34]; P < .001) reports compared with teleradiology (mean [SE] score, 2.74 [0.34]) reports. AI and radiologist reports were not significantly different. On secondary analysis, there was no difference in the probability of no clinically significant discrepancy between the 3 report types. Further stratification of reports by presence of cardiomegaly, pulmonary edema, pleural effusion, infiltrate, pneumothorax, and support devices also yielded no difference in the probability of containing no clinically significant discrepancy between the report types.Conclusions and relevanceIn a representative sample of emergency department chest radiographs, results suggest that the generative AI model produced reports of similar clinical accuracy and textual quality to radiologist reports while providing higher textual quality than teleradiologist reports. Implementation of the model in the clinical workflow could enable timely alerts to life-threatening pathology while aiding imaging interpretation and documentation.
| S-EPMC10556963 | biostudies-literature
Project description:Rapid on-site diagnosis of emerging pathogens is key for early identification of infected individuals and for prevention of further spreading in a population. Currently available molecular diagnostic tests are instrument-based whereas rapid antibody and antigen tests are often not sufficiently sensitive for detection in pre-symptomatic subjects. There is a need for rapid point of care molecular screening tests that can be easily adapted to emerging pathogens and are selective, sensitive, reliable in different settings around the world. We have developed a simple, rapid (<30 min), and inexpensive test for SARS-CoV-2 that is based on combination of isothermal reverse transcription recombinase polymerase amplification (RT-RPA) using modified primers and visual detection with paper-based microfluidics. Our test (CoRapID) is specific for SARS-CoV-2 (alpha to omicron variants) and does not detect other coronaviruses and pathogens by in silico and in vitro analysis. A two-step test protocol was developed with stable lyophilized reagents that reduces handling by using portable and disposable components (droppers, microapplicators/swabs, paper-strips). After optimization of assay components and conditions, we have achieved a limit of detection (LoD) of 1 copy/reaction by adding a blocking primer to the lateral flow assay. Using a set of 138 clinical samples, a sensitivity of 88.1% (P < 0.05, CI: 78.2-93.8%) and specificity of 93.9% (P < 0.05, CI: 85.4-97.6%) was determined. The lack of need for instrumentation for our CoRapID makes it an ideal on-site primary screening tool for local hospitals, doctors' offices, senior homes, workplaces, and in remote settings around the world that often do not have access to clinical laboratories.
| S-EPMC9890548 | biostudies-literature
Project description:Until vaccines and effective therapeutics become available, the practical solution to transit safely out of the current coronavirus disease 19 (CoVID-19) lockdown may include the implementation of an effective testing, tracing and tracking system. However, this requires a reliable and clinically validated diagnostic platform for the sensitive and specific identification of SARS-CoV-2. Here, we report on the development of a de novo, high-resolution and comparative genomics guided reverse-transcribed loop-mediated isothermal amplification (LAMP) assay. To further enhance the assay performance and to remove any subjectivity associated with operator interpretation of results, we engineered a novel hand-held smart diagnostic device. The robust diagnostic device was further furnished with automated image acquisition and processing algorithms and the collated data was processed through artificial intelligence (AI) pipelines to further reduce the assay run time and the subjectivity of the colorimetric LAMP detection. This advanced AI algorithm-implemented LAMP (ai-LAMP) assay, targeting the RNA-dependent RNA polymerase gene, showed high analytical sensitivity and specificity for SARS-CoV-2. A total of ~200 coronavirus disease (CoVID-19)-suspected NHS patient samples were tested using the platform and it was shown to be reliable, highly specific and significantly more sensitive than the current gold standard qRT-PCR. Therefore, this system could provide an efficient and cost-effective platform to detect SARS-CoV-2 in resource-limited laboratories.
| S-EPMC7552048 | biostudies-literature