Rapid Screening Tool Research Articles

Pilot-scale microfluidic solvent extraction of high-value metals

• A multistream microchip was numbered-up to successfully extract high-value metals. • The platform provides a throughput up to 1 L/h. • Extraction efficiency was monitored with increasing throughput. • The approach to steady state in multistage extraction was monitored by pH and ICP-MS. Numbering-up of processing units is essential for industrial-scale microfluidic solvent extraction. In the present work, a multistream microchip was numbered-up to successfully extract high-value metals (including platinum and rare earth elements) from industrial leach solutions. The platform provides orders of magnitude higher throughput (up to 1 L/h) compared with previously reported stream-based microfluidic solvent extraction, without compromising extraction performance. Extraction efficiency was monitored with increasing throughput. The approach to steady state in a three-stage counter-current extraction circuit was monitored by pH and inductively coupled plasma mass spectrometry of the aqueous raffinate. This pilot-scale microfluidic solvent extraction shows potential for refining high-value (technology or precious) metals and as a laboratory tool for rapid screening of reagents and process conditions.

Machine learning and geometric morphometrics to predict obstructive sleep apnea from 3D craniofacial scans

BackgroundObstructive sleep apnea (OSA) remains massively underdiagnosed, due to limited access to polysomnography (PSG), the highly complex gold standard for diagnosis. Performance scores in predicting OSA are evaluated for machine learning (ML) analysis applied to 3D maxillofacial shapes. MethodsThe 3D maxillofacial shapes were scanned on 280 Caucasian men with suspected OSA. All participants underwent single night in-home or in-laboratory sleep testing with PSG (Nox A1, Resmed, Australia), with concomitant 3D scanning (Sense v2, 3D systems corporation, USA). Anthropometric data, comorbidities, medication, BERLIN, and NoSAS questionnaires were also collected at baseline. The PSG recordings were manually scored at the reference sleep center. The 3D craniofacial scans were processed by geometric morphometrics, and 13 different supervised algorithms, varying from simple to more advanced, were trained and tested. Results for OSAS recognition by ML models were then compared with scores for specificity and sensitivity obtained using BERLIN and NoSAS questionnaires. ResultsAll valid scans (n = 267) were included in the analysis (patient mean age: 59 ± 9 years; BMI: 27 ± 4 kg/m2). For PSG-derived AHI≥15 events/h, the 56% specificity obtained for ML analysis of 3D craniofacial shapes was higher than for the questionnaires (Berlin: 50%; NoSAS: 40%). A sensitivity of 80% was obtained using ML analysis, compared to nearly 90% for NoSAS and 61% for the BERLIN questionnaire. The auROC score was further improved when 3D geometric morphometrics were combined with patient anthropometrics (auROC = 0.75). ConclusionThe combination of 3D geometric morphometrics with ML is proposed as a rapid, efficient, and inexpensive screening tool for OSA. Trial registration number: NCT03632382; Date of registration: 15-08-2018.

Adapting to heatwave-induced seagrass loss: Prioritizing management areas through environmental sensitivity mapping

Seagrass meadows support complex species assemblages and provide ecosystem services with a multitude of socio-economic benefits. However, they are sensitive to anthropogenic pressures such as coastal development, agricultural run-off, and overfishing. The increasing prevalence of marine heatwaves (MHWs) due to climate change poses an additional and growing threat. In this study, we apply the environmental sensitivity mapping approach MESA (Mapping Environmentally Sensitive Assets) to explore the potential consequences of MHWs on the ecosystem services that Posidonia oceanica provides to coastal communities. Under the intermediate climate change scenario Representative Concentration Pathway 4.5, Mediterranean marine heatwaves will be severe by 2050, and will very likely increase mortality of P. oceanica. However, the societal risk of seagrass loss is not evenly distributed across the Mediterranean. The spatial distribution of socio-economic implications of seagrass loss is highlighted through two case studies on seagrass-dependent fisheries and coastal hazards. Coastal communities in Tunisia and Libya show very high sensitivity to a loss of fisheries due to a combination of increasingly intense and frequent MHWs, coupled with high proportions of regional seagrass-dependent fisheries catch. The coastlines of Italy, Tunisia, and Cyprus are shown to potentially be highly sensitive to loss of seagrass due to high levels of coastal hazards, and seagrass meadows susceptible to MHW-induced degradation. These coastlines are likely to suffer from reduced coastal protection services provided by intact seagrass meadows. We demonstrate the implications of MHWs for ecosystem service provision to coastal communities in the Mediterranean and the need for policy instruments to help mitigate and adapt to its effect. We also highlight the potential for environmental sensitivity mapping to help support policymakers with rapid screening tools to prioritize resources more effectively to areas where in-depth local planning is needed.

Quantitative detection of caffeine in beverages using flowing atmospheric-pressure afterglow (FAPA) ionization high-resolution mass spectrometry imaging and performance evaluation of different thin-layer chromatography plates as sample substrates

Ambient desorption/ionization mass spectrometry (ADI-MS) is widely used as a rapid screening tool of samples in their native state without sample preparation. While analysis times are much less than 1 min per sample, one challenge of ADI-MS is the possibility to perform quantitative analysis of analytes in complex matrices. Typically, the goal is to probe a variety of different analytes in a complex matrix from a solid, liquid, or otherwise uncharacterized surface in the open air in front of the MS inlet. In this study, it is demonstrated that a carefully selected surface for analyte spot sampling and co-deposited isotopically labeled standards both significantly improve the capabilities of flowing atmospheric-pressure afterglow (FAPA) high-resolution (HR) MS for direct quantitative analysis. Specifically, a systematic study of different surfaces (glass, steel mesh, high-performance thin-layer chromatography (HPTLC) stationary phases including silica, reversed-phase (RP)-modified silica, and cyano (CN)-modified silica) and their suitability for spot sampling with FAPA-MS was performed. A set of different caffeine-containing standards and beverages (Red Bull, Coca-Cola, coffee, and black tea) was deposited on the surfaces and direct FAPA-HR-MS analysis of caffeine was performed using internal calibration with co-deposited 13C3-caffeine. For TLC surfaces, it was demonstrated that quantitative results could be achieved with the matrix and concomitants present and that a preceding chromatographic separation was not mandatory for this application. In addition, the use of a CN-HPTLC surface resulted in a significantly more intense caffeine signal in the beverage samples compared to the other surfaces studied, with the highest increase compared to the silica (200-fold higher) and the lowest increase compared to the steel mesh (30-fold higher). The utilization of TLC-based surfaces as sample carriers is considered an attractive tool in the ADI-MS toolbox for fast and efficient mass spectrometric investigations of complex samples without time-consuming sample preparation.Graphical

Host-guest interactions and confinement effects in HZSM-5 and chabazite zeolites studied by low-field NMR spin relaxation

The characterization of fluid/solid interactions in porous materials is crucial for their design and optimization, most notably in applications such as adsorption and catalysis. Yet, probing interfacial phenomena of fluids confined in porous systems is particularly challenging. Nuclear magnetic resonance (NMR) spin relaxation has emerged in recent years as a rapid, non-invasive experimental technique to probe adsorbate/adsorbent interactions in mesoporous catalytic materials. More recently, NMR relaxation measurements performed on high-field (300 MHz) superconducting magnets have been successfully validated as a robust method to characterize acidity in HZSM-5 zeolites. Expanding such techniques in the context of low-field, bench-top NMR instruments would be highly beneficial as it would make NMR relaxation a much more appealing and accessible tool for non-invasive, rapid characterization of adsorbate/adsorbent interactions in zeolites and microporous materials. Herein, we validate the use of low-field, bench-top NMR spin relaxation as an indicator for characterizing host-guest interactions in microporous zeolitic materials, using water as guest molecules confined within two different zeolite frameworks, HZSM-5 and chabazite, with varying silica/alumina ratio. The results reported here demonstrate the robustness and sensitivity of low-field NMR relaxation measurements as a rapid screening tool for characterizing adsorption and molecular dynamics in microporous materials, with important implications for both academics and industrialists in terms of making the method more widely accessible, hence expanding the set of tools for materials chemistry and characterization.

Rapid Detection of Actinobacillus pleuropneumoniae From Clinical Samples Using Recombinase Polymerase Amplification.

Actinobacillus pleuropneumoniae (APP) is the causative agent of porcine pleuropneumonia, resulting in high economic impact worldwide. There are currently 19 known serovars of APP, with different ones being predominant in specific geographic regions. Outbreaks of pleuropneumonia, characterized by sudden respiratory difficulties and high mortality, can occur when infected pigs are brought into naïve herds, or by those carrying different serovars. Good biosecurity measures include regular diagnostic testing for surveillance purposes. Current gold standard diagnostic techniques lack sensitivity (bacterial culture), require expensive thermocycling machinery (PCR) and are time consuming (culture and PCR). Here we describe the development of an isothermal point-of-care diagnostic test - utilizing recombinase polymerase amplification (RPA) for the detection of APP, targeting the species-specific apxIVA gene. Our APP-RPA diagnostic test achieved a sensitivity of 10 copies/μL using a strain of APP serovar 8, which is the most prevalent serovar in the UK. Additionally, our APP-RPA assay achieved a clinical sensitivity and specificity of 84.3 and 100%, respectively, across 61 extracted clinical samples obtained from farms located in England and Portugal. Using a small subset (n = 14) of the lung tissue samples, we achieved a clinical sensitivity and specificity of 76.9 and 100%, respectively) using lung imprints made on FTA cards tested directly in the APP-RPA reaction. Our results demonstrate that our APP-RPA assay enables a suitable rapid and sensitive screening tool for this important veterinary pathogen.

Production and Characterization of Biotinylated Anti-fenitrothion Nanobodies and Development of Sensitive Fluoroimmunoassay.

A simple and sensitive fluoroimmunoassay (FIA) based on a heavy-chain antibody (VHH) for rapid detection of fenitrothion was developed. A VHH library was constructed from an immunized alpaca, and one clone recognizing fenitrothion (namely, VHHjd8) was achieved after careful biopanning. It was biotinylated by fusing with the Avi tag and biotin ligase to obtain a fusion protein (VHHjd8-BT), showing both binding capacity to fenitrothion and the streptavidin poly-horseradish peroxidase conjugate (SA-polyHRP). Based on a competitive assay format, the absorbance spectrum of oxidized 3,3',5,5'-tetramethylbenzidine generated by SA-polyHRP overlapped the emission spectrum of carbon dots, which resulted in quenching of signals due to the inner-filter effect. The developed FIA showed an IC50 value of 1.4 ng/mL and a limit of detection of 0.03 ng/mL, which exhibited 15-fold improvement compared with conventional enzyme-linked immunosorbent assay. The recovery test of FIA was validated by standard GC-MS/MS, and the results showed good consistency, indicating that the assay is an ideal tool for rapid screening of fenitrothion in bulk food samples.

A Rapid and Affordable Screening Tool for Early-Stage Ovarian Cancer Detection Based on MALDI-ToF MS of Blood Serum

Ovarian cancer is a worldwide health issue that grows at a rate of almost 250,000 new cases every year. Its early detection is key for a good prognosis and even curative surgery. However, current medical examination methods and tests have been inefficient in detecting ovarian cancer at the early stage, leading to preventable death. So far, new screening tests based on molecular biomarker analysis techniques have not resulted in any substantial improvement in early-stage diagnosis and increased survival. Thus, whilst there remains clear potential to improve outcomes through early detection, novel approaches are needed. Here, we postulated that MALDI-ToF-mass-spectrometry-based tests can be a solution for effective screening of ovarian cancer. In this retrospective cohort study, we generated and analyzed the mass spectra of 181 serum samples of women with and without ovarian cancer. Using bioinformatics pipelines for analysis, including predictive modeling and machine learning, we found distinct mass spectral patterns composed of 9–20 key combinations of peak intensity or peak enrichment features for each stage of ovarian cancer. Based on a scoring algorithm and obtained patterns, the optimal sensitivity for detecting each stage of cancer was 95–97% with a specificity of 97%. Scoring all algorithms simultaneously could detect all stages of ovarian cancer at 99% sensitivity and 92% specificity. The results further demonstrate that the matrix and mass range analyzed played a key role in improving the mass spectral data quality and diagnostic power. Altogether, with the results reported here and increasing evidence of the MS assay’s diagnostic accuracy and instrument robustness, it has become imminent to consider MS in the clinical application for ovarian cancer screening.

Development of a sensitive and rapid fluorescence polarization immunoassay for high throughput screening eight glucocorticoids in beef

In this study, a sensitive, rapid, homogeneous, and high-throughput fluorescence polarization immunoassay (FPIA) for the rapid screening of eight glucocorticoids (GCs) in beef samples was successfully established. Two tracers including 5-aminofluorescein-labeled dexamethasone (5-AF-DMS) and fluorescein isothiocyanate-labeled dexamethasone (FITC-DMS) were studied to select appropriate antibody-tracer pairs using four previously produced broad-specific monoclonal antibodies. An optimal combination of the antibody 12D9 and the tracer FITC-DMS was selected. Under optimal detection conditions, the half inhibitory concentrations of dexamethasone (DMS), betamethasone (BMS), prednisolone (PNS), hydrocortisone (HCS), beclomethasone (BCMS), cortisone (CS), 6-α-methylprednisone (6-α-MPNS), fludrocortisone acetate (HFCS) were 1.00, 2.17, 3.49, 12.45, 1.20, 5.66, 6.85 and 3.45 ng/mL, respectively. The average recoveries of the proposed method in beef samples ranged from 77.3–91.7% with the coefficient of variation less than 12%. The developed FPIA was time-saving that could be completed within 10 min. The FPIA was applied to beef samples and showed a good correlation with liquid chromatography-tandem mass spectrometry (LC-MS/MS) (R2 = 0.9894). Thus, the proposed method provides a rapid, reliable, sensitive, and high-throughput screening tool for the simultaneous screening of eight GCs in beef, which shows great potential in the food safety analysis.

Effective Macrosomia Prediction Using Random Forest Algorithm.

(1) Background: Macrosomia is prevalent in China and worldwide. The current method of predicting macrosomia is ultrasonography. We aimed to develop new predictive models for recognizing macrosomia using a random forest model to improve the sensitivity and specificity of macrosomia prediction; (2) Methods: Based on the Shandong Multi-Center Healthcare Big Data Platform, we collected the prenatal examination and delivery data from June 2017 to May 2018 in Jinan, including the macrosomia and normal-weight newborns. We constructed a random forest model and a logistic regression model for predicting macrosomia. We compared the validity and predictive value of these two methods and the traditional method; (3) Results: 405 macrosomia cases and 3855 normal-weight newborns fit the selection criteria and 405 pairs of macrosomia and control cases were brought into the random forest model and logistic regression model. On the basis of the average decrease of the Gini coefficient, the order of influencing factors was: interspinal diameter, transverse outlet, intercristal diameter, sacral external diameter, pre-pregnancy body mass index, age, the number of pregnancies, and the parity. The sensitivity, specificity, and area under curve were 91.7%, 91.7%, and 95.3% for the random forest model, and 56.2%, 82.6%, and 72.0% for logistic regression model, respectively; the sensitivity and specificity were 29.6% and 97.5% for the ultrasound; (4) Conclusions: A random forest model based on the maternal information can be used to predict macrosomia accurately during pregnancy, which provides a scientific basis for developing rapid screening and diagnosis tools for macrosomia.

Triage in the time of COVID-19

Triage in the time of COVID-19

Convolutional neural networks for expediting the determination of minimum volume requirements for studies of microstructurally small cracks, Part I: Model implementation and predictions

Convolutional neural networks (CNNs) are implemented to expedite the determination of representative volume elements for microstructurally small cracks (RVEMSC). By definition, RVEMSC is the minimum volume of microstructure required around a microstructurally small crack (MSC) to achieve convergence of crack-front parameters with respect to volume size. In a previous study, RVEMSC was determined using a computationally expensive finite-element (FE) framework involving the simulation of many microstructural instantiations. With the aim of increasing the computational efficiency of determining RVEMSC, CNNs are leveraged herein to reduce the number of FE simulations required to determine RVEMSC. Using data from the previous FE-based RVEMSC study, CNNs are trained to predict RVEMSC,ip values, which quantify crack-front parameter convergence with respect to volume size for microstructural instantiation i evaluated at individual crack-front points p, given local microstructural and geometrical information. Predicted RVEMSC,ip values are subsequently used to estimate RVEMSC values. Studies are carried out to determine the optimal amount of training data, assess CNN-based RVEMSC estimation performance, and demonstrate the use of CNNs as microstructural-instantiation screening tools by enabling downselection of microstructures that are considered critical in terms of volume requirements. Individual and ensemble CNN predictions are compared. While CNNs are not found to be accurate enough to replace all FE simulations, CNNs are found to be effective as a rapid screening tool for improving the efficiency of the FE-based RVEMSC determination framework and for expediting future RVEMSC studies.

Rapid detection of hysteromyoma and cervical cancer based on serum surface-enhanced Raman spectroscopy and a support vector machine.

In this study, we investigated the feasibility of using surface-enhanced Raman spectroscopy (SERS) combined with a support vector machine (SVM) algorithm to discriminate hysteromyoma and cervical cancer from healthy volunteers rapidly. SERS spectra of serum samples were recorded from 30 hysteromyoma patients, 36 cervical cancer patients as well as 30 healthy subjects. SVM was used to establish the classification models, and three types of kernel functions, namely linear, polynomial, and Gaussian radial basis function (RBF), were utilized for comparison. When the polynomial kernel function was employed, the overall diagnostic accuracy for classifying the three groups could achieve 86.5%. In addition, when the optimal kernel function was selected, the diagnostic accuracy for identifying healthy versus hysteromyoma, healthy versus cervical cancer, and hysteromyoma versus cervical cancer reached 98.3%, 93.9%, and 90.9%, respectively. The current results indicate that serum SERS technology, together with the SVM algorithm, is expected to become a clinical tool for rapid screening of hysteromyoma and cervical cancer.

Ultrafast and Sensitive Screening of Pathogens by Functionalized Janus Microbeads‐Enabled Rotational Diffusometry in Combination with Isothermal Amplification

Polymerase chain reaction (PCR) is widely considered the gold standard in molecular diagnostics because of its high accuracy. However, conventional PCR is very time‐consuming (turnaround time 4–6 h), labor‐intensive, and high‐tech instruments dependent. Therefore, developing an ultrafast and sensitive nucleic amplification‐based detection method is still in high demand. Herein, a rapid screening of Escherichia coli using an advanced and sensitive biosensing method known as rotational diffusometry in combination with loop‐mediated isothermal amplification (LAMP) is reported. Given that the solution viscosity is increased with nucleic acid amplification, the viscosity change can be measured by the functionalized Janus microbeads‐enabled rotational diffusometry. The rotational diffusivity derived from capturing images of microbeads is expressed in terms of blinking signals. Under such conditions, this method can achieve a limit of detection of 42.8 fg μL−1 in 10 min for E. coli with sample volume as low as 2 μL. Additionally, detection of E. coli whole cells extracted from artificially contaminated milk, juice, and water is performed by the developed method and validated with real‐time PCR. Through this biosensing technique, a rapid, reliable, and low sample volume screening tool can be established, improving the shortcomings of the current time‐consuming and labor‐intensive methods.

Prevalence of Helicobacter pylori infection by rapid urease test among patients with dyspeptic symptoms who underwent upper gastrointestinal endoscopy in a secondary care hospital

Background: Functional dyspepsia is referred to wide spectrum of upper gastrointestinal symptoms which are nonspecific and without organic pathology of upper gastrointestinal tract, and accounts for >60% of patients undergoing upper gastrointestinal endoscopy. The aim of our study was to determine the prevalence of active Helicobacter pylori (H. pylori) infection by retrospective review of patient records using only rapid urease test reports which gives us the magnitude of the H. pylori prevalence.Methods: 200 patients who presented with dyspepsia were studied clinically according to the proforma over a period of 18 months from April 2020 to October 2021, Patients underwent upper gastro-intestinal endoscopy under topical anesthesia, and biopsies were taken from the antrum. Biopsy specimens were immediately placed onto rapid urease test kit containing urea and an indicator. Positive test for H. pylori was confirmed by the change in color of the medium from yellow to pink or red and when the rapid urease test was positive, patient was considered as H. pylori positive.Results: Out of 200 patients, most commonly affected age group are patients between 18-35years and is more in males than females (48:36), 86 patients were found to be H. pylori positive (43%) associated with acid peptic disease and duodenal ulcers, gastric antral ulcers.Conclusions: H. pylori is a potential risk factor of gastric malignancy. After being aware of the prevalence of H. pylori by early detection of active H. pylori infections, rapid urease test serves only as a rapid screening tool in dyspeptic symptoms of patients with acid peptic disease than all other tests.

Braden Skin Score Subdomains Predict Mortality Among Cardiac Intensive Care Patients

BackgroundThe Braden Skin Score (BSS) is a bedside nursing assessment that may be a measure of frailty and predicts mortality among patients in the cardiac intensive care unit (CICU). We examined the association between each of the 6 individual BSS subscores with hospital mortality in patients in the CICU. We hypothesized that BSS subscores reflecting patient frailty would have a stronger association with outcomes. MethodsRetrospective cohort study of unique adult patients admitted to the Mayo Clinic CICU from 2007 to 2018 with BSS documented on admission. Primary outcome was all-cause hospital mortality. Odds ratios (ORs) were determined using multivariable logistic regression. ResultsThe 11,954 included patients had a mean age of 67.4 ± 15.2 years (37.8% women). Each individual BSS subscore was lower among patients who died in the hospital (all P < .001). The total BSS was inversely associated with in-hospital mortality across admission diagnoses and among patients with coma or mechanical ventilation; each individual subscore was inversely associated with in-hospital mortality. On multivariable regression, all subscores were inversely associated with hospital mortality after full adjustment. Shear had the strongest association (adjusted OR 0.59), followed by nutrition (adjusted OR 0.67), skin moisture (adjusted OR 0.76), mobility (adjusted OR 0.76), sensory perception (adjusted OR 0.82), and activity level (adjusted OR 0.85). ConclusionBSS can serve as a rapid noninvasive screening tool for identifying poor outcomes in patients in the CICU. BSS subdomains that are more strongly associated with mortality appear to reflect physical frailty. Insofar as the BSS and its subscores measure frailty, a low BSS may identify frail patients.

Pediatric Prediction Model for Low Immunoglobulin G Level Based on Serum Globulin and Illness Status.

Hypogammaglobulinemia is a condition that requires prompt diagnosis and treatment. Unfortunately, serum immunoglobulin (Ig) measurements are not widely accessible in numerous developing countries. Serum globulin is potentially the best candidate for screening of low IgG level (IgGLo) due to its high availability, low cost, and rapid turnover time. However, multiple factors may influence the probability of prediction. Our study aimed to establish a simple prediction model using serum globulin to predict the likelihood of IgGLo in children. For retrospective data of patients who were suspected of having IgGLo, both serum IgG and globulin were simultaneously collected and measured. Potential factors interfering with serum globulin and IgG levels were investigated for their impact using bivariate binary logistic regression. A multivariate binary logistic regression was used to generate a formula and score to predict IgGLo. We obtained 953 samples from 143 pediatric patients. A strong positive correlation between serum globulin and IgG levels was observed (r=0.83, p < 0.001). A screening test model using serum globulin and illness status was constructed to predict IgGLo. The formula for predicting IgGLo was generated as follows; Predicted score = (2 x globulin (g/dl)) – illness condition score (well=0, sick=1). When the score was <4, the patient has the probability of having IgGLo with a sensitivity of 0.78 (0.71, 0.84), a specificity of 0.71 (0.68, 0.74), PPV of 0.34 (0.29, 0.40) and NPV of 0.94 (0.92, 0.96). This formula will be useful as rapid and inexpensive screening tool for early IgGLo detection, particularly in countries/locations where serum IgG measurement is inaccessible.

Real-Time RT-PCR Allelic Discrimination Assay for Detection of N501Y Mutation in the Spike Protein of SARS-CoV-2 Associated with B.1.1.7 Variant of Concern.

ABSTRACTThe N501Y amino acid mutation caused by a single point substitution A23063T in the spike gene of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is possessed by three variants of concern (VOCs), B.1.1.7, B.1.351, and P.1. A rapid screening tool using this mutation is important for surveillance during the coronavirus disease 2019 (COVID-19) pandemic. We developed and validated a single nucleotide polymorphism real-time reverse transcription PCR assay using allelic discrimination of the spike gene N501Y mutation to screen for potential variants of concern and differentiate them from SARS-CoV-2 lineages without the N501Y mutation. A total of 160 clinical specimens positive for SARS-CoV-2 were characterized as mutant (N501Y) or N501 wild type by Sanger sequencing and were subsequently tested with the N501Y single nucleotide polymorphism real-time reverse transcriptase PCR assay. Our assay, compared to Sanger sequencing for single nucleotide polymorphism detection, demonstrated positive percent agreement of 100% for all 57 specimens displaying the N501Y mutation, which were confirmed by Sanger sequencing to be typed as A23063T, including one specimen with mixed signal for wild type and mutant. Negative percent agreement was 100% in all 103 specimens typed as N501 wild type, with A23063 identified as wild type by Sanger sequencing. The identification of circulating SARS-CoV-2 lineages carrying an N501Y mutation is critical for surveillance purposes. Current identification methods rely primarily on Sanger sequencing or whole-genome sequencing, which are time consuming, labor intensive, and costly. The assay described herein is an efficient tool for high-volume specimen screening for SARS-CoV-2 VOCs and for selecting specimens for confirmatory Sanger or whole-genome sequencing.IMPORTANCE During the coronavirus disease 2019 (COVID-19) pandemic, several variants of concern (VOCs) have been detected, for example, B.1.1.7, B.1.351, P.1, and B.1.617.2. The VOCs pose a threat to public health efforts to control the spread of the virus. As such, surveillance and monitoring of these VOCs is of the utmost importance. Our real-time RT-PCR assay helps with surveillance by providing an easy method to quickly survey SARS-CoV-2 specimens for VOCs carrying the N501Y single nucleotide polymorphism (SNP). Samples that test positive for the N501Y mutation in the spike gene with our assay can be sequenced to identify the lineage. Thus, our assay helps to focus surveillance efforts and decrease turnaround times.

Rapid antibody conformational screening by matrix-assisted laser desorption/ionization hydrogen-deuterium exchange mass spectrometry.

Recent advances in the field of cancer biology have accelerated the discovery and development of novel biopharmaceuticals. At the forefront of these drug development efforts are high-throughput screening, compressed timelines, and limited sample quantities, all characteristic of the discovery space. To meet program targets, large numbers of protein variants must be produced, screened, and characterized, presenting a daunting analytical challenge. Additionally, the higher-order structure is paramount for protein function and must be monitored as a critical quality attribute. Matrix-assisted laser desorption/ionization mass spectrometry has been utilized as an ultra-fast, automatable, sample-sparing analytical tool for biomolecules. Our group has published applications integrating hydrogen-deuterium exchange mass spectrometry with matrix-assisted laser desorption/ionization mass spectrometry for the rapid conformational characterization of small proteins, the current work expands this application to monoclonal and bi-specific antibodies. This study demonstrates the ability of the methodology, matrix-assisted laser desorption/ionization hydrogen-deuterium exchange mass spectrometry, to detect conformational differences between bi-specific antibodies from different expression hosts. These conformational differences were validated by orthogonal techniques including circular dichroism, nuclear magnetic resonance, and size-exclusion chromatography hydrogen-deuterium exchange mass spectrometry. This work demonstrates the utility of applying the developed methodology as a rapid conformational screening tool to triage samples for further analytical characterization.

Saliva-based detection of COVID-19 infection in a real-world setting using reagent-free Raman spectroscopy and machine learning.

.Significance: The primary method of COVID-19 detection is reverse transcription polymerase chain reaction (RT-PCR) testing. PCR test sensitivity may decrease as more variants of concern arise and reagents may become less specific to the virus.Aim: We aimed to develop a reagent-free way to detect COVID-19 in a real-world setting with minimal constraints on sample acquisition. The machine learning (ML) models involved could be frequently updated to include spectral information about variants without needing to develop new reagents.Approach: We present a workflow for collecting, preparing, and imaging dried saliva supernatant droplets using a non-invasive, label-free technique—Raman spectroscopy—to detect changes in the molecular profile of saliva associated with COVID-19 infection.Results: We used an innovative multiple instance learning-based ML approach and droplet segmentation to analyze droplets. Amongst all confounding factors, we discriminated between COVID-positive and COVID-negative individuals yielding receiver operating coefficient curves with an area under curve (AUC) of 0.8 in both males (79% sensitivity and 75% specificity) and females (84% sensitivity and 64% specificity). Taking the sex of the saliva donor into account increased the AUC by 5%.Conclusion: These findings may pave the way for new rapid Raman spectroscopic screening tools for COVID-19 and other infectious diseases.