DENGUE PROGNOSTIC BIOMARKERS PREDICT DENGUE HAEMORRHAGIC FEVER WITH HIGH SENSITIVITY AND SPECIFICITY

Ninety-six million people are symptomatically infected with Dengue globally every year. Under the current standard-of-care, up to 20% of Dengue patients may be hospitalized, while only 500,000 develop Dengue Haemorrhagic Fever (DHF) and require hospitalization. This leads to unnecessary overwhelming of hospitals in tropical countries during large Dengue epidemics, especially when healthcare systems are grappling with large numbers of COVID-19 patients. Our research team set out to discover biomarkers to prognosticate Dengue patients, and augment the infectious disease clinician's decision-making process to hospitalize Dengue patients. Host biomarkers with concentrations significantly different between pooled serum samples of Dengue Fever (DF) patients and DHF patients were identified using protein array. The prognostication capabilities of selected biomarkers were then validated over 283 adult Dengue patients recruited from three Singapore tertiary hospitals, prior to the diagnosis of DHF. Three biomarkers (A2M, CMA1 and VEGFA) were identified that provide independent prognostication value from one another, and from clinical parameters commonly monitored in Dengue patients. The combination of all three biomarkers was able to identify from as early as Day 1 after the onset of fever, DF patients whose conditions will deteriorate into DHF. The biomarkers are robust and able to predict DHF well when trained on different AI/ML algorithms (logistic regression, support vector machine, decision tree, random forest, AdaBoost and gradient boosting). When stacked, prediction models based on the biomarkers were able to predict DHF with 97.3% sensitivity, 92.7% specificity, 66.7% PPV, 99.6% NPV and an AUC of 0.978. To the best of our knowledge, our panel of three biomarkers offers the highest accuracy in prognosticating Dengue to date. Further studies are required to validate the biomarkers in different geographical settings and pilot their implementation as part of the standard-of-care workflow for Dengue patients. [ FROM AUTHOR] Copyright of International Journal of Infectious Diseases is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Transcriptome in Human Mycoses

Mycoses are infectious diseases caused by fungi, which incidence has increased in recent decades due to the increasing number of immunocompromised patients and improved diagnostic tests. As eukaryotes, fungi share many similarities with human cells, making it difficult to design drugs without side effects. Commercially available drugs act on a limited number of targets and have been reported fungal resistance to commonly used antifungal drugs. Therefore, elucidating the pathogenesis of fungal infections, the fungal strategies to overcome the hostile environment of the host, and the action of antifungal drugs is essential for developing new therapeutic approaches and diagnostic tests. Large-scale transcriptional analyses using microarrays and RNA sequencing (RNA-seq), combined with improvements in molecular biology techniques, have improved the study of fungal pathogenicity. Such techniques have provided insights into the infective process by identifying molecular strategies used by the host and pathogen during the course of human mycoses. This chapter will explore the latest discoveries regarding the transcriptome of major human fungal pathogens. Further we will highlight genes essential for host–pathogen interactions, immune response, invasion, infection, antifungal drug response, and resistance. Finally, we will discuss their importance to the discovery of new molecular targets for antifungal drugs. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2014, 2022.

Spatially Addressable Multiplex Biodetection by Calibrated Micro/Nanostructured Surfaces.

A challenge of any biosensing technology is the detection of very low concentrations of analytes. The fluorescence interference contrast (FLIC) technique improves the fluorescence-based sensitivity by selectively amplifying, or suppressing, the emission of a fluorophore-labeled biomolecule immobilized on a transparent layer placed on top of a mirror basal surface. The standing wave of the reflected emission light means that the height of the transparent layer operates as a surface-embedded optical filter for the fluorescence signal. FLIC extreme sensitivity to wavelength is also its main problem: small, e.g., 10 nm range, variations of the vertical position of the fluorophore can translate in unwanted suppression of the detection signal. Herein, we introduce the concept of quasi-circular lenticular microstructured domes operating as continuous-mode optical filters, generating fluorescent concentric rings, with diameters determined by the wavelengths of the fluorescence light, in turn modulated by FLIC. The critical component of the lenticular structures was the shallow sloping side wall, which allowed the simultaneous separation of fluorescent patterns for virtually any fluorophore wavelength. Purposefully designed microstructures with either stepwise or continuous-slope dome geometries were fabricated to modulate the intensity and the lateral position of a fluorescence signal. The simulation of FLIC effects induced by the lenticular microstructures was confirmed by the measurement of the fluorescence profile for three fluorescent dyes, as well as high-resolution fluorescence scanning using stimulated emission depletion (STED) microscopy. The high sensitivity of the spatially addressable FLIC technology was further validated on a diagnostically important target, i.e., the receptor-binding domain (RBD) of the SARS-Cov2 via the detection of RBD:anti-S1-antibody.

Late acceptance hill climbing aided chaotic harmony search for feature selection: An empirical analysis on medical data

In today's era of data-driven digital society, there is a huge demand for optimized solutions that essentially reduce the cost of operation, thereby aiming to increase productivity. Processing a huge amount of data, like the Microarray based gene expression data, using machine learning and data mining algorithms has certain limitations in terms of memory and time requirements. This would be more concerning, when a dataset comes with redundant and non-important information. For example, many report-based medical datasets have several non-informative attributes which mislead the classification algorithms. To this end, researchers have been developing several feature selection algorithms that try to discard the redundant information from the raw datasets before feeding them to machine learning algorithms. Metaheuristic based optimization algorithms provide an excellent option to solve feature selection problems. In this paper, we propose a music-inspired harmony search (HS) algorithm based wrapper feature selection method. At the beginning, we use a chaotic mapping to initialize the population of the HS algorithm in order to better coverage of the search space. Further to complement the inferior exploitation of the HS algorithm, we integrate it with the Late Acceptance Hill Climbing (LAHC) method. Thus the combination of these two algorithms provides a good balance between the exploration and exploitation of the HS algorithm. We evaluate the proposed feature selection method on 15 UCI datasets and the obtained results are found to be better than many state-of-the-art methods both in terms of the classification accuracy and the number of features selected. To evaluate the effectiveness of our algorithm, we utilize a combination of precision, recall, F1 score, fitness value, and execution time as performance indicators. These metrics enable us to obtain a comprehensive assessment of the algorithm's abilities and limitations. We also apply our method on 3 microarray based gene expression datasets used for prediction of cancer to ensure the scalability and robustness as a feature selection method in real-life scenarios. In addition to this, we test our approach using the COVID-19 dataset, and it performs better than several metaheuristic based optimization techniques. © 2023

Evaluating SARS-CoV-2 antibody reactivity to natural exposure and inactivated vaccination with peptide microarrays.

Objective: Vaccination is effective tool for preventing and controlling SARS-CoV-2 infections, and inactivated vaccines are the most widely used type of vaccine. In order to identify antibody-binding peptide epitopes that can distinguish between individuals who have been vaccinated and those who have been infected, this study aimed to compare the immune responses of vaccinated and infected individuals. Methods: SARS-CoV-2 peptide microarrays were used to assess the differences between 44 volunteers inoculated with the inactivated virus vaccine BBIBP-CorV and 61 patients who were infected with SARS-CoV-2. Clustered heatmaps were used to identify differences between the two groups in antibody responses to peptides such as M1, N24, S15, S64, S82, S104, and S115. Receiver operating characteristic curve analysis was used to determine whether a combined diagnosis with S15, S64, and S104 could effectively distinguish infected patients from vaccinated individuals. Results: Our findings showed that the specific antibody responses against S15, S64, and S104 peptides were stronger in vaccinators than in infected persons, while responses to M1, N24, S82, and S115 were weaker in asymptomatic patients than in symptomatic patients. Additionally, two peptides (N24 and S115) were found to correlate with the levels of neutralizing antibodies. Conclusion: Our results suggest that antibody profiles specific to SARS-CoV-2 can be used to distinguish between vaccinated individuals and those who are infected. The combined diagnosis with S15, S64, and S104 was found to be more effective in distinguishing infected patients from those who have been vaccinated than the diagnosis using individual peptides. Moreover, the specific antibody responses against the N24 and S115 peptides were found to be consistent with the changing trend of neutralizing antibodies.

FLU-LISA (fluorescence-linked immunosorbent assay): high-throughput antibody profiling using antigen microarrays.

Vaccination and natural infection both elicit potent humoral responses that provide protection from subsequent infections. The immune history of an individual following such exposures is in part encoded by antibodies. While there are multiple immunoassays for measuring antibody responses, the majority of these methods measure responses to a single antigen. A commonly used method for measuring antibody responses is ELISA-a semiquantitative assay that is simple to perform in research and clinical settings. Here, we present FLU-LISA (fluorescence-linked immunosorbent assay)-a novel antigen microarray-based assay for rapid high-throughput antibody profiling. The assay can be used for profiling immunoglobulin (Ig) G, IgA and IgM responses to multiple antigens simultaneously, requiring minimal amounts of sample and antigens. Using several influenza and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) antigen microarrays, we demonstrated the specificity and sensitivity of our novel assay and compared it with the traditional ELISA, using samples from mice, chickens and humans. We also showed that our assay can be readily used with dried blood spots, which can be collected from humans and wild birds. FLU-LISA can be readily used to profile hundreds of samples against dozens of antigens in a single day, and therefore offers an attractive alternative to the traditional ELISA.

Thermal-annealing-regulated plasmonic enhanced fluorescence platform enables accurate detection of antigen/antibody against infectious diseases.

Plasmonic enhanced fluorescence (PEF) technology is a powerful strategy to improve the sensitivity of immunofluorescence microarrays (IFMA), however, current approaches to constructing PEF platforms are either expensive/time-consuming or reliant on specialized instruments. Here, we develop a completely alternative approach relying on a two-step protocol that includes the self-assembly of gold nanoparticles (GNPs) at the water-oil interface and subsequent annealing-assisted regulation of gold nanogap. Our optimized thermal-annealing GNPs (TA-GNP) platform generates adequate hot spots, and thus produces high-density electromagnetic coupling, eventually enabling 240-fold fluorescence enhancement of probed dyes in the near-infrared region. For clinical detection of human samples, TA-GNP provides super-high sensitivity and low detection limits for both hepatitis B surface antigen and SARS-CoV-2 binding antibody, coupled with a much-improved detection dynamic range up to six orders of magnitude. With fast detection, high sensitivity, and low detection limit, TA-GNP could not only substantially improve the outcomes of IFMA-based precision medicine but also find applications in fields of proteomic research and clinical pathology. Electronic Supplementary Material: Supplementary material (UV-Vis absorption and transmission spectra of GNPs, SEM, microscopy and digital images of PEF platforms, and fluorescence images of IFMA on PEF platforms) is available in the online version of this article at 10.1007/s12274-022-5035-6.

Hybrid Peptide–Agarose Hydrogels for 3D Immunoassays

Recent advances in biosensing analytical platforms have brought relevant outcomes for novel diagnostic and therapy-oriented applications. In this context, 3D droplet microarrays, where hydrogels are used as matrices to stably entrap biomolecules onto analytical surfaces, potentially provide relevant advantages over conventional 2D assays, such as increased loading capacity, lower nonspecific binding, and enhanced signal-to-noise ratio. Here, we describe a hybrid hydrogel composed of a self-assembling peptide and commercial agarose (AG) as a suitable matrix for 3D microarray bioassays. The hybrid hydrogel is printable and self-adhesive and allows analyte diffusion. As a showcase example, we describe its application in a diagnostic immunoassay for the detection of SARS-CoV-2 infection. © 2023, The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.

Peptide microarray analysis of in-silico predicted B-cell epitopes in SARS-CoV-2 sero-positive healthcare workers in Bulawayo, Zimbabwe.

Immunogenic peptides that mimic linear B-cell epitopes coupled with immunoassay validation may improve serological tests for emerging diseases. This study reports a general approach for profiling linear B-cell epitopes derived from SARS-CoV-2 using an in-silico method and peptide microarray immunoassay, using healthcare workers' SARS-CoV-2 sero-positive sera. SARS-CoV-2 was tested using rapid chromatographic immunoassays and real-time reverse-transcriptase polymerase chain reaction. Immunogenic peptides mimicking linear B-cell epitopes were predicted in-silico using ABCpred. Peptides with the lowest sequence identity with human protein and proteins from other human pathogens were selected using the NCBI Protein BLAST. IgG and IgM antibodies against the SARS-CoV-2 spike protein, membrane glycoprotein and nucleocapsid derived peptides were measured in sera using peptide microarray immunoassay. Fifty-three healthcare workers included in the study were RT-PCR negative for SARS-CoV-2. Using rapid chromatographic immunoassays, 10 were SARS-CoV-2 IgM sero-positive and 7 were SARS-CoV-2 IgG sero-positive. From a total of 10 SARS-CoV-2 peptides contained on the microarray, 3 (QTH34388.1-1-14, QTN64908.1-135-148, and QLL35955.1-22-35) showed reactivity against IgG. Three peptides (QSM17284.1-76-89, QTN64908.1-135-148 and QPK73947.1-8-21) also showed reactivity against IgM. Based on the results we predicted one peptide (QSM17284.1-76-89) that had an acceptable diagnostic performance. Peptide QSM17284.1-76-89 was able to detect IgM antibodies against SARS-CoV-2 with area under the curve (AUC) 0.781 when compared to commercial antibody tests. In conclusion in silico peptide prediction and peptide microarray technology may provide a platform for the development of serological tests for emerging infectious diseases such as COVID-19. However, we recommend using at least three in-silico peptide prediction tools to improve the sensitivity and specificity of B-cell epitope prediction, to predict peptides with excellent diagnostic performances.

Microarray Profiling of Vaccination-Induced Antibody Responses to SARS-CoV-2 Variants of Interest and Concern.

Mutations in surface proteins enable emerging variants of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) to escape a substantial fraction of neutralizing antibodies and may thus weaken vaccine-driven immunity. To compare available vaccines and justify revaccination, rapid evaluation of antibody (Ab) responses to currently circulating SARS-CoV-2 variants of interest (VOI) and concern (VOC) is needed. Here, we developed a multiplex protein microarray-based system for rapid profiling of anti-SARS-CoV-2 Ab levels in human sera. The microarray system was validated using sera samples from SARS-CoV-2-free donors and those diagnosed with COVID-19 based on PCR and enzyme immunoassays. Microarray-based profiling of vaccinated donors revealed a substantial difference in anti-VOC Ab levels elicited by the replication-deficient adenovirus vector-base (Sputnik V) and whole-virion (CoviVac Russia COVID-19) vaccines. Whole-virion vaccine-induced Abs showed minor but statistically significant cross-reactivity with the human blood coagulation factor 1 (fibrinogen) and thrombin. However, their effects on blood clotting were negligible, according to thrombin time tests, providing evidence against the concept of pronounced cross-reactivity-related side effects of the vaccine. Importantly, all samples were collected in the pre-Omicron period but showed noticeable responses to the receptor-binding domain (RBD) of the Omicron spike protein. Thus, using the new express Ab-profiling system, we confirmed the inter-variant cross-reactivity of the anti-SARS-CoV-2 Abs and demonstrated the relative potency of the vaccines against new VOCs.

Epitope Mapping on Microarrays Highlights a Sequence on the N Protein with Strong Immune Response in SARS-CoV-2 Patients

In SARS-CoV-2 pandemic scenario, the identification of rapid methods to detect antibodies against coronavirus has been a wide and urgent issue. Epitope mapping on peptide microarrays is a rapid way to identify sequences with a high immunoreactivity. The process begins with a proteome-wide screening, based on immune affinity;the use of a high-density microarray is followed by a validation phase, where a restricted panel of probes is tested using peptide microarrays;peptide sequences are immobilized through a click-based strategy.COVID-19-positive sera are tested and immuno-domains regions are identified on SARS-CoV-2 spike (S), nucleocapsid (N) protein, and Orf1ab polyprotein. An epitope on N protein (region 155-171) provided good diagnostic performance in discriminating COVID-19-positive vs. healthy individuals. Using this sequence, 92% sensitivity and 100% specificity are reached for IgG detection in COVID-19 samples, and no cross-reactivity with common cold coronaviruses is detected. Overall, epitope 155-171 from N protein represents a promising candidate for further development and rapid implementation in serological tests.

Hybrid Peptide-Agarose Hydrogels for 3D Immunoassays

Recent advances in biosensing analytical platforms have brought relevant outcomes for novel diagnostic and therapy-oriented applications. In this context, 3D droplet microarrays, where hydrogels are used as matrices to stably entrap biomolecules onto analytical surfaces, potentially provide relevant advantages over conventional 2D assays, such as increased loading capacity, lower nonspecific binding, and enhanced signal-to-noise ratio. Here, we describe a hybrid hydrogel composed of a self-assembling peptide and commercial agarose (AG) as a suitable matrix for 3D microarray bioassays. The hybrid hydrogel is printable and self-adhesive and allows analyte diffusion. As a showcase example, we describe its application in a diagnostic immunoassay for the detection of SARS-CoV-2 infection.

Design of Hydrogel Silk-Based Microarrays and Molecular Beacons for Reagentless Point-of-Care Diagnostics.

We have developed a novel microarray system based on three technologies: 1) molecular beacons designed to interact with DNA targets at room temperature (25-27°C), 2) tridimensional silk-based microarrays containing the molecular beacons immersed in the silk hydrogel, and 3) shallow angle illumination, which uses separated optical pathways for excitation and emission. Unlike conventional microarrays that exhibit reduced signal-to-background ratio, require several stages of incubation, rinsing, and stringency control, and measure only end-point results, our microarray technology provides enhanced signal-to-background ratio (achieved by separating the optical pathways for excitation and emission, resulting in reduced stray light), performs analysis rapidly in one step without the need for labeling DNA targets, and measures the entire course of association kinetics between target DNA and the molecular beacons. To illustrate the benefits of our technology, we conducted microarray assays designed for the identification of influenza viruses. We show that in a single microarray slide, we can identify the virus subtype according to the molecular beacons designed for hemagglutinin (H1, H2, and H3) and neuraminidase (N1, N2). We also show the identification of human and swine influenza using sequence-specific molecular beacons. This microarray technology can be easily implemented for reagentless point-of-care diagnostics of several contagious diseases, including coronavirus variants responsible for the current pandemic.

COVID-BIOCHIP: A Web Tool to Analyse COVID-19 Antigen Microarrays.

During the COVID-19 pandemic, there was a growing need to characterise the disease. A very important aspect is the ability to measure the immunisation extent, which can be achieved using antigen microarrays that quantitively measure the presence of COVID-related antibodies. A significant limitation for these tests was the complexity of manually analysing the results, and the limited availability of software for its analysis. In this paper, we describe the development of COVID-BIOCHIP, an ad-hoc web-based solution for the automatic analysis and visualisation of COVID-19 antigen microarray data results.

Rise of the SARS-CoV-2 Variants: can proteomics be the silver bullet?

INTRODUCTION: The challenges posed by emergent strains of SARS-CoV-2 need to be tackled by contemporary scientific approaches, with proteomics playing a significant role. AREAS COVERED: In this review, we provide a brief synthesis of the impact of proteomics technologies in elucidating disease pathogenesis and classifiers for the prognosis of COVID-19 and propose proteomics methodologies that could play a crucial role in understanding emerging variants and their altered disease pathology. From aiding the design of novel drug candidates to facilitating the identification of T cell vaccine targets, we have discussed the impact of proteomics methods in COVID-19 research. Techniques varied as mass spectrometry, single-cell proteomics, multiplexed ELISA arrays, high-density proteome arrays, surface plasmon resonance, immunopeptidomics, and in silico docking studies that have helped augment the fight against existing diseases were useful in preparing us to tackle SARS-CoV-2 variants. We also propose an action plan for a pipeline to combat emerging pandemics using proteomics technology by adopting uniform standard operating procedures and unified data analysis paradigms. EXPERT OPINION: The knowledge about the use of diverse proteomics approaches for COVID-19 investigation will provide a framework for future basic research, better infectious disease prevention strategies, improved diagnostics, and targeted therapeutics.

InfectionCMA: A Cell MicroArray Approach for Efficient Biomarker Screening in In Vitro Infection Assays.

The recently emerged severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has forced the scientific community to acquire knowledge in real-time, when total lockdowns and the interruption of flights severely limited access to reagents as the global pandemic became established. This unique reality made researchers aware of the importance of designing efficient in vitro set-ups to evaluate infectious kinetics. Here, we propose a histology-based method to evaluate infection kinetics grounded in cell microarray (CMA) construction, immunocytochemistry and in situ hybridization techniques. We demonstrate that the chip-like organization of the InfectionCMA has several advantages, allowing side-by-side comparisons between diverse cell lines, infection time points, and biomarker expression and cytolocalization evaluation in the same slide. In addition, this methodology has the potential to be easily adapted for drug screening.

SARS-CoV-2 Infection Triggers Auto-Immune Response in ARDS.

Acute respiratory distress syndrome (ARDS) is a severe pulmonary disease, which is one of the major complications in COVID-19 patients. Dysregulation of the immune system and imbalances in cytokine release and immune cell activation are involved in SARS-CoV-2 infection. Here, the inflammatory, antigen, and auto-immune profile of patients presenting COVID-19-associated severe ARDS has been analyzed using functional proteomics approaches. Both, innate and humoral responses have been characterized through acute-phase protein network and auto-antibody signature. Severity and sepsis by SARS-CoV-2 emerged to be correlated with auto-immune profiles of patients and define their clinical progression, which could provide novel perspectives in therapeutics development and biomarkers of COVID-19 patients. Humoral response in COVID-19 patients' profile separates with significant differences patients with or without ARDS. Furthermore, we found that this profile can be correlated with COVID-19 severity and results more common in elderly patients.

Composite Peptide-Agarose Hydrogels for Robust and High-Sensitivity 3D Immunoassays.

Canonical immunoassays rely on highly sensitive and specific capturing of circulating biomarkers by interacting biomolecular baits. In this frame, bioprobe immobilization in spatially discrete three-dimensional (3D) spots onto analytical surfaces by hydrogel encapsulation was shown to provide relevant advantages over conventional two-dimensional (2D) platforms. Yet, the broad application of 3D systems is still hampered by hurdles in matching their straightforward fabrication with optimal functional properties. Herein, we report on a composite hydrogel obtained by combining a self-assembling peptide (namely, Q3 peptide) with low-temperature gelling agarose that is proved to have simple and robust application in the fabrication of microdroplet arrays, overcoming hurdles and limitations commonly associated with 3D hydrogel assays. We demonstrate the real-case scenario feasibility of our 3D system in the profiling of Covid-19 patients' serum IgG immunoreactivity, which showed remarkably improved signal-to-noise ratio over canonical assays in the 2D format and exquisite specificity. Overall, the new two-component hydrogel widens the perspectives of hydrogel-based arrays and represents a step forward towards their routine use in analytical practices.

Mass spectrometry analysis of human tear fluid biomarkers specific for ocular and systemic diseases in the context of 3P medicine.

Over the last two decades, a large number of non-communicable/chronic disorders reached an epidemic level on a global scale such as diabetes mellitus type 2, cardio-vascular disease, several types of malignancies, neurological and eye pathologies-all exerted system's enormous socio-economic burden to primary, secondary, and tertiary healthcare. The paradigm change from reactive to predictive, preventive, and personalized medicine (3PM/PPPM) has been declared as an essential transformation of the overall healthcare approach to benefit the patient and society at large. To this end, specific biomarker panels are instrumental for a cost-effective predictive approach of individualized prevention and treatments tailored to the person. The source of biomarkers is crucial for specificity and reliability of diagnostic tests and treatment targets. Furthermore, any diagnostic approach preferentially should be noninvasive to increase availability of the biomaterial, and to decrease risks of potential complications as well as concomitant costs. These requirements are clearly fulfilled by tear fluid, which represents a precious source of biomarker panels. The well-justified principle of a "sick eye in a sick body" makes comprehensive tear fluid biomarker profiling highly relevant not only for diagnostics of eye pathologies but also for prediction, prognosis, and treatment monitoring of systemic diseases. One prominent example is the Sicca syndrome linked to a cascade of severe complications that include dry eye, neurologic, and oncologic diseases. In this review, protein profiles in tear fluid are highlighted and corresponding biomarkers are exemplified for several relevant pathologies, including dry eye disease, diabetic retinopathy, cancers, and neurological disorders. Corresponding analytical approaches such as sample pre-processing, differential proteomics, electrophoretic techniques, high-performance liquid chromatography (HPLC), enzyme-linked immuno-sorbent assay (ELISA), microarrays, and mass spectrometry (MS) methodology are detailed. Consequently, we proposed the overall strategies based on the tear fluid biomarkers application for 3P medicine practice. In the context of 3P medicine, tear fluid analytical pathways are considered to predict disease development, to target preventive measures, and to create treatment algorithms tailored to individual patient profiles.

A Serological Snapshot of COVID-19 Initial Stages in Israel by a 6-Plex Antigen Array.

The first case of SARS-CoV-2 was discovered in Israel in late February 2020. Three major outbreaks followed, resulting in over 800,000 cases and over 6,000 deaths by April 2021. Our aim was characterization of a serological snapshot of Israeli patients and healthy adults in the early months of the COVID-19 pandemic. Sera from 55 symptomatic COVID-19 patients and 146 healthy subjects (early-pandemic, reverse transcription-quantitative PCR [qRT-PCR]-negative), collected in Israel between March and April 2020, were screened for SARS-CoV-2-specific IgG, IgM, and IgA antibodies, using a 6-plex antigen microarray presenting the whole inactivated virus and five viral antigens: a stabilized version of the spike ectodomain (S2P), spike subunit 1 (S1), receptor-binding-domain (RBD), N-terminal-domain (NTD), and nucleocapsid (NC). COVID-19 patients, 4 to 40 days post symptom onset, presented specific IgG to all of the viral antigens (6/6) in 54 of the 55 samples (98% sensitivity). Specific IgM and IgA antibodies for all six antigens were detected in only 10% (5/55) and 4% (2/55) of the patients, respectively, suggesting that specific IgG is a superior serological marker for COVID-19. None of the qRT-PCR-negative sera reacted with all six viral antigens (100% specificity), and 48% (70/146) were negative throughout the panel. Our findings confirm a low seroprevalence of anti-SARS-CoV-2 antibodies in the Israeli adult population prior to the COVID-19 outbreak. We further suggest that the presence of low-level cross-reacting antibodies in naive individuals calls for a combined, multiantigen analysis for accurate discrimination between naive and exposed individuals. IMPORTANCE A 6-plex protein array presenting the whole inactivated virus and five nucleocapsid and spike-derived SARS-CoV-2 antigens was used to generate a serological snapshot of SARS-CoV-2 seroprevalence and seroconversion in Israel in the early months of the pandemic. Our findings confirm a very low seroprevalence of anti-SARS-CoV-2 antibodies in the Israeli adult population. We further propose that the presence of low-level nonspecific antibodies in naive individuals calls for a combined, multiantigen analysis for accurate discrimination between naive and exposed individuals enabling accurate determination of seroconversion. The developed assay is currently applied to evaluate immune responses to the Israeli vaccine during human phase I/II trials.