A GMR-based assay for quantification of the human response to influenza

Real-time temperature correction for magnetoresistive biosensors integrated with temperature modulator

Magnetoresistance-based biosensors utilize changes in electrical resistance upon varying magnetic fields to measure biological molecules or events involved with magnetic tags. However, electrical resistance fluctuates with temperature. To decouple unwanted temperature-dependent signals from the signal of interest, various methods have been proposed to correct signals from magnetoresistance-based biosensors. Yet, there is still a need for a temperature correction method capable of instantaneously correcting signals from all sensors in an array, as multiple biomarkers need to be detected simultaneously with a group of sensors in a central laboratory or point-of-care setting. Here we report a giant magnetoresistive biosensor system that enables real-time temperature correction for individual sensors using temperature correction coefficients obtained through a temperature sweep generated by an integrated temperature modulator. The algorithm with individual temperature correction coefficients obviously outperformed that using the average temperature correction coefficient. Further, temperature regulation did not eliminate temperature-dependent signals completely. To demonstrate that the method can be used in biomedical applications where large temperature variations are involved, binding kinetics experiments and melting curve analysis were conducted with the temperature correction method. The method successfully removed all temperature-dependent artifacts and thus produced more precise kinetic parameters and melting temperatures of DNA hybrids.

Rapid, Point-of-Care Host-Based Gene Expression Diagnostics Using Giant Magnetoresistive Biosensors

Host-based gene expression analysis is a promising tool for a broad range of clinical applications, including rapid infectious disease diagnostics and real-time disease monitoring. However, the complex instrumentation requirements and slow turnaround-times associated with traditional gene expression analysis methods have hampered their widespread adoption at the point-of-care (POC). To overcome these challenges, we have developed an automated and portable platform that utilizes polymerase chain reaction (PCR) and giant magnetoresistive (GMR) biosensors to perform rapid multiplexed, targeted gene expression analysis at the POC. As proof-of-concept, we utilized our platform to amplify and measure the expression of four genes (HERC5, HERC6, IFI27, and IFIH1) that were previously shown to be upregulated in hosts infected with influenza viruses. The compact instrument conducted highly automated PCR amplification and GMR detection to measure the expression of the four genes in multiplex, then utilized Bluetooth communication to relay results to users on a smartphone application. To validate the platform, we tested 20 cDNA samples from symptomatic patients that had been previously diagnosed as either influenza-positive or influenza-negative using a RT-PCR virology panel. A non-parametric Mann-Whitney test revealed that day 0 (day of symptom onset) gene expression was significantly different between the two groups (p < 0.0001, n = 20). Hence, we preliminarily demonstrated that our platform could accurately discriminate between symptomatic influenza and non-influenza populations based on host gene expression in ∼30 min. This study not only establishes the potential clinical utility of our proposed assay and device for influenza diagnostics but it also paves the way for broadscale and decentralized implementation of host-based gene expression diagnostics at the POC.

Advances in point-of-care genetic testing for personalized medicine applications

Breakthroughs within the fields of genomics and bioinformatics have enabled the identification of numerous genetic biomarkers that reflect an individual's disease susceptibility, disease progression, and therapy responsiveness. The personalized medicine paradigm capitalizes on these breakthroughs by utilizing an individual's genetic profile to guide treatment selection, dosing, and preventative care. However, integration of personalized medicine into routine clinical practice has been limited-in part-by a dearth of widely deployable, timely, and cost-effective genetic analysis tools. Fortunately, the last several decades have been characterized by tremendous progress with respect to the development of molecular point-of-care tests (POCTs). Advances in microfluidic technologies, accompanied by improvements and innovations in amplification methods, have opened new doors to health monitoring at the point-of-care. While many of these technologies were developed with rapid infectious disease diagnostics in mind, they are well-suited for deployment as genetic testing platforms for personalized medicine applications. In the coming years, we expect that these innovations in molecular POCT technology will play a critical role in enabling widespread adoption of personalized medicine methods. In this work, we review the current and emerging generations of point-of-care molecular testing platforms and assess their applicability toward accelerating the personalized medicine paradigm.

Point-of-Care Testing of Enzyme Polymorphisms for Predicting Hypnotizability and Postoperative Pain

Hypnotizability is a stable trait that moderates the benefit of hypnosis for treating pain, but limited availability of hypnotizability testing deters widespread use of hypnosis. Inexpensive genotyping of four single-nucleotide polymorphisms in the catechol-o-methyltransferase (COMT) gene was performed using giant magnetoresistive biosensors to determine if hypnotizable individuals can be identified for targeted hypnosis referrals. For individuals with the proposed optimal COMT diplotypes, 89.5% score highly on the Hypnotic Induction Profile (odds ratio, 6.12; 95% CI, 1.26-28.75), which identified 40.5% of the treatable population. Mean hypnotizability scores of the optimal group were significantly higher than the total population (P = 0.015; effect size = 0.60), an effect that was present in women (P = 0.0015; effect size = 0.83), but not in men (P = 0.28). In an exploratory cohort, optimal individuals also reported significantly higher postoperative pain scores (P = 0.00030; effect size = 1.93), indicating a greater need for treatment.

The IFN-stimulated gene IFI27 counteracts innate immune responses after viral infections by interfering with RIG-I signaling

The recognition of viral nucleic acids by host pattern recognition receptors (PRRs) is critical for initiating innate immune responses against viral infections. These innate immune responses are mediated by the induction of interferons (IFNs), IFN-stimulated genes (ISGs) and pro-inflammatory cytokines. However, regulatory mechanisms are critical to avoid excessive or long-lasting innate immune responses that may cause detrimental hyperinflammation. Here, we identified a novel regulatory function of the ISG, IFN alpha inducible protein 27 (IFI27) in counteracting the innate immune responses triggered by cytoplasmic RNA recognition and binding. Our model systems included three unrelated viral infections caused by Influenza A virus (IAV), Severe Acute Respiratory Syndrome coronavirus 2 (SARS-CoV-2), and Sendai virus (SeV), and transfection with an analog of double-stranded (ds) RNA. Furthermore, we found that IFI27 has a positive effect on IAV and SARS-CoV-2 replication, most likely due to its ability to counteract host-induced antiviral responses, including in vivo. We also show that IFI27 interacts with nucleic acids and PRR retinoic acid-inducible gene I (RIG-I), being the interaction of IFI27 with RIG-I most likely mediated through RNA binding. Interestingly, our results indicate that interaction of IFI27 with RIG-I impairs RIG-I activation, providing a molecular mechanism for the effect of IFI27 on modulating innate immune responses. Our study identifies a molecular mechanism that may explain the effect of IFI27 in counterbalancing innate immune responses to RNA viral infections and preventing excessive innate immune responses. Therefore, this study will have important implications in drug design to control viral infections and viral-induced pathology.

One-Shot Full-Range Quantification of Multi-Biomarkers With Different Abundance by a Tandem Giant Magnetoresistance Assay

In this study, we reported a tandem giant magnetoresistance (GMR) assay that realized the one-shot quantification of multi-biomarkers of infection, C-reactive protein (CRP) with procalcitonin (PCT), and neutrophil gelatinase-associated lipocalin (NGAL), all of which could cover their clinically relevant concentration ranges under a different principle. In the presence of co-determined assay, we quantified these three biomarkers in undiluted human blood serum in a single test. The tandem principle, based on which quantification of CRP occurs, combines a sandwich assay and an indirect competitive assay, which allows for the discrimination of the concentration values resulting from the multivalued dose-response curve (‘Hook’ effect), which characterizes the one-step sandwich assay at high CRP concentrations. However, the entire diagnostically dynamic range, in the quantification of PCT and NGAL, was achieved by differential coating of two identical GMR sensors operated in tandem and by combining two standard curves. The sensor quantified low detection limits and a broader dynamic range for the detection of infection biomarkers. The noticeable features of the assay are its dynamic range and small sample volume requirement (50 μL), and the need for a short measurement time of 15 min. These figures of merit render it a prospective candidate for practical use in point-of-care analysis.

A Dynamic Hysteresis Model for TMR-Current Sensors Based on Probability Estimation of Hysteresis Operator and Its Switching Time

Hysteresis is one of the main factors affecting the measurement accuracy of TMR sensors, especially in dynamic measurements. The commonly used Preisach hysteresis compensation model has some problems, such as complex modeling and difficulty in accurately measuring the step time, resulting in low accuracy in dynamic measurements. In this paper, considering the distribution characteristics of the conversion time of the hysteresis operator in dynamic measurements, a dynamic hysteresis model based on the probability estimation of the hysteresis operator and its conversion time is proposed. Compared with the existing methods, this method only needs to calculate the distribution of the sensor hysteresis operator to realize the calculation of hysteresis characteristics without a physical model or fitting algorithm. It has good generalization performance and a corresponding fast speed. In the test of two typical TMR sensors, compared with the transmission Preisach model, the maximum error of this method is reduced by 46.7%, the variance can be reduced by 90.2%, and the average value can be reduced by 65.1%.