Advances in biosensor technologies for in vitro diagnostics have the potential to transform the practice of medicine. Despite considerable work in the biosensor field, there is still no general sensing platform that can be ubiquitously applied to detect the constellation of biomolecules in diverse clinical samples (for example, serum, urine, cell lysates or saliva) with high sensitivity and large linear dynamic range. A major limitation confounding other technologies is signal distortion that occurs in various matrices due to heterogeneity in ionic strength, pH, temperature and autofluorescence. Here we present a magnetic nanosensor technology that is matrix insensitive yet still capable of rapid, multiplex protein detection with resolution down to attomolar concentrations and extensive linear dynamic range. The matrix insensitivity of our platform to various media demonstrates that our magnetic nanosensor technology can be directly applied to a variety of settings such as molecular biology, clinical diagnostics and biodefense.

Magnetic nanotags (MNTs) are a promising alternative to fluorescent labels in biomolecular detection assays, because minute quantities of MNTs can be detected with inexpensive giant magnetoresistive (GMR) sensors, such as spin valve (SV) sensors. However, translating this promise into easy to use and multilplexed protein assays, which are highly sought after in molecular diagnostics such as cancer diagnosis and treatment monitoring, has been challenging. Here, we demonstrate multiplex protein detection of potential cancer markers at subpicomolar concentration levels and with a dynamic range of more than four decades. With the addition of nanotag amplification, the analytic sensitivity extends into the low fM concentration range. The multianalyte ability, sensitivity, scalability, and ease of use of the MNT-based protein assay technology make it a strong contender for versatile and portable molecular diagnostics in both research and clinical settings.A consensus is emerging that early detection and personalized treatment in clinics based on genetic and proteomic profiles of perhaps 4-20 biomarkers are the key to improving the survival rate of patients with complex diseases, such as cancer, autoimmune disorders, infectious diseases, and cardiovascular diseases (1-3). Although the tools for large-scale biomarker discovery with hundreds to thousands of biomarkers are available, there are few biomolecular detection tools capable of multiplex and sensitive detection of protein biomarkers that can be readily adopted in clinical settings for biomarker validation and for personalized diagnosis and treatment. This need, we believe, can be fulfilled by the magnetic nanotag (MNT)-based biomolecular assay technology reported here. We demonstrate the feasibility and implementation of this technology with multiple potential cancer markers.Several research groups are investigating MNT (4-6)-based analyte quantification as a highly sensitive alternative to optical biosensors and biochips (7-10). By labeling the target analyte of interest with MNTs (see Fig. 1), analyte detection and quantification can occur when the analyte binds to capture probes on the surface of giant magnetoresistive (GMR) sensors (11-15) such as spin valve (SV) sensors (16), which have been developed and optimized for use in hard disk drives on a scale of hundreds of millions of units annually with great economy and reliability. Such sensors, when modified for use in biological applications, were previously shown to be capable of detecting as few as 10 MNTs (13, 16). ResultsGiven the recent efforts to develop methods for early cancer detection via quantification of cancer-related cytokines, we chose the following analytes for our MNT-based protein assays: cancer embryonic antigen (CEA), eotaxin, granulocyte colonystimulating factor (G-CSF), interleukin-1-alpha (IL-1␣), interleukin-10 (IL-10), IFN gamma (IFN-␥), lactoferrin, and tumor necrosis factor alpha (TNF-␣). Fig. 1 outlines the detection scheme in which analyte is captured on the sensor surface and qu...

Monitoring the kinetics of protein interactions on a high density sensor array is vital to drug development and proteomic analysis. Label-free kinetic assays based on surface plasmon resonance are the current gold standard, but they have poor detection limits, suffer from non-specific binding, and are not amenable to high throughput analyses. Here we show that magnetically responsive nanosensors that have been scaled to over 100,000 sensors/cm2 can be used to measure the binding kinetics of various proteins with high spatial and temporal resolution. We present an analytical model that describes the binding of magnetically labeled antibodies to proteins that are immobilized on the sensor surface. This model is able to quantify the kinetics of antibody-antigen binding at sensitivities as low as 20 zeptomoles of solute.

A giant magnetoresistive (GMR) biochip based on spin valve sensor array and magnetic nanoparticle labels was developed for inexpensive, sensitive and reliable DNA detection. The DNA targets detected in this experiment were PCR products amplified from Human Papillomavirus (HPV) plasmids. The concentrations of the target DNA after PCR were around 10 nM in most cases, but concentrations of 10 pM were also detectable, which is demonstrated by experiments with artificial DNA samples. A mild but highly specific surface chemistry was used for probe oligonucleotide immobilization. Double modulation technique was used for signal detection in order to reduce the 1/f noise in the sensor. Twelve assays were performed with an accuracy of approximately 90%. Magnetic signals were consistent with particle coverage data measured with Scanning Electron Microscopy. More recent research on microfluidics showed the potential of reducing the assay time below one hour. This is the first demonstration of magnetic DNA detection using plasmid-derived samples. This study provides a direct proof that GMR sensors can be used for biomedical applications.

Radiographic imaging is routinely used to evaluate treatment response in solid tumors. Current imaging response metrics do not reliably predict the underlying biological response. Here, we present a multi-task deep learning approach that allows simultaneous tumor segmentation and response prediction. We design two Siamese subnetworks that are joined at multiple layers, which enables integration of multi-scale feature representations and in-depth comparison of pre-treatment and post-treatment images. The network is trained using 2568 magnetic resonance imaging scans of 321 rectal cancer patients for predicting pathologic complete response after neoadjuvant chemoradiotherapy. In multi-institution validation, the imaging-based model achieves AUC of 0.95 (95% confidence interval: 0.91–0.98) and 0.92 (0.87–0.96) in two independent cohorts of 160 and 141 patients, respectively. When combined with blood-based tumor markers, the integrated model further improves prediction accuracy with AUC 0.97 (0.93–0.99). Our approach to capturing dynamic information in longitudinal images may be broadly used for screening, treatment response evaluation, disease monitoring, and surveillance.

Objective: We aimed to develop a deep learning-based signature to predict prognosis and benefit from adjuvant chemotherapy using preoperative computed tomography (CT) images. Background: Current staging methods do not accurately predict the risk of disease relapse for patients with gastric cancer. Methods: We proposed a novel deep neural network (S-net) to construct a CT signature for predicting disease-free survival (DFS) and overall survival in a training cohort of 457 patients, and independently tested it in an external validation cohort of 1158 patients. An integrated nomogram was constructed to demonstrate the added value of the imaging signature to established clinicopathologic factors for individualized survival prediction. Prediction performance was assessed with respect to discrimination, calibration, and clinical usefulness. Results: The DeLIS was associated with DFS and overall survival in the overall validation cohort and among subgroups defined by clinicopathologic variables, and remained an independent prognostic factor in multivariable analysis (P < 0.001). Integrating the imaging signature and clinicopathologic factors improved prediction performance, with C-indices: 0.792-0.802 versus 0.719-0.724, and net reclassification improvement 10.1%-28.3%. Adjuvant chemotherapy was associated with improved DFS in stage II patients with high-DeLIS [hazard ratio ¼ 0.362 (95% confidence interval 0.149-0.882)] and stage III patients with high-and intermediate-DeLIS [hazard ratio ¼ 0.611 (0.442-0.843); 0.633 (0.433-0.925)]. On the other hand, adjuvantchemotherapy did not affect survival for patients with low-DeLIS, suggesting a predictive effect (P interaction ¼ 0.048, 0.016 for DFS in stage II and III disease). Conclusions: The proposed imaging signature improved prognostic prediction and could help identify patients most likely to benefit from adjuvant chemotherapy in gastric cancer.

Rapid and multiplexed measurement is vital in the detection of food-borne pathogens. While highly specific and sensitive, traditional immunochemical assays such as enzyme-linked immunosorbent assays (ELISAs) often require expensive read-out equipment (e.g. fluorescent labels) and lack the capability of multiplex detection. By combining the superior specificity of immunoassays with the sensitivity and simplicity of magnetic detection, we have developed a novel multiplex magnetic nanotag-based detection platform for mycotoxins that functions on a sub-picomolar concentration level. Unlike fluorescent labels, magnetic nanotags (MNTs) can be detected with inexpensive giant magnetoresistive (GMR) sensors such as spin-valve sensors. In the system presented here, each spinvalve sensor has an active area of 90 × 90 µm 2 , arranged in an 8×8 array. Sample is added to the antibody-immobilized sensor array prior to the addition of the biotinylated detection antibody. The sensor response is recorded in real time upon the addition of streptavidin-linked MNTs on the chip. Here we demonstrate the simultaneous detection of multiple mycotoxins (aflatoxins B 1 , zearalenone and HT-2) and show that a detection limit of 50 pg/mL can be achieved.

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