Low-cost standalone magnetic particle spectroscopy device for fast and sensitive immunoassays

The recent pandemic has shown how important reliable assays are for determining whether someone is infectious. One promising alternative to the established testing methods are immunoassays with magnetic nanoparticle (MNP) markers using magnetic particle spectroscopy (MPS). In this work we present the development of our “immunoMPS” which was especially built at low cost for immunoassays with infectious samples. It is a completely self-contained, mobile device with total costs of only 300€ that could be used in S2+ laboratories. The device delivers high performance on par or exceeding our lab equipment. Thus, we achieved a significantly lower limit of detection (LOD) of 4x10^8 viruses/mL of our magnetic immunoassays (MIAs) for the detection of mimic SARS-CoV-2 which is about one order of magnitude better than previous results in this research topic. In addition, it is possible to further improve the limit by optimizing the experimental setup and using DC fields

Improvements of magnetic nanoparticle assays for SARS-CoV-2 detection using a mimic virus approach

Immunoassays with magnetic nanoparticles (MNPs) as markers are a promising approach for the fast and sensitive virus detection. Upon binding of antibody-functionalized MNP on virus proteins, the hydrodynamic diameter increases and a change in the Brownian relaxation time can be measured. In this study, we detect the whole SARS-CoV-2 by mimicking it with streptavidin-coated polystyrene beads with biotinylated spike proteins. Changes of the MNP dynamics are measured by alternating current susceptometry and magnetic particle spectroscopy. Due to the multiple binding sites of MNP and virus, crosslinking enlarges the change of the hydrodynamic diameter. In order to improve the sensitivity and the limit of detection of the assay, the ratio of the virus to the MNP amount RMV/MNP is investigated in detail. High RMV/MNP ratios lead to a saturation of the MNPs with viruses, so that the cluster size and therefore the sensitivity decrease again. Additionally, it is found that the smallest virus concentrations can be detected for small MNP concentrations. It is also shown that the RMV/MNP range that can be used for an unambiguous detection of viruses depends on the virus/MNP concentration; it shifts with increasing MNP concentration to smaller RMV/MNP values. For very small virus concentrations, an increase of the Brownian relaxation time is detected implying a decrease of the hydrodynamic diameter. Furthermore, the optimal antibody concentration for MNP functionalization was determined. It is also found that a washing process with a centrifuge improves the sensitivity by reliably removing unbound antibodies and eliminating small MNPs with improper functionalization

Decoupling the Characteristics of Magnetic Nanoparticles for Ultrahigh Sensitivity

Immunoassays exploiting magnetization dynamics of magnetic nanoparticles are highly promising for mix-and-measure, quantitative, and point-of-care diagnostics. However, how single-core magnetic nanoparticles can be employed to reduce particle concentration and concomitantly maximize assay sensitivity is not fully understood. Here, we design monodisperse Néel and Brownian relaxing magnetic nanocubes (MNCs) of different sizes and compositions. We provide insights into how to decouple physical properties of these MNCs to achieve an ultrahigh sensitivity. We find that a tri-component-based Zn0.06Co0.80Fe2.14O4 particles, with out-of-phase to initial magnetic susceptibility χ^\u27\u27/χ_0 ratio of 0.47 out of nominal ratio of 0.50 for thoroughly magnetically blocked particles, show the ultrahigh magnetic sensitivity by providing rich magnetic particle spectroscopy harmonics spectrum despite bearing a lower saturation magnetization value than di-component Zn0.1Fe2.9O4 with a high value of saturation magnetization. The Zn0.06Co0.80Fe2.14O4 MNCs, coated with polyethylene glycol-based ligands, show three orders of magnitude better sensitivity than commercially available particles of comparable size

Artificial intelligence for tumour tissue detection and histological regression grading in oesophageal adenocarcinomas: a retrospective algorithm development and validation study

Background: Oesophageal adenocarcinoma and adenocarcinoma of the oesophagogastric junction are among the most common malignant epithelial tumours. Most patients receive neoadjuvant therapy before complete tumour resection. Histological assessment after resection includes identification of residual tumour tissue and areas of regressive tumour, data which are used to calculate a clinically relevant regression score. We developed an artificial intelligence (AI) algorithm for tumour tissue detection and tumour regression grading in surgical specimens from patients with oesophageal adenocarcinoma or adenocarcinoma of the oesophagogastric junction. Methods: We used one training cohort and four independent test cohorts to develop, train, and validate a deep learning tool. The material consisted of histological slides from surgically resected specimens from patients with oesophageal adenocarcinoma and adenocarcinoma of the oesophagogastric junction from three pathology institutes (two in Germany, one in Austria) and oesophageal cancer cohort of The Cancer Genome Atlas (TCGA). All slides were from neoadjuvantly treated patients except for those from the TCGA cohort, who were neoadjuvant-therapy naive. Data from training cohort and test cohort cases were extensively manually annotated for 11 tissue classes. A convolutional neural network was trained on the data using a supervised principle. First, the tool was formally validated using manually annotated test datasets. Next, tumour regression grading was assessed in a retrospective cohort of post-neoadjuvant therapy surgical specimens. The grading of the algorithm was compared with that of a group of 12 board-certified pathologists from one department. To further validate the tool, three pathologists processed whole resection cases with and without AI assistance. Findings: Of the four test cohorts, one included 22 manually annotated histological slides (n=20 patients), one included 62 sides (n=15), one included 214 slides (n=69), and the final one included 22 manually annotated histological slides (n=22). In the independent test cohorts the AI tool had high patch-level accuracy for identifying both tumour and regression tissue. When we validated the concordance of the AI tool against analyses by a group of pathologists (n=12), agreement was 63·6% (quadratic kappa 0·749; p<0·0001) at case level. The AI-based regression grading triggered true reclassification of resected tumour slides in seven cases (including six cases who had small tumour regions that were initially missed by pathologists). Use of the AI tool by three pathologists increased interobserver agreement and substantially reduced diagnostic time per case compared with working without AI assistance. Interpretation: Use of our AI tool in the diagnostics of oesophageal adenocarcinoma resection specimens by pathologists increased diagnostic accuracy, interobserver concordance, and significantly reduced assessment time. Prospective validation of the tool is required. Funding: North Rhine-Westphalia state, Federal Ministry of Education and Research of Germany, and the Wilhelm Sander Foundation