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Magnetic nanoparticle (MNP)-based immunochromatographic tests (ICTs) display long-term stability and an enhanced capability for multiplex biomarker detection, surpassing conventional gold nanoparticles (AuNPs) and fluorescence-based ICTs. In this study, we innovatively developed zwitterionic silica-coated MNPs (MNP@Si-Zwit/COOH) with outstanding antifouling capabilities and effectively utilised them for the simultaneous identification of the nucleocapsid protein (N protein) of the severe acute respiratory syndrome coronavirus (SARS-CoV-2) and influenza A/B. The carboxyl-functionalised MNPs with 10% zwitterionic ligands (MNP@Si-Zwit 10/COOH) exhibited a wide linear dynamic detection range and the most pronounced signal-to-noise ratio when used as probes in the ICT. The relative limit of detection (LOD) values were achieved in 12 min by using a magnetic assay reader (MAR), with values of 0.0062 ng/mL for SARS-CoV-2 and 0.0051 and 0.0147 ng/mL, respectively, for the N protein of influenza A and influenza B. By integrating computer vision and deep learning to enhance the image processing of immunoassay results for multiplex detection, a classification accuracy in the range of 0.9672-0.9936 was achieved for evaluating the three proteins at concentrations of 0, 0.1, 1, and 10 ng/mL. The proposed MNP-based ICT for the multiplex diagnosis of biomarkers holds substantial promise for applications in both medical institutions and self-administered diagnostic settings., Competing Interests: Declaration of competing interest The authors declare no competing financial interests., (Copyright © 2024. Published by Elsevier B.V.)

Breast density is an important risk factor for breast cancer development; however, imager inconsistency in density reporting can lead to patient and clinician confusion. A deep learning (DL) model for mammographic density grading was examined in a retrospective multi-reader multi-case study consisting of 928 image pairs and assessed for impact on inter- and intra-reader variability and reading time. Seven readers assigned density categories to the images, then re-read the test set aided by the model after a 4-week washout. To measure intra-reader agreement, 100 image pairs were blindly double read in both sessions. Linear Cohen Kappa (κ) and Student's t-test were used to assess the model and reader performance. The model achieved a κ of 0.87 (95% CI: 0.84, 0.89) for four-class density assessment and a κ of 0.91 (95% CI: 0.88, 0.93) for binary non-dense/dense assessment. Superiority tests showed significant reduction in inter-reader variability (κ improved from 0.70 to 0.88, p ≤ 0.001) and intra-reader variability (κ improved from 0.83 to 0.95, p ≤ 0.01) for four-class density, and significant reduction in inter-reader variability (κ improved from 0.77 to 0.96, p ≤ 0.001) and intra-reader variability (κ improved from 0.89 to 0.97, p ≤ 0.01) for binary non-dense/dense assessment when aided by DL. The average reader mean reading time per image pair also decreased by 30%, 0.86 s (95% CI: 0.01, 1.71), with six of seven readers having reading time reductions. [ABSTRACT FROM AUTHOR]

The break accident process of a supercritical carbon dioxide (SCO2) reactor system presents a transcritical phenomenon. Operating under high pressure and a wide parameter range, the SCO2 system introduces multiphase characteristics to the critical flow of carbon dioxide (CO2) at various system positions. Nevertheless, current research lacks a comprehensive critical flow model capable of accommodating a broad parameter range with high precision. Data-driven approaches offer the potential to enhance accuracy by leveraging an expanding training database. To precisely forecast SCO2 critical flow efficiency, this study established an SCO2 critical flow model using deep learning techniques. The conservation equations and sensitivity analysis of experimental data informed the feature selection for the deep learning model, accomplished through the recurrent neural network (RNN) method, employing K-fold cross-validation and L2 regularization. The result was the SCO2-RNN critical flow model, boasting improved prediction accuracy and enhanced generalization capabilities. Subsequently, the optimal hyperparameters were determined by using a genetic algorithm. This model yielded an average error of 4.88% in predicted results, with a maximum error of 14.24%. The average error when extrapolating to the generalization data was 5.73%, with a maximum error of 20.45%. After the implementation of transfer learning, the average error decreased to 1.75%, with a final maximum error of 4.15%. Generalization results, using new data based on the trained model, underscore that the deep learning model meets engineering requirements for both efficiency and accuracy. • A wide-parameter, high-precision critical flow model was constructed based on the RNN deep learning model. • K-fold cross-validation was used to overcome the problem of insufficient training data, whereas L2 regularization was used to prevent overfitting. • A genetic algorithm was used to optimize the hyperparameters, and transfer learning was introduced to expand the applicability of the model. • The average model error is as low as 1.68% and the maximum error is as low as 4.15%. [ABSTRACT FROM AUTHOR]