Challenges of implementing computer-aided diagnostic models for neuroimages in a clinical setting

Advances in artificial intelligence have cultivated a strong interest in developing and validating the clinical utilities of computer-aided diagnostic models. Machine learning for diagnostic neuroimaging has often been applied to detect psychological and neurological disorders, typically on small-scale datasets or data collected in a research setting. With the collection and collation of an ever-growing number of public datasets that researchers can freely access, much work has been done in adapting machine learning models to classify these neuroimages by diseases such as Alzheimer’s, ADHD, autism, bipolar disorder, and so on. These studies often come with the promise of being implemented clinically, but despite intense interest in this topic in the laboratory, limited progress has been made in clinical implementation. In this review, we analyze challenges specific to the clinical implementation of diagnostic AI models for neuroimaging data, looking at the differences between laboratory and clinical settings, the inherent limitations of diagnostic AI, and the different incentives and skill sets between research institutions, technology companies, and hospitals. These complexities need to be recognized in the translation of diagnostic AI for neuroimaging from the laboratory to the clinic.</p

Chemical Science Membrane protein biosensing with plasmonic nanopore arrays and pore-spanning lipid membranes †

Integration of solid-state biosensors and lipid bilayer membranes is important for membrane protein research and drug discovery. In these sensors, it is critical that the solid-state sensing material does not have adverse effects on the conformation or functionality of membrane-bound molecules. In this work, pore-spanning lipid membranes are formed over an array of periodic nanopores in free-standing gold films for surface plasmon resonance (SPR) kinetic binding assays. The ability to perform kinetic assays with a transmembrane protein is demonstrated with a-hemolysin (a-HL). The incorporation of a-HL into the membrane followed by specific antibody binding (anti-a-HL) red-shifts the plasmon resonance of the gold nanopore array, which is optically monitored in real time. Subsequent fluorescence imaging reveals that the antibodies primarily bind in nanopore regions, indicating that a-HL incorporation preferentially occurs into areas of pore-spanning lipid membranes

Design and clinical validation of a point-of-care device for the diagnosis of lymphoma via contrast-enhanced microholography and machine learning

The file contains all numerical values used for the graphs in Figures 2-7

Multi-confound regression adversarial network for deep learning-based diagnosis on highly heterogenous clinical data

Automated disease detection in medical images using deep learning holds promise to improve the diagnostic ability of radiologists, but routinely collected clinical data frequently contains technical and demographic confounding factors that differ between hospitals, negatively affecting the robustness of diagnostic deep learning models. Thus, there is a critical need for deep learning models that can train on imbalanced datasets without overfitting to site-specific confounding factors. In this work, we developed a novel deep learning architecture, MUCRAN (Multi-Confound Regression Adversarial Network), to train a deep learning model on highly heterogeneous clinical data while regressing demographic and technical confounding factors. We trained MUCRAN using 16,821 clinical T1 Axial brain MRIs collected from Massachusetts General Hospital before 2019 and tested it using post-2019 data to distinguish Alzheimer's disease (AD) patients, identified using both prescriptions of AD drugs and ICD codes, from a non-medicated control group. In external validation tests using MRI data from other hospitals, the model showed a robust performance of over 90% accuracy on newly collected data. This work shows the feasibility of deep learning-based diagnosis in real-world clinical data

Atomic layer deposition: A versatile technique for plasmonics and nanobiotechnology

Although atomic layer deposition (ALD) has been used for many years as an industrial manufacturing method for microprocessors and displays, this versatile technique is finding increased use in the emerging fields of plasmonics and nanobiotechnology. In particular, ALD coatings can modify metallic surfaces to tune their optical and plasmonic properties, to protect them against unwanted oxidation and contamination, or to create biocompatible surfaces. Furthermore, ALD is unique among thin film deposition techniques in its ability to meet the processing demands for engineering nanoplasmonic devices, offering conformal deposition of dense and ultrathin films on high-aspect-ratio nanostructures at temperatures below 100°C. In this review, we present key features of ALD and describe how it could benefit future applications in plasmonics, nanosciences, and biotechnology

Plasmon color-preserved gold nanoparticle clusters for high sensitivity detection of SARS-CoV-2 based on lateral flow immunoassay

Lateral flow immunoassays (LFI) have shown great promise for point-of-care (POC) sensing applications, however, its clinical translation is often hindered by insufficient sensitivity for early detection of diseases, including severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). This is mainly due to weak absorption signals of single gold nanoparticles (AuNPs). Here, we developed AuNP clusters that maintain the red color of isolated individual AuNPs, but increase the colorimetric readout to improve the detection sensitivity. The plasmon color-preserved (PLASCOP) AuNP clusters is simply made by mixing streptavidin-coated AuNP core with satellite AuNPs coated with biotinylated antibodies. The biotinylated antibody-streptavidin linker forms a gap size over 15 nm to avoid plasmon coupling between AuNPs, thus maintaining the plasmonic color while increasing the overall light absorption. LFI sensing using PLASCOP AuNP clusters composed of 40 nm AuNPs showed a high detection sensitivity for SARS-CoV-2 nucleocapsid proteins with a limit of detection (LOD) of 0.038 ng mL(−1), which was 23.8- and 5.9-times lower value than that of single 15 nm and 40 nm AuNP conjugates, respectively. The PLASCOP AuNP clusters-based LFI sensing also shows good specificity for SARS-CoV-2 nucleocapsid proteins from other influenza and coronaviruses. In a clinical feasibility test, we demonstrated that SARS-CoV-2 particles spiked in human saliva could be detected with an LOD of 54 TCID(50) mL(−1). The developed PLASCOP AuNP clusters are promising colorimetric sensing reporters that present improved sensitivity in LFI sensing for broad POC sensing applications beyond SARS-CoV-2 detection

Nanostar Clustering Improves the Sensitivity of Plasmonic Assays

Star-shaped Au nanoparticles (Au nanostars, AuNS) have been developed to improve the plasmonic sensitivity, but their application has largely been limited to single-particle probes. We herein describe a AuNS clustering assay based on nanoscale self-assembly of multiple AuNS and which further increases detection sensitivity. We show that each cluster contains multiple nanogaps to concentrate electric fields, thereby amplifying the signal via plasmon coupling. Numerical simulation indicated that AuNS clusters assume up to 460-fold higher field density than Au nanosphere clusters of similar mass. The results were validated in model assays of protein biomarker detection. The AuNS clustering assay showed higher sensitivity than Au nanosphere. Minimizing the size of affinity ligand was found important to tightly confine electric fields and improve the sensitivity. The resulting assay is simple and fast and can be readily applied to point-of-care molecular detection schemes

Integrated Dual-Mode Chromatography to Enrich Extracellular Vesicles from Plasma

Purifying extracellular vesicles (EVs) from complex biological fluids is a critical step in analyzing EVs molecularly. Plasma lipoprotein particles (LPPs) are a significant confounding factor as they outnumber EVs >104-fold. Given their overlap in size, LPPs cannot be completely removed using standard size-exclusion chromatography. Density-based separation of LPPs can be applied but is impractical for routine use in clinical research and practice. Here a new separation approach, known as dual-mode chromatography (DMC), capable of enriching plasma EVs, and depleting LPPs is reported. DMC conveniently integrates two orthogonal separation steps in a single column device: i) size exclusion to remove high-density lipoproteins (HDLs) that are smaller than EVs; and ii) cation exchange to clear positively charged ApoB100-containing LPPs, mostly (very) low-density lipoproteins (V)LDLs, from negatively charged EVs. The strategy enables DMC to deplete most LPPs (>97% of HDLs and >99% of (V)LDLs) from human plasma, while retaining EVs (>30% of input). Furthermore, the two-in-one operation is fast (15 min per sample) and equipment-free. With abundant LPPs removed, DMC-prepared samples facilitate EV identification in imaging analyses and improve the accuracy for EV protein analysis11Nsciescopu