Plasmon-Free Surface-Enhanced Raman Spectroscopy Using α-Type MoO Semiconductor Nanorods with Strong Light Scattering in the Visible Regime.

Recent developments in semiconductor-based surface-enhanced Raman scattering (SERS) have achieved numerous advancements, primarily centered on the chemical mechanism. However, the role of the electromagnetic (electromagnetic mechanism) contribution in advancing semiconductor SERS substrates is still underexplored. In this study, we developed a SERS substrate based on densely aligned α-type MoO (α-MoO) semiconductor nanorods (NRs) with rectangular parallelepiped ribbon shapes with width measuring several hundred nanometers.

Screening and diagnosis of cardiovascular disease using artificial intelligence-enabled cardiac magnetic resonance imaging.

Cardiac magnetic resonance imaging (CMR) is the gold standard for cardiac function assessment and plays a crucial role in diagnosing cardiovascular disease (CVD). However, its widespread application has been limited by the heavy resource burden of CMR interpretation. Here, to address this challenge, we developed and validated computerized CMR interpretation for screening and diagnosis of 11 types of CVD in 9,719 patients.

Corrigendum: OPN promotes pro-inflammatory cytokine expression via ERK/JNK pathway and M1 macrophage polarization in Rosacea.

[This corrects the article DOI: 10.3389/fimmu.2023.

Towards trustworthy seizure onset detection using workflow notes.

A major barrier to deploying healthcare AI is trustworthiness. One form of trustworthiness is a model's robustness across subgroups: while models may exhibit expert-level performance on aggregate metrics, they often rely on non-causal features, leading to errors in hidden subgroups. To take a step closer towards trustworthy seizure onset detection from EEG, we propose to leverage annotations that are produced by healthcare personnel in routine clinical workflows-which we refer to as workflow notes-that include multiple event descriptions beyond seizures.

Artificial Intelligence Predictive Model for Hormone Therapy Use in Prostate Cancer.

BACKGROUND: Androgen deprivation therapy (ADT) with radiotherapy can benefit patients with localized prostate cancer. However, ADT can negatively impact quality of life, and there remain no validated predictive models to guide its use. METHODS: We used digital pathology images from pretreatment prostate tissue and clinical data from 5727 patients enrolled in five phase 3 randomized trials, in which treatment was radiotherapy with or without ADT, as our data source to develop and validate an artificial intelligence (AI)–derived predictive patient-specific model that would determine which patients would develop the primary end point of distant metastasis.

OPN promotes pro-inflammatory cytokine expression via ERK/JNK pathway and M1 macrophage polarization in Rosacea.

Rosacea is a chronic inflammatory dermatosis that involves dysregulation of innate and adaptive immune systems. Osteopontin (OPN) is a phosphorylated glycoprotein produced by a broad range of immune cells such as macrophages, keratinocytes, and T cells. However, the role of OPN in rosacea remains to be elucidated.

Real-time detection of acetone gas molecules at ppt levels in an air atmosphere using a partially suspended graphene surface acoustic wave skin gas sensor.

To improve the quality of modern life in the current society, low-power, highly sensitive, and reliable healthcare technology is necessary to monitor human health in real-time. In this study, we fabricated partially suspended monolayer graphene surface acoustic wave gas sensors (G-SAWs) with a love-mode wave to effectively detect ppt-level acetone gas molecules at room temperature. The sputtered SiO thin film on the surface of a black 36°YX-LiTaO (B-LT) substrate acted as a guiding layer, effectively reducing the noise and insertion loss.

Investigation on the interaction mechanisms for stability of preheated whey protein isolate with anthocyanins from blueberry.

Proteins and anthocyanins coexist in complex food systems. This research mainly studied the steady-state protective design and mechanism of the preheated protein against anthocyanins. Multispectral and molecular dynamics are utilized to illustrate the interaction mechanism between preheated whey protein isolate (pre-WPI) and anthocyanins.

Chiroptical Response Inversion and Enhancement of Room-Temperature Exciton-Polaritons Using 2D Chirality in Perovskites.

Although chiral semiconductors have shown promising progress in direct circularly polarized light (CPL) detection and emission, they still face potential challenges. A chirality-switching mechanism or approach integrating two enantiomers is needed to discriminate the handedness of a given CPL; additionally, a large material volume is required for sufficient chiroptical interaction. These two requirements pose significant obstacles to the simplification and miniaturization of the devices.

Fusion of imaging and non-imaging data for disease trajectory prediction for coronavirus disease 2019 patients.

Purpose: Our study investigates whether graph-based fusion of imaging data with non-imaging electronic health records (EHR) data can improve the prediction of the disease trajectories for patients with coronavirus disease 2019 (COVID-19) beyond the prediction performance of only imaging or non-imaging EHR data.

Approach: We present a fusion framework for fine-grained clinical outcome prediction [discharge, intensive care unit (ICU) admission, or death] that fuses imaging and non-imaging information using a similarity-based graph structure. Node features are represented by image embedding, and edges are encoded with clinical or demographic similarity.

Artificial Intelligence Predictive Model for Hormone Therapy Use in Prostate Cancer.

Background: Androgen deprivation therapy (ADT) with radiotherapy can benefit patients with localized prostate cancer. However, ADT can negatively impact quality of life and there remain no validated predictive models to guide its use.

Methods: Digital pathology image and clinical data from pre-treatment prostate tissue from 5,727 patients enrolled on five phase III randomized trials treated with radiotherapy +/- ADT were used to develop and validate an artificial intelligence (AI)-derived predictive model to assess ADT benefit with the primary endpoint of distant metastasis.

Graph-based Fusion Modeling and Explanation forDisease Trajectory Prediction.

We propose a relational graph to incorporate clinical similarity between patients while building personalized clinical event predictors with a focus on hospitalized COVID-19 patients. Our graph formation process fuses heterogeneous data, i.e.

Predicting 30-day all-cause hospital readmission using multimodal spatiotemporal graph neural networks.

Reduction in 30-day readmission rate is an important quality factor for hospitals as it can reduce the overall cost of care and improve patient post-discharge outcomes. While deep-learning-based studies have shown promising empirical results, several limitations exist in prior models for hospital readmission prediction, such as: (a) only patients with certain conditions are considered, (b) do not leverage data temporality, (c) individual admissions are assumed independent of each other, which ignores patient similarity, (d) limited to single modality or single center data. In this study, we propose a multimodal, spatiotemporal graph neural network (MM-STGNN) for prediction of 30-day all-cause hospital readmission, which fuses in-patient multimodal, longitudinal data and models patient similarity using a graph.

Reconfigurable magnon interference by on-chip dynamic wavelength conversion.

Spin waves (SWs), an ultra-low power magnetic excitation in ferro or antiferromagnetic media, have tremendous potential as transport less data carriers for post-CMOS technology using their wave interference properties. The concept of magnon interference originates from optical interference, resulting in a historical taboo of maintaining an identical wavevector for magnon interference-based devices. This makes the attainment of on-chip design reconfigurability challenging owing to the difficulty in phase tuning via external fields.

Graph-based Fusion Modeling and Explanation for Disease Trajectory Prediction.

We propose a relational graph to incorporate clinical similarity between patients while building personalized clinical event predictors with a focus on hospitalized COVID-19 patients. Our graph formation process fuses heterogeneous data, i.e.

An updated review on the stability of anthocyanins regarding the interaction with food proteins and polysaccharides.

The health benefits of anthocyanins are compromised by their chemical instability and susceptibility to external stress. Researchers found that the interaction between anthocyanins and macromolecular components such as proteins and polysaccharides substantially determines the stability of anthocyanins during food processing and storage. The topic thus has attracted much attention in recent years.

Sensitivity enhancement in magnetic sensor using CoFeB/YFeO resonator.

Magnonics, an emerging research field that uses the quanta of spin waves as data carriers, has a potential to dominate the post-CMOS era owing to its intrinsic property of ultra-low power operation. Spin waves can be manipulated by a wide range of parameters; thus, they are suitable for sensing applications in a wide range of physical fields. In this study, we designed a highly sensitive, simple structure, and ultra-low power magnetic sensor using a simple CoFeB/YFeO bilayer structure.

Preparing for the next pandemic via transfer learning from existing diseases with hierarchical multi-modal BERT: a study on COVID-19 outcome prediction.

Developing prediction models for emerging infectious diseases from relatively small numbers of cases is a critical need for improving pandemic preparedness. Using COVID-19 as an exemplar, we propose a transfer learning methodology for developing predictive models from multi-modal electronic healthcare records by leveraging information from more prevalent diseases with shared clinical characteristics. Our novel hierarchical, multi-modal model ([Formula: see text]) integrates baseline risk factors from the natural language processing of clinical notes at admission, time-series measurements of biomarkers obtained from laboratory tests, and discrete diagnostic, procedure and drug codes.

Effect of bovine serum albumin on the stability and antioxidant activity of blueberry anthocyanins during processing and in vitro simulated digestion.

The application of blueberry anthocyanins (ANs) was limited due to their low in-process stability and bioavailability. In our study, the stability and antioxidant capacity of ANs before and after adding bovine serum albumin (BSA) were examined by simulating various processing, storage (light, sucrose, and vitamin C (Vc)), and in vitro simulated digestion parameters. For this purpose, pH-differential method, high performance liquid chromatography-tandem mass spectrometry (HPLC-MS/MS), peroxyl scavenging capacity assay, and cellular antioxidant assay were conducted.

The adsorption of aromatic macromolecules on graphene with entropy-tailored behavior and its utilization in exfoliating graphite.

Aromatic macromolecules tend to form a compact conformation after physically adsorbed on graphene and it brings about great entropy loss for physisorption, due to the strong interaction between aromatic macromolecules and graphene. However, previous researches have validated the availability of aromatic macromolecules to stabilize graphene based on physisorption. In order to clarify the underlying mechanism of this physisorption process on graphene, a series of aromatic polyamide copolymers are used as models in this research.

Data valuation for medical imaging using Shapley value and application to a large-scale chest X-ray dataset.

The reliability of machine learning models can be compromised when trained on low quality data. Many large-scale medical imaging datasets contain low quality labels extracted from sources such as medical reports. Moreover, images within a dataset may have heterogeneous quality due to artifacts and biases arising from equipment or measurement errors.

Comparison of segmentation-free and segmentation-dependent computer-aided diagnosis of breast masses on a public mammography dataset.

Purpose: To compare machine learning methods for classifying mass lesions on mammography images that use predefined image features computed over lesion segmentations to those that leverage segmentation-free representation learning on a standard, public evaluation dataset.

Methods: We apply several classification algorithms to the public Curated Breast Imaging Subset of the Digital Database for Screening Mammography (CBIS-DDSM), in which each image contains a mass lesion. Segmentation-free representation learning techniques for classifying lesions as benign or malignant include both a Bag-of-Visual-Words (BoVW) method and a Convolutional Neural Network (CNN).