Clinical Decision Support Using Speech Signal Analysis: Systematic Scoping Review of Neurological Disorders.

Background: Digital biomarkers are increasingly used in clinical decision support for various health conditions. Speech features as digital biomarkers can offer insights into underlying physiological processes due to the complexity of speech production. This process involves respiration, phonation, articulation, and resonance, all of which rely on specific motor systems for the preparation and execution of speech.

EnrichRBP: an automated and interpretable computational platform for predicting and analyzing RNA-binding protein events.

Motivation: Predicting RNA-binding proteins (RBPs) is central to understanding post-transcriptional regulatory mechanisms. Here, we introduce EnrichRBP, an automated and interpretable computational platform specifically designed for the comprehensive analysis of RBP interactions with RNA.

Results: EnrichRBP is a web service that enables researchers to develop original deep learning and machine learning architectures to explore the complex dynamics of RNA-binding proteins.

Integrated of Hyperspectral Imaging and Machine Learning Algorithms for Nondestructive Detection of Therapeutic Properties of Plants.

The approaches used to determine the medicinal properties of the plants are often destructive, labor-intensive, time-consuming, and expensive, making it impossible to analyze their quality analysis online. Performance of hyperspectral imaging (HSI) integrated with intelligent techniques to overcome these problems was investigated in this research. For this purpose, three classification methods-support vector machine, random forest (RF), and extreme gradient boosting-were studied for the classification of plants in three classes of medicinal, edible, and ornamental for the organs of leaf, stem, flower, and root.

Global Minimization of Electronic Hamiltonian 1-Norm via Linear Programming in the Block Invariant Symmetry Shift (BLISS) Method.

The cost of encoding a system Hamiltonian in a digital quantum computer as a linear combination of unitaries (LCU) grows with the 1-norm of the LCU expansion. The Block Invariant Symmetry Shift (BLISS) technique reduces this 1-norm by modifying the Hamiltonian action on only the undesired electron-number subspaces. Previously, BLISS required a computationally expensive nonlinear optimization that was not guaranteed to find the global minimum.

Automated characterisation of cerebral microbleeds using their size and spatial distribution on brain MRI.

Cerebral microbleeds (CMBs) are small, hypointense hemosiderin deposits in the brain measuring 2-10 mm in diameter. As one of the important biomarkers of small vessel disease, they have been associated with various neurodegenerative and cerebrovascular diseases. Hence, automated detection, and subsequent extraction of clinically useful metrics (e.

Application of deep learning in automated localization and interpretation of coronary artery calcification in oncological PET/CT scans.

Coronary artery calcification (CAC) is a key marker of coronary artery disease (CAD) but is often underreported in cancer patients undergoing non-gated CT or PET/CT scans. Traditional CAC assessment requires gated CT scans, leading to increased radiation exposure and the need for specialized personnel. This study aims to develop an artificial intelligence (AI) method to automatically detect CAC from non-gated, freely-breathing, low-dose CT images obtained from positron emission tomography/computed tomography scans.

Evaluation of a content-based image retrieval system for radiologists in high-resolution CT of interstitial lung diseases.

Background: This retrospective study aims to evaluate the impact of a content-based image retrieval (CBIR) application on diagnostic accuracy and confidence in interstitial lung disease (ILD) assessment using high-resolution computed tomography CT (HRCT).

Methods: Twenty-eight patients with verified pattern-based ILD diagnoses were split into two equal datasets (1 and 2). The images were assessed by two radiology residents (3rd and 5th year) and one expert radiologist in four sessions.

Targeting audiences' moral values shapes misinformation sharing.

Does aligning misinformation content with individuals' core moral values facilitate its spread? We investigate this question in three behavioral experiments ( = 615; = 505; ₂ = 533) that examine how the alignment of audience values and misinformation framing affects sharing behavior, in conjunction with analyzing real-world Twitter data ( = 20,235; 809,414 tweets) that explores how aligning the moral values of message senders with misinformation content influences its dissemination in the context of COVID-19 vaccination misinformation. First, we investigate how aligning messages' moral framing with participants' moral values impacts participants' intentions to share true and false news headlines and whether this effect is driven by a lack of analytical thinking. Our results show that framing a post such that it aligns with audiences' moral values leads to increased sharing intentions, independent of headline familiarity, and participants' political ideology but find no effect of analytical thinking.

Preliminary Development and Validation of Automated Nociception Recognition Using Computer Vision in Perioperative Patients.

Background: Effective pain recognition and treatment in perioperative environments reduce length of stay and decrease risk of delirium and chronic pain. We sought to develop and validate preliminary computer vision-based approaches for nociception detection in hospitalized patients.

Methods: Prospective observational cohort study using red-green-blue camera detection of perioperative patients.

Dextran stabilised hematite: a sustainable anode in aqueous electrolytes.

During the last decades, the use of innovative hybrid materials in energy storage devices has led to notable advances in the field. However, further enhancement of their electrochemical performance faces significant challenges nowadays, imposed by the materials used in the electrodes and the electrolyte. Such problems include the high solubility of both the organic and the inorganic anode components in the electrolyte as well as the limited intrinsic electronic conductivity and substantial volume variation of the materials during cycling.

A framework of multi-view machine learning for biological spectral unmixing of fluorophores with overlapping excitation and emission spectra.

The accuracy of assigning fluorophore identity and abundance, known as spectral unmixing, in biological fluorescence microscopy images remains a significant challenge due to the substantial overlap in emission spectra among fluorophores. In traditional laser scanning confocal spectral microscopy, fluorophore information is acquired by recording emission spectra with a single combination of discrete excitation wavelengths. However, organic fluorophores possess characteristic excitation spectra in addition to their unique emission spectral signatures.

Exploring the potential of compound-protein complex structure-free models in virtual screening using BlendNet.

Identifying new compounds that interact with a target is a crucial time-limiting step in the initial phases of drug discovery. Compound-protein complex structure-based affinity prediction models can expedite this process; however, their dependence on high-quality three-dimensional (3D) complex structures limits their practical application. Prediction models that do not require 3D complex structures for binding-affinity estimation offer a theoretically attractive alternative; however, accurately predicting affinity without interaction information presents significant challenges.

CO-templated [LnNi] heterometallic compounds for enhanced magnetocaloric effects at low fields.

In the history of magnetochemistry development, lanthanide-transition (3d-4f) heterometallic compounds have been considered an attractive candidate for magnetic refrigerants. Herein, a series of heterometallic compounds have been designed and templated by CO anions, that is, {[LnNi(L)(CO)(HO)]·HO} [Ln = Gd (. Gd2Ni) = Sm (.

Factors associated with abusive head trauma in young children presenting to emergency medical services using a large language model.

Objectives: Abusive head trauma (AHT) is a leading cause of death in young children. Analyses of patient characteristics presenting to Emergency Medical Services (EMS) are often limited to structured data fields. Artificial Intelligence (AI) and Large Language Models (LLM) may identify rare presentations like AHT through factors not found in structured data.

Predicting Age and Visual-Motor Integration Using Origami Photographs: Deep Learning Study.

Background: Origami is a popular activity among preschool children and can be used by therapists as an evaluation tool to assess children's development in clinical settings. It is easy to implement, appealing to children, and time-efficient, requiring only simple materials-pieces of paper. Furthermore, the products of origami may reflect children's ages and their visual-motor integration (VMI) development.

Distal Access Catheter Improves Balloon Guide and Stent Retriever Thrombectomy Outcomes in Nonagenarians.

Background And Purpose: The safety and effectiveness of endovascular techniques in elderly patients with large vessel occlusion (LVO) remain controversial. We investigated the angiographic and clinical outcomes of nonagenarians treated with different endovascular techniques using a balloon guide catheter (BGC), distal aspiration catheter (DAC), and/or stent retriever (SR).

Methods: We analyzed the data from the Registry of Combined versus Single Thrombectomy Techniques (ROSSETTI) of consecutive nonagenarian patients with anterior circulation LVO and compared the outcomes of those treated with BGC+noDAC+SR (101-group), BGC+DAC+SR (111-group), and noBGC+DAC+SR (011-group).

Preliminary validation of the virtual bariatric endoscopic simulator.

Background And Aims: Obesity is a global health concern. Bariatric surgery offers reliably effective and durable weight loss and improvements of other comorbid conditions. However, the accessibility of bariatric surgery remains limited.

Novel Transfer Learning Approach for Detecting Infected and Healthy Maize Crop Using Leaf Images.

Maize is a staple crop worldwide, essential for food security, livestock feed, and industrial uses. Its health directly impacts agricultural productivity and economic stability. Effective detection of maize crop health is crucial for preventing disease spread and ensuring high yields.

Enhanced neurological anomaly detection in MRI images using deep convolutional neural networks.

Introduction: Neurodegenerative diseases, including Parkinson's, Alzheimer's, and epilepsy, pose significant diagnostic and treatment challenges due to their complexity and the gradual degeneration of central nervous system structures. This study introduces a deep learning framework designed to automate neuro-diagnostics, addressing the limitations of current manual interpretation methods, which are often time-consuming and prone to variability.

Methods: We propose a specialized deep convolutional neural network (DCNN) framework aimed at detecting and classifying neurological anomalies in MRI data.

Molecular Characterization and Potential Inhibitors Prediction of Protein Arginine Methyltransferase-2 in Carcinoma: An Insight from Molecular Docking, ADMET Profiling and Molecular Dynamics Simulation Studies.

Objectives: To predict and characterize the three-dimensional (3D) structure of protein arginine methyltransferase 2 (PRMT2) using homology modeling, besides, the identification of potent inhibitors for enhanced comprehension of the biological function of this protein arginine methyltransferase (PRMT) family protein in carcinogenesis.

Materials And Methods: An method was employed to predict and characterize the three-dimensional structure. The bulk of PRMTs in the PDB shares just a structurally conserved catalytic core domain.

Live and mediated user engagements: A comparative dataset from two Bengali audio-story based youtube channels.

The dataset contains user engagement and language-related information from two audio story-producing channels on YouTube. It offers a comparative view of live and mediated engagements, which includes information pertinent to the user's interaction of audio-story based YouTube contents. The speciality of this dataset is the inclusion of textual data of live comments on YouTube videos.

Data-driven score tuning for ChooseLD: A structure-based drug design algorithm with empirical scoring and evaluation of ligand-protein docking predictability.

Computerized molecular docking methodologies are pivotal in screening, a crucial facet of modern drug design. ChooseLD, a docking simulation software, combines structure- and ligand-based drug design methods with empirical scoring. Despite advancements in computerized molecular docking methodologies, there remains a gap in optimizing the predictive capabilities of docking simulation software.

Uncertainty estimation in diagnosis generation from large language models: next-word probability is not pre-test probability.

Objective: To evaluate large language models (LLMs) for pre-test diagnostic probability estimation and compare their uncertainty estimation performance with a traditional machine learning classifier.

Materials And Methods: We assessed 2 instruction-tuned LLMs, Mistral-7B-Instruct and Llama3-70B-chat-hf, on predicting binary outcomes for Sepsis, Arrhythmia, and Congestive Heart Failure (CHF) using electronic health record (EHR) data from 660 patients. Three uncertainty estimation methods-Verbalized Confidence, Token Logits, and LLM Embedding+XGB-were compared against an eXtreme Gradient Boosting (XGB) classifier trained on raw EHR data.

Developing a Research Center for Artificial Intelligence in Medicine.

Artificial intelligence (AI) and machine learning (ML) are driving innovation in biosciences and are already affecting key elements of medical scholarship and clinical care. Many schools of medicine are capitalizing on the promise of these new technologies by establishing academic units to catalyze and grow research and innovation in AI/ML. At Stanford University, we have developed a successful model for an AI/ML research center with support from academic leaders, clinical departments, extramural grants, and industry partners.

Non-targeted LC-MS metabolomics reveal shifts from wound-induced enzymatic browning to lignification during extended storage of fresh-cut lettuce in modified atmosphere packaging.

Modified Atmosphere Packaging (MAP) is a conventional method used to prolong the shelf-life of fresh-cut vegetables, including lettuce. However, MAP-stored lettuce remains perishable, and its deterioration mechanism is not fully understood. Here, we utilized non-targeted LC-MS metabolomics to evaluate the effects of cutting and extended storage time on metabolite profiles of lettuce stored in MAP.