Automatic detection and multi-component segmentation of brain metastases in longitudinal MRI.

Manual segmentation of lesions, required for radiotherapy planning and follow-up, is time-consuming and error-prone. Automatic detection and segmentation can assist radiologists in these tasks. This work explores the automated detection and segmentation of brain metastases (BMs) in longitudinal MRIs.

A lung cancer diagnosis and treatment dataset with geno- and phenotypical characteristics of the patient.

This dataset comprises information about 1242 lung cancer patients collected by the Medical Oncology Department of the Puerta de Hierro University Hospital of Majadahonda in Madrid, Spain. It includes information about cancer diagnosis and treatment, as well as personal and medical data recorded during anamneses. The dataset could assist in data analysis with the aim of discovering relationships between the applied treatment(s), the evolution of the disease and the associated adverse effects.

- a large-scale dataset of 3D medical shapes for computer vision.

Objectives: The shape is commonly used to describe the objects. State-of-the-art algorithms in medical imaging are predominantly diverging from computer vision, where voxel grids, meshes, point clouds, and implicit surface models are used. This is seen from the growing popularity of ShapeNet (51,300 models) and Princeton ModelNet (127,915 models).

A proficient approach for the classification of Alzheimer's disease using a hybridization of machine learning and deep learning.

Alzheimer's disease (AD) is a neurodegenerative disorder. It causes progressive degeneration of the nervous system, affecting the cognitive ability of the human brain. Over the past two decades, neuroimaging data from Magnetic Resonance Imaging (MRI) scans has been increasingly used in the study of brain pathology related to the birth and growth of AD.

Gaming Platforms for People with ASD.

Autism spectrum disorder (ASD) has a significant impact on a person's social, emotional, and communication functioning. According to research, individualized instruction can significantly improve these deficits. One of the most successful methods of achieving this outcome is by gaming platforms that provide serious games (SGs).

The role of sonic hedgehog signaling in the oropharyngeal epithelium during jaw development.

Sonic hedgehog (Shh) is expressed in the oropharyngeal epithelium, including the frontonasal ectodermal zone (FEZ), which is defined as the boundary between Shh and Fgf8 expression domains in the frontonasal epithelium. To investigate the role of SHH signaling from the oropharyngeal epithelium, we generated mice in which Shh expression is specifically deleted in the oropharyngeal epithelium (Isl1-Cre; Shh). In the mutant mouse, Shh expression was excised in the oropharyngeal epithelium as well as FEZ and ventral forebrain, consistent with the expression pattern of Isl1.

Enhancing the reverse transcriptase function in Taq polymerase via AI-driven multiparametric rational design.

Introduction: Modification of natural enzymes to introduce new properties and enhance existing ones is a central challenge in bioengineering. This study is focused on the development of Taq polymerase mutants that show enhanced reverse transcriptase (RTase) activity while retaining other desirable properties such as fidelity, 5'- 3' exonuclease activity, effective deoxyuracyl incorporation, and tolerance to locked nucleic acid (LNA)-containing substrates. Our objective was to use AI-driven rational design combined with multiparametric wet-lab analysis to identify and validate Taq polymerase mutants with an optimal combination of these properties.

Optimizing sequence data analysis using convolution neural network for the prediction of CNV bait positions.

Background: Accurate prediction of copy number variations (CNVs) from targeted capture next-generation sequencing (NGS) data relies on effective normalization of read coverage profiles. The normalization process is particularly challenging due to hidden systemic biases such as GC bias, which can significantly affect the sensitivity and specificity of CNV detection. In many cases, the kit manifests provide only the genome coordinates of the targeted regions, and the exact bait design of the oligo capture baits is not available.

Early diagnosis of Alzheimer's disease and mild cognitive impairment using MRI analysis and machine learning algorithms.

Unlabelled: Early diagnosis of Alzheimer's disease (AD) and mild cognitive impairment (MCI) is crucial to prevent their progression. In this study, we proposed the analysis of magnetic resonance imaging (MRI) based on features including; hippocampus (HC) area size, HC grayscale statistics and texture features (mean, standard deviation, skewness, kurtosis, contrast, correlation, energy, homogeneity, entropy), lateral ventricle (LV) area size, gray matter area size, white matter area size, cerebrospinal fluid area size, patient age, weight, and cognitive score. Five machine learning classifiers; K-nearest neighborhood (KNN), support vector machine (SVM), random forest (RF), decision tree (DT), and multi-layer perception (MLP) were used to distinguish between groups: cognitively normal (CN) vs AD, early MCI (EMCI) vs late MCI (LMCI), CN vs EMCI, CN vs LMCI, AD vs EMCI, and AD vs LMCI.

The smartHEALTH European Digital Innovation Hub experiences and challenges for accelerating the transformation of public and private organizations within the innovation ecosystem.

Digital innovation can significantly enhance public health services, environmental sustainability, and social welfare. To this end, the European Digital Innovation Hub (EDIH) initiative was funded by the European Commission and national governments aiming to facilitate the digital transformation on various domains (including health) via the setup of relevant ecosystems consisting of academic institutions, research centres, start-ups, small and medium-sized enterprises, larger companies, public organizations, technology transfer offices, innovation clusters, and financial institutions. The ongoing goal of the EDIHs initiative is to bridge the gap between high-tech research taking place in universities and research centres and its deployment in real-world conditions by fostering innovation ecosystems.

Cyto-Safe: A Machine Learning Tool for Early Identification of Cytotoxic Compounds in Drug Discovery.

Cytotoxicity is essential in drug discovery, enabling early evaluation of toxic compounds during screenings to minimize toxicological risks. assays support high-throughput screening, allowing for efficient detection of toxic substances while considerably reducing the need for animal testing. Additionally, AI-based Quantitative Structure-Activity Relationship (AI-QSAR) models enhance early stage predictions by assessing the cytotoxic potential of molecular structures, which helps prioritize low-risk compounds for further validation.

Validation of a Wearable Sensor Prototype for Measuring Heart Rate to Prescribe Physical Activity: Cross-Sectional Exploratory Study.

Background: Wearable sensors are rapidly evolving, particularly in health care, due to their ability to facilitate continuous or on-demand physiological monitoring.

Objective: This study aimed to design and validate a wearable sensor prototype incorporating photoplethysmography (PPG) and long-range wide area network technology for heart rate (HR) measurement during a functional test.

Methods: We conducted a transversal exploratory study involving 20 healthy participants aged between 20 and 30 years without contraindications for physical exercise.

The role of deep learning in myocardial perfusion imaging for diagnosis and prognosis: A systematic review.

The development of state-of-the-art algorithms for computer visualization has led to a growing interest in applying deep learning (DL) techniques to the field of medical imaging. DL-based algorithms have been extensively utilized in various aspects of cardiovascular imaging, and one notable area of focus is single-photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI), which is regarded as the gold standard for non-invasive diagnosis of myocardial ischemia. However, due to the complex decision-making process of DL based on convolutional neural networks (CNNs), the explainability of DL results has become a significant area of research, particularly in the field of medical imaging.

Comparison of the Quality of Lymphatic Vessel Images Obtained Using Two Photoacoustic Imaging Systems.

Photoacoustic imaging (PAI) can evaluate lymphatic vessels with a high resolution (0.2 mm) compared with other methods. LUB0, a new PAI device that is smaller than the PAI-05 used since 2020 (both from Luxonus, Inc.

Synthetic ECG signals generation: A scoping review.

The scientific community has recently shown increasing interest in generating synthetic ECG data. In particular, synthetic ECG signals can be beneficial for understanding cardiac electrical activity, developing large and heterogeneous unbiased datasets, and anonymizing data to favour knowledge sharing and open science. In the present scoping review, various methodologies to generate synthetic ECG data have been thoroughly analysed, highlighting their limitations and possibilities.

Dimensions of Job Demands Among New-Generation Employees Based on Online Reviews by Employees.

Based on employees' online reviews, this article analyzes the dimensions of job demand for new-generation employees, using a combination model of Latent Dirichlet Allocation, decision-making trials and evaluation laboratory, interpretative structural modeling, and cross-impact matrix multiplication (LDA-DEMATEL-ISM-MICMAC). The results show that job demand is composed of 10 dimensions, and there is significant interdependence between the dimensions. Changing one dimension will quickly affect the other dimensions.

Efficient Search Algorithms for Identifying Synergistic Associations in High-Dimensional Datasets.

In recent years, there has been a notably increased interest in the study of multivariate interactions and emergent higher-order dependencies. This is particularly evident in the context of identifying synergistic sets, which are defined as combinations of elements whose joint interactions result in the emergence of information that is not present in any individual subset of those elements. The scalability of frameworks such as partial information decomposition (PID) and those based on multivariate extensions of mutual information, such as O-information, is limited by combinational explosion in the number of sets that must be assessed.

Catalytic enhancement in the performance of the microscopic two-stroke heat engine.

We consider a model of heat engine operating in the microscopic regime: the two-stroke engine. It produces work and exchanges heat in two discrete strokes that are separated in time. The working body of the engine consists of two d-level systems initialized in thermal states at two distinct temperatures.