- 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).

Internal consistency of the Mental Health Professional Culture Inventory: A pilot study in Romanian population.

The objective of this study (registered under number 2020 006) was to assess the internal consistency of the revised Mental Health Professional Culture Inventory (MHPCI) scale, which comprises 15 items, among mental health service workers in Romania. To examine the psychometric properties of the MHPCI questionnaire within the Romanian population, we employed two main methods: The partial credit model (PCM) and Exploratory factor analysis (EFA). A total of 94 individuals were interviewed, and among them, 71 provided complete responses to the questionnaire.

Relationship Between Depression and Falls Among Nursing Home Residents: Integrative Review.

Background: Depression is a highly prevalent psychopathological condition among older adults, particularly those institutionalized in nursing homes (NHs). Unfortunately, it is poorly identified and diagnosed. NH residents are twice as likely to fall as community-dwelling older adults.

Native learning ability and not age determines the effects of brain stimulation.

Healthy aging often entails a decline in cognitive and motor functions, affecting independence and quality of life in older adults. Brain stimulation shows potential to enhance these functions, but studies show variable effects. Previous studies have tried to identify responders and non-responders through correlations between behavioral change and baseline parameters, but results lack generalization to independent cohorts.

A Multi-Scale CNN for Transfer Learning in sEMG-Based Hand Gesture Recognition for Prosthetic Devices.

Advancements in neural network approaches have enhanced the effectiveness of surface Electromyography (sEMG)-based hand gesture recognition when measuring muscle activity. However, current deep learning architectures struggle to achieve good generalization and robustness, often demanding significant computational resources. The goal of this paper was to develop a robust model that can quickly adapt to new users using Transfer Learning.

Effects of intrinsic and extrinsic growth factors on virulence gene expression of foodborne pathogens in vitro and in food model systems; a review.

Since foodborne diseases are one of the major causes of human hospitalization and death, one of the main challenges to food safety is the elimination or reduction of pathogens from food products throughout the food production chain. Pathogens, such as species, , , species, , , species, etc., enter the consumer's body through the consumption of contaminated food and eventually cause disease, disability, and death in humans.

Between green hills and green bills: Unveiling the green shades of sustainability and burden shifting through multi-objective optimization in Swiss energy system planning.

The Paris Agreement is the first-ever universally accepted and legally binding agreement on global climate change. It is a bridge between today's and climate-neutrality policies and strategies before the end of the century. Critical to this endeavor is energy system modeling, which, while adept at devising cost-effective carbon-neutral strategies, often overlooks the broader environmental and social implications.

The Code of Ethics for Chemists between Universal Moral Values and Local Reality.

In an increasingly globalized world, threatened by resource depletion, global warming and pollution, scientists in general and the chemists in particular are obliged to rapidly integrate ethical values into their work. This article shows that the codes of ethics are a valuable aid in this process. Two real life examples are highlighted.

The future backbone of nutritional science: integrating public health priorities with system-oriented precision nutrition.

Adopting policies that promote health for the entire biosphere (One Health) requires human societies to transition towards a more sustainable food supply as well as to deepen the understanding of the metabolic and health effects of evolving food habits. At the same time, life sciences are experiencing rapid and groundbreaking technological developments, in particular in laboratory analytics and biocomputing, placing nutrition research in an unprecedented position to produce knowledge that can be translated into practice in line with One Health policies. In this dynamic context, nutrition research needs to be strategically organised to respond to these societal expectations.

Improving deep learning-based automatic cranial defect reconstruction by heavy data augmentation: From image registration to latent diffusion models.

Modeling and manufacturing of personalized cranial implants are important research areas that may decrease the waiting time for patients suffering from cranial damage. The modeling of personalized implants may be partially automated by the use of deep learning-based methods. However, this task suffers from difficulties with generalizability into data from previously unseen distributions that make it difficult to use the research outcomes in real clinical settings.

An Overview of Public Retinal Optical Coherence Tomography Datasets: Access, Annotations, and Beyond.

In ophthalmology, Optical Coherence Tomography (OCT) has become a daily used tool in the diagnostics and therapeutic planning of various diseases. Publicly available datasets play a crucial role in advancing research by providing access to diverse imaging data for algorithm development. The accessibility, data format, annotations, and metadata are not consistent across OCT datasets, making it challenging to efficiently use the available resources.

Multimodal representations of biomedical knowledge from limited training whole slide images and reports using deep learning.

The increasing availability of biomedical data creates valuable resources for developing new deep learning algorithms to support experts, especially in domains where collecting large volumes of annotated data is not trivial. Biomedical data include several modalities containing complementary information, such as medical images and reports: images are often large and encode low-level information, while reports include a summarized high-level description of the findings identified within data and often only concerning a small part of the image. However, only a few methods allow to effectively link the visual content of images with the textual content of reports, preventing medical specialists from properly benefitting from the recent opportunities offered by deep learning models.

Development of an artificial intelligence-based algorithm for predicting the severity of myxomatous mitral valve disease from thoracic radiographs by using two grading systems.

A heart-convolutional neural network (heart-CNN) was designed and tested for the automatic classification of chest radiographs in dogs affected by myxomatous mitral valve disease (MMVD) at different stages of disease severity. A retrospective and multicenter study was conducted. Lateral radiographs of dogs with concomitant X-ray and echocardiographic examination were selected from the internal databases of two institutions.

Exploring Publicly Accessible Optical Coherence Tomography Datasets: A Comprehensive Overview.

Artificial intelligence has transformed medical diagnostic capabilities, particularly through medical image analysis. AI algorithms perform well in detecting abnormalities with a strong performance, enabling computer-aided diagnosis by analyzing the extensive amounts of patient data. The data serve as a foundation upon which algorithms learn and make predictions.

: a library for automatic merging and preprocessing of multiple EEG repositories.

This study aims to address the challenges associated with data-driven electroencephalography (EEG) data analysis by introducing a standardised library called. This library efficiently processes and merges heterogeneous EEG datasets from different sources into a common standard template. The goal of this work is to create an environment that allows to preprocess public datasets in order to provide data for the effective training of deep learning (DL) architectures.

Improving quality control of whole slide images by explicit artifact augmentation.

The problem of artifacts in whole slide image acquisition, prevalent in both clinical workflows and research-oriented settings, necessitates human intervention and re-scanning. Overcoming this challenge requires developing quality control algorithms, that are hindered by the limited availability of relevant annotated data in histopathology. The manual annotation of ground-truth for artifact detection methods is expensive and time-consuming.

Graph-Based Electroencephalography Analysis in Tinnitus Therapy.

Tinnitus is the perception of sounds like ringing or buzzing in the ears without any external source, varying in intensity and potentially becoming chronic. This study aims to enhance the understanding and treatment of tinnitus by analyzing a dataset related to tinnitus therapy, focusing on electroencephalography (EEG) signals from patients undergoing treatment. The objectives of the study include applying various preprocessing techniques to ensure data quality, such as noise elimination and standardization of sampling rates, and extracting essential features from EEG signals, including power spectral density and statistical measures.

Adjustments of iodinated contrast media using lean body weight for abdominopelvic computed tomography: A systematic review and meta-analysis.

Purpose: This systematic review aimed to compare the effect of contrast media (CM) dose adjustment based on lean body weight (LBW) method versus other calculation protocols for abdominopelvic CT examinations.

Method: Studies published from 2002 onwards were systematically searched in June 2024 across Medline, Embase, CINAHL, Cochrane CENTRAL, Web of Science, Google Scholar and four other grey literature sources, with no language limit. Randomised controlled trials (RCT) and quasi-RCT of abdominopelvic or abdominal CT examinations in adults with contrast media injection for oncological and acute diseases were included.

Graphical Insight: Revolutionizing Seizure Detection with EEG Representation.

Epilepsy is characterized by recurring seizures that result from abnormal electrical activity in the brain. These seizures manifest as various symptoms including muscle contractions and loss of consciousness. The challenging task of detecting epileptic seizures involves classifying electroencephalography (EEG) signals into ictal (seizure) and interictal (non-seizure) classes.

ROCOv2: Radiology Objects in COntext Version 2, an Updated Multimodal Image Dataset.

Automated medical image analysis systems often require large amounts of training data with high quality labels, which are difficult and time consuming to generate. This paper introduces Radiology Object in COntext version 2 (ROCOv2), a multimodal dataset consisting of radiological images and associated medical concepts and captions extracted from the PMC Open Access subset. It is an updated version of the ROCO dataset published in 2018, and adds 35,705 new images added to PMC since 2018.

HaN-Seg: The head and neck organ-at-risk CT and MR segmentation challenge.

Background And Purpose: To promote the development of auto-segmentation methods for head and neck (HaN) radiation treatment (RT) planning that exploit the information of computed tomography (CT) and magnetic resonance (MR) imaging modalities, we organized HaN-Seg: The Head and Neck Organ-at-Risk CT and MR Segmentation Challenge.

Materials And Methods: The challenge task was to automatically segment 30 organs-at-risk (OARs) of the HaN region in 14 withheld test cases given the availability of 42 publicly available training cases. Each case consisted of one contrast-enhanced CT and one T1-weighted MR image of the HaN region of the same patient, with up to 30 corresponding reference OAR delineation masks.