Genetic analysis of L 1758 (Mollusca, Bivalvia, Pinnidae) in the Northwest Cabo Verde Islands (Central-East Atlantic).

The rough pen shell Linnaeus, 1758 (family Pinnidae) is a mollusc with an Atlantic-Mediterranean distribution, typically inhabiting coarse sandy substrates. Habitat degradation is considered the primary cause of population decline, leading to the designation 'Vulnerable' in certain regions. In this study, we conducted a genetic analysis of populations of from Cabo Verde and compared them with populations from the Mediterranean and Macaronesia.

Developing, Characterizing, and Modeling CRISPR-Based Point-of-Use Pathogen Diagnostics.

Recent years have seen intense interest in the development of point-of-care nucleic acid diagnostic technologies to address the scaling limitations of laboratory-based approaches. Chief among these are combinations of isothermal amplification approaches with CRISPR-based detection and readouts of target products. Here, we contribute to the growing body of rapid, programmable point-of-care pathogen tests by developing and optimizing a one-pot NASBA-Cas13a nucleic acid detection assay.

Development of cell-free platforms for discovering, characterizing, and engineering post-translational modifications.

Post-translational modifications (PTMs) are important for the stability and function of many therapeutic proteins and peptides. Current methods for studying and engineering PTM installing proteins often suffer from low-throughput experimental techniques. Here we describe a generalizable, workflow coupling cell-free protein synthesis (CFPS) with AlphaLISA for the rapid expression and testing of PTM installing proteins.

Oncological safety of fertility preservation treatment in ovarian cancer: A Spanish multicenter study.

Objective: To assess the safety of fertility-sparing treatments for early-stage ovarian cancer in women younger than 40 years old.

Methods: We performed a retrospective multicenter study including women aged 18-40 years diagnosed with early-stage (FIGO I-II) ovarian cancer in 55 Spanish hospitals, from January 2010 to December 2019. Benign and borderline tumors were excluded, as well as advanced stages (FIGO III-IV).

MIMt: a curated 16S rRNA reference database with less redundancy and higher accuracy at species-level identification.

Motivation: Accurate determination and quantification of the taxonomic composition of microbial communities, especially at the species level, is one of the major issues in metagenomics. This is primarily due to the limitations of commonly used 16S rRNA reference databases, which either contain a lot of redundancy or a high percentage of sequences with missing taxonomic information. This may lead to erroneous identifications and, thus, to inaccurate conclusions regarding the ecological role and importance of those microorganisms in the ecosystem.

A benchmark for 2D foetal brain ultrasound analysis.

Brain development involves a sequence of structural changes from early stages of the embryo until several months after birth. Currently, ultrasound is the established technique for screening due to its ability to acquire dynamic images in real-time without radiation and to its cost-efficiency. However, identifying abnormalities remains challenging due to the difficulty in interpreting foetal brain images.

Developing, characterizing and modeling CRISPR-based point-of-use pathogen diagnostics.

Recent years have seen intense interest in the development of point-of-care nucleic acid diagnostic technologies to address the scaling limitations of laboratory-based approaches. Chief among these are combinations of isothermal amplification approaches with CRISPR-based detection and readouts of target products. Here, we contribute to the growing body of rapid, programmable point-of-care pathogen tests by developing and optimizing a one-pot NASBA-Cas13a nucleic acid detection assay.

Improving multiple sclerosis lesion segmentation across clinical sites: A federated learning approach with noise-resilient training.

Accurately measuring the evolution of Multiple Sclerosis (MS) with magnetic resonance imaging (MRI) critically informs understanding of disease progression and helps to direct therapeutic strategy. Deep learning models have shown promise for automatically segmenting MS lesions, but the scarcity of accurately annotated data hinders progress in this area. Obtaining sufficient data from a single clinical site is challenging and does not address the heterogeneous need for model robustness.

An Analysis of Loss Functions for Heavily Imbalanced Lesion Segmentation.

Heavily imbalanced datasets are common in lesion segmentation. Specifically, the lesions usually comprise less than 5% of the whole image volume when dealing with brain MRI. A common solution when training with a limited dataset is the use of specific loss functions that rebalance the effect of background and foreground voxels.

Translation and linguistic validation into Spanish of the Owner-Reported Outcome Measure "Liverpool Osteoarthritis in Dogs".

Introduction: Assessing chronic pain in dogs has been greatly favoured by the development of Owner-Reported Outcome Measures. Among them, the Liverpool Osteoarthritis in Dogs (LOAD) has been widely used for this purpose. Most of these tools have been written in English and its use by non English natives requires not only translation but also linguistic validation for use by veterinarians and owners.

Attitudes to acute pain and the use of pain assessment scales among Spanish small animal veterinarians.

Unlabelled: Evaluation of acute pain in dogs and cats is the basis for an appropriate treatment and improved well-being. The difficulties involved in pain assessment pose a challenge for veterinarians, and recent findings indicate that the most effective tools available-validated pain assessment scales-may not be widely utilized.

Objectives: This study aimed to characterize the level of concern among Spanish veterinarians regarding acute pain in companion animals.

Dermoscopic Evaluation of Actinic Changes in the Lips of Indigenous Children Living at High Altitude in Ecuador: A Descriptive Study.

BACKGROUND High altitude increases sunlight exposure, resulting in actinic keratosis, which predisposes people to skin cancer. The dermoscopy procedure evaluates keratotic and pigmented skin changes. This study aimed to describe the clinical and dermoscopic actinic changes in the lips of 25 indigenous children living at high altitude in Ecuador.

Deep learning distinguishes connectomes from focal epilepsy patients and controls: feasibility and clinical implications.

The application of deep learning models to evaluate connectome data is gaining interest in epilepsy research. Deep learning may be a useful initial tool to partition connectome data into network subsets for further analysis. Few prior works have used deep learning to examine structural connectomes from patients with focal epilepsy.

A real-world clinical validation for AI-based MRI monitoring in multiple sclerosis.

Modern management of MS targets No Evidence of Disease Activity (NEDA): no clinical relapses, no magnetic resonance imaging (MRI) disease activity and no disability worsening. While MRI is the principal tool available to neurologists for monitoring clinically silent MS disease activity and, where appropriate, escalating treatment, standard radiology reports are qualitative and may be insensitive to the development of new or enlarging lesions. Existing quantitative neuroimaging tools lack adequate clinical validation.

High angular diffusion tensor imaging estimation from minimal evenly distributed diffusion gradient directions.

Diffusion-weighted Imaging (DWI) is a non-invasive imaging technique based on Magnetic Resonance Imaging (MRI) principles to measure water diffusivity and reveal details of the underlying brain micro-structure. By fitting a tensor model to quantify the directionality of water diffusion a Diffusion Tensor Image (DTI) can be derived and scalar measures, such as fractional anisotropy (FA), can then be estimated from the DTI to summarise quantitative microstructural information for clinical studies. In particular, FA has been shown to be a useful research metric to identify tissue abnormalities in neurological disease (e.

A scoping review of food and nutrition literacy programs.

The impact of food literacy (FL) and nutrition literacy (NL) programs on lifestyles and health is recognized in the literature, yet few studies systematize it. This study aims to deepen the understanding of FL/NL from the characterization of programs in terms of (i) theoretical foundations, conceptualization and measuring instruments; (ii) characterization of the FL/NL programs and results and (iii) limitations and future directions declared. A scoping review of original articles addressing FL/NL programs from the Web of Science, Scopus and PubMed databases published between 2015 and 2023.

Validation of deep learning techniques for quality augmentation in diffusion MRI for clinical studies.

The objective of this study is to evaluate the efficacy of deep learning (DL) techniques in improving the quality of diffusion MRI (dMRI) data in clinical applications. The study aims to determine whether the use of artificial intelligence (AI) methods in medical images may result in the loss of critical clinical information and/or the appearance of false information. To assess this, the focus was on the angular resolution of dMRI and a clinical trial was conducted on migraine, specifically between episodic and chronic migraine patients.

Adaptation and Validation of the Autism Mental Status Examination (AMSE) in Chile: seeking to reduce the diagnostic gap.

Unlabelled: Early detection of Autism is a challenge in Chile and Latin America. Gold-standard evaluations are expensive and difficult to access. The Autism Mental Status Exam (AMSE) is a brief, structured, observational instrument for clinical use with promising results.

Learning from pseudo-labels: deep networks improve consistency in longitudinal brain volume estimation.

Introduction: Brain atrophy is a critical biomarker of disease progression and treatment response in neurodegenerative diseases such as multiple sclerosis (MS). Confounding factors such as inconsistent imaging acquisitions hamper the accurate measurement of brain atrophy in the clinic. This study aims to develop and validate a robust deep learning model to overcome these challenges; and to evaluate its impact on the measurement of disease progression.

Translation to Spanish and linguistic validation of the Canine Brief Pain Inventory.

Introduction: Pain scales for the assessment of chronic pain have been developed for dogs but they should be translated and linguistically validated to be used by owners with different native languages. The Canine Brief Pain Inventory (CBPI) is widely employed for this purpose but has not been translated into Spanish. Thus, the aim was to produce a validated translation of the Spanish CBPI.

Historical Lattice Trees.

We prove that the rescaled historical processes associated to critical spread-out lattice trees in dimensions converge to historical Brownian motion. This is a functional limit theorem for measure-valued processes that encodes the genealogical structure of the underlying random trees. Our results are applied elsewhere to prove that random walks on lattice trees, appropriately rescaled, converge to Brownian motion on super-Brownian motion.

COVID-19 mortality and risk factors in patients with cancer in Ecuador: A retrospective, non-randomized, controlled study.

The 2019 coronavirus disease (COVID-19) pandemic has affected different human populations since March 2020 and challenged healthcare systems, especially in chronic non-communicable diseases such as cancer. The present study aimed to evaluate the mortality, risk factors, and symptoms of cancer patients and control subjects, diagnosed with COVID-19 and admitted to intensive care unit (ICU). This retrospective, observational, non-randomized, controlled study of patients admitted to ICU was conducted between March and August 2020 in an Ecuadorian oncology center.

The deleted in oral cancer (DOC1 aka CDK2AP1) tumor suppressor gene is downregulated in oral squamous cell carcinoma by multiple microRNAs.

Cyclin-dependent kinase 2-associated protein 1 (CDK2AP1; also known as deleted in oral cancer or DOC1) is a tumor suppressor gene known to play functional roles in both cell cycle regulation and in the epigenetic control of embryonic stem cell differentiation, the latter as a core subunit of the nucleosome remodeling and histone deacetylation (NuRD) complex. In the vast majority of oral squamous cell carcinomas (OSCC), expression of the CDK2AP1 protein is reduced or lost. Notwithstanding the latter (and the DOC1 acronym), mutations or deletions in its coding sequence are extremely rare.

First Molecular Identification of a Goussia Parasite from a New World Invasive Blenny.

Purpose: Introduced or invasive fish are susceptible to new parasites but can simultaneously carry infectious parasites from their native range towards new hosts. Screening these parasites is key to address the health of fish populations and spread of diseases.

Methods: In this study, we sequenced a Coccidia parasite, for the first time from the blenny Omobranchus sewalli, introduced in the northern coast of Brazil with an Indo-Pacific origin.