Effects of data transformation and model selection on feature importance in microbiome classification data.

Background: Accurate classification of host phenotypes from microbiome data is crucial for advancing microbiome-based therapies, with machine learning offering effective solutions. However, the complexity of the gut microbiome, data sparsity, compositionality, and population-specificity present significant challenges. Microbiome data transformations can alleviate some of the aforementioned challenges, but their usage in machine learning tasks has largely been unexplored.

Transcriptomic profiling of "brain-eating amoeba" infection in mice: the host and the protozoa perspectives.

The free-living amoeba (NF) causes a rare but lethal parasitic meningoencephalitis (PAM) in humans. Currently, this disease lacks effective treatments and the specific molecular mechanisms that govern NF pathogenesis and host brain response remain unknown. To address some of these issues, we sought to explore naturally existing virulence diversity within environmental NF isolates.

Sensitivity analysis of closed-loop one-chamber and four-chamber models with baroreflex.

The baroreflex is one of the most important control mechanisms in the human cardiovascular system. This work utilises a closed-loop in silico model of baroreflex regulation, coupled to pulsatile mechanical models with (i) one heart chamber and 36-parameters and (ii) four chambers and 51 parameters. We perform the first global sensitivity analysis of these closed-loop systems which considers both cardiovascular and baroreflex parameters, and compare the models with their respective unregulated equivalents.

MICCAI-CDMRI 2023 QuantConn Challenge Findings on Achieving Robust Quantitative Connectivity through Harmonized Preprocessing of Diffusion MRI.

White matter alterations are increasingly implicated in neurological diseases and their progression. International-scale studies use diffusion-weighted magnetic resonance imaging (DW-MRI) to qualitatively identify changes in white matter microstructure and connectivity. Yet, quantitative analysis of DW-MRI data is hindered by inconsistencies stemming from varying acquisition protocols.

Virtual Screening Approaches to Identify Promising Multitarget-Directed Ligands for the Treatment of Autism Spectrum Disorder.

Autism spectrum disorder is a complex neurodevelopmental disorder. The available medical treatment options for autism spectrum disorder are very limited. While the etiology and pathophysiology of autism spectrum disorder are still not fully understood, recent studies have suggested that wide alterations in the GABAergic, glutamatergic, cholinergic, and serotonergic systems play a key role in its development and progression.

Multifunctional, Fluorene-Based Modulator of Cholinergic and GABAergic Neurotransmission as a Novel Drug Candidate for Palliative Treatment of Alzheimer's Disease.

Alzheimer's disease (AD) is a neurodegenerative disorder characterized by memory loss and behavioral and psychological symptoms of dementia (BPSD). Given that cholinergic neurons are predominantly affected in AD, current treatments primarily aim to enhance cholinergic neurotransmission. However, imbalances in other neurotransmitters, such as γ-aminobutyric acid (GABA), also contribute to AD symptomatology.

Artificial intelligence and machine learning algorithms in diagnosis and therapy of the ankle joint.

The recent advancement of computational systems provides fast information exchange and the collection of large amounts of data. Growing number of those systems allow for effective processing of huge amounts of information, utilizing advanced algorithms that are called artificial intelligence (AI). AI has been used for many years, and the number of its applications is growing in various areas.

Targeted isolation of Methanobrevibacter strains from fecal samples expands the cultivated human archaeome.

Archaea are vital components of the human microbiome, yet their study within the gastrointestinal tract (GIT) is limited by the scarcity of cultured representatives. Our study presents a method for the targeted enrichment and isolation of methanogenic archaea from human fecal samples. The procedure combines methane breath testing, in silico metabolic modeling, media optimization, FACS, dilution series, and genomic sequencing through Nanopore technology.

Neural networks associated with eye movements in congenital blindness.

Recent studies have shown that during the typical resting-state, echo planar imaging (EPI) time series obtained from the eye orbit area correlate with brain regions associated with oculomotor control and lower-level visual cortex. Here, we asked whether congenitally blind (CB) shows similar patterns, suggesting a hard-wired constraint on connectivity. We find that orbital EPI signals in CB do correlate with activity in the motor cortex, but less so with activity in the visual cortex.

Functional and structural reorganization in brain tumors: a machine learning approach using desynchronized functional oscillations.

Neuroimaging studies have allowed for non-invasive mapping of brain networks in brain tumors. Although tumor core and edema are easily identifiable using standard MRI acquisitions, imaging studies often neglect signals, structures, and functions within their presence. Therefore, both functional and diffusion signals, as well as their relationship with global patterns of connectivity reorganization, are poorly understood.

Refining antimicrobial photodynamic therapy: effect of charge distribution and central metal ion in fluorinated porphyrins on effective control of planktonic and biofilm bacterial forms.

Antibiotic resistance represents a pressing global health challenge, now acknowledged as a critical concern within the framework of One Health. Photodynamic inactivation of microorganisms (PDI) offers an attractive, non-invasive approach known for its flexibility, independence from microbial resistance patterns, broad-spectrum efficacy, and minimal risk of inducing resistance. Various photosensitizers, including porphyrin derivatives have been explored for pathogen eradication.

Can gamification reduce the burden of self-reporting in mHealth applications? A feasibility study using machine learning from smartwatch data to estimate cognitive load.

The effectiveness of digital treatments can be measured by requiring patients to self-report their state through applications, however, it can be overwhelming and causes disengagement. We conduct a study to explore the impact of gamification on self-reporting. Our approach involves the creation of a system to assess cognitive load (CL) through the analysis of photoplethysmography (PPG) signals.

GraphTar: applying word2vec and graph neural networks to miRNA target prediction.

Background: MicroRNAs (miRNAs) are short, non-coding RNA molecules that regulate gene expression by binding to specific mRNAs, inhibiting their translation. They play a critical role in regulating various biological processes and are implicated in many diseases, including cardiovascular, oncological, gastrointestinal diseases, and viral infections. Computational methods that can identify potential miRNA-mRNA interactions from raw data use one-dimensional miRNA-mRNA duplex representations and simple sequence encoding techniques, which may limit their performance.

Minimal data requirement for realistic endoscopic image generation with Stable Diffusion.

Purpose: Computer-assisted surgical systems provide support information to the surgeon, which can improve the execution and overall outcome of the procedure. These systems are based on deep learning models that are trained on complex and challenging-to-annotate data. Generating synthetic data can overcome these limitations, but it is necessary to reduce the domain gap between real and synthetic data.

BabyNet＋＋: Fetal birth weight prediction using biometry multimodal data acquired less than 24 hours before delivery.

Accurate prediction of fetal weight at birth is essential for effective perinatal care, particularly in the context of antenatal management, which involves determining the timing and mode of delivery. The current standard of care involves performing a prenatal ultrasound 24 hours prior to delivery. However, this task presents challenges as it requires acquiring high-quality images, which becomes difficult during advanced pregnancy due to the lack of amniotic fluid.

Prediction of misfolded proteins spreading in Alzheimer's disease using machine learning and spreading models.

The pervasive impact of Alzheimer's disease on aging society represents one of the main challenges at this time. Current investigations highlight 2 specific misfolded proteins in its development: Amyloid-$\beta$ and tau. Previous studies focused on spreading for misfolded proteins exploited simulations, which required several parameters to be empirically estimated.

Deep learning for estimation of fetal weight throughout the pregnancy from fetal abdominal ultrasound.

Background: Fetal weight is currently estimated from fetal biometry parameters using heuristic mathematical formulas. Fetal biometry requires measurements of the fetal head, abdomen, and femur. However, this examination is prone to inter- and intraobserver variability because of factors, such as the experience of the operator, image quality, maternal characteristics, or fetal movements.

Longitudinal Functional Connectome in Pediatric Concussion: An Advancing Concussion Assessment in Pediatrics Study.

Advanced magnetic resonance imaging (MRI) techniques indicate that concussion (i.e., mild traumatic brain injury) disrupts brain structure and function in children.

Comprehensive Analysis of Circular RNAs in Endothelial Cells.

Non-coding RNAs constitute a heterogeneous group of molecules that lack the ability to encode proteins but retain the potential ability to influence cellular processes through a regulatory mechanism. Of these proteins, microRNAs, long non-coding RNAs, and more recently, circular RNAs have been the most extensively described. However, it is not entirely clear how these molecules interact with each other.

Longitudinal Gray Matter Trajectories in Pediatric Mild Traumatic Brain Injury.

Background And Objectives: This prospective, longitudinal cohort study examined trajectories of brain gray matter macrostructure after pediatric mild traumatic brain injury (mTBI).

Methods: Children aged 8-16.99 years with mTBI or mild orthopedic injury (OI) were recruited from 5 pediatric emergency departments.

Altered longitudinal structural connectome in paediatric mild traumatic brain injury: an Advancing Concussion Assessment in Paediatrics study.

Advanced diffusion-weighted imaging techniques have increased understanding of the neuropathology of paediatric mild traumatic brain injury (i.e. concussion).

SATO (IDEAS expAnded wiTh BCIO): Workflow for designers of patient-centered mobile health behaviour change intervention applications.

Designing effective theory-driven digital behaviour change interventions (DBCI) is a challenging task. To ease the design process, and assist with knowledge sharing and evaluation of the DBCI, we propose the SATO (IDEAS expAnded wiTh BCIO) design workflow based on the IDEAS (Integrate, Design, Assess, and Share) framework and aligned with the Behaviour Change Intervention Ontology (BCIO). BCIO is a structural representation of the knowledge in behaviour change domain supporting evaluation of behaviour change interventions (BCIs) but it is not straightforward to utilise it during DBCI design.