Effects of one-to-one music therapy in older adults with cognitive impairment: A randomized controlled trial.

This study investigated the effects of music therapy (MT), a nonpharmacological therapy, on cognitive, behavioral, and physiological outcomes in older adults with mild-to-moderate cognitive decline residing in care home settings. A randomized controlled trial design was employed, with 42 care home residents (Mage = 86.25 years) randomly assigned to either a one-to-one 16-week MT intervention or an active control group receiving storytelling.

Predicting functional impairment trajectories in amyotrophic lateral sclerosis: a probabilistic, multifactorial model of disease progression.

Objective: To employ Artificial Intelligence to model, predict and simulate the amyotrophic lateral sclerosis (ALS) progression over time in terms of variable interactions, functional impairments, and survival.

Methods: We employed demographic and clinical variables, including functional scores and the utilisation of support interventions, of 3940 ALS patients from four Italian and two Israeli registers to develop a new approach based on Dynamic Bayesian Networks (DBNs) that models the ALS evolution over time, in two distinct scenarios of variable availability. The method allows to simulate patients' disease trajectories and predict the probability of functional impairment and survival at different time points.

A metaproteomic-based gut microbiota profiling in children affected by autism spectrum disorders.

During the last decade, the evidences on the relationship between neurodevelopmental disorders and the microbial communities of the intestinal tract have considerably grown. Particularly, the role of gut microbiota (GM) ecology and predicted functions in Autism Spectrum Disorders (ASD) has been especially investigated by 16S rRNA targeted and shotgun metagenomics, trying to assess disease signature and their correlation with cognitive impairment or gastrointestinal (GI) manifestations of the disease. Herein we present a metaproteomic approach to point out the microbial gene expression profiles, their functional annotations, and the taxonomic distribution of gut microbial communities in ASD children.

Thioredoxin reductase-1 levels are associated with NRF2 pathway activation and tumor recurrence in non-small cell lung cancer.

Activating mutations in the KEAP1/NRF2 pathway characterize a subset of non-small cell lung cancer (NSCLC) associated with chemoresistance and poor prognosis. We herein evaluated the relationship between 64 oxidative stress-related genes and overall survival data from 35 lung cancer datasets. Thioredoxin reductase-1 (TXNRD1) stood out as the most significant predictor of poor outcome.

Addressing practical issues of predictive models translation into everyday practice and public health management: a combined model to predict the risk of type 2 diabetes improves incidence prediction and reduces the prevalence of missing risk predictions.

Introduction: Many predictive models for incident type 2 diabetes (T2D) exist, but these models are not used frequently for public health management. Barriers to their application include (1) the problem of model choice (some models are applicable only to certain ethnic groups), (2) missing input variables, and (3) the lack of calibration. While (1) and (2) drives to missing predictions, (3) causes inaccurate incidence predictions.

Integrative Network Fusion: A Multi-Omics Approach in Molecular Profiling.

Recent technological advances and international efforts, such as The Cancer Genome Atlas (TCGA), have made available several pan-cancer datasets encompassing multiple omics layers with detailed clinical information in large collection of samples. The need has thus arisen for the development of computational methods aimed at improving cancer subtyping and biomarker identification from multi-modal data. Here we apply the Integrative Network Fusion (INF) pipeline, which combines multiple omics layers exploiting Similarity Network Fusion (SNF) within a machine learning predictive framework.

A Dynamic Bayesian Network model for the simulation of Amyotrophic Lateral Sclerosis progression.

Background: Amyotrophic lateral sclerosis (ALS) is an adult-onset neurodegenerative disease progressively affecting upper and lower motor neurons in the brain and spinal cord. Mean life expectancy is three to five years, with paralysis of muscles, respiratory failure and loss of vital functions being the common causes of death. Clinical manifestations of ALS are heterogeneous due to the mix of anatomic regions involvement and the variability in disease course; consequently, diagnosis and prognosis at the level of individual patient is really challenging.

Multi-omics integration for neuroblastoma clinical endpoint prediction.

Background: High-throughput methodologies such as microarrays and next-generation sequencing are routinely used in cancer research, generating complex data at different omics layers. The effective integration of omics data could provide a broader insight into the mechanisms of cancer biology, helping researchers and clinicians to develop personalized therapies.

Results: In the context of CAMDA 2017 Neuroblastoma Data Integration challenge, we explore the use of Integrative Network Fusion (INF), a bioinformatics framework combining a similarity network fusion with machine learning for the integration of multiple omics data.

Gut microbiota profiling of pediatric nonalcoholic fatty liver disease and obese patients unveiled by an integrated meta-omics-based approach.

Unlabelled: There is evidence that nonalcoholic fatty liver disease (NAFLD) is affected by gut microbiota. Therefore, we investigated its modifications in pediatric NAFLD patients using targeted metagenomics and metabolomics. Stools were collected from 61 consecutive patients diagnosed with nonalcoholic fatty liver (NAFL), nonalcoholic steatohepatitis (NASH), or obesity and 54 healthy controls (CTRLs), matched in a case-control fashion.