Addictions

Co-occurrence patterns and related risk factors of dementia and Parkinson's disease among older adults across 204 countries and territories: a spatial correspondence and systematic analysis.

BACKGROUND: Dementia and Parkinson's disease (PD) are among the most prevalent neurological disorders globally. Most previous research has focused on these two diseases in isolation; however, their co-occurrence has rarely been examined, limiting understanding of shared mechanisms and hindering integrated prevention and resource planning. This study aimed to characterize global co-occurrence patte...

Characterization of volatile compounds of irradiated and fermented cherry juice by SPME-GC-MS, SPME-GC × GC-MS and HS-GC-IMS combined with machine learning algorithm.

To explore the effects of irradiation and fermentation on the flavor of cherry juice, this study characterized the volatile components of cherry juice by gas chromatography-mass spectrometry (GC-MS), gas chromatography-ion mobility spectrometry (GC-IMS) and comprehensive two-dimensional gas chromatography-mass spectrometry (GC × GC-MS) combined with machine learning techniques. Electron beam irrad...

Acute pancreatitis: mechanisms and therapeutic approaches.

Acute pancreatitis is a complex inflammatory condition characterized by sudden onset and rapid progression, with severe cases often associated with hi...

Nalmefene and naltrexone reduce alcohol intake via selective efficacy in subpopulations distinguished by behavioral and blood-based biomarkers.

BACKGROUND: The relative efficacies of nalmefene versus naltrexone for alcohol use disorder is the subject of intense and ongoing debate. The two pan-...

Comparative study of 11 machine learning algorithms for predicting recurrence risk after atrial fibrillation catheter ablation based on a real-world cohort: a retrospective study.

BACKGROUND: Atrial fibrillation (AF) is the most common arrhythmia worldwide, with catheter ablation being an effective yet recurrence-prone treatment...

Toward clarity in antiseizure medication withdrawal decisions: Clinical challenges and machine learning-based scoring systems.

Rumination is more important than other variables in the association between social media use and depression: A multi-method study.

The relationship between social media use (SMU) and mental health, particularly depression, remains inconclusive. The present study recruited 925 coll...

HDFLStyler: Hierarchical domain-invariant feature learning for source-free domain generalization.

Source-Free Domain Generalization (SFDG) aims to generalize a model to unknown domains without using any specific source domain data. Currently, SFDG ...

Artificial Intelligence and Its Role in Endoscopic Adenoma and Cancer Detection.

Colorectal cancer incidence and mortality have declined over time, due in part to high-quality screening and surveillance colonoscopy. Nevertheless, p...

A multicenter multinational retrospective study of the 1-year natural history of LI-RADS 3 observations in patients with cirrhosis.

OBJECTIVES: To assess the 1-year natural history of liver imaging reporting and data system (LI-RADS) 3 observations on contrast-enhanced MRI in cirrh...

Elucidating hydrogen bonding and synergistic adsorption mechanisms of morphine on deep eutectic solvent-functionalized biochar through machine learning and density functional theory.

Adsorbent materials effectively separated psychoactive substances from the ambient medium through adsorption, serving as a sustainable removal strateg...

Data-Driven Learning of Optimal Position-Dependent Exact-Exchange Energy Density Mixing for Improved Density Functionals.

A transparent, data-driven route to improve approximate density functionals by learning only the position dependence of an exact-exchange admixture in...

Precision prediction of posttraumatic stress disorder symptom surges: A pilot study integrating real-time daily data with supervised learning.

The application of machine learning algorithms to daily diary data represents a valuable tool for improving dynamic prediction of posttraumatic stress...

Integrated metabolomics and transcriptomics reveal biomarkers for detecting fentanyl analog abuse through machine learning.

Fentanyl analogs' (FA) rampant proliferation causes widespread abuse, fatalities, and severe social issues globally. Their high toxicity, rapid metabo...

Machine Learning Models to Predict Withdrawal of Life-Sustaining Therapy in Patients With Severe Traumatic Brain Injury.

Machine learning analysis of clinical, psychological and sociodemographic factors predicting mental well-being in patients with severe mental illness: Insights from the French REHABase cohort.

PURPOSE: Mental well-being is a cornerstone of recovery for people with mental disorders. Unfortunately, despite many studies on the topic, the litera...

Resting-state fMRI analysis of functional connectivity and temporal dynamics differences between cocaine users and healthy controls.

Understanding alterations in functional connectivity among individuals with substance use disorder (SUD) is critical for elucidating the neural mechan...

Co3[Co(CN)6]2-Mediated Olfactory and Visual Dual-Modality Ammonia Sensing.

In the emerging ammonia (NH3) economy, both olfactory and visual sensing are desired for leakage detection, in which olfactory sensing enables a fast ...

Cerebral blood flow and modern approaches for clinical assessment & monitoring: a view to the future.

Cerebral blood flow (CBF) is under homeostatic control via cerebral autoregulation, maintaining a constant blood supply to brain parenchyma by integra...

Artificial intelligence and machine learning applications in ambulatory surgery - A systematic review.

BACKGROUND: We aimed to systematically review applications of artificial intelligence (AI) technologies for ambulatory surgical patients. METHODS: We ...