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Evidence-backed gain

A Risk Prediction Model for Normal-Tension Glaucoma Progression Integrating Genetic Markers and Corneal Biomechanics

Objective Normal-tension glaucoma (NTG) is characterized by progressive optic nerve damage despite intraocular pressure remaining consistently within the normal range. Predicting disease progression in patients with confirmed NTG remains challenging. This study aimed to develop and validate an interpretable machine learning model integrating genetic risk scores and corneal biomechanical parameters to predict progression risk in patients with NTG, identify independent predictors, and quantify the contribution of…

Seminars in Ophthalmology · Health

Both readings

An Umbrella Review of Artificial Intelligence Applications in Mental Health Care

Background Artificial intelligence (AI) is increasingly used in mental health care to address rising demand, workforce shortages and access barriers; however, evidence remains scattered across multiple systematic reviews, limiting synthesis and practical application. Objective To synthesise evidence on AI applications in mental health care, including trends, uses, benefits, challenges and risk-mitigation strategies, guided by an ethics of care framework. Methods This umbrella review of systematic reviews followe…

Health

Evidence-backed gain

General Atomics and SDSC use AI to advance fusion laser research

Collaboration combines high-performance computing and artificial intelligence to accelerate the design and optimisation of laser systems for inertial confinement fusion research

Science

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‘I feel like I’m at war’: are we losing the battle against machine-made music?

This year, the battle for song of the summer has been eclipsed by a much more complicated – some would even say disturbing – debate. That’s because we find ourselves asking not “What’s the song of the summer?” but rather “Is the song of the summer even real?” Among the top contenders for the title is Fenix Flexin’s Rubberz, a single released in June that has ascended to No 58 on the Billboard Hot 100 and racked up more than 35m Spotify streams. It’s not the sort of fare Fenix usually cooks up. The artist is know…

Media & Arts

Both readings

UK to use Ukraine battlefield data to train AI to protect sensitive sites

AI models trained on Ukrainian battlefield data will be used to stop protesters and foreign states targeting UK defence sites, railways and energy plants under a deal struck between London and Kyiv. Private companies will also be given access to the vast trove of data from Ukraine’s Avengers AI lab to help build new systems, the first agreement of its kind in the UK. The scheme will be piloted at a UK defence site, building a model to identify protesters or a hostile-state attack. It uses AI-optimised sensors in…

Business

Evidence-backed gain

Revolutionizing the circular economy through new technologies: A new era of sustainable progress

Nowadays the pace of production and consumption is reaching environmentally unsustainable levels. In this regard, the great technological advances developed in recent years are postulated as a source of opportunities to boost the circular economy and sustainable development. This wide range of possibilities offered by new technologies to create a more sustainable reality has aroused the curiosity and interest of the academic world, especially in recent years. The main objective of this research is to reveal the…

Business

Evidence-backed gain

Adaptive Learning Using Artificial Intelligence in e-Learning: A Literature Review

The rapid evolution of e-learning platforms, propelled by advancements in artificial intelligence (AI) and machine learning (ML), presents a transformative potential in education. This dynamic landscape necessitates an exploration of AI/ML integration in adaptive learning systems to enhance educational outcomes. This study aims to map the current utilization of AI/ML in e-learning for adaptive learning, elucidating the benefits and challenges of such integration and assessing its impact on student engagement, re…

Education

Agentic AI in Newsrooms: Towards a multi-dimensional framework for evaluating trust, editorial accountability, and workflow quality

As artificial intelligence (AI) systems evolve from assistive to agentic capable of autonomous planning, decision-making, and content generation existing evaluation frameworks struggle to capture their broader organizational and ethical implications. Most assessments of newsroom AI focus narrowly on technical accuracy or efficiency, overlooking how such systems reshape trust, governance, and human collaboration. This study conducts a systematic literature review of 46 peer-reviewed and institutional sources (201…

Media & Arts

Agentic AI in Newsrooms: Towards a multi-dimensional framework for evaluating trust, editorial accountability, and workflow quality

Targeting GLS and LPIN2 in renal fibroblasts: potential therapeutic targets for kidney stone disease identified by integrated multi-omics analysis

Kidney stones (KS) are a common urological condition, the aetiology of which remains incompletely understood. This study aimed to investigate the key cell types involved in the formation of kidney stones and the molecular mechanisms associated with calcium metabolism. Single-cell and bulk RNA-seq datasets related to kidney stones were downloaded from the GEO database. Single-cell analysis was performed to explore the heterogeneity of kidney stones and identify differentially expressed genes (DEGs). Candidate gen…

Health

Targeting GLS and LPIN2 in renal fibroblasts: potential therapeutic targets for kidney stone disease identified by integrated multi-omics analysis

The Lymph Node Ratio as a Predictive Biomarker for Individualized Benefit from Adjuvant Chemotherapy in Gastric Cancer: A Retrospective Cohort and Causal Machine Learning Study

Background Optimizing adjuvant chemotherapy (AC) for gastric cancer (GC) remains challenging due to patient heterogeneity. While the lymph node ratio (LNR) is a known prognostic factor, its role in predicting individualized AC benefit remains underexplored. This study aimed to leverage causal machine learning to explore LNR's role for personalized treatment. Methods We conducted a retrospective cohort study of 2,748 patients undergoing radical gastrectomy (2007-2017, re-staged by AJCC 8th edition). While the ful…

Health

Integrative single-cell and machine-learning analysis identifies LGALS1 as a macrophage-associated diagnostic and prognostic biomarker in hepatocellular carcinoma

Background Hepatocellular carcinoma (HCC) is characterized by marked heterogeneity and an immunosuppressive microenvironment in which tumor-associated macrophages contribute to disease progression. This study aimed to identify macrophage-associated biomarkers with diagnostic, prognostic, and translational relevance in HCC. Methods Single-cell RNA-sequencing datasets were integrated with bulk transcriptomic and clinical data from TCGA-LIHC, GEO, and ICGC cohorts. Macrophage markers were intersected with tumor-ass…

Health

Identifying risk factors for marijuana use among male high school students using machine learning: Implications for public health

Objectives To identify risk and protective factors associated with lifetime marijuana use among male high school students through an interpretable machine learning model, providing evidence to support early and targeted public health interventions. Study design Cross-sectional analysis of 2023 Youth Risk Behavior Surveillance System (YRBS) data for boys in grades 9-12 across the United States. Methods The final analytical sample included 8285 boys after excluding missing outcomes. Thirty-six predictors spanning…

Health

Organ-at-risk contouring education in the era of AI-assisted practice: Insights from an Australian undergraduate radiation therapy program

Introduction The integration of artificial intelligence (AI) tools into radiation therapy workflows offers significant opportunities to improve efficiency by automating tasks such as contouring organs at risk (OARs). However, this also raises concerns regarding future practitioners' ability to critically evaluate auto-generated contours. This educational perspective examines how OAR contouring education can be integrated into undergraduate radiation therapy programs to support the development of foundational con…

Education

Triglyceride-glucose frailty index, metabolic-frailty phenotypes, and mortality in critically ill patients with acute kidney injury: Derivation, interpretation, and external validation

Background Prognosis remains heterogeneous among critically ill patients with acute kidney injury (AKI). We evaluated the triglyceride-glucose frailty index (TyG-FI), a composite of metabolic burden and laboratory-based frailty, for mortality risk characterization, phenotype identification, prediction, and external validation. Methods We included 2230 adults with KDIGO-defined AKI from MIMIC-IV. Associations between TyG-FI and ICU, in-hospital, 28-day, 90-day, and 365-day mortality were assessed using multivaria…

Health

Triglyceride-glucose frailty index, metabolic-frailty phenotypes, and mortality in critically ill patients with acute kidney injury: Derivation, interpretation, and external validation

Impact of allometric reference selection on management scale aboveground biomass estimation via Sentinel-2 and machine learning

Accurate estimation of aboveground biomass (AGB) is essential for sustainable forest management, carbon accounting, and climate change mitigation. In remote sensing-based biomass mapping, field-derived AGB values are commonly used as reference data; however, these values are strongly influenced by the selected allometric equation. This study evaluates how alternative allometric reference datasets affect Sentinel-2-based AGB estimation at the forest management scale in Pinus brutia stands. Reference AGB values we…

Climate

Impact of allometric reference selection on management scale aboveground biomass estimation via Sentinel-2 and machine learning