Prediction of Green Solvent Applicability in Cultural Heritage Using Hansen Solubility Parameters, Cremonesi Method and Integrated Toxicity Index

Sustainability, Journal Year: 2025, Volume and Issue: 17(7), P. 2944 - 2944

Published: March 26, 2025

The transition toward sustainable conservation practices requires a scientifically ground approach to substituting traditional solvent systems with green alternatives. This study aims facilitate the adoption of solvents by restoration professionals systematically evaluating their chemical compatibility and toxicological safety. By integrating Hansen solubility parameters (HSP), Relative Energy Difference (RED), Integrated Toxicity Index (ITI), we identified high potential for replacing Cremonesi mixtures. analysis revealed that ether-based solvents, such as 2,5-dimethyltetrahydrofuran cyclopentyl methyl ether, exhibit affinity mixtures, while esters fatty acid (FAMEs) offer balanced combination low toxicity. However, also underscores significant gaps in safety data (SDS) many innovative highlighting need further evaluation before widespread implementation.

Language: Английский

The role of humans in the future of medicine: Completing the cycle

Metaverse, Journal Year: 2025, Volume and Issue: 6(1), P. 3129 - 3129

Published: Jan. 15, 2025

<p>The progression of Artificial Intelligence (AI) has reshaped our understanding intelligence, consciousness, and the human condition, challenging long-held assumptions about mind its relationship with machines. Starting Alan Turing’s Imitation Game, narrative assessment AI continually evolved. This historical context underlines importance moving beyond mere facts to confront philosophical questions AI’s role limitations, especially in capacity for consciousness emotional resonance. In healthcare, evolution reflects a transformative cycle. Historically, medicine began as an empathic endeavor, where caregivers provided comfort amid limited knowledge. Over centuries, advancements science elevated physicians authoritative figures, creating paternalistic doctor-patient dynamic. Today, advent technologies like metaverse, healthcare knowledge is becoming democratized. Patients can increasingly access AI-driven diagnostics interactions, potential era “<em>algorithmic paternalism</em>” machines dominate hierarchy. Looking future, assumes cognitive diagnostic responsibilities, aspect will gain renewed importance. Physicians return their foundational caregivers, focusing on connection support—qualities that AI, despite advances, cannot fully replicate today. shift completes cycle, reaffirming enduring value humanity positioning physician central figure emotionally nuanced landscape healthcare.</p>

Language: Английский

Using artificial intelligence system for assisting the classification of breast ultrasound glandular tissue components in dense breast tissue

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: April 6, 2025

To investigate the potential of employing artificial intelligence (AI) -driven breast ultrasound analysis models for classification glandular tissue components (GTC) in dense tissue. A total 1,848 healthy women with mammograms classified as were enrolled this prospective study. Residual Network (ResNet) 101 model and ResNet fully Convolutional Networks (ResNet + FCN) segmentation trained. The better effective was selected to appraise performance 3 radiologists non-breast radiologists. evaluation metrics included sensitivity, specificity, positive predictive value (PPV). ResNet101 demonstrated superior compared FCN model. It significantly enhanced sensitivity all by 0.060, 0.021, 0.170, 0.009, 0.052, 0.047, respectively. For P1 P4 glandular, PPVs increased 0.154, 0.178, 0.027, 0.109 Ai-assisted. Notably, experienced a particularly substantial rise PPV (p < 0.01). This study trained deep learning is reliable accurate system assisting different differentiate images.

Language: Английский