ARUd’A (Università “G. d’Annunzio CHIETI -PESCARA)
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Psychotic-Like Experiences in Young Recreational Users of Ketamine: A Case Study
No full textThis study explores the psychotic-like experiences (PLEs) associated with recreational ketamine use among young adults. Ketamine, initially introduced as an anesthetic, is now widely used recreationally for its dissociative effects, raising concerns about its impact on mental health. Ten participants aged 18–24, who used ketamine recreationally multiple times a week, were assessed using the Community Assessment of Psychic Experiences (CAPE-42). Results showed a significant positive correlation between the frequency of ketamine use and PLEs, with no significant impact from other substances like THC, MDMA, and alcohol. These findings confirm ketamine’s potential to induce psychotic-like symptoms by antagonizing NMDA receptors, similar to schizophrenia. The study underscores the need for preventive measures and targeted interventions to address the mental health risks of frequent ketamine use, particularly among young adults. However, limitations such as the small sample size and reliance on self-reported data suggest that further research is needed to establish causality and examine long-term effects. Overall, this study highlights the significant association between recreational ketamine use and increased PLEs, emphasizing the importance of early detection and intervention strategies
Synthesis and Characterization of Na-X Zeolite Using a Natural Opaline Diatomite Rock from SE Spain
No full textChemical exploration of different extracts from Phytolacca americana leaves and their potential utilization for global health problems: ın silico and network pharmacology validation
No full textPhytolacca americana L. is of great interest as a traditional additive in various folk remedies in several countries, including Turkey. We aimed to determine the chemical profile (assisted by high-Performance liquid chromatography-electrospray ionization-tandem mass apectrometry (HPLC-ESI-MS/MS) experiments of three extracts obtained by different polarity solvents viz. ethyl acetate (to extract semipolar compounds), methanol and water (to extract highly polar metabolites) from P. americana leaves. Their anti-diabetic effects were investigated in vitro by assessing their inhibition toα-amylase and α-glucosidase. Assessment of the neuroprotective potential of the three extracts was carried out against acetyl-(AChE) and butyryl-(BChE) cholinesterase enzymes. HPLC-ESI-MS/MS experiments showed a total of 17 chromatographic peaks primarily classified to six flavonoids, two saponins, and six fatty acids. Antioxidant assays revealed remarkable activity for the ethyl acetate and methanol extracts. The BChE inhibition was considerably more significant (4.08 mg galantamine equivalent (GALAE)/g) for the ethyl acetate extract, whereas the methanol extract had good inhibitory efficacy for AChE (2.05 mg GALAE/g). Through network pharmacology, the compounds’ mechanism of action of targeted key gene in their associated diseases were identified. The hubb gene signal transducer and activator of transcription 3 (STAT3) and tumour necrosis factor (TNFα) where the P. americana compound’s site of action in inflammation bowel disease. The results offer possibilities for the prospective application of P. americana in metabolic regulation, blood glucose control, and as a source of bioactive compounds with cholinesterase enzyme inhibitory characteristics which could be of relevance in the cosmetic or pharmaceutical industry for combating melanogenesis
Assessing and Comparing Free Large Language Models’ Responses to a Clinical Case: Accuracy, Safety, and Reliability
No full textThis research examines how five free of charge Large Language Models (LLMs)-Zephyr, Mistral 7B, LLAMA 2 7B, ChatGPT 3.5 and Copilot Precise-perform when faced with a nursing clinical scenario involving a neuropsychiatric emergency. Their responses were evaluated based on established guidelines by a Delphi consensus using a 5-point Likert scale to rate safety, accuracy, reliability and the potential for improvement. The findings underscore the greatest importance of safety and accuracy metrics. LLAMA 2 7B exhibits balanced but poor performance, scoring 3 out of 5 in Safety, Accuracy, and References, and 4 out of 5 in providing Improvement suggestions. ChatGPT 3.5 demonstrates adequate performance in Safety, Accuracy, and References, each with a score of 4 out of 5, indicating its proficiency in generating accurate, reliable content and ensuring patient safety, though there is room for improvement in enhancement suggestions (3 out of 5). Copilot Precise shows a unique profile, with balanced scores of 3 out of 5 in Safety, Accuracy, and Improvements, and a perfect score of 5 out of 5 only in References, highlighting its high accuracy in generating references. Reliability was reported in terms of both reference precision criteria and consistency over time computed through automated assessment. These preliminary results underscore the importance of developing language models that focus on ensuring safety and precision, in clinical decision-making scenarios. Further studies should aim to improve the accuracy and dependability of these models by examining a range of situations and incorporating real-time feedback mechanisms from experts. This will enhance their usefulness in clinical environments
107) Commento all’art. 3, Testo unico imposte sui redditi a cura di A. Magliaro, A. Scaglia, S. Ariatti
No full textIntrinsic brain mapping of cognitive abilities: A multiple-dataset study on intelligence and its components
No full textThis study investigates how functional brain network features contribute to general intelligence and its cognitive components by analyzing three independent cohorts of healthy participants. Cognitive scores were derived from 1) the Wechsler Adult Intelligence Scale (WAIS-IV), 2) the Raven Standard Progressive Matrices (RPM), and 3) the NIH and Penn cognitive batteries from the Human Connectome Project. Factor analysis on the NIH and Penn cognitive batteries yielded latent variables that closely resembled the content of the WAIS-IV indices and RPM. We employed graph theory and a multi-resolution network analysis by varying the modularity parameter (γ) to investigate hierarchical brain-behavior relationships across different scales of brain organization. Brain-behavior associations were quantified using multi-level robust regression analyses to accommodate variability and confounds at the subject-level, node-level, and resolution-level. Our findings reveal consistent brain-behavior relationships across the datasets. Nodal efficiency in fronto-parietal sensorimotor regions consistently played a pivotal role in fluid reasoning, whereas efficiency in visual networks was linked to executive functions and memory. A broad, low-resolution 'task-positive' network emerged as predictive of full-scale IQ scores, indicating a hierarchical brain-behavior coding. Conversely, increased cross-network connections involving default mode and subcortical-limbic networks were associated with reductions in both general and specific cognitive performance. These outcomes highlight the relevance of network efficiency and integration, as well as of the hierarchical organization in supporting specific aspects of intelligence, while recognizing the inherent complexity of these relationships. Our multi-resolution network approach offers new insights into the interplay between multilayer network properties and the structure of cognitive abilities, advancing the understanding of the neural substrates of the intelligence construct
Cognition in Climate Change: Is It Just a Matter of Time?
No full textClimate change (CC) is a global phenomenon characterized by long-term shifts in temperatures and weather patterns. Aside from natural causes, we have been facing a full-blown climate crisis primarily driven by human activity, leading to increasingly frequent and extreme weather events that put a strain on people's mental capacities. Addressing CC necessitates a temporal perspective as both causes and potential solutions extend beyond the present. However, despite being a significant challenge for humanity, CC is often considered temporally distant, leading to abstract thinking and reduced urgency for action. Considering the diverse dimensions that concur to define CC, this review will explore the link between CC and time cognition, building on insights from cognitive sciences. Upon considering the tangible effects of the anthropogenic CC (Changing Place), we argue that change in the social construction of time is inherent to CC and drifts to the point of affecting psychological well-being (Changing Time). Moreover, considering that time is central to cognition and interlinked with several cognitive functions, we will consider the literature investigating the impact of CC-related eco-anxiety on cognitive abilities within the framework of time cognition. Furthermore, we assess how eco-anxiety and time cognition interact, potentially serving as markers of mental well-being (Changing Thoughts). By framing CC within the realm of time cognition, we offer an interdisciplinary perspective on cognition and well-being, advocating for the integration of cognitive science into climate adaptation and mitigation efforts to foster more effective, psychologically sustainable long-term climate strategies (Changing Future). This article is categorized under: Neuroscience > Cognition
AI-assisted Real-Time Spatial Delphi: integrating artificial intelligence models for advancing future scenarios analysis
No full textThe Real-Time Spatial Delphi represents an innovative method tailored to navigate the complexities of uncertain spatial issues. Adopted in Future Studies contexts, this method excels in developing spatial scenarios and leveraging the collaborative insights of experts within a virtual environment to achieve a consensus regarding territorial dynamics. However, while this method yields invaluable spatial insights and statistical metrics, the final outputs often remain confined to expert circles due to their technical complexity. In addition, the outcomes often lack direct policy implications, as they primarily provide an expansive overview of potential future scenarios. In response to these challenges, this paper proposes integrating text-to-image models and generative pre-trained transformers, into the Real-Time Spatial Delphi process. By adopting these advanced tools during the visioning and planning phases, the method endeavors to transform spatial judgments into visually immersive scenarios, while concurrently crafting actionable policy recommendations suitable for evaluation. To validate the approach, we present a case study in the environmental context, for the cities of Cork, Galway, and Limerick, located in Ireland. Through this application, we contribute to Futures Studies by illustrating the method’s capacity to envision plausible futures in the form of real images, considering the formulation of policies to support decision-making
