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ObjectiveTo explore the impact of antithrombotic therapy discontinuation in the postacute phase of cervical artery dissection (CeAD) on the mid-term outcome of these patients.MethodsIn a cohort of consecutive patients with first-ever CeAD, enrolled in the setting of the multicentre Italian Project on Stroke in Young Adults Cervical Artery Dissection, we compared postacute (beyond 6 months since the index CeAD) outcomes between patients who discontinued antithrombotic therapy and patients who continued taking antithrombotic agents during follow-up. Primary outcome was a composite of ischaemic stroke and transient ischaemic attack. Secondary outcomes were (1) Brain ischaemia ipsilateral to the dissected vessel and (2) Recurrent CeAD. Associations with the outcome of interest were assessed by the propensity score (PS) method.ResultsOf the 1390 patients whose data were available for the outcome analysis (median follow-up time in patients who did not experience outcome events, 36.0 months (25th–75th percentile, 62.0)), 201 (14.4%) discontinued antithrombotic treatment. Primary outcome occurred in 48 patients in the postacute phase of CeAD. In PS-matched samples (201 vs 201), the incidence of primary outcomes among patients taking antithrombotics was comparable with that among patients who discontinued antithrombotics during follow-up (5.0% vs 4.5%; p(log rank test)=0.526), and so was the incidence of the secondary outcomes ipsilateral brain ischaemia (4.5% vs 2.5%; p(log rank test)=0.132) and recurrent CeAD (1.0% vs 1.5%; p(log rank test)=0.798).ConclusionsDiscontinuation of antithrombotic therapy in the postacute phase of CeAD does not appear to increase the risk of brain ischaemia during follow-up.

Frontotemporal dementia (FTD) is a complex disorder characterised by a broad range of clinical manifestations, differential pathological signatures, and genetic variability. Mutations in three genes—MAPT, GRN, and C9orf72—have been associated with FTD. We sought to identify novel genetic risk loci associated with the disorder. We did a two-stage genome-wide association study on clinical FTD, analysing samples from 3526 patients with FTD and 9402 healthy controls. To reduce genetic heterogeneity, all participants were of European ancestry. In the discovery phase (samples from 2154 patients with FTD and 4308 controls), we did separate association analyses for each FTD subtype (behavioural variant FTD, semantic dementia, progressive non-fluent aphasia, and FTD overlapping with motor neuron disease [FTD-MND]), followed by a meta-analysis of the entire dataset. We carried forward replication of the novel suggestive loci in an independent sample series (samples from 1372 patients and 5094 controls) and then did joint phase and brain expression and methylation quantitative trait loci analyses for the associated (p<5 × 10−8) single-nucleotide polymorphisms. We identified novel associations exceeding the genome-wide significance threshold (p<5 × 10−8). Combined (joint) analyses of discovery and replication phases showed genome-wide significant association at 6p21.3, HLA locus (immune system), for rs9268877 (p=1·05 × 10−8; odds ratio=1·204 [95% CI 1·11–1·30]), rs9268856 (p=5·51 × 10−9; 0·809 [0·76–0·86]) and rs1980493 (p value=1·57 × 10−8, 0·775 [0·69–0·86]) in the entire cohort. We also identified a potential novel locus at 11q14, encompassing RAB38/CTSC (the transcripts of which are related to lysosomal biology), for the behavioural FTD subtype for which joint analyses showed suggestive association for rs302668 (p=2·44 × 10−7; 0·814 [0·71–0·92]). Analysis of expression and methylation quantitative trait loci data suggested that these loci might affect expression and methylation in cis. Our findings suggest that immune system processes (link to 6p21.3) and possibly lysosomal and autophagy pathways (link to 11q14) are potentially involved in FTD. Our findings need to be replicated to better define the association of the newly identified loci with disease and to shed light on the pathomechanisms contributing to FTD. The National Institute of Neurological Disorders and Stroke and National Institute on Aging, the Wellcome/MRC Centre on Parkinson's disease, Alzheimer's Research UK, and Texas Tech University Health Sciences Center.

LRRK2 genetic screening should be taken into account in atypical Parkinson disease cases. 1 Introduction Parkinson Disease (PD) is a neurodegenerative disorders with a well recognised genetic basis. Among at least six PD-causing genes in predominantly early-onset forms, leucine-rich repeat kinase 2 (LRRK2) mutations have emerged as the most commonly involved in both familial and sporadic PD. LRRK2 mutations frequently simulate typical features of PD with a wide range of age at disease onset [1].

This paper investigates the suitability of the community cooperatives (CC) model for the implementation of renewable energy communities (REC), as prescribed by art. 22 of EU Directive 2018/2001, and temporarily transposed into the Italian law by art. 42-bis of the Law Decree n. 162/2019. The hypothesis explored analyses the potential synergies between RECs and CC, based on their similarities. In particular, the article takes into consideration: the actors involved in both the RECs and the CCs; the geographical scope in which they develop, and the purposes that these two legal forms intended to achieve. Through a literature review and the analysis of EU, national and regional legislations, the paper aims at (1) clarifying the main features of RECs and the CCs in Italy; (2) exploring the main differences between CCs and the other legal forms of cooperative (e.g., mutual cooperative, cooperative benefit, etc.) and assessing the extent to which CCs are more suitable to implement renewable energy communities. As a result of the literature and regulatory review, several similarities between CCs and RECs can be detected, particularly, in reference to the strategic valorization of the cooperation between citizens and the local public entities. These similarities allow the authors to provisionally conclude that, in Italy, CCs may be adopted as a tool to implement RECs.

Major depression (MDD) has been associated with an altered EEG frontal asymmetry measured in resting state; nevertheless, this association has showed a weak consistency across studies. In the present study, which starts from an evolutionistic view of psychiatric disorders, we investigated frontal asymmetry in MDD, using language as a probe to test the integrity of large inter- and intra-hemispheric networks and processes. Thirty MDD patients (22 women) and 32 matched controls (HC) were recruited for an EEG recording in resting state and during two linguistic tasks, phonological and semantic. Normalized alpha and beta EEG spectral bands were measured across all three conditions in the two groups. EEG alpha amplitude showed no hemispheric asymmetry, regardless of group, both at rest and during linguistic tasks. During resting state, analysis of EEG beta revealed a lack of hemispheric asymmetry in both groups, but during linguistic tasks, HC exhibited the typical greater left frontal beta activation, whereas MDD patients showed a lack of frontal asymmetry and a significantly lower activation of left frontal sites. In depressed patients, positive affect was negatively correlated with depression levels and positively correlated with left frontal EEG beta amplitude. Language represents the human process that requires the largest level of integration between and within the hemispheres; thus, language asymmetry was a valid probe to test the left frontal alteration encompassing highly impairing psychiatric disorders, such as schizophrenia and MDD. Indeed, these severe diseases are marked by delusions, ruminations, thought disorders, and hallucinations, all of which have a clear linguistic or metalinguistic basis.

This study offers a comparative evaluation of fifteen generative artificial intelligence models using 397 Italian multiple-choice questions on occupational medicine. Model accuracy ranged from 75.06% to 95.72%. The results highlight the need to assess large language models in specialized fields to support their safe and effective integration into medical education and occupational medicine practice.This study offers a comparative evaluation of fifteen generative artificial intelligence models using 397 Italian multiple-choice questions on occupational medicine. Model accuracy ranged from 75.06% to 95.72%. The results highlight the need to assess large language models in specialized fields to support their safe and effective integration into medical education and occupational medicine practice.

In recent years, collective action initiatives in the energy field, such as energy communities and cooperatives, have been gaining relevance both in the policy and in the social research domains as primary actors of energy transition. [...]

This conceptual paper applies a mobilization model to Collective Action Initiatives (CAIs) in the energy sector. The goal is to synthesize aspects of sustainable transition theories with social movement theory to gain insights into how CAIs mobilize to bring about niche-regime change in the context of the sustainable energy transition. First, we demonstrate how energy communities, as a representation of CAIs, relate to social innovation. We then discuss how CAIs in the energy sector are understood within both sustainability transition theory and institutional dynamics theory. While these theories are adept at describing the role energy CAIs have in the energy transition, they do not yet offer much insight concerning the underlying social dimensions for the formation and upscaling of energy CAIs. Therefore, we adapt and apply a mobilization model to gain insight into the dimensions of mobilization and upscaling of CAIs in the energy sector. By doing so we show that the expanding role of CAIs in the energy sector is a function of their power acquisition through mobilization processes. We conclude with a look at future opportunities and challenges of CAIs in the energy transition.

Diffuse low-grade gliomas (LGG) represent a heterogeneous group of primary brain tumors arising from supporting glial cells and usually affecting young adults. Advances in the knowledge of molecular profile of these tumors, including mutations in the isocitrate dehydrogenase genes, or 1p/19q codeletion, and in neuroradiological techniques have contributed to the diagnosis, prognostic stratification, and follow-up of these tumors. Optimal post-operative management of LGG is still controversial, though radiation therapy and chemotherapy remain the optimal treatments after surgical resection in selected patients. In this review, we report the most important and recent research on clinical and molecular features, new neuroradiological techniques, the different therapeutic modalities, and new opportunities for personalized targeted therapy and supportive care.

The objective of this study is to evaluate ChatGPT’s accuracy and reliability in answering complex medical questions related to occupational health and explore the implications and limitations of AI in occupational health medicine. The study also provides recommendations for future research in this area and informs decision-makers about AI’s impact on healthcare. A group of physicians was enlisted to create a dataset of questions and answers on Italian occupational medicine legislation. The physicians were divided into two teams, and each team member was assigned a different subject area. ChatGPT was used to generate answers for each question, with/without legislative context. The two teams then evaluated human and AI-generated answers blind, with each group reviewing the other group’s work. Occupational physicians outperformed ChatGPT in generating accurate questions on a 5-point Likert score, while the answers provided by ChatGPT with access to legislative texts were comparable to those of professional doctors. Still, we found that users tend to prefer answers generated by humans, indicating that while ChatGPT is useful, users still value the opinions of occupational medicine professionals.