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Biomarkers for advancing diagnosis and prognosis in stroke
No full text: The complexity and incomplete understanding of the pathophysiology of stroke poses substantial challenges to personalised medicine and the development of novel therapies, as reflected in the neutral results of many randomised trials. Current clinical algorithms lack the precision to diagnose stroke before hospital admission, predict disease progression and recovery, or assess recurrence risk. Biomarkers are urgently needed, yet the absence of a robust evidence base hinders their clinical translation. To lay the groundwork for addressing this knowledge gap, an international multidisciplinary panel of stroke experts conducted a literature review that informed a Delphi consensus process. The stroke experts panel proposes 17 research priorities and five minimum reporting datasets for stroke biomarker studies, each corresponding to a key domain of stroke care: prehospital diagnosis, ischaemic stroke progression, atrial cardiopathy, plaque vulnerability, and intracerebral haemorrhage. This framework will promote consistency and collaboration towards the discovery, validation, and clinical translation of biomarkers to improve stroke care
Effect of metformin on metabolomic and circulating miRNAs profile in type 2 diabetes mellitus patients: a pilot analysis of sex differences
No full textThe impact of sex on metformin (METF) activity has been largely overlooked. This pilot study investigates sex influences metabolic and microRNA (miRNAs) profiles in diabetic patients treated with METF therapy compared to those not treated with METF. Fifty-six outpatients with type 2 diabetes mellitus (29 men and 27 women), Caucasian, non-smokers, normotensive, normolipidemic, in good metabolic control, and free from diabetic complications were recruited. They were divided into two groups, drug-naïve and METF-treated, and stratified by sex. Serum samples were collected and analyzed for amino acids (AA) and acylcarnitines (AC), advanced glycation end products (AGEs), malondialdehyde (MDA), and 8 selected circulating miRNAs. Women treated with METF (W-METF) showed lower MDA than the W-drug-naïve, while AGEs were reduced in both the W-METF and men treated with METF (M-METF). In the W-METF, five miRNAs were upregulated compared to the W-drug-naïve, whereas three miRNAs were upregulated in the M-METF group compared to the M-drug-naïve. AA and AC are sex-dependent: W-METF showed more differences than W-drug-naïve, while men had fewer changes. METF impacted women's AC profiles more, with 23 out of 39 AC being significantly reduced, compared to only 4 AC being reduced in men. METF amplifies sex differences increasing their number from 11 in the drug-naïve patients to 39 in the METF-treated patients. MiR-223-3p was upregulated only in W-METF. This pilot study highlights that METF induces significant sex-dependent changes in the blood metabolome and 8 circulating miRNAs, paving the way for large-scale studies and suggesting that METF therapy could be personalized based on sex
Add a seat at the table. Religions, food and music as expression of freedom between past and future
No full textConsiglio scientifico della Rivista telematica La Cittadinanza europea. Rivista di studi e documentazione sull’integrazione europea (LCE on line).
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Investigating breathing dynamics of a 110 kV oil-immersed transformer using thermal-fluid-structure interaction modelling
No full textThe breathing system of an oil-immersed transformer is of vital importance for maintaining
equilibrium between internal and external pressures, with any malfunction potentially disrupting the
transformer’s stable operation. This study develops a thermal-fluid-structure interaction (TFSI)
model to simulate the breathing phenomenon in a 110 kV oil-immersed transformer, addressing the
limitations of current manual inspection methods. This model analyses key breathing parameters
such as breathing volume and maximum breathing rate, along with their dynamic characteristics
under various load factors and ambient temperatures, revealing nonlinear and linear relationships,
respectively. To validate the model, the breathing parameters of a 110 kV oil-immersed transformer
were monitored over a 7-day period using a breather equipped with a breathing monitoring
functionality. Comparison between simulation results and monitoring data for the period from 7
p.m. March 7 to 7 p.m. March 8 revealed discrepancies of 6.93% for the maximum daily breathing
rate, and 6.73% for maximum daily breathing volume. This study contributes to the field by
providing a more accurate and efficient method for analysing transformer breathing dynamics,
potentially enhancing early detection of breathing system malfunctions, optimising breather
maintenance schedules, and improving diagnosis of pressure-related faults in oil-immersed
transformers
Grass is always dark(er) on the other side: Exploring the dark side of artificial intelligence humanitarian supply chain operations
Get PDFHumanitarian supply chains (HSCs) have undergone significant changes over the years, shifting from traditional systems to more intelligent and, eventually, AI-enabled operations. With technological advancements accelerating across sectors, humanitarian organizations have also begun adopting artificial intelligence (AI) to enhance their workflows, improve efficiency, and reduce losses. While much of the existing research has focused on the benefits of AI in business and logistics, there is still limited understanding of its potential downsides—particularly within humanitarian settings. This study addresses that gap by exploring how AI may negatively affect HSC activities, both at the individual (micro) and organizational (macro) levels. To guide our analysis, we draw on the Belief-Action-Outcome (BAO) framework, which helps connect personal and institutional beliefs to actions and resulting outcomes. Humanitarian supply chains operate in complex environments where technology use intersects with human behavior, organizational culture, and social values. To better understand these dynamics, we conducted qualitative interviews with professionals working in humanitarian organizations. These insights allowed us to identify and map various challenges—what we refer to as the “dark side” of AI—onto specific functions within HSC operations. Our findings not only highlight areas of concern but also contribute to the broader application of the BAO model in the humanitarian field
Spatial Reasoning and Risk Assessment for Autonomous Vehicles on Consumer Electronics Platforms Using a Customized Vision–Language Model with Data Augmentation
No full textAccurate spatial reasoning and risk assessment from monocular video on consumer electronics platforms are prerequisites for safe decision-making in autonomous vehicles, yet general-purpose vision–language model (VLM) remains unreliable at lane-level localization and temporally grounded risk estimation. We present a deployment-oriented, spatially enhanced VLM that learns implicit 3D reconstructive information from monocular sequences via a reconstructive 3D tokenization pipeline and a spatial–visual fusion encoder built on MobileVLM. Concretely, the model fuses 3D reconstructive tokens with CLIP visual tokens through cross-attention to produce 3D-aware visual tokens, enabling lane-level localization and depth-aware reasoning about object orientation and inter-object relations. The unified decoder jointly generates scenario descriptions, object identities and positions, and converts spatial estimates into interpretable multi-level risk scores using a Time-to-Collision (TTC) mapping. Experimental results show that our system outperforms strong VLM baselines for multi-level risk classification on the nuScenes dataset. The generalization in CARLA and CoVLA further corroborates robust spatial reasoning and risk assessment. The findings indicate that coupling a lightweight VLM with monocular 3D cues via cross-attentional spatial–visual fusion yields accurate spatial localization and interpretable, transferable risk estimation that is robust under shift and practical for edge deployment in downstream tasks
Seasonal physical performance changes in U12-U15 male youth soccer players
No full textThe present study aimed to investigate the development of physical performance attributes across one competition year in male youth soccer players from different playing levels, while controlling for baseline performance, chronological age, and biological maturity. A total of 175 male Scottish youth soccer players from three distinct playing levels: grassroots (GR); professional youth (PY); performance school (PS) were recruited. Physical testing (linear sprint, change of direction, squat jump, and Yo-Yo Intermittent Recovery Test Level 1) was conducted for all players to establish baseline fitness and then repeated under matched conditions at the end of the same competition year. A Bayesian approach was used to estimate the size of any change in each physical test over the season, estimate the uncertainty around these changes, and estimate the probability of direction of these changes. In all cases, the players improved fitness testing metrics. The GR group made the greatest changes in physical performances but did not match absolute performance of the PY and PS groups. Our results provide meaningful benchmark data for evaluating and interpreting isolated physical fitness metrics between distinct youth playing levels and may augment the ongoing critique of the discriminative ability of isolated physical fitness tests in youth soccer