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Brain clocks, which quantify discrepancies between brain age and chronological age, hold promise for understanding brain health and disease. However, the impact of diversity (including geographical, socioeconomic, sociodemographic, sex and neurodegeneration) on the brain-age gap is unknown. We analyzed datasets from 5,306 participants across 15 countries (7 Latin American and Caribbean countries (LAC) and 8 non-LAC countries). Based on higher-order interactions, we developed a brain-age gap deep learning architecture for functional magnetic resonance imaging (2,953) and electroencephalography (2,353). The datasets comprised healthy controls and individuals with mild cognitive impairment, Alzheimer disease and behavioral variant frontotemporal dementia. LAC models evidenced older brain ages (functional magnetic resonance imaging: mean directional error = 5.60, root mean square error (r.m.s.e.) = 11.91; electroencephalography: mean directional error = 5.34, r.m.s.e. = 9.82) associated with frontoposterior networks compared with non-LAC models. Structural socioeconomic inequality, pollution and health disparities were influential predictors of increased brain-age gaps, especially in LAC (R² = 0.37, F² = 0.59, r.m.s.e. = 6.9). An ascending brain-age gap from healthy controls to mild cognitive impairment to Alzheimer disease was found. In LAC, we observed larger brain-age gaps in females in control and Alzheimer disease groups compared with the respective males. The results were not explained by variations in signal quality, demographics or acquisition methods. These findings provide a quantitative framework capturing the diversity of accelerated brain aging.

Le Burkina Faso, comme d'autres pays ouest-africains, a connu diverses techniques de production du fer. Cette activité a duré plusieurs siècles avant de s'arrêter au début du XXe siècle de notre ère avec l'arrivée de l'administration coloniale française. Les recherches archéologiques menées à Godyr, dans la province du Sanguié, ont révélé un important développement sidérurgique, avec au moins trois traditions techniques distinctes. Pour ce faire, quelles sont les caractéristiques de chacune des traditions sidérurgiques développées autrefois par les métallurgistes de Godyr ? Cette étude vise à dresser un bilan des recherches dans cette région, encore peu explorée sur le plan archéologique.

This study investigated the potential of natural deep eutectic solvents (NADESs) for extracting bioactive compounds from the aerial parts of two mountain tea taxa, Sideritis clandestina ssp. peloponnesiaca (Boiss. & Heldr.) Baden and Sideritis raeseri Boiss. & Heldr. ssp. raeseri. Five NADEs, composed of betaine, glycerol, glucose, urea, citric acid, and sucrose, were evaluated for their extraction efficiency compared to conventional solvents (water and 70% ethanol). The total phenolic content (TPC) and antioxidant activity (FRAP and DPPH assays) were determined. Results showed that water was not a good extraction solvent. Despite the great solvent-dependent differences, most NADEs, particularly the betaine–glycerol–glucose mixture (BGG4), exhibited comparable or even superior extraction efficiency and antioxidant activity compared to 70% ethanol. The secondary metabolites in the BGG4 and 70% ethanol extracts were determined with HPLC-MS. The BGG4 extracts of both Sideritis taxa had a rich phenolic profile, with the major ingredients being chlorogenic acid, verbascoside, and non-, mono- and di-acetylated allosyl hypolaetin glycosides. Although distinct quantitative differences in their composition compared to the respective 70% ethanol extracts, and between them were noted, overall, the content of secondary metabolites in both S. raeseri extracts was lower than that of the S. clandestina extracts. These findings suggest that NADEs, particularly BGG4, are promising green solvents for extracting bioactive compounds from Sideritis taxa, paving the way for potential applications in the development of natural and sustainable cosmetic products.

Objective.The differential diagnosis of behavioral variant frontotemporal dementia (bvFTD) and Alzheimers disease (AD) remains challenging in underrepresented, underdiagnosed groups, including Latinos, as advanced biomarkers are rarely available. Recent guidelines for the study of dementia highlight the critical role of biomarkers. Thus, novel cost-effective complementary approaches are required in clinical settings.Approach. We developed a novel framework based on a gradient boosting machine learning classifier, tuned by Bayesian optimization, on a multi-feature multimodal approach (combining demographic, neuropsychological, magnetic resonance imaging (MRI), and electroencephalography/functional MRI connectivity data) to characterize neurodegeneration using site harmonization and sequential feature selection. We assessed 54 bvFTD and 76 AD patients and 152 healthy controls (HCs) from a Latin American consortium (ReDLat).Main results. The multimodal model yielded high area under the curve classification values (bvFTD patients vs HCs: 0.93 (±0.01); AD patients vs HCs: 0.95 (±0.01); bvFTD vs AD patients: 0.92 (±0.01)). The feature selection approach successfully filtered non-informative multimodal markers (from thousands to dozens).Results. Proved robust against multimodal heterogeneity, sociodemographic variability, and missing data.Significance. The model accurately identified dementia subtypes using measures readily available in underrepresented settings, with a similar performance than advanced biomarkers. This approach, if confirmed and replicated, may potentially complement clinical assessments in developing countries.

Diversity in brain health is influenced by individual differences in demographics and cognition. However, most studies on brain health and diseases have typically controlled for these factors rather than explored their potential to predict brain signals. Here, we assessed the role of individual differences in demographics (age, sex, and education; n = 1298) and cognition (n = 725) as predictors of different metrics usually used in case-control studies. These included power spectrum and aperiodic (1/f slope, knee, offset) metrics, as well as complexity (fractal dimension estimation, permutation entropy, Wiener entropy, spectral structure variability) and connectivity (graph-theoretic mutual information, conditional mutual information, organizational information) from the source space resting-state EEG activity in a diverse sample from the global south and north populations. Brain-phenotype models were computed using EEG metrics reflecting local activity (power spectrum and aperiodic components) and brain dynamics and interactions (complexity and graph-theoretic measures). Electrophysiological brain dynamics were modulated by individual differences despite the varied methods of data acquisition and assessments across multiple centers, indicating that results were unlikely to be accounted for by methodological discrepancies. Variations in brain signals were mainly influenced by age and cognition, while education and sex exhibited less importance. Power spectrum activity and graph-theoretic measures were the most sensitive in capturing individual differences. Older age, poorer cognition, and being male were associated with reduced alpha power, whereas older age and less education were associated with reduced network integration and segregation. Findings suggest that basic individual differences impact core metrics of brain function that are used in standard case-control studies. Considering individual variability and diversity in global settings would contribute to a more tailored understanding of brain function.

Abstract The Latin American Brain Health Institute (BrainLat) has released a unique multimodal neuroimaging dataset of 780 participants from Latin American. The dataset includes 530 patients with neurodegenerative diseases such as Alzheimer’s disease (AD), behavioral variant frontotemporal dementia (bvFTD), multiple sclerosis (MS), Parkinson’s disease (PD), and 250 healthy controls (HCs). This dataset (62.7 ± 9.5 years, age range 21–89 years) was collected through a multicentric effort across five Latin American countries to address the need for affordable, scalable, and available biomarkers in regions with larger inequities. The BrainLat is the first regional collection of clinical and cognitive assessments, anatomical magnetic resonance imaging (MRI), resting-state functional MRI (fMRI), diffusion-weighted MRI (DWI), and high density resting-state electroencephalography (EEG) in dementia patients. In addition, it includes demographic information about harmonized recruitment and assessment protocols. The dataset is publicly available to encourage further research and development of tools and health applications for neurodegeneration based on multimodal neuroimaging, promoting the assessment of regional variability and inclusion of underrepresented participants in research.

Abstract Situated models suggest that social concepts are grounded in interpersonal experience. However, few studies have tested this notion experimentally, and none has targeted individuals with reduced social interaction. Here, we assessed comprehension of text-level social and non-social concepts in persons with and without autistic-like traits. Participants read a social and a non-social text and answered questionnaires targeting social and non-social concepts, respectively. We compared behavioral outcomes, gauged their contribution to subject-level classification, and examined their association with validated measures of autism. Persons with autistic-like traits showed selective deficits in grasping text-level social concepts, even adjusting for intelligence, memory, and vocabulary. Also, social concept comprehension was the only variable that significantly classified between groups. Finally, social concept outcomes correlated negatively with measures of autism, including social interaction. Our results suggest that reduced interpersonal experience selectively compromises text-level social concept processing, offering empirical constraints for situated models of social semantics.

Abstract We present a case of a 47‐year‐old woman with a history of asthma and mononeuritis who presented with shortness of breath and fatigue. Heart failure was diagnosed and echocardiography revealed large floating thrombi attached to the left ventricular walls. Cardiac magnetic resonance imaging showed evidence of myocarditis and angiitis. Blood count revealed eosinophilia. She was diagnosed with eosinophilic granulomatosis with polyangiitis or Churg–Strauss syndrome (CSS) according to recently updated criteria. Medical management with specific aetiology (anticoagulation or immunosuppression) and heart failure treatment resulted in clinical improvement. We further discuss the diagnostic approach of CSS with cardiovascular complications and therapeutic management.

Abstract Introduction Harmonization protocols that address batch effects and cross‐site methodological differences in multi‐center studies are critical for strengthening electroencephalography (EEG) signatures of functional connectivity (FC) as potential dementia biomarkers. Methods We implemented an automatic processing pipeline incorporating electrode layout integrations, patient–control normalizations, and multi‐metric EEG source space connectomics analyses. Results Spline interpolations of EEG signals onto a head mesh model with 6067 virtual electrodes resulted in an effective method for integrating electrode layouts. Z‐score transformations of EEG time series resulted in source space connectivity matrices with high bilateral symmetry, reinforced long‐range connections, and diminished short‐range functional interactions. A composite FC metric allowed for accurate multicentric classifications of Alzheimer's disease and behavioral variant frontotemporal dementia. Discussion Harmonized multi‐metric analysis of EEG source space connectivity can address data heterogeneities in multi‐centric studies, representing a powerful tool for accurately characterizing dementia.

Background: The WHO Global Malaria Programme advocates for a comprehensive, strategic approach to managing insecticide resistance, highlighting the importance of using multiple insecticides with different modes of action through rotations and combinations. To slow the spread of resistance, it is essential to develop and evaluate new formulations that feature unique modes of action and extended residual effects. Addressing this need, Mitsui Chemicals Crop & Life Solutions, Inc., developed VECTRON™ T500, a new indoor residual spraying (IRS) formulation using broflanilide, applied at a dosage of 100 mg AI/m2. This formulation was tested in a Phase III community trial, alongside Actellic® 300CS, a commonly used IRS product containing pirimiphos-methyl, applied at the recommended dosage of 1000 mg AI/m2. Methods: Monthly WHO wall cone bioassays were performed to assess the efficacy of the interventions using three mosquito strains: the laboratory-bred, insecticide-susceptible Anopheles gambiae s.s. Kisumu strain, the insecticide-resistant Anopheles coluzzii VKPer strain, and wild Anopheles gambiae s.l. mosquitoes from the Vallée du Kou, where the study was conducted. Vector surveillance was carried out to compare the results between sites treated with VECTRON™ T500, Actellic® 300CS, and an untreated control site. In addition, any reported adverse effects were closely monitored to evaluate the community's acceptance of VECTRON™ T500. Results: VECTRON™ T500 consistently achieved 100% mortality across all wall types for both susceptible and resistant mosquito strains over the 12-month period. In comparison, Actellic® 300CS induced < 80% mortality for both strains, irrespective of the wall substrate. When assessing delayed mortality in An. gambiae s.l. mosquitoes collected from sites treated with Actellic® 300CS (VK1) and VECTRON™ T500 (VK3), a statistically significant difference was noted after a 72-h holding period compared to the control site (RR = 0.51, CI95% = [0.31–0.6], P = 0.0026). Additionally, no adverse events were reported in households sprayed with VECTRON™ T500. Conclusions: The residual efficacy of VECTRON™ T500 extended for 12 months post-spraying, effectively covering the full malaria transmission season while maintaining high mortality rates in pyrethroid-resistant malaria vectors. VECTRON™ T500 demonstrated non-inferiority in performance compared to Actellic® 300CS, the standard reference product. This new IRS formulation has the potential to play a crucial role in managing insecticide resistance by being integrated into a rotational strategy alongside other IRS products containing insecticides with different modes of action. [ABSTRACT FROM AUTHOR]