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BackgroundStudies have shown that cardiovascular health (CVH) is related to depression. We aimed to identify gene networks jointly associated with depressive symptoms and cardiovascular health metrics using the whole blood transcriptome.Materials and methodsWe analyzed human blood transcriptomic data to identify gene co-expression networks, termed gene modules, shared by Beck’s depression inventory (BDI-II) scores and cardiovascular health (CVH) metrics as markers of depression and cardiovascular health, respectively. The BDI-II scores were derived from Beck’s Depression Inventory, a 21-item self-report inventory that measures the characteristics and symptoms of depression. CVH metrics were defined according to the American Heart Association criteria using seven indices: smoking, diet, physical activity, body mass index (BMI), blood pressure, total cholesterol, and fasting glucose. Joint association of the modules, identified with weighted co-expression analysis, as well as the member genes of the modules with the markers of depression and CVH were tested with multivariate analysis of variance (MANOVA).ResultsWe identified a gene module with 256 genes that were significantly correlated with both the BDI-II score and CVH metrics. Based on the MANOVA test results adjusted for age and sex, the module was associated with both depression and CVH markers. The three most significant member genes in the module were YOD1, RBX1, and LEPR. Genes in the module were enriched with biological pathways involved in brain diseases such as Alzheimer’s, Parkinson’s, and Huntington’s.ConclusionsThe identified gene module and its members can provide new joint biomarkers for depression and CVH.

Jatkuvasti kehittyvällä digitaalisella aikakaudella sosiaalisen median rooli yritysten, erityisesti mikroyrittäjien, viestintästrategioiden kehittämisessä on tärkeämpää kuin koskaan. Mikroyrittäjien ulkoinen viestintä usein lähtee liikkeelle yrityksen tarpeista, tahdosta ja suunnittelusta. Laadukkaan viestinnän tulisi sisältää kahdensuuntaista vuorovaikutusta potentiaalisten asiakkaiden kanssa, sillä sosiaalinen media altistaa yrityksen julkisuudelle. Hallinnan puute ja yksisuuntainen viestintä ilman selkeää strategiaa altistavat yritykset resurssien tuhlaamiselle.

Silver nanoparticles deposits were produced with liquid flame spray (LFS) on glass and TiO2 substrates to study their optical response and photocatalytic enhancement. The correlation between extinction spectrum of the nanoparticle coating and the LFS process parameters was studied. The spectra consisted of two partly overlapping peaks: one centered in the UV region and the other in the visible light region. The visible light peak redshifted as either the silver mass concentration in the precursor solution or the precursor solution feed rate was increased, which also correlated with growing primary particle size. However, simultaneous correlation with photocatalytic activity of the decorated TiO2 surfaces was not observed, which was attributed to particle sintering on the surface. Instead, the photocatalytic activity was seen to change as the surface coverage of silver nanoparticles was varied. When the surface coverage was raised from ∼10 % to roughly 30 %, the activity, and then decreased as the loading was further raised. The increase was assumed to originate from plasmonic activation, and the decrease was attributed to the excessive amount of silver either blocking reactive area of the TiO2 or absorbing/scattering too much of the incoming light, which hindered the photocatalytic activity.

Abstract Objectives: To characterise nutritionally adequate, climate-friendly diets that are culturally acceptable across socio-demographic groups. To identify potential equity issues linked to more climate-friendly and nutritionally adequate dietary changes. Design: An optimisation model minimises distance from observed diets subject to nutritional, greenhouse gas emissions (GHGE) and food-habit constraints. It is calibrated to socio-demographic groups differentiated by sex, education and income levels using dietary intake data. The environmental coefficients are derived from life cycle analysis and an environmentally extended input–output model. Setting: Finland. Participants: Adult population. Results: Across all population groups, we find large synergies between improvements in nutritional adequacy and reductions in GHGE, set at one-third or half of the current level. Those reductions result mainly from the substitution of meat with cereals, potatoes and roots and the intra-category substitution of foods, such as beef with poultry in the meat category. The simulated more climate-friendly diets are thus flexitarian. Moving towards reduced-impact diets would not create major inadequacies related to protein and fatty acid intakes, but Fe could be an issue for pre-menopausal females. The initial socio-economic gradient in the GHGE of diets is small, and the patterns of adjustments to more climate-friendly diets are similar across socio-demographic groups. Conclusions: A one-third reduction in GHGE of diets is achievable through moderate behavioural adjustments, but achieving larger reductions may be difficult. The required changes are similar across socio-demographic groups and do not raise equity issues. A population-wide policy to promote behavioural change for diet sustainability would be appropriate.

More than 10 million Europeans show signs of mild cognitive impairment (MCI), a transitional stage between normal brain aging and dementia stage memory disorder. The path MCI takes can be divergent; while some maintain stability or even revert to cognitive norms, alarmingly, up to half of the cases progress to dementia within 5 years. Current diagnostic practice lacks the necessary screening tools to identify those at risk of progression. The European patient experience often involves a long journey from the initial signs of MCI to the eventual diagnosis of dementia. The trajectory is far from ideal. Here, we introduce the AI-Mind project, a pioneering initiative with an innovative approach to early risk assessment through the implementation of advanced artificial intelligence (AI) on multimodal data. The cutting-edge AI-based tools developed in the project aim not only to accelerate the diagnostic process but also to deliver highly accurate predictions regarding an individual's risk of developing dementia when prevention and intervention may still be possible. AI-Mind is a European Research and Innovation Action (RIA H2020-SC1-BHC-06-2020, No. 964220) financed between 2021 and 2026. First, the AI-Mind Connector identifies dysfunctional brain networks based on high-density magneto- and electroencephalography (M/EEG) recordings. Second, the AI-Mind Predictor predicts dementia risk using data from the Connector, enriched with computerized cognitive tests, genetic and protein biomarkers, as well as sociodemographic and clinical variables. AI-Mind is integrated within a network of major European initiatives, including The Virtual Brain, The Virtual Epileptic Patient, and EBRAINS AISBL service for sensitive data, HealthDataCloud, where big patient data are generated for advancing digital and virtual twin technology development. AI-Mind's innovation lies not only in its early prediction of dementia risk, but it also enables a virtual laboratory scenario for hypothesis-driven personalized intervention research. This article introduces the background of the AI-Mind project and its clinical study protocol, setting the stage for future scientific contributions.

ObjectiveAssess the quality of critical congenital heart disease (CCHD) screening data reports in California, where CCHD screening is not mandatory but reporting is.Study designRetrospective review of California hospital-level CCHD screening data to evaluate data reliability and adherence to state screening and reporting recommendations. Data were evaluated for internal consistency and compared to two databases.ResultsOver one-third of hospitals did not submit data. Only 70.7% of the Vital Records live births were reported in CCHD screening data. Only 46% of reporting hospitals submitted data with matching numbers of completed screens and results, and 22% matched their respective live births in a second database.ConclusionCCHD data reporting in California is incomplete, which may miss 359 CCHD cases/year from non-reporting. Data inconsistencies may miss additional cases. Mandatory screening, reporting, and improvements in data reliability are urgently needed to inform screening modifications and enhance timely detection and disease surveillance.