The dual concepts of neurotechnology and artificial intelligence (AI) form an intriguing but also potentially explosive mixture because of its many ethical and legal implications. The advent of AI and the progress in neurotechnologies are reshaping the landscape not only in all scientific fields but also in everyday life both individually and collectively, ushering in a new era where the centrality, integrity and identity of humans is no longer a fact. Such tumultuous progress has implications at all levels, individual, societal, economical and political. Without the pretension of exploring the whole set of relevant aspects, we aim at providing a multi-disciplinary view on the main ethical, legal and societal issues stemming from neurotechnology and AI, by assessing them using keywords like trustworthiness, fairness, awareness, security, and privacy. In this paper, we propose an overview on the current scenario, taking a philosophical perspective in the light of ethics, and boiling it down to aspects closely related to the technological developments and the regulatory measures that are currently in-place and called for.

BACKGROUND: Hypertension is a major contributor to cardiovascular morbidity and mortality. Its heterogeneity complicates risk stratification. Unsupervised machine learning can uncover risk profiles and refine preventative strategies. This study applied a data-driven approach to identify clinical phenotypes of hypertension, examine their associations with cardiovascular imaging characteristics and adverse outcomes, and assess the mediating role of cardiac imaging features in these associations. METHODS: Fourteen thousand eight hundred forty UK Biobank participants with diagnosed hypertension and cardiovascular magnetic resonance imaging were analyzed. K-means clustering was applied to 77 clinical variables. Associations with incident heart failure, atrial fibrillation, atherosclerotic events, all-cause mortality, and major adverse cardiovascular events were examined and adjusted for cardiovascular risk factors. Mediation analyses assessed the role of cardiovascular imaging features in the association between clusters and outcomes. RESULTS: Three clusters emerged. Cluster 1, predominantly female with the most favorable metabolic profile, had the lowest risk. Cluster 2, predominantly male with the highest atherosclerosis burden, carried the greatest risk for all adverse events, independent of cardiovascular risk factors. They showed severe cardiac remodeling, impaired cardiac mechanisms, and global left atrial dysfunction. Cluster 3 had a profile resembling metabolic syndrome, with moderate risk for atrial fibrillation and all-cause death (hazard ratio, 1.65 and 1.58; P<0.05). Although in cluster 2 the risk was largely mediated by left ventricular hypertrophy, in cluster 3 its role was attenuated and more evenly balanced with left atrial dysfunction. CONCLUSIONS: Clustering analysis identified distinct hypertension phenotypes with specific risk profiles, suggesting potential for improved stratification and more tailored treatment approaches.

Background This study evaluated observational and causal relationships between rheumatoid arthritis (RA) and cardiovascular disease and imaging phenotypes in the UK Biobank. Methods RA was defined using linked hospital records, self-reported diagnostics, and medication data. Controls were participants without a record of RA. Cardiovascular diseases (CVDs) were defined using linked hospital records over an average of 14 years of prospective follow-up, including: ischaemic heart diseases (IHD), acute myocardial infarction (AMI), atrial fibrillation, any arrhythmia, non-ischaemic cardiomyopathies, pericardial disease, stroke, peripheral vascular disease, and venous thromboembolism. For participants with cardiovascular magnetic resonance (CMR) available as part of the UK Biobank Imaging Study, we considered measures of cardiac structure and function extracted using automated pipelines. Associations of RA with prevalent and incident CVDs were calculated using logistic and Cox regression. Linear regression was used to examine associations with CMR metrics. Models were adjusted for demographic, lifestyle, and clinical confounders. Causal associations were assessed using two-sample Mendelian randomisation. Genetic instruments for RA (22,350 cases and 74,823 controls), nine CVDs (FinnGen, n = 224,737), and 11 CMR phenotypes (UK Biobank) were extracted and associations assessed using inverse-variance weighting with pleiotropy adjustments and multiple testing corrections. Results The analysis included 1,436 RA cases (mean age 59.9 years; 70.6% female) and 476,975 controls (mean age 56.5 years; 54.3% female). Participants with RA lived in more socioeconomically deprived areas (as per the Townsend Deprivation Index), had lower physical activity levels, were more likely to smoke, and had a higher baseline prevalence of CVDs. In fully adjusted models, participants with RA had a significantly higher hazard of multiple incident CVDs, with the greatest risks related to pericardial disease (HR 2.63 (1.85, 3.74)), heart failure (HR 1.68 (1.42, 1.99)), and AMI (HR 1.53 (1.20, 1.96)). Mendelian Randomisation analyses supported causal links between RA and AMI (OR 1.07 (1.02, 1.09), p = 0.009), arrhythmias (OR 1.05 (1.02, 1.06), p = 0.0007), and IHD (OR 1.05 (1.01, 1.06), p = 0.036). No significant associations were identified between RA and CMR phenotypes. Conclusions People with RA have a heightened risk of multiple prevalent and incident CVDs, independent of shared risk factors, with suggestions of causal links with IHD, AMI, and arrhythmias.

INTRODUCTION Dementia is a rising global health challenge. Advances in large-scale proteomics and genetic databases have enabled high-throughput screening approaches to uncover novel mechanistic pathways and therapeutic targets. METHODS This study used a Mendelian randomization framework to examine genetic associations of 2172 plasma proteins (UK Biobank, n = 54,219) with: (1) dementia subtypes (FinnGen, n = 429,209), including Alzheimer's disease (n = 12,348), vascular dementia (n = 2667), and Parkinson's disease dementia (n = 589); and (2) global neuroimaging markers (UK Biobank), including white matter hyperintensities (n = 42,310), fractional anisotropy (n = 17,663), and mean diffusivity (n = 17,467). RESULT Multiple potential causal protein–outcome relationships were identified, corroborating known associations (e.g., apolipoprotein E, synaptosomal-associated protein 25) and uncovering more novel proteins (e.g., butyrophilin subfamily 3 member A2, granzyme A, contactin-2, and trefoil factor 3) potentially involved in dementia disease processes. DISCUSSION The identified proteins have diverse functions spanning immune regulation, cellular proliferation, neuronal stability, and neuroinflammation. The findings increase our understanding of disease processes governing cognitive health and highlight candidate proteins with potential as new disease biomarkers or therapeutic targets.

ackground Cardiac and cerebral ischaemic diseases share multiple vascular risk factors (VRFs), largely driving atherosclerosis, and only partially explaining their interconnections. Cardiovascular magnetic resonance (CMR) radiomics, which detects subtle imaging features beyond conventional techniques, can enhance disease characterisation. We previously identified common radiomic features in cardiac and cerebral ischaemia, illustrating heart-brain relationships.1 However, the extent to which VRFs influence these features remains unclear. This study aims to clarify the role of VRFs in the radiomic characteristics shared by cardiac and cerebral ischaemia. Methods We examined UK Biobank participants with prevalent ischaemic heart disease (IHD, n=781) or ischaemic cerebrovascular disease (n=360)—with no overlapping diagnoses—and their respective subsets (myocardial infarction [MI], n=542; ischaemic stroke [IS], n=119). After defining a radiomics signature for each condition, as previously described, we extracted the top 20 features common to all groups. Multivariable regression models, adjusted for age, sex, and body surface area, were then used to assess associations of these features with hypertension (HTN), adiposity (waist-hip ratio [WHR]), smoking, hypercholesterolaemia, and diabetes. Results VRFs had more significant associations with radiomics in cardiac than in cerebral ischaemia (table 1). HTN and WHR were consistently linked across all groups. HTN had larger effects on cardiac remodelling in cerebral ischaemia, particularly increasing left ventricular (LV) systolic kurtosis, and that effect was greater in the IS subset (IS: β=0.42, CI [0.30; 0.55], p<0.001; cerebrovascular disease: β=0.23, CI [0.15; 0.32], p<0.001). In cardiac ischaemia, HTN presented smaller but distinct associations, including elevated myocardial volume in diastole (IHD: β=0.08, CI [0.05; 0.11]; p<0.0001; MI: β=0.11, CI [0.06; 0.16]; p =0.0002) and systolic remodelling in MI. WHR showed the most extensive effects in all groups, predominantly involving diastolic-focused remodelling marked by increased LV sphericity. Other VRFs, although with smaller effects, emerged in specific scenarios: smoking in IHD (subtle LV alterations during diastole), hypercholesterolaemia in cerebrovascular disease (systolic LV remodelling), and diabetes in MI (diastolic myocardial changes).

Background Cardiovascular magnetic resonance (CMR) and electrocardiographic (ECG) abnormalities after coronavirus disease 2019 (COVID-19) are widely reported. However, the absence of pre-infection assessments limits causal inference from these studies. This study aims to compare interval change in CMR and ECG measures in participants with incident COVID-19 and matched uninfected controls in UK Biobank. Methods UK Biobank participants with documented COVID-19 who had CMR and ECG performed before the pandemic were invited for repeat assessment, along with uninfected participants matched on age, sex, ethnicity, location, and date of baseline imaging. Automated pipelines were used to extract ECG phenotypes and CMR measures of cardiac structure and function, aortic distensibility, aortic flow, and myocardial native T1. Logistic regression was used to examine associations of baseline metrics with incident COVID-19. Standardized residual approach was used to compare the degree of interval change in CMR and ECG metrics between cases and controls. Results We analyzed 2092 participants (1079 cases and 1013 controls) with average age of 60 ± 7 years. 47.1% were male. There was 3.2 ± 1.5 years between pre- and post-infection assessments. 3.6% of cases were hospitalized. Lower baseline left ventricular ejection fraction and worse longitudinal, circumferential, and radial strain were associated with higher risk of incident COVID-19. There were no significant differences in interval change of any CMR or ECG metric between cases and controls. Conclusion While pre-existing cardiovascular abnormalities are linked to higher risk of COVID-19, exposure to infection does not alter interval change of highly sensitive CMR and ECG indicators of cardiovascular health.

Artificial intelligence (AI) is rapidly transforming cardiovascular imaging by automating tasks such as image segmentation, feature extraction, and risk prediction - leading to significant improvements in diagnostic precision and efficiency. However, the integration of AI into clinical workflows comes with critical risks that must be addressed to ensure safe and reliable patient care. This review explores the technical, clinical, and ethical challenges of AI in cardiovascular imaging, particularly highlighting the risks of model errors, data drift and inappropriate usage. We also examine concerns about explainability, the potential for deskilling of healthcare professionals, generalisability across diverse populations, and accountability in AI implementation. We present real-world examples of where these risks have been realised, along with attempts at mitigations, including the adoption of explainable AI techniques, rigorous validation frameworks to ensure fairness and broad applicability, continuous performance monitoring, and transparency at every stage of model development and deployment. The successful adoption of AI in cardiovascular imaging relies on striking a balance between innovation and the need for ethical and legal safeguards. Achieving this requires collaborative efforts between clinicians, data scientists, patients and regulators. Evaluating and addressing these challenges is essential for responsible AI implementation and advancing patient care while maintaining high safety standards.

The link between BMD and cardiovascular disease (CVD) remains a topic of extensive debate in observational studies, with inconsistent reports regarding the causality of this relationship. This study implements robust methodologies to evaluate the causal relationship between BMD and various CVDs. Two sample Mendelian randomization (MR) method was used to estimate the relationship between genetically predicted BMD and seven key CVDs: atrial fibrillation and flutter, angina, ischemic heart disease, heart failure, hypertension, myocardial infarction, and non-ischemic cardiomyopathy. Data were obtained from independent publicly available genome-wide association studies (GWAS) for BMD and CVDs, using two separate datasets for the cardiovascular outcomes: the UK Biobank cohort (primary analysis) and the FinnGen cohort (validation analysis). The MR Pleiotropy RESidual Sum and Outlier test assessed the heterogeneity and pleiotropy of selected instrumental variables (IVs). We applied the inverse variance weighted model (IVW), weighted median, weighted mode method, and MR-Egger regression model to estimate causal effects. MR results indicate no relationship between BMD and atrial fibrillation and flutter (IVW, beta-estimate: 0.011, SE: 0.03, p = .73), angina (IVW, beta-estimate: 0.04, SE: 0.03, p = .17), chronic ischemic heart disease (IVW, beta-estimate: 0.009, SE: 0.03, p = .74), heart failure (IVW, beta-estimate: 0.004, SE: 0.04, p = .91), hypertension (IVW, beta-estimate: −0.01, SE: 0.01, p = .44), myocardial infarction (IVW, beta-estimate: 0.02, SE: 0.03, p = .36), or non-ischemic cardiomyopathy (IVW, beta-estimate: 0.1, SE: 0.08, p = .20). These findings remained consistent across all complementary analyses (MR-Egger, weighted median and weighted mode) and were validated using the FinnGen cohort GWAS dataset. This comprehensive analysis identified no evidence for a causal link between genetically predicted BMD and a range of key CVDs. Previously reported observational associations between bone and cardiovascular health likely represent shared risk factors rather than direct causal mechanisms.

Background Explainable artificial intelligence (XAI) emerged to improve the transparency of machine learning models and increase understanding of how models make actions and decisions. It helps to present complex models in a more digestible form from a human perspective. However, XAI is still in the development stage and must be used carefully in sensitive domains including paediatrics, where misuse might have adverse consequences. Objective This commentary paper discusses concerns and challenges related to implementation and interpretation of XAI methods, with the aim of rising awareness of the main concerns regarding their adoption in paediatrics. Methods A comprehensive literature review was undertaken to explore the challenges of adopting XAI in paediatrics. Results Although XAI has several favorable outcomes, its implementation in paediatrics is prone to challenges including generalizability, trustworthiness, causality and intervention, and XAI evaluation. Conclusion Paediatrics is a very sensitive domain where consequences of misinterpreting AI outcomes might be very significant. XAI should be adopted carefully with focus on evaluating the outcomes primarily by including paediatricians in the loop, enriching the pipeline by injecting domain knowledge promoting a cross-fertilization perspective aiming at filling the gaps still preventing its adoption.

Background Explainable artificial intelligence (XAI) emerged to improve the transparency of machine learning models and increase understanding of how models make actions and decisions. It helps to present complex models in a more digestible form from a human perspective. However, XAI is still in the development stage and must be used carefully in sensitive domains including paediatrics, where misuse might have adverse consequences. Objective This commentary paper discusses concerns and challenges related to implementation and interpretation of XAI methods, with the aim of rising awareness of the main concerns regarding their adoption in paediatrics. Methods A comprehensive literature review was undertaken to explore the challenges of adopting XAI in paediatrics. Results Although XAI has several favorable outcomes, its implementation in paediatrics is prone to challenges including generalizability, trustworthiness, causality and intervention, and XAI evaluation. Conclusion Paediatrics is a very sensitive domain where consequences of misinterpreting AI outcomes might be very significant. XAI should be adopted carefully with focus on evaluating the outcomes primarily by including paediatricians in the loop, enriching the pipeline by injecting domain knowledge promoting a cross-fertilization perspective aiming at filling the gaps still preventing its adoption.

Background Automated algorithms are regularly used to analyse cardiac magnetic resonance (CMR) images. Validating data output reliability from this method is crucial for enabling widespread adoption. We outline a visual quality control (QC) process for image analysis using automated batch processing. We assess the performance of automated analysis and the reliability of replacing visual checks with statistical outlier removal approach in UK Biobank CMR scans. Methods We included 1,987 CMR scans from the UK Biobank COVID imaging study. We used batch processing software (Circle Cardiovascular Imaging Inc. - CVI42) to automatically extract chamber volumetric data, strain, native T1, and aortic flow data. The automated analysis outputs (∼62,000 videos and 2,000 images) were visually checked by six experienced clinicians using a standardised approach and a custom-built R Shiny app. Inter-observer variability was assessed. Data from scans passing visual QC was compared with a statistical outlier removal QC method in a subset of healthy individuals (n = 1069). Results Automated segmentation was highly rated, with over 95% of scans passing visual QC. Overall inter-observer agreement was very good (Gwet’s AC2 0.91; 95% confidence interval [0.84,0.94]). No difference in overall data derived from visual QC or statistical outlier removal in healthy individuals was observed. Conclusion Automated image analysis using CVI42 prototypes for UK Biobank CMR scans demonstrated high quality. Larger UK Biobank datasets analysed using these automated algorithms do not require in-depth visual QC. Statistical outlier removal is sufficient as a QC measure, with operator discretion for visual checks based on population or research objectives.

Explainable artificial intelligence (XAI) elucidates the decision-making process of complex AI models and is important in building trust in model predictions. XAI explanations themselves require evaluation as to accuracy and reasonableness and in the context of use of the underlying AI model. This review details the evaluation of XAI in cardiac AI applications and has found that, of the studies examined, 37% evaluated XAI quality using literature results, 11% used clinicians as domain-experts, 11% used proxies or statistical analysis, with the remaining 43% not assessing the XAI used at all. We aim to inspire additional studies within healthcare, urging researchers not only to apply XAI methods but to systematically assess the resulting explanations, as a step towards developing trustworthy and safe models.

Aims Automated algorithms are regularly used to analyse cardiac magnetic resonance (CMR) images. Validating data output reliability from this method is crucial for enabling widespread adoption. We outline a visual quality control (VQC) process for image analysis using automated batch processing. We assess the performance of automated analysis and the reliability of replacing visual checks with statistical outlier (SO) removal approach in UK Biobank CMR scans. Methods and results We included 1987 CMR scans from the UK Biobank COVID-19 imaging study. We used batch processing software (Circle Cardiovascular Imaging Inc.—CVI42) to automatically extract chamber volumetric data, strain, native T1, and aortic flow data. The automated analysis outputs (∼62 000 videos and 2000 images) were visually checked by six experienced clinicians using a standardized approach and a custom-built R Shiny app. Inter-observer variability was assessed. Data from scans passing VQC were compared with a SO removal QC method in a subset of healthy individuals (n = 1069). Automated segmentation was highly rated, with over 95% of scans passing VQC. Overall inter-observer agreement was very good (Gwet’s AC2 0.91; 95% confidence interval 0.84, 0.94). No difference in overall data derived from VQC or SO removal in healthy individuals was observed. Conclusion Automated image analysis using CVI42 prototypes for UK Biobank CMR scans demonstrated high quality. Larger UK Biobank data sets analysed using these automated algorithms do not require in-depth VQC. SO removal is sufficient as a QC measure, with operator discretion for visual checks based on population or research objectives.

eXplainable artificial intelligence (XAI) methods have emerged to convert the black box of machine learning (ML) models into a more digestible form. These methods help to communicate how the model works with the aim of making ML models more transparent and increasing the trust of end-users in their output. SHapley Additive exPlanations (SHAP) and Local Interpretable Model Agnostic Explanation (LIME) are two widely used XAI methods, particularly with tabular data. In this perspective piece, the way the explainability metrics of these twomethods are generated is discussed and a framework for the interpretation of their outputs, highlighting their weaknesses and strengths is proposed.Specifically, their outcomes in terms of model-dependency and in the presence of collinearity among the features, relying on a case study from the biomedical domain (classification of individuals with or without myocardial infarction) are discussed. The results indicate that SHAP and LIME are highly affected by the adopted ML model and feature collinearity, raising a note of caution on their usage and interpretation

Explainable Artificial Intelligence (XAI) provides tools to help understanding how AI models work and reach a particular decision or outcome. It helps to increase the interpretability of models and makes them more trustworthy and transparent. In this context, many XAI methods have been proposed to make black-box and complex models more digestible from a human perspective. However, one of the main issues that XAI methods have to face especially when dealing with a high number of features is the presence of multicollinearity, which casts shadows on the robustness of the XAI outcomes, such as the ranking of informative features. Most of the current XAI methods either do not consider the collinearity or assume the features are independent which, in general, is not necessarily true. Here, we propose a simple, yet useful, proxy that modifies the outcome of any XAI feature ranking method allowing to account for the dependency among the features, and to reveal their impact on the outcome. The proposed method was applied to SHAP, as an example of XAI method which assume that the features are independent. For this purpose, several models were exploited for a well-known classification task (males versus females) using nine cardiac phenotypes extracted from cardiac magnetic resonance imaging as features. Principal component analysis and biological plausibility were employed to validate the proposed method. Our results showed that the proposed proxy could lead to a more robust list of informative features compared to the original SHAP in presence of collinearity.

Population aging is one of the most important demographic transformations of our time. Increasing the “health span”—the proportion of life spent in good health—is a global priority. Biological aging comprises molecular and cellular modifications over many years, which culminate in gradual physiological decline across multiple organ systems and predispose to age-related illnesses. Cardiovascular disease is a major cause of ill health and premature death in older people. The rate at which biological aging occurs varies across individuals of the same age and is influenced by a wide range of genetic and environmental exposures. The authors review the hallmarks of biological cardiovascular aging and their capture using imaging and other noninvasive techniques and examine how this information may be used to understand aging trajectories, with the aim of guiding individual- and population-level interventions to promote healthy aging.

Background The absence of population-stratified cardiovascular magnetic resonance (CMR) reference ranges from large cohorts is a major shortcoming for clinical care. Objectives This paper provides age-, sex-, and ethnicity-specific CMR reference ranges for atrial and ventricular metrics from the Healthy Hearts Consortium, an international collaborative comprising 9,088 CMR studies from verified healthy individuals, covering the complete adult age spectrum across both sexes, and with the highest ethnic diversity reported to date. Methods CMR studies were analyzed using certified software with batch processing capability (cvi42, version 5.14 prototype, Circle Cardiovascular Imaging) by 2 expert readers. Three segmentation methods (smooth, papillary, anatomic) were used to contour the endocardial and epicardial borders of the ventricles and atria from long- and short-axis cine series. Clinically established ventricular and atrial metrics were extracted and stratified by age, sex, and ethnicity. Variations by segmentation method, scanner vendor, and magnet strength were examined. Reference ranges are reported as 95% prediction intervals. Results The sample included 4,452 (49.0%) men and 4,636 (51.0%) women with average age of 61.1 ± 12.9 years (range: 18-83 years). Among these, 7,424 (81.7%) were from White, 510 (5.6%) South Asian, 478 (5.3%) mixed/other, 341 (3.7%) Black, and 335 (3.7%) Chinese ethnicities. Images were acquired using 1.5-T (n = 8,779; 96.6%) and 3.0-T (n = 309; 3.4%) scanners from Siemens (n = 8,299; 91.3%), Philips (n = 498; 5.5%), and GE (n = 291, 3.2%). Conclusions This work represents a resource with healthy CMR-derived volumetric reference ranges ready for clinical implementation.

This study examined the association of estimated heel bone mineral density (eBMD, derived from quantitative ultrasound) with: 1) prevalent and incident cardiovascular diseases (CVDs: ischaemic heart disease (IHD), myocardial infarction (MI), heart failure, non-ischaemic cardiomyopathy (NICM), arrhythmia), 2) mortality (all-cause, CVD, IHD), and 3) cardiovascular magnetic resonance (CMR) measures of left ventricular and atrial structure and function and aortic distensibility, in the UK Biobank. Clinical outcomes were ascertained using health record linkage over 12.3 years of prospective follow-up. Two-sample Mendelian randomization (MR) was conducted to assess causal associations between BMD and CMR metrics using genetic instrumental variables identified from published genome-wide association studies. The analysis included 485 257 participants (55% women, mean age 56.5 ± 8.1 years). Higher heel eBMD was associated with lower odds of all prevalent CVDs considered. The greatest magnitude of effect was seen in association with heart failure and NICM, where 1-SD increase in eBMD was associated with 15% lower odds of heart failure and 16% lower odds of NICM. Association between eBMD and incident IHD and MI were non-significant; the strongest relationship was with incident heart failure (SHR: 0.90 [95% CI: 0.89-0.92]). Higher eBMD was associated with a decreased risk in all-cause, CVD, and IHD mortality, in the fully adjusted model. Higher eBMD was associated with greater aortic distensibility; associations with other CMR metrics were null. Higher heel eBMD is linked to reduced risk of a range of prevalent and incident CVD and mortality outcomes. While observational analyses suggest associations between higher eBMD and greater aortic compliance, MR analysis did not support a causal relationship between genetically predicted BMD and CMR phenotypes. These findings support the notion that bone-cardiovascular associations reflect shared risk factors/mechanisms rather than direct causal pathways.

BACKGROUNDExisting research demonstrates the association of shorter leukocyte telomere length with increased risk of age‐related health outcomes including cardiovascular diseases. However, the direct causality of these relationships has not been definitively established. Cardiovascular aging at an organ level may be captured using image‐derived phenotypes of cardiac anatomy and function. METHODS AND RESULTSIn the current study, we use 2‐sample Mendelian randomization to assess the causal link between leukocyte telomere length and 54 cardiac magnetic resonance imaging measures representing structure and function across the 4 cardiac chambers. Genetically predicted shorter leukocyte telomere length was causally linked to smaller ventricular cavity sizes including left ventricular end‐systolic volume, left ventricular end‐diastolic volume, lower left ventricular mass, and pulmonary artery. The association with left ventricular mass (β =0.217, Pfalse discovery rate=0.016) remained significant after multiple testing adjustment, whereas other associations were attenuated. CONCLUSIONSOur findings support a causal role for shorter leukocyte telomere length and faster cardiac aging, with the most prominent relationship with left ventricular mass.

As the use of technology is increasing rapidly, the amount of shared, sent, and received information is also increas-ing in the same way. As a result, this necessitates the need for finding techniques that can save and secure the information over the net. There are many methods that have been used to protect the information such as hiding information and encryption. In this study, we propose a new encryption method making use of amino acid and DNA sequences. In addition, several criteria including data size, key size and the probability of cracking are used to evaluate the proposed method. The results show that the performance of the proposed method is better than many common encryption methods, such as RSA in terms of evaluation criteria.

XML files are used widely to save the information especially in the field of bioinformatics about the whole genome. There are many programming languages and modules used to parse XML files in different platforms such as Linux, Macintosh and Windows. The aim of this report is to reveal and determine which common programming language to use and on which platform is better to parse XML files in terms of memory usage, CPU time consumption and executing time.

The process involve in building a successful website goes beyond knowing web developing languages such as HTML and CSS. The success or failure of any website depends heavily on the use of some aspects of Graphical User Interface. These aspects are usually reserved for client’s wishes and desires. The content features of the GUI are used to stay in touch with visitors. Therefore, this article will highlight some important GUI features used in building an attractive and a successful website.