Systematic under-estimation of the epigenetic clock and age acceleration in older subjects

Background: The Horvath epigenetic clock is widely used. It predicts age quite well from 353 CpG sites in the DNA methylation profile in unknown samples and has been used to calculate 'age acceleration’ in various tissues and environments. Results: The model systematically underestimates age in tissues from older people. This is seen in all examined tissues but most strongly in the cerebellum and is consistently observed in multiple datasets. Age acceleration is thus age-dependent, and this can lead to spurious associations. The current literature includes examples of association tests with age acceleration calculated in a wide variety of ways. Conclusions: The concept of an epigenetic clock is compelling, but caution should be taken in interpreting associations with age acceleration. Association tests of age acceleration should include age as a covariate

Recalibrating the epigenetic clock: implications for assessing biological age in the human cortex.

Human DNA methylation data have been used to develop biomarkers of ageing, referred to as 'epigenetic clocks', which have been widely used to identify differences between chronological age and biological age in health and disease including neurodegeneration, dementia and other brain phenotypes. Existing DNA methylation clocks have been shown to be highly accurate in blood but are less precise when used in older samples or in tissue types not included in training the model, including brain. We aimed to develop a novel epigenetic clock that performs optimally in human cortex tissue and has the potential to identify phenotypes associated with biological ageing in the brain. We generated an extensive dataset of human cortex DNA methylation data spanning the life course (n = 1397, ages = 1 to 108 years). This dataset was split into 'training' and 'testing' samples (training: n = 1047; testing: n = 350). DNA methylation age estimators were derived using a transformed version of chronological age on DNA methylation at specific sites using elastic net regression, a supervised machine learning method. The cortical clock was subsequently validated in a novel independent human cortex dataset (n = 1221, ages = 41 to 104 years) and tested for specificity in a large whole blood dataset (n = 1175, ages = 28 to 98 years). We identified a set of 347 DNA methylation sites that, in combination, optimally predict age in the human cortex. The sum of DNA methylation levels at these sites weighted by their regression coefficients provide the cortical DNA methylation clock age estimate. The novel clock dramatically outperformed previously reported clocks in additional cortical datasets. Our findings suggest that previous associations between predicted DNA methylation age and neurodegenerative phenotypes might represent false positives resulting from clocks not robustly calibrated to the tissue being tested and for phenotypes that become manifest in older ages. The age distribution and tissue type of samples included in training datasets need to be considered when building and applying epigenetic clock algorithms to human epidemiological or disease cohorts

Genome-wide identification and phenotypic characterization of seizure-associated copy number variations in 741,075 individuals

Copy number variants (CNV) are established risk factors for neurodevelopmental disorders with seizures or epilepsy. With the hypothesis that seizure disorders share genetic risk factors, we pooled CNV data from 10,590 individuals with seizure disorders, 16,109 individuals with clinically validated epilepsy, and 492,324 population controls and identified 25 genome-wide significant loci, 22 of which are novel for seizure disorders, such as deletions at 1p36.33, 1q44, 2p21-p16.3, 3q29, 8p23.3-p23.2, 9p24.3, 10q26.3, 15q11.2, 15q12-q13.1, 16p12.2, 17q21.31, duplications at 2q13, 9q34.3, 16p13.3, 17q12, 19p13.3, 20q13.33, and reciprocal CNVs at 16p11.2, and 22q11.21. Using genetic data from additional 248,751 individuals with 23 neuropsychiatric phenotypes, we explored the pleiotropy of these 25 loci. Finally, in a subset of individuals with epilepsy and detailed clinical data available, we performed phenome-wide association analyses between individual CNVs and clinical annotations categorized through the Human Phenotype Ontology (HPO). For six CNVs, we identified 19 significant associations with specific HPO terms and generated, for all CNVs, phenotype signatures across 17 clinical categories relevant for epileptologists. This is the most comprehensive investigation of CNVs in epilepsy and related seizure disorders, with potential implications for clinical practice

Chronic Kidney Disease: Vitamin D Treatment Regimens and Novel Assay Development for Kidney and Cardiovascular Function Biomarkers

Chronic kidney disease (CKD) is highly prevalent in the US population and has high incidence of cardiovascular and all-cause mortality. A known complication of CKD is secondary hyperparathyroidism that is caused by bone and mineral imbalances, including vitamin D deficiency. Supplementation of CKD patients with vitamin D is based on guidelines issued by the Kidney Disease Quality Outcomes Initiative (K/DOQI), which recommend administration of vitamin D2 in variable doses depending on the severity of vitamin D deficiency. Retrospective and pilot studies have shown that vitamin D2 was not as effective as vitamin D3 in treating vitamin D deficiency. In Chapter I, we investigated the effectiveness of vitamin D2 versus vitamin D3 treatment in resolving vitamin D deficiency in the pre-dialysis CKD population. This study was a double blinded, randomized, single center study that involved 22 CKD subjects. Data showed that vitamin D3 elicited a more rapid increase in 25-hydroxyvitamin D (25OHD) levels than vitamin D2, but both forms became equivalent in terms of the number of people who reached target 25OHD levels by the end of study.Glomerular filtration rate (GFR) is the best overall index of kidney function. GFR is determined by measuring the urinary clearance of a radioactive exogenous biomarker, such as iothalamate, or estimated (eGFR) by measuring creatinine and adjusting for race, gender and age using equations. There are several known limitations to using creatinine-based equations and radioactive substances exposure for eGFR and GFR determinations. In the remaining Chapters, solutions are proposed for measurement of GFR and eGFR, which involve liquid chromatography-tandem mass spectrometry (LC-MS/MS). Chapter II discusses this technique and the process of development and validation of bioanlaytical methods by LC-MS/MS. Chapter III introduces a LC-MS/MS method for the measurement of L-arginine, symmetric dimethylarginine (SDMA), and asymmetric dimethylarginine (ADMA). SDMA was correlated with biomarkers of kidne

Τεχνολογίες τεχνητής νοημοσύνης στην ανάπτυξη εργαλείων εντός της γεώτρησης για τον έλεγχο παραγωγής σε ταμιευτήρες πετρελαίου / φυσικού αερίου

Summarization: The introduction of revolutionizing methods in the Gas and Oil sector is a challenging task, especially when these methods interfere with high-risk activities in terms of operation, investment, personnel welfare and the environment. Despite the great caution in introducing new technologies, the oil and gas industry remains open to new technologies to optimize and streamline existing processes. The low price period for oil and gas that came along with the Covid-19 restrictions further fueled the rate at which the specific industry adopts new technologies in order to cope with such challenges. The scope of the thesis is to review the artificial intelligence methods that apply to downhole equipment in the production sector of the oil/gas industry. In the first part of this work, the history of the two distinct disciplines (oil and gas and the artificial intelligence) will be connected throughout a timeline and explain the one can benefit from the other. Subsequently, two case studies will be discussed in order to study the efficiency of AI in production and workover. The first case study is a virtual flow meter, which is a convenient substitute of physical sensors it uses available data in instrumented wells to predict other measurements (oil, water and gas flowrates). Most of the oil and gas production flowrates change with the production time, so it is necessary to recalibrate the virtual flow meter model periodically. The model runs on a dataset generated from a physics-based software (Pipesim software). The dataset was initially modified in python to make the dataset more suitable for introducing it to the virtual flowmeter model in order to set the initial parameters of the instrument. The same python code can be implemented to predict the behavior of an ESP throughout the lifetime of a field and to optimize the performance of an ESP according to the current reservoir and well conditions. The virtual flowmeter aims at predicting the multiphase flowrates based on the readings of the ESP pump downhole. Several models were considered as candidates for the virtual flowmeter. The final model (MLP) was then selected after cross-validation and hyperparameters tuning of each candidate model. The accuracy of the selected model exceeded 99% of the dataset values. The second case study is a Bayesian network model for predicting the root cause of ESP breakdown (or stoppage). The results match with the actual assessment of the service engineer, when the latter inspected the motor caused after the pump’s failure. A third case study describes a reinforcement learning technique to autonomously make decisions for an injection well in order to keep the reservoir pressure within an optimal range, while decrease the cost. The model was able to reduce the cost by 57%

A framework for categorising mobile applications in mathematics education

New learning technologies have brought fresh challenges to teachers in selecting appropriate educational resources, particularly in regards to mobile devices. There are an impressive number of educational mobile learning applications, more commonly known as apps, that teachers need to understand before integrating them into the classroom. To make this process more effective, educational apps can be categorised on their specific role in the teaching and learning of mathematics along with their media richness. A framework of nine distinct categories of apps emerged, grouped into three main clusters, namely, investigative, productivity and instructive tools. The framework was validated with examples from a K-12 context.6 page(s

International Perspectives on GFR Estimation and Race-Based Adjustments.

peer reviewe

Heparan-mimetics: Potential agents of tissue regeneration for bone and periodontal therapies

Heparan sulfate glycosaminoglycans are key players of tissue repair and can be regarded as useful compounds for regenerative medicines. Unfortunately, their therapeutic uses face many technical, industrial, and regulatory hurdles due to their animal origin. So, some non-animal sulfated polysaccharides mimic heparan sulfate properties and offer interesting solutions to replace them. Among them, dextran derivatives, seaweed polysaccharides, or marine bacterial polysaccharides are the best known and have demonstrated their pro-regenerative capabilities by promoting both extracellular matrix structuring and angiogenesis and limiting degenerative processes such as inflammatory cell migration or tissue proteolysis. These polysaccharides have also shown their ability to specifically promote osteoblastic differentiation and bone wound healing. Furthermore, recent works shows that heparan-mimetics can be used as an additive to improve the cytocompatibility of bone substitutes commonly used in periodontal surgery. The use of these polysaccharides can be regarded as a clever approach to improve the biointegration of bone substitutes

Low molecular weight fucoidans improve human medullar stromal cell settlement and osteoid-like tissue formation in organo-mineral bone substitute

International audienc