A simulation system for biomarker evolution in neurodegenerative disease

We present a framework for simulating cross-sectional or longitudinal biomarker data sets from neurodegenerative disease cohorts that reflect the temporal evolution of the disease and population diversity. The simulation system provides a mechanism for evaluating the performance of data-driven models of disease progression, which bring together biomarker measurements from large cross-sectional (or short term longitudinal) cohorts to recover the average population-wide dynamics. We demonstrate the use of the simulation framework in two different ways. First, to evaluate the performance of the Event Based Model (EBM) for recovering biomarker abnormality orderings from cross-sectional datasets. Second, to evaluate the performance of a differential equation model (DEM) for recovering biomarker abnormality trajectories from short-term longitudinal datasets. Results highlight several important considerations when applying data-driven models to sporadic disease datasets as well as key areas for future work. The system reveals several important insights into the behaviour of each model. For example, the EBM is robust to noise on the underlying biomarker trajectory parameters, under-sampling of the underlying disease time course and outliers who follow alternative event sequences. However, the EBM is sensitive to accurate estimation of the distribution of normal and abnormal biomarker measurements. In contrast, we find that the DEM is sensitive to noise on the biomarker trajectory parameters, resulting in an over estimation of the time taken for biomarker trajectories to go from normal to abnormal. This over estimate is approximately twice as long as the actual transition time of the trajectory for the expected noise level in neurodegenerative disease datasets. This simulation framework is equally applicable to a range of other models and longitudinal analysis techniques

Genetic variation of TLR4 influences immunoendocrine stress response: an observational study in cardiac surgical patients

Introduction: Systemic inflammation (e.g. following surgery) involves Toll-like receptor (TLR) signaling and leads to an endocrine stress response. This study aims to investigate a possible influence of TLR2 and TLR4 single nucleotide polymorphisms (SNPs) on perioperative adrenocorticotropic hormone (ACTH) and cortisol regulation in serum of cardiac surgical patients. To investigate the link to systemic inflammation in this context, we additionally measured 10 different cytokines in the serum. Methods: 338 patients admitted for elective cardiac surgery were included in this prospective observational clinical cohort study. Genomic DNA of patients was screened for TLR2 and TLR4 SNPs. Serum concentrations of ACTH, cortisol, interferon (IFN)-, interleukin (IL)-1, IL-2, IL-4, IL-5, IL-6, IL-8, IL-10, tumor necrosis factor (TNF)- and granulocyte macro-phage-colony stimulating factor (GM-CSF) were determined before surgery, immediately post surgery and on the first postoperative day. Results: 13 patients were identified as TLR2 SNP carrier, 51 as TLR4 SNP carrier and 274 pa-tients as non-carrier. Basal levels of ACTH, cortisol and cytokines did not differ between groups. In all three groups a significant, transient perioperative rise of cortisol could be ob-served. However, only in the non-carrier group this was accompanied by a significant ACTH rise, TLR4 SNP carriers had significant lower ACTH levels compared to non-carriers ((mean[95% confidence intervals]) non-carriers: 201.9[187.7 to 216.1]pg/ml; TLR4 SNP car-riers: 149.9[118.4 to 181.5]pg/ml; TLR2 SNP carriers: 176.4[110.5 to 242.3]pg/ml). Compared to non-carriers, TLR4 SNP carriers showed significant lower serum IL-8, IL-10 and GM-CSF peaks ((mean[95% confidence intervals]): IL-8: non-carriers: 42.6[36.7 to 48.5]pg/ml, TLR4 SNP carriers: 23.7[10.7 to 36.8]pg/ml; IL-10: non-carriers: 83.8[70.3 to 97.4]pg/ml, TLR4 SNP carriers: 54.2[24.1 to 84.2]pg/ml; GM-CSF: non-carriers: 33.0[27.8 to 38.3]pg/ml, TLR4 SNP carriers: 20.2[8.6 to 31.8]pg/ml). No significant changes over time or between the groups were found for the other cytokines. Conclusions: Regulation of the immunoendocrine stress response during systemic inflamma-tion is influenced by the presence of a TLR4 SNP. Cardiac surgical patients carrying this ge-notype showed decreased serum concentrations of ACTH, IL-8, IL-10 and GM-CSF. This finding might have impact on interpreting previous and designing future trials on diagnosing and modulating immunoendocrine dysregulation (e.g. adrenal insufficiency) during systemic inflammation and sepsis

Data-Driven Sequence of Changes to Anatomical Brain Connectivity in Sporadic Alzheimer's Disease

Model-based investigations of transneuronal spreading mechanisms in neurodegenerative diseases relate the pattern of pathology severity to the brain’s connectivity matrix, which reveals information about how pathology propagates through the connectivity network. Such network models typically use networks based on functional or structural connectivity in young and healthy individuals, and only end-stage patterns of pathology, thereby ignoring/excluding the effects of normal aging and disease progression. Here, we examine the sequence of changes in the elderly brain’s anatomical connectivity over the course of a neurodegenerative disease. We do this in a data-driven manner that is not dependent upon clinical disease stage, by using event-based disease progression modeling. Using data from the Alzheimer’s Disease Neuroimaging Initiative dataset, we sequence the progressive decline of anatomical connectivity, as quantified by graph-theory metrics, in the Alzheimer’s disease brain. Ours is the first single model to contribute to understanding all three of the nature, the location, and the sequence of changes to anatomical connectivity in the human brain due to Alzheimer’s disease. Our experimental results reveal new insights into Alzheimer’s disease: that degeneration of anatomical connectivity in the brain may be a viable, even early, biomarker and should be considered when studying such neurodegenerative diseases

ADSORPTION ESSAYS OF PALLADIUM IN MODIFIED SILICA GEL WITH THIOURONIUM GROUPS: EXPERIMENTAL AND THEORICAL STUDIES

Indexación: Web of Science; ScieloThe silylant 3-cloropropyltriethoxysilyl was anchored over silica gel in anhydrous conditions in order to react with thiourea to obtain modified silica gel with thiouronium. The aim to obtain an inorganic support that is able to hijack metals from the VIII group such as palladium. The product was characterized by Sbet and FTIR infrared spectroscopy. For the determination of the structure in the modified silica gel NMR spectra of silicon and carbon were preformed in solid state. The coordination form of the modified silica gel to the metal was studied computationally in the context of the DFT theory, using the ADF code. This was a collaborative work with "Fundación Chile" for the recuperation of precious metals from the mining industry.http://ref.scielo.org/gk7rm

Towards cascading genetic risk in Alzheimers disease.

Alzheimers disease typically progresses in stages, which have been defined by the presence of disease-specific biomarkers: amyloid (A), tau (T) and neurodegeneration (N). This progression of biomarkers has been condensed into the ATN framework, in which each of the biomarkers can be either positive (+) or negative (-). Over the past decades, genome-wide association studies have implicated ∼90 different loci involved with the development of late-onset Alzheimers disease. Here, we investigate whether genetic risk for Alzheimers disease contributes equally to the progression in different disease stages or whether it exhibits a stage-dependent effect. Amyloid (A) and tau (T) status was defined using a combination of available PET and CSF biomarkers in the Alzheimers Disease Neuroimaging Initiative cohort. In 312 participants with biomarker-confirmed A-T- status, we used Cox proportional hazards models to estimate the contribution of APOE and polygenic risk scores (beyond APOE) to convert to A+T- status (65 conversions). Furthermore, we repeated the analysis in 290 participants with A+T- status and investigated the genetic contribution to conversion to A+T+ (45 conversions). Both survival analyses were adjusted for age, sex and years of education. For progression from A-T- to A+T-, APOE-e4 burden showed a significant effect [hazard ratio (HR) = 2.88; 95% confidence interval (CI): 1.70-4.89; P < 0.001], whereas polygenic risk did not (HR = 1.09; 95% CI: 0.84-1.42; P = 0.53). Conversely, for the transition from A+T- to A+T+, the contribution of APOE-e4 burden was reduced (HR = 1.62; 95% CI: 1.05-2.51; P = 0.031), whereas the polygenic risk showed an increased contribution (HR = 1.73; 95% CI: 1.27-2.36; P < 0.001). The marginal APOE effect was driven by e4 homozygotes (HR = 2.58; 95% CI: 1.05-6.35; P = 0.039) as opposed to e4 heterozygotes (HR = 1.74; 95% CI: 0.87-3.49; P = 0.12). The genetic risk for late-onset Alzheimers disease unfolds in a disease stage-dependent fashion. A better understanding of the interplay between disease stage and genetic risk can lead to a more mechanistic understanding of the transition between ATN stages and a better understanding of the molecular processes leading to Alzheimers disease, in addition to opening therapeutic windows for targeted interventions

Data-driven modelling of neurodegenerative disease progression: thinking outside the black box

Data-driven disease progression models are an emerging set of computational tools that reconstruct disease timelines for long-term chronic diseases, providing unique insights into disease processes and their underlying mechanisms. Such methods combine a priori human knowledge and assumptions with large-scale data processing and parameter estimation to infer long-term disease trajectories from short-term data. In contrast to 'black box' machine learning tools, data-driven disease progression models typically require fewer data and are inherently interpretable, thereby aiding disease understanding in addition to enabling classification, prediction and stratification. In this Review, we place the current landscape of data-driven disease progression models in a general framework and discuss their enhanced utility for constructing a disease timeline compared with wider machine learning tools that construct static disease profiles. We review the insights they have enabled across multiple neurodegenerative diseases, notably Alzheimer disease, for applications such as determining temporal trajectories of disease biomarkers, testing hypotheses about disease mechanisms and uncovering disease subtypes. We outline key areas for technological development and translation to a broader range of neuroscience and non-neuroscience applications. Finally, we discuss potential pathways and barriers to integrating disease progression models into clinical practice and trial settings