Dempseyhamann7278

Initiatives such as the UK Biobank provide joint cardiac and brain imaging information for thousands of individuals, representing a unique opportunity to study the relationship between heart and brain. read more Most of research on large multimodal databases has been focusing on studying the associations among the available measurements by means of univariate and multivariate association models. However, these approaches do not provide insights about the underlying mechanisms and are often hampered by the lack of prior knowledge on the physiological relationships between measurements. For instance, important indices of the cardiovascular function, such as cardiac contractility, cannot be measured in-vivo. While these non-observable parameters can be estimated by means of biophysical models, their personalisation is generally an ill-posed problem, often lacking critical data and only applied to small datasets. Therefore, to jointly study brain and heart, we propose an approach in which the parameter personalisation of a lumped cardiovascular model is constrained by the statistical relationships observed between model parameters and brain-volumetric indices extracted from imaging, i.e. ventricles or white matter hyperintensities volumes, and clinical information such as age or body surface area. We explored the plausibility of the learnt relationships by inferring the model parameters conditioned on the absence of part of the target clinical features, applying this framework in a cohort of more than 3 000 subjects and in a pathological subgroup of 59 subjects diagnosed with atrial fibrillation. Our results demonstrate the impact of such external features in the cardiovascular model personalisation by learning more informative parameter-space constraints. Moreover, physiologically plausible mechanisms are captured through these personalised models as well as significant differences associated to specific clinical conditions.Reliable patient-specific ventricular repolarization times (RTs) can identify regions of functional block or afterdepolarizations, indicating arrhythmogenic cardiac tissue and the risk of sudden cardiac death. Unipolar electrograms (UEs) record electric potentials, and the Wyatt method has been shown to be accurate for estimating RT from a UE. High-pass filtering is an important step in processing UEs, however, it is known to distort the T-wave phase of the UE, which may compromise the accuracy of the Wyatt method. The aim of this study was to examine the effects of high-pass filtering, and improve RT estimates derived from filtered UEs. We first generated a comprehensive set of UEs, corresponding to early and late activation and repolarization, that were then high-pass filtered with settings that mimicked the CARTO filter. We trained a deep neural network (DNN) to output a probabilistic estimation of RT and a measure of confidence, using the filtered synthetic UEs and their true RTs. Unfiltered ex-vivo human UEs were also filtered and the trained DNN used to estimate RT. Even a modest 2 Hz high-pass filter imposes a significant error on RT estimation using the Wyatt method. The DNN outperformed the Wyatt method in 62.75% of cases, and produced a significantly lower absolute error (p=8.99E-13), with a median of 16.91 ms, on 102 ex-vivo UEs. We also applied the DNN to patient UEs from CARTO, from which an RT map was computed. In conclusion, DNNs trained on synthetic UEs improve the RT estimation from filtered UEs, which leads to more reliable repolarization maps that help to identify patient-specific repolarization abnormalities.Severe dental tissue damage induces odontoblast death, after which dental pulp stem and progenitor cells (DPSCs) differentiate into odontoblast-like cells, contributing to reparative dentin. However, the damage-induced mechanism that triggers this regeneration process is still not clear. We aimed to understand the effect of odontoblast death without hard tissue damage on dental regeneration. Herein, using a Cre/LoxP-based strategy, we demonstrated that cell-rich zone (CZ)-localizing Nestin-GFP-positive and Nestin-GFP-negative cells proliferate and differentiate into odontoblast-like cells in response to odontoblast depletion. The regenerated odontoblast-like cells played a role in reparative dentin formation. RNA-sequencing analysis revealed that the expression of odontoblast differentiation- and activation-related genes was upregulated in the pulp in response to odontoblast depletion even without damage to dental tissue. In this regenerative process, the expression of type I parathyroid hormone receptor (PTH1R) increased in the odontoblast-depleted pulp, thereby boosting dentin formation. The levels of PTH1R and its downstream mediator, i.e., phosphorylated cyclic AMP response element-binding protein (Ser133) increased in the physically damaged pulp. Collectively, odontoblast death triggered the PTH1R cascade, which may represent a therapeutic target for inducing CZ-mediated dental regeneration.

Skeletal fragility is a major burden for individuals with cerebral palsy (CP), but little is known clinically about when to prevent fractures or monitor bone health for this population. Critical periods of bone health (CPBH) are important windows for intervention to augment bone growth or mitigate bone loss. However, CPBH from the general population may not align with the needs or timing of skeletal fragility for individuals with CP. The objective of this study was to identify discrepancies when evaluating individuals with CP using CPBH across the lifespan from the general population, and propose new CP-specific CPBH.

Data from 2016 administrative claims databases were used, including the Optum's De-identified Clinformatics® Data Mart Database and a random 20% sample of the Medicare fee-for-service database from the Centers for Medicare and Medicaid Services. Sex-stratified fracture prevalence was compared between individuals with and without CP across the lifespan starting at 2years of age using age groushed CPBH from the general population. We therefore propose new CP- and sex-specific CPBH for fracture monitoring and prevention.

This study identified discrepancies in evaluating fracture risk for individuals with CP if using established CPBH from the general population. We therefore propose new CP- and sex-specific CPBH for fracture monitoring and prevention.

Vertebral fracture assessment (VFA) images are acquired in dual-energy (DE) or single-energy (SE) scan modes. Automated identification of vertebral compression fractures, from VFA images acquired using GE Healthcare scanners in DE mode, has achieved high accuracy through the use of convolutional neural networks (CNNs). Due to differences between DE and SE images, it is uncertain whether CNNs trained on one scan mode will generalize to the other.

To evaluate the ability of CNNs to generalize between GE DE and GE SE VFA scan modes.

12,742 GE VFA images from the Manitoba Bone Mineral Density Program, obtained between 2010 and 2017, were exported in both DE and SE modes. VFAs were classified by imaging specialists as fracture present or absent using the modified algorithm-based qualitative (mABQ) method. VFA scans were randomly divided into independent training (60%), validation (10%), and test (30%) sets. Three CNN models were constructed by training separately on DE only, SE only, and a composite dataset for vertebral fracture identification are highly sensitive to scan mode. Training CNNs on a composite dataset, comprised of both GE DE and GE SE VFAs, allows CNNs to generalize to both scan modes and may facilitate the development of manufacturer-independent machine learning models for vertebral fracture detection.

CNNs for vertebral fracture identification are highly sensitive to scan mode. Training CNNs on a composite dataset, comprised of both GE DE and GE SE VFAs, allows CNNs to generalize to both scan modes and may facilitate the development of manufacturer-independent machine learning models for vertebral fracture detection.

Osteogenesis imperfecta (OI) is a genetic disorder characterized by bone fragility and craniofacial and dental abnormalities such as congenitally missing teeth and teeth that failed to erupt which are believed to be doubled in OI patients than normal populations and were associated with low oral health quality of life. However, the etiology of these abnormalities remains unclear. To understand the factors influencing missing and unerupted teeth, we investigated their prevalence in a cohort of OI patients as a function of the clinical phenotype (OI type), the genetic variant type, the tooth type and the onset of bisphosphonate treatment.

A total of 144 OI patients were recruited from The Shriners Hospital, Montreal, Canada, between 2016 and 2017. Patients were evaluated using intraoral photographs and panoramic radiographs. Missing teeth were evaluated in all patients, and unerupted teeth were assessed only in patients ≥15years old (n=82).

On average, each OI patient had 2.4 missing teeth and 0.8 unerupt the nature of the collagen variants and the OI type. These findings highlight the role of collagen in tooth development and eruption.Osteonecrosis resulting from heavy ethanol consumption is one of the major causes of nontraumatic osteonecrosis of the femoral head (ONFH). The underlying pathological and molecular mechanisms remain elusive. In this study, we performed deep RNA sequencing from femoral heads of patients diagnosed with late-stage alcohol-induced ONFH (AIONFH), other types of ONFH and traumatic injury (bone fracture). Genome-wide gene expression analyses identified 690 differentially expressed mRNAs in AIONFH. Gene annotation and pathway analyses revealed significant dysregulated genes involved in hemostasis, angiogenesis and bone remodeling processes from the late-stage AIONFH. Notably, ADH1B, which codes for one of the major alcohol dehydrogenases, is significantly upregulated in AIONFH samples. Further, we found that the ADH1B protein was primarily expressed in smooth muscle cells of the blood vessels, stromal cells and adipocytes of the femoral heads of AIONFH patients; but was absent in other ONFH samples. Our analyses also revealed unique long non-coding RNA (lncRNA) expression profiles and identified novel lncRNAs in AIONFH. In addition, we observed a close co-expression correlation between lncRNAs and mRNAs in AIONFH suggesting that cis-gene regulation represents a major mechanism of action of human femoral lncRNAs. Further, the expression signature of lncRNAs, but not mRNAs, distinguishes AIONFH from other types of ONFH. Taken together, our studies uncovered novel molecular signatures associated with late-stage AIONFH in which the dysregulation of several key signaling pathways within the femoral head may be involved in AIONFH. link2 Subsequently, lncRNAs may serve as potential biomarkers for diagnosis and therapeutic treatment of AIONFH. Further studies are needed to confirm that ADH1B is specifically upregulated in AIONFH and not generally upregulated in patients who consume alcohol excessively.GHB related acids (3,4-dihydroxy butyric acid, 2,4-dihydroxy butyric acid and glycolic acid) are produced through oxidative GHB metabolism. These analytes could be potential biomarkers to ensure the diagnosis of a GHB intoxication and even prolong the detection window. Within this study, forensic routine cases were measured to consider the potential of additional gas chromatographic mass spectrometric analysis on these acids. 17 GHB positive real cases (10 serum samples and 7 urine samples) and 40 cases with suspicion of drugging in DFC cases and negative GHB results (21 serum samples and 19 urine samples) were evaluated. link3 Increased GHB related acid concentrations were detected in all serum and most urine samples positive on GHB. In some GHB negative cases, especially in serum samples, concentrations of GHB related acids gave hints that GHB actually was taken. We recommend to use the following cut-offs for a more reliable interpretation of potential GHB intoxication cases 3,4-OH-BA>3 mg/L in serum and>50 mg/L in urine; 2,4-OH-BA>2 mg/L in serum and>25 mg/L in urine; GA>5 mg/L in serum and>400 mg/L in urine.