Ectopy on a single 12‐lead ECG, incident cardiac myopathy, and death in the community

BackgroundAtrial fibrillation and heart failure are 2 of the most common diseases, yet ready means to identify individuals at risk are lacking. The 12-lead ECG is one of the most accessible tests in medicine. Our objective was to determine whether a premature atrial contraction observed on a standard 12-lead ECG would predict atrial fibrillation and mortality and whether a premature ventricular contraction would predict heart failure and mortality.Methods and resultsWe utilized the CHS (Cardiovascular Health) Study, which followed 5577 participants for a median of 12 years, as the primary cohort. The ARIC (Atherosclerosis Risk in Communities Study), the replication cohort, captured data from 15 792 participants over a median of 22 years. In the CHS, multivariable analyses revealed that a baseline 12-lead ECG premature atrial contraction predicted a 60% increased risk of atrial fibrillation (hazard ratio, 1.6; 95% CI, 1.3-2.0; P<0.001) and a premature ventricular contraction predicted a 30% increased risk of heart failure (hazard ratio, 1.3; 95% CI, 1.0-1.6; P=0.021). In the negative control analyses, neither predicted incident myocardial infarction. A premature atrial contraction was associated with a 30% increased risk of death (hazard ratio, 1.3; 95% CI, 1.1-1.5; P=0.008) and a premature ventricular contraction was associated with a 20% increased risk of death (hazard ratio, 1.2; 95% CI, 1.0-1.3; P=0.044). Similarly statistically significant results for each analysis were also observed in ARIC.ConclusionsBased on a single standard ECG, a premature atrial contraction predicted incident atrial fibrillation and death and a premature ventricular contraction predicted incident heart failure and death, suggesting that this commonly used test may predict future disease

Peripheral Arterial Disease and Risk of Atrial Fibrillation and Stroke: The Multi�Ethnic Study of Atherosclerosis

Background Peripheral arterial disease (PAD) shares several risk factors with atrial fibrillation (AF), and persons with PAD have an increased risk of stroke. It is unclear if PAD is associated with an increased risk for AF and whether this potential association explains the increased risk of stroke observed in those with PAD. Methods and Results We examined the association between PAD, measured by ankle�brachial index (ABI), and incident AF and incident stroke, separately, in 6568 participants (mean age 62±10 years, 53% women, 62% nonwhite) from the Multi�Ethnic Study of Atherosclerosis (MESA). ABI values 1.4 defined PAD. AF was ascertained through review of hospital discharge records and from Medicare claims data until December 31, 2010. An independent adjudication committee ascertained stroke events. Cox regression was used to estimate hazard ratios and 95% CIs for the association between PAD and AF and stroke. Over a median follow�up of 8.5 years, 301 (4.6%) participants developed AF and 140 (2.1%) developed stroke. In a model adjusted for sociodemographics, cardiovascular risk factors, and potential confounders, PAD was associated with an increased risk of AF (hazard ratio 1.5, 95% CI 1.1 to 2.0). In a similar model, PAD was associated with incident stroke (hazard ratio 1.7, 95% CI 1.1 to 2.5), and the magnitude of risk was not different after inclusion of AF as a time�dependent covariate (hazard ratio 1.7, 95% CI 1.1 to 2.5). Conclusions PAD is associated with an increased risk of AF and stroke in MESA. Potentially, the relationship between PAD and stroke is not mediated by AF

Two loop electroweak corrections to Bˉ→Xsγ\bar B\rightarrow X_s\gamma and Bs0→μ+μ−B_s^0\rightarrow \mu^+\mu^- in the B-LSSM

The rare decays $\bar B\rightarrow X_s\gamma$ and $B_s^0\rightarrow \mu^+\mu^-$ are important to research new physics beyond standard model. In this work, we investigate two loop electroweak corrections to $\bar B\rightarrow X_s\gamma$ and $B_s^0\rightarrow \mu^+\mu^-$ in the minimal supersymmetric extension of the SM with local $B-L$ gauge symmetry (B-LSSM), under a minimal flavor violating assumption for the soft breaking terms. In this framework, new particles and new definition of squarks can affect the theoretical predictions of these two processes, with respect to the MSSM. Considering the constraints from updated experimental data, the numerical results show that the B-LSSM can fit the experimental data for the branching ratios of $\bar B\rightarrow X_s\gamma$ and $B_s^0\rightarrow \mu^+\mu^-$. The results of the rare decays also further constrain the parameter space of the B-LSSM.Comment: 33 pages, 9 figures, Published in EPJ

Prospects for terahertz imaging the human skin cancer with the help of gold-nanoparticles-based terahertz-to-infrared converter

The design is suggested, and possible operation parameters are discussed, of an instrument to inspect a skin cancer tumour in the terahertz (THz) range, transferring the image into the infrared (IR) and making it visible with the help of standard IR camera. The central element of the device is the THz-to-IR converter, a Teflon or silicon film matrix with embedded 8.5 nm diameter gold nanoparticles. The use of external THz source for irradiating the biological tissue sample is presumed. The converter's temporal characteristics enable its performance in a real-time scale. The details of design suited for the operation in transmission mode (in vitro) or on the human skin in reflection mode {in vivo) are specified.Comment: To be published in the proceedings of the FANEM2018 workshop - Minsk, 3-5 June 201

Enhancing lepton flavour violation in the supersymmetric inverse seesaw beyond the dipole contribution

In minimal supersymmetric models the $Z$-penguin usually provides sub-dominant contributions to charged lepton flavour violating observables. In this study, we consider the supersymmetric inverse seesaw in which the non-minimal particle content allows for dominant contributions of the $Z$-penguin to several lepton flavour violating observables. In particular, and due to the low-scale (TeV) seesaw, the penguin contribution to, for instance, \Br(\mu \to 3e) and $\mu-e$ conversion in nuclei, allows to render some of these observables within future sensitivity reach. Moreover, we show that in this framework, the $Z$-penguin exhibits the same non-decoupling behaviour which had previously been identified in flavour violating Higgs decays in the Minimal Supersymmetric Standard Model.Comment: 29 pages, 9 figures, 4 tables; v2: minor corrections, version to appear in JHE

Evaluation of anti-thyroglobulin antibodies and thyroid stimulating hormone level in cases of recurrent early pregnancy loss

Background: Autoimmune thyroid disease (AITD) is by far the most frequent cause of hypothyroidism in women in reproductive age. The prevalence of hypothyroidism in the general population of reproductive age is 2-3%. The objective of this study was to evaluate maternal anti-thyroglobulin (ATG) concentrations and thyroid stimulating hormone (TSH) level in cases of recurrent miscarriage.Methods: 200 female patients divided into two groups. Group A: 100 female patients with history of recurrent miscarriage. Group B: 100 female patients with at least 2 living children and without history of recurrent early miscarriage.  Antithyroglobulin antibodies using chemilumeniscence immunoassay (normal level up to 115 IU/ml) and TSH level using chemilumeniscence immunoassay (normal level 0.350-2 U/ml) were assessed.Results: 8.0% of cases (n = 100) and 2.0% of control group (n = 100) were positive for anti TG antibodies. There was no significant relationship between the presence of anti TG antibodies and RPL (p = 0.052).  19% of cases (n = 100) were positive for TSH level. On the other hand, 14% of control group (n = 100) were positive for TSH level there was no significant relation between recurrent pregnancy loss and TSH, (P = 0.34).Conclusions: Neither TSH nor ATG showed significant difference in cases with recurrent miscarriage

An enhanced deep learning approach for vascular wall fracture analysis

This work outlines an efficient deep learning approach for analyzing vascular wall fractures using experimental data with openly accessible source codes (https://doi.org/10.25835/weuhha72) for reproduction. Vascular disease remains the primary cause of death globally to this day. Tissue damage in these vascular disorders is closely tied to how the diseases develop, which requires careful study. Therefore, the scientific community has dedicated significant efforts to capture the properties of vessel wall fractures. The symmetry-constrained compact tension (symconCT) test combined with digital image correlation (DIC) enabled the study of tissue fracture in various aorta specimens under different conditions. Main purpose of the experiments was to investigate the displacement and strain field ahead of the crack tip. These experimental data were to support the development and verification of computational models. The FEM model used the DIC information for the material parameters identification. Traditionally, the analysis of fracture processes in biological tissues involves extensive computational and experimental efforts due to the complex nature of tissue behavior under stress. These high costs have posed significant challenges, demanding efficient solutions to accelerate research progress and reduce embedded costs. Deep learning techniques have shown promise in overcoming these challenges by learning to indicate patterns and relationships between the input and label data. In this study, we integrate deep learning methodologies with the attention residual U-Net architecture to predict fracture responses in porcine aorta specimens, enhanced with a Monte Carlo dropout technique. By training the network on a sufficient amount of data, the model learns to capture the features influencing fracture progression. These parameterized datasets consist of pictures describing the evolution of tissue fracture path along with the DIC measurements. The integration of deep learning should not only enhance the predictive accuracy, but also significantly reduce the computational and experimental burden, thereby enabling a more efficient analysis of fracture response

Genetic risk prediction of atrial fibrillation

Background—Atrial fibrillation (AF) has a substantial genetic basis. Identification of individuals at greatest AF risk could minimize the incidence of cardioembolic stroke. Methods—To determine whether genetic data can stratify risk for development of AF, we examined associations between AF genetic risk scores and incident AF in five prospective studies comprising 18,919 individuals of European ancestry. We examined associations between AF genetic risk scores and ischemic stroke in a separate study of 509 ischemic stroke cases (202 cardioembolic [40%]) and 3,028 referents. Scores were based on 11 to 719 common variants (≥5%) associated with AF at P-values ranging from <1x10-3 to <1x10-8 in a prior independent genetic association study. Results—Incident AF occurred in 1,032 (5.5%) individuals. AF genetic risk scores were associated with new-onset AF after adjusting for clinical risk factors. The pooled hazard ratio for incident AF for the highest versus lowest quartile of genetic risk scores ranged from 1.28 (719 variants; 95%CI, 1.13-1.46; P=1.5x10-4) to 1.67 (25 variants; 95%CI, 1.47-1.90; P=9.3x10-15). Discrimination of combined clinical and genetic risk scores varied across studies and scores (maximum C statistic, 0.629-0.811; maximum ΔC statistic from clinical score alone, 0.009-0.017). AF genetic risk was associated with stroke in age- and sex-adjusted models. For example, individuals in the highest versus lowest quartile of a 127-variant score had a 2.49-fold increased odds of cardioembolic stroke (95%CI, 1.39-4.58; P=2.7x10-3). The effect persisted after excluding individuals (n=70) with known AF (odds ratio, 2.25; 95%CI, 1.20-4.40; P=0.01). Conclusions—Comprehensive AF genetic risk scores were associated with incident AF beyond associations for clinical AF risk factors, though offered small improvements in discrimination. AF genetic risk was also associated with cardioembolic stroke in age- and sex-adjusted analyses. Efforts are warranted to determine whether AF genetic risk may improve identification of subclinical AF or help distinguish between stroke mechanisms

Biomechanics of traumatic brain injury

A biomechanical model for traumatic brain injury and soft tissue damage is presented. A variational constitutive model for soft biological tissues is utilized to reproduce axonal damage and cavitation injury through inelastic deformation. The material response is split into elastoplastic and viscoelastic components, including rate effects, shear and porous plasticity, and finite viscoelasticity. Mechanical damage of brain tissue is classified as volumetric (compression/tension) and shear-type. Finite element simulations of brain injuries are presented, examining frontal and oblique head impacts with external objects. Localization, extension, intensity and reversibility/irreversibility of tissue damage are predicted. Future directions of this work, relating mechanical damage and physiological brain dysfunction, and application to relevant medical and engineering problems are discussed