Prediction of incident cardiovascular events using machine learning and CMR radiomics.

OBJECTIVES: Evaluation of the feasibility of using cardiovascular magnetic resonance (CMR) radiomics in the prediction of incident atrial fibrillation (AF), heart failure (HF), myocardial infarction (MI), and stroke using machine learning techniques. METHODS: We identified participants from the UK Biobank who experienced incident AF, HF, MI, or stroke during the continuous longitudinal follow-up. The CMR indices and the vascular risk factors (VRFs) as well as the CMR images were obtained for each participant. Three-segmented regions of interest (ROIs) were computed: right ventricle cavity, left ventricle (LV) cavity, and LV myocardium in end-systole and end-diastole phases. Radiomics features were extracted from the 3D volumes of the ROIs. Seven integrative models were built for each incident cardiovascular disease (CVD) as an outcome. Each model was built with VRF, CMR indices, and radiomics features and a combination of them. Support vector machine was used for classification. To assess the model performance, the accuracy, sensitivity, specificity, and AUC were reported. RESULTS: AF prediction model using the VRF+CMR+Rad model (accuracy: 0.71, AUC 0.76) obtained the best result. However, the AUC was similar to the VRF+Rad model. HF showed the most significant improvement with the inclusion of CMR metrics (VRF+CMR+Rad: 0.79, AUC 0.84). Moreover, adding only the radiomics features to the VRF reached an almost similarly good performance (VRF+Rad: accuracy 0.77, AUC 0.83). Prediction models looking into incident MI and stroke reached slightly smaller improvement. CONCLUSIONS: Radiomics features may provide incremental predictive value over VRF and CMR indices in the prediction of incident CVDs. KEY POINTS: • Prediction of incident atrial fibrillation, heart failure, stroke, and myocardial infarction using machine learning techniques. • CMR radiomics, vascular risk factors, and standard CMR indices will be considered in the machine learning models. • The experiments show that radiomics features can provide incremental predictive value over VRF and CMR indices in the prediction of incident cardiovascular diseases

The Effect of Mindfulness-based Programs on Cognitive Function in Adults: A Systematic Review and Meta-analysis

Mindfulness-based programs (MBPs) are increasingly utilized to improve mental health. Interest in the putative effects of MBPs on cognitive function is also growing. This is the first meta-analysis of objective cognitive outcomes across multiple domains from randomized MBP studies of adults. Seven databases were systematically searched to January 2020. Fifty-six unique studies (n = 2,931) were included, of which 45 (n = 2,238) were synthesized using robust variance estimation meta-analysis. Meta-regression and subgroup analyses evaluated moderators. Pooling data across cognitive domains, the summary effect size for all studies favored MBPs over comparators and was small in magnitude (g = 0.15; [0.05, 0.24]). Across subgroup analyses of individual cognitive domains/subdomains, MBPs outperformed comparators for executive function (g = 0.15; [0.02, 0.27]) and working memory outcomes (g = 0.23; [0.11, 0.36]) only. Subgroup analyses identified significant effects for studies of non-clinical samples, as well as for adults aged over 60. Across all studies, MBPs outperformed inactive, but not active comparators. Limitations include the primarily unclear within-study risk of bias (only a minority of studies were considered low risk), and that statistical constraints rendered some p-values unreliable. Together, results partially corroborate the hypothesized link between mindfulness practices and cognitive performance. This review was registered with PROSPERO [CRD42018100904]

Cataloguing functionally relevant polymorphisms in gene DNA ligase I: a computational approach

A computational approach for identifying functionally relevant SNPs in gene LIG1 has been proposed. LIG1 is a crucial gene which is involved in excision repair pathways and mutations in this gene may lead to increase sensitivity towards DNA damaging agents. A total of 792 SNPs were reported to be associated with gene LIG1 in dbSNP. Different web server namely SIFT, PolyPhen, CUPSAT, FASTSNP, MAPPER and dbSMR were used to identify potentially functional SNPs in gene LIG1. SIFT, PolyPhen and CUPSAT servers predicted eleven nsSNPs to be intolerant, thirteen nsSNP to be damaging and two nsSNPs have the potential to destabilize protein structure. The nsSNP rs11666150 was predicted to be damaging by all three servers and its mutant structure showed significant increase in overall energy. FASTSNP predicted twenty SNPs to be present in splicing modifier binding sites while rSNP module from MAPPER server predicted nine SNPs to influence the binding of transcription factors. The results from the study may provide vital clues in establishing affect of polymorphism on phenotype and in elucidating drug response

Coinfection with Different Trypanosoma cruzi Strains Interferes with the Host Immune Response to Infection

A century after the discovery of Trypanosoma cruzi in a child living in Lassance, Minas Gerais, Brazil in 1909, many uncertainties remain with respect to factors determining the pathogenesis of Chagas disease (CD). Herein, we simultaneously investigate the contribution of both host and parasite factors during acute phase of infection in BALB/c mice infected with the JG and/or CL Brener T. cruzi strains. JG single infected mice presented reduced parasitemia and heart parasitism, no mortality, levels of pro-inflammatory mediators (TNF-α, CCL2, IL-6 and IFN-γ) similar to those found among naïve animals and no clinical manifestations of disease. On the other hand, CL Brener single infected mice presented higher parasitemia and heart parasitism, as well as an increased systemic release of pro-inflammatory mediators and higher mortality probably due to a toxic shock-like systemic inflammatory response. Interestingly, coinfection with JG and CL Brener strains resulted in intermediate parasitemia, heart parasitism and mortality. This was accompanied by an increase in the systemic release of IL-10 with a parallel increase in the number of MAC-3+ and CD4+ T spleen cells expressing IL-10. Therefore, the endogenous production of IL-10 elicited by coinfection seems to be crucial to counterregulate the potentially lethal effects triggered by systemic release of pro-inflammatory mediators induced by CL Brener single infection. In conclusion, our results suggest that the composition of the infecting parasite population plays a role in the host response to T. cruzi in determining the severity of the disease in experimentally infected BALB/c mice. The combination of JG and CL Brener was able to trigger both protective inflammatory immunity and regulatory immune mechanisms that attenuate damage caused by inflammation and disease severity in BALB/c mice

Usefulness of PCR-based assays to assess drug efficacy in Chagas disease chemotherapy: value and limitations

One major goal of research on Chagas disease is the development of effective chemotherapy to eliminate the infection from individuals who have not yet developed cardiac and/or digestive disease manifestations. Cure evaluation is the more complex aspect of its treatment, often leading to diverse and controversial results. The absence of reliable methods or a diagnostic gold standard to assess etiologic treatment efficacy still constitutes a major challenge. In an effort to develop more sensitive tools, polymerase chain reaction (PCR)-based assays were introduced to detect low amounts of Trypanosoma cruzi DNA in blood samples from chagasic patients, thus improving the diagnosis and follow-up evaluation after chemotherapy. In this article, I review the main problems concerning drug efficacy and criteria used for cure estimation in treated chagasic patients, and the work conducted by different groups on developing PCR methodologies to monitor treatment outcome of congenital infections as well as recent and late chronic T. cruzi infections

Encoding of Spatio-Temporal Input Characteristics by a CA1 Pyramidal Neuron Model

The in vivo activity of CA1 pyramidal neurons alternates between regular spiking and bursting, but how these changes affect information processing remains unclear. Using a detailed CA1 pyramidal neuron model, we investigate how timing and spatial arrangement variations in synaptic inputs to the distal and proximal dendritic layers influence the information content of model responses. We find that the temporal delay between activation of the two layers acts as a switch between excitability modes: short delays induce bursting while long delays decrease firing. For long delays, the average firing frequency of the model response discriminates spatially clustered from diffused inputs to the distal dendritic tree. For short delays, the onset latency and inter-spike-interval succession of model responses can accurately classify input signals as temporally close or distant and spatially clustered or diffused across different stimulation protocols. These findings suggest that a CA1 pyramidal neuron may be capable of encoding and transmitting presynaptic spatiotemporal information about the activity of the entorhinal cortex-hippocampal network to higher brain regions via the selective use of either a temporal or a rate code