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Propensity-matched patients with DM and no known CAD have similar MACE risk compared to patients with known CAD and no DM. DM is synergistic with mode of stress testing and TPD in predicting the risk of cardiac stress test patients.

Propensity-matched patients with DM and no known CAD have similar MACE risk compared to patients with known CAD and no DM. DM is synergistic with mode of stress testing and TPD in predicting the risk of cardiac stress test patients.

Data involved the association between myocardial ischaemia and the outcome for unrevascularized coronary chronic total occlusion (CTO) patients were limited. The purpose of this study was to evaluate the predictive value of ischaemia detected by single photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI) for the adverse events in unrevascularized CTO patients. We further explored whether ischaemia generated from CTO vessel can independently predict the outcome.

Patients with at least one unrevascularized CTO on coronary angiography were enrolled in this study. Exercise stress/rest SPECT MPI was performed in all patients. All patients were then followed by telephone interview and reviewing of medical records.

Patients with ischaemia experienced significantly higher rate of adverse events than non-ischaemia patients (40.7% vs 7.1%, P = 0.002). Ischaemia demonstrated on MPI [odds ratio (OR) = 7.656; 95% confidence interval (CI) 1.598-36.677; P = 0.011] was an independent predictor for adverse events. Moreover, CTO-ischaemia (OR = 5.466; 95% CI 1.015-29.420; P = 0.048), non-CTO ischaemia (OR = 29.174; 95% CI 3.245-262.322; P = 0.003), mixed-ischaemia (OR = 7.130, 95% CI 1.257-40.445; P = 0.027) were all independent predictors for outcome.

Ischaemia demonstrated on MPI, especially CTO-ischaemia were independent predictors for the adverse events. SPECT MPI can aid to identify patients at risk of adverse events, who may benefit from subsequent CTO percutaneous coronary intervention.

Ischaemia demonstrated on MPI, especially CTO-ischaemia were independent predictors for the adverse events. SPECT MPI can aid to identify patients at risk of adverse events, who may benefit from subsequent CTO percutaneous coronary intervention.

Investigations of foot strike patterns during overground distance running have foci on prevalence, performance and change in foot strike pattern with increased distance. To date, synthesised analyses of these findings are scarce.

The key objectives of this review were to quantify the prevalence of foot strike patterns, assess the impact of increased running distance on foot strike pattern change and investigate the potential impact of foot strike pattern on performance.

Relevant peer-reviewed literature was obtained by searching EBSCOhost CINAHL, Ovid Medline, EMBASE and SPORTDiscus (inception-2021) for studies investigating foot strike patterns in overground distance running settings (> 10km). Random effects meta-analyses of prevalence data were performed where possible.

The initial search identified 2210 unique articles. After removal of duplicates and excluded articles, 12 articles were included in the review. Meta-analysis of prevalence data revealed that 79% of long-distance overground runnersrfoot strike pattern in favour of a rearfoot strike pattern.

Most overground distance runners rearfoot strike early, and the prevalence of this pattern increases with distance. Of those that do change foot strike pattern, the majority transition from non-rearfoot to rearfoot. The current literature provides inconclusive evidence of a competitive advantage being associated with long-distance runners who use a non-rearfoot strike pattern in favour of a rearfoot strike pattern.Small extracellular vesicles (sEVs) obtained from mesenchymal stromal cells (MSCs) promote neurological recovery after middle cerebral artery occlusion (MCAO) in young rodents. Ischemic stroke mainly affects aged humans. MSC-sEV effects on stroke recovery in aged rodents had not been assessed. In a head-to-head comparison, we exposed young (4-5 months) and aged (19-20 months) male Sprague-Dawley rats to permanent distal MCAO. At 24 h, 3 and 7 days post-stroke, vehicle or MSC-sEVs (2 × 106 or 2 × 107 MSC equivalents/kg) were intravenously administered. Neurological deficits, ischemic injury, brain inflammatory responses, post-ischemic angiogenesis, and endogenous neurogenesis were evaluated over 28 days. Post-MCAO, aged vehicle-treated rats exhibited more severe motor-coordination deficits evaluated by rotating pole and cylinder tests and larger brain infarcts than young vehicle-treated rats. Although infarct volume was not influenced by MSC-sEVs, sEVs at both doses effectively reduced motor-coordination deficits in young and aged rats. Brain macrophage infiltrates in periinfarct tissue, which were evaluated as marker of a recovery-aversive inflammatory environment, were significantly stronger in aged than young vehicle-treated rats. sEVs reduced brain macrophage infiltrates in aged, but not young rats. The tolerogenic shift in immune balance paved the way for structural brain tissue remodeling. Hence, sEVs at both doses increased periinfarct angiogenesis evaluated by CD31/BrdU immunohistochemistry in young and aged rats, and low-dose sEVs increased neurogenesis in the subventricular zone examined by DCX/BrdU immunohistochemistry. Our study provides robust evidence that MSC-sEVs promote functional neurological recovery and brain tissue remodeling in aged rats post-stroke. This study encourages further proof-of-concept studies in clinic-relevant stroke settings.Lung cancer is one of the most prevalent causes of morbidity and mortality in both men and women across the globe. The disease has a quiet phenotype at first, which leads to chronic tumor development. Non-small cell lung cancer (NSCLC) is the most common kind of lung cancer, accounting for 85 percent of all lung malignancies. Autophagy has been described as an intracellular "recycle bin" where damaged proteins and molecules are degraded. Autophagy regulation is mainly dependent on signaling pathways such as phosphoinositide 3-kinases (PI3K), AKT, and the mammalian target of rapamycin (mTOR). In the context of NSCLC, studies on these signaling pathways are inconsistent, but our literature review suggests that the inhibition of mTOR, PI3K/AKT, and epidermal growth factor receptor signaling pathways by different medications can active autophagy and inhibit NSCLC progression. In conclusion, signaling pathways related to autophagy are effective therapeutic approaches for the treatment of NSCLC.

Left ventricular hypertrophy (LVH) is an independent prognostic factor for cardiovascular events and it can be detected by echocardiography in the early stage. In this study, we aim to develop a semi-automatic diagnostic network based on deep learning algorithms to detect LVH.

We retrospectively collected 1610 transthoracic echocardiograms, included 724 patients [189 hypertensive heart disease (HHD), 218 hypertrophic cardiomyopathy (HCM), and 58 cardiac amyloidosis (CA), along with 259 controls]. fMLP The diagnosis of LVH was defined by two experienced clinicians. For the deep learning architecture, we introduced ResNet and U-net++ to complete classification and segmentation tasks respectively. The models were trained and validated independently. Then, we connected the best-performing models to form the final framework and tested its capabilities.

In terms of individual networks, the view classification model produced AUC = 1.0. The AUC of the LVH detection model was 0.98 (95% CI 0.94-0.99), with corresponding sensitivity and specificity of 94.0% (95% CI 85.3-98.7%) and 91.6% (95% CI 84.6-96.1%) respectively. For etiology identification, the independent model yielded good results with AUC = 0.90 (95% CI 0.82-0.95) for HCM, AUC = 0.94 (95% CI 0.88-0.98) for CA, and AUC = 0.88 (95% CI 0.80-0.93) for HHD. Finally, our final integrated framework automatically classified four conditions (Normal, HCM, CA, and HHD), which achieved an average of AUC 0.91, with an average sensitivity and specificity of 83.7% and 90.0%.

Deep learning architecture has the ability to detect LVH and even distinguish the latent etiology of LVH.

Deep learning architecture has the ability to detect LVH and even distinguish the latent etiology of LVH.

To evaluate metabolic alterations along with thecarcinoma ex pleomorphic adneoma (CXPA) development of lacrimal glands (LG).

Four samples of the normal LG (NLG), 9 of pleomorphic adenoma (PA), 4 of residual PA (rPA), and 4 of CXPA of LGwere included. GLUT-1, HIF-1α, FASN, and adipophilin by immunohistochemical stains were performed in the selected cases.

Was observed higher expression of markers associated with glycolytic and lipid metabolism in the tumor tissue samples when compared to the NLG samples. Additionally, GLUT-1, FASN, and Adipophilin were more expressed in CXPA samples while HIF-1α in PA samples.

In conclusion, our results demonstrate overexpression of FASN and Adipophilin in CXPA which may reflect a metabolic shift toward lipogenesis in cancer cells.

In conclusion, our results demonstrate overexpression of FASN and Adipophilin in CXPA which may reflect a metabolic shift toward lipogenesis in cancer cells.Lack of physical activity is a risk factor for dementia, however, the utility of interventional physical activity programs as a protective measure against brain atrophy and cognitive decline is uncertain. Here we present the effect of a randomized controlled trial of a 24-month physical activity intervention on global and regional brain atrophy as characterized by longitudinal voxel-based morphometry with T1-weighted MRI images. The study sample consisted of 98 participants at risk of dementia, with mild cognitive impairment or subjective memory complaints, and having at least one vascular risk factor for dementia, randomized into an exercise group and a control group. Between 0 and 24 months, there was no significant difference detected between groups in the rate of change in global, or regional brain volumes.Analyzing the relation between intelligence and neural activity is of the utmost importance in understanding the working principles of the human brain in health and disease. In existing literature, functional brain connectomes have been used successfully to predict cognitive measures such as intelligence quotient (IQ) scores in both healthy and disordered cohorts using machine learning models. However, existing methods resort to flattening the brain connectome (i.e., graph) through vectorization which overlooks its topological properties. To address this limitation and inspired from the emerging graph neural networks (GNNs), we design a novel regression GNN model (namely RegGNN) for predicting IQ scores from brain connectivity. On top of that, we introduce a novel, fully modular sample selection method to select the best samples to learn from for our target prediction task. However, since such deep learning architectures are computationally expensive to train, we further propose a learning-based sample selection method that learns how to choose the training samples with the highest expected predictive power on unseen samples. For this, we capitalize on the fact that connectomes (i.e., their adjacency matrices) lie in the symmetric positive definite (SPD) matrix cone. Our results on full-scale and verbal IQ prediction outperforms comparison methods in autism spectrum disorder cohorts and achieves a competitive performance for neurotypical subjects using 3-fold cross-validation. Furthermore, we show that our sample selection approach generalizes to other learning-based methods, which shows its usefulness beyond our GNN architecture.