Diabetes severely affects attentional performance after coronary artery bypass grafting.

J Cardiothorac Surg

Department of Cardiothoracic Surgery, University Hospital Würzburg, Oberdürrbacherstraße 6, Würzburg, 97080, Germany.

Published: November 2012

AI Article Synopsis

Article Abstract

Background: Diabetes is a risk factor for (micro) vascular damage of the brain, too. Therefore cognitive performance after coronary artery bypass grafting may be hypothesized worse in diabetics. To avoid observational errors a reliable tool for testing attentional performance was used. We evaluated whether diabetes mellitus disposes to distinct cognitive dysfunction after coronary artery bypass grafting (CABG).

Methods: Three aspects in attentional performance were prospectively tested with three different tests (alertness: composed of un-cued and cued reaction, divided attention, and selective attention) by a computerized tool one day before and seven days after CABG in a highly selected cohort of 30 males, 10 of whom had diabetes. Statistical comparisons were done with analysis of variance for repeated measurements and Fisher's LSD.

Results: Prior to CABG there was no statistically meaningful difference between diabetics and non-diabetics. Postoperatively, diabetic patients performed significantly worse than non-diabetics in tests for un-cued (p=0.01) and cued alertness (p=0.03). Test performance in divided attention was worse after CABG but independent of diabetes status. Selective attention was neither affected by diabetes status nor by CABG itself.

Conclusions: Diabetes may have an impact on cognitive performance after CABG. More severe deficits in alertness may point to underlying microvascular disease.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3504518PMC
http://dx.doi.org/10.1186/1749-8090-7-115DOI Listing

Publication Analysis

Top Keywords

attentional performance
12
coronary artery
12
artery bypass
12
bypass grafting
12
performance coronary
8
cognitive performance
8
divided attention
8
selective attention
8
diabetes status
8
diabetes
7

Similar Publications

Background: Drug-drug interactions (DDIs) especially antagonistic ones present significant risks to patient safety, underscoring the urgent need for reliable prediction methods. Recently, substructure-based DDI prediction has garnered much attention due to the dominant influence of functional groups and substructures on drug properties. However, existing approaches face challenges regarding the insufficient interpretability of identified substructures and the isolation of chemical substructures.

View Article and Find Full Text PDF

Practice makes better? The influence of increased practice on task conflict in the Stroop task.

Mem Cognit

January 2025

Department of Cognitive and Brain Sciences, and School of Brain Sciences and Cognition , Ben-Gurion University of the Negev, 84105, Beer-Sheva, Israel.

The Stroop task is widely used to study attentional control and cognitive flexibility. However, questions about its sensitivity to training and the impact of task conflict on attentional control remain open. We investigated the effects of practice and task conflict on attentional control in the Stroop task, with participants completing four sessions of a Stroop task over 3 weeks in low and high task-conflict conditions.

View Article and Find Full Text PDF

Total population for a resource-limited single consumer model.

J Math Biol

January 2025

Department of Integrative Biology, Oklahoma State University, Stillwater, OK, 74078, USA.

In the past several decades, much attention has been focused on the effects of dispersal on total populations of species. In Zhang (EL 20:1118-1128, 2017), a rigorous biological experiment was performed to confirm the mathematical conclusion: Dispersal tends to enhance populations under a suitable hypothesis. In addition, mathematical models keeping track of resource dynamics in population growth were also proposed in Zhang (EL 20:1118-1128, 2017) to understand this remarkable phenomenon.

View Article and Find Full Text PDF

An automatic cervical cell classification model based on improved DenseNet121.

Sci Rep

January 2025

Department of Biomedical Engineering, School of Life Science and Technology, Changchun University of Science and Technology, Changchun, 130022, China.

The cervical cell classification technique can determine the degree of cellular abnormality and pathological condition, which can help doctors to detect the risk of cervical cancer at an early stage and improve the cure and survival rates of cervical cancer patients. Addressing the issue of low accuracy in cervical cell classification, a deep convolutional neural network A2SDNet121 is proposed. A2SDNet121 takes DenseNet121 as the backbone network.

View Article and Find Full Text PDF

Human behavior is strongly influenced by anticipation, but the underlying neural mechanisms are poorly understood. We obtained intracranial electrocephalography (iEEG) measurements in neurosurgical patients as they performed a simple sensory-motor task with variable (short or long) foreperiod delays that affected anticipation of the cue to respond. Participants showed two forms of anticipatory response biases, distinguished by more premature false alarms (FAs) or faster response times (RTs) on long-delay trials.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!