Biology subjects

Krauel, K.

Publications and source records attributed to Krauel, K..

3 recordsLinked to original sources

bioRxiv2026

Platelet C5aR1 Aggravates Myocardial Infarction through Platelet-Neutrophil Interactions and CXCL4-Dependent NET Release

Platelet activation is a central driver of myocardial infarction. The complement anaphylatoxin C5a is abundantly generated during myocardial infarction, and its receptor C5aR1 is highly expressed on platelets. However, the functional role of platelet C5aR1 in myocardial infarction (MI) remains unknown. Here, we show that platelet&[ndash]expressed C5aR1 critically amplifies thromboinflammatory cardiac injury by promoting platelet&[ndash]mediated neutrophil activation after MI. Platelet&[ndash]specific C5aR1 deletion reduced infarct size, fibrosis, and adverse remodeling while enhancing neovascularization and preserving cardiac function. Mechanistically, cell&[ndash]specific C5aR1 deletion markedly reduced myocardial platelet&[ndash]neutrophil accumulation and neutrophil extracellular trap (NET) formation, while circulating platelet&[ndash]neutrophil complexes were increased. Ex vivo, C5a&[ndash]stimulated platelets robustly induced NET release from neutrophils in a platelet C5aR1&[ndash] and CXCL4&[ndash]dependent manner, whereas platelets lacking C5aR1 failed to trigger NET formation. Pharmacological C5aR1 inhibition with PMX205 phenocopied the genetic platelet&[ndash]specific deletion, resulting in comparable cardioprotection. Together, these findings identify platelet C5aR1 as a druggable target of platelet&[ndash]neutrophil&[ndash]NET signaling that exacerbates myocardial injury and limits reparative healing after MI, highlighting platelet C5aR1 as a potential therapeutic approach to restrain thromboinflammation.

immunology↗

bioRxiv2022

Stratification of responses to tDCS intervention in a healthy paediatric population based on resting-state EEG profiles

Transcranial Direct Current Stimulation (tDCS) is a non-invasive neuromodulation technique with a wide variety of applications in both the clinical and cognitive psychology domains. As increasingly acknowledged, its effectiveness is subject dependent, which may lead to timely and costly treatments with ineffective results if this variability is not taken into account. We propose the usage of electroencephalography (EEG) for the analysis and prediction of individual responses to tDCS. In this context the application of machine learning can be of enormous help. We analysed resting-state EEG activity to identify subgroups of participants with an homogeneous electrophysiological profile and their response to different tDCS interventions. The study described herein, which focuses on healthy controls, was conducted within a clinical trial for the development of treatments based on tDCS for age-matched children diagnosed with Attention Deficit Hyperactivity Disorder (ADHD) and Autism Spectrum Disorder (ASD). We have studied a randomized, double-blind, sham-controlled tDCS intervention in 56 healthy children and adolescents aged 10-17, applied in 2 parallel groups over 2 target regions, namely left Dorsolateral Prefrontal Cortex (lDLPFC) and right Inferior Frontal Gyrus (rIFG). Cognitive behavioural tasks were used to both activate particular brain areas during the stimulation and to assess the impact of the intervention afterwards. We have implemented an unsupervised learning approach to stratify participants based on their resting-state EEG spectral features before the tDCS application. We have then applied a correlational analysis to identify EEG profiles associated with tDCS subject response to the specific stimulation sites and the presence or not of concurrent tasks during the intervention. In the results we found specific digital electrophysiological profiles that can be associated to a positive response, whereas subjects with other profiles respond negatively or do not respond to the intervention. Findings suggest that unsupervised machine learning procedures, when associated with proper visualization features, can be successfully used to interpret and eventually to predict responses of individuals to tDCS treatment.

neuroscience↗

bioRxiv2019

Learning in anticipation of reward and punishment: Perspectives across the human lifespan

Pavlovian biases influence the interaction between action and valence by coupling reward seeking to action invigoration and punishment avoidance to action suppression. In this study we used an orthogonalised go/no-go task to investigate learning in 247 individuals across the human lifespan (7-80 years) to demonstrate that all participants, independently of age, demonstrated an influence of Pavlovian control. Computational modeling revealed peak performance in young adults was attributable to greater sensitivity to both rewards and punishment. However in children and adolescents an increased bias towards action but not reward sensitivity was observed. In contrast, reduced learning in midlife and older adults was accompanied with decreased reward sensitivity and especially punishment sensitivity. These findings reveal distinct learning capabilities across the human lifespan that cannot be probed using conventional go/reward no-go/punishment style paradigms that have important implications in life-long education.

neuroscience↗