Cerebrovascular and coronary vascular function in anxiety disorders
Bibliographic record
Abstract
Anxiety disorders, including generalized anxiety disorder (GAD), have been linked to an increased risk of developing cardiovascular diseases (CVD).Studies have shown that anxiety disorders can have a negative impact on the cardiovascular system, leading to changes in blood pressure, heart rate, and inflammation that can contribute to the development of CVD.Dysfunction or imbalances in the heart-brain axis have been associated with various diseases and conditions, including CVD, depression, and anxiety.Understanding and promoting the health of the heartbrain axis is important for maintaining overall health and well-being.Altered autonomic control, endothelial dysfunction and respiratory dysregulation seen in anxiety disorders could all affect coronary and cerebrovascular microvascular function.These interrelated disruptions may impact the intricate bidirectional communication between the heart and brain, involving both neurohumoral interactions and physiological processes.Such disturbances could contribute to the development of CVD.Thus, this heart-brain axis and associated vascular function in GAD may be clinically relevant for preventive or therapeutic decision-making.The exact nature of the link between CVD and GAD, however, remains remarkably unclear.This is in part due to a lack of safe, robust, noninvasive, affordable techniques for assessing vascular function.Cognitive behavioral therapy (CBT) is a common treatment for anxiety, but its impact on psychiatric and cardiovascular health needs further study.Oxygenation-sensitive magnetic resonance imaging (OS-MR) has emerged as an efficient technique for visualizing tissue oxygenation changes and has been utilized in functional magnetic resonance imaging (fMRI) since more than 30 years.The OS-MR image signal intensity is determined by the intrinsic paramagnetic characteristics of deoxygenated hemoglobin (deoxyhemoglobin), which results in a reduction of the transverse relaxation time, also referred to as the blood oxygen level dependent (BOLD)
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".