Background and Objectives: Changes in autonomic cardiac activity during night sleep are well documented. However, there is limited information regarding changes in the autonomic cardiac profile during daytime naps. Heart rate variability (HRV) and baroreflex sensitivity (BRS) are reliable measures of autonomic cardiac activity. The purpose of this study was to determine the changes in HRV and BRS during daytime naps in healthy men. Methods: This was a cross-sectional study of 25 healthy men. Polysomnographic recording with electrocardiogram monitoring was conducted for all volunteers during a 50– 80 min nap between 3.30 pm and 5.30 pm. Five-minute segments during pre-nap wakefulness, non-rapid eye movement (NREM) sleep stages (N1, N2, and N3), rapid eye movement (REM) sleep stage, and post-nap wakefulness were used to measure changes in the variation in HRV parameters, including inter-beat interval (RR-interval), total spectral power (TP), high-frequency power (HF), low-frequency power (LF), and low frequency/high-frequency ratio (LF/HF). BRS was also measured for 10 min during pre- and post-nap wakefulness using finger arterial pressure measurement (Finometer Pro ^®). Results: HRV increased significantly during NREM sleep compared with that during pre-nap wakefulness (p < 0.05), as reflected by RR-interval prolongation, higher HF, and increased HF_nu (normalized units). Furthermore, there was a parallel reduction in TP, LF, and LF/HF ratio during NREM sleep, indicating parasympathetic predominance over cardiac autonomic activity. HF and HF_nu were significantly reduced during REM sleep compared with that during NREM sleep (p < 0.05). BRS did not show significant differences between pre- and post-nap wakefulness. Conclusion: We observed a progressive increase in parasympathetic activity during daytime sleep as NREM sleep deepened compared with that during wakefulness and REM sleep. Daytime nap may have a favorable cardiovascular impact.

Respiratory medicine is transitioning from symptom-driven, standardized care to a more precise, patient-specific approach guided by molecular profiling. This evolution is being enabled by advances in liquid biopsy, multiomics, and artificial intelligence (AI) analytics. Fractional exhaled nitric oxide (FeNO) and blood eosinophils, the two commonly used markers in asthma, are now being joined by more precise airway markers such as galectin-10, which could aid clinicians in making more informed decisions for biological treatments. In chronic obstructive pulmonary disease (COPD) similar progress is underway, with treatment now emphasizing inflammation endotypes, especially eosinophilic patterns, to direct therapeutic choices. Alongside these developments, routine blood-based ratios (e.g., platelet-to-lymphocyte and neutrophil-to-lymphocyte) are being explored as predictors of exacerbation risk, and forced oscillation testing (FOT) is proving useful for picking up early disease shifts. In more severe conditions, biomarkers are linked to an early and better prognosis, enabling timely intervention. Markers like Matrix metalloproteinase-7 (MMP-7) and CC chemokine ligand 18 (CCL18) have proven to be reliable indicators of mortality and disease progression in idiopathic pulmonary fibrosis. Meanwhile, in lung cancer, liquid biopsies, especially those measuring circulating tumor DNA and micro-RNA (miRNA) panels, are enhancing screening accuracy while helping to cut down on the high false-positive rates seen with low-dose computerised tomography (CT). Other respiratory conditions such as bronchiectasis, pulmonary embolism, pneumonia, and acute respiratory distress syndrome (ARDS) are also benefiting from biomarker advances. At the same time there is a growing push to standardize how these biomarkers are measured. AI-based clinical decision support systems are also playing an increasingly important role in the translation of all these complicated data into actionable clinical insights. Together these developments pave the way for improved respiratory care that is precise and responsive to individual patient needs.

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