Abstract
Diagnosis of health disorders relies heavily on detecting biological data and accurately observing pathological changes. A significant challenge lies in detecting targeted biological signals and developing reliable sensing technology for clinically relevant results. The combination of data analytics with the sensing abilities of Optical Fiber Tweezers (OFT) provides a high-capability, multifunctional biosensing approach for biophotonic tools. In this work, we introduced phase as a new domain to obtain light patterns in OFT back-scattering signals. By applying a multivariate data analysis procedure, we extract phase spectral information for discriminating micro and nano (bio)particles. A newly proposed method—Hilbert Phase Slope—presented high suitability for differentiation problems, providing features able to discriminate with statistical significance two optically trapped human tumoral cells (MKN45 gastric cell line) and two classes of non-trapped cancer-derived extracellular nanovesicles – an important outcome in view of the current challenges of label-free bio-detection for multifunctional single-molecule analytic tools. © The Author(s) 2024.

Abstract
Direct blood pressure (BP) measurements require cuff compression, which not only is time-consuming but also inconvenient for frequent monitoring. This study addresses the challenge of continuous BP estimation (both Systolic (SBP) and Diastolic (DBP)) during exercise in a cuff-less manner, utilizing photoplethysmography (PPG) signals acquired by low-cost wear-ables. Leveraging Reflective Pulse-wave Transit Time (R-PTT), state-of-the-art algorithms were put to the test in two datasets (total subjects = 18). DATASET1 contains PPG signal and BP measurements of subjects in resting state, while DATASET2 comprises data of subjects in resting state and during exercise. The results reveal competitive performance, with Mean Absolute Error (MAE) of the estimation algorithm for DATASET1 being SBP=7.9 mmHg and DBP=5.2 mmHg and SBP=14.4 mmHg and DBP=7.7 mmHg for DATASET2. DATASET1 consistently outperforms DATASET2, affirming the algorithm's efficacy during resting states and that estimation during physical activity introduces challenges, requiring further refinement and research for real-world applications. In conclusion, this research unveils a viable solution for continuous cuff-less BP monitoring, while emphasizing the need for refinement and validation to enhance its clinical applicability and accessibility. © 2024 IEEE.