Application of a Machine Learning Algorithms in a Wrist-Wearable Sensor for Patient Health Monitoring during Autonomous Hospital Bed Transport.

Smart sensors, coupled with artificial intelligence (AI)-enabled remote automated monitoring (RAMs), can free a nurse from the task of in-person patient monitoring during the transportation process of patients between different wards in hospital settings. Automation of hospital beds using advanced robotics and sensors has been a growing trend exacerbated by the COVID crisis. In this exploratory study, a polynomial regression (PR) machine learning (ML) RAM algorithm based on a Dreyfusian descriptor for immediate wellbeing monitoring was proposed for the autonomous hospital bed transport (AHBT) application. This method was preferred over several other AI algorithm for its simplicity and quick computation. The algorithm quantified historical data using supervised photoplethysmography (PPG) data for 5 min just before the start of the autonomous journey, referred as pre-journey (PJ) dataset. During the transport process, the algorithm continued to quantify immediate measurements using non-overlapping sets of 30 PPG waveforms, referred as in-journey (IJ) dataset. In combination, this algorithm provided a binary decision condition that determined if AHBT should continue its journey to destination by checking the degree of polynomial (DoP) between PJ and IJ. Wrist PPG was used as algorithm's monitoring parameter. PPG data was collected simultaneously from both wrists of 35 subjects, aged 21 and above in postures mimicking that in AHBT and were given full freedom of upper limb and wrist movement. It was observed that the top goodness-of-fit which indicated potentials for high data accountability had 0.2 to 0.6 cross validation score mean (CVSM) occurring at 8th to 10th DoP for PJ datasets and 0.967 to 0.994 CVSM at 9th to 10th DoP for IJ datasets. CVSM was a reliable metric to pick out the best PJ and IJ DoPs. Central tendency analysis showed that coinciding DoP distributions between PJ and IJ datasets, peaking at 8th DoP, was the precursor to high algorithm stability. Mean algorithm efficacy was 0.20 as our proposed algorithm was able to pick out all signals from a conscious subject having full freedom of movement. This efficacy was acceptable as a first ML proof of concept for AHBT. There was no observable difference between subjects' left and right wrists.

Label-free quantitative lymphocyte activation profiling using microfluidic impedance cytometry

Circulating lymphocytes are integral components of our adaptive immunity with emerging clinical applications in immune status monitoring in infectious diseases and cell-mediated cancer immunotherapies. Herein we present a novel impedance-based microfluidic assay for label-free lymphocyte activation profiling based on native or antigen-specific T-lymphocyte biophysical responses. Single cell impedance profiling of T-lymphocytes first revealed distinct biophysical differences in cell size and membrane electrical impedance of healthy, activated (CD3/CD28) and dead lymphocyte populations. Impedance characterization of peripheral blood mononuclear cells (PBMCs) stimulated with mitogen phytohemagglutinin (PHA) or Tuberculin Purified Protein Derivative antigen (PPD) after 24 hours also showed an increase in lymphocyte cell size (∼8 to 10 µm) which corresponded to activated lymphocytes (CD69+CD137+). We next developed a spiral inertial microfluidics cell sorter integrated with coplanar electrodes for direct impedance quantification of activated lymphocytes. By removing non-activated smaller lymphocytes (< 8 µm) and employing hydrodynamic-based single stream particle focusing, we demonstrated significant enrichment of activated lymphocytes (∼11.7-fold) to electrically detect low levels of lymphocyte activation (< 5%). Finally, the developed biochip is coupled with magnetic activated cell sorting (MACS) to quantify CD4+ T-lymphocytes response in PBMCs stimulated with PPD. A differential impedance cell count ratio (stimulated/unstimulated) was defined to distinguish activated T-lymphocytes, which showed better sensitivity as compared to immunophenotyping by flow cytometry. Taken together, the integrated impedance biosensor can be further developed as a rapid multiplexed screening assay to detect antigen-specific T-lymphocyte responses to characterize host immunity and diagnosis of infectious diseases (e.g tuberculosis, dengue and COVID-19).