We propose a nanometer-scale displacement or vibration measurement system, using an optical quadrature interferometer and the post-processing technique that extracts the parameters necessary for characterizing the interferometric system. Using a 3 × 3 fiber-optic coupler, the entire complex interference signal could be reconstructed with two interference signals measured at two return ports of the coupler. The intrinsic phase difference between the return ports was utilized to obtain the quadratic part of the interference signal, which allowed one to reconstruct the entire complex interference signal. However, the two measured signals were appreciably affected by the unequal detector gains and non-uniform intrinsic phases of the coupler. Fortunately, we could find that the Lissajous curve plotted by the two signals of the interferometric system would form an ellipse. Therefore, by fitting the measured Lissajous curve to an ellipse, we could extract the parameters characterizing the actual system, which allowed the nanometer-scale measurement. Experimental results showed that a 20 kHz sinusoidal vibration with an amplitude of 1.5 nm could be measured with a standard deviation of 0.4 nm.

Dynamic full-field optical coherence microscopy (DFFOCM) was used to characterize the intracellular dynamic activities and cytoskeleton of HeLa cells in different viability states. HeLa cell samples were continuously monitored for 24 hours and compared with histological examination to confirm the cell viability states. The averaged mean frequency and magnitude observed in healthy cells were 4.79±0.5 Hz and 2.44±1.06, respectively. In dead cells, the averaged mean frequency was shifted to 8.57±0.71 Hz, whereas the magnitude was significantly decreased to 0.53±0.25. This cell dynamic activity analysis using DFFOCM is expected to replace conventional time-consuming and biopsies-required histological or biochemical methods.

We present a novel method that can assay cellular viability in real-time using supervised machine learning and intracellular dynamic activity data that is acquired in a label-free, non-invasive, and non-destructive manner. Cell viability can be an indicator for cytology, treatment, and diagnosis of diseases. We applied four supervised machine learning models on the observed data and compared the results with a trypan blue assay. The cell death assay performance by the four supervised models had a balanced accuracy of 93.92 ± 0.86%. Unlike staining techniques, where criteria for determining viability of cells is unclear, cell viability assessment using machine learning could be clearly quantified.

A noncontact photoacoustic imaging method based on optical quadrature detection is proposed. The photo-induced acoustic signal is detected by an optical method without contacting the specimen. By utilizing the intrinsic phase difference of a multiport optical interferometer, the quadrature signal of a conventional interferometric signal could be obtained. With this quadratic signal pair, we could reconstruct the photoacoustic signal without suffering from the initial phase drift that usually occurs in a conventional interferometric system. The performance of the proposed system is verified by imaging human hairs embedded in a polydimethylsiloxane resin block. The system’s lateral and axial resolutions are measured to be 84 and 86 μm at a 1.5 mm depth of a PDMS resin block, respectively. The experimental result is good enough to distinguish the hairs staggered in depth.

This study aimed to assess transabdominal placental oxygenation levels non-invasively. A wearable device was designed and tested in 12 pregnant women with an anterior placenta, 5 of whom had maternal pregnancy complications. Preliminary results revealed that the placental oxygenation level is closely related to pregnancy complications and placental pathology. Women with maternal pregnancy complications were found to have a lower placental oxygenation level (69.4% ± 6.7%) than those with uncomplicated pregnancy (75.0% ± 5.8%). This device is a step in the development of a point-of-care method designed to continuously monitor placental oxygenation and to assess maternal and fetal health.

The worldwide outbreak of the novel Coronavirus (COVID-19) has highlighted the need for a screening and monitoring system for infectious respiratory diseases in the acute and chronic phase. The purpose of this study was to examine the feasibility of using a wearable near-infrared spectroscopy (NIRS) sensor to collect respiratory signals and distinguish between normal and simulated pathological breathing. Twenty-one healthy adults participated in an experiment that examined five separate breathing conditions. Respiratory signals were collected with a continuous-wave NIRS sensor (PortaLite, Artinis Medical Systems) affixed over the sternal manubrium. Following a three-minute baseline, participants began five minutes of imposed difficult breathing using a respiratory trainer. After a five minute recovery period, participants began five minutes of imposed rapid and shallow breathing. The study concluded with five additional minutes of regular breathing. NIRS signals were analyzed using a machine learning model to distinguish between normal and simulated pathological breathing. Three features: breathing interval, breathing depth, and O₂Hb signal amplitude were extracted from the NIRS data and, when used together, resulted in a weighted average accuracy of 0.87. This study demonstrated that a wearable NIRS sensor can monitor respiratory patterns continuously and non-invasively and we identified three respiratory features that can distinguish between normal and simulated pathological breathing.