Journal of Innovative Optical Health Sciences

There is an ongoing technological revolution in the field of biomedical instruments. Consequently, high performance healthcare devices have led to remarkable economic developments in the medical hardware industry. Until now, nearly all optical bio-imaging systems are based on the 2-dimensional imaging chip architecture. In fact, recent developments in digital micromirror devices (DMDs) are gradually making their way from conventional optical projection displays into biomedical instruments. As an ultrahigh-speed spatial light modulator, the DMD may offer a range of new applications including real-time biomedical sensing or imaging, as well as orientation tracking and targeted screening. Given its short history, the use of DMD in biomedical and healthcare instruments has emerged only within the past decade. In this paper, we first provide an overview by summarizing all reported cases found in the literature. We then critically analyze the general pros and cons of using DMD, specifically in terms of response speed, stability, accuracy, repeatability, robustness, and degree of automation, in relation to the performance outcome of the designated instrument. Particularly, we shall focus our discussion on the use of Micro-Electro-Mechanical System (MEMS)-based devices in a set of representative instruments including the surface plasmon resonance biosensor, optical microscopes, Raman spectrometers, ophthalmoscopes, and the micro stereolithographic system. Finally, the prospects of using the DMD approach in biomedical or healthcare systems and possible next generation DMD-based biomedical devices are presented.

We present a robust and fiducial-marker-free algorithm that can identify and correct stick-slip distortion caused by nonuniform rotation (or beam scanning) in distally scanned catheters for endoscopic optical coherence tomography (OCT) images. This algorithm employs spatial frequency analysis to select and remove distortions. We demonstrate the feasibility of this algorithm on images acquired from ex vivo rat colon with a distally scanned DC motor-based endoscope. The proposed algorithm can be applied to general endoscopic OCT images for correcting nonuniform rotation distortion.

Intensity-based quantitative fluorescence resonance energy transfer (FRET) is a technique to measure the distance of molecules in scale of a few nanometers which is far beyond optical diffraction limit. This widely used technique needs complicated experimental process and manual image analyses to obtain precise results, which take a long time and restrict the application of quantitative FRET especially in living cells. In this paper, a simplified and automatic quantitative FRET (saqFRET) method with high efficiency is presented. In saqFRET, photoactivatable acceptor PA-mCherry and optimized excitation wavelength of donor enhanced green fluorescent protein (EGFP) are used to simplify FRET crosstalk elimination. Traditional manual image analyses are time consuming when the dataset is large. The proposed automatic image analyses based on deep learning can analyze 100 samples within 30s and demonstrate the same precision as manual image analyses.

The main objective of this study is to evaluate the antibacterial effect of antibacterial photodynamic therapy (aPDT) on Streptococcus mutans (S. mutans) biofilm model in vitro. The selection of photosensitizers is the key step for the efficacy of photodynamic therapy (PDT). However, no studies have been conducted in the oral field to compare the functional characteristics and application effects of PDT mediated by various photosensitizers. In this research, the antibacterial effect of Methylene blue (MB)/650nm laser and Hematoporphyrin monomethyl ether (HMME)/532nm laser on S. mutans biofilm was compared under different energy densities to provide experimental reference for the clinical application of the two PDT. The yield of lactic acid was analyzed by Colony forming unit (CFU) and spectrophotometry, and the complete biofilm activity was measured by Confocal Laser Scanning Microscopy (CLSM) to evaluate the bactericidal effect on each group. Based on the results of CFU, the bacterial colonies formed by 30.4J/cm² 532nm MB-aPDT group and 30.4J/cm² 532nm HMME-aPDT group were significantly less than those in other groups, and the bacterial colonies in HMME-aPDT group were less than those in HMME-aPDT group. Lactic acid production in all treatment groups except the photosensitizer group was statistically lower than that in the normal saline control group. The activity of bacterial plaque biofilm was significantly decreased in the two groups treated with 30.4J/cm² aPDT. Therefore, aPDT suitable for energy measurement can kill S. mutans plaque biofilm, and MB-aPDT is better than HMME-aPDT.

Melanoma is the deadliest skin cancer and is responsible for over 7000 deaths in the US annually. The spread of cancer, or metastasis, is responsible for these deaths, as secondary tumors interrupt normal organ function. Circulating tumor cells, or those cells that spread throughout the body from the primary tumor, are thought to be responsible for metastasis. We developed an optical method, photoacoustic flow cytometry, in order to detect and enumerate circulating melanoma cells (CMCs) from blood samples of patients. We tested the blood of Stage IV melanoma patients to show the ability of the photoacoustic flow cytometer to detect these rare cells in blood. We then tested the system on archived blood samples from Stage III melanoma patients with known outcomes to determine if detection of CMCs can predict future metastasis. We detected between 0 and 66 CMCs in Stage IV patients. For the Stage III study, we found that of those samples with CMCs, two remained disease free and five developed metastasis. Of those without CMCs, six remained disease free and one developed metastasis. We believe that photoacoustic detection of CMCs provides valuable information for the prediction of metastasis and we postulate a system for more accurate prognosis.

The paper presents the results of a study of the female urethra in cases of urethral pain syndrome (UPS) and inflammatory diseases of the lower urinary tract using cross-polarization optical coherence tomography (CP OCT). Urethral wall structure was studied in 86 patients; 233 CP OCT images were collected. A comparative qualitative analysis of three groups of CP OCT images — “norm”, “Inflammation” and “UPS” — identified that despite the absence of a clear inflammatory factor in the patient’s examination, the urethral tissues in UPS were in an altered state. The changes in the urethral wall with UPS and in cases of inflammation were similar. Using a point scale, three experts independently performed visual scoring of the CP OCT images. Three parameters: epithelial contrast, cavities and the minimum signal depth in the co-channel were evaluated. It was found that, individually, the parameters correlate only weakly with the diagnosis. Area under the receiver operating characteristic (ROC) curve was from 0.51 to 0.78. The joint use of a number of visual signs has a greater diagnostic value than the use of the criteria individually. Area under the ROC curve using the developed cumulative criterion reached up to 0.87–0.89. This study could form the basis of a scoring system for assessing the state of the urethral tract using CP OCT images in real time. The CP OCT method provides information on the state of urethral tissues that cannot be obtained with traditional cystoscopy.

Creatinine (Cr) is a biochemical waste molecule generated from muscle metabolism and primarily cleared from the bloodstream by the kidneys. If kidney function declines, Cr levels in the blood tend to increase. Therefore, Cr serves as an indicator of kidney function. In this work, we present a simple method for the rapid screening for impaired renal function based on the subject’s Cr concentration. In our setup, broadband white light is delivered to a finger clamp through a fiber-optic cable to illuminate the patient’s finger. The light is transmitted through the finger and collected by a second optical fiber coupled to a visible–near-infrared (VisNIR) spectrometer which covers the spectral range from 400nm to 1100nm. During the calibration process, the transmitted spectra acquired from 60 patients were measured. An average was calculated using the peak level of the transmitted, diffused intensity at three different wavelengths to create a “Cr intensity index”. Patients were divided into five groups according to their Cr concentration levels, ranging from 1mg/dL to 13mg/dL. Our observations indicated that each group featured a unique spectral fingerprint. Next, we tested the index on 20 patients not included in the calibration procedure (unknown samples). We were able to classify patients into groups according to their Cr level with moderate prediction accuracy ($R^2=0.55$) and mean screening error of up to 16%. Future efforts will evaluate the accuracy of this approach with larger patient populations representing a broad range of Cr concentration. Still, this preliminary work is an essential step toward developing this useful noninvasive Cr screening platform using NIR light spectroscopy.

Chronic kidney disease (CKD) is becoming a major public health problem worldwide, and excessive potassium intake is a health threat to patients with CKD. In this study, visible–short-wave near-infrared (Vis–SWNIR) spectroscopy and chemometric algorithms were investigated as nondestructive methods for assessing the potassium concentration in fresh lettuce to benefit the CKD patients’ health. Interactance and transmittance measurements were performed and the competencies were compared based on the multivariate methods of partial least-square regression (PLS) and support vector machine regression (SVR). Meanwhile, several preprocessing methods [first- and second-order derivatives in combination with standard normal variate (SNV)] and wavelength selection method of competitive adaptive reweighted sampling (CARS) were applied to eliminate noise and highlight the spectral characteristics. The PLS models yielded better prediction than the SVR models with higher correlation coefficients ($R^2$) and residual predictive deviation (RPD), and lower root-mean-square error of prediction (RMSEP). Excellent prediction of green leaves was obtained by the interactance measurement with $R^2=0.93$, $\mathrm{\text{RMSEP}}=24.86$mg/100g, and $\mathrm{\text{RPD}}=3.69$; while the transmittance spectra of petioles provided optimal prediction with $R^2=0.92$, $\mathrm{\text{RMSEP}}=27.80$mg/100g, and RPD=3.34, respectively. Therefore, the results indicated that Vis–SWNIR spectroscopy is capable of intelligently detecting potassium concentration in fresh lettuce to benefit CKD patients around the world in maintaining and enhancing their health.