This PDF file contains the front matter associated with SPIE Proceedings Volume 10055, including the Title Page, Copyright information, Table of Contents, and Conference Committee listing.

Surface Enhanced Raman spectroscopy (SERS) provides significant improvements over conventional methods for single and multianalyte quantification. Specifically, the spectroscopic fingerprint provided by Raman scattering allows for a direct multiplexing potential far beyond that of fluorescence and colorimetry. Additionally, SERS generates a comparatively low financial and spatial footprint compared with common fluorescence based systems. Despite the advantages of SERS, it has remained largely an academic pursuit. In the field of biosensing, techniques to apply SERS to molecular diagnostics are constantly under development but, most often, assay protocols are redesigned around the use of SERS as a quantification method and ultimately complicate existing protocols.

Our group has sought to rethink common SERS methodologies in order to produce translational technologies capable of allowing SERS to compete in the evolving, yet often inflexible biosensing field. This work will discuss the development of two techniques for quantification of microRNA, a promising biomarker for homeostatic and disease conditions ranging from cancer to HIV. First, an inkjet-printed paper SERS sensor has been developed to allow on-demand production of a customizable and multiplexable single-step lateral flow assay for miRNA quantification. Second, as miRNA concentrations commonly exist in relatively low concentrations, amplification methods (e.g. PCR) are therefore required to facilitate quantification. This work presents a novel miRNA assay alongside a novel technique for quantification of nuclease driven nucleic acid amplification strategies that will allow SERS to be used directly with common amplification strategies for quantification of miRNA and other nucleic acid biomarkers.

An innovative and novel quartz-enhanced photoacoustic spectroscopy (QEPAS) sensor for highly sensitive and selective breath gas analysis is introduced. The QEPAS sensor consists of two acoustically coupled micro- resonators (mR) with an off-axis 20 kHz quartz tuning fork (QTF). The complete acoustically coupled mR system is optimized based on finite element simulations and experimentally verified. Due to the very low fabrication costs the QEPAS sensor presents a clear breakthrough in the field of photoacoustic spectroscopy by introducing novel disposable gas chambers in order to avoid cleaning after each test. The QEPAS sensor is pumped resonantly by a nanosecond pulsed single-mode mid-infrared optical parametric oscillator (MIR OPO). Spectroscopic measurements of methane and methanol in the 3.1 μm to 3.7 μm wavelength region is conducted. Demonstrating a resolution bandwidth of 1 cm^-1. An Allan deviation analysis shows that the detection limit at optimum integration time for the QEPAS sensor is 32 ppbv@190s for methane and that the background noise is solely due to the thermal noise of the QTF. Spectra of both individual molecules as well as mixtures of molecules were measured and analyzed. The molecules are representative of exhaled breath gasses that are bio-markers for medical diagnostics.

Vitamin A and iron deficiency are common malnutrition affecting billions of people worldwide. However, in infrastructure limited settings, access to blood vitamin A and iron status test is limited because of the complexity and cost of traditional diagnostic methods. Direct measurements of vitamin A and iron level is not easy to perform, and it is necessary to measure approximate marker for obtaining vitamin A and iron deficiency status. Measurement of inflammatory marker is also necessary because the vitamin A and iron level are altered by inflammation status. Here we introduced a multiplex rapid point-of-care (POC) diagnostic devices that simultaneously characterize three markers relevant to vitamin A, iron and inflammation status: retinol binding protein 4, ferritin and C-reactive protein with lateral flow immunoassay test strips. Level of retinol binding protein 4, ferritin and C-reactive protein are indicated by excitation intensity of fluorescence tags with three different colors. The test can be done within 15 minutes and a complete sample-answer quantitative results of vitamin A, iron and inflammation status level can be obtained with assists of a smartphone and an external device. We also demonstrated the device is able to perform colorimetric analysis on single test area. which gives the device potential to perform more tests simultaneously at the same time.

Multispectral imaging holds great promise as a non-contact tool for the assessment of tissue composition. Performing multi - spectral imaging on a hand held mobile device would allow to bring this technology and with it knowledge to low resource settings to provide a state of the art classification of tissue health. This modality however produces considerably larger data sets than white light imaging and requires preliminary image analysis for it to be used. The data then needs to be analyzed and logged, while not requiring too much of the system resource or a long computation time and battery use by the end point device. Cloud environments were designed to allow offloading of those problems by allowing end point devices (smartphones) to offload computationally hard tasks. For this end we present a method where the a hand held device based around a smartphone captures a multi - spectral dataset in a movie file format (mp4) and compare it to other image format in size, noise and correctness. We present the cloud configuration used for segmenting images to frames where they can later be used for further analysis.

Digital holographic on-chip microscopy achieves large space-bandwidth-products (e.g., >1 billion) by making use of pixel super-resolution techniques. To synthesize a digital holographic color image, one can take three sets of holograms representing the red (R), green (G) and blue (B) parts of the spectrum and digitally combine them to synthesize a color image. The data acquisition efficiency of this sequential illumination process can be improved by 3-fold using wavelength-multiplexed R, G and B illumination that simultaneously illuminates the sample, and using a Bayer color image sensor with known or calibrated transmission spectra to digitally demultiplex these three wavelength channels. This demultiplexing step is conventionally used with interpolation-based Bayer demosaicing methods. However, because the pixels of different color channels on a Bayer image sensor chip are not at the same physical location, conventional interpolation-based demosaicing process generates strong color artifacts, especially at rapidly oscillating hologram fringes, which become even more pronounced through digital wave propagation and phase retrieval processes. Here, we demonstrate that by merging the pixel super-resolution framework into the demultiplexing process, such color artifacts can be greatly suppressed. This novel technique, termed demosaiced pixel super-resolution (D-PSR) for digital holographic imaging, achieves very similar color imaging performance compared to conventional sequential R,G,B illumination, with 3-fold improvement in image acquisition time and data-efficiency. We successfully demonstrated the color imaging performance of this approach by imaging stained Pap smears. The D-PSR technique is broadly applicable to high-throughput, high-resolution digital holographic color microscopy techniques that can be used in resource-limited-settings and point-of-care offices.

Research laboratories and the industry rely on yeast viability and concentration measurements to adjust fermentation parameters such as pH, temperature, and pressure. Beer-brewing processes as well as biofuel production can especially utilize a cost-effective and portable way of obtaining data on cell viability and concentration. However, current methods of analysis are relatively costly and tedious. Here, we demonstrate a rapid, portable, and cost-effective platform for imaging and measuring viability and concentration of yeast cells. Our platform features a lens-free microscope that weighs 70 g and has dimensions of 12 × 4 × 4 cm. A partially-coherent illumination source (a light-emitting-diode), a band-pass optical filter, and a multimode optical fiber are used to illuminate the sample. The yeast sample is directly placed on a complementary metal-oxide semiconductor (CMOS) image sensor chip, which captures an in-line hologram of the sample over a large field-of-view of >20 mm². The hologram is transferred to a touch-screen interface, where a trained Support Vector Machine model classifies yeast cells stained with methylene blue as live or dead and measures cell viability as well as concentration. We tested the accuracy of our platform against manual counting of live and dead cells using fluorescent exclusion staining and a bench-top fluorescence microscope. Our regression analysis showed no significant difference between the two methods within a concentration range of 1.4 × 10⁵ to 1.4 × 10⁶ cells/mL. This compact and cost-effective yeast analysis platform will enable automatic quantification of yeast viability and concentration in field settings and resource-limited environments.

Giardia lamblia causes a disease known as giardiasis, which results in diarrhea, abdominal cramps, and bloating. Although conventional pathogen detection methods used in water analysis laboratories offer high sensitivity and specificity, they are time consuming, and need experts to operate bulky equipment and analyze the samples. Here we present a field-portable and cost-effective smartphone-based waterborne pathogen detection platform that can automatically classify Giardia cysts using machine learning. Our platform enables the detection and quantification of Giardia cysts in one hour, including sample collection, labeling, filtration, and automated counting steps. We evaluated the performance of three prototypes using Giardia-spiked water samples from different sources (e.g., reagent-grade, tap, non-potable, and pond water samples). We populated a training database with >30,000 cysts and estimated our detection sensitivity and specificity using 20 different classifier models, including decision trees, nearest neighbor classifiers, support vector machines (SVMs), and ensemble classifiers, and compared their speed of training and classification, as well as predicted accuracies. Among them, cubic SVM, medium Gaussian SVM, and bagged-trees were the most promising classifier types with accuracies of ~ 94.1%, 94.2%, and 95%, respectively; we selected the latter as our preferred classifier for the detection and enumeration of Giardia cysts that are imaged using our mobile-phone fluorescence microscope. Without the need for any experts or microbiologists, this field-portable pathogen detection platform can present a useful tool for water quality monitoring in resource-limited-settings.

Antimicrobial susceptibility testing (AST) is commonly used for determining microbial drug resistance, but routine testing, which can significantly reduce the spread of multi-drug resistant organisms, is not regularly performed in resource-limited and field-settings due to technological challenges and lack of trained diagnosticians. We developed a portable cost-effective smartphone-based colorimetric 96-well microtiter plate (MTP) reader capable of automated AST without the need for a trained diagnostician. This system is composed of a smartphone used in conjunction with a 3D-printed opto-mechanical attachment, which holds a set of inexpensive light-emitting-diodes and fiber-optic cables coupled to the 96-well MTP for enabling the capture of the transmitted light through each well by the smartphone camera. Images of the MTP plate are captured at multiple exposures and uploaded to a local or remote server (e.g., a laptop) for automated processing/analysis of the results using a custom-designed smartphone application. Each set of images are combined to generate a high dynamic-range image and analyzed for well turbidity (indicative of bacterial growth), followed by interpretative analysis per plate to determine minimum inhibitory concentration (MIC) and drug susceptibility for the specific bacterium. Results are returned to the originating device within ~1 minute and shown to the user in tabular form. We demonstrated the capability of this platform using MTPs prepared with 17 antibiotic drugs targeting Gram-negative bacteria and tested 82 patient isolate MTPs of Klebsiella pneumoniae, achieving well turbidity accuracy of 98.19%, MIC accuracy of 95.15%, and drug susceptibility interpretation accuracy of 99.06%, meeting the FDA defined criteria for AST.

Cervical cancer disproportionately affects women living in low- and middle-income countries. To address this global crisis, many governments and NGOs have implemented community-based screening and treatment programs at outreach camps. Here, high volumes of patients are able to access care: screening and diagnosis followed by immediate treatment of precancerous lesions onsite. However, monitoring and evaluation (M&E) of these efforts presents challenges, since each event typically relies on a different health workforce, and refers patients to different facilities for follow up and advanced care. To address these challenges, a digital imaging intervention was deployed at several screening camps in East Africa. Trained nurses screened women using a connected low-cost mobile colposcope built around a smartphone. A decision support job aid was integrated into the app controlling the device, guiding nurses and recording their diagnosis and treatment decisions. Aggregating the data from the job aid allowed M&E of the screening camp in real-time. In this paper, the M&E data from 2 different screening camps in East Africa are compared. Additionally, screening camps are compared to stationary clinics. Differences in the patient screening times, treatment rates, and individual nurse statistics were all documented through the job aid allowing for much improved epidemiological information following outreach events thus enabling targeted program improvements and provider training. Reporting data from screening camps were also shared online via public web pages, facilitating broader dissemination of health needs in specific East African communities, and sparking conversations with regional stakeholders about local disease burden.

Herpes is one of the most widespread sexually transmitted viral diseases. Timely detection of Herpes Simplex Virus (HSV) can help prevent the rampant spreading of the virus. Current detection techniques such as viral culture, immuno-assays or Polymerase-Chain-Reaction, are time extensive and require expert handling. Here we present a field-portable, easy-to-use, and cost-effective biosensor for the detection of HSV based on holographic imaging. The virus is first captured from a target solution onto specifically developed substrates, prepared by coating glass coverslips with HSV-specific antibodies, and imaged using a lensfree holographic microscope. Several light-emitting-diodes (LEDs), coupled to multi-mode optical-fibers, are used to illuminate the sample containing the viruses. A micro-controller is used to activate the LEDs one at a time and in-line holograms are recorded using a CMOS imager placed immediately above the substrate. These sub-pixel shifted holograms are used to generate a super-resolved hologram, which is reconstructed to obtain the phase and amplitude images of the viruses. The signal of the viruses is enhanced using self-assembled PEG-based nanolenses, formed around the viral particles. Based on the phase information of the reconstructed images we can estimate the size of the viral particles, with an accuracy of ± 11 nm, as well as quantify the viral load. The limit-of-detection of this system is estimated to be <500 viral copies per 100 μL sample volume that is imaged over 30 mm^2 field-of-view. This holographic microscopy based biosensor is label-free, cost-effective and field-portable, providing results in 2 hours, including sample preparation and imaging time.

Currently, one-third of humanity is still suffering from anemia. In China the most common forms of anemia are iron deficiency and Thalassemia minor. Differentiating these two is the key to effective treatment. Iron deficiency is caused by malnutrition and can be cured by iron supplementation. Thalassemia is a hereditary disease in which the hemoglobin β chain is lowered or absent. Iron therapy is not effective, and there is evidence that iron therapy may be harmful to patients with Thalassemia. Both anemias can be diagnosed using red blood cell morphology: Iron deficiency presents a smaller mean cell volume compared to normal cells, but with a wide distribution; Thalassemia, meanwhile, presents a very small cell size and tight particle size distribution. Several researchers have proposed diagnostic indices based on red cell morphology to differentiate these two diseases. However, these indices lack sensitivity and specificity and are constructed without statistical rigor. Using multivariate methods we demonstrate a new classification method based on red cell morphology that diagnoses anemia in a Chinese population with enough accuracy for its use as a screening method. We further demonstrate a low cost instrument that precisely measures red cell morphology using elastic light scattering. This instrument is combined with an automated analysis program that processes scattering data to report red cell morphology without the need for user intervention. Despite using consumer-grade components, when comparing our experimental results with gold-standard measurements, the device can still achieve the high precision required for sensing clinically significant changes in red cell morphology.

Biophotonics-based diagnostic and imaging modalities have been widely used in various applications associated with the non-invasive imaging of the internal structure of a range biological media from a range of cells cultures to biological tissues. With the fast growing interest in food securities there remains strong demand to apply reliable and cost effective biophotonics-based technologies for rapid screening of freshness, internal defects and quality of major agricultural products. In current presentation the results of application of optical coherence tomography (OCT) and encapsulated optical bio-sensors for quantitative assessment of freshness of agricultural products, such as meat and sea foods, are presented, and their further perspectives are discussed.

We present a cost-effective and highly-portable plastic prototype that can be interfaced with a cell phone to implement an optofluidic imaging cytometry platform. It is based on a PMMA microfluidic chip that fits inside an opto-mechanical platform fabricated by a 3D printer. The fluorescence excitation and imaging is performed using the LED and the CMOS from the cell phone increasing the compactness of the system. A custom developed application is used to analyze the images and provide a value of particle concentration.

This Conference Presentation was recorded at SPIE Photonics West 2017 held in San Francisco, California, United States.

Lens-free holographic microscopy (LHM) is a promising imaging technique for life science and industrial applications, yet system miniaturization and cost reduction without compromising imaging performance remain challenging for field applications in low-resource settings. We demonstrate a cost-effective LHM system without needs for precision optical and mechanical parts (such as lenses, beam-splitters, or kinematic stages) and relies solely on robust optoelectronic hardware and software co-design for high performance imaging. The compact and lightweight form-factor is achieved through integration of light sources, an image sensor and all control electronics with automated calibration and multiwavelength reconstruction algorithms. Amplitude and phase images of a sample can be reconstructed in a few seconds with a micron level optical resolution in a field-of-view of 16.5 mm². The method offers a portable and scalable solution for microscopic imaging applications.

Scanning confocal microscopy is a standard choice for many fluorescence imaging applications in basic biomedical research. It is able to produce optically sectioned images and provide acquisition versatility to address many samples and application demands. However, scanning a focused point across the specimen limits the speed of image acquisition. As a result, scanning confocal microscope only works well with stationary samples. Researchers have performed parallel confocal scanning using digital-micromirror-device (DMD), which was used to project a scanning multi-point pattern across the sample. The DMD based parallel confocal systems increase the imaging speed while maintaining the optical sectioning ability. In this paper, we report the development of an add-on kit for high-speed and low-cost confocal microscopy. By adapting this add-on kit to an existing regular microscope, one can convert it into a confocal microscope without significant hardware modifications. Compared with current DMD-based implementations, the reported approach is able to recover multiple layers along the z axis simultaneously. It may find applications in wafer inspection and 3D metrology of semiconductor circuit. The dissemination of the proposed add-on kit under $1000 budget could also lead to new types of experimental designs for biological research labs, e.g., cytology analysis in cell culture experiments, genetic studies on multicellular organisms, pharmaceutical drug profiling, RNA interference studies, investigation of microbial communities in environmental systems, and etc.

Microscopic imaging of biological samples such as pathology slides is one of the standard diagnostic methods for screening various diseases, including cancer. These biological samples are usually imaged using traditional optical microscopy tools; however, the high cost, bulkiness and limited imaging throughput of traditional microscopes partially restrict their deployment in resource-limited settings. In order to mitigate this, we previously demonstrated a cost-effective and compact lens-less on-chip microscopy platform with a wide field-of-view of >20-30 mm^2. The lens-less microscopy platform has shown its effectiveness for imaging of highly connected biological samples, such as pathology slides of various tissue samples and smears, among others. This computational holographic microscope requires a set of super-resolved holograms acquired at multiple sample-to-sensor distances, which are used as input to an iterative phase recovery algorithm and holographic reconstruction process, yielding high-resolution images of the samples in phase and amplitude channels. Here we demonstrate that in order to reconstruct clinically relevant images with high resolution and image contrast, we require less than 50% of the previously reported nominal number of holograms acquired at different sample-to-sensor distances. This is achieved by incorporating a loose sparsity constraint as part of the iterative holographic object reconstruction. We demonstrate the success of this sparsity-based computational lens-less microscopy platform by imaging pathology slides of breast cancer tissue and Papanicolaou (Pap) smears.

The limited space-bandwidth-product of microscopy systems in general forces users to sacrifice either resolution or field-of-view (FOV). Here we introduce a wide-field and high-resolution imaging method that uses a stack of out-of-focus images of the specimen to increase the space-bandwidth-product of lens-based microscopes. Although modern microscope objective-lenses are designed for high-resolution imaging and can achieve a relatively large FOV, often the active area of the imager chip sets a limitation. To best utilize the full field-of-view of an objective-lens in our microscope, we first added a demagnification camera adaptor (e.g., 0.35×) to match the CCD sensor chip active area to the FOV of a 10X objective-lens (~5 mm2) that has an NA of 0.3. This demagnification, while increasing the FOV, downgrades the image resolution and results in pixelation. We illustrate that this spatial undersampling can be overcome through an iterative pixel super-resolution algorithm that uses a stack of out-of-focus images of the sample to restore a high-resolution image across a large FOV. We demonstrated the success of this approach using a resolution test-target and showed that our technique reduces the number of measurements required to achieve the same effective space-bandwidth-product using e.g., lateral scanning and digital stitching of different FOVs. Phase retrieval capability of this approach is also demonstrated by reconstructing unstained Papanicolaou (Pap) smear samples without the need for phase-contrast objective-lenses. This technique might be useful to maximize the throughput of lens-based optical imaging systems and inspire new microscopy designs that utilize auto-focusing steps to increase resolution.

We developed two types of cantilever-based biosensors for portable diagnostics applications. One sensor is based on MEMS cantilever chip mounted in a microfluidic channel and the other sensor is based on a movable optical fiber placed across a microfluidic channel. Both types of sensors were aimed at direct mechanical measurement of coagulation time in a disposable cartridge using plasma or whole blood samples. There are several similarities and also some important differences between the MEMS based and the optical fiber based solutions. The aim of this paper is to provide a comparison between the two solutions and the results. For both types of sensors, actuation of the cantilever or the moving fiber is achieved using an electro coil and the readout is optical. Since both the actuation and sensing are remote, no electrical connections are required for the cartridge. Therefore it is possible to build low cost disposable cartridges. The reader unit for the cartridge contains light sources, photodetectors, the electro coil, a heater, analog electronics, and a microprocessor. The reader unit has different optical interfaces for the cartridges that have MEMS cantilevers and moving fibers. MEMS based platform has better sensitivity but optomechanical alignment is a challenge and measurements with whole blood were not possible due to high scattering of light by the red blood cells. Fiber sensor based platform has relaxed optomechanical tolerances, ease of manufacturing, and it allows measurements in whole blood. Both sensors were tested using control plasma samples for activated-Partial-Thromboplastin-Time (aPTT) measurements. Control plasma test results matched with the manufacturer’s datasheet. Optical fiber based system was tested for aPTT tests with human whole blood samples and the proposed platform provided repeatable test results making the system method of choice for portable diagnostics.

Digital pathology and telepathology require imaging tools with high-throughput, high-resolution and accurate color reproduction. Lens-free on-chip microscopy based on digital in-line holography is a promising technique towards these needs, as it offers a wide field of view (FOV >20 mm²) and high resolution with a compact, low-cost and portable setup. Color imaging has been previously demonstrated by combining reconstructed images at three discrete wavelengths in the red, green and blue parts of the visible spectrum, i.e., the RGB combination method. However, this RGB combination method is subject to color distortions. To improve the color performance of lens-free microscopy for pathology imaging, here we present a wavelet-based color fusion imaging framework, termed “digital color fusion microscopy” (DCFM), which digitally fuses together a grayscale lens-free microscope image taken at a single wavelength and a low-resolution and low-magnification color-calibrated image taken by a lens-based microscope, which can simply be a mobile phone based cost-effective microscope. We show that the imaging results of an H&E stained breast cancer tissue slide with the DCFM technique come very close to a color-calibrated microscope using a 40x objective lens with 0.75 NA. Quantitative comparison showed 2-fold reduction in the mean color distance using the DCFM method compared to the RGB combination method, while also preserving the high-resolution features of the lens-free microscope. Due to the cost-effective and field-portable nature of both lens-free and mobile-phone microscopy techniques, their combination through the DCFM framework could be useful for digital pathology and telepathology applications, in low-resource and point-of-care settings.

Mobile phone cameras employ sensors with near-infrared (NIR) sensitivity, yet this capability has not been exploited for biomedical purposes. Removing the IR-blocking filter from a phone-based camera opens the door to a wide range of techniques and applications for inexpensive, point-of-care biophotonic imaging and sensing. This study provides proof of principle for one of these modalities – phone-based NIR fluorescence imaging. An imaging system was assembled using a 780 nm light source along with excitation and emission filters with 800 nm and 825 nm cut-off wavelengths, respectively. Indocyanine green (ICG) was used as an NIR fluorescence contrast agent in an ex vivo rodent model, a resolution test target and a 3D-printed, tissue-simulating vascular phantom. Raw and processed images for red, green and blue pixel channels were analyzed for quantitative evaluation of fundamental performance characteristics including spectral sensitivity, detection linearity and spatial resolution. Mobile phone results were compared with a scientific CCD. The spatial resolution of CCD system was consistently superior to the phone, and green phone camera pixels showed better resolution than blue or green channels. The CCD exhibited similar sensitivity as processed red and blue pixels channels, yet a greater degree of detection linearity. Raw phone pixel data showed lower sensitivity but greater linearity than processed data. Overall, both qualitative and quantitative results provided strong evidence of the potential of phone-based NIR imaging, which may lead to a wide range of applications from cancer detection to glucose sensing.

Multispectral imaging of human tissue is a powerful method that allows for quantify scattering and absorption parameters of the tissue and differentiate tissue types or identify pathology. This method requires imaging at multiple wavelengths and then fitting the measured data to a model based on light transport theory. Earlier, a mobile phone based multi-spectral imaging system was developed to image the uterine cervix from the colposcopy geometry, outside the patient’s body at a distance of 200-300 mm. Such imaging of a distance object has inherent challenges, as bright and homogenous illumination is required. Several solutions addressing this problem were developed, with varied degrees of success. In this paper, several multi-spectral illumination setups were developed and tested for brightness and uniformity. All setups were specifically designed with low cost in mind, utilizing a printed circuit board with surface-mounted LEDs. The three setups include: LEDs illuminating the target directly, LEDs illuminating focused by a 3D printed miniature lens array, and LEDs coupled to a mixing lens and focusing optical system. In order to compare the illumination uniformity and intensity performance two experiments were performed. Test results are presented, and various tradeoffs between the three system configurations are discussed. Test results are presented, and various tradeoffs between the three system configurations are discussed.

The need for sensitive, portable diagnostic tests at the point of care persists. We report on a simple method to obtain improved detection of biomolecules by a two-fold mechanism. Silica (SiO2) is coated on pre-stressed thermoplastic shrink-wrap film. When the film retracts, the resulting micro- and nanostructures yield far-field fluorescence signal enhancements over their planar or wrinkled counterparts. Because the film shrinks by 95% in surface area, there is also a 20x concentration effect. The SiO2 structured substrate is therefore used for improved detection of labeled proteins and DNA hybridization via both fluorescent and bright field. Through optical characterization studies, we attribute the fluorescence signal enhancements of ~100x to increased surface density and light scattering from the rough SiO2 structures. Combining with our open channel self-wicking microfluidics, we can achieve extremely low cost yet sensitive point of care diagnostics.

Infants in the US are routinely screened for risk of neurodevelopmental impairment due to neonatal jaundice using transcutaneous bilirubinometry (TcB). In low-resource settings, such as sub-Saharan Africa, TcB devices are not common, however, mobile camera-phones are now widespread. We provide an update on the development of TcB using the built-in camera and flash of a mobile phone, along with a snap-on adapter containing optical filters. We will present Monte Carlo Extreme modeling of diffuse reflectance in neonatal skin, implications in design, and refined analysis methods.

We have presented the plastic based ultra-compact aspheric lens for disposable epidural spinal endoscope. We have also showed the analysis of the stray light distribution on the image plane using optical illumination system design software (Light Tools). The optical system consists of the aspheric lens with a size of 1.4mm (total track of optical system). The effective length and field of view (FOV) is 0.66mm and 90 degrees. The distortion of the optical system is below 25%. The curves of modulation transfer function (MTF) are higher than 0.3 at 80 line pairs/mm (lps/mm) in image space. For the analysis of stray light, we assumed that the 98 percent of incident light is absorbed inside lens barrel and the rest is scattered on the inner surfaces of the lens barrel. The average value of stray light is 0.16% in the image intensity. The maximum stray light and minimum stray light of the proposed optical system is 0.57% and 0.0005% in the image intensity, respectively. The effective transmission rate of the proposed optical system is 89.6%.

An ideal laser is a useful tool for the analysis of biological systems. In particular, the polarization property of lasers can allow for the concentration of important organic molecules in the human body, such as proteins, amino acids, lipids, and carbohydrates, to be estimated. However, lasers do not always work as intended and there can be effects such as mode hopping and thermal drift that can cause time-varying intensity fluctuations. The causes of these effects can be from the surrounding environment, where either an unstable current source is used or the temperature of the surrounding environment is not temporally stable. This intensity fluctuation can cause bias and error in typical organic molecule concentration estimation techniques. In a low-resource setting where cost must be limited and where environmental factors, like unregulated power supplies and temperature, cannot be controlled, the hardware required to correct for these intensity fluctuations can be prohibitive. We propose a method for computational laser intensity stabilisation that uses Bayesian state estimation to correct for the time-varying intensity fluctuations from electrical and thermal instabilities without the use of additional hardware. This method will allow for consistent intensities across all polarization measurements for accurate estimates of organic molecule concentrations.

Portable and low-cost optical coherence tomography (OCT) is increasingly used to improve the accuracy of point-of-care applications. The increasing research efforts to use photonics integrated circuits (PIC) is without doubts the future for highest packaging densities in miniature optical systems. MR-OCT is another technology that is using the advantages of well known CD/DVD-ROM technology to build miniaturized and low-cost OCT systems and may be more readily available before PICs reach their full potential. For MR-OCT it is essential to separate the the multiple signals originating from the multiple reflections of the partial mirror in the reference arm of Michelson interferometer. An image analysis on MR-OCT scans is performed for filter types such as a Chebychev2 and an elliptic filter, but also an FFT filter with Gaussian window is discussed. The SNR is compared on a variety of test signals generated with a mirror in the sample arm at different attenuation levels and the CNR is used to compare the image quality.

Video Photoplethysmography (VPPG) is a numerical technique to process standard RGB video data of exposed human skin and extracting the heart-rate (HR) from the skin areas. Being a non-contact technique, VPPG has the potential to provide estimates of subject’s heart-rate, respiratory rate, and even the heart rate variability of human subjects with potential applications ranging from infant monitors, remote healthcare and psychological experiments, particularly given the non-contact and sensor-free nature of the technique. Though several previous studies have reported successful correlations in HR obtained using VPPG algorithms to HR measured using the gold-standard electrocardiograph, others have reported that these correlations are dependent on controlling for duration of the video-data analyzed, subject motion, and ambient lighting. Here, we investigate the ability of two commonly used VPPG-algorithms in extraction of human heart-rates under three different laboratory conditions. We compare the VPPG HR values extracted across these three sets of experiments to the gold-standard values acquired by using an electrocardiogram or a commercially available pulseoximeter. The two VPPG-algorithms were applied with and without KLT-facial feature tracking and detection algorithms from the Computer Vision MATLAB® toolbox. Results indicate that VPPG based numerical approaches have the ability to provide robust estimates of subject HR values and are relatively insensitive to the devices used to record the video data. However, they are highly sensitive to conditions of video acquisition including subject motion, the location, size and averaging techniques applied to regions-of-interest as well as to the number of video frames used for data processing.

In the present work, the use of smartphone for colorimetric quantification of biomolecules has been demonstrated. As a proof-of-concept, BSA protein and carbohydrate have been used as biomolecular sample. BSA protein and carbohydrate at different concentrations have been treated with Lowry's reagent and Anthrone's reagent respectively . The change in color of the reagent-treated samples at different concentrations have been recorded with the camera of a smartphone in combination with a custom designed optomechanical hardware attachment. This change in color of the reagent-treated samples has been correlated with color channels of two different color models namely RGB (Red Green Blue) and HSV (Hue Saturation and Value) model. In addition to that, the change in color intensity has also been correlated with the grayscale value for each of the imaged sample. A custom designed android app has been developed to quantify the bimolecular concentration and display the result in the phone itself. The obtained results have been compared with that of standard spectrophotometer usually considered for the purpose and highly reliable data have been obtained with the designed sensor. The device is robust, portable and low cost as compared to its commercially available counterparts. The data obtained from the sensor can be transmitted to anywhere in the world through the existing cellular network. It is envisioned that the designed sensing device would find wide range of applications in the field of analytical and bioanalytical sensing research.