Feature-Based Classification of Mild Cognitive Impairment and Alzheimer's Disease Based on Optical Coherence Tomographic Angiographic Image.

Alzheimer's disease is a type of neurodegenerative disorder that is characterized by the progressive degeneration of brain cells, leading to cognitive decline and memory loss. It is the most common cause of dementia and affects millions of people worldwide. While there is currently no cure for Alzheimer's disease, early detection and treatment can help to slow the progression of symptoms and improve quality of life. This research presents a diagnostic tool for classifying mild cognitive impairment and Alzheimer's diseases using feature-based machine learning applied to optical coherence tomographic angiography images (OCT-A). Several features are extracted from the OCT-A image, including vessel density in five sectors, the area of the foveal avascular zone, retinal thickness, and novel features based on the histogram of the range-filtered OCT-A image. To ensure effectiveness for a diverse population, a large local database for our study was collected. The promising results of our study, with the best accuracy of 92.17,% will provide an efficient diagnostic tool for early detection of Alzheimer's disease.

Cry4Aa and Cry4Ba Mosquito-Active Toxins Utilize Different Domains in Binding to a Particular Culex ALP Isoform: A Functional Toxin Receptor Implicating Differential Actions on Target Larvae.

The three-domain Cry4Aa toxin produced from Bacillus thuringiensis subsp. israelensis was previously shown to be much more toxic to Culex mosquito larvae than its closely related toxin-Cry4Ba. The interaction of these two individual toxins with target receptors on susceptible larval midgut cells is likely to be the critical determinant in their differential toxicity. Here, two full-length membrane-bound alkaline phosphatase (mALP) isoforms from Culex quinquefasciatus larvae, Cq-mALP1263and Cq-mALP1264, predicted to be GPI-linked was cloned and functionally expressed in Spodoptera frugiperda (Sf9) cells as 57- and 61-kDa membrane-bound proteins, respectively. Bioinformatics analysis disclosed that both Cq-mALP isoforms share significant sequence similarity to Aedes aegypti-mALP-a Cry4Ba toxin receptor. In cytotoxicity assays, Sf9 cells expressing Cq-mALP1264, but not Cq-mALP1263, showed remarkably greater susceptibility to Cry4Aa than Cry4Ba, while immunolocalization studies revealed that both toxins were capable of binding to each Cq-mALP expressed on the cell membrane surface. Molecular docking of the Cq-mALP1264-modeled structure with individual Cry4 toxins revealed that Cry4Aa could bind to Cq-mALP1264 primarily through particular residues on three surface-exposed loops in the receptor-binding domain-DII, including Thr512, Tyr513 and Lys514 in the ß10-ß11loop. Dissimilarly, Cry4Ba appeared to utilize only certain residues in its C-terminal domain-DIII to interact with such a Culex counterpart receptor. Ala-substitutions of selected ß10-ß11loop residues (T512A, Y513A and K514A) revealed that only the K514A mutant displayed a drastic decrease in biotoxicity against C. quinquefasciatus larvae. Further substitution of Lys514 with Asp (K514D) revealed a further decrease in larval toxicity. Furthermore, in silico calculation of the binding affinity change (ΔΔGbind) in Cry4Aa-Cq-mALP1264 interactions upon these single-substitutions revealed that the K514D mutation displayed the largest ΔΔGbind value as compared to three other mutations, signifying an adverse impact of a negative charge at this critical receptor-binding position. Altogether, our present study has disclosed that these two related-Cry4 mosquito-active toxins conceivably exploited different domains in functional binding to the same Culex membrane-bound ALP isoform-Cq-mALP1264 for mediating differential toxicity against Culex target larvae.

Biosensors for Klebsiella pneumoniae with Molecularly Imprinted Polymer (MIP) Technique.

Nosocomial infection is one of the most important problems that occurs in hospitals, as it directly affects susceptible patients or patients with immune deficiency. Klebsiella pneumoniae (K. pneumoniae) is the most common cause of nosocomial infections in hospitals. K. pneumoniae can cause various diseases such as pneumonia, urinary tract infections, septicemias, and soft tissue infections, and it has also become highly resistant to antibiotics. The principal routes for the transmission of K. pneumoniae are via the gastrointestinal tract and the hands of hospital personnel via healthcare workers, patients, hospital equipment, and interventional procedures. These bacteria can spread rapidly in the hospital environment and tend to cause nosocomial outbreaks. In this research, we developed a MIP-based electrochemical biosensor to detect K. pneumoniae. Quantitative detection was performed using an electrochemical technique to measure the changes in electrical signals in different concentrations of K. pneumoniae ranging from 10 to 105 CFU/mL. Our MIP-based K. pneumoniae sensor was found to achieve a high linear response, with an R2 value of 0.9919. A sensitivity test was also performed on bacteria with a similar structure to that of K. pneumoniae. The sensitivity results show that the MIP-based K. pneumoniae biosensor with a gold electrode was the most sensitive, with a 7.51 (% relative current/log concentration) when compared with the MIP sensor applied with Pseudomonas aeruginosa and Enterococcus faecalis, where the sensitivity was 2.634 and 2.226, respectively. Our sensor was also able to achieve a limit of detection (LOD) of 0.012 CFU/mL and limit of quantitation (LOQ) of 1.61 CFU/mL.

Analysis of Deep Learning-Based Phase Retrieval Algorithm Performance for Quantitative Phase Imaging Microscopy.

Quantitative phase imaging has been of interest to the science and engineering community and has been applied in multiple research fields and applications. Recently, the data-driven approach of artificial intelligence has been utilized in several optical applications, including phase retrieval. However, phase images recovered from artificial intelligence are questionable in their correctness and reliability. Here, we propose a theoretical framework to analyze and quantify the performance of a deep learning-based phase retrieval algorithm for quantitative phase imaging microscopy by comparing recovered phase images to their theoretical phase profile in terms of their correctness. This study has employed both lossless and lossy samples, including uniform plasmonic gold sensors and dielectric layer samples; the plasmonic samples are lossy, whereas the dielectric layers are lossless. The uniform samples enable us to quantify the theoretical phase since they are established and well understood. In addition, a context aggregation network has been employed to demonstrate the phase image regression. Several imaging planes have been simulated serving as input and the label for network training, including a back focal plane image, an image at the image plane, and images when the microscope sample is axially defocused. The back focal plane image plays an essential role in phase retrieval for the plasmonic samples, whereas the dielectric layer requires both image plane and back focal plane information to retrieve the phase profile correctly. Here, we demonstrate that phase images recovered using deep learning can be robust and reliable depending on the sample and the input to the deep learning.

Measurement precision enhancement of surface plasmon resonance based angular scanning detection using deep learning.

Angular scanning-based surface plasmon resonance measurement has been utilized in label-free sensing applications. However, the measurement accuracy and precision of the surface plasmon resonance measurements rely on an accurate measurement of the plasmonic angle. Several methods have been proposed and reported in the literature to measure the plasmonic angle, including polynomial curve fitting, image processing, and image averaging. For intensity detection, the precision limit of the SPR is around 10-5 RIU to 10-6 RIU. Here, we propose a deep learning-based method to locate the plasmonic angle to enhance plasmonic angle detection without needing sophisticated post-processing, optical instrumentation, and polynomial curve fitting methods. The proposed deep learning has been developed based on a simple convolutional neural network architecture and trained using simulated reflectance spectra with shot noise and speckle noise added to generalize the training dataset. The proposed network has been validated in an experimental setup measuring air and nitrogen gas refractive indices at different concentrations. The measurement precision recovered from the experimental reflectance images is 4.23 × 10-6 RIU for the proposed artificial intelligence-based method compared to 7.03 × 10-6 RIU for the cubic polynomial curve fitting and 5.59 × 10-6 RIU for 2-dimensional contour fitting using Horner's method.

Analysis of the surface plasmon resonance interferometric imaging performance of scanning confocal surface plasmon microscopy.

Here, we apply rigorous coupled-wave theory to analyze the optical phase imaging performance of scanning confocal surface plasmon microscope. The scanning confocal surface plasmon resonance microscope is an embedded interferometric microscope interfering between two integrated optical beams. One beam is provided by the central part around the normal incident angle of the back focal plane, and the other beam is the incident angles beyond the critical angle, exciting the surface plasmon. Furthermore, the two beams can form an interference signal inside a confocal pinhole in the image plane, which provides a well-defined path for the surface plasmon propagation. The scanning confocal surface plasmon resonance microscope operates by scanning the sample along the optical axis z, so-called V(z). The study investigates two imaging modes: non-quantitative imaging and quantitative imaging modes. We also propose a theoretical framework to analyze the scanning confocal surface plasmon resonance microscope compared to non-interferometric surface plasmon microscopes and quantify quantitative performance parameters including spatial resolution and optical contrast for non-quantitative imaging; sensitivity and crosstalk for quantitative imaging. The scanning confocal SPR microscope can provide a higher spatial resolution, better sensitivity, and lower crosstalk measurement. The confocal SPR microscope configuration is a strong candidate for high throughput measurements since it requires a smaller sensing channel than the other SPR microscopes.

Bone Mineral Density Screening System Using CMOS-Sensor X-ray Detector.

This research concerns a design and construction of a bone mineral density (BMD) and bone mineral content (BMC) measurement system based on dual energy X-ray absorptiometry (DEXA). An indirect X-ray detector is designed by optical coupling CMOS sensor with image on the intensifying screen. A dedicated microcontroller X-ray apparatus is used as an X-ray source to capture two energy level X-ray of middle phalanges bone of middle finger. The captured image is processed based on modified Beer-Lambert law to compute bone mineral density. Bone mineral content is also computed by determining the area of the phalanges bone using active contour. The designed bone mineral density (BMD) and bone mineral content (BMC) measurement system is low-cost and hence can be distributed at district hospital for screening purposes of Osteoporosis of the elderly. Compared with BMD measured from commercial model, BMD measurement of our system acquires linear relation with R2 equals 0.969. The mean square error between the normalized BMD value and that of the commercial model is 0.0000981.

Deep learning-based single-shot phase retrieval algorithm for surface plasmon resonance microscope based refractive index sensing application.

A deep learning algorithm for single-shot phase retrieval under a conventional microscope is proposed and investigated. The algorithm has been developed using the context aggregation network architecture; it requires a single input grayscale image to predict an output phase profile through deep learning-based pattern recognition. Surface plasmon resonance imaging has been employed as an example to demonstrate the capability of the deep learning-based method. The phase profiles of the surface plasmon resonance phenomena have been very well established and cover ranges of phase transitions from 0 to 2π rad. We demonstrate that deep learning can be developed and trained using simulated data. Experimental validation and a theoretical framework to characterize and quantify the performance of the deep learning-based phase retrieval method are reported. The proposed deep learning-based phase retrieval performance was verified through the shot noise model and Monte Carlo simulations. Refractive index sensing performance comparing the proposed deep learning algorithm and conventional surface plasmon resonance measurements are also discussed. Although the proposed phase retrieval-based algorithm cannot achieve a typical detection limit of 10-7 to 10-8 RIU for phase measurement in surface plasmon interferometer, the proposed artificial-intelligence-based approach can provide at least three times lower detection limit of 4.67 × 10-6 RIU compared to conventional intensity measurement methods of 1.73 × 10-5 RIU for the optical energy of 2500 pJ with no need for sophisticated optical interferometer instrumentation.

Performance Analysis of Non-Interferometry Based Surface Plasmon Resonance Microscopes.

Surface plasmon microscopy has been of interest to the science and engineering community and has been utilized in broad aspects of applications and studies, including biochemical sensing and biomolecular binding kinetics. The benefits of surface plasmon microscopy include label-free detection, high sensitivity, and quantitative measurements. Here, a theoretical framework to analyze and compare several non-interferometric surface plasmon microscopes is proposed. The scope of the study is to (1) identify the strengths and weaknesses in each surface plasmon microscopes reported in the literature; (2) quantify their performance in terms of spatial imaging resolution, imaging contrast, sensitivity, and measurement accuracy for quantitative and non-quantitative imaging modes of the microscopes. Six types of non-interferometric microscopes were included in this study: annulus aperture scanning, half annulus aperture scanning, single-point scanning, double-point scanning, single-point scanning, at 45 degrees azimuthal angle, and double-point scanning at 45 degrees azimuthal angle. For non-quantitative imaging, there is a substantial tradeoff between the image contrast and the spatial resolution. For the quantitative imaging, the half annulus aperture provided the highest sensitivity of 127.058 rad/µm2 RIU-1, followed by the full annulus aperture of 126.318 rad/µm2 RIU-1. There is a clear tradeoff between spatial resolution and sensitivity. The annulus aperture and half annulus aperture had an optimal resolution, sensitivity, and crosstalk compared to the other non-interferometric surface plasmon resonance microscopes. The resolution depends strongly on the propagation length of the surface plasmons rather than the numerical aperture of the objective lens. For imaging and sensing purposes, the recommended microfluidic channel size and protein stamping size for surface plasmon resonance experiments is at least 25 µm for accurate plasmonic measurements.

Optical-Based Foot Plantar Pressure Measurement System for Potential Application in Human Postural Control Measurement and Person Identification.

Plantar pressure, the pressure exerted between the sole and the supporting surface, has great potentialities in various research fields, including footwear design, biometrics, gait analysis and the assessment of patients with diabetes. This research designs an optical-based foot plantar pressure measurement system aimed for human postural control and person identification. The proposed system consists of digital cameras installed underneath an acrylic plate covered by glossy white paper and mounted with LED strips along the side of the plate. When the light is emitted from the LED stripes, it deflects the digital cameras due to the pressure exerted between the glossy white paper and the acrylic plate. In this way, the cameras generate color-coded plantar pressure images of the subject standing on the acrylic-top platform. Our proposed system performs personal identification and postural control by extracting static and dynamic features from the generated plantar pressure images. Plantar pressure images were collected from 90 individuals (40 males, 50 females) to develop and evaluate the proposed system. In posture balance evaluation, we propose the use of a posture balance index that contains both magnitude and directional information about human posture balance control. For person identification, the experimental results show that our proposed system can achieve promising results, showing an area under the receiver operating characteristic (ROC) curve of 0.98515 (98.515%), an equal error rate (EER) of 5.8687%, and efficiency of 98.515%.

Automated Segmentation of Infarct Lesions in T1-Weighted MRI Scans Using Variational Mode Decomposition and Deep Learning.

Automated segmentation methods are critical for early detection, prompt actions, and immediate treatments in reducing disability and death risks of brain infarction. This paper aims to develop a fully automated method to segment the infarct lesions from T1-weighted brain scans. As a key novelty, the proposed method combines variational mode decomposition and deep learning-based segmentation to take advantages of both methods and provide better results. There are three main technical contributions in this paper. First, variational mode decomposition is applied as a pre-processing to discriminate the infarct lesions from unwanted non-infarct tissues. Second, overlapped patches strategy is proposed to reduce the workload of the deep-learning-based segmentation task. Finally, a three-dimensional U-Net model is developed to perform patch-wise segmentation of infarct lesions. A total of 239 brain scans from a public dataset is utilized to develop and evaluate the proposed method. Empirical results reveal that the proposed automated segmentation can provide promising performances with an average dice similarity coefficient (DSC) of 0.6684, intersection over union (IoU) of 0.5022, and average symmetric surface distance (ASSD) of 0.3932, respectively.

Single-reversal charge in the ß10-ß11 receptor-binding loop of Bacillus thuringiensis Cry4Aa and Cry4Ba toxins reflects their different toxicity against Culex spp. larvae.

Bacillus thuringiensis Cry4Aa toxin was previously shown to be much more toxic to Culex mosquito-larvae than its closely related toxin - Cry4Ba, conceivably due to their sequence differences within the ß10-ß11 receptor-binding loop. Here, single-Ala substitutions of five residues (Pro(510), Thr(512), Tyr(513), Lys(514) and Thr(515)) within the Cry4Aa ß10-ß11 loop revealed that only Lys(514) corresponding to the relative position of Cry4Ba-Asp(454) is crucial for toxicity against Culex quinquefasciatus larvae. Interestingly, charge-reversal mutations at Cry4Ba-Asp(454) (D454R and D454K) revealed a marked increase in toxicity against such less-susceptible larvae. In situ binding analyses revealed that both Cry4Ba-D454R and D454K mutants exhibited a significant increase in binding to apical microvilli of Culex larval midguts, albeit at lower-binding activity when compared with Cry4Aa. Altogether, our present data suggest that a positively charged side-chain near the tip of the ß10-ß11 loop plays a critical role in determining target specificity of Cry4Aa against Culex spp., and hence a great increase in the Culex larval toxicity of Cry4Ba was obtained toward an opposite-charge conversion of the corresponding Asp(454).