Nanoparticle Classification in Wide-field Interferometric Microscopy by Supervised Learning from Model

Interference enhanced wide-field nanoparticle imaging is a highly sensitive technique that has found numerous applications in labeled and label-free sub-diffraction-limited pathogen detection. It also provides unique opportunities for nanoparticle classification upon detection. More specif- ically, the nanoparticle defocus images result in a particle-specific response that can be of great utility for nanoparticle classification, particularly based on type and size. In this work, we com- bine a model based supervised learning algorithm with a wide-field common-path interferometric microscopy method to achieve accurate nanoparticle classification. We verify our classification schemes experimentally by using gold and polystyrene nanospheres.Comment: 5 pages, 2 figure

High-resolution Imaging of nanoparticles in wide-field interferometric scattering microscopy

Single particle interferometric scattering microscopy has demonstrated great capability in label-free imaging of sub-wavelength dielectric nanoparticles (r<25 nm); however, it suffers from diffraction-limited resolution. Here, we demonstrate ~2-fold improvement in lateral resolution upon asymmetric illumination.Published versio

Robust visualization and discrimination of nanoparticles by interferometric imaging

Single-molecule and single-nanoparticle biosensors are a growing frontier in diagnostics. Digital biosensors are those which enumerate all specifically immobilized biomolecules or biological nanoparticles, and thereby achieve limits of detection usually beyond the reach of ensemble measurements. Here we review modern optical techniques for single nanoparticle detection and describe the single-particle interferometric reflectance imaging sensor (SP-IRIS). We present challenges associated with reliably detecting faint nanoparticles with SP-IRIS, and describe image acquisition processes and software modifications to address them. Specifically, we describe a image acquisition processing method for the discrimination and accurate counting of nanoparticles that greatly reduces both the number of false positives and false negatives. These engineering improvements are critical steps in the translation of SP-IRIS towards applications in medical diagnostics.R01 AI096159 - NIAID NIH HHSFirst author draf

Advanced wide-field interferometric microscopy for nanoparticle sensing and characterization

Nanoparticles have a key role in today's biotechnological research owing to the rapid advancement of nanotechnology. While metallic, polymer, and semiconductor based artificial nanoparticles are widely used as labels or targeted drug delivery agents, labeled and label-free detection of natural nanoparticles promise new ways for viral diagnostics and therapeutic applications. The increasing impact of nanoparticles in bio- and nano-technology necessitates the development of advanced tools for their accurate detection and characterization. Optical microscopy techniques have been an essential part of research for visualizing micron-scale particles. However, when it comes to the visualization of individual nano-scale particles, they have shown inadequate success due to the resolution and visibility limitations. Interferometric microscopy techniques have gained significant attention for providing means to overcome the nanoparticle visibility issue that is often the limiting factor in the imaging techniques based solely on the scattered light. In this dissertation, we develop a rigorous physical model to simulate the single nanoparticle optical response in a common-path wide-field interferometric microscopy (WIM) system. While the fundamental elements of the model can be used to analyze nanoparticle response in any generic wide-field imaging systems, we focus on imaging with a layered substrate (common-path interferometer) where specular reflection of illumination provides the reference light for interferometry. A robust physical model is quintessential in realizing the full potential of an optical system, and throughout this dissertation, we make use of it to benchmark our experimental findings, investigate the utility of various optical configurations, reconstruct weakly scattering nanoparticle images, as well as to characterize and discriminate interferometric nanoparticle responses. This study investigates the integration of advanced optical schemes in WIM with two main goals in mind: (i) increasing the visibility of low-index nanoscale particles via pupil function engineering, pushing the limit of sensitivity; (ii) improving the resolution of sub-diffraction-limited, low-index particle images in WIM via reconstruction strategies for shape and orientation information. We successfully demonstrate an overall ten-fold improvement in the visibility of the low-index sub-wavelength nanoparticles as well as up to two-fold extended spatial resolution of the interference-enhanced nanoparticle images. We also systematically examine the key factors that determine the signal in WIM. These factors include the particle type, size, layered substrate design, defocus and nanoparticle polarizability. We use the physical model to demonstrate how these factors determine the signal levels, and demonstrate how the layered substrate can be designed to optimize the overall signal, while defocus scan can be used to maximize it, as well as its signature can be utilized for particle discrimination purposes for both dielectric particles and resonant metallic particles. We introduce a machine learning based particle characterization algorithm that relies on supervised learning from model. The particle characterization is limited to discrimination based on nanosphere size and type in the scope of this dissertation

Is My Model Up-to-date? Detecting CoViD-19 Variants by Machine Learning

Machine learning extracts models from huge quantities of data. Models trained and validated over past data can be deployed in making forecasts as well as in classifying new incoming data. The real world which generates data may change over time, making the deployed model an obsolete one. To preserve the quality of the currently deployed model, continuous machine learning is required. Our approach retrospectively evaluates in an online fashion the behaviour of the currently deployed model. A drift detector detects any performance slump, and, in case, can replace the previous model with an up-to-date one. The approach experiments on a dataset of 8642 hematochemical examinations from hospitalized patients gathered over 6 months: the outcome of the model predicts the RT-PCR test result about CoViD-19. The method reached an area under the curve (AUC) of 0.794, 6% better than offline and 5% better than standard online-binary classification techniques

Comparison of Explainable Artificial Intelligence Model and Radiologist Review Performances to Detect Breast Cancer in 752 Patients

Objectives Breast cancer is a type of cancer caused by the uncontrolled growth of cells in the breast tissue. In a few cases, erroneous diagnosis of breast cancer by specialists and unnecessary biopsies can lead to various negative consequences. In some cases, radiologic examinations or clinical findings may raise the suspicion of breast cancer, but subsequent detailed evaluations may not confirm cancer. In addition to causing unnecessary anxiety and stress to patients, such diagnosis can also lead to unnecessary biopsy procedures, which are painful, expensive, and prone to misdiagnosis. Therefore, there is a need for the development of more accurate and reliable methods for breast cancer diagnosis. Methods In this study, we proposed an artificial intelligence (AI)-based method for automatically classifying breast solid mass lesions as benign vs malignant. In this study, a new breast cancer dataset (Breast-XD) was created with 791 solid mass lesions belonging to 752 different patients aged 18 to 85 years, which were examined by experienced radiologists between 2017 and 2022. Results Six classifiers, support vector machine (SVM), K-nearest neighbor (K-NN), random forest (RF), decision tree (DT), logistic regression (LR), and XGBoost, were trained on the training samples of the Breast-XD dataset. Then, each classifier made predictions on 159 test data that it had not seen before. The highest classification result was obtained using the explainable XGBoost model (X2GAI) with an accuracy of 94.34%. An explainable structure is also implemented to build the reliability of the developed model. Conclusions The results obtained by radiologists and the X2GAI model were compared according to the diagnosis obtained from the biopsy. It was observed that our developed model performed well in cases where experienced radiologists gave false positive results

Determination of lipid peroxidation biomarkers in vero cell line inoculated with bovine ephemeral fever virus

Aim: The aim of the present study was to determine of lipid peroxidation biomarkers in Vero cell line inoculated with Bovine Ephemeral Fever Virus (BEFV, Genbank No: GQ229452.1). Materials and Methods: Cell supernatants were collected 4 h/day for 5 days after BEFV inoculation. Superoxide dismutase (SOD), catalase (CAT), glutathione peroxidase (GPX) enzymes, glutathione (GSH) and malondialdehyde (MDA) values were analyzed from the test media by commercially available ELISA kits. In addition to this, cytopathogenic effects (CPE) of BEFV in cell culture were evaluated periodically by invert microscope. Results: In this research, CPE of BEFV was observed at 72 h post-inoculation. Maximum level of SOD was determined at 56 h, while minimum levels of CAT and GPX were determined at 8 and 104 hours, respectively. Maximum GSH levels were determined at 30, 60, 84 and 120 hours while minimum GSH concentrations were measured at 44, 92 and 112 hours. A sudden decrease of MDA level was observed in the first 8 hours occurred. In addition, CAT, MDA and SOD levels decreased before developing BEFV-caused CPE. Conclusion: It is concluded that lipid peroxidation biomarkers can be useful in the pathogenesis of BEFV. It may prove helpful in the design of future protect from decreasing of oxidative damage associated with BEFV infection

Impact of deformed wing virus master variants (dwv-a, dwv-b, and dwv-c) in managed honey bee colonies of türkiye

Aim: This study aimed to determine the deformed wing virus (DWV) master variants in managed honey bee hives in Central Anatolia and the Mediterranean Regions of Türkiye. Also, the relationship of DWV genotypes circulating in the apiaries with clinical signs observed in honey bee hives was investigated. Materials and Methods: For this study, adult honey bees were collected from the same 25 hives in the spring-summer and autumn seasons of 2019 from the provinces of Aksaray, Isparta, Karaman, Konya and Nigde. DWV-specific nucleic acid and DWV genotypes were detected by DWV real-time RT-PCR assay and ABC assay, respectively. Results: Deformed wing virus infection was detected in each sampling season. While many colonies were without any clinical signs, in some of the apiaries where samples were collected, wing deformity, trembling, paralysis, swelling in the abdomen, loss of productivity, and dead bees were observed. The prevalences of DWV-A, DWV-B, and DWV-C in adult honey bees were 62%, 82%, and 24%, respectively. The dominant genotype detected in bee hives was the DWV-B master variant (98%). Also, the virus load of the DWV-A master variant was high in all of the honey bee hives with wintering losses. Conclusion: In this present study, data on the current status of DWV master variants circulating in Turkey and their impacts on honey bee colonies are reported for the first time. Thus, it is thought that DWV, which causes yield losses at varying rates in every season of the year in Turkish bee hives, should be carefully monitored

Serological and virological investigation of bovine viral diarrhea virus infection in cattle with abortion problem

Aim: The aim of this study is to determine the presence of Bovine Viral Diarrhea Virus (BVDV) infection in a cattle herd with abortion problem in Konya. Materials and Methods: Totally 228 blood serum and 228 leukocytes taken from cattle selected according to criteria for infertile and abortion problems were examined for antigens and antibodies to BVDV by Enzyme Linked Immunosorbent Assay. Results: In this research, 41 (17.9%) sera were found seropositive and 4 (1.7%) leukocytes were BVDV antigen positive. Of these 4 BVDV antigen positive cattle, a number of 2 (0.8%) were detected seropositive while 2 (0.8%) were seronegative. The animals being antigen positive and antibody negative were sampled second time after two weeks. The same results were detected for two seronegative cattle. The animals detecting persistent infection status were sent to slaughter. Conclusion: It is recommended that the animals should be checked in terms of BVDV for being negative both antigen and antibody before accepting them to the herds

The Serological and Virological Investigation of Canine Adenovirus Infection on the Dogs

Two types of Canine Adenovirus (CAVs), Canine Adenovirus type 1 (CAV-1), the virus which causes infectious canine hepatitis, and Canine Adenovirus type 2 (CAV-2), which causes canine infectious laryngotracheitis, have been found in dogs. In this study, blood samples taken from 111 dogs, which were admitted to the Internal Medicine Clinic of Selcuk University, Faculty of Veterinary Medicine, with clinical symptoms. Seventy-seven dogs were sampled from Isparta and Burdur dog shelters by random sampling, regardless of the clinical findings. Dogs showed a systemic disease, characterized by fever, diarrhea, vomiting, oculonasal discharge, conjunctivitis, severe moist cough, signs of pulmonary disease and dehydration. Two dogs had corneal opacity and photophobia. In serological studies, 188 serum samples were investigated on the presence of CAV antibodies by ELISA. Total 103 (103/188–54.7%) blood samples were detected to be positive for CAV antibodies by ELISA. However, 85 (85/188–45.2%) blood samples were negative. Blood leukocyte samples from dogs were processed and inoculated onto confluent monolayers of MDCK cells using standard virological techniques. After third passage, cells were examined by direct immunoflourescence test for virus isolation. But positive result was not detected. In conclusion, this study clearly demonstrates the high prevalence of CAV infection in dogs