Acoustic Photometry of Biomedical Parameters for Association with Diabetes and Covid-19

Because of their mortality rate, diabetes and COVID-19 are serious diseases. Moreover, people with diabetes are at a higher risk of developing COVID-19 complications. This article therefore proposes a single, non-invasive system that can help people with diabetes and COVID-19 to monitor their health parameters by measuring oxygen saturation (SPO2), heart rate, and body temperature. This is in contrast to other pulse oximeters and previous work reported in the literature. A Max30102 sensor, consisting of two light-emitting diodes (LEDs), can serve as a transmission spectrum to enable three synchronous parameter measurements. Hence, the Max30102 sensor facilitates identification of the relationship between COVID-19 and diabetes in a cost-effective manner. Fifty subjects (20 healthy, 20 diabetic, and 10 with COVID-19), aged 18-61 years, were recruited to provide data on heart rate, body temperature, and oxygen saturation, measured in a variety of activities and scenarios. The results showed accuracy of ±97% for heart rate, ±98% for body temperature, and ±99% for oxygen saturation with an enhanced time efficiency of 5-7 seconds in contrast to a commercialized pulse oximeter, which took 10-12 seconds. The results were then compared with those of commercially available pulse oximetry (Oxitech Pulse Oximeter) and a thermometer (Medisana Infrared Thermometer). These results revealed that uncontrolled diabetes can be as dangerous as COVID-19 in terms of high resting heart rate and low oxygen saturation. Doi: 10.28991/esj-2022-SPER-04 Full Text: PD

Photoacoustic Image Analysis for Cancer Detection and Building a Novel Ultrasound Imaging System

Photoacoustic (PA) imaging is a rapidly emerging non-invasive soft tissue imaging modality which has the potential to detect tissue abnormality at early stage. Photoacoustic images map the spatially varying optical absorption property of tissue. In multiwavelength photoacoustic imaging, the soft tissue is imaged with different wavelengths, tuned to the absorption peaks of the specific light absorbing tissue constituents or chromophores to obtain images with different contrasts of the same tissue sample. From those images, spatially varying concentration of the chromophores can be recovered. As multiwavelength PA images can provide important physiological information related to function and molecular composition of the tissue, so they can be used for diagnosis of cancer lesions and differentiation of malignant tumors from benign tumors. In this research, a number of parameters have been extracted from multiwavelength 3D PA images of freshly excised human prostate and thyroid specimens, imaged at five different wavelengths. Using marked histology slides as ground truths, region of interests (ROI) corresponding to cancer, benign and normal regions have been identified in the PA images. The extracted parameters belong to different categories namely chromophore concentration, frequency parameters and PA image pixels and they represent different physiological and optical properties of the tissue specimens. Statistical analysis has been performed to test whether the extracted parameters are significantly different between cancer, benign and normal regions. A multidimensional [29 dimensional] feature set, built with the extracted parameters from the 3D PA images, has been divided randomly into training and testing sets. The training set has been used to train support vector machine (SVM) and neural network (NN) classifiers while the performance of the classifiers in differentiating different tissue pathologies have been determined by the testing dataset. Using the NN classifier, performance of parameters belonging to different categories in differentiating malignant tissue from nonmalignant tissue has been determined. It has been found that, among different categories, the frequency parameters performed best in differentiating malignant from nonmalignant tissue [sensitivity and specificity with testing dataset are 85% and 84%] while performance of all the categories combined was better than that [sensitivity and specificity with testing dataset are 93% and 91%]. However, PA imaging cannot be used to provide the anatomical cues required to determine the position of the detected or suspected malignant tumor region relative to familiar organ landmarks. On the other hand, although accuracy of Ultrasound (US) imaging in detecting cancer lesions is low, major anatomical cues like organ boundaries or presence of nearby major organs are visible in US images. A dual mode PA and US imaging system can potentially detect as well as localize cancer lesions with high accuracy. In this study, we have developed a novel pulse echo US imaging system which can be easily integrated with our existing ex-vivo PA imaging system to produce the dual mode imaging system. Here a Polyvinylidene fluoride (PVDF) film has been used as US transmitter. To improve the anticipated low signal to noise ratio (SNR) of the received US signal due to the low electromechanical coupling coefficient of the PVDF film, we implemented pulse compression technique using chirp signals. Comparisons among the different SNR values obtained with short pulse and after pulse compression with chirp signal show a clear improvement of the SNR for the compressed pulse. The axial resolution of the imaging system improved with increasing sweep bandwidth of input chirp signals, whereas the lateral resolution remained almost constant. This work demonstrates the feasibility of using a PVDF film transducer as an US transmitter and implementing pulse compression technique in an acoustic lens focusing based imaging system

On the Selectivity of Planar Microwave Glucose Sensors with Multicomponent Solutions

The development of glucose concentration sensors by means of microwave planar resonant technology is an active field attracting considerable attention from the scientific community. Although showing promising results, the current experimental sensors are facing some fundamental challenges. Among them, the most critical one seems to be the selectivity of glucose concentration against the variations of the concentrations of other components or parameters. In this article, we investigate the selectivity of microwave planar resonant sensors when measuring multicomponent solutions. Three sensors are involved, two of them having been designed looking for a more simplified system with a reduced size, and the third one has been specially developed to improve the sensitivity. The performance of these sensors is thoroughly assessed with a large set of measurements involving multicomponent solutions composed of pure water, NaCl, albumin at different concentrations and glucose at different concentrations. The impact of the simultaneous variations of the concentrations of glucose and albumin on the final measurements is analyzed, and the effective selectivity of the sensors is discussed. The results show a clear influence of the albumin concentration on the measurements of the glucose concentration, thereby pointing to a lack of selectivity for all sensors. This influence has been modeled, and strategies to manage this selectivity challenge are inferredThis research was partially funded by AEI (Spanish Research State Agency) through the Race project (reference PID2019-111023RB-C32). The work of C.G.J. was funded by the Ministry of Universities in the Government of Spain, the European Union–NextGenerationEU and the Miguel Hernández University of Elche through the Margarita Salas postdoctoral program, and also by Conselleria d’Innovació, Universitats, Ciència i Societat Digital in Generalitat Valenciana (Government of Valencia Region) and European Social Fund through the APOSTD postdoctoral program, grant number CIAPOS/2021/267. Partial funding for open access charge: Universidad de Málaga

Optical Methods in Sensing and Imaging for Medical and Biological Applications

The recent advances in optical sources and detectors have opened up new opportunities for sensing and imaging techniques which can be successfully used in biomedical and healthcare applications. This book, entitled ‘Optical Methods in Sensing and Imaging for Medical and Biological Applications’, focuses on various aspects of the research and development related to these areas. The book will be a valuable source of information presenting the recent advances in optical methods and novel techniques, as well as their applications in the fields of biomedicine and healthcare, to anyone interested in this subject

Multispectral imaging for preclinical assessment of rheumatoid arthritis models

Rheumatoid arthritis (RA) is a chronic inflammatory autoimmune condition affecting multiple body systems. Murine models of RA are vital in progressing understanding of the disease. The severity of arthritis symptoms is currently assessed in vivo by observations and subjective scoring which are time-consuming and prone to bias and inaccuracy. The main aim of this thesis is to determine whether multispectral imaging of murine arthritis models has the potential to assess the severity of arthritis symptoms in vivo in an objective manner. Given that pathology can influence the optical properties of a tissue, changes may be detectable in the spectral response. Monte Carlo modelling of reflectance and transmittance for varying levels of blood volume fraction, blood oxygen saturation, and water percentage in the mouse paw tissue demonstrated spectral changes consistent with the reported/published physiological markers of arthritis. Subsequent reflectance and transmittance in vivo spectroscopy of the hind paw successfully detected significant spectral differences between normal and arthritic mice. Using a novel non-contact imaging system, multispectral reflectance and transmittance images were simultaneously collected, enabling investigation of arthritis symptoms at different anatomical paw locations. In a blind experiment, Principal Component (PC) analysis of four regions of the paw was successful in identifying all 6 arthritic mice in a total sample of 10. The first PC scores for the TNF dARE arthritis model were found to correlate significantly with bone erosion ratio results from microCT, histology scoring, and the manual scoring method. In a longitudinal study at 5, 7 and 9 weeks the PC scores identified changes in spectral responses at an early stage in arthritis development for the TNF dARE model, before clinical signs were manifest. Comparison of the multispectral image data with the Monte Carlo simulations suggest that in this study decreased oxygen saturation is likely to be the most significant factor differentiating arthritic mice from their normal littermates. The results of the experiments are indicative that multispectral imaging performs well as an assessor of arthritis for RA models and may outperform existing techniques. This has implications for better assessment of preclinical arthritis and hence for better experimental outcomes and improvement of animal welfare

Molecular Imaging

The present book gives an exceptional overview of molecular imaging. Practical approach represents the red thread through the whole book, covering at the same time detailed background information that goes very deep into molecular as well as cellular level. Ideas how molecular imaging will develop in the near future present a special delicacy. This should be of special interest as the contributors are members of leading research groups from all over the world

Harnessing label-free Raman spectroscopy for metastatic cancer diagnosis and biologic development

Optical spectroscopy is unique amongst experimental techniques in that it can be performed in near-physiological conditions, achieve high molecular specificity, and explore dynamics on timescales ranging from nanoseconds to days. In particular, Raman spectroscopy has emerged in the last two decades as a uniquely versatile method to investigate the structures and properties of molecules in diverse environments through interpreting vibrational transitions. In this thesis, we present four interconnected biomedical and biopharmaceutical applications of Raman spectroscopy that exploit its exquisite molecular specificity, non-perturbative nature, and near real-time measurement capability. In the first presented study, we harness spontaneous Raman spectroscopy in conjunction with multivariate analysis to rapidly and quantitatively determine antibody-drug conjugate aggregation with the goal of eventual application as an in-line tool for monitoring protein particle formation. By exploring subtle, but consistent, differences in spectral vibrational modes of various monoclonal antibodies (mAb) aggregations, a support vector machine-based regression model is developed which is able to accurately predict a wide range of protein aggregation. In addition, the investigation of these spectral vibrational modes also offers new insights into mAb product-specific aggregation mechanisms. Second, leveraging surface-enhanced Raman scattering (SERS) and localized surface plasmon resonance (LSPR), we present a design of plasmonic nanostructures based on rationally structured metal-dielectric combinations, which we call composite scattering probes (CSP). Specifically, we design CSP configurations that have several prominent resonance peaks enabling higher tunability and sensitivity for self-referenced multiplexed analyte sensing. The CSP prototypes were used to demonstrate differentiation of subtle changes in refractive index (as low as 0.001) as well as acquire complementary untargeted plasmon-enhanced Raman measurements from the biospecimen’s compositional contributors. In the third study, we demonstrate that Raman spectroscopy offers vital biomolecular information for early diagnosis and precise localization of breast cancer-colonized bone alterations. We show that as early as two weeks after intracardiac injections of breast cancer cells in mouse models, Raman measurements in femur and spine uncover consistent changes in both bone matrix and mineral composition. This research effort opens the door for improved understanding of breast metastatic tumor-related bone remodeling and establishing a non-invasive tool for detection of early metastasis and prediction of fracture risk. In parallel with this effort, we also seek to identify the differences between organ-specific isogenic metastatic breast cancer cells. By interpreting the informative spectral bands, we are able to unambiguously identify these isogenic cell lines as unique biological entities. Our spectroscopic study and corresponding metabolic research indicate that tissue-specific adaptations generate biomolecular alterations on cancer cells