Digital Image Processing And Metabolic Parameter Linearity To Noninvasively Detect Analyte Concentration

Spectroscopy is the scientific technique of quantifying and measuring electromagnetic, or light, reflectance or absorption. Atoms emit and/or absorb light when light passes through. The excitations provide specific energy signatures that relate to the element that is emitting or absorbing the light. Non-invasive biosensors monitor physical health properties such as heart rate, oxygen saturation, and tissue blood flow as a result of spectroscopy. Several attempts have been made to non-invasively detect metabolic chemical, or analyte, concentration with various spectroscopic techniques. The primary limitation is due to signal-to-noise ratio. This research focuses on a unique method that combines emission spectroscopy and machine learning to non-invasively detect glucose and other metabolic analyte concentrations. Artificial neural network is applied to train a predictive model that enables the remote sensing capability. The data acquisition requires capturing digital images of the spectral reflectance. Image processing and segmentation determines discrete variables that correlate with the metabolic analytes. The clinical trial protocol includes n=90 subjects, and a venipuncture comprehensive metabolic panel blood test within two minutes prior to a non-invasive spectral reading. Results indicate a strong correlation between the spectral system and the clinical gold standard, relative to metabolic analyte concentration

Genetic and autoimmune modulators of brain function in neuropsychiatric illness and health

In the present thesis, the synergetic interaction between the nervous and immune systems and the potential pathological outcomes mediated by autoimmune processes targeting the brain was addressed, with a particular focus on autoantibodies targeting NMDAR. The first two projects were designed to understand the role of these autoantibodies beyond this pathological condition and gain insight to its effects upon access to the brain. Specifically, Project I aimed at (i) determining the functional properties of NMDAR-ABs of different isotypes; for this purpose a new assay employing human induced pluripotent stem cell-derived neurons was developed. (ii) Identifying which NMDAR epitopes are recognized by these autoantibodies. Project II focused on (i) determining if these NMDAR-AB are present and functional in other mammal species; (ii) assessing the protective role of the BBB and the effects of endogenously produced NMDAR-AB on the brain, in the presence of an open BBB. Additionally, I have briefly mentioned that disruption of the balance between excitation and inhibition in the brain can contribute to brain diseases as autism and schizophrenia. The contributors for such disruption are not completely understood and might have a common ground between diseases. In Project III, we focused on dissecting the relationship between the severity of autistic traits in schizophrenic patients and imbalances in excitation and inhibition. Specifically, using transcranial magnetic stimulation (TMS), we aimed at determining if individuals with low severity of autistic traits and individuals with high severity of autistic traits would differ in terms of glutamatergic or GABAergic neurotransmission

Novel approaches for effective design of controlled drug release systems, employing hybrid semi-parametric mathematical systems

The controlled release of a drug from a carrier into a medium over a defined period of time is referred to as Controlled Drug Release (CDR). A major challenge for a sustainable and reproducible CDR is the unintentional initial burst, which occurs in the first hours/days of immersion and during which a large amount of drug is released. Also it can have deleterious effects on the host. Burst release happens with both small drug molecules and large proteins and for both drug-loaded PLGA micro- and nanoparticles. Particle design can, in principal, be used to control the amount of burst but no systematic methods are to date available and the design process is governed by trial and error. One reason might be that the available models for burst release do not explicitly account for the particle design parameters. This thesis proposes novel methodologies that allow for rational design of drug-loaded PLGA micro- and nanoparticles. It is divided in three main parts. Firstly, a quantitative analysis of the physicochemical factors that impact on the amount of burst release and the burst release rate using partial least squares and decision tree methods is performed. The factors with the greatest impact are selected for the subsequent modelling activities. Next, a bootstrap aggregated hybrid model (HM) is developed, which can successfully predict the cumulative drug release of an independent set of CDR experiments. Lastly, a new rational design method is presented for the optimization of the formulation characteristics of protein-loaded PLGA nanoparticles. The method is successfully applied to design the carrier of a mock-protein, α- chymotrypsin, yielding a close to desired release profile. The method can also help to judge upon the similarity of the mock protein with a target protein in terms of their similarities in burst release behavior. This thesis proposes the first rational PLGA particle design method requiring only the specification of the drug and the desired burst release profile. The application of the method can be expected to significantly reduce the time for PLGA particle development. With the increasing availability of CDR data the predictive power of the method can be further improved towards a systematic and reliable tool. The engine of the method is the hybrid model which links the release profile to the design parameters and is the first of its kind in drug release modeling

Aerospace medicine and biology: A continuing bibliography with indexes, supplement 125

This special bibliography lists 323 reports, articles, and other documents introduced into the NASA scientific and technical information system in January 1974

Artificial Intelligence and Corneal Confocal Microscopy: The Start of a Beautiful Relationship.

Corneal confocal microscopy (CCM) is a rapid non-invasive in vivo ophthalmic imaging technique that images the cornea. Historically, it was utilised in the diagnosis and clinical management of corneal epithelial and stromal disorders. However, over the past 20 years, CCM has been increasingly used to image sub-basal small nerve fibres in a variety of peripheral neuropathies and central neurodegenerative diseases. CCM has been used to identify subclinical nerve damage and to predict the development of diabetic peripheral neuropathy (DPN). The complex structure of the corneal sub-basal nerve plexus can be readily analysed through nerve segmentation with manual or automated quantification of parameters such as corneal nerve fibre length (CNFL), nerve fibre density (CNFD), and nerve branch density (CNBD). Large quantities of 2D corneal nerve images lend themselves to the application of artificial intelligence (AI)-based deep learning algorithms (DLA). Indeed, DLA have demonstrated performance comparable to manual but superior to automated quantification of corneal nerve morphology. Recently, our end-to-end classification with a 3 class AI model demonstrated high sensitivity and specificity in differentiating healthy volunteers from people with and without peripheral neuropathy. We believe there is significant scope and need to apply AI to help differentiate between peripheral neuropathies and also central neurodegenerative disorders. AI has significant potential to enhance the diagnostic and prognostic utility of CCM in the management of both peripheral and central neurodegenerative diseases

Current and future roles of artificial intelligence in retinopathy of prematurity

Retinopathy of prematurity (ROP) is a severe condition affecting premature infants, leading to abnormal retinal blood vessel growth, retinal detachment, and potential blindness. While semi-automated systems have been used in the past to diagnose ROP-related plus disease by quantifying retinal vessel features, traditional machine learning (ML) models face challenges like accuracy and overfitting. Recent advancements in deep learning (DL), especially convolutional neural networks (CNNs), have significantly improved ROP detection and classification. The i-ROP deep learning (i-ROP-DL) system also shows promise in detecting plus disease, offering reliable ROP diagnosis potential. This research comprehensively examines the contemporary progress and challenges associated with using retinal imaging and artificial intelligence (AI) to detect ROP, offering valuable insights that can guide further investigation in this domain. Based on 89 original studies in this field (out of 1487 studies that were comprehensively reviewed), we concluded that traditional methods for ROP diagnosis suffer from subjectivity and manual analysis, leading to inconsistent clinical decisions. AI holds great promise for improving ROP management. This review explores AI's potential in ROP detection, classification, diagnosis, and prognosis.Comment: 28 pages, 8 figures, 2 tables, 235 references, 1 supplementary tabl