Design and Engineering of a Palm-Sized Optical Immunosensing Device for the Detection of a Kidney Dysfunction Biomarker

Creatinine is one of the most common and specific biomarkers for renal diseases, usually found in the serum and urine of humans. Its level is extremely important and critical to know, not only in the case of renal diseases, but also for various other pathological conditions. Hence, detecting creatinine in clinically relevant ranges in a simplistic and personalized manner is interesting and important. In this direction, an optical sensing device has been developed for the simple, point-of-care detection of creatinine. The developed biosensor was able to detect creatinine quantitatively based on optical signals measured through a change in color. The sensor has been integrated with a smartphone to develop a palm-sized device for creatinine analysis in personalized settings. The sensor has been developed following facile chemical modification steps to anchor the creatinine-selective antibody to generate a sensing probe. The fabricated sensor has been thoroughly characterized by FTIR, AFM, and controlled optical analyses. The quantitative analysis is mediated through the reaction between picric acid and creatinine which was detected by the antibody-functionalized sensor probe. The differences in color intensity and creatinine concentrations show an excellent dose-dependent correlation in two different dynamic ranges from 5 to 20 μM and 35 to 400 μM, with a detection limit of 15.37 (±0.79) nM. Several interfering molecules, such as albumin, glucose, ascorbic acid, citric acid, glycine, uric acid, Na⁺, K⁺, and Cl^-, were tested using the biosensor, in which no cross-reactivity was observed. The utility of the developed system to quantify creatinine in spiked serum samples was validated and the obtained percentage recoveries were found within the range of 89.71-97.30%. The fabricated biosensor was found to be highly reproducible and stable, and it retains its original signal for up to 28 days.

A Personalized Computer-Aided Diagnosis System for Mild Cognitive Impairment (MCI) Using Structural MRI (sMRI)

Alzheimer's disease (AD) is a neurodegenerative disorder that targets the central nervous system (CNS). Statistics show that more than five million people in America face this disease. Several factors hinder diagnosis at an early stage, in particular, the divergence of 10-15 years between the onset of the underlying neuropathological changes and patients becoming symptomatic. This study surveyed patients with mild cognitive impairment (MCI), who were at risk of conversion to AD, with a local/regional-based computer-aided diagnosis system. The described system allowed for visualization of the disorder's effect on cerebral cortical regions individually. The CAD system consists of four steps: (1) preprocess the scans and extract the cortex, (2) reconstruct the cortex and extract shape-based features, (3) fuse the extracted features, and (4) perform two levels of diagnosis: cortical region-based followed by global. The experimental results showed an encouraging performance of the proposed system when compared with related work, with a maximum accuracy of 86.30%, specificity 88.33%, and sensitivity 84.88%. Behavioral and cognitive correlations identified brain regions involved in language, executive function/cognition, and memory in MCI subjects, which regions are also involved in the neuropathology of AD.

Immunological Aspects of Diagnosis and Management of Childhood Tuberculosis

The diagnosis of tuberculosis (TB) in children is difficult because of the low sensitivity and specificity of traditional microbiology techniques in this age group. Whereas in adults the culture of Mycobacterium tuberculosis (M. tuberculosis), the gold standard test, detects 80% of positive cases, it only detects around 30-40% of cases in children. The new methods based on the immune response to M. tuberculosis infection could be affected by many factors. It is necessary to evaluate the medical record, clinical features, presence of drug-resistant M. tuberculosis strains, comorbidities, and BCG vaccination history for the diagnosis in children. There is no ideal biomarker for all TB cases in children. A new strategy based on personalized diagnosis could be used to evaluate specific molecules produced by the host immune response and make therapeutic decisions in each child, thereby changing standard immunological signatures to personalized signatures in TB. In this way, immune diagnosis, prognosis, and the use of potential immunomodulators as adjunct TB treatments will meet personalized treatment.

A Personalized Autism Diagnosis CAD System Using a Fusion of Structural MRI and Resting-State Functional MRI Data

Autism spectrum disorder is a neuro-developmental disorder that affects the social abilities of the patients. Yet, the gold standard of autism diagnosis is the autism diagnostic observation schedule (ADOS). In this study, we are implementing a computer-aided diagnosis system that utilizes structural MRI (sMRI) and resting-state functional MRI (fMRI) to demonstrate that both anatomical abnormalities and functional connectivity abnormalities have high prediction ability of autism. The proposed system studies how the anatomical and functional connectivity metrics provide an overall diagnosis of whether the subject is autistic or not and are correlated with ADOS scores. The system provides a personalized report per subject to show what areas are more affected by autism-related impairment. Our system achieved accuracies of 75% when using fMRI data only, 79% when using sMRI data only, and 81% when fusing both together. Such a system achieves an important next step towards delineating the neurocircuits responsible for the autism diagnosis and hence may provide better options for physicians in devising personalized treatment plans.

A Novel Early Diagnosis System for Mild Cognitive Impairment Based on Local Region Analysis: A Pilot Study

Alzheimer's disease (AD) is an irreversible neurodegenerative disorder that accounts for 60–70% of cases of dementia in the elderly. An early diagnosis of AD is usually hampered for many reasons including the variable clinical and pathological features exhibited among affected individuals. This paper presents a computer-aided diagnosis (CAD) system with the primary goal of improving the accuracy, specificity, and sensitivity of diagnosis. In this system, PiB-PET scans, which were obtained from the ADNI database, underwent five essential stages. First, the scans were standardized and de-noised. Second, an Automated Anatomical Labeling (AAL) atlas was utilized to partition the brain into 116 regions or labels that served for local (region-based) diagnosis. Third, scale-invariant Laplacian of Gaussian (LoG) was used, per brain label, to detect the discriminant features. Fourth, the regions' features were analyzed using a general linear model in the form of a two-sample t-test. Fifth, the support vector machines (SVM) and their probabilistic variant (pSVM) were constructed to provide local, followed by global diagnosis. The system was evaluated on scans of normal control (NC) vs. mild cognitive impairment (MCI) (19 NC and 65 MCI scans). The proposed system showed superior accuracy, specificity, and sensitivity as compared to other related work.