Fourier Transform Infrared Spectroscopy Based Complementary Diagnosis Tool for Autism Spectrum Disorder in Children and Adolescents

Fabrication of Glucose Fluorescent Aptasensor Based on CdTe Quantum Dots

Diabetes is a chronic metabolic disease characterized by high blood glucose (or blood sugar) levels, which harms the heart, blood vessels, eyes, kidneys, and nerves over time. So, it is crucial to regularly control glucose concentration in biological fluids to check its targets, reduce unpleasant symptoms of high and low blood sugar, and avoid long-term diabetes complications. This study developed a simple, rapid, sensitive, and cost-effective fluorescence system for glucose determination. The fluorescent Aptasensor was fabricated using cadmium telluride quantum dots (CdTe QDs) modified with thioglycolic acid and functionalized with thiol-glucose-aptamer through ligand exchange. The thiol-glucose-aptamer interacted directly with CdTe QDs, increasing fluorescence intensity. However, it decreased when the target molecules of glucose were introduced. The structural and morphological characteristics of the Aptasensor were confirmed by various analytical methods such as UV-visible spectroscopy, Fourier-transform infrared spectroscopy (FT-IR), field emission scanning electron microscopy (FESEM), energy dispersive x-ray spectroscopy (EDX), transmission electron microscopy (TEM), atomic force microscopy (AFM), and dynamic light scattering (DLS). According to the typical Stern-Volmer equation, the relationship between fluorescent quenching and target concentration was linear with a detection limit (LOD) of 0.13 ± 1.95 × 10-11 mol L-1 and a relative standard deviation (RSD) of 1.05%. The Aptasensor demonstrated high specificity towards the target and stability over 28 days. Furthermore, it detected glucose in human serum and urine with a recovery rate of up to 99.74%. The results indicate that the fluorescent Aptasensor could be valuable in developing robust sensing technology for low-concentrated analytes.

Exploring the Steps of Infrared (IR) Spectral Analysis: Pre-Processing, (Classical) Data Modelling, and Deep Learning

Infrared (IR) spectroscopy has greatly improved the ability to study biomedical samples because IR spectroscopy measures how molecules interact with infrared light, providing a measurement of the vibrational states of the molecules. Therefore, the resulting IR spectrum provides a unique vibrational fingerprint of the sample. This characteristic makes IR spectroscopy an invaluable and versatile technology for detecting a wide variety of chemicals and is widely used in biological, chemical, and medical scenarios. These include, but are not limited to, micro-organism identification, clinical diagnosis, and explosive detection. However, IR spectroscopy is susceptible to various interfering factors such as scattering, reflection, and interference, which manifest themselves as baseline, band distortion, and intensity changes in the measured IR spectra. Combined with the absorption information of the molecules of interest, these interferences prevent direct data interpretation based on the Beer-Lambert law. Instead, more advanced data analysis approaches, particularly artificial intelligence (AI)-based algorithms, are required to remove the interfering contributions and, more importantly, to translate the spectral signals into high-level biological/chemical information. This leads to the tasks of spectral pre-processing and data modeling, the main topics of this review. In particular, we will discuss recent developments in both tasks from the perspectives of classical machine learning and deep learning.

A Review on Integrated ZnO-Based SERS Biosensors and Their Potential in Detecting Biomarkers of Neurodegenerative Diseases

Surface-enhanced Raman spectroscopy (SERS) applications in clinical diagnosis and spectral pathology are increasing due to the potential of the technique to bio-barcode incipient and differential diseases via real-time monitoring of biomarkers in fluids and in real-time via biomolecular fingerprinting. Additionally, the rapid advancements in micro/nanotechnology have a visible influence in all aspects of science and life. The miniaturization and enhanced properties of materials at the micro/nanoscale transcended the confines of the laboratory and are revolutionizing domains such as electronics, optics, medicine, and environmental science. The societal and technological impact of SERS biosensing by using semiconductor-based nanostructured smart substrates will be huge once minor technical pitfalls are solved. Herein, challenges in clinical routine testing are addressed in order to understand the context of how SERS can perform in real, in vivo sampling and bioassays for early neurodegenerative disease (ND) diagnosis. The main interest in translating SERS into clinical practice is reinforced by the practical advantages: portability of the designed setups, versatility in using nanomaterials of various matter and costs, readiness, and reliability. As we will present in this review, in the frame of technology readiness levels (TRL), the current maturity reached by semiconductor-based SERS biosensors, in particular that of zinc oxide (ZnO)-based hybrid SERS substrates, is situated at the development level TRL 6 (out of 9 levels). Three-dimensional, multilayered SERS substrates that provide additional plasmonic hot spots in the z-axis are of key importance in designing highly performant SERS biosensors for the detection of ND biomarkers.

Investigation of menopause-induced changes on hair by Raman spectroscopy and chemometrics

The ending of estrogen production in the ovaries after menopause results in a series of important physiologic changes, including hair texture and growth. In this study we demonstrate that Raman spectroscopy can be used successfully as a tool to probe menopause-induced changes on hair, in particular when coupled with suitable chemometrics approaches. The detailed analysis of the average Raman spectra (in particular of the Amide I and νS-S stretching spectral regions) of the hair samples of women pre- and post-menopause allowed to estimate that absence of estrogen in post-menopause women leads to an average reduction of ∼12% in the thickness of the hair cuticle, compared to that of pre-menopause women, and revealed the strong prevalence of disulphide bonds in the most stable gauche-gauche-gauche conformation in the hair cuticle. From the analysis of the νS-S stretching spectral region it could also be concluded that the amount of α-helix keratin is slightly higher for post-menopause than for pre-menopause women. A series of statistical models were developed in order to classify the hair samples. Outperforming the traditional PCA-LDA (principal component analysis - linear discriminant analysis) approach, in the present study a GA-LDA (genetic algorithm - linear discriminant analysis) strategy was used for variable reduction/selection and samples' classification. This strategy allowed to develop of a statistical model (L16), which has exceptional prediction capability (total accuracy of 96.6%, with excellent sensitivity and selectivity) and can be used as an efficient instrument for the hair samples' classification. In addition, a new chemometrics approach is here presented, which allows to overcome the intrinsic limitations of the GA algorithm and that can be used to develop statistical models that use GA as the variable reduction/selection method, but superseding its stochastic nature. Three suitable models for classification of the hair samples according to the menopause status of the women were developed using this novel approach (LV17, BLV20 and PLS7 models), which are based on the Fisher's and Bayers' LDA approaches and the PLS-DA method. The followed new chemometrics approach uses the results of a large set of GA-LDA runs over the full data matrix for the selection of the reduced data matrices. The criterion for the selection of the variables is their statistical significance in terms of number of occurrences as solutions of the whole set of GA-LDA runs.

PLS-DA Model for the Evaluation of Attention Deficit and Hyperactivity Disorder in Children and Adolescents through Blood Serum FTIR Spectra

Attention deficit and hyperactivity disorder (ADHD) is one of the most common neurodevelopmental disorders of childhood. It affects ~10% of the world's population of children, and about 30-50% of those diagnosed in childhood continue to show ADHD symptoms later, with 2-5% of adults having the condition. Current diagnosis of ADHD is based on the clinical evaluation of the patient, and on interviews performed by clinicians with parents and teachers of the children, which, together with the fact that it shares common symptoms and frequent comorbidities with other neurodevelopmental disorders, makes the accurate and timely diagnosis of the disorder a difficult task. Despite the large effort to identify reliable biomarkers that can be used in a clinical environment to support clinical diagnosis, this goal has never been achieved hitherto. In the present study, infrared spectroscopy was used together with multivariate statistical methods (hierarchical clustering and partial least-squares discriminant analysis) to develop a model based on the spectra of blood serum samples that is able to distinguish ADHD patients from healthy individuals. The developed model used an approach where the whole infrared spectrum (in the 3700-900 cm^-1 range) was taken as a holistic imprint of the biochemical blood serum environment (spectroscopic biomarker), overcoming the need for the search of any particular chemical substance associated with the disorder (molecular biomarker). The developed model is based on a sensitive and reliable technique, which is cheap and fast, thus appearing promising to use as a complementary diagnostic tool in the clinical environment.