Amir Altinawi

Interleukin-8 (IL-8) is a critical biomarker associated with inflammation and disability in both adults and newborns. Conventional detection methods are often labor-intensive, time-consuming, and require highly trained personnel. Non-Faradaic impedimetric biosensors offer a label-free, rapid, and direct approach for IL-8 detection. While previous studies have primarily focused on capacitance and phase changes, the potential of other impedimetric parameters remains underexplored. In this study, a gold interdigitated electrode (Au-IDE)-based non-Faradaic biosensor was developed for IL-8 detection, evaluating multiple impedimetric parameters, including capacitance, impedance magnitude (Z_mod), real impedance (Z_real), and imaginary impedance (Z_imag). Among these, Z_imag exhibited the lowest limit of detection (LoD) at 90 pg/mL, followed by Z_mod at 120 pg/mL, and capacitance at 140 pg/mL, all significantly below the clinical threshold of 600 pg/mL. In contrast, Z_real displayed the highest LoD at 1.3 ng/mL. Sensitivity analysis revealed that Z_imag provided the highest sensitivity at 13.1 kΩ/log (ng/mL), making it the most effective parameter for detecting IL-8 at low concentrations. The sensitivity of Z_mod and Z_real was lower, while capacitance sensitivity was measured at 20 nF/log (ng/mL). These findings highlight the importance of investigating alternative impedimetric parameters beyond capacitance to optimize biosensor performance for biomarker detection. This study demonstrates that non-Faradaic biosensors, despite their capacitive-based nature, can achieve enhanced sensitivity and detection limits by leveraging additional impedimetric parameters, offering a promising approach for rapid and effective IL-8 detection.

Currently, several methods are being applied to assess auditory temporal resolution in a controlled clinical environment via the measurements of gap detection thresholds (GDTs). However, these methods face two issues: the relatively long time required to perform the gap detection test in such settings and the potential of inaccessibility to such facilities. This article proposes a fast, affordable, and reliable application-based method for the determination of GDT either inside or outside the soundproof booth. The proposed test and the acoustic stimuli were both developed using the MATLAB® programming platform. GDT is determined when the subject is able to distinguish the shortest silent gap inserted randomly in one of two segments of white noise. GDTs were obtained from 42 normal-hearing subjects inside and outside the soundproof booth. The results of this study indicated that average GDTs measured inside the booth (5.12 ± 1.02 ms) and outside (4.78 ± 1.16 ms) were not significantly different. The measured GDTs were also comparable to that reported in the literature. In addition, the GDT screening time of the proposed method was approximately 5 minutes, a screening time that is much less than that reported by the literature. Data show that the proposed application was fast and reliable to screen GDT compared to the standard method currently used in clinical settings.

Recently, vertebral tumor prediction using image-based vertebral bio-mathematical modeling with accurate segmentation has been considered as a significant area of research. Further, precise lumbar spinal segmentation is essential to the clinicians for tumor analysis. Therefore, precise and reliable segmentation process is required to help radiologists and doctors to identify different vertebrae tumors with better prediction ratio. The exact vertebral disks segmentation of spinal bones from medical images is a complex process in dealing with different deformities and pathologies in accordance to the conventional techniques such as Deep Convolutional Neural Network (DCNN), Finite Element Analysis (FEA), Principal Component and Factor Analysis (PCFA), Multi-Parameter Ensemble Learning (MPEL), Hierarchical Conditional Random Forest (HCRF), and Deep Siamese Neural Network (DSNN). Therefore, to overcome the present drawbacks, Analytical Transform Assisted Statistical Characteristic Decomposition Model (ATS-CDM) is proposed in this paper for the accurate prediction of vertebral tumor detection and segmentation. This technique is used for the calibration of the segmentation procedures in vertebral tumor image prediction and Receiver Operating Curve (ROC) grading for the lumbar spines. The significant objective of the model-fitting algorithm iterates the tumor regions and measures the current region's variance for the accurate identification of tumor. The outcomes show potential and promising results at lab scale evaluation through analyzing the vertebral datasets with Intra-Discal Pressure (IDP) images for experimental validation with 98.7% bending and 98.88 % segmentation accuracy leads to 94.2 ± 0.2 % to 97.02 ± 0.2 % average ROC grading.