Hussamaldeen Jaradat

Sweat analysis has significant potential in personalized health monitoring and exercise physiology assessment due to its non-invasive and real-time nature. However, the complexity of sweat composition limits the applicability of existing potentiometric or voltammetric single sensors and e-tongue systems in complex analysis, as they rely on the selective detection of a single target analyte using specific electrodes, followed by pattern recognition based on that information. To address this limitation, this study proposes a flexible impedimetric e-tongue system based on a sensor array and machine learning-based pattern recognition for the holistic assessment of artificial sweat, enabling the high-accuracy classification of six exercise-related physiological states. The system is constructed using a laser-induced graphene (LIG) interdigitated electrode array, integrated with multi-walled carbon nanotubes (MWCNTs) and metal oxide nanoparticles (MONPs) composite sensing films, ensuring high sensitivity and stability. Experimental results demonstrate that different sensing elements exhibit complementary responses to major sweat analytes (Na⁺, K⁺, lactic acid, and glucose), forming distinctive sweat fingerprint signals. Key parameters were extracted from impedance spectroscopy (IS) and further analyzed using principal component analysis (PCA) for features transformation and support vector machine (SVM) for classification, achieving an overall 97.69% classification accuracy in identifying exercise-related physiological states. Furthermore, the sensor array exhibited high sensitivity, a broad linear range, excellent repeatability (RSD ≤ 1.67%), reproducibility (RSD ≤ 2.9%), and stability over ~ 20 days, confirming its feasibility for sweat analysis. Compared to potentiometric and voltammetric single sensors and e-tongue systems, the proposed impedimetric e-tongue system does not rely on the selectivity of individual sensors but instead analyzes the overall impedance pattern, significantly enhancing its capability for complex biofluid analysis. In conclusion, this study presents a novel impedimetric e-tongue system as a potential solution for exercise monitoring, health assessment, and personalized medicine, providing new insights into the development of non-invasive sweat analysis technologies.

This review article dives into the complex world of biocompatibility testing: chemical, mechanical, and biological characterization, including many elements of biocompatibility, such as definitions, descriptive examples, and the practical settings. The focus extends to evaluating standard documents obtained from reliable organizations; with a particular focus on open-source information, including FDA-USA, ISO 10933 series, and TÜV SÜD. We found a significant gap in this field: biomaterial scientists and those involved in the realm of medical device development in general, and implants in particular, lack access to a tool that reorganizes the process of selecting the appropriate biocompatibility test for the implant being examined. This work progressed through two key phases that aimed to provide a solution to this gap. A straightforward “yes or no” flowchart was initially developed to guide biocompatibility testing decisions based on the previously accumulated information. Subsequently, the Python code was employed, generating a framework through targeted questions. This work reshapes biocompatibility evaluation, bridging theory and practical implementation. An integrated approach via a flowchart and the Python code empowers stakeholders to navigate biocompatibility testing effortlessly. To conclude, researchers are now better equipped for a safer, more effective implant development, propelling the field towards improved patient care and innovative progress.

Helicobacter pylori (H. pylori) is a highly contagious pathogenic bacterium that can cause gastrointestinal ulcers and may gradually lead to gastric cancer. H. pylori expresses the outer membrane HopQ protein at the earliest stages of infection. Therefore, HopQ is a highly reliable candidate as a biomarker for H. pylori detection in saliva samples. In this work, an H. pylori immunosensor is based on detecting HopQ as an H. pylori biomarker in saliva. The immunosensor was developed by surface modification of screen-printed carbon electrodes (SPCE) with MWCNT-COOH decorated with gold nanoparticles (AuNP) followed by HopQ capture antibody grafting on SPCE/MWCNT/AuNP surface using EDC/S-NHS chemistry. The sensor performance was investigated utilizing various methods, such as cyclic voltammetry (CV), electrochemical impedance spectroscopy (EIS), and scanning electron microscope (SEM) coupled with energy-dispersive X-ray spectroscopy (EDX). H. pylori detection performance in spiked saliva samples was evaluated by square wave voltammetry (SWV). The sensor is suitable for HopQ detection with excellent sensitivity and linearity in the 10 pg/mL–100 ng/mL range, with a 2.0 pg/mL limit of detection (LOD) and an 8.6 pg/mL limit of quantification (LOQ). The sensor was tested in saliva at 10 ng/mL, and recovery of 107.6% was obtained by SWV. From Hill’s model, the dissociation constant Kd for HopQ/HopQ antibody interaction is estimated to be 4.60 × 10⁻¹⁰ mg/mL. The fabricated platform shows high selectivity, good stability, reproducibility, and cost-effectiveness for H. pylori early detection due to the proper choice of biomarker, the nanocomposite material utilization to boost the SPCE electrical performance, and the intrinsic selectivity of the antibody–antigen approach. Additionally, we provide insight into possible future aspects that researchers are recommended to focus on.

Background Helicobacter pylori (H. pylori) is a highly contagious pathogenic bacterium that can cause gastrointestinal ulcers and may gradually lead to gastric cancer. H. pylori expresses the outer membrane HopQ protein at the earliest stages of infection. Therefore, HopQ is a highly reliable candidate as a biomarker for H. pylori detection in saliva samples. Materials and Methods: An H. pylori immunosensor is developed based on detecting HopQ as a biomarker in saliva by a screen-printed carbon electrode (SPCE) modified with MWCNT-COOH decorated with gold nanoparticles (AuNP). The HopQ antibodies are grafted on the SPCE/MWCNT/AuNP surface using EDC/S-NHS chemistry. The sensor performance is investigated by various methods and H. pylori detection performance in spiked saliva samples is evaluated by square wave voltammetry. Results: The sensor is suitable for HopQ detection with high sensitivity and excellent linearity in the 10 pg/mL - 100 ng/mL range and with a 10 pg/ml limit of detection. The sensor was tested in saliva at 10 ng/mL and returned an 107.6% recovery. The dissociation constant Kd for HopQ/HopQ antibody interaction, estimated from Hill's model, is calculated with a value of an order of 4.605 × 10−10 mg/mL. Conclusions: Due to the strategical choice of biomarker, the utilization of nanocomposite material to enhance the SPCE electrical performance, the intrinsic selectivity of the antibody-antigen interaction, and effective immobilization, the fabricated platform shows high selectivity, good stability, reproducibility, and cost-effectiveness for early H. pylori detection. Additionally, we provide insight into possible future aspects the researchers are recommended to focus on.

The sensor units are considered one of the significant technologies that use solar energy as an assistant power source to the batteries. Despite their advantages over the other forms of renewables, solar energy has an intermittent nature which negatively affects the operation of these units. Reaching an effective operation ensuring sustainable units requires a prior prediction of the harvested solar energy. Artificial neural networks (ANNs) appeared recently as a promising prediction approach with those units. This is attributed to the high accuracy compared to the conventional stochastic and statistical ones. Till now, the optimal neural network that fits with sensor units has not been precisely determined. This paper is aimed at finding the optimal neural network that would be applied with solar-supplied sensor units. This is performed by applying a cascaded input/structure direct optimization. The optimization process handles the aspects of accuracy, computational efforts, and complexity. It mainly identifies the type and number of parameters that would be utilized as inputs in the first stage. Then, it optimizes the structure by addressing the number of hidden layers and hidden neurons. The corresponding analysis has been implemented for premeasured real data over five-year time period. The results showed that the optimal neural network can be achieved by using three input parameters which are the air temperature (AT), the relative humidity (RH), and the zenith angle (). For the structure, it has been concluded that the proposed optimal ANN should have two hidden layers with ten neurons in each of them. Lastly, the proposed optimal ANN was verified against the associated prediction error which is minimized to less than 2%.

Carbon is a well-known material for its properties, versatility, availability, and wide range of applications. Screen-printed carbon electrodes (SPCEs) have several attractive properties like mass production, price effectiveness, and technical properties. However, SPCEs performance usually needs to be enhanced with several approaches, such as modifying with nanocomposite materials, electrochemical activation, or doping. In this work, an investigation focused on the effect of 2-(N-morpholino)ethanesulfonic-acid (MES) – electrochemical activation of SPCEs was conducted. Electrochemical methods, i.e., cyclic voltammetry and impedance spectroscopy, were utilized as characterization techniques. This study utilizes the concept of charge transfer resistance Ret and its drop as a measure to assess the enhancement in conductivity. The results show a significant enhancement in charge transfer resistance (R ct ). For graphite electrodes, Ret was reduced by 52% after pretreatment. Meanwhile, the R ct dropped by 54% for the carbon nanotube-modified graphite electrodes.