Ahmed Choukri Abdullah

Human monkeypox (Mpox) is a zoonotic disease caused by the Monkeypox virus (MPXV). As of 14 August 2024, the World Health Organization (WHO) has declared it a global health emergency. For Mpox, this was the second public health emergency of global significance in the past two years. MPXV belongs to the Poxviridae family and is phylogenetically and epidemically divided into two clades: the Congo Basin (Clade-I) and the West African (Clade-II) clades. Clade-I has been associated with more severe disease progression and higher mortality compared to Clade-II, and thus the differentiation between clades can play an important role in predicting disease prognosis. The LAMP technique has the advantages of not requiring thermal cycling and achieving higher amplification in a shorter time compared to qPCR. Different types of LAMP assays were developed in this study to benefit from these advantages. We report the development of LAMP-1 and LAMP-2 assays using the LAMP method to detect MPXV Clade-I and Clade-II, respectively. The LAMP-1 assay includes both fluorescence and visible colorimetric readout tests developed with sensitivities of 10³ and 10⁷ copies, respectively. For the LAMP-2 assay, a probe-based test utilizing the Novel R-Duplex DARQ probe was developed, offering fluorescence detection at a sensitivity of 10³ copies. As a result, we successfully developed three highly specific molecular diagnostic tests that distinctly differentiate between MPXV clades, delivering essential tools for the precise diagnosis and effective control of Mpox.

Microneedles (MNs), that is, a matrix of micrometer-scale needles, have diverse applications in drug delivery, skincare therapy, and health monitoring. MNs offer a minimally invasive alternative to hypodermic needles, characterized by rapid and painless procedures, cost-effective fabrication methods, and reduced tissue damage. This study explores four MN designs, cone-shaped, tapered cone-shaped, pyramidal with a square base, and pyramidal with a triangular-shaped base, and their optimization based on predefined criteria. The workflow encompasses three loading conditions: compressive load during insertion, critical buckling load, and bending loading resulting from incorrect insertion. Geometric parameters such as base radius/width, tip radius/width, height, and tapered angle tip influence the output criteria, namely, total deformation, critical buckling loads, factor of safety (FOS), and bending stress. The comprehensive framework employing a design of experiment approach within the ANSYS workbench toolbox establishes a mathematical model and a response surface fitting model. The resulting regression model, sensitivity chart, and response curve are used to create a multiobjective optimization problem that helps achieve an optimized MN geometrical design across the introduced four shapes, integrating machine learning (ML) techniques. This study contributes valuable insights into a potential ML-augmented optimization framework for MNs via needle designs to stay durable for various physiologically relevant conditions.

Microneedles (MNs) have emerged as a promising solution for drug delivery and extraction of body fluids. Pain is an important physiological attribute to be examined when designing MNs. There is no known representation of pain with geometric features of a MN despite the focus on experimental work. This study focuses on optimizing MN designs with the aim of minimizing pain through means of machine learning, finite element analysis, and optimization tools. Three distinct approaches are proposed. The first approach involves training multiple regression models on data obtained through finite element analysis in COMSOL. The second approach uses COMSOL's built‐in nonlinear optimization solver. Finally, the third approach utilizes the LiveLink interface between COMSOL and MATLAB, combined with Bayesian optimization. Each approach presents unique strengths and challenges, with the third approach demonstrating significant promise due to its efficiency, practicality, and time‐saving.

Melt electrowriting (MEW) is a solvent‐free (i.e., no volatile chemicals), a high‐resolution three‐dimensional (3D) printing method that enables the fabrication of semi‐flexible structures with rigid polymers. Despite its advantages, the MEW process is sensitive to changes in printing parameters (e.g., voltage, printing pressure, and temperature), which can cause fluid column breakage, jet lag, and/or fiber pulsing, ultimately deteriorating the resolution and printing quality. In spite of the commonly used error‐and‐trial method to determine the most suitable parameters, here, we present a machine learning (ML)‐enabled image analysis‐based method for determining the optimum MEW printing parameters through an easy‐to‐use graphical user interface (GUI). We trained five different ML algorithms using 168 MEW 3D print samples, among which the Gaussian process regression ML model yielded 93% accuracy of the variability in the dependent variable, 0.12329 on root mean square error for the validation set and 0.015201 mean square error in predicting line thickness. Integration of ML with a control feedback loop and MEW can reduce the error‐and‐trial steps prior to the 3D printing process, decreasing the printing time (i.e., increasing the overall throughput of MEW) and material waste (i.e., improving the cost‐effectiveness of MEW). Moreover, embedding a trained ML model with the feedback control system in a GUI facilitates a more straightforward use of ML‐based optimization techniques in the industrial section (i.e., for users with no ML skills).

Melt electrowriting (MEW) is a solvent-free (i.e., no volatile chemicals), high-resolution 3D printing method that enables the fabrication of semi-flexible structures with rigid polymers. Despite its advantages, the MEW process is sensitive to changes in printing parameters (e.g., voltage, printing pressure, and temperature), which can cause fluid column breakage, jet lag, and/or fiber pulsing, ultimately deteriorating the resolution and printing quality. In spite of the commonly used error-and-trial method to determine the most suitable parameters, here, we present a machine learning (ML)-enabled image analysis-based method for determining the optimum MEW printing parameters through an easy-to-use graphical user interface (GUI). We trained 5 different ML algorithms using 168 MEW 3D print samples, among which the gaussian process regression ML model yielded 93% accuracy of the variability in the dependent variable, 0.12329 on root mean square error for the validation set and 0.015201 mean square error in predicting line thickness. Integration of ML with control feedback loop and MEW can reduce the error-and-trial steps prior to the 3D printing process, decreasing the printing time (i.e., increasing the overall throughput of MEW) and material waste (i.e., improving the cost-effectiveness of MEW). Moreover, embedding trained ML model with the feedback control system in a GUI facilitates a more straightforward use of ML-based optimization techniques in the industrial section (i.e., for users with no ML skills).