Magnetic nanoparticles have attracted significant attention in various disciplines, including engineering and medicine. Microfluidic chips and lab-on-a-chip devices, with precise control over small volumes of fluids and tiny particles, are appropriate tools for the synthesis, manipulation, and evaluation of nanoparticles. Moreover, the controllability and automation offered by the microfluidic chips in combination with the unique capabilities of the magnetic nanoparticles and their ability to be remotely controlled and detected, have recently provided tremendous advances in biotechnology. In particular, microfluidic chips with magnetic nanoparticles serve as sensitive, high throughput, and portable devices for contactless detecting and manipulating DNAs, RNAs, living cells, and viruses. In this work, we review recent fundamental advances in the field with a focus on biomedical applications. First, we study novel microfluidic-based methods in synthesizing magnetic nanoparticles as well as microparticles encapsulating them. We review both continues-flow and droplet-based microreactors, including the ones based on the cross-flow, co-flow, and flow-focusing methods. Then, we investigate the microfluidic-based methods for manipulating tiny magnetic particles. These manipulation techniques include the ones based on external magnets, embedded micro-coils, and magnetic thin films. Finally, we review techniques invented for the detection and magnetic measurement of magnetic nanoparticles and magnetically labeled bioparticles. We include the advances in anisotropic magnetoresistive, giant magnetoresistive, tunneling magnetoresistive, and magnetorelaxometry sensors. Overall, this review covers a wide range of the field uniquely and provides essential information for designing “lab-on-a-chip” systems for synthesizing magnetic nanoparticles, labeling bioparticles with them, and sorting and detecting them on a single chip.

The high rate of knee osteoarthritis has raised the need for accurate diagnostic methods. In this study, we propose a precise detection method using the center of pressure data obtained from the patients. The introduced automatic detection pipeline is based on the two modern algorithms of grey wolf and BAT. The extracted statistical features and the obtained data from healthy individuals and patients are processed with the grey wolf binary algorithm. The results are fed into the binary bat algorithm to select important features and increase the pipeline accuracy. Then the groups are classified using a four-layer neural network. We show that the proposed method with a simple four-layer neural network offers fantastic accuracy in high-speed processing large data and classifies the high-dimensional knee osteoarthritis center of pressure data with appropriate precision, recall, specificity, and F1 values. The proposed method has direct applications in knee osteoarthritis diagnostics in clinics.