Ayoub Glia, Muhammedin Deliorman, Mohammad A. Qasaimeh
Cancer progression is shaped by coupled molecular, biochemical, and mechanical changes across multiple scales, from altered signaling and cytoskeletal organization to cellular deformation, adhesion, and metastatic dissemination. Circulating tumor cells (CTCs) are rare and heterogeneous in blood, occurring as single cells and multicellular clusters that provide access to evolving tumor phenotypes through liquid biopsy. Their apparent elasticity, deformability, adhesion, and viscoelastic behavior may reflect invasive potential, treatment response, and phenotypic state, while cytoskeletal proteins, membrane lipids, cell surface glycans, and receptors provide molecular context for these mechanical signatures. This review examines the complementary roles of microfluidics and atomic force microscopy (AFM) in multiscale CTC mechanobiology. Microfluidic platforms support label-free and affinity-based enrichment, capture, positioning, and recovery of viable cells. AFM provides localized measurements of apparent elasticity, deformation, adhesion, viscoelastic response, and nanoscale surface architecture in liquid, while functionalized probes enable molecular recognition and molecule-specific force measurements. We evaluate physical, affinity-based, cluster-preserving, open-space, and artificial intelligence-assisted microfluidic strategies together with reversible device assembly, automated AFM targeting, fluidic probes, advanced mapping modes, and treatment-response applications. Existing studies establish feasibility, including direct AFM measurements of individual patient-derived CTCs and automated analysis following microfluidic enrichment in model rare-cell mixtures. Clinical evidence remains limited, many integrated workflows rely on cultured cells or spiked samples, and within the literature identified in this review, direct AFM characterization of intact patient-derived CTC clusters was not found. Clinical translation will require confirmation of tumor identity, standardized acquisition, analysis, and reporting, increased throughput, statistical analysis that preserves the hierarchy of patients, cells, and force curves, and validation in independent patient cohorts. Integrated AFM-microfluidic platforms could extend CTC liquid biopsy beyond enumeration toward functional mechanobiological analysis relevant to metastasis, therapeutic response, and biomarker development.
C. Bishayee, C. Herrmann, M. Feinaeugle, M. Skolimowski, G. R. B. E. Römer
Precise flow control using valves is essential for the functionality of microfluidic systems as valves serve as critical components for regulating fluid pathways. This work presents a pneumatic elastomeric membrane-based valving mechanism integrated into microfluidic devices and demonstrates laser welding as an advanced bonding technique to enhance performance and manufacturing efficiency. The polymer parts, fluid paths and valving seat are fabricated by micromilling and the elastomeric membrane is cut and placed over the valving seat. Laser welding delivers localised heat with minimal thermal spread, enabling wrinkle-free bonding and significantly accelerating production compared with conventional thermal bonding methods. The proposed valve operates by membrane expansion under pneumatic actuation to block channels and by relaxation to restore flow. Experimental validation demonstrates that laser-welded valves maintain structural integrity, exhibit consistent actuation over 300 cycles, and reduce fabrication time by a factor of 600, offering a scalable approach for high-throughput microfluidic device manufacturing. The welded valves produced require less bonding area than those produced via thermal bonding.
Understanding electroosmotic flow of non-Newtonian fluids in micro- and nanochannels is crucial for the electrokinetic handling of chemical and biological samples that often exhibit viscoelastic, shear-thinning, or shear-thickening behavior. A theoretical model is developed for the generalized electroosmotic core–annular flow of power-law fluids in cylindrical microchannels. Both approximate and simplified analytical solutions are derived under their respective appropriate assumptions and validated by benchmarking against the exact solution. The simplified analytical solutions under the thin electric double layer assumption are further used to characterize the parametric effects of the annular-fluid thickness and flow behavior index on the electroosmotic velocity and flow rate. The presented analytical solutions are useful for predicting the wall-induced polymer depletion or adsorption effect on the electroosmotic velocity of power-law fluids. They will also benefit the study of electroosmotic pumping and electrokinetic energy conversion in non-Newtonian fluids.
Droplet microfluidics has become a modern technology for chemical, biological, electrochemical and energy applications, which helps in the manipulation of a discrete droplet in a size range from 10 pL to 100 nL. Compared to conventional techniques, the droplet-based platform provides a 10–100-fold faster mass-transfer rate, reduces reagent usage by up to 95%, decreases reaction time from hours to minutes, and enables ultra-efficient screening of 10 3 –10 4 droplets s -1. The capabilities have greatly advanced the discovery and optimization of advanced materials, catalysts, and bioelectrochemical systems. This review covers all aspects of droplet generation, manipulation, detection methods and the latest progress in device manufacturing for energy and bioelectrochemical applications. Representative studies show that the droplet-assisted microfluidic platforms can be used to screen electrocatalysts within minutes, can be used for membrane-less electrochemical systems with current densities over 100–500 mA cm -2, and can be used to reduce material consumption by over 80% with enhanced catalyst utilization. Droplet microfluidics has enabled rapid microbial screening, improved control of biofilms, removal of up to 95% chemical oxygen demand (COD), and microbial electrolysis system with hydrogen production rates up to 314 ± 17 m 3 H 2 m -3 reactor per day with optimized electrode configuration in bioelectrochemical applications. The review also emphasizes the use of AI, machine learning, digital microfluidics and automation for autonomous experimentation and optimization based on data. Finally, the current challenges regarding scalability and standardization of devices, long-term operational stability and commercialization of devices are critically discussed. Overall, droplet microfluidics is a versatile and scalable platform that has the potential to speed up the next-generation of sustainable energy technologies and to bioelectrochemical systems by providing more precise control of reactions, high throughput experimentation, and more efficient use of resources.
Viral encephalitis causes substantial neurological morbidity and mortality through the combined effects of direct viral injury, blood–brain barrier (BBB) dysfunction, and dysregulated communication among neural, vascular, and immune cells. Extracellular vesicles (EVs) are increasingly recognized as mediators of this communication because they transfer proteins, lipids, viral constituents, cytokine-related signals, and regulatory RNAs between neurons, astrocytes, microglia, endothelial cells, and peripheral immune cells. Depending on their cellular origin and cargo, EVs may support antiviral defense or propagate viral dissemination, neuroinflammation, barrier injury, and persistent neurological sequelae. This narrative review synthesizes evidence for an EV–neuroimmune axis in viral encephalitis and evaluates how that evidence could inform the design of brain organoid-on-a-chip (BOoC) models. The review distinguishes established findings from a proposed integrated platform that would combine patient-derived brain organoids, a BBB-interposed vascular and immune compartment, real-time EV tracking, transendothelial electrical resistance and electrophysiological sensing, and multi-omics analysis. Existing studies support several individual components of this framework, but a fully integrated EV–BOoC system with closed-loop artificial intelligence (AI) control has not yet been demonstrated for viral encephalitis. Accordingly, digital twins, autonomous treatment optimization, and patient-specific clinical prediction are presented as future research directions rather than current capabilities. This calibrated framework identifies measurable design requirements, evidence gaps, and translational priorities for mechanistic studies, biomarker discovery, and precision therapeutic screening.
