Traditional exercise training monitoring is based on invasive blood testing methods. As sweat can reveal abundant blood-related physiological information about health, wearable sweat sensors have received significant research attention and become increasingly popular in the field of exercise training monitoring. However, most of these sensors are used to measure physical indicators such as heart rate, blood pressure, respiration, etc., demanding a versatile sensor that can detect relevant biochemical indicators in body fluids. In this work, we proposed a wearable microfluidic sweat chip combined with smartphone image processing to realize non-invasive in situ analysis of epidermal sweat for sports practitioners. The polydimethylsiloxane (PDMS) based chip was modified with nonionic surfactants to ensure good hydrophilicity for the automatic collection of sweat. Besides, a simple, reliable, and low-cost paper-based sensor was prepared for high-performance sensing of glucose concentration and pH in sweat. Under optimized conditions, this proposed chip can detect glucose with low concentrations from 0.05 mM to 0.40 mM, with a pH range of 4.0 to 6.5 for human sweat. The ability of this microfluidic chip for human sweat analysis was demonstrated by dynamically tracking the changes in glucose concentration and pH in long-distance running subjects.

The gingival epithelium–capillary interface is a unique feature of periodontal soft tissue, preserving periodontal tissue homeostasis and preventing microorganism and toxic substances from entering the subepithelial tissue. However, the function of the interface is disturbed in periodontitis, and mechanisms of the breakdown of the interface are incompletely understood. To address these limitations, we developed a microfluidic epithelium–capillary barrier with a thin culture membrane (10 μm) that closely mimics the in vivo gingival epithelial barrier with an immune micro-environment. To test the validity of the fabricated gingival epithelial barrier model, epithelium–capillary interface-on-a-chip was cultured with human gingival epithelial cells (HGECs) and human vascular endothelial cells (HUVEC). Their key properties were tested using optical microscope, transepithelial/transendothelial electrical resistance (TEER), and permeability assays. The clear expression of VE-cadherin revealed the tight junctions in endothelial cells. Live/dead assays indicated a high cell viability, and the astrocytic morphology of HGE cells was confirmed by F-actin immunostaining. By the third day of cell culture, TEER levels typically exceeded in co-cultures. The resultant permeability coefficients showed a significant difference between 70 kDa and 40 kDa FITC-dextran. The expression of protein intercellular cell adhesion molecule (ICAM-1) and human beta defensin-2 (HBD2) decreased when exposed to TNF-α and LPS, but recovered with the NF-κB inhibitor treatment- Pyrrolidinedithiocarbamic acid (PDTC), indicating the stability of the fabricated chip. These results demonstrate that the developed epithelium-capillary interface system is a valid model for studying periodontal soft tissue function and drug delivery.

This paper introduces an enclosed microfluidic chip that integrates sample preparation and the chamber-based digital polymerase chain reaction (cdPCR). The sample preparation of the chip includes nucleic acid extraction and purification based on magnetic beads, which adsorb nucleic acids by moving around the reaction chambers to complete the reactions including lysis, washing, and elution. The cdPCR area of the chip consists of tens of thousands of regularly arranged microchambers. After the sample preparation processes are completed, the purified nucleic acid can be directly introduced into the microchambers for amplification and detection on the chip. The nucleic acid extraction performance and digital quantification performance of the system were examined using synthetic SARS-CoV-2 plasmid templates at concentrations ranging from 10¹–10⁵ copies per μL. Further on, a simulated clinical sample was used to test the system, and the integrated chip was able to accurately detect SARS-CoV-2 virus particle samples doped with interference (saliva) with a detection limit of 10 copies per μL. This integrated system could provide a promising tool for point-of-care testing of pathogenic infections.

Diagnosis of cancer by biomarkers plays an important role in human health and life. However, current laboratory techniques for detecting cancer biomarkers still require laborious and time-consuming operation by skilled operators and associated laboratory instruments. This work presents a colorimetric biosensor for the rapid and sensitive detection of carcinoembryonic antigen (CEA) based on an automated immunomagnetic separation platform and a droplet array microfluidic chip with the aid of an image analysis system. Immunomagnetic nanoparticles (MNPs) were used to capture CEA in the samples. CEA-detecting antibodies and horseradish peroxidase (HRP) were modified on polystyrene microspheres (PS), catalysing hydrogen peroxide and 3,3′,5,5′-tetramethylbenzidine (TMB) as signal outputs. Color reaction data were analyzed to establish a CEA concentration standard curve. The movement of MNPs between droplets in the microfluidic chip is achieved using an automatically programmable magnetic control system. This colorimetric biosensor has been used for the simultaneous detection of six CEA samples ranging from 100 pg mL⁻¹ to 100 ng mL⁻¹ with a detection limit of 14.347 pg mL⁻¹ in 10 min, following the linear equation: y = −4.773 ln(x) + 156.26 with a correlation of R² = 0.9924, and the entire workflow can be completed within 80 minutes. The microfluidic immunosensor designed in this paper has the advantages of low cost, automation, low sample consumption, high throughput, and promising applications in biochemistry.

The concept of a motor-like microlaser with a controllable direction, velocity, and spatial motions was introduced, providing a novel approach for laser writing, laser switching, and localized cell stimulation and imaging.