High-Efficiency Inertial Separation of Microparticles Using Elevated Columned Reservoirs and Vortex Technique for Lab-on-a-Chip Applications.

The discovery of circulating tumor cells (CTCs) has envisioned an excellent outlook for cancer diagnosis and prognosis. Among numerous efforts proposed for CTCs isolation, vortex separation is a well-known method for capturing CTCs from blood due to its applicability, low sample volume requirement, and ability to retain cell viability. It is a label-free, passive, low-cost, and automated method, making it an ideal solution for lab-on-a-chip applications. The previous designs that employed vortex technology have shown reaching high throughput and 70% separation efficiency although it was after three processing cycles which are not desired. Inspired by our earlier design, in this work, we redesigned the chip geometry by elevating the columned reservoir height to capture more particles and consequently reduce particle-particle collision, eventually improving efficiency. So, a height-variable chip with fewer elevated columned reservoirs (ECRs) was employed to isolate 20 µm microparticles representing CTCs from 8 µm microparticles. Also, numerical simulations were conducted to investigate the third axis contribution to the separation mechanism. The new design with ECRs resulted in a 14% increase in average efficiency, reaching ∼80% ± 8.3% in microparticle separation and 61% purity. Moreover, the proposed chip geometry demonstrated more than three times higher capacity in retaining orbiting particles up to 1300 in peak performance without sacrificing efficiency compared to earlier single-layer designs. We came up with an upgraded injection system to facilitate this chip characterization. We also presented an effortless and straightforward approach for purging air bubbles trapped inside the reservoirs to preserve regular chip operation, especially where there is a mismatch between channel and reservoir heights.

A multimodal sensor dataset for continuous stress detection of nurses in a hospital.

Advances in wearable technologies provide the opportunity to monitor many physiological variables continuously. Stress detection has gained increased attention in recent years, mainly because early stress detection can help individuals better manage health to minimize the negative impacts of long-term stress exposure. This paper provides a unique stress detection dataset created in a natural working environment in a hospital. This dataset is a collection of biometric data of nurses during the COVID-19 outbreak. Studying stress in a work environment is complex due to many social, cultural, and psychological factors in dealing with stressful conditions. Therefore, we captured both the physiological data and associated context pertaining to the stress events. We monitored specific physiological variables such as electrodermal activity, Heart Rate, and skin temperature of the nurse subjects. A periodic smartphone-administered survey also captured the contributing factors for the detected stress events. A database containing the signals, stress events, and survey responses is publicly available on Dryad.