Intelligent image-activated cell sorting 2.0.

The advent of intelligent image-activated cell sorting (iIACS) has enabled high-throughput intelligent image-based sorting of single live cells from heterogeneous populations. iIACS is an on-chip microfluidic technology that builds on a seamless integration of a high-throughput fluorescence microscope, cell focuser, cell sorter, and deep neural network on a hybrid software-hardware data management architecture, thereby providing the combined merits of optical microscopy, fluorescence-activated cell sorting (FACS), and deep learning. Here we report an iIACS machine that far surpasses the state-of-the-art iIACS machine in system performance in order to expand the range of applications and discoveries enabled by the technology. Specifically, it provides a high throughput of ∼2000 events per second and a high sensitivity of ∼50 molecules of equivalent soluble fluorophores (MESFs), both of which are 20 times superior to those achieved in previous reports. This is made possible by employing (i) an image-sensor-based optomechanical flow imaging method known as virtual-freezing fluorescence imaging and (ii) a real-time intelligent image processor on an 8-PC server equipped with 8 multi-core CPUs and GPUs for intelligent decision-making, in order to significantly boost the imaging performance and computational power of the iIACS machine. We characterize the iIACS machine with fluorescent particles and various cell types and show that the performance of the iIACS machine is close to its achievable design specification. Equipped with the improved capabilities, this new generation of the iIACS technology holds promise for diverse applications in immunology, microbiology, stem cell biology, cancer biology, pathology, and synthetic biology.

Revealing hub pathway cross-talk for premature newborns with bronchopulmonary dysplasia by the integration of pathway analysis and Monte Carlo Cross-Validation.

The objective of this study was to reveal hub pathway cross-talk for premature newborns with bronchopulmonary dysplasia (BPD) based on the pathway enrichment analysis and Monte Carlo Cross-Validation (MCCV) method. The inference of key pathway cross-talk consisted of four parts: i) Identifying differentially expressed genes (DEGs); ii) detecting differentially expressed pathways (DEPs); iii) computing discriminating score (DS) for each pair of DEPs or cross-talk and investigating seed cross-talk through the random forest (RF) algorithm and iv) extracting hub cross-talk dependent on the MCCV method. The results showed that a total of 132 DEGs and 137 DEPs were obtained across BPD patients and normal controls. Using the DS and RF algorithm, 10 seed DEP cross-talk were detected. By conducting the MCCV on seed cross-talk, 3 hub cross-talk for BPD were uncovered: i) The pair of pathways role of interleukin-17F (IL-17F) in allergic inflammatory airway diseases and role of IL-17A in psoriasis; ii) the pair of pathways role of IL random forest 17A in psoriasis and IL-17A signaling in fibroblasts and ii) the pair of pathways IL-17A signaling in airway cells and role of hypercytokinemia/hyperchemokinemia in the pathogenesis of influenza. These 3 hub cross-talk among DEPs might give an insight to reveal the molecular mechanism underlying the premature newborns with BPD.

Facile Fabrication of a Flexible LiNbO3 Piezoelectric Sensor through Hot Pressing for Biomechanical Monitoring.

Wearable pressure sensors have attracted increasing attention for biomechanical monitoring due to their portability and flexibility. Although great advances have been made, there are no facile methods to produce sensors with good performance. Here, we present a simple method for manufacturing flexible and self-powered piezoelectric sensors based on LiNbO3 (LN) particles. The LN particles are dispersed in polypropylene (PP) doped with multiwalled carbon nanotubes (MWCNTs) by hot pressing (200 °C) to form a flexible LN/MWCNT/PP piezoelectric composite film (PCF) sensor. This cost-effective sensor has high sensitivity (8 Pa), fast response time (ca. 40 ms), and long-term stability (>3000 cycles). Measurements of pressure changes from peripheral arteries demonstrate the applicability of the LN/MWCNT/PP PCF sensor to biomechanical monitoring as well as its potential for biomechanics-related clinical diagnosis and forecasting.

Renewable and Ultralong Nanoelectrochemical Sensor: Nanoskiving Fabrication and Application for Monitoring Cell Release.

Nanoscaled electrode has been attracting increasing attention because of striking fundamentals and practical applications. Usually, the nanoscaled electrode is fabricated by manual or photo or electron-beam lithography, which is not easy to reproducibly fabricate with simple equipment. In this paper, a cost-effective method, nanoskiving, is developed to fabricate an ultralong nanowire electrode (ULNE). The ULNE is reproducibly obtained by simply sectioning a sandwich epoxy block with a Au film. The width of ULNE could be down to nanometer dependence on the thickness of the Au film, while the length could reach to the millimeter. Thus, the created Au ULNE shows steady-state microamperometric current, characteristic of the nanoelectrode array attributed to its macroscopic length and nanoscaled width without considering the overlap of the diffusion layer of the neighboring nanoelectrode. The electrodeposited Pt/Au ULNE displays unusual electrocatalytic performance toward both the oxidation and reduction of hydrogen peroxide and, as a nanosensor, gives rise to high sensitivity and selectivity of monitoring hydrogen peroxide released from cells stimulated by ascorbic acid.