Single-cell and microarray chip analysis revealed the underlying pathogenesis of ulcerative colitis and validated model genes in diagnosis and drug response.

The morbidity rate of ulcerative colitis (UC) in the world is increasing year by year, recurrent episodes of diarrhea, mucopurulent and bloody stools, and abdominal pain are the main symptoms, reducing the quality of life of the patient and affecting the productivity of the society. In this study, we sought to develop robust diagnostic biomarkers for UC, to uncover potential targets for anti-TNF-ɑ drugs, and to investigate their associated pathway mechanisms. We collected single-cell expression profile data from 9 UC or healthy samples and performed cell annotation and cell communication analysis. Revealing the possible pathogenesis of ulcerative colitis by Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSEA) analysis. Based on the disease-related modules obtained from weighted correlation network analysis (WGCNA) analysis, we used Lasso regression analysis and random forest algorithm to identify the genes with the greatest impact on disease (EPB41L3, HSD17B3, NDRG1, PDIA5, TRPV3) and further validated the diagnostic value of the model genes by various means. To further explore the relationship and mechanism between model genes and drug sensitivity, we collected gene expression profiles of 185 UC patients before receiving anti-tumor necrosis factor drugs, and we performed functional analysis based on the results of differential analysis between NR tissues and R tissues, and used single-sample GSEA (ssGSEA) and CIBERSORT algorithms to explore the important role of immune microenvironment on drug sensitivity. The results suggest that our model is not only helpful in aiding diagnosis, but also has implications for predicting drug efficacy; in addition, model genes may influence drug sensitivity by affecting immune cells. We suggest that this study has developed a diagnostic model with higher specificity and sensitivity, and also provides suggestions for clinical administration and drug efficacy prediction.

A high-throughput microfluidic device inspired by the Wheatstone bridge principle for characterizing the mechanical properties of single cells.

The mechanical properties of single cells have been recognized as biomarkers for identifying individual cells and diagnosing human diseases. Microfluidic devices based on the flow cytometry principle, which are not limited by the vision field of a microscope and can achieve a very high throughput, have been extensively adopted to measure the mechanical properties of single cells. However, these kinds of microfluidic devices usually required pressure-driven pumps with a very low flow rate and high precision. In this study, we developed a high-throughput microfluidic device inspired by the Wheatstone bridge principle for characterizing the mechanical properties of single cells. The microfluidic analogue of the Wheatstone bridge not only took advantage of flow cytometry, but also allowed precise control of a very low flow rate through the constricted channel with a higher input flow rate generated by a commercially available pressure-driven pump. Under different input flow rates of the pump, the apparent elastic moduli and the fluidity of osteosarcoma (U-2OS) cells and cervical carcinoma (HeLa) cells were measured by monitoring their dynamic deformations passing through the bridge-channel with different sizes of rectangular constrictions. The results showed that the input flow rate had little effect on measuring the mechanical properties of the cells, while the ratio of cell radius to effective constriction radius was different, i.e., for U-2OS cells it was 1.20 and for HeLa cells it was 1.09. Under this condition compared with predecessors, our statistic results of cell mechanical properties exhibited minimal errors. Furthermore, the cell viability after measurements was kept above 90% that demonstrated the non-destructive property of our proposed method.

A passive pump-assisted microfluidic assay for quantifying endothelial wound healing in response to fluid shear stress.

There as an urgent need to quantify the endothelial wound-healing process in response to fluid shear stress to improve the biological and clinical understanding of healing mechanisms, which is of great importance for preventing healing impairment, chronic wounds, and postoperative in-stent restenosis. However, current experimental platforms not only require expensive, cumbersome, and powered pumping devices (to, e.g., generate cell scratches and load shear stress stimulation) but also lack quantitative controls for quantitative analysis. In this paper, a passive pump-assisted microfluidic assay is developed to quantify endothelial wound healing in response to fluid shear stress. Our assay consists of passive constant-flow pumps based on the siphon principle and a three-inlet microfluidic chip for cell wound-healing experiments. We also propose a method for quantitatively adjusting cell scratch size by controlling trypsin flow. Both numerical simulations and fluorescein experiments validate the effectiveness of this method. Moreover, we use the designed microfluidic assay to successfully generate cell scratches, load a 12-h shear stress of 5 dyn/cm2 to the cells, and observe wound healing. The results indicate that the healing of a cell scratch is significantly accelerated under the stimulation of shear stress. In conclusion, our passive pump-assisted microfluidic assay shows versatility, applicability, and the potential for quantifying endothelial wound healing in response to fluid shear stress.

Impact of RIM-BPs in neuronal vesicles release.

Accurate signal transmission between neurons is accomplished by vesicle release with high spatiotemporal resolution in the central nervous system. The vesicle release occurs mainly in the active zone (AZ), a unique area on the presynaptic membrane. Many structural proteins expressed in the AZ connect with other proteins nearby. They can also regulate the precise release of vesicles through protein-protein interactions. RIM-binding proteins (RIM-BPs) are one of the essential proteins in the AZ. This review summarizes the structures and functions of three subtypes of RIM-BPs, including the interaction between RIM-BPs and other proteins such as Bassoon and voltage-gated calcium channel, their significance in stabilizing the AZ structure in the presynaptic region and collecting ion channels, and ultimately regulating the fusion and release of neuronal vesicles.

A Microfluidic Micropipette Aspiration Device to Study Single-Cell Mechanics Inspired by the Principle of Wheatstone Bridge.

The biomechanical properties of single cells show great potential for early disease diagnosis and effective treatments. In this study, a microfluidic device was developed for quantifying the mechanical properties of a single cell. Micropipette aspiration was integrated into a microfluidic device that mimics a classical Wheatstone bridge circuit. This technique allows us not only to effectively alter the flow direction for single-cell trapping, but also to precisely control the pressure exerted on the aspirated cells, analogous to the feature of the Wheatstone bridge that can precisely control bridge voltage and current. By combining the micropipette aspiration technique into the microfluidic device, we can effectively trap the microparticles and Hela cells as well as measure the deformability of cells. The Young's modulus of Hela cells was evaluated to be 387 ± 77 Pa, which is consistent with previous micropipette aspiration studies. The simplicity, precision, and usability of our device show good potential for biomechanical trials in clinical diagnosis and cell biology research.

Improved artificial bee colony algorithm for vehicle routing problem with time windows.

This paper investigates a well-known complex combinatorial problem known as the vehicle routing problem with time windows (VRPTW). Unlike the standard vehicle routing problem, each customer in the VRPTW is served within a given time constraint. This paper solves the VRPTW using an improved artificial bee colony (IABC) algorithm. The performance of this algorithm is improved by a local optimization based on a crossover operation and a scanning strategy. Finally, the effectiveness of the IABC is evaluated on some well-known benchmarks. The results demonstrate the power of IABC algorithm in solving the VRPTW.

Towards a Semantic Web of Things: A Hybrid Semantic Annotation, Extraction, and Reasoning Framework for Cyber-Physical System.

Web of Things (WoT) facilitates the discovery and interoperability of Internet of Things (IoT) devices in a cyber-physical system (CPS). Moreover, a uniform knowledge representation of physical resources is quite necessary for further composition, collaboration, and decision-making process in CPS. Though several efforts have integrated semantics with WoT, such as knowledge engineering methods based on semantic sensor networks (SSN), it still could not represent the complex relationships between devices when dynamic composition and collaboration occur, and it totally depends on manual construction of a knowledge base with low scalability. In this paper, to addresses these limitations, we propose the semantic Web of Things (SWoT) framework for CPS (SWoT4CPS). SWoT4CPS provides a hybrid solution with both ontological engineering methods by extending SSN and machine learning methods based on an entity linking (EL) model. To testify to the feasibility and performance, we demonstrate the framework by implementing a temperature anomaly diagnosis and automatic control use case in a building automation system. Evaluation results on the EL method show that linking domain knowledge to DBpedia has a relative high accuracy and the time complexity is at a tolerant level. Advantages and disadvantages of SWoT4CPS with future work are also discussed.