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Chemotherapy is one of the most effective cancer treatments. Starting from the discovery of new molecular entities, it usually takes about 10 years and 2 billion U.S. dollars to bring an effective anti-cancer drug from the benchtop to patients. Due to the physiological differences between animal models and humans, more than 90% of drug candidates failed in phase I clinical trials. Thus, a more efficient drug screening system to identify feasible compounds and pre-exclude less promising drug candidates is strongly desired. For their capability to accurately construct in vitro tumor models derived from human cells to reproduce pathological and physiological processes, microfluidic tumor chips are reliable platforms for preclinical drug screening, personalized medicine, and fundamental oncology research. This review summarizes the recent progress of the microfluidic tumor chip and highlights tumor vascularization strategies. In addition, promising imaging modalities for enhancing data acquisition and machine learning-based image analysis methods to accurately quantify the dynamics of tumor spheroids are introduced. It is believed that the microfluidic tumor chip will serve as a high-throughput, biomimetic, and multi-sensor integrated system for efficient preclinical drug evaluation in the future. [ABSTRACT FROM AUTHOR]

Detection and characterization of rare circulating tumor cells (CTCs) in patients' blood is important for the diagnosis and monitoring of cancer. The traditional way of counting CTCs via fluorescent images requires a series of tedious experimental procedures and often impacts the viability of cells. Here we present a method for label-free detection of CTCs from patient blood samples, by taking advantage of data analysis of bright field microscopy images. The approach uses the convolutional neural network, a powerful image classification and machine learning algorithm to perform label-free classification of cells detected in microscopic images of patient blood samples containing white blood cells and CTCs. It requires minimal data pre-processing and has an easy experimental setup. Through our experiments, we show that our method can achieve high accuracy on the identification of rare CTCs without the need for advanced devices or expert users, thus providing a faster and simpler way for counting and identifying CTCs. With more data becoming available in the future, the machine learning model can be further improved and can serve as an accurate and easy-to-use tool for CTC analysis. [ABSTRACT FROM AUTHOR]

[Display omitted] • Unbinding forces of PSGL-1/E-selectin and PSGL-1/E-selectin liposome were measured with AFM. • Binding kinetics of the two binding pairs were derived from measured unbinding forces. • Compared to E-selectin, E-selectin liposome has much higher association rate constant to PSGL-1. • E-selectin on liposome has much higher binding affinity to PSGL-1 than E-selectin. Various ligand-functionalized liposomes have been developed for targeted therapies. Typically, the binding properties of the ligands and targeted proteins are measured with surface plasmon resonance (SPR), where the proteins are immobilized on a rigid surface. However, the difference of protein-ligand binding kinetics between liposome-conjugated protein and rigid surface-conjugated protein is not fully understood. In this work, the binding kinetics of P-selectin glycoprotein ligand-1 (PSGL-1) and E-selectin conjugated on liposome and on rigid surfaces are investigated with Atomic Force Microscopy (AFM). The results suggest that protein orientation and diffusion on liposomal membrane can alter the binding kinetics of the protein-ligand interaction. Specifically, the association and dissociation rate constant of AFM probe-conjugated E-selectin and glass-conjugated PSGL-1 are measured as 9.32 × 104 M−1s−1 and 1.54 s−1, respectively. While for the liposome-conjugated E-selectin and glass-conjugated PSGL-1, the kinetic constants are measured as 5.00 × 107 M−1s-1 and 2.76 s−1, respectively. Thus, there is an order's magnitude increase of binding affinity (from k d = 16.51 μM to k d = 0.06 μM) when protein is attached to liposome compared to attached to a rigid surface. The results might provide better understanding and pave the way for the future design of the ligand-targeted liposomes. [ABSTRACT FROM AUTHOR]

Abstract: In Schweiger (2003) , Fritz Schweiger introduced the algorithm of the generalized continued fraction (GCF), and in Zhong (2008) , T. Zhong studied some basic metric properties of the GCF. In this paper, under the restriction of , the “0–1” law and the central limit theorem of quotients in the GCF expansions are studied. [Copyright &y& Elsevier]

Background: Hematopoietic stem cells (HSCs) and multipotent progenitors (MPPs) play a pivotal role in maintaining lifelong hematopoiesis. The distinction between stem cells and other progenitors, as well as the assessment of their functions, has long been a central focus in stem cell research. In recent years, deep learning has emerged as a powerful tool for cell image analysis and classification/prediction. Methods: In this study, we explored the feasibility of employing deep learning techniques to differentiate murine HSCs and MPPs based solely on their morphology, as observed through light microscopy (DIC) images. Results: After rigorous training and validation using extensive image datasets, we successfully developed a three-class classifier, referred to as the LSM model, capable of reliably distinguishing long-term HSCs, short-term HSCs, and MPPs. The LSM model extracts intrinsic morphological features unique to different cell types, irrespective of the methods used for cell identification and isolation, such as surface markers or intracellular GFP markers. Furthermore, employing the same deep learning framework, we created a two-class classifier that effectively discriminates between aged HSCs and young HSCs. This discovery is particularly significant as both cell types share identical surface markers yet serve distinct functions. This classifier holds the potential to offer a novel, rapid, and efficient means of assessing the functional states of HSCs, thus obviating the need for time-consuming transplantation experiments. Conclusion: Our study represents the pioneering use of deep learning to differentiate HSCs and MPPs under steady-state conditions. This novel and robust deep learning-based platform will provide a basis for the future development of a new generation stem cell identification and separation system. It may also provide new insight into the molecular mechanisms underlying stem cell self-renewal. [ABSTRACT FROM AUTHOR]

Abstract: By using the continuation theorem of coincidence degree theory and Liapunov function, we obtain some sufficient criteria to ensure the existence and global exponential stability of periodic solution to the bidirectional associative memory (BAM) neural networks with periodic coefficients and continuously distributed delays. These results improve and generalize the works of papers [J. Cao, L. Wang, Phys. Rev. E 61 (2000) 1825] and [Z. Liu, A. Chen, J. Cao, L. Huang, IEEE Trans. Circuits Systems I 50 (2003) 1162]. An example is given to illustrate that the criteria are feasible. [Copyright &y& Elsevier]

Intracellular drug delivery by rapid squeezing is one of the most recent and simple cell membrane disruption-mediated drug encapsulation approaches. In this method, cell membranes are perforated in a microfluidic setup due to rapid cell deformation during squeezing through constricted channels. While squeezing-based drug loading has been successful in loading drug molecules into various cell types, such as immune cells, cancer cells, and other primary cells, there is so far no comprehensive understanding of the pore opening mechanism on the cell membrane and the systematic analysis on how different channel geometries and squeezing speed influence drug loading. This article aims to develop a three-dimensional computational model to study the intracellular delivery for compound cells squeezing through microfluidic channels. The Lattice Boltzmann method, as the flow solver, integrated with a spring-connected network via frictional coupling, is employed to capture compound capsule dynamics over fast squeezing. The pore size is proportional to the local areal strain of triangular patches on the compound cell through mathematical correlations derived from molecular dynamics and coarse-grained molecular dynamics simulations. We quantify the drug concentration inside the cell cytoplasm by introducing a new mathematical model for passive diffusion after squeezing. Compared to the existing models, the proposed model does not have any empirical parameters that depend on operating conditions and device geometry. Since the compound cell model is new, it is validated by simulating a nucleated cell under a simple shear flow at different capillary numbers and comparing the results with other numerical models reported in literature. The cell deformation during squeezing is also compared with the pattern found from our compound cell squeezing experiment. Afterward, compound cell squeezing is modeled for different cell squeezing velocities, constriction lengths, and constriction widths. We reported the instantaneous cell center velocity, variations of axial and vertical cell dimensions, cell porosity, and normalized drug concentration to shed light on the underlying physics in fast squeezing-based drug delivery. Consistent with experimental findings in the literature, the numerical results confirm that constriction width reduction, constriction length enlargement, and average cell velocity promote intracellular drug delivery. The results show that the existence of the nucleus increases cell porosity and loaded drug concentration after squeezing. Given geometrical parameters and cell average velocity, the maximum porosity is achieved at three different locations: constriction entrance, constriction middle part, and outside the constriction. Our numerical results provide reasonable justifications for experimental findings on the influences of constriction geometry and cell velocity on the performance of cell-squeezing delivery. We expect this model can help design and optimize squeezing-based cargo delivery. [ABSTRACT FROM AUTHOR]

The efficacy of non-equilibrium atmospheric pressure-pulsed dielectric barrier discharge (cold plasma)-treated mist in disinfecting baby kale leaves, and its effect on color values and cuticle composition were evaluated. UV-treated baby kale leaves were spot-inoculated (0.04-mL of 106 CFU/mL) with rifampicin-resistant strain E. coli O157:H7 ATCC 700728 incubated (0–4 °C) overnight and treated with plasma mist at 60, 120, 180, 240 or 300 s. Treated and untreated leaves were analyzed for change in color values (L*, a*, b*, chroma, hue, and browning index (BI)) using a Chroma meter. Functional groups (alcohols, esters, aldehydes, and ketones) were determined in the cuticle using FTIR. Color stability of treated leaves was also evaluated after refrigerated storage (4 °C) for 12 days. Levels of E. coli O157:H7 on the kale leaves were reduced below the detection limit of 5.5 × 103 CFU/mL after plasma treatment for 300 s with no significant change in color values. Visible change in color (browning or leaf damage) was observed after 600 s of plasma treatment. Color stability of plasma-treated leaves was enhanced during refrigerated storage (4 °C), indicated by a lower BI value of 34.4 ± 5.4 at 120-s plasma treatment compared to untreated leaves after 12 days of storage. Fourier transform infrared (FTIR) spectroscopy analysis concluded that plasma treatment of kale did not negatively affect functional groups in the cuticle. This study demonstrated that cold plasma mist has the potential to reduce E. coli on the surface of baby kale leaves with no significant change in the color values. Kale leaves are consumed worldwide owing to their health-promoting and nutritional benefits. However, in the past two decades there has been an increase in foodborne diseases outbreaks caused by microorganisms associated with leafy vegetables. The food industry faces challenges in improving food safety while maintaining quality of the food. Chemicals in wash water or heat treatment (70–121 °C), commonly used methods for killing microorganisms, can have negative effect on the aesthetic (color) and nutritional quality of the product. Cold plasma treatment of fresh produce, an emerging food preservation technology, is an efficient, quick, and environmentally-friendly method of disinfecting the product at low temperature (30–40 °C). This study demonstrated that treatment of baby kale with cold plasma mist not only inactivated E. coli , but also enhanced its color stability during refrigerated storage. Hence, cold plasma mist has a potential application as an efficient disinfection technology, thus enhancing shelf-life of fresh leafy greens. • Disinfection and quality of spot-inoculated kale leaves by cold plasma was evaluated. • Plasma treatment resulted in inactivation (0 CFU/mL) of E. coli O157:H7 on kale leaves. • No significant color change was observed in leaves treated with plasma for 300 s. • Refrigerated storage of treated leaves showed lower Browning Index values than untreated leaves. [ABSTRACT FROM AUTHOR]