To identify factors that regulate gut microbiota density and the impact of varied microbiota density on health, we assayed this fundamental ecosystem property in fecal samples across mammals, human disease, and therapeutic interventions. Physiologic features of the host (carrying capacity) and the fitness of the gut microbiota shape microbiota density. Therapeutic manipulation of microbiota density in mice altered host metabolic and immune homeostasis. In humans, gut microbiota density was reduced in Crohn’s disease, ulcerative colitis, and ileal pouch-anal anastomosis. The gut microbiota in recurrent Clostridium difficile infection had lower density and reduced fitness that were restored by fecal microbiota transplantation. Understanding the interplay between microbiota and disease in terms of microbiota density, host carrying capacity, and microbiota fitness provide new insights into microbiome structure and microbiome targeted therapeutics.Editorial note: This article has been through an editorial process in which the authors decide how to respond to the issues raised during peer review. The Reviewing Editor's assessment is that all the issues have been addressed (see decision letter).
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. Circulating tumor cells (CTCs) found in peripheral blood are originated from solid tumors. They are cells shed by a primary tumor into the vasculature, circulating through bloodstream of cancer patients, and colonizing at distant sites which may form metastatic tumors 1. CTCs are an important biomarker for early tumor diagnosis and early evaluation of disease recurrence and metastatic spread in various types of cancer 2-6. Early detection of CTCs provides high chances for patients to survive before severe cancer growth occurs 7. The CTC count is also an important prognostic factor for patients with metastatic cancer 8-12. For example, a study has shown that the number of CTCs is an independent predictor of survival in patients for breast cancer and prostate cancer 8-10 , and the changes of the CTC count predict the survival in patients for lung cancer 12. However, the identification of the CTCs population is a challenging problem. Various approaches to identifying and isolating CTCs including antibody-based methods and physical-characteristics-based methods have been developed 13-19. This task is difficult because of the low concentration of CTCs existing in a patient's peripheral blood-a few CTCs out of 10 billion blood cells 20,21 , as well as heterogeneity in the characteristics of CTCs 22,23. For example, the mechanism of CTCs maintaining metastatic potential during circulating is not well understood 24 ; CTCs derived from some patients allow a cell line to be established, but CTCs from some others lose the capability of proliferation after a few hours of blood drawing 13. Therefore, the incapability to draw a large volume of blood from patients leads to the need for improvements of CTC isolation methods so that CTCs can be detected in small sample volumes. Further, the inconsistency in the viability of CTCs hinders further explorati...
Summary 34To identify factors that regulate the absolute microbiota and the impact of varied microbiota density on health, we assayed 35 gut microbiota density across mammals, disease, and therapeutic interventions. Physiologic features of the host (carrying 36 capacity) and the fitness of the gut microbiota shape microbiota density. Therapeutic manipulation of microbiota density in 37 mice altered host metabolic and immune homeostasis. In humans, gut microbiota density was reduced in Crohn's disease, 38 ulcerative colitis, and ileal pouch-anal anastomosis. The gut microbiota in recurrent Clostridium difficile infection had lower 39 density and reduced fitness that were restored by fecal microbiota transplantation. Understanding the interplay between 40 microbiota and disease through the conceptual framework of microbiota density, host carrying capacity, and microbiota fitness 41 could provide biomarkers to identify candidates for microbiota therapeutics and monitor their response.
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.
The vascular system in living tissues is a highly organized system that consists of vessels with various diameters for nutrient delivery and waste transport. In recent years, many vessel construction methods have been developed for building vascularized on-chip tissue models. These methods usually focused on constructing vessels at a single scale. In this work, a method that can build a hierarchical and perfusable vessel networks was developed. By providing flow stimuli and proper HUVEC concentration, spontaneous anastomosis between endothelialized lumens and the self-assembled capillary network was induced; thus, a perfusable network containing vessels at different scales was achieved. With this simple method, an in vivolike hierarchical vessel-supported tumor model was prepared and its application in anticancer drug testing was demonstrated. The tumor growth rate was predicted by combining computational fluid dynamics simulation and a tumor growth mathematical model to understand the vessel perfusability effect on tumor growth rate in the hierarchical vessel network. Compared to the tumor model without capillary vessels, the hierarchical vessel-supported tumor shows a significantly higher growth rate and drug delivery efficiency.
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