Functional and kinetic characterization of granulocyte colony-stimulating factor-primed CD34- human stem cells

We assessed the functional properties and the kinetic status in vitro, and the engraftment potential in vivo of human haematopoietic stem cells according to the expression of CD34 antigen. Lin-CD34- and Lin-CD34+ cells were isolated from granulocyte colony-stimulating factor-primed peripheral blood (PB) cells of healthy donors. The CD34- cell fraction did not contain either clonogenic cells in semisolid culture or long-term culture initiating cells (LTC-IC). However, stroma-dependent liquid cultures and cytokines induced CD34 expression on a minority of stem cells, acquisition of clonogenic capacity and generation of LTC-IC. Significantly higher percentages of quiescent G0 cells and lower percentages of cycling G1 cells were found in Lin-CD34- cells when compared with Lin-CD34+ cells. Kinetic quiescence of Lin-CD34- cells was associated with a significantly higher expression of the negative regulators of the cell cycle, p27Kip1 and p21(cip1/waf1). Cytokine-mediated induction of CD34, in vitro, resulted in cycling of stem cells and downregulation of p27. There was a higher rate of human long-term engraftment in immunocompromised non-obese diabetic (NOD)/recombination activating gene 1null and NOD/severe combined immunodeficient-beta2microglobulin(null) mice injected with CD34+ cells. Thus, our study indicated that CD34 expression on human PB stem cells was associated with haematopoietic activity, cell-cycle recruitment and downregulation of p27Kip1 in vitro and higher engraftment capacity in vivo.     

Artificial intelligence technologies for the detection of colorectal lesions: The future is now

Several studies have shown a significant adenoma miss rate up to 35% during screening colonoscopy, especially in patients with diminutive adenomas. The use of artificial intelligence(AI) in colonoscopy has been gaining popularity by helping endoscopists in polyp detection, with the aim to increase their adenoma detection rate(ADR) and polyp detection rate(PDR) in order to reduce the incidence of interval cancers. The efficacy of deep convolutional neural network(DCNN)-based AI system for polyp detection has been trained and tested in ex vivo settings such as colonoscopy still images or videos. Recent trials have evaluated the real-time efficacy of DCNN-based systems showing promising results in term of improved ADR and PDR. In this review we reported data from the preliminary ex vivo experiences and summarized the results of the initial randomized controlled trials.