Searching for Preclinical Models of Acute Decompensated Heart Failure: a Concise Narrative Overview and a Novel Swine Model

Cardiovascular Drugs and Therapy - Available animal models of acute heart failure (AHF) and their limitations are discussed herein. A novel and preclinically relevant porcine model of decompensated...     

Can leukocyte telomere shortening be a possible biomarker to track Huntington's disease progression?

<正>Huntington's disease(HD):HD is an autosomal dominant neurodegenerative disease,caused by a CAG trinucleotide repeat expansion in the first exon of the HTT gene encoding the huntingtin protein.The mutant protein contains an expanded polyglutamine sequence that confers a toxic gain-of-function and causes neurodegeneration.Moreover,several studies indicate that loss of the normal protein beneficial     

A perivascular niche in the bone marrow hosts quiescent and proliferating tumorigenic colorectal cancer cells

Disseminated tumor cells (dTCs) can frequently be detected in the bone marrow (BM) of colorectal cancer (CRC) patients, raising the possibility that the BM serves as a reservoir for metastatic tumor cells. Identification of dTCs in BM aspirates harbors the potential of assessing therapeutic outcome and directing therapy intensity with limited risk and effort. Still, the functional and prognostic relevance of dTCs is not fully established. We have previously shown that CRC cell clones can be traced to the BM of mice carrying patient-derived xenografts. However, cellular interactions, proliferative state and tumorigenicity of dTCs remain largely unknown. Here, we applied a coculture system modeling the microvascular niche and used immunofluorescence imaging of the murine BM to show that primary CRC cells migrate toward endothelial tubes. dTCs in the BM were rare, but detectable in mice with xenografts from most patient samples (8/10) predominantly at perivascular sites. Comparable to primary tumors, a substantial fraction of proliferating dTCs was detected in the BM. However, most dTCs were found as isolated cells, indicating that dividing dTCs rather separate than aggregate to metastatic clones—a phenomenon frequently observed in the microvascular niche model. Clonal tracking identified subsets of self-renewing tumor-initiating cells in the BM that formed tumors out of BM transplants, including one subset that did not drive primary tumor growth. Our results indicate an important role of the perivascular BM niche for CRC cell dissemination and show that dTCs can be a potential source for tumor relapse and tumor heterogeneity.     

Biparametric prostate MRI: impact of a deep learning-based software and of quantitative ADC values on the inter-reader agreement of experienced and inexperienced readers

Objective

To investigate the impact of an artificial intelligence (AI) software and quantitative ADC (qADC) on the inter-reader agreement, diagnostic performance, and reporting times of prostate biparametric MRI (bpMRI) for experienced and inexperienced readers.

Materials and methods

A total of 170 multiparametric MRI (mpMRI) of patients with suspicion of prostate cancer (PCa) were retrospectively reviewed by one experienced and one inexperienced reader three times, following a wash-out period. First, only the bpMRI sequences, including T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI) sequences, and apparent diffusion coefficient (ADC) maps, were used. Then, bpMRI and quantitative ADC values were used. Lastly, bpMRI and the AI software were used. Inter-reader agreement between the two readers and between each reader and the mpMRI original reports was calculated. Detection rates and reporting times were calculated for each group.

Results

Inter-reader agreement with respect to mpMRI was moderate for bpMRI, Quantib, and qADC for both the inexperienced (weighted k of 0.42, 0.45, and 0.41, respectively) and the experienced radiologists (weighted k of 0.44, 0.46, and 0.42, respectively). Detection rate of PCa was similar between the inexperienced (0.24, 0.26, and 0.23) and the experienced reader (0.26, 0.27 and 0.27), for bpMRI, Quantib, and qADC, respectively. Reporting times were lower for Quantib (8.23, 7.11, and 9.87 min for the inexperienced reader and 5.62, 5.07, and 6.21 min for the experienced reader, for bpMRI, Quantib, and qADC, respectively).

Conclusions

AI and qADC did not have a significant impact on the diagnostic performance of both readers. The use of Quantib was associated with lower reporting times.