We report here that cells co-purifying with mesenchymal stem cells--termed here multipotent adult progenitor cells or MAPCs--differentiate, at the single cell level, not only into mesenchymal cells, but also cells with visceral mesoderm, neuroectoderm and endoderm characteristics in vitro. When injected into an early blastocyst, single MAPCs contribute to most, if not all, somatic cell types. On transplantation into a non-irradiated host, MAPCs engraft and differentiate to the haematopoietic lineage, in addition to the epithelium of liver, lung and gut. Engraftment in the haematopoietic system as well as the gastrointestinal tract is increased when MAPCs are transplanted in a minimally irradiated host. As MAPCs proliferate extensively without obvious senescence or loss of differentiation potential, they may be an ideal cell source for therapy of inherited or degenerative diseases.

This pilot study explores the potential of noninvasive diffuse correlation spectroscopy (DCS) and diffuse reflectance spectroscopy (DRS) for monitoring early relative blood flow (rBF), tissue oxygen saturation (StO(2)), and total hemoglobin concentration (THC) responses to chemo-radiation therapy in patients with head and neck tumors. rBF, StO(2), and THC in superficial neck tumor nodes of eight patients are measured before and during the chemo-radiation therapy period. The weekly rBF, StO(2), and THC kinetics exhibit different patterns for different individuals, including significant early blood flow changes during the first two weeks. Averaged blood flow increases (52.7+/-9.7)% in the first week and decreases (42.4+/-7.0)% in the second week. Averaged StO(2) increases from (62.9+/-3.4)% baseline value to (70.4+/-3.2)% at the end of the second week, and averaged THC exhibits a continuous decrease from pretreatment value of (80.7+/-7.0) [microM] to (73.3+/-8.3) [microM] at the end of the second week and to (63.0+/-8.1) [microM] at the end of the fourth week of therapy. These preliminary results suggest daily diffuse-optics-based therapy monitoring is feasible during the first two weeks and may have clinical promise.

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Background The Liver Imaging Reporting and Data System (LI‐RADS) is widely adopted for noninvasive diagnosis of hepatocellular carcinoma (HCC). It's updated to version 2018 recently, with some major changes compared with v2017. However, the diagnostic performance of LI‐RADS v2018 and its difference with v2017 are yet to be validated. Purpose To compare the diagnostic performances of LI‐RADS on MR for diagnosing HCC between v2017 and v2018. Study Type Retrospective. Subjects In all, 181 patients with 217 hepatic observations (146 HCCs, 16 non‐HCC malignancies and 55 benign lesions) with liver MRI and pathological or follow‐up imaging diagnoses. Field Strength/Sequence 1.5 T or 3 T MRI. Dual‐echo T1WI, T2WI, diffusion‐weighted imaging, and a liver acquisition with volume acceleration. AssessmentImages were independently interpreted by three radiologists, and then in consensus for observations with different LR categories, according to LI‐RADS v2017 and v2018, separately. Statistical Tests Sensitivity, specificity, accuracy, positive predictive value (PPV), negative predictive value (NPV), positive likelihood ratio (+LR), and Youden index. Results When adopting LR‐5 as a predictor of HCC, the sensitivity (80.8% vs. 71.2%), NPV (69.6% vs. 60.7%), and accuracy (83.9% vs. 77.9%) were all increased for LI‐RADS v2018 compared with v2017, with a greater Youden index (0.709 vs. 0.627). However, the diagnostic performances of MRI for diagnosing HCC were not changed while adopting LR‐4/5 as a predictor. The threshold growths of 76% (19/25) observations in v2017 were revised to subthreshold growth in v2018, and 16 LR‐4 observations in v2017 were changed to LR‐5 based on v2018. Data Conclusion The diagnostic performance of LI‐RADS v2018 for diagnosing HCC is superior to v2017, with a greater sensitivity, NPV, and accuracy. The revisions in v2018 mainly affect the categorization when adopting LR‐5 as a predictor of HCC. Level of Evidence: 4 Technical Efficacy Stage: 2 J. Magn. Reson. Imaging 2019;50:746–755.

Most bone loss during the development of osteoporosis occurs in cortical bone at the peripheral skeleton. Decreased cortical thickness (Ct.Th) and the prevalence of large pores at the tibia are associated with reduced bone strength at the hip. Ct.Th and cortical sound velocity, i.e., a surrogate marker for changes of cortical porosity (Ct.Po), are key biomarkers for the identification of patients at high fracture risk. In this study, we have developed a method using a conventional ultrasound array transducer to determine thickness (Ct.Th) and the compressional sound velocity propagating in the radial bone direction (Ct.ν 11 ) using a refraction-corrected multifocus imaging approach. The method was validated in-silico on porous bone plate models using a 2-D finite-difference time-domain method and ex vivo on plate-shaped plastic reference materials and on plate-shaped cortical bovine tibia samples. Plane-wave pulse-echo measurements provided reference values to assess precision and accuracy of our method. In-silico results revealed the necessity to account for inclinationdependent transmission losses at the bone surface. Moreover, the dependence of Ct.ν 11 on both porosity and pore density was observed. Ct.Th and Ct.ν 11 obtained ex vivo showed a high correlation (R2 > 0.99) with reference values. The ex-vivo accuracy and precision for Ct.ν 11 were 29.9 m/s and 0.94%, respectively, and those for Ct.Th were 0.04 mm and 1.09%, respectively. In conclusion, this numerical and experimental study demonstrates an accurate and precise estimation of Ct.Th and Ct.ν 11 . The developed multifocus technique may have high clinical potential to improve fracture risk prediction using noninvasive and nonionizing conventional ultrasound technology with image guidance. Index Terms-Medical beamforming and beam steering, medical signal and image processing, medical tissue characterization, pulse-echo ultrasound. I. INTRODUCTIONO STEOPOROSIS (OP) is one of the most important global health problems of our aging population, which reduces mobility and quality of life, increases mortality,

Multi-channel pulse-echo ultrasound using linear arrays and single-channel data acquisition systems opens new perspectives for the evaluation of cortical bone. In combination with spectral backscatter analysis, it can provide quantitative information about cortical microstructural properties. We present a numerical study, based on the finite-difference time-domain (FDTD) method, to estimate the backscatter cross-section of randomly distributed circular pores in a bone matrix. A model that predicts the backscatter coefficient using arbitrary pore diameter distributions was derived. In an ex-vivo study on 19 human tibia bones (6 males, 13 females, 83.7 ± 8.4 years), multidirectional ultrasound backscatter measurements were performed using an ultrasound scanner equipped with a 6-MHz 128-element linear array with sweep motor control. A normalized depth-dependent spectral analysis was performed to derive backscatter and attenuation coefficients. Site-matched reference values of tissue acoustic impedance Z, cortical thickness Ct.Th, pore density Ct.Po.Dn, porosity Ct.Po and characteristic parameters of the pore diameter (Ct.Po.Dm) distribution were obtained from 100-MHz scanning-acoustic microscopy images. Proximal femur areal bone mineral density (aBMD), stiffness S and ultimate force Fu from the same donors were available from a previous study. All pore structure and material properties could be predicted using linear combinations of backscatter parameters with median to high accuracy (0.28  adjusted R²  0.59). The combination of cortical thickness and backscatter parameter provided similar or better prediction accuracies than aBMD. For the first time, a method for the non-invasive assessment of the pore diameter distribution in cortical bone by ultrasound is proposed. The combined assessment of cortical thickness, sound velocity, and pore size distribution in a mobile, non-ionizing measurement system could have a major impact to prevent osteoporotic fractures.

Objectives We aimed to understand the clinical characteristics and better predict the prognosis of patients with mucosal melanoma of the head and neck (MMHN) using a nomogram. Methods Three hundred patients with nometastatic MMHN were included. Multivariable Cox regression was performed to analyze independent prognostic factors for overall survival (OS), disease-free survival (DFS), distant metastasis-free survival (DMFS), and locoregional relapse-free survival (LRRFS), and these factors were used to develop a nomogram. Concordance indexes (C-indexes), calibration plots, and receiver operating characteristic (ROC) analysis were performed to test the predictive performance of the nomogram in both the primary (n = 300) and validation cohorts (n = 182). Results The primary tumor site, T stage and N stage were independent risk factors for survival and were included in the nomogram to predict the 3- and 5-year OS, DFS, DMFS, and LRRFS in the primary cohort. The C-indexes (both > 0.700), well-fit calibration plots, and area under the ROC curve (both > 0.700) indicated the high diagnostic accuracy of the nomogram, in both the primary and validation cohorts. The patients were divided into three groups (high-risk, intermediate-risk, and low-risk groups) according to their nomogram scores. The survival curves of OS, DFS, DMFS, and LRRFS were well separated by the risk groups in both cohorts (all P < 0.001). Conclusions The nomogram can stratify MMHN patients into clinically meaningful taxonomies to provide individualized treatment.

The pandemic of coronavirus Disease 2019 (COVID-19) caused enormous loss of life globally. 1-3 Case identification is critical. The reference method is using real-time reverse transcription PCR (rRT-PCR) assays, with limitations that may curb its prompt large-scale application. COVID-19 manifests with chest computed tomography (CT) abnormalities, some even before the onset of symptoms. We tested the hypothesis that application of deep learning (DL) to the 3D CT images could help identify COVID-19 infections. Using the data from 920 COVID-19 and 1,073 non-COVID-19 pneumonia patients, we developed a modified DenseNet-264 model, COVIDNet, to classify CT images to either class. When tested on an independent set of 233 COVID-19 and 289 non-COVID-19 patients. COVIDNet achieved an accuracy rate of 94.3% and an area under the curve (AUC) of 0.98. Application of DL to CT images may improve both the efficiency and capacity of case detection and long-term surveillance.