Short cell cycle duration is a phenotype of human epidermal stem cells.

BACKGROUND: A traditional view is that stem cells (SCs) divide slowly. Meanwhile, both embryonic and pluripotent SCs display a shorter cell cycle duration (CCD) in comparison to more committed progenitors (CPs). METHODS: We examined the in vitro proliferation and cycling behavior of somatic adult human cells using live cell imaging of passage zero keratinocytes and single-cell RNA sequencing. RESULTS: We found two populations of keratinocytes: those with short CCD and protracted near exponential growth, and those with long CCD and terminal differentiation. Applying the ergodic principle, the comparative numbers of cycling cells in S phase in an enriched population of SCs confirmed a shorter CCD than CPs. Further, analysis of single-cell RNA sequencing of cycling adult human keratinocyte SCs and CPs indicated a shortening of both G1 and G2M phases in the SC. CONCLUSIONS: Contrary to the pervasive paradigm, SCs progress through cell cycle more quickly than more differentiated dividing CPs. Thus, somatic human adult keratinocyte SCs may divide infrequently, but divide rapidly when they divide. Additionally, it was found that SC-like proliferation persisted in vitro.

Clinical evaluation of the agreement between virtual and clinical occlusal registrations: A comparative clinical study.

STATEMENT OF PROBLEM: The accuracy of virtual interocclusal records has been evaluated, but clinical studies comparing the clinical output to the virtual design are lacking. PURPOSE: The purpose of this clinical study was to evaluate the agreement between the virtual occlusal scheme designed by a computer-aided design and computer-aided manufacturing software program and the occlusal scheme obtained clinically on the definitive prosthesis assessed with articulating paper. MATERIAL AND METHODS: The virtual occlusal scheme design of 20 single monolithic crowns and their adjacent teeth was obtained by using an intraoral scanning system in 17 participants. These registrations were compared with conventional occlusal records obtained by applying articulating paper in 2 stages: first with 200-µm blue film and the second with 12-µm metallic red articulation tape. The analysis included both the quantity and the quality of the contacts of the conventional occlusal records referred to as the standard method. For accuracy analysis, virtual record sensitivity was calculated per crown as the percentage of true positive virtual contacts of the actual contacts identified by articulating paper. Specificity was also calculated as the percentage of true negative virtual contacts of the actual sites of clearance. RESULTS: The virtual record sensitivity was 98.5 (95% confidence interval [CI] 96-100) for the crowns and 95 (95% CI 85-100) for the adjacent teeth. The virtual record specificity was 88.6 (95% CI 82.4-94.8) for the crowns and 82.6 (95% CI 77.5-87.6) for the adjacent teeth. The agreement between the clinical and virtual contact intensities on the crowns was 83 (95% CI 73-93) and 67.3 (95% CI 56-78.7) for the adjacent teeth. The positive predictive value was 72.83 (95% CI 60-86). The negative predictive value was 100% (95% CI 100-100). CONCLUSIONS: The intraoral scanning system provided clinically sufficient sensitivity and specificity for identifying the occlusal surface contacts of monolithic crowns. A slight decrease was detected in the system's sensitivity and specificity in identifying contacts on adjacent teeth and a larger decrease identifying the intensity of these contacts.

Multiple two-dimensional active shape model framework for right ventricular segmentation.

Segmentation of the right ventricle (RV) in MRI short axis images is very challenging due to its complex shape and various appearance among the different subjects and cross-sections. Active shape models (ASM) have shown potential for segmenting the complex structures, including the RV, through two formulations: two- and three-dimensional modeling with a reported trade-off between accuracy and complexity of each formulation. In this work, we propose a new framework for modeling the RV surface using multiple 2D contours, where information from multiple cross-sectional images are incorporated into the same model. The proposed method was tested using cardiac MRI images from 56 human subjects. Compared to a golden reference of manually delineated RV contours, the proposed method resulted in significantly lower error than (almost one half) that of the conventional 2D ASM especially at the apical slices. The mean absolute distance of the proposed method was 2.9 ± 2 mm while the conventional 2D ASM resulted in an error of 6.6 ± 4.5 mm. In addition, the computation time of the proposed method was 5 s compared to 4 ± 1 min previously reported for the 3D ASM formulation.

Decreased p53 is associated with a decline in asymmetric stem cell self-renewal in aged human epidermis.

With age, the epidermis becomes hypoplastic and hypoproliferative. Hypoproliferation due to aging has been associated with decreased stem cell (SC) self-renewal in multiple murine tissues. The fate of SC self-renewal divisions can be asymmetric (one SC, one committed progenitor) or symmetric (two SCs). Increased asymmetric SC self-renewal has been observed in inflammatory-mediated hyperproliferation, while increased symmetric SC self-renewal has been observed in cancers. We analyzed SC self-renewal divisions in aging human epidermis to better understand the role of SCs in the hypoproliferation of aging. In human subjects, neonatal to 78 years, there was an age-dependent decrease in epidermal basal layer divisions. The balance of SC self-renewal shifted toward symmetric SC self-renewal, with a decline in asymmetric SC self-renewal. Asymmetric SC divisions maintain epidermal stratification, and this decrease may contribute to the hypoplasia of aging skin. P53 decreases in multiple tissues with age, and p53 has been shown to promote asymmetric SC self-renewal. Fewer aged than adult ALDH+CD44+ keratinocyte SCs exhibited p53 expression and activity and Nutlin-3 (a p53 activator) returned p53 activity as well as asymmetric SC self-renewal divisions to adult levels. Nutlin-3 increased Notch signaling (NICD, Hes1) and DAPT inhibition of Notch activation prevented Nutlin-3 (p53)-induced asymmetric SC self-renewal divisions in aged keratinocytes. These studies indicate a role for p53 in the decreased asymmetric SC divisions with age and suggest that in aged keratinocytes, Notch is required for p53-induced asymmetric SC divisions.

Perceptions of Clinical Dental Students Toward Online Education During the COVID-19 Crisis: An Egyptian Multicenter Cross-Sectional Survey.

Objectives: To evaluate the perceptions of clinical dental students on the role of online education in providing dental education during the COVID-19 crisis. Materials and Methods: A cross-sectional online survey was sent to four Egyptian dental schools from the 20th of January 2021 to the 3rd of February 2021. Survey questions included the demographics, uses, experiences, perceived benefits, and barriers of distance learning in dentistry during the COVID-19 pandemic. Responses were collected from the clinical dental school students. Categorical data were presented as frequencies (n) and percentages (%) and were analyzed using Fisher's exact test. Results: Three hundred thirty-seven clinical dental students across four Egyptian dental schools responded. Most students used either Google Classroom or Microsoft Teams to access the online content. The data showed that the COVID-19 pandemic affected the academic performance of most participants (97.4%) with varying degrees. On average, students were neutral when asked to rate the online lectures, but did not find online practical education as effective (81.3%) as online theoretical teaching. The commonly described barriers to online teaching included loss of interaction with educators, inappropriateness in gaining clinical skills, and the instability of the internet connection. Conclusion: Despite the reported benefits, clinical dental students in Egypt preferred the hybrid approach in dental education as distance learning represented a prime challenge to gain adequate clinical dental skills.

Detecting liver fibrosis using a machine learning-based approach to the quantification of the heart-induced deformation in tagged MR images.

Liver disease causes millions of deaths per year worldwide, and approximately half of these cases are due to cirrhosis, which is an advanced stage of liver fibrosis that can be accompanied by liver failure and portal hypertension. Early detection of liver fibrosis helps in improving its treatment and prevents its progression to cirrhosis. In this work, we present a novel noninvasive method to detect liver fibrosis from tagged MRI images using a machine learning-based approach. Specifically, coronal and sagittal tagged MRI imaging are analyzed separately to capture cardiac-induced deformation of the liver. The liver is manually delineated and a novel image feature, namely, the histogram of the peak strain (HPS) value, is computed from the segmented liver region and is used to classify the liver as being either normal or fibrotic. Classification is achieved using a support vector machine algorithm. The in vivo study included 15 healthy volunteers (10 males; age range 30-45 years) and 22 patients (15 males; age range 25-50 years) with liver fibrosis verified and graded by transient elastography, and 10 patients only had a liver biopsy and were diagnosed with a score of F3-F4. The proposed method demonstrates the usefulness and efficiency of extracting the HPS features from the sagittal slices for patients with moderate fibrosis. Cross-validation of the method showed an accuracy of 83.7% (specificity = 86.6%, sensitivity = 81.8%).

Influence of the analysis technique on estimating hepatic iron content using MRI.

PURPOSE: To investigate the effect of the analysis technique on estimating hepatic iron content using MRI. MATERIALS AND METHODS: We evaluated the influences of single-exponential (EXP), bi-exponential (BEXP), and exponential-plus-constant (CEXP) models; and pixel-wise (MAP), average (AVG), and median (MED) signal calculation methods on T2* measurement using numerical simulations, calibrated phantoms, and nine patients scanned on 3 Tesla MRI, based on regression, correlation, and t-test statistical analysis. RESULTS: The T2* measurement error varied from 9 to 51% in the numerical simulations (T2*: 5-20 ms), depending on signal-to-noise ratio (SNR; range: 8-233) with significant (P < 0.05) difference between actual and predicted values. The MAP method performed well (error < 10%) at high SNR (>100), but resulted in severe estimation errors at low SNR (<50). The EXP model resulted in significant measurement differences (P < 0.05) compared with all other methods, irrespective of SNR. In vivo T2* values ranged from 3.1 to 53.6 ms, depending on the amount of iron overload and implemented analysis method. The BEXP (range: 3.7-50 ms) and CEXP (range: 3.8-53.6 ms) models, and the AVG (range: 3.2-38.8 ms) and MED (range: 3.1-38.5 ms) methods provided more accurate measurements than the EXP model (range: 3.1-18.3 ms) and MAP (range: 3.8-53.6 ms) method, respectively (P < 0.05). The BEXP and CEXP models provided very similar measurements (P > 0.87). Similarly, the AVG and MED methods provided very similar results (P > 0.97), with slightly better performance of the AVG method. CONCLUSION: Different analysis techniques show different performances based on the fitting model and signal calculation method. Based on this study, the CEXP model and AVG method are recommended due to simpler implementation and less influence by the selected analysis region. J. Magn. Reson. Imaging 2016;44:1448-1455.

MRI T2* imaging for assessment of liver iron overload: study of different data analysis approaches.

Background Recently, magnetic resonance imaging (MRI) has been established as an effective technique for evaluating iron overload by measuring T2* in the liver. Purpose To investigate the effects of various factors associated with T2* calculation on the resulting measurement and to determine the analysis criterion that provides the most accurate T2* measurements. Material and Methods Both phantom and in vivo MRI experiments were conducted to study the effects of the selected region of interest (ROI) location and size, signal-averaging method, exponential-fitting model, echo truncation, iron-overload severity, and inter-/intra-observer variabilities on T2* measurements. The results were compared to reference values from the scanner processing software. Results The pixel-by-pixel calculation method provided results in better agreement with the reference values from the MRI scanner than the average or median methods. The choice of the exponential fitting model affected the results, depending on signal-to-noise ratio, number of echoes, minimum and maximum echo times, and tissue composition inside the selected ROI. The single-exponential model resulted in smaller error than the bi-exponential or exponential-plus-constant models, where the latter two models showed similar results. The relative performance of the different models and methods was not affected by the degree of iron-overload. Conclusion Various factors associated with the adopted T2* calculation method affect the resulting measurement. In this study, the pixel-by-pixel calculation method and single-exponential model provided the most accurate results based on the conducted phantom and in vivo MRI experiments.

Cine viability magnetic resonance imaging of the heart without increased scan time.

Cardiac magnetic resonance imaging (MRI) provides information about myocardial morphology, function, and viability from cine, tagged, and late gadolinium enhancement (LGE) images, respectively. While the cine and tagged images are acquired in a time-resolved fashion, the LGE images are acquired at a single timeframe. The purpose of this work is to develop a method for generating cine LGE images without additional scan time. The motion field is extracted from the tagged images, and is then used to guide the deformation of the infarcted region from the acquired LGE image at the acquired timeframe to any other timeframe. Major techniques for motion estimation, including harmonic phase (HARP) and optical flow analysis, are tested in this work for motion estimation. The proposed method is tested on numerical phantom and images from four human subjects. The generated cine LGE images showed both viability and wall motion information in the same set of images without additional scan time or image misregistration problems. The band-pass optical flow analysis resulted in the most accurate motion estimation compared to other methods, especially HARP, which fails to track points at the myocardial boundary. Infarct transmurality from the generated images showed good agreement with myocardial strain, and wall thickening showed good agreement with that measured from conventional cine images. In conclusion, the developed technique allows for generating cine LGE images that enable simultaneous display of wall motion and viability information. The generated images could be useful for estimating myocardial contractility reserve and for treatment prognosis.

Assessment of cardiac mass from tagged magnetic resonance images.

PURPOSE: Tagged and cine magnetic resonance imaging (tMRI and cMRI) techniques are used for evaluating regional and global heart function, respectively. Measuring global function parameters directly from tMRI is challenging due to the obstruction of the anatomical structure by the tagging pattern. The purpose of this study was to develop a method for processing the tMRI images to improve the myocardium-blood contrast in order to estimate global function parameters from the processed images. MATERIALS AND METHODS: The developed method consists of two stages: (1) removing the tagging pattern based on analyzing and modeling the signal distribution in the image's k-space, and (2) enhancing the blood-myocardium contrast based on analyzing the signal intensity variability in the two tissues. The developed method is implemented on images from twelve human subjects. RESULTS: Ventricular mass measured with the developed method showed good agreement with that measured from gold-standard cMRI images. Further, preliminary results on measuring ventricular volume using the developed method are presented. CONCLUSION: The promising results in this study show the potential of the developed method for evaluating both regional and global heart function from a single set of tMRI images, with associated reduction in scan time and patient discomfort.

Tag removal in cardiac tagged MRI images using coupled dictionary learning.

Tagged Magnetic Resonance Imaging (tMRI) is considered to be the gold standard for quantitative assessment of the cardiac local functions. However, the tagging patterns and low myocardium-to-blood-pool contrast of tagged images bring great challenges to cardiac image processing and analysis tasks such as myocardium segmentation and tracking. Hence, there has been growing interest in techniques for removing tagging lines. In this work, a method for removing tagging patterns in tagged MR images using a coupled dictionary learning (CDL) model is proposed. In this model, identical sparse representations are assumed for image patches in the tagged MRI and corresponding cine MRI image spaces. First, we learn a dictionary for the tagged MRI image space. Then, we compute a dictionary for the cine MRI image space so that corresponding tagged and cine patches have the same sparse codes in terms of their respective dictionaries. Finally, in order to produce the de-tagged (cine version) of a test tagged image, the sparse codes of the tagged patches and the trained cine dictionary are used together to construct the de-tagged patches. We have tested this tag removal method on a dataset of tagged cardiac MR images. Our experimental results compared favorably with a recently proposed tag removal method that removes tags in the frequency domain using an optimal band-stop filter of harmonic peaks.

Accurate estimation of the myocardium global function from reduced magnetic resonance image acquisitions.

Evaluating the heart global function from magnetic resonance images is based on estimating a number of functional parameters such as the left ventricular (LV) volume, LV mass, ejection fraction, and stroke volume. Estimating these parameters requires accurate calculation of the volumes enclosed by the inner and outer surfaces of the LV chamber at the max contraction and relaxation states of the heart. Currently, this is achieved through acquisition and segmentation of a large number of short-axis (SAX) views of the LV, which is time-consuming and expensive. Reducing the number of acquisitions results in undersampling the LV surfaces and hence increases the calculation errors. In this work, we describe and evaluate a method for estimating the cardiac parameters from a small number of image acquisitions that includes one long-axis (LAX) view of the LV. In this method, the LAX contour is used to swipe the SAX contours to fill in the missed LV surface between the SAX slices. Results on 25 patients and CT phantoms shows that, given the same number of slices, the proposed method is superior to other methods.

A collaborative resource to build consensus for automated left ventricular segmentation of cardiac MR images.

A collaborative framework was initiated to establish a community resource of ground truth segmentations from cardiac MRI. Multi-site, multi-vendor cardiac MRI datasets comprising 95 patients (73 men, 22 women; mean age 62.73±11.24years) with coronary artery disease and prior myocardial infarction, were randomly selected from data made available by the Cardiac Atlas Project (Fonseca et al., 2011). Three semi- and two fully-automated raters segmented the left ventricular myocardium from short-axis cardiac MR images as part of a challenge introduced at the STACOM 2011 MICCAI workshop (Suinesiaputra et al., 2012). Consensus myocardium images were generated based on the Expectation-Maximization principle implemented by the STAPLE algorithm (Warfield et al., 2004). The mean sensitivity, specificity, positive predictive and negative predictive values ranged between 0.63 and 0.85, 0.60 and 0.98, 0.56 and 0.94, and 0.83 and 0.92, respectively, against the STAPLE consensus. Spatial and temporal agreement varied in different amounts for each rater. STAPLE produced high quality consensus images if the region of interest was limited to the area of discrepancy between raters. To maintain the quality of the consensus, an objective measure based on the candidate automated rater performance distribution is proposed. The consensus segmentation based on a combination of manual and automated raters were more consistent than any particular rater, even those with manual input. The consensus is expected to improve with the addition of new automated contributions. This resource is open for future contributions, and is available as a test bed for the evaluation of new segmentation algorithms, through the Cardiac Atlas Project (www.cardiacatlas.org).

Hybrid intensity- and phase-based optical flow tracking of tagged MRI.

Accurate tracking of the myocardium tissues in tagged Magnetic Resonance Images (MRI) is essential for evaluating the cardiac function. Current tracking methods utilize either the image intensity or the image phase as landmarks that can be tracked. In either case, the performance is vulnerable to the image quality and the fading of the tag lines. In this work, we propose a hybrid optical flow tracking method that combines both the intensity and the phase features of the image. The method is validated using numerical cardiac phantom as well as real MRI data experiments. Both experiments showed that the proposed method outperforms current intensity-based optical flow tracking and the phase-based HARP method with maximum error of 1 pixel at extreme conditions of tag fading.

The influence of the analysis technique on estimating liver iron overload using magnetic resonance imaging T2* quantification.

Iron toxicity is the major cause of tissue damage in patients with iron overload. Iron deposits mainly in the liver, where its concentration closely correlates with whole body iron overload. Different techniques have been proposed for estimating iron content, with liver biopsy being the gold standard despite its invasiveness and influence by sampling error. Recently, magnetic resonance imaging (MRI) has been established as an effective technique for evaluating iron overload by measuring T2(*) in the liver. However, various factors associated with the adopted analysis technique, mainly the exponential fitting model and signal averaging method, affect the resulting measurements. In this study, we evaluate the influences of these factors on T2(*) measurement in numerical phantom, calibrated phantoms, and nine patients with different degrees of iron overload. The results show different performances among the fitting models and signal averaging methods, which are affected by SNR, image quality and signal homogeneity inside the selected ROI for analysis.

Automatic synthesis of cine viability MRI images for evaluation of coronary heart disease.

Coronary heart disease (CHD) is the leading cause of death worldwide. Cardiac magnetic resonance imaging (MRI) is a valuable imaging modality, as it can noninvasively provide information about myocardial function, viability, and morphology. Viability delayed-enhancement (DE) images are acquired at a single timeframe while myocardial functional (tagged) images are acquired as a cine loop of timeframes throughout the cardiac cycle. In this work, we propose a method for estimating DE images at all timeframes in the cardiac cycle without additional scan time to show both viability and functional information in the same image. The method is based on generating a dense motion field of the heart from the acquired tagged images, and then applying the extracted field to the acquired DE image. The developed technique is accurate in generating cine DE images and providing simultaneous information about myocardial viability and wall motion for comprehensive patient evaluation and optimal treatment selection.

Improved Harmonic Phase (HARP) Method for Motion Tracking a Tagged Cardiac MR images.

The diagnosis of cardiovascular disease requires precise assessment of both morphology and function of the heart. Currently, magnetic resonance imaging (MRI) provides a useful tool for accurate and reproducible assessment of regional function of the left ventricle noninvasively. MR tagging produces images of the heart that can be analyzed using harmonic phase (HARP) method to describe the regional function of the heart. In order to calculate regional function, a circular mesh is manually built at a specific timeframe, after which the points of such mesh are tracked using the HARP technique. The tracking is not perfect and some individual points on the mesh could fail in tracking. In this work, a new method is presented to improve the tracking by combining HARP with active contour methods. This modified HARP technique is more robust than the previous HARP technique.