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Myelinated retinal nerve fibers (MRNFs) result from a developmental anomaly in which ectopic oligodendrocytes myelinate retinal ganglion cell fibers. These fibers typically remain stable over time in the absence of pathology. We identified unilateral MRNFs in a patient during an ophthalmological examination associated with ocular hypertension. Over an 8-year follow-up period, we observed a progressive decrease in the retinal nerve fiber layer and a rarefaction of MRNFs, which we attributed to the lack of regular follow-up and treatment compliance. The absence of neuropathy control in this patient makes this description close to the natural evolution of the disease. Similar progressive disappearance of MRNFs has been observed in cases of Behcet's disease, pituitary adenoma, and open-angle glaucoma. In patients with known MRNFs, their disappearance should alert clinicians to potential optic nerve damage and prompt further examinations to determine the underlying cause. Keywords: Open-angle glaucoma, myelinated retinal nerve fibers, glaucomatous optic neuropathy

During the development of the retina and the nervous system, high levels of energy are required by the axons of retinal ganglion cells (RGCs) to grow towards their brain targets. This energy demand leads to an increase of glycolysis and L-lactate concentrations in the retina. L-lactate is known to be the endogenous ligand of the GPR81 receptor. However, the role of L-lactate and its receptor in the development of the nervous system has not been studied in depth. In the present study, we used immunohistochemistry to show that GPR81 is localized in different retinal layers during development, but is predominantly expressed in the RGC of the adult rodent. Treatment of retinal explants with L-lactate or the exogenous GPR81 agonist 3,5-DHBA altered RGC growth cone (GC) morphology (increasing in size and number of filopodia) and promoted RGC axon growth. These GPR81-mediated modifications of GC morphology and axon growth were mediated by protein kinases A and C, but were absent in explants from gpr81−/− transgenic mice. Living gpr81−/− mice showed a decrease in ipsilateral projections of RGCs to the dorsal lateral geniculate nucleus (dLGN). In conclusion, present results suggest that L-lactate and its receptor GPR81 play an important role in the development of the visual nervous system.

Objectives: We aimed to study classical, publicly available convolutional neural networks (3D-CNNs) using a combination of several cine-MR orientation planes for the estimation of left ventricular ejection fraction (LVEF) without contour tracing. Methods: Cine-MR examinations carried out on 1082 patients from our institution were analysed by comparing the LVEF provided by the CVI42 software (V5.9.3) with the estimation resulting from different 3D-CNN models and various combinations of long- and short-axis orientation planes. Results: The 3D-Resnet18 architecture appeared to be the most favourable, and the results gradually and significantly improved as several long-axis and short-axis planes were combined. Simply pasting multiple orientation views into composite frames increased performance. Optimal results were obtained by pasting two long-axis views and six short-axis views. The best configuration provided an R2 = 0.83, a mean absolute error (MAE) = 4.97, and a root mean square error (RMSE) = 6.29; the area under the ROC curve (AUC) for the classification of LVEF < 40% was 0.99, and for the classification of LVEF > 60%, the AUC was 0.97. Internal validation performed on 149 additional patients after model training provided very similar results (MAE 4.98). External validation carried out on 62 patients from another institution showed an MAE of 6.59. Our results in this area are among the most promising obtained to date using CNNs with cardiac magnetic resonance. Conclusion: (1) The use of traditional 3D-CNNs and a combination of multiple orientation planes is capable of estimating LVEF from cine-MRI data without segmenting ventricular contours, with a reliability similar to that of traditional methods. (2) Performance significantly improves as the number of orientation planes increases, providing a more complete view of the left ventricle.

Although the benefits of physical exercise to preserve bone quality are now widely recognized, the intimate mechanisms leading to the underlying cell responses still require further investigations. Interval training running, for instance, appears as a generator of impacts on the skeleton, and particularly on the progenitor cells located in the bone marrow. Therefore, if this kind of stimulus initiates bone cell proliferation and differentiation, the activation of a devoted signaling pathway by mechano-transduction seems likely. This study aimed at investigating the effects of an interval running program on the appearance of the zinc finger protein FHL2 in bone cells and their anatomical location. Twelve 5-week-old male Wistar rats were randomly allocated to one of the following groups (n = 6 per group): sedentary control (SED) or high-intensity interval running (EX, 8 consecutive weeks). FHL2 identification in bone cells was performed by immuno-histochemistry on serial sections of radii. We hypothesized that impacts generated by running could activate, in vivo, a specific signaling pathway, through an integrin-mediated mechano-transductive process, leading to the synthesis of FHL2 in bone marrow cells. Our data demonstrated the systematic appearance of FHL2 (% labeled cells: 7.5%, p < 0.001) in bone marrow obtained from EX rats, whereas no FHL2 was revealed in SED rats. These results suggest that the mechanical impacts generated during high-intensity interval running activate a signaling pathway involving nuclear FHL2, such as that also observed with dexamethasone administration. Consequently, interval running could be proposed as a non-pharmacological strategy to contribute to bone marrow cell osteogenic differentiation.

The aim of this work was to compare the classification of cardiac MR-images of AL versus ATTR amyloidosis by neural networks and by experienced human readers. Cine-MR images and late gadolinium enhancement (LGE) images of 120 patients were studied (70 AL and 50 TTR). A VGG16 convolutional neural network (CNN) was trained with a 5-fold cross validation process, taking care to strictly distribute images of a given patient in either the training group or the test group. The analysis was performed at the patient level by averaging the predictions obtained for each image. The classification accuracy obtained between AL and ATTR amyloidosis was 0.750 for cine-CNN, 0.611 for Gado-CNN and between 0.617 and 0.675 for human readers. The corresponding AUC of the ROC curve was 0.839 for cine-CNN, 0.679 for gado-CNN (p < 0.004 vs. cine) and 0.714 for the best human reader (p < 0.007 vs. cine). Logistic regression with cine-CNN and gado-CNN, as well as analysis focused on the specific orientation plane, did not change the overall results. We conclude that cine-CNN leads to significantly better discrimination between AL and ATTR amyloidosis as compared to gado-CNN or human readers, but with lower performance than reported in studies where visual diagnosis is easy, and is currently suboptimal for clinical practice.

The purpose of this work was to evaluate CMR T1 and T2 mapping sequences in patients with intracardiac thrombi and masses in order to assess T1 and T2 relaxometry usefulness and to allow better etiological diagnosis. This observational study of patients scheduled for routine CMR was performed from September 2014 to August 2015. All patients referred to our department for a 1.5 T CMR were screened to participate. T1 mapping were acquired before and after Gadolinium injection; T2 mapping images were obtained before injection. 41 patients were included. 22 presented with cardiac thrombi and 19 with cardiac masses. The native T1 of thrombi was 1037 ± 152 ms (vs 1032 ± 39 ms for myocardium, p = 0.88; vs 1565 ± 88 ms for blood pool, p < 0.0001). T2 were 74 ± 13 ms (vs 51 ± 3 ms for myocardium, p < 0.0001; vs 170 ± 32 ms for blood pool, p < 0.0001). Recent thrombi had a native T1 shorter than old thrombi (911 ± 177 vs 1169 ± 107 ms, p = 0.01). The masses having a shorter T1 than the myocardium were lipomas (278 ± 29 ms), calcifications (621 ± 218 ms), and melanoma (736 ms). All other masses showed T1 values higher than myocardial T1, with T2 consistently >70 ms. T1 and T2 mapping CMR sequences can be useful and represent a new approach for the evaluation of cardiac thrombi and masses.

Background: Diagnosing cardiac amyloidosis (CA) from cine-CMR (cardiac magnetic resonance) alone is not reliable. In this study, we tested if a convolutional neural network (CNN) could outperform the visual diagnosis of experienced operators. Method: 119 patients with cardiac amyloidosis and 122 patients with left ventricular hypertrophy (LVH) of other origins were retrospectively selected. Diastolic and systolic cine-CMR images were preprocessed and labeled. A dual-input visual geometry group (VGG ) model was used for binary image classification. All images belonging to the same patient were distributed in the same set. Accuracy and area under the curve (AUC) were calculated per frame and per patient from a 40% held-out test set. Results were compared to a visual analysis assessed by three experienced operators. Results: frame-based comparisons between humans and a CNN provided an accuracy of 0.605 vs. 0.746 (p < 0.0008) and an AUC of 0.630 vs. 0.824 (p < 0.0001). Patient-based comparisons provided an accuracy of 0.660 vs. 0.825 (p < 0.008) and an AUC of 0.727 vs. 0.895 (p < 0.002). Conclusion: based on cine-CMR images alone, a CNN is able to discriminate cardiac amyloidosis from LVH of other origins better than experienced human operators (15 to 20 points more in absolute value for accuracy and AUC), demonstrating a unique capability to identify what the eyes cannot see through classical radiological analysis.

Improving the assessment and prediction of soil organic nitrogen (N) mineralization is essential: it contributes significantly to the N nutrition of crops and remains a major economic and environmental challenge. Consequently, a network of 137 fields was established in Brittany, France, to represent the wide diversity of soils and cultivation practices in this region. The experimental design was developed to measure net N mineralization for three consecutive years, in order to improve the accuracy of measuring it. Net N mineralization was quantified by the mineral N mass balance, which was estimated from March to October for a maize crop with no N fertilization. The effect of climate on mineralization was considered by calculating normalized time (ndays) and, then, calculating the N mineralization rate (Vn) as the ratio of the mineral N mass balance to normalized time. Strict screening of the experimental data, using agronomic and statistical criteria, resulted in the selection of a subset of 67 fields for data analysis. Mean Vn was relatively high (0.99 kg N ha−1 nday−1) over the period and varied greatly, from 0.62 to 1.46 kg N ha−1 nday−1 for the 10th and 90th percentiles, respectively. The upper soil layer (0–30 cm) was sampled to estimate its physical and chemical properties, particulate organic matter carbon and N fractions (POM-C and POM-N, respectively), soil microbial biomass (SMB), and extractable organic N (EON) determined in a phosphate borate extractant. The strongest correlations between Vn and these variables were observed with EON (r = 0.47), SMB (r = 0.45), POM-N (r = 0.43), and, to a lesser extent, the soil N stock (r = 0.31). Vn was also strongly correlated with a cropping system indicator (r = 0.39). A modeling approach, using generalized additive models, was used to identify and rank the variables with the greatest ability to predict net N mineralization.

The automatic classification of various types of cardiomyopathies is desirable but has never been performed using a convolutional neural network (CNN). The purpose of this study was to evaluate currently available CNN models to classify cine magnetic resonance (cine-MR) images of cardiomyopathies. Method: Diastolic and systolic frames of 1200 cine-MR sequences of three categories of subjects (395 normal, 411 hypertrophic cardiomyopathy, and 394 dilated cardiomyopathy) were selected, preprocessed, and labeled. Pretrained, fine-tuned deep learning models (VGG) were used for image classification (sixfold cross-validation and double split testing with hold-out data). The heat activation map algorithm (Grad-CAM) was applied to reveal salient pixel areas leading to the classification. Results: The diastolic–systolic dual-input concatenated VGG model cross-validation accuracy was 0.982 ± 0.009. Summed confusion matrices showed that, for the 1200 inputs, the VGG model led to 22 errors. The classification of a 227-input validation group, carried out by an experienced radiologist and cardiologist, led to a similar number of discrepancies. The image preparation process led to 5% accuracy improvement as compared to nonprepared images. Grad-CAM heat activation maps showed that most misclassifications occurred when extracardiac location caught the attention of the network. Conclusions: CNN networks are very well suited and are 98% accurate for the classification of cardiomyopathies, regardless of the imaging plane, when both diastolic and systolic frames are incorporated. Misclassification is in the same range as inter-observer discrepancies in experienced human readers.

T1 mapping is now a clinically feasible method, providing pixel-wise quantification of the cardiac structure's T1 values. Beyond focal lesions, well depicted by late gadolinium enhancement sequences, it has become possible to discriminate diffuse myocardial alterations, previously not assessable by noninvasive means. The strength of this method includes the high reproducibility and immediate clinical applicability, even without the use of contrast media injection (native or pre-contrast T1). The two most important determinants of native T1 augmentation are (1) edema related to tissue water increase (recent infarction or inflammation) and (2) interstitial space increase related to fibrosis (infarction scar, cardiomyopathy) or to amyloidosis. Conversely, lipid (Anderson–Fabry) or iron overload diseases are responsible for T1 reduction. In this pictorial review, the main features provided by native T1 mapping are discussed and illustrated, with a special focus on the awaited clinical purpose of this unique, promising new method.