Aqueous outflow channels and its lymphatic association: A review

The human eye has a unique immune architecture and behavior. While the conjunctiva is known to have a well-defined lymphatic drainage system, the cornea, sclera, and uveal tissues were historically considered “alymphatic” and thought to be immune privileged. The very fact that the aqueous outflow channels carry a clear fluid (aqueous humor) along the outflow pathway makes it hard to ignore its lymphatic-like characteristics. The development of novel lymphatic lineage markers and expression of these markers in aqueous outflow channels and improved imaging capabilities has sparked a renewed interest in the study of ocular lymphatics. Ophthalmic lymphatic research has had a directional shift over the last decade, offering an exciting new physiological platform that needs further in-depth understanding. The evidence of a presence of distinct lymphatic channels in the human ciliary body is gaining significant traction. The uveolymphatic pathway is an alternative new route for aqueous outflow and adds a new dimension to pathophysiology and management of glaucoma. Developing novel animal models, markers, and non-invasive imaging tools to delineate the core anatomical structure and physiological functions may help pave some crucial pathways to understand disease pathophysiology and help develop novel targeted therapeutic approaches for glaucoma.     

Novel pathogenic variants in an Indian cohort with epidermolysis bullosa: Expanding the genotypic spectrum

BackgroundEpidermolysis bullosa (EB) is a genodermatosis characterized by skin fragility and blisters with variable severity. Patients with Dystrophic EB (DEB) or Junctional EB (JEB) mainly present to clinic due to greater functional impairment. Pathogenic sequence variations in COL7A1 are implicated in DEB.ObjectiveWe have tried to decipher the molecular spectrum and genotype phenotype correlation of 21 Indian patients with EB.MethodsNext generation sequencing (NGS) was performed to determine the pathogenic variants. Sanger sequencing was also done for validation of the variants in eleven individuals.ResultsPathogenic variants were detected in 20 individuals (diagnostic yield of 95%). Majority of them (90%) had sequence variation in COL7A1 while two had pathogenic variants in ITGB4 and KRT14 respectively. Out of the 18 patients confirmed to have DEB, 3 had Dominant DEB (DDEB) whereas 15 patients had Recessive DEB (RDEB). Amongst 23 sequence variations identified, 12 were found to be novel (3 were missense, 5 were premature termination codon variants while 4 were splice-site changes).ConclusionGenotype phenotype correlation was noted with milder manifestations in those with dominant inheritance types. Exact molecular diagnosis can be ascertained by NGS in majority of cases.     

Quantifying and leveraging predictive uncertainty for medical image assessment

The interpretation of medical images is a challenging task, often complicated by the presence of artifacts, occlusions, limited contrast and more. Most notable is the case of chest radiography, where there is a high inter-rater variability in the detection and classification of abnormalities. This is largely due to inconclusive evidence in the data or subjective definitions of disease appearance. An additional example is the classification of anatomical views based on 2D Ultrasound images. Often, the anatomical context captured in a frame is not sufficient to recognize the underlying anatomy. Current machine learning solutions for these problems are typically limited to providing probabilistic predictions, relying on the capacity of underlying models to adapt to limited information and the high degree of label noise. In practice, however, this leads to overconfident systems with poor generalization on unseen data. To account for this, we propose a system that learns not only the probabilistic estimate for classification, but also an explicit uncertainty measure which captures the confidence of the system in the predicted output. We argue that this approach is essential to account for the inherent ambiguity characteristic of medical images from different radiologic exams including computed radiography, ultrasonography and magnetic resonance imaging. In our experiments we demonstrate that sample rejection based on the predicted uncertainty can significantly improve the ROC-AUC for various tasks, e.g., by 8% to 0.91 with an expected rejection rate of under 25% for the classification of different abnormalities in chest radiographs. In addition, we show that using uncertainty-driven bootstrapping to filter the training data, one can achieve a significant increase in robustness and accuracy. Finally, we present a multi-reader study showing that the predictive uncertainty is indicative of reader errors.