LinguAPP: An m-Health Application for Teledentistry Diagnostics

An Android/iOS application for low-cost mobile devices to aid in dental diagnosis through questionnaire and photos is presented in this paper. The main purposes of our app lie in the ease of use even for nonexperienced users, in the limited hardware requirements that allow a wide diffusion, and in the possibility to modify the questionnaire for different pathologies. This tool was developed in about a month at the beginning of the COVID-19 (SARS-CoV-2) pandemic and is still in use in Italy to allow support to patients without going to the hospital, if not strictly necessary.

A novel system for the classification of diseased retinal ganglion cells

Retinal ganglion cell (RGC) dendritic atrophy is an early feature of many forms of retinal degeneration, providing a challenge to RGC classification. The characterization of these changes is complicated by the possibility that selective labeling of any particular class can confound the estimation of dendritic remodeling. To address this issue we have developed a novel, robust, and quantitative RGC classification based on proximal dendritic features which are resistant to early degeneration. RGCs were labeled through the ballistic delivery of DiO and DiI coated tungsten particles to whole retinal explants of 20 adult Brown Norway rats. RGCs were grouped according to the Sun classification system. A comprehensive set of primary and secondary dendrite features were quantified and a new classification model derived using principal component (PCA) and discriminant analyses, to estimate the likelihood that a cell belonged to any given class. One-hundred and thirty one imaged RGCs were analyzed; according to the Sun classification, 24% (n = 31) were RGCA, 29% (n = 38) RGCB, 32% (n = 42) RGCC, and 15% (n = 20) RGCD. PCA gave a 3 component solution, separating RGCs based on descriptors of soma size and primary dendrite thickness, proximal dendritic field size and dendritic tree asymmetry. The new variables correctly classified 73.3% (n = 74) of RGCs from a training sample and 63.3% (n = 19) from a hold out sample indicating an effective model. Soma and proximal dendritic tree morphological features provide a useful surrogate measurement for the classification of RGCs in disease. While a definitive classification is not possible in every case, the technique provides a useful safeguard against sample bias where the normal criteria for cell classification may not be reliable.

Functional morphometric analysis in cellular behaviors: shape and size matter

Cellular morphogenesis in response to biophysical and topographical cues provides insights into cytoskeletal status, biointerface communications, and phenotypic adaptations in an incessant signaling feedback that governs cellular fate. Morphometric characterization is an important element in the study of the dynamic cellular behaviors, in their interactive response to environmental influence exerted by culture system. They collectively serve to reflect cellular proliferation, migration, and differentiation, which may serve as prognostic indices for clinical and pathological diagnosis. Various parameters are proposed to categorize morphological adaptations in relation to cellular function. In this review, the underlying principles, assumptions, and limitations of morphological characterizations are discussed. The significance, challenges, and implications of quantitative morphometric characterization of cell shapes and sizes in determining cellular functions are discussed.