ABSTRACT
Studies recently accomplished on the Enteric Nervous System have shown that chronic degenerative diseases affect the Enteric Glial Cells (EGC) and, thus, the development of recognition methods able to identify whether or not the EGC are affected by these type of diseases may be helpful in its diagnoses. In this work, we propose the use of pattern recognition and machine learning techniques to evaluate if a given animal EGC image was obtained from a healthy individual or one affect by a chronic degenerative disease. In the proposed approach, we have performed the classification task with handcrafted features and deep learning-based techniques, also known as non-handcrafted features. The handcrafted features were obtained from the textural content of the ECG images using texture descriptors, such as the Local Binary Pattern (LBP). Moreover, the representation learning techniques employed in the approach are based on different Convolutional Neural Network (CNN) architectures, such as AlexNet and VGG16, with and without transfer learning. The complementarity between the handcrafted and non-handcrafted features was also evaluated with late fusion techniques. The datasets of EGC images used in the experiments, which are also contributions of this paper, are composed of three different chronic degenerative diseases: Cancer, Diabetes Mellitus, and Rheumatoid Arthritis. The experimental results, supported by statistical analysis, show that the proposed approach can distinguish healthy cells from the sick ones with a recognition rate of 89.30% (Rheumatoid Arthritis), 98.45% (Cancer), and 95.13% (Diabetes Mellitus), being achieved by combining classifiers obtained on both feature scenarios.
ABSTRACT
BACKGROUND: The intestinal wall has a complex topographical architecture. The multi-layered network of the enteric nervous system and its intercellular interactions are difficult to map using traditional section-based or whole-mount histology. With the advent of optical clearing techniques, it has become feasible to visualize intact tissue and organs in 3D. However, as yet, a gap still needs to be filled in that no in-depth analysis has been performed yet on the potential of different clearing techniques for the small intestine. AIM: The goal of this study was to identify an optimal clearing protocol for in toto imaging of mouse intestinal tissue. METHODS: Five aqueous-based clearing protocols (SeeDB2, CUBIC, ScaleS, Ce3D, and UbasM) and four organic reagent-based clearing protocols (3DISCO, iDISCO+, uDISCO, and Visikol® ) were assessed in segments of small intestine from CX3CR1GFP/GFP and wild-type mice. Following clearing, optical transparency, tissue morphology, green fluorescent protein (GFP) fluorescence retention, and compatibility with (immuno-)labeling were analyzed. KEY RESULTS: All organic reagent-based clearing protocols-except for Visikol-rendered tissue highly transparent but led to substantial tissue shrinkage and deformation. Of the aqueous-based protocols, only Ce3D yielded full-thickness tissue transparency. In addition, Ce3D displayed excellent GFP retention and preservation of tissue morphology. CONCLUSIONS: Ce3D emerged as a most efficient protocol for enabling rapid full-thickness 3D mapping of the mouse intestinal wall.
Subject(s)
Histocytological Preparation Techniques/methods , Imaging, Three-Dimensional/methods , Intestines , Animals , MiceABSTRACT
OBJECTIVE: To assess the susceptibility of Trypanosoma cruzi strains from Amazon to benznidazole. METHODS: We studied 23 strains of T. cruzi obtained from humans in the acute phase of Chagas disease, triatomines and marsupials in the state of Amazonas and from chronic patients and triatomines in the state of Paraná, Brazil. The strains were classified as TcI (6), TcII (4) and TcIV (13). For each strain, 20 Swiss mice were inoculated: 10 were treated orally with benznidazole 100 mg/kg/day (TBZ group) for 20 consecutive days and 10 comprised the untreated control group (NT). Fresh blood examination, haemoculture (HC), PCR, and ELISA were used to monitor the cure. RESULTS: The overall cure rate was 60.5% (109/180 mice) and varied widely among strains. The strains were classified as resistant, partially resistant or susceptible to benznidazole, irrespective of discrete typing units (DTUs), geographical origin or host. However, the TcI strains from Amazonas were significantly (P = 0.028) more sensitive to benznidazole than the TcI strains from Paraná. The number of parasitological, molecular and serological parameters that were significantly reduced by benznidazole treatment also varied among the DTUs; the TBZ group of mice inoculated with TcIV strains showed more reductions (8/9) than those with TcI and TcII strains. CONCLUSIONS: Benznidazole resistance was observed among natural populations of the parasite in the Amazon, even in those never exposed to the drug.
