In this work, the analogous treatment between coupled temperature-displacement problems and material failure models is explored within the context of a commercial software (Abaqus®). The implicit gradient Lemaitre damage and phase field models are implemented utilizing the software underlying capabilities for coupled temperature-displacement problems. The heat conduction equation is made compatible with the diffusive regularization of such material models and calculations are carried out at the material point level. This bypasses the need to implement explicitly the weak form resultant from the coupling between the momentum conservation and the evolution of the diffusive field. Throughout benchmarking examples, the proposed methodology is assessed and validated by investigating typical issues risen from the considered local inelastic-based deformation models, such as mesh dependency and the difficulties to predict cracked regions.
Background and aims
Capsule endoscopy is a central element in the management of patients with suspected or known Crohn’s disease. In 2017, PillCam™ Crohn’s Capsule was introduced and demonstrated greater accuracy in the evaluation of extension of disease in these patients. Artificial Intelligence is expected to enhance the diagnostic accuracy of capsule endoscopy. This study aims to develop an AI algorithm for the automatic detection of ulcers and erosions of the small intestine and colon in PillCam™ Crohn’s Capsule images.
Methods
A total of 8085 PillCam™ Crohn’s Capsule images were extracted between 2017-2020, constituted by 2855 images of ulcers and 1975 erosions; the remaining images showed normal enteric and colonic mucosa. This pool of images was subsequently split into training and validation datasets. The performance of the network was subsequently assessed in an independent test set.
Results
The model had an overall sensitivity and specificity of 90.0% and 96.0%, respectively. The precision and accuracy of this model were 97.1% and 92.4%, respectively. Particularly, the algorithm detected ulcers with a sensitivity of 83% and specificity of 98%, and erosions with sensitivity and specificity of 91% and 93%, respectively.
Conclusion
A deep learning model capable of automatically detecting ulcers and erosions in PillCam™ Crohn’s Capsule images was developed for the first time. These findings pave the way for the development of automatic systems for detection of clinically significant lesions, optimizing diagnostic performance and efficiency of monitoring Crohn’s disease activity.
Background NF-kB (nuclear factor kappa B) is a transcription factor composed of two subunits, p50 and p65, which plays a key role in the inflammatory process. Melatonin has oncostatic, antiangiogenic and antimetastatic properties, and some recent studies have indicated an inhibitory effect of melatonin on NF-kB in some types of cancer. This work aims to investigate the effects of melatonin treatment on the expression of NF-kB in breast and liver cancer models. Methods The breast cancer xenographic model was performed using female Balb/c nude athymic mice injected with MDA-MB-231 cells. The animals were treated with 40 mg/Kg of melatonin for 21 days. Volume of the tumors was measured with a digital capiler. Hepatocarcinoma model was developed by using the HepG2 cells in vitro, treated with 1 mM melatonin for 24 h. The expression of NF-kB protein was verified by immunohistochemistry and immunocytochemistry and quantified by optical densitometry, in vivo study and in vitro study, respectively. NF-kB gene expression was performed by quantitative RT-PCR. Results The breast cancer xenografts nude mice treated with melatonin showed reduced tumor size (P=0.0022). There was a decrease in NF-kB protein staining (P=0.0027) and gene expression (P=0.0185) in mice treated with melatonin. The opposite results were observed for the hepatocarcinoma model. HepG2 cells treated with melatonin showed an increase in the NF-kB immunostaining when compared to control cells (P=0.0042). Conclusion Our results indicated that treatment with melatonin was able to decrease both gene and protein expressions of NF-kB in breast cancer cells and, conversely, increase the transcription factor protein expression in hepatocarcinoma cells. These data highlighted a double role in the expression of NF-kB, depending on the cell type. Further studies are needed to better elucidate the action of melatonin in NF-kB, since this transcription factor acts on different signaling pathways that are fundamental for carcinogenesis.
The highly nonlinear mechanical behaviour of soft tissues solicited within the physiological range usually involves degradation of the material properties. Mechanically, having these biostructures undergoing such stretch patterns may bring about pathological conditions related to the steady deterioration of both collagen fibres and material's ground substance. Tissue and subject variability observed in the phenomenological mechanical characterisation of soft tissues often hinder the choice of the computational constitutive model. Therefore, this contribution brings forth a detailed overview of the constitutive implementation in a computational framework of anisotropic hyperelastic materials with damage. Surmounting the challenge posed by the mesh dependency pathology requires the incorporation of an integral-type non-local averaging, which seeks to include the effects of the microstructure in order to limit the localisation phenomena of the damage variables. By adopting this approach, one can make use of multiple developed material models available in the literature, a combination of those, or even propose new models within the same numerical framework. The numerical examples of three-dimensional displacement and force-driven boundary value problems highlight the possibility of using multiple material models within the same numerical framework. Particularities concerning the considered material models and the damage effect implications to represent the Mullins effect, induced anisotropy, hysteresis, and mesh dependency are discussed.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.