This study highlights the importance of addressing emotional regulation jointly with attachment to deepen the comprehension of the relational processes implicated in adaptation to breast cancer. Results supported a mediational hypothesis, presenting emotional regulation processes as relevant dimensions for the understanding of attachment associations with adaptation to breast cancer.
Background Cowden’s syndrome is an autosomal dominant disease with variable penetrance, involving the tumor suppressor phosphatase and tension homolog gene, located on chromosome 10q22-23, responsible for cell proliferation, migration, and cellular apoptosis. Its clinical presentation encompasses mucocutaneous lesions, which are present around 99% of the time; macrocephaly; and cognitive impairment, and it precedes the appearance of neoplasms such as thyroid carcinoma, breast cancer, among others. In addition to these malformations, arteriovenous malformations of the brain and spine, endocrine abnormalities, skeletal defects, and cardiopulmonary lesions may also be found. The relevance of the case is due to the fact that, through a certain phenotype, the patient’s genotype can be inferred and thus followed up closely. Case representation The clinical case concerns a 28-year-old Caucasian and Portuguese woman with palmar pits, macrocephaly, and cognitive impairment. She was diagnosed with papillary thyroid carcinoma at 22 years of age and proposed total thyroidectomy. At age 27, a pregnancy was diagnosed with a Breast Imaging-Reporting and Data System 2-rated breast lump. After the histological verification, it was concluded that it was a high metastatic breast sarcoma, opting for palliative mastectomy. A genetic evaluation confirmed alteration in the phosphatase and tension homolog gene, confirming Cowden’s syndrome. The patient died at age 29 due to neoplastic pathology. Conclusion This report aims to alert to the clinical signs of this entity and the clinical supervision and follow-up of these patients. In order to prevent premature deaths and to improve patient’s quality of life, genetic diseases with cancer impact should be diagnosed as early as possible.
Mental health is on the agenda, especially in times of pandemic due to the COVID-19 virus. Nowadays, psychological problems, such as anxiety and depression, have been more discussed and have become even more critical during confinement. This paper proposes the development process of an innovative product whose active ingredient - ellagic acid, extracted from the chestnut shell - has anxiolytic properties and can be potentially used in consumers' daily lives, preventing this type of illness. The product presented, Do Nut Stress – Acid ellagic enriched pancakes for anxiety control, consists of a pancake powder, considered a healthy and nutritious meal.
Optical trapping is a versatile and non-invasive technique for single particle manipulation. As such, it can be widely applied in the domains of particle identification and classification and thus used as a tool for monitoring physical and chemical processes. This creates an opportunity for integrating the method seamlessly into optofluidic chips, provided it can be automatized. Yet even though OT is well established in multiple scientific domains, a full stack approach to its integration into other technological devices is still lacking. This calls for solutions in tasks such as automatic trapping and signal analysis. In this manuscript, we describe the implementation of an algorithm seeking autonomous particle location and trapping. The methodology is based upon image-processing, allowing for particle location using real time image segmentation. A local thresholding algorithm is applied, followed by morphological techniques for closing shapes and excluding non-bounded regions - after which only the particles remain on the image. Once the centroid is identified, the stage is translated accordingly by piezo-electric actuators, followed by the laser activation. In this way, trapping is achieved, and one may proceed to analyze the forward scattered optical signal, after which a new particle inside the actuators range may be automatically trapped. This development, when compared with existent solutions involving holographic optical tweezers, allows for similar capabilities without using a spatial light modulator, thus dramatically reducing the setup costs of autonomous OT solutions. Therefore, when combined with particle classification techniques, this method is well suited for integration into possible optofluidic chips for autonomous sensing and monitoring of biochemical samples.
Optical trapping provides a way to isolate, manipulate, and probe a wide range of microscopic particles. Moreover, as particle dynamics are strongly affected by their shape and composition, optical tweezers can also be used to identify and classify particles, paving the way for multiple applications such as intelligent microfluidic devices for personalized medicine purposes, or integrated sensing for bioengineering. In this work, we explore the possibility of using properties of the forward scattered radiation of the optical trapping beam to analyze properties of the trapped specimen and deploy an autonomous classification algorithm. For this purpose, we process the signal in the Fourier domain and apply a dimensionality reduction technique using UMAP algorithms, before using the reduced number of features to feed standard machine learning algorithms such as K-nearest neighbors or random forests. Using a stratified 5-fold cross-validation procedure, our results show that the implemented classification strategy allows the identification of particle material with accuracies up to 80%, demonstrating the potential of using signal processing techniques to probe properties of optical trapped particles based on the forward scattered light. Furthermore, preliminary results of an autonomous implementation in a standard experimental optical tweezers setup show similar differentiation capabilities for real-time applications, thus opening some opportunities towards technological applications such as intelligent microfluidic devices and solutions for biochemical and biophysical sensing.
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.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.