Diabetic Mastopathy: a Case Report

Diabetic mastopathy (DMP) is an uncommon collection of clinical, radiological, and histological features, classically described in premenopausal women with long-term insulin-dependent diabetes mellitus. This entity can mimic breast carcinoma, but, in the appropriate clinical and imaging setting, the diagnosis can be made by core biopsy, avoiding unnecessary surgeries. We report the case of a 34-year-old female, with a 12-year history of type 1 diabetes, who presented with bilateral breast lumps. Mammography, ultrasonography, and magnetic resonance imaging could not exclude the suspicion of malignancy, and a core biopsy was performed showing the typical histologic features of DMP. The literature is briefly reviewed

Artificial intelligence (AI) in rare diseases: is the future brighter?

The amount of data collected and managed in (bio)medicine is ever-increasing. Thus, there is a need to rapidly and efficiently collect, analyze, and characterize all this information. Artificial intelligence (AI), with an emphasis on deep learning, holds great promise in this area and is already being successfully applied to basic research, diagnosis, drug discovery, and clinical trials. Rare diseases (RDs), which are severely underrepresented in basic and clinical research, can particularly benefit from AI technologies. Of the more than 7000 RDs described worldwide, only 5% have a treatment. The ability of AI technologies to integrate and analyze data from different sources (e.g., multi-omics, patient registries, and so on) can be used to overcome RDs' challenges (e.g., low diagnostic rates, reduced number of patients, geographical dispersion, and so on). Ultimately, RDs' AI-mediated knowledge could significantly boost therapy development. Presently, there are AI approaches being used in RDs and this review aims to collect and summarize these advances. A section dedicated to congenital disorders of glycosylation (CDG), a particular group of orphan RDs that can serve as a potential study model for other common diseases and RDs, has also been included.info:eu-repo/semantics/publishedVersio

Transistor sizing analysis of regular fabrics

This paper presents an extensive transistor sizing analysis for regular transistor fabrics. Several evaluation methods have been exploited, such as DC simulations, ring oscillators and single-gate open chain structures. Different design aspects are addressed taking into account stacked transistors, cells with drive strengths and circuit critical paths. The performance degradation of using regular fabrics in comparison to standard cells is naturally expected, but it is quite important to evaluate the dimension of such impact. The results were obtained for predictive PTM45 CMOS parameters, and the conclusions can be easily extrapolated to other technology nodes and fabrication processesPostprint (published version

Empirical evaluation of the potential of low-cost and open source “on-the-person” ECG for cardiopathy pre-screening

Electrocardiographic (ECG) data analysis can reveal crucial information about the cardiovascular physiologi- cal phenomenon, which is modulated by the Autonomic Nervous System. Hereupon, beyond cardiovascular diagnosis, ECG markers can also reflect workload levels, or even physical and mental performance, through Heart Rate Variability (HRV) analysis. Building upon previous work found within the state-of-the-art, this pilot research explores the potential of using a low-cost device for cardiopathy pre-screening, through ECG signal analysis. With the aim of performing the rhythmical analysis, we performed empirical tests from a population of 21 control subjects in a resting position, and an additional 2 subjects, one of them in dynamic condition, in the scope of an exploratory research, using ECG wave segments analysis and HRV features extraction for nu- merical analysis. Results have demonstrated that the signal quality allows reliable ECG acquisition for further rhythmical and HRV analysis, in stationary and dynamic monitoring, for the bipolar leads applied. There was also evidence to suggest a benefit from including ECG morphological analysis with this hardware and software setup for prevention and diagnosis of cardiovascular disorders, although requiring further investigation.info:eu-repo/semantics/publishedVersio

THE STUDY OF SWIMMERS’S HAND AND FOREARM USING COMPUTATIONAL FLUID DYNAMICS

Computational Fluid Dynamics has been widely used in biomechanics studies applied to medicine and sport. In this study we developed a 3-D model for swimmer’s hand/forearm forces using Computational Fluid Dynamics. Models used in the simulations were created in CAD, based on realistic dimensions of a right adult human hand/forearm. The governing system of equations considered was the incompressible Reynolds averaged Navier-Stokes equations implemented with Fluent® code. The drag coefficient was the main responsible for propulsion, with a maximum value of force propulsion corresponding to a pitch angle of 90º. The lift coefficient seemed to play a less important role in the generation of propulsive force with pitch angles of 0º and 90º but it is important with a pitch angle of 45º. It was demonstrated the relevance of applying CFD in the propulsive force measurements, using a more realistic model of a human segment

The development of an excellence model integrating the Shingo model and sustainability

Companies are continuously looking to improve their production systems using excellence models, with lean thinking, the Shingo model, six sigma and lean six sigma being the most comprehensive and applied. It is expected that the initial focus for the survival of companies is their economic profitability, but when economic needs are met, the next step is to achieve operational excellence. For this, in addition to economic objectives, it is necessary to include social and environmental objectives, i.e., the other two pillars of sustainability. This study aims to propose a conceptual model identifying the tools that can help achieve the desired results in the three pillars of sustainability aligned with operational excellence. The design of the conceptual model was based on a bibliometric analysis of the literature that relates the concepts of lean thinking, six sigma, lean six sigma and the Shingo model. The Web of Science was the platform selected for the collection of data, and the timeframe considered was 2010 to 2021. A total of 125 articles were analyzed using the VosViewer software, through which it was possible to analyze different topics of study related to the literature. The bibliometric analysis allowed for the identification of the temporal distribution of publications, the categorization of topics, different areas of application and the importance of the tools used in different practical cases. This study points out that companies have at their disposal several tools to achieve economic objectives. On the other hand, there is a set of more restricted tools that are used to meet the objectives of the social and environmental pillars. Future research should focus on identifying tools that meet social and environmental goals in order to strengthen these pillars that are essential for operational excellence and for the sustainability of companies.The work of the author Vanda Lima is supported by national funds, through the FCT-Portuguese Foundation for Science and Technology under the project UIDB/04728/2020

Hydrophobic-electrostatic balance driving the LCST offset aggregation-redissolution behavior of N-alkylacrylamide-based ionic terpolymers

A series of random terpolymers composed of N-isopropylacrylamide (NIPAAm), 2-acrylamido-2-methyl-1-propanesulfonic acid (AMPS), and N-tert-butylacrylamide (NTBAAm) monomers were synthesized by free radical polymerization. The molar fraction of the negatively charged monomer (AMPS) was maintained constant (0.05) for all studied terpolymer compositions. Turbidity measurements were used to evaluate the influence of the relative amount of NIPAAm and NTBAAm, polymer concentration, and solution ionic strength on the cloud point and redissolution temperatures (macroscopic phase separation). Dynamic light scattering (DLS) was employed to elucidate some aspects regarding the molecular scale mechanism of the temperature-induced phase separation and to determine the low critical solution temperature (LCST). The aqueous solutions of terpolymers remained clear at all studied temperatures; turbidity was only observed in the presence of NaCl. The cloud point temperature (CPT) determined by turbidimetry was found to be systematically much higher than the LCST determined by DLS; nanosized aggregates were observed at temperatures between the LCST and the CPT. Both CPT and LCST decreased when increasing the molar ratio of NTBAAm (increased hydrophobicity). It was found that above a critical molar fraction of NTBAAm (0.25-0.30) the aggregation rate suddenly decreased. Polymers with NTBAAm content lower than 0.25 showed a fast macroscopic phase separation, but the formed large aggregates are disaggregating during the cooling ramp at temperatures still higher than the LCST. On the contrary, polymers withNTBAAmcontents above 0.30 showed a slow macroscopic phase separation, and the formed large aggregates only redissolved when LCST was reached. These differences were explained on the basis of a delicate balance between the electrostatic repulsion and the hydrophobic attractive forces, which contribute cooperatively to the formation of metastable nanosized aggregates.The authors acknowledge funding from EU Marie Curie Actions, Alea Jacta Est (MEST-CT-2004-008104), and Portuguese Foundation For Science and Technology (FCT) (SFRH/BPD/34545/2007). This work was carried out under the scope of the European NoE EXPERTISSUES (NMP3-CT-2004-500283)

Fine-tuning artificial neural networks automatically

To get the most out of powerful tools expert knowledge is often required. Experts are the ones with the suitable knowledge to tune the tools parameters. In this paper we assess several techniques which can automatically fine tune ANN parameters. Those techniques include the use of GA and Stratified Sampling. The tuning includes the choice of the best ANN structure and the best network biases and their weights. Empirical resultsachieved in experiments performed using nine heterogeneous data sets show that the use of the proposed Stratified Sampling technique is advantageous