Chitosan-PEG Gels Loaded with Jatropha mollissima (Pohl) Baill. Ethanolic Extract: An Efficient and Effective Biomaterial in Hemorrhage Control.

A lack of control over blood loss can have catastrophic implications, including death. Although several hemostatic medications have been employed to reduce bleeding, a vast majority of them are ineffective, expensive, or pose health risks to the patient. To overcome these constraints, chitosan-polyethylene glycol (CS-PEG) hemostatic gels loaded with ethanolic extract of Jatropha mollissima sap (EES) were prepared and their hemostatic, physicochemical, and cytotoxic properties were evaluated. The gels were produced by mixing CS with PEG (an external plasticizer) and EES. The phytochemical analysis revealed a significant concentration of total polyphenols and tannins content in the extract and catechin was identified as one of the key compounds of EES. Infrared spectroscopy analysis revealed the presence of EES in the gels, as well as the chemical interaction between CS and PEG. The gels were thermally stable between 25 and 37 °C (ambient and human body temperature range), had pseudoplastic deformation behavior (rheological properties preserved after shearing), were simple to inject (compression force 30 N), and were biocompatible. In vivo experiments showed that both CS-PEG-EES gels exhibited greater hemostatic action in preventing tail hemorrhage in Wistar rats, with decreased bleeding time and blood weight compared with unloaded CS-PEG gels (control groups) and Hemostank, a commercial product. However, the gel prepared with acetic acid was more efficient in controlling bleeding. These findings reveal that CS-PEG-EES gels can reduce hemorrhages and are a potent, simple, and safe hemostatic agent.

Aircraft Fuselage Corrosion Detection Using Artificial Intelligence.

Corrosion identification and repair is a vital task in aircraft maintenance to ensure continued structural integrity. Regarding fuselage lap joints, typically, visual inspections are followed by non-destructive methodologies, which are time-consuming. The visual inspection of large areas suffers not only from subjectivity but also from the variable probability of corrosion detection, which is aggravated by the multiple layers used in fuselage construction. In this paper, we propose a methodology for automatic image-based corrosion detection of aircraft structures using deep neural networks. For machine learning, we use a dataset that consists of D-Sight Aircraft Inspection System (DAIS) images from different lap joints of Boeing and Airbus aircrafts. We also employ transfer learning to overcome the shortage of aircraft corrosion images. With precision of over 93%, we demonstrate that our approach detects corrosion with a precision comparable to that of trained operators, aiding to reduce the uncertainties related to operator fatigue or inadequate training. Our results indicate that our methodology can support specialists and engineers in corrosion monitoring in the aerospace industry, potentially contributing to the automation of condition-based maintenance protocols.

Big data and machine learning for materials science.

Herein, we review aspects of leading-edge research and innovation in materials science that exploit big data and machine learning (ML), two computer science concepts that combine to yield computational intelligence. ML can accelerate the solution of intricate chemical problems and even solve problems that otherwise would not be tractable. However, the potential benefits of ML come at the cost of big data production; that is, the algorithms demand large volumes of data of various natures and from different sources, from material properties to sensor data. In the survey, we propose a roadmap for future developments with emphasis on computer-aided discovery of new materials and analysis of chemical sensing compounds, both prominent research fields for ML in the context of materials science. In addition to providing an overview of recent advances, we elaborate upon the conceptual and practical limitations of big data and ML applied to materials science, outlining processes, discussing pitfalls, and reviewing cases of success and failure.

On the convergence of nanotechnology and Big Data analysis for computer-aided diagnosis.

An overview is provided of the challenges involved in building computer-aided diagnosis systems capable of precise medical diagnostics based on integration and interpretation of data from different sources and formats. The availability of massive amounts of data and computational methods associated with the Big Data paradigm has brought hope that such systems may soon be available in routine clinical practices, which is not the case today. We focus on visual and machine learning analysis of medical data acquired with varied nanotech-based techniques and on methods for Big Data infrastructure. Because diagnosis is essentially a classification task, we address the machine learning techniques with supervised and unsupervised classification, making a critical assessment of the progress already made in the medical field and the prospects for the near future. We also advocate that successful computer-aided diagnosis requires a merge of methods and concepts from nanotechnology and Big Data analysis.

A ubiquitous reflective e-portfolio architecture.

BACKGROUND: In nurse and in medicine courses, the use of reflective portfolios as a pedagogical tool is becoming a common practice; in the last years, this practice has gradually migrated from paper-based to electronic-based portfolios. Current approaches for reflective e-portfolios, however, do not widely operate at outdoor sites, where data networks are limited or nonexistent. Considering that many of the activities related to nurse and medicine courses relate to professional practices conducted in such conditions, these network shortcomings restrict the adoption of e-portfolios. PURPOSE: The present study describes the requirements specification, design, implementation, and evaluation of the Ubiquitous Reflective E-Portfolio Architecture, a solution proposed to support the development of systems based on mobile and wired access for both online and offline operation. METHODS: We have implemented a prototype named Professional Practice Module to evaluate the Ubiquitous Reflective E-Portfolio Architecture; the module was based on requirements observed during the professional practice, the paper-based portfolio in use, and related learning meetings in the Medicine Course of a Brazilian University. The evaluation of the system was carried out with a learning group of 2nd year students of the medicine course, who answered to extensive evaluation questionnaires. RESULTS: The prototype proved to be operational in the activities of the professional practice of the Medicine Course object of the study, including homework tasks, patient care, data sharing, and learning meetings. It also demonstrated to be versatile with respect to the availability of the computer network that, many times, was not accessible. Moreover, the students considered the module useful and easy to use, but pointed out difficulties about the keyboard and the display sizes of the netbook devices, and about their operational system. Lastly, most of the students declared preference for the electronic Professional Practice Module in internal and in group activities, and for the paper-based version while in patient attendance. CONCLUSIONS: There is evidence that the environment where the professional practice takes place influences the usage of the e-portfolio. Mobile devices were able to support students in their professional practice; however, these devices present characteristics that must be judiciously selected, otherwise, they may limit the execution of important tasks. The main shortcoming identified during the evaluation tests was about the use of the module, and of the access device, during patient attendance. For this reason, we have envisioned a new version of the Professional Practice Module that shall follow a twofold requisite: by one side, it will include all the features of the module, to be used at the university or in the students' homes; from the other side, it will include only the features that are essential for the practice of patient attendance.