Analyzing COVID-19 Medical Papers Using Artificial Intelligence: Insights for Researchers and Medical Professionals

Since the beginning of the COVID-19 pandemic almost two years ago, there have been more than 700,000 scientific papers published on the subject. An individual researcher cannot possibly get acquainted with such a huge text corpus and, therefore, some help from artificial intelligence (AI) is highly needed. We propose the AI-based tool to help researchers navigate the medical papers collections in a meaningful way and extract some knowledge from scientific COVID-19 papers. The main idea of our approach is to get as much semi-structured information from text corpus as possible, using named entity recognition (NER) with a model called PubMedBERT and Text Analytics for Health service, then store the data into NoSQL database for further fast processing and insights generation. Additionally, the contexts in which the entities were used (neutral or negative) are determined. Application of NLP and text-based emotion detection (TBED) methods to COVID-19 text corpus allows us to gain insights on important issues of diagnosis and treatment (such as changes in medical treatment over time, joint treatment strategies using several medications, and the connection between signs and symptoms of coronavirus, etc.).

The GA4GH Phenopacket schema: A computable representation of clinical data for precision medicine

Despite great strides in the development and wide acceptance of standards for exchanging structured information about genomic variants, there is no corresponding standard for exchanging phenotypic data, and this has impeded the sharing of phenotypic information for computational analysis. Here, we introduce the Global Alliance for Genomics and Health (GA4GH) Phenopacket schema, which supports exchange of computable longitudinal case-level phenotypic information for diagnosis and research of all types of disease including Mendelian and complex genetic diseases, cancer, and infectious diseases. To support translational research, diagnostics, and personalized healthcare, phenopackets are designed to be used across a comprehensive landscape of applications including biobanks, databases and registries, clinical information systems such as Electronic Health Records, genomic matchmaking, diagnostic laboratories, and computational tools. The Phenopacket schema is a freely available, community-driven standard that streamlines exchange and systematic use of phenotypic data and will facilitate sophisticated computational analysis of both clinical and genomic information to help improve our understanding of diseases and our ability to manage them.

Unraveling the Interconnections Between Statistical Learning and Dyslexia: A Review of Recent Empirical Studies

One important aspect of human cognition involves the learning of structured information encountered in our environment, a phenomenon known as statistical learning. A growing body of research suggests that learning to read print is partially guided by learning the statistical contingencies existing between the letters within a word, and also between the letters and sounds to which the letters refer. Research also suggests that impairments to statistical learning ability may at least partially explain the difficulties experienced by individuals diagnosed with dyslexia. However, the findings regarding impaired learning are not consistent, perhaps partly due to the varied use of methodologies across studies – such as differences in the learning paradigms, stimuli used, and the way that learning is assessed – as well as differences in participant samples such as age and extent of the learning disorder. In this review, we attempt to examine the purported link between statistical learning and dyslexia by assessing a set of the most recent and relevant studies in both adults and children. Based on this review, we conclude that although there is some evidence for a statistical learning impairment in adults with dyslexia, the evidence for an impairment in children is much weaker. We discuss several suggestive trends that emerge from our examination of the research, such as issues related to task heterogeneity, possible age effects, the role of publication bias, and other suggestions for future research such as the use of neural measures and a need to better understand how statistical learning changes across typical development. We conclude that no current theoretical framework of dyslexia fully captures the extant research findings on statistical learning.

Draft of the clinical practice guidelines “Adrenal incidentaloma”

The wider application and technical improvement of abdominal imaging procedures in recent years has led to an increasingly frequent detection of adrenal gland masses — adrenal incidentaloma, which have become a common clinical problem and need to be investigated for evidence of hormonal hypersecretion and/or malignancy. Clinical guidelines are the main working tool of a practicing physician. Laconic, structured information about a specific nosology, methods of its diagnosis and treatment, based on the principles of evidence-based medicine, make it possible to give answers to questions in a short time, to achieve maximum efficiency and personalization of treatment. These clinical guidelines include data on the prevalence, etiology, radiological features and assessment of hormonal status of adrenal incidentalomas. In addition, this clinical practice guideline provides information on indications for surgery, postoperative rehabilitation and follow-up.

Effectiveness of Two Different Methods on the Perceived Pain and Satisfaction During Intramuscular Antibiotic Injection: ShotBlocker and Local Vibration

In this study aimed to examine the effectiveness of ShotBlocker and local vibration on the perceived pain and satisfaction during intramuscular antibiotic injection. The sample of the randomized controlled experimental study consisted of 100 patients (32 in vibration group, 35 in ShotBlocker group, 33 in control group) who applied to the adult emergency clinic for antibiotic (amoxicillin/cefuroxime sodium) injection between April and May 2021. The study data were collected using the Structured Information Form, VAS for Pain and VAS for Satisfaction. CONSORT statement was followed for reporting. After the intramuscular antibiotic injection, a significant difference was found between the groups in terms of the mean scores of VAS for Pain and VAS for Injection Satisfaction ( p < .001). It was determined that local vibration application was more effective in reducing the pain and in increasing satisfaction that occurs during intramuscular antibiotic injection according to ShotBlocker and control groups.

Obtaining the Dynamic Coefficients of Structuredness for Assessing a Domain

Today, effective management of information requires an in-depth study of its internal organization. The structural organization of information affects the efficiency of choosing a method for solving the problem and the qualitative presentation of information about the subject area. Therefore, the article proposes a new semiotic structural approach to assessing the structuredness of information in a subject area, as well as theoretical, practical, and general logical methods for studying the process of search research as a single system. The authors proposed and investigated the structured information coeffi-cient, which the authors propose to consider in several aspects - with respect to the search research model presented by the traditional algorithm, and the structured subject area. The article presents theo-retical positions, derives the formula of coefficients for different cases, carries out calculations on the example of the subject area “optimization methods”, constructs graphs based on the calculated data, and draws conclusions.

Consideration of requirements for materials for different bioprinting methods

The object of research is the characteristics of the materials used in the bioprinting process. One of the biggest problems in the field of bioprinting is the materials used for printing organs, in particular, the lack of mechanical properties of these materials, such as strength, elasticity, ductility, wear resistance, and the like. They are essential to achieve the stabilization of printed structures. During the study, the requirements for materials used in the technology of three-dimensional bioprinting, including hydrogels, were discussed. Three main methods were considered (extrusion bioprinting, drip bioprinting, laser bioprinting), for each of which separate requirements for materials are put forward. Comparative assessment of these materials for different types of printing techniques are obtained. It is also determined that the extrusion printing technique is the most used for this direction of use, however, there remains the problem of the viability of living cells through the force of the bias stress, which occurs when the substance is squeezed out from the side of the nozzle walls. It is determined that the main requirements are the ability to gel, low surface tension, wettability and viscosity of the substance. Through understanding and structured information, it is possible to provide biological connections for better cellular interactions and improve the nutrient medium for the creation of physiologically relevant, functional tissues that can be engrafted by the human body after implantation. With such initial data, it is possible to develop new materials and improve existing ones that would meet all these requirements. By identifying the key problem, new ways of solving it can be developed. The above problems are some of the main reasons why researchers are still far from the bioprinting of clinically significant functional organs. Nonetheless, thanks to the new development, bioprinting will become a key technology for future tissue engineering, regenerative medicine and pharmaceuticals.

An Abstract System for Converting and Recovering Texts like Structured Information

This paper introduces an abstract system for converting texts into structured information. The proposed architecture incorporates several strategies based on scientific models of how the brain records and recovers memories, and approaches that convert texts into structured data. The applications of this proposal are vast because, in general, the information that can be expressed like a text way, such as reports, emails, web contents, etc., is considered unstructured and, hence, the repositories based on a SQL do not capable to deal efficiently with this kind of data. The model in which was based on this proposal divides a sentence into clusters of words which in turn are transformed into members of a taxonomy of algebraic structures. The algebraic structures must comply properties of Abelian groups. Methodologically, an incremental prototyping approach has been applied to develop a satisfactory architecture that can be adapted to any language. A special case is studied, this deals with the Spanish language. The developed abstract system is a framework that permits to implements applications that convert unstructured textual information to structured information, this can be useful in contexts such as Natural Language Generation, Data Mining, dynamically generation of theories, among others.