Host defence peptides in diabetes mellitus type 2 patients with periodontal disease. A systematic review

The aim of the study was to critically assess and review the latest evidence relating the associations between host defence peptides (HDPs), periodontal diseases (PD) and diabetes mellitus type 2 (DM2). To explore studies on HDPs, periodontal disease, and DM2, researchers utilised specific key phrases to search the electronic databases PubMed (National Library of Medicine), Embase (Ovid), Medline (EBSCO), and Dentistry and Oral Sciences (EBSCO). Quality assessment was conducted by means of the Newcastle Ottawa scale and the Systematic Review Centre for Laboratory Animal Experimentation (SYRCLE) tool. Following a thorough screening process, a total of 12 papers (4 case‐control, 6 cross‐sectional, 1 animal, and 1 in vitro) fulfilled the selection criteria and were included. The majority of research found that HDPs were upregulated in DM2 patients with PD. Three investigations, however, found that HDPs were downregulated in DM2 patients with PD. HDPs play a part in the pathophysiology of PD and DM2. Nonetheless, more human, animal and laboratory investigations are needed to fully understand validation of the link, as the evidence is limited. Understanding HDPs as common moderators is critical, aimed at unlocking their potential as therapeutic and diagnostic agents

Prediction of the intention to use a smartwatch : a comparative approach using machine learning and partial least squares structural equation modeling

This study makes use of a cohesive yet innovative research model to identify the determinants of the adoption of smart watches using constructs from the Technology Acceptance Model (TAM) and constructs of smartwatches, including effectiveness, content richness, and personal innovativeness. The chief objective of the study was to encourage the use of smartwatches for medical purposes so that the role of doctors can be made more effective and to facilitate access to patient records. Our conceptual framework highlights the association of TAM constructs (i.e., perceived usefulness and perceived ease of use) with the content richness, the construct of user satisfaction, and innovativeness. To measure the effectiveness of the smartwatch, an external factor based on the flow theory was added, which emphasizes the control over the smartwatch and the degree of involvement. The study employs data from 385 respondents involved in the field of medicine, such as doctors, patients, and nurses. The data were gathered through a survey and used for evaluation of the research model using partial least squares structural equation modeling (PLS-SEM) and machine learning (ML) models. The significance and performance of factors impacting THE adoption of smartwatches were also identified using Importance-Performance Map Analysis (IPMA). User satisfaction is the most important predictor of intention to adopt a medical smartwatch according to the ML and IPMA analyses. The fitting of the structural equation model to the sample showed a high dependence of user satisfaction on perceived usefulness and perceived ease of use. Furthermore, two critical factors, innovativeness and content richness, are demonstrated to enhance perceived usefulness. However, one should consider that perceived usefulness or behavioral intention could not be determined based on perceived ease of use. In general, the findings suggest that smartwatch usage could become critically important in the medical field as a mediator that allows doctors, patients, and other users to access essential information

The effectiveness of online platforms after the pandemic : will face-to-face classes affect students’ perception of their Behavioural Intention (BIU) to use online platforms?

The purpose of this study is to investigate students’ intention to continue using online learning platforms during face-to-face traditional classes in a way that is parallel to their usage during online virtual classes (during the pandemic). This investigation of students’ intention is based on a conceptual model that uses newly used external factors in addition to the technology acceptance model (TAM) contrasts; hence, it takes into consideration users’ satisfaction, the external factor of information richness (IR) and the quality of the educational system and information disseminated. The participants were 768 university students who have experienced the teaching environments of both traditional face-to-face classes and online classes during the pandemic. A structural equation modelling (SEM) test was conducted to analyse the independent variables, including the users’ situation awareness (SA), perceived ease of use, perceived usefulness, satisfaction, IR, education system quality and information quality. An online questionnaire was used to explore students’ perceptions of their intention to use online platforms accessibly in a face-to-face learning environment. The results showed that (a) students prefer online platforms that have a higher level of content richness, to be able to implement the three dimensions of users’ situation awareness (perception, comprehension and projection); (b) there were significant effects of TAM constructs on students’ satisfaction and acceptance; (c) students are in favour of using a learning platform that is characterised by a high level of educational system quality and information quality and (d) students with a higher level of satisfaction have a more positive attitude in their willingness to use the online learning system

Text Mining the History of Medicine

Historical text archives constitute a rich and diverse source of information, which is becoming increasingly readily accessible, due to large-scale digitisation efforts. However, it can be difficult for researchers to explore and search such large volumes of data in an efficient manner. Text mining (TM) methods can help, through their ability to recognise various types of semantic information automatically, e.g., instances of concepts (places, medical conditions, drugs, etc.), synonyms/variant forms of concepts, and relationships holding between concepts (which drugs are used to treat which medical conditions, etc.). TM analysis allows search systems to incorporate functionality such as automatic suggestions of synonyms of user-entered query terms, exploration of different concepts mentioned within search results or isolation of documents in which concepts are related in specific ways. However, applying TM methods to historical text can be challenging, according to differences and evolutions in vocabulary, terminology, language structure and style, compared to more modern text. In this article, we present our efforts to overcome the various challenges faced in the semantic analysis of published historical medical text dating back to the mid 19th century. Firstly, we used evidence from diverse historical medical documents from different periods to develop new resources that provide accounts of the multiple, evolving ways in which concepts, their variants and relationships amongst them may be expressed. These resources were employed to support the development of a modular processing pipeline of TM tools for the robust detection of semantic information in historical medical documents with varying characteristics. We applied the pipeline to two large-scale medical document archives covering wide temporal ranges as the basis for the development of a publicly accessible semantically-oriented search system. The novel resources are available for research purposes, while the processing pipeline and its modules may be used and configured within the Argo TM platform

Using Twitter to Detect Hate Crimes and Their Motivations: The HateMotiv Corpus

With the rapidly increasing use of social media platforms, much of our lives is spent online. Despite the great advantages of using social media, unfortunately, the spread of hate, cyberbullying, harassment, and trolling can be very common online. Many extremists use social media platforms to communicate their messages of hatred and spread violence, which may result in serious psychological consequences and even contribute to real-world violence. Thus, the aim of this research was to build the HateMotiv corpus, a freely available dataset that is annotated for types of hate crimes and the motivation behind committing them. The dataset was developed using Twitter as an example of social media platforms and could provide the research community with a very unique, novel, and reliable dataset. The dataset is unique as a consequence of its topic-specific nature and its detailed annotation. The corpus was annotated by two annotators who are experts in annotation based on unified guidelines, so they were able to produce an annotation of a high standard with F-scores for the agreement rate as high as 0.66 and 0.71 for type and motivation labels of hate crimes, respectively

Building a semantically annotated corpus for chronic disease complications using two document types.

Narrative information in electronic health records (EHRs) contains a wealth of information related to patient health conditions. In addition, people use Twitter to express their experiences regarding personal health issues, such as medical complaints, symptoms, treatments, lifestyle, and other factors. Both genres of text include different types of health-related information concerning disease complications and risk factors. Knowing detailed information about controlling disease risk factors has a great impact on modifying these risks and subsequently preventing disease complications. Text-mining tools provide efficient solutions to extract and integrate vital information related to disease complications hidden in the large volume of the narrative text. However, the development of text-mining tools depends on the availability of an annotated corpus. In response, we have developed the PrevComp corpus, which is annotated with information relevant to the identification of disease complications, underlying risk factors, and prevention measures, in the context of the interaction between hypertension and diabetes. The corpus is unique and novel in terms of the very specific topic in the biomedical domain and as an integration of information from both EHRs and tweets collected from Twitter. The annotation scheme was designed with guidance by a domain expert, and two further domain experts performed the annotation, resulting in a high-quality annotation, with agreement rate F-scores as high as 0.60 and 0.75 for EHRs and tweets, respectively