Key Phrases Research Articles

Phakic intraocular lens implantation in patients with stabilized keratoconus: Current state of the issue (review)

Objective: to present current data on the efficacy and safety of phakic intraocular lenses in keratoconus to correct ametropia in patients with stabilized keratoconus. Methods. The PubMed and Google Academy databases were used to search for articles. Queries were formulated taking into account the main topic of the review; the main key search phrases were: "phakic intraocular lenses", "keratoconus" in Russian and English. These keywords were used to find 72 articles. A total of 26 literature references published between 2003 and 2021 were used in this review. Conclusion. The studies presented so far indicate sufficient predictability and efficacy of the above technique. However, there are certain difficulties and possible side effects associated with this method. One of the main problems in the use of phakic intraocular lenses in patients with keratoconus is the difficulty in calculating the lens power, especially the toric component.

Identification of Early Mobilization Interventions in Cardiovascular Disease Patients in Hospital

This study aims to categorize various early mobilization therapies in heart disease patients. The research method used is a systematic review by searching for articles using the Science Direct, PubMed, and Google Scholar databases. Key phrases included "early mobilization," "exercise training," and "cardiovascular disease and heart failure." The results showed that the interventions had 6MWT, CPET, progressive mobilization I, muscle breathing/inhalation training on day I, leg and limb exercises on the second and third days, ambulation, limb exercises, sitting, and walking. Around the bed, low-intensity resistance exercises in bed, and standing and tiptoe exercises on the floor beside the bed. Patients with acute heart failure often receive 20 to 40 minutes of therapy daily, including bedside exercises and a 50-step walk. In conclusion, there are still differences in time, scope, and implementation of early mobilization initiatives. However, mobilization therapy can be given to cardiac patients of all ages and is safe and practical. They help reduce re-hospitalization risk and improve a patient's VO2 and quality of life (QOL).
 
 Keywords: Heart Failure, Exercise, Early Mobilization, Exercise, Cardiovascular Disease

Evaluation of the Weightage on Dermatological Pharmacotherapy in Undergraduate Pharmacology Education of Bangladesh

Background: The aim of pharmacology education is to make graduate competent enough to prescribe safely and effectively. So it is expected that pharmacology education would incorporate all the important aspects of prescribing. Skin diseases are associated with a considerable disease burden in Bangladesh and mostly treated in primary care settings by junior doctors. Materials and methods: This descriptive cross-sectional study was conducted to analyze the undergraduate pharmacology curriculum and written question papers (SAQ) of MBBS curriculum of 07 different universities (Bangladesh University of Professionals, University of Dhaka, University of Chittagong, University of Rajshahi, Shahjalal University of Science and Technology, University of Science and Technology, Chittagong and Gono Bishwabidyalay) of Bangladesh in last 10 years (January 2010 to November 2019). The evaluation was conducted through searching certain key phrases. Results: In Pharmacology & Therapeutics portion of the MBBS curricula, there was not a single hour to teach dermatological pharmacotherapy. Highest presence of dermatological pharmacotherapy related question was observed in Bangladesh University of Professionals (BUP) followed by University of Rajshahi (RU).And statistically significant difference (p<0.00001) was observed among seven universities. Conclusion: Current study revealed negligible presence of dermatological pharmacotherapy in undergraduate pharmacology education of Bangladesh over 10 years period. IAHS Medical Journal Vol 5(2), Dec 2022; 34-37

Разработка программного модуля семантичского анализа патентного массива

While designing the software module, the authors developed an algorithm for parsing the texts of patents and patent applications, an algorithm for searching patents-analogues based on full-text search using Amazon Twinword technology, an algorithm for searching patents-analogues based on key phrases identified applying Amazon Comprehend technology, clustering the patent array. The software was developed to cluster the patent array and speed up the patent office examiner’s work by searching for key phrases and patent analogues

Aspect-based sentiment analysis on multi-domain reviews through word embedding

Abstract The finest resource for consumers to evaluate products is online product reviews, and finding such reviews that are accurate and helpful can be difficult. These reviews may sometimes be corrupted, biased, contradictory, or lacking in detail. This opens the door for customer-focused review analysis methods. A method called “Multi-Domain Keyword Extraction using Word Vectors” aims to streamline the customer experience by giving them reviews from several websites together with in-depth assessments of the evaluations. Using the specific model number of the product, inputs are continuously grabbed from different e-commerce websites. Aspects and key phrases in the reviews are properly identified using machine learning, and the average sentiment for each keyword is calculated using context-based sentiment analysis. To precisely discover the keywords in massive texts, word embedding data will be analyzed by machine learning techniques. A unique methodology developed to locate trustworthy reviews considers several criteria that determine what makes a review credible. The experiments on real-time data sets showed better results compared to the existing traditional models.

Challenges to the Availability and Affordability of Essential Medicines in African Countries: A Scoping Review.

The availability and affordability of safe, effective, accessible, and high-quality essential medicines is a critical benchmark for achieving the right to good health, and it is also one of the goals of the global health development agenda. To that end, it is critical to conduct rigorous studies to identify the major challenges confronting developing countries, particularly those in Africa. The purpose of this review was to identify the major challenges that Africans face in obtaining reasonably priced and readily available essential medicines. Generally the Boolean operators "AND" and "OR" were employed. Making progress also involves using duplicate checks, field definitions, and comparisons of articles and criteria. The analysis included all English-language papers published in any African country between 2005 and 2022, depending on the year of publication. The technique searches electronic databases for key phrases related to essential medication availability and affordability, such as PubMed, Web of Science, Scopus, Science Direct, Plos Medicine, and Google Scholar. A total of 91 articles; by using search engines and handpicking including duplicates, were primarily searched. The electronic database search earned 78 articles while only eleven studies met the criteria for review and were reviewed of which 5 (50%) were from East African countries. Inadequate human resources, financial constraints, high cost of available medications on the market, poor inventory management, manual consumption forecasting, inefficiencies in drug registration, and trade-related aspects of intellectual property rights agreement regulations are all obstacles to the availability of essential medicines in African nations. This review revealed that in Africa, the availability and affordability of essential medicines face numerous challenges. The primary challenge, according to the review research, is a lack of adequate financing to pay for an appropriate set of essential medications, which account for a significant portion of household spending.

Text Analysis of Trends in Health Equity and Disparities From the Internal Revenue Service Tax Documentation Submitted by US Nonprofit Hospitals Between 2010 and 2019: Exploratory Study.

Many US hospitals are classified as nonprofits and receive tax-exempt status partially in exchange for providing benefits to the community. Proof of compliance is collected with the Schedule H form submitted as part of the annual Internal Revenue Service Form 990 (F990H), including a free-response text section that is known for being ambiguous and difficult to audit. This research is among the first to use natural language processing approaches to evaluate this text section with a focus on health equity and disparities. This study aims to determine the extent to which the free-response text in F990H reveals how nonprofit hospitals address health equity and disparities, including alignment with public priorities. We used free-response text submitted by hospital reporting entities in Part V and VI of the Internal Revenue Service Form 990 Schedule H between 2010 and 2019. We identified 29 main themes connected to health equity and disparities, and 152 related key phrases. We tallied occurrences of these phrases through term frequency analysis, calculated the Moran I statistic to assess geographic variation in 2018, analyzed Google Trends use for the same terms during the same period, and used semantic search with Sentence-BERT in Python to understand contextual use. We found increased use from 2010 to 2019 across all the 29 phrase themes related to health equity and disparities. More than 90% of hospital reporting entities used terms in 2018 and 2019 related to affordability (2018: 2117/2131, 99.34%; 2019: 1620/1627, 99.57%), government organizations (2018: 2053/2131, 96.33%; 2019: 1577/1627, 96.93%), mental health (2018: 1937/2131, 90.9%; 2019: 1517/1627, 93.24%), and data collection (2018: 1947/2131, 91.37%; 2019: 1502/1627, 92.32%). The themes with the largest relative increase were LGBTQ (lesbian, gay, bisexual, transgender, and queer; 1676%; 2010: 12/2328, 0.51%; 2019: 149/1627, 9.16%) and social determinants of health (958%; 2010: 68/2328, 2.92%; 2019: 503/1627, 30.92%). Terms related to homelessness varied geographically from 2010 to 2018, and terms related to equity, health IT, immigration, LGBTQ, oral health, rural, social determinants of health, and substance use showed statistically significant (P<.05) geographic variation in 2018. The largest percentage point increase was for terms related to substance use (2010: 403/2328, 17.31%; 2019: 1149/1627, 70.62%). However, use in themes such as LGBTQ, disability, oral health, and race and ethnicity ranked lower than public interest in these topics, and some increased mentions of themes were to explicitly say that no action was taken. Hospital reporting entities demonstrate an increasing awareness of health equity and disparities in community benefit tax documentation, but these do not necessarily correspond with general population interests or additional action. We propose further investigation of alignment with community health needs assessments and make suggestions for improvements to F990H reporting requirements.

Screen distractions during Memrise MALL experience: Course-of-action study on students’ performance and perception

This study employed Course-of-Action methodology in a mixed-methods approach to examine the impact of screen distractions on Memrise MALL experience in an EFL flipped classroom setting. An Exploratory Sequential Design was utilized to analyze the two-week Memrise MALL experience and its impact on the screen behaviors of 46 Indonesian first-year university students. Descriptive and nonparametric inferential statistics were employed to analyze the quantitative data, while confrontative interview data were transcribed and encoded using in vivo coding to mark key phrases in the participants' comments. Results of the study indicated that social media and entertainment apps were the most common type of distraction and that these distractions had a detrimental effect on task performance. The participants expressed their appreciation for the customizable learning experience and found the app layouts and gamification features were encouraging; however, they found it challenging to switch between multiple tabs or windows on a mobile screen. There was a small correlation between total screen distractions and total vocabulary learned, as well as a strong correlation between total screen distractions and the total time spent on the task.

Artificial intelligence and its influence in adult learning in China

PurposeThis research aimed to examine the current status of artificial intelligence's (AI's) integration into Chinese adult education, by analyzing the influences that AI has had on current adult education practices in China and by discussing the opportunities and challenges that adult education in China is faced with under the rapid AI development in the past 12 years.Design/methodology/approachThis research employed systematic literature analysis. CNKI (China National Knowledge Infrastructure) Chinese Journals Full-text Database was used to collect scholarly publications on the use of AI in adult education in China that was published in the past decade. Data analysis included the following steps: identifying key words and phrases, detecting underlying meanings, searching for logical connections and relationships, collecting and connecting evidence to the research questions, and drawing logical and credible conclusions.FindingsThe findings indicated that AI has been gradually integrated into Chinese adult education through innovations and explorations and AI's influence is broad and profound. More specifically, the following five main themes were identified. The field's understanding of AI technology and AI's influence on adult education has evolved and become more comprehensive; AI challenges traditional Chinese adult education practices by helping to actualize personalized learning and precision education; AI transforms adult learning resource development; AI helps to turn learning environment into an open intelligent learning system; and lastly, AI urges the shift of adult educator's role in adult learning.Research limitations/implicationsThis study is not without limitations. Contextualized in China, this study shares the limitations with other single country studies. One such limitation is “cumulation” issue. This study should be replicated in other country contexts to further validate the generalizability of the five main themes identified in this research.Practical implicationsThe five themes identified in this study can help understand the promises and challenges that AI brings to the field of adult education in China. These five themes can also serve as an integrated lens through which one can make sense of AI's integration into other countries' adult education practices.Originality/valueThis paper fulfills an identified need of understanding the current status of AI's integration into and influence on the field of adult education in China.

METHOD OF AUTOMATED DETECTION OF ARTICLE TERMS USING A DECISION TREE

Every day, the number of users of virtual communities is increasing, and therefore the data that occurs during communication between them. The posted data can contain valuable information because they contain not only the manufacturer’s opinion, but also consumer experience about a certain product. But, due to the fact that virtual communities have a weak structure in terms of providing information, they are more focused on entertaining content – they may contain data that do not carry a meaningful load, and also, when placing data, not all users foresee techniques that will help increase the relevance of the search for this data. Therefore, the search for target data requires significant time costs. To improve the search for data in the article, a method is proposed that allows you to analyze the content of posted posts and identify keywords from a certain subject area. This method is automated and works on the basis of a previously developed dictionary of key phrases or regular expressions with weighting coefficients of belonging to one or another term. As a result, a decision-making tree is built for each term, which determines the weight of the term to the content of the post, article. At the same time, the level of location of the post in the discussion is taken into account, because the discussion contains a set of chronologically ordered posts. Posts placed at higher levels have a higher coefficient in the calculation. While posts are placed at lower levels – lower weighting factors. Identified key phrases before the specified term are ordered in descending order of weight. At each level of the tree, the total weight of key phrases must be equal to one. To process the data from the virtual communities, they were downloaded using the data consolidation technique. As a result, the concept of consolidated data storage was introduced, which allows collecting data from disparate sources. The paper presents the weight calculation for one term from part of the CodeProject community post.

Experiences of Prejudice, Stereotype, and Discrimination Exposure of Secondary School Students

Although being exposed to prejudice, stereotype and discrimination reduces the self-esteem and quality of life of individuals, the effect of it on secondary school students is not fully understood. This study aimed to reveal the meaning, structure and essence of secondary school students' experiences of being exposed to prejudice, stereotype and discrimination. The present study was conducted with phenomenology, one of the qualitative research designs. Maximum variety and criterion sampling from one of the purposive sampling strategies were used to determine the participants. In this study, research participants consisted of 12 students studying in 7th and 8th grades in two public schools in Ankara, Turkiye. Research data were collected by semi-structured interview method. Within the scope of this study, 24 in-depth interviews were conducted with each participant twice. In data analysis, phenomenological analysis, bracketing, listing key phrases, developing sets of meanings, textural description, structural description, and extracting essences, were used. The findings showed that the students exposed to prejudice, stereotyping and discrimination suffered from self-esteem and self-confidence, their academic motivation decreased, they developed sympathy for the in-group and antipathy to the out-group and they were in search of equality and justice.

Gerontophobia in “The Coming of Age” by Simone De Beauvoir: an Interdisciplinary Study

This research aims to analyse “The Coming of Age” by Simone de Beauvoir, classify the word associations with the phenomenon of ageing and identify the changes in the perception of ageing by the population. The scientific methods used in this research are determined by the objectives and the research questions: methods of empirical research (survey, observation, semantic analysis, method of free-associative experiment). Text analysis was performed with the help of Voyant Tools (quantitative confirmation of patterns in the text, identifying key words and phrases within a large corpus). The semantic analysis of the text made it possible to distinguish three main connotations used by Simone de Beauvoir to describe old age: existential, romantic, pejorative and it was proved that “The Coming of Age” belongs to “sad” texts using V. Belyanin’s (2000) classification. The results of the associative experiment conducted with 340 participants showed that 45% of associations with the word ageing carry an existential connotation; 40% – pejorative; 10% – romantic and 5% of the associations have a connotation that cannot be included in the three main categories. The results of the text analysis showed that the content and the language of “The Coming of Age” by Simone de Beauvoir directly collate with the author’s personality. Moreover, modern society demonstrates similar stereotypes, phobias, and discrimination to those depicted in the literary work more than fifty years ago. The results open the gate to further research on gerontophobia and authors’ personality.

Keeping your teeth for life: Why use a disclosing agent?

Keeping your teeth for life: Why use a disclosing agent? Dr. Neha Dixit and Dr. Marcel Donnet argue that you can't ignore what you see when it comes to keeping your teeth for life. Particular focus is given to Guided Biofilm Therapy and the use of a disclosing agent. Maintaining a healthy mouth is a never-ending battle, with the activity of the bacteria in the mouth never stopping. Therefore, an effective strategy is needed to keep our teeth for life and win this battle. In this matter, the key phrase is “good teamwork” between the patient and the dental professional delivering professional teeth cleaning.

Cystic fibrosis - Ways to improve the quality of life of patients

Introduction Cystic fibrosis (CF) is a serious, multi-system disease. In the Caucasian population CF is the most common autosomal recessive disease, which happens for 1 person in 3,500 births [1]. In the course of CF, there is a dysfunction of many organs, mainly the respiratory and digestive systems. Symptomatic treatment of cystic fibrosis is very important. The change in daily eating habits, the use of proper breathing techniques and psychotherapy also have an impact on improving the quality of life of patients. In the past patients died at a young age. The progress in the diagnosis and treatment of cystic fibrosis has meant that nowadays more and more people are reaching adulthood. In recent years, the median survival of patients has increased to 46.2 years. [2] Purpose The aim of this review is to present the current state of knowledge about ways to improve the quality and length of life of patients with CF. Methods The literature available in the Pubmed and Google Scholarship databases was reviewed using key phrases. Results Additional methods of treatment, such as: learning the correct breathing technique, oxygen therapy, physical exercise or proper nutrition, significantly improve the living conditions of patients.

Multi-Document News Web Page Summarization Using Content Extraction and Lexical Chain Based Key Phrase Extraction

In the area of text summarization, there have been significant advances recently. In the meantime, the current trend in text summarization is focused more on news summarization. Therefore, developing a synthesis approach capable of extracting, comparing, and ranking sentences is vital to create a summary of various news articles in the context of erroneous online data. It is necessary, however, for the news summarization system to be able to deal with multi-document summaries due to content redundancy. This paper presents a method for summarizing multi-document news web pages based on similarity models and sentence ranking, where relevant sentences are extracted from the original article. English-language articles are collected from five news websites that cover the same topic and event. According to our experimental results, our approach provides better results than other recent methods for summarizing news.

Towards Usable Neural Comment Generation via Code-Comment Linkage Interpretation: Method and Empirical Study

Code comment is important to facilitate code comprehension for developers. Recent studies suggest to generate comments automatically with deep learning, in particular, based on neural machine translation models. However, such a promising Neural Comment Generation (NCG) technique suffers from unsatisfactory performance, as well as poor usability, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.,</i> developers cannot easily understand and modify the auto-generated comments. This paper suggests that a proper interpretation of how the comments are generated can significantly improve the usability of NCG approaches. We propose a novel model-independent framework, namely CCLink, to interpret the auto-generated comments. CCLink generates a set of code mutants and obtains their corresponding comments. Based on these data, several contribution mining algorithms are designed to infer the key elements in code that contributes to the generation of the key phrases in the comments. The links between code and its auto-generated comment can thus be constructed. This in turn allows CCLink to visualize the links as the comment interpretations to developers. It greatly facilitates manual verification and correction of the comments. We examine the performance of CCLink with different contribution mining algorithms, NCG approaches, and real-world datasets. We also conduct an empirical study on 32 experienced Java programmers to evaluate the effectiveness of CCLink. The results show that CCLink is promising in making NCG more usable with a proper interpretation of the auto-generated comments.

A small sample data-driven method: User needs elicitation from online reviews in new product iteration

Eliciting user needs from mass online reviews is playing a significant role in the product iteration process. Efficient user needs elicitation does achieve considerable benefits for maintaining higher competitiveness and a speedier lifecycle. However, there is inevitably an online review scarcity about new products due to the short time on the market and low buyer recognition compared with commonly used products. This paper proposes a small sample data-driven method for user needs elicitation from online reviews in new product iteration. In the first stage, a scraped initial online review dataset is pre-processed roughly to improve the data quality. And then, reviews are classified into multiple categories according to different topics using ERNIE. In the second stage, each topic-based dataset is reprocessed in detail. Thereafter, the key user needs set is determined and facilitated by extracting key product information phrases from every single dataset using improved SIFRank. Moreover, the case study of a smart cat feeder is carried out to demonstrate the feasibility and potential of the ERNIE-ISIFRank methodology. Finally, comparative experiments are conducted to verify the advantages of the proposed method which is primarily based on the pre-trained language model to enhance the deep understanding of the semantics of online reviews. The experimental results confirm that the proposed method can assist in identifying key user needs with high efficiency.

Therapeutic approaches for pathological gamblers with psychiatric comorbidities – a literature review

Research has shown that gambling disorders often coexist with other mental disorders, with worse treatment outcomes in the affected patients than those with a single disorder. This population is also characterised by higher hospitalisation rates, non- compliance and poor response to pharmacotherapy. The aim of this review was to identify, describe and analyse therapeutic approaches used for pathological gamblers with other mental health issues. The EBSCOhost database was used for literature review. Combinations of key phrases referring to gambling disorders and the most frequently identified mental comorbidities were used for the search. The review showed that therapeutic guidelines for gamblers with mental comorbidities are missing. According to previous studies, the cognitive-behavioural approach in individual and group therapy seems to be an effective strategy for gambling disorders overlapping with other psychiatric comorbidities. Motivational interviewing and desensitisation with exposure to stimuli have also been shown to be effective. Pharmacotherapy is an indispensable component of treatment. Therapy via the Internet, which may be particularly attractive for those reluctant to opt for individual or group therapy, is an alternative to traditional sessions. It is necessary to develop treatment guidelines for patients with gambling disorders co-occurring with other mental conditions to provide support for practitioners working with these patients.

Eradicating the tumor "seeds": nanomedicines-based therapies against cancer stem cells.

Cancer stem cells (CSCs), a small subpopulation of cells with high tumorigenesis and strong intrinsic drug resistance, exhibit self-renewal and differentiation abilities. CSCs play a crucial role in tumor progression, drug resistance, recurrence and metastasis,and conventional therapy is not enough to eradicate them. Therefore, developing novel therapies targeting CSCs to increase drug sensitivity and preventing relapse is essential. The objective of this review is to present nanotherapies that target and eradicate the tumor "seeds". Evidence was collected and sorted from the literature ranging from 2000 to 2022, using appropriate keywords and key phrases as search terms within scientific databases such as Web of Science, PubMed and Google Scholar. Nanoparticle drug delivery systems have been successfully applied to gain longer circulation time, more precise targeting capability and better stability during cancer treatment. Nanotechnology-based strategies that have been used to target CSCs, include (1) encapsulating small molecular drugs and genes by nanotechnology, (2) targeting CSC signaling pathways, (3) utilizing nanocarriers targeting for specific markers of CSCs, (4) improving photothermal/ photodynamic therapy (PTT/PDT), 5)targeting the metabolism of CSCs and 6) enhancing nanomedicine-aided immunotherapy. This review summarizes the biological hallmarks and markers of CSCs, and the nanotechnology-based therapies to kill them. Nanoparticle drug delivery systems are appropriate means for delivering drugs to tumors through enhanced permeability and retention (EPR) effect. Furthermore, surface modification with special ligands or antibodies improves the recognition and uptake of tumor cells or CSCs. It is expected that this review can offer insights into features of CSCs and the exploration of targeting nanodrug delivery systems.

Systematic Literature Review on Robust Optimization in Solving Sustainable Development Goals (SDGs) Problems during the COVID-19 Pandemic

Handling uncertainty is important in decision making, especially for SDGs problems. Robust Optimization (RO) is an applied optimization method that can be employed to handle optimization under uncertain data. With SDGs problems, many uncertain data have been considered in decision making. With RO, the data uncertainties are assumed to lay within a compact, convex continuous set. There are three special sets that can be used to represent the data, i.e., box, ellipsoidal, or polyhedral uncertainty sets. These special sets lead the SDGs problems to a computationally tractable optimization model, such that the global optimal solution is attained. However, literature reviews on the application of RO in SDGs decision-making is sparse, especially during the COVID-19 pandemic period. This paper examines the following topics: (1) the purposes of studies of RO and SDGs during the COVID-19 pandemic, (2) the state-of-the-art in RO-SDGs to determine the research objectives, and (3) the SDGs type of problems that have been modeled using RO. A systematic literature review is conducted in this paper, wherein discussion is based on a PRISMA (Preferred Reporting Items for Systematic Review and Meta-Analyses) flowchart. To this end, the database reference searching conducted on the Scopus, Science Direct, and SAGE databases, is completed using the help RStudio software. The analysis was carried out on two datasets, assisted by the output visualization using RStudio software with the “bibliometrix” package, and using the ‘biblioshiny()’ command to create a link to the “shiny web interface”. In this paper, the research gap on application of RO to SDGs problems is analyzed in order to identify the research objectives, methods, and specific RO-SDGs problems. As a result, the application of RO to SDGs problems is rare; this finding provides a motivation to conduct a further study of RO and SDGs during the COVID-19 pandemic. An expansion is presented using the key phrase “Operations Research and Optimization Modeling”, or “OROM”. SDGs in Indonesia may be referenced as an example of the capacity building available through RO/OROM.