Search Queries Research Articles

When toxicity is not just from gluten: a systematic review on financial toxicity and celiac disease

Abstract Background With an incidence of up to ∼2% globally, Celiac Disease (CD) is probably one of the most life-long common chronic conditions. Although ‘Financial toxicity’ (FT) has been mainly linked to oncology, significant financial burdens have been described across all healthcare domains, even in CD and to the unexpected economic effort resulting from illness and the need for treatments. This study aimed to investigate the potential relationship between CD and FT. Methods A systematic literature review was performed according to the PRISMA Statement 2020 guidelines. The search query queried the following databases: PubMed, Scopus, CINAHL and APA PsycInfo. No time and study design limits were included. Results 105 articles were found, and 25 were included in the review. The main results revealed that CD impacts physical health and imposes a significant financial burden on patients and their families. FT in CD is primarily due to the high costs of diagnosis, lifestyle changes, and expenses for gluten-free foods. Several studies highlighted the scarce adherence to the therapeutic diet due to the cost of food and poor acceptance of CD. Furthermore, since 2007, there has been increased attention to the difficulty of finding gluten-free products and their expensive fees. All these aspects, even for low-income countries, were faced differently globally, as documented by the heterogeneity of government subsidy policies highlighting the health inequity. Conclusions Considering the prevalence, a comprehensive health assessment with a global health approach should consider clinical, social, and economic aspects. This regard could improve the quality of life for CD patients and ensure fair and personalised access to care, regardless of their geographical location or financial situation. This review has highlighted the fragmentation of resources available internationally, underlining the relevance from a public health point of view. Key messages • Celiac Disease is one of the most common life-long disorders, and financial toxicity is an aspect that should be taken into account also from a public health point of view to ensure health equity. • The gluten-free diet represents an important therapeutic choice in CD; the related expensive fees could increase scarce adherence and worse health outcomes.

ANALYZING USER BEHAVIOR PATTERNS FOR PERSONALIZED RECOMMENDER SYSTEMS IN E-COMMERCE: A LITERATURE REVIEW

Abstract: E-commerce thrives on a user-centric strategy, and recommender systems are at the cutting edge of personalizing the purchasing experience. These systems may forecast preferences and recommend appropriate items by analyzing user behavior patterns, resulting in many benefits such as increased customer satisfaction, increased sales and conversions, and increased efficiency. To accomplish these benefits, recommender systems utilize complex algorithms that examine numerous elements of user behavior such as purchase history, browsing behavior, search queries, demographic data, and implicit feedback. Sophisticated algorithms can recognise complicated patterns in user data, resulting in more accurate and personalized suggestions. Analyzing user reviews, product descriptions, and social media interactions may help you better understand consumer preferences and product features. Systems can make real-time suggestions depending on a user’s current browsing session, resulting in a more dynamic purchasing experience. Personalized recommender systems will play an increasingly important role in molding the future of e-commerce as user behavior analysis techniques are constantly refined. The study intends to make important advances to the field of personalized recommender systems by undertaking a thorough research of user behavior patterns in the e-commerce domain. We strive to improve the performance of recommender systems by extracting insightful features from various data sources and exploring sophisticated machine learning techniques, resulting in a more engaging and tailored user experience that fosters customer satisfaction and drives business growth. A comprehensive review of user behavior patterns and their influence on personalized recommender systems in the e-commerce industry reveals the critical role of data analysis and machine learning algorithms in tailoring product suggestions to individual preferences, thereby enhancing customer satisfaction and driving sales growth. By implementing the tactics and approaches expressed in this study, e-commerce platforms may stay ahead of the curve, providing a smooth and tailored purchasing experience that surpasses customer expectations and contributes to their competitive advantage in the changing e-commerce environment.

Pain Management Methods and Techniques in Lumbar Disc Surgery: A Narrative Review Study

Background and Aim: This study examines the complex field of pain treatment related to lumbar disc disorders in a narrative review. Methods and Materials/Patients: Our search strategy involved utilizing specific terms, including ‘pain management,’ ‘lumbar disc surgery,’ ‘methods,’ and ‘techniques.’ We formulated a search query using Boolean operators: (‘Pain management’ OR ‘analgesia’) AND (‘lumbar disc surgery’ OR ‘spinal surgery’) AND (‘techniques’ OR ‘procedures’ OR ‘approaches’ OR ‘methods’) AND ‘narrative review.’ A systematic search was conducted across reputable medical databases including PubMed, MEDLINE, and Cochrane Library. Filters were applied to refine search results based on publication type and date, with a focus on material published within the past two decades to ensure the inclusion of current, high-quality literature. Results: This study synthesized information from several sources, demonstrating the changing methods used to address pain in lumbar disc disorders. The article explored the historical background of medical treatments, emphasized both contemporary surgical and non-surgical methods, and investigated the predicted factors that affect the results. The study investigated the range of surgical incidents and examined the clinical factors to be taken into account for treatments. The value of efficient pain treatment in alleviating chronic back pain and maximizing patient results was emphasized. Conclusion: In conclusion, this review underscores the paramount importance of comprehensive management of lumbar disc pain. It stresses that effective pain control not only alleviates immediate suffering but also plays a pivotal role in ensuring positive surgical experiences, enhancing patient outcomes, and promoting long-term well-being.

Terminology of medicines used to control seizure disorders: 2024 ILAE Recommendations and profile of real-world term use

Background. The International League Against Epilepsy (ILAE) has provided recommendations on the terminology of medicines for control of seizure disorders, which should be adapted into different languages.Objective: to evaluate the usage profile of terms describing the effects of medicines used in patients with epilepsy.Material and methods. The search query analysis systems Google Trends and Yandex Wordstat as well as PubMed/MEDLINE, Google Scholar and eLibrary databases were used to evaluate the general public and professional usage profile, respectively.The following terms in Russian and English were studied: “antiepileptic”, “antiepileptic drug”, “antiseizure”, “antiseizure drug”. In order to ascertain a prevailing opinion within professional and patient communities, a questionnaire survey was conducted on the public online resources of the non-profit partnership “The Association of Epileptologists and Patients” carried out via the cloud-based cross-platform social media as well as instant messaging service Telegram and the social media platform VK.Results. In the English-speaking internet segment, the term “antiepileptic” is used more than 5 times frequently than “antiseizure” by the general public. Conversely, in the Russian-language segment, the term “antiseizure” is almost 2.5 times more common. The term “antiepileptic” is more prevalent in scientific community in both segments. This finding aligns with the profile of terminology usage observed among epileptologists and epilepsy patients. The questionnaire survey results revealed that 56.42% of 179 respondents were in favor of the terms “antiepileptic drug”, “AED” and “antiepileptic therapy”, whereas as few as 17.32% of those endorsed the ILAE-proposed terminology.Conclusion. In light of the established practice of terminology use in both professional scientific community and among practicing epileptologists and epileptic patients, as well as the differences in the semantics of English- and Russian-language terms and the identified lack of precision in the ILAE definition of epilepsy, it can be concluded that for the Russian-speaking population, the terms “antiepileptic drug”, “AED” are more preferable rather than the terms “antiseizure medication”, “ASM”. Additionally, it is preferable to use the terms “convulsive ictus” and “epileptic ictus” due to negative connotation with term “seizure” in Russian. The terms “antiseizure” and “antiepileptic” should not be regarded as mutually exclusive in the context of treatment that has a direct impact on epilepsy course. Finally, it is necessary to further refine epilepsy definition and classification.

Health Care Language Models and Their Fine-Tuning for Information Extraction: Scoping Review.

In response to the intricate language, specialized terminology outside everyday life, and the frequent presence of abbreviations and acronyms inherent in health care text data, domain adaptation techniques have emerged as crucial to transformer-based models. This refinement in the knowledge of the language models (LMs) allows for a better understanding of the medical textual data, which results in an improvement in medical downstream tasks, such as information extraction (IE). We have identified a gap in the literature regarding health care LMs. Therefore, this study presents a scoping literature review investigating domain adaptation methods for transformers in health care, differentiating between English and non-English languages, focusing on Portuguese. Most specifically, we investigated the development of health care LMs, with the aim of comparing Portuguese with other more developed languages to guide the path of a non-English-language with fewer resources. This study aimed to research health care IE models, regardless of language, to understand the efficacy of transformers and what are the medical entities most commonly extracted. This scoping review was conducted using the PRISMA-ScR (Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews) methodology on Scopus and Web of Science Core Collection databases. Only studies that mentioned the creation of health care LMs or health care IE models were included, while large language models (LLMs) were excluded. The latest were not included since we wanted to research LMs and not LLMs, which are architecturally different and have distinct purposes. Our search query retrieved 137 studies, 60 of which met the inclusion criteria, and none of them were systematic literature reviews. English and Chinese are the languages with the most health care LMs developed. These languages already have disease-specific LMs, while others only have general-health care LMs. European Portuguese does not have any public health care LM and should take examples from other languages to develop, first, general-health care LMs and then, in an advanced phase, disease-specific LMs. Regarding IE models, transformers were the most commonly used method, and named entity recognition was the most popular topic, with only a few studies mentioning Assertion Status or addressing medical lexical problems. The most extracted entities were diagnosis, posology, and symptoms. The findings indicate that domain adaptation is beneficial, achieving better results in downstream tasks. Our analysis allowed us to understand that the use of transformers is more developed for the English and Chinese languages. European Portuguese lacks relevant studies and should draw examples from other non-English languages to develop these models and drive progress in AI. Health care professionals could benefit from highlighting medically relevant information and optimizing the reading of the textual data, or this information could be used to create patient medical timelines, allowing for profiling.

Ethical and Methodological Considerations in LGBTQ-focused Online Ethnography: A Proposal of a Systematic Review

Lesbian, gay, bisexual, transgender, and queer (LGBTQ) people constitute a vulnerable population that requires research about their needs, representations, and challenges. Among the variant research methods, ethnography is particularly effective in understanding participants and their lived experience. The development of information and communication technologies has transformed traditional ethnography research and contributed to the emergence of online ethnography research that holds potential for studying vulnerable populations (e.g., LGBTQ people) and relevant topics. We propose a systematic review to explore how researchers have considered and addressed ethical and methodological issues in LGBTQ-focused online ethnography research. We used the core collection of the Web of Science to search for relevant literature. We crafted the search query based on studies of LGBTQ populations, online ethnography studies, and other data sources like the Homosaurus vocabulary. Two groups of search terms were compiled and used in combination, including population-related terms (e.g., “queer”, “LGB*”, “GLB*”, and “sexual minorit*”) and method-related terms (e.g., “online ethnograph*”, “netnograph*”, “digital ethnograph*”, and “virtual ethnograph*”). In this review, we focus on empirical journal articles using any type or variant of online ethnography to investigate LGBTQ-focused topics. The eligible articles may include LGBTQ people as participants or stakeholders (e.g., LGBTQ staff), or analyze LGBTQ-related content (e.g., YouTube videos). Most importantly, we will analyze the ethical concerns (e.g., informed consent, power imbalance, cultural sensitivity, and beneficence and justice) and methodological aspects (e.g., research design, sampling, recruitment, data collection, data analysis, and roles of LGBTQ people) of these studies.

Navigating the Challenges of Legacy Data: A Systematic Literature Analysis

Legacy data is data collected systematically in the past, for which contemporary use and reuse are at-risk, difficult, or impossible due to issues such as missing metadata, obsolete storage media, outdated file formats, and unsupported software. As we introduced in Buchanan, et al. (2023), there is a demonstrated need for more systematic understanding of legacy research data efforts and the value of these data and efforts as perceived across stakeholder groups. We are conducting a literature analysis of publications on the topics of legacy research data use, preservation, curation, and management. Using an established search query and online literature databases, we retrieved and manually reviewed a pool of 1,338 results and identified 69 papers that fit the following criteria for our study: peer-reviewed articles published in the last 15 years including studies discussing methodologies and best practices; studies that describe repurposing legacy data; studies focusing on storage or archival methods for older data; and studies that discuss software and migration challenges. We are analyzing the sample of papers using content analysis and bibliometric methods, focusing on references to individuals involved, processes, data types, formats, locations, and problems being addressed.

Confirming the Query Search Method: Balancing AI Advancements in Reference Services

Although artificial intelligence (AI) has quickly become a tool that offers quick, simplistic answers to reference questions (Chen, 2023), in this paper, we interrogate AI’s ability to consider the human-to-human interpersonal nuances that occur and are vital to answering queries. We ask: what is most essential for reference services - human knowledge sharing or the machine’s artificial intelligence? AI, as a mechanistic language model, cannot discern the subtleties of humanity’s questions; AI itself admits that librarians are more efficient at answering reference queries from a humanistic approach and lens (Yang & Mason, 2023). This paper is the second part of our introduction to our methodology for reference services, the Query Search Method (QSM). We present conceptual substantiation that, when enacted, the QSM can incorporate AI as an addition to the librarian’s toolbox for honoring patrons’ ways of knowing that, through the sententiousness of the human experience, invariably seek knowledge beyond “the machine.”

Clinical Evaluation of Blood Culture Identification Gram-Positive Panel on Methicillin Resistant Gene Testing in a Large Metropolitan Teaching Hospital

Abstract Introduction The ePlex blood culture identification (BCID) gram-positive panel is a PCR-based approach used for the detection of common pathogens associated with bloodstream infections and corresponding antibiotic-resistance genes. One of the genes, mecA, is associated with methicillin, penicillin, and other penicillin-like-antibiotic-resistant gram-positive bacteria (GPB). It is unclear whether the BCID panel has predictive utility and accuracy in treating hospital infections due to GPB. This retrospective study attempts to determine the consistency and accuracy of BCID gene testing compared to more traditional susceptibility methods at a large public hospital. Method A search query was performed for all BCID results at one teaching hospital in a large metropolitan area for one year (2022). The query included demographic information, BCID results, culture results, and combined susceptibility data. The results were filtered to include only cases in which the mecA gene was detected. The filtered results were then analyzed to determine if the susceptibility results from the susceptibility platform (VITEK) were consistent with the expected susceptibility of the target gene, specifically for oxacillin and vancomycin. The susceptibility cutoff was set as >0.5 mcg/mL and <1 mcg/mL respectively, the standard threshold used at the institution. Results A total of 1211 culture results were obtained, of which the mecA gene was identified by BCID in 321 cases, along with the following GPB: Staphylococcus aureus, Staphylococcus epidermidis, and Staphylococcus sp. (not aureus). The corresponding cultures grew the following isolates: S. aureus, S. epidermidis, S. hominis, and S. haemolyticus. Of the 321 BCID results, 75 were identified as S. aureus. The isolates grew methicillin-resistant Staph aureus (MRSA) (69/75) 92%, while the typical S. aureus grew (6/75) in 8% of the isolates. In these cases, we compared susceptibility testing for both isolates as either sensitive or resistant, with a MIC of >0.5 mcg/mL for oxacillin to be designated as resistant, and <1 mcg/mL to be designated as sensitive to vancomycin. Of the 75 specimens, the concordance rate was 97% (73/75) for oxacillin and 100% (75/75) for vancomycin. The average receipt to verification turnaround time (TAT) was 36 hours for BCID results and 121 hours (about 5 days) for culture sensitivity results. Conclusion The results show that oxacillin resistance can be predicted by detecting S. aureus and the mecA gene using the BCID panel. However, the mecA gene can be identified in other GPB such as S. epidermidis, in which it should not be used to infer oxacillin or vancomycin susceptibilities. In the case of mecA and S. aureus detection by BCID, treating the patient as a MRSA infection may be appropriate before the susceptibilities have been performed. This approach can reduce the turnaround time for antimicrobial resistance reporting and improve antimicrobial stewardship efforts.

Research trends in innovation ecosystem and circular economy

Understanding innovation ecosystems and the circular economy is crucial for systemic change in business and industry, fostering eco-innovation and advancing sustainable development. This study uses bibliometric analysis to uncover the research trends, patterns, and collaborations, revealing a significant gap in understanding the interactions between innovation ecosystems and the circular economy and offering potential avenues for future research that align with sustainable development. The analysis was carried out with the help of Biblioshiny and VOSviewer on the final selected documents of 2981 from the Scopus database through a search query and the SPAR-4-SLR stages of filtration. The key findings of the study are as follows: collaboration among countries involves accessing countries’ resources, knowledge, markets, and location. The study explores research trends in the innovation ecosystem and circular economy, focusing on five key clusters: the circular economy for eco-innovation and resource recovery, circular business models for fostering sustainable innovation in the bioeconomy, sustainability through renewable energy and sdgs, the sustainable business model for enhancing green innovation through entrepreneurship, and Artificial Intelligence (AI) and the circular business model in Industry 4.0. The bibliometric analysis identifies trends, research gaps, and collaboration patterns, revealing potential avenues for future exploration in industrial symbiosis, energy transition, and the system of innovation. The study analyzed only the documents available in Scopus. The exclusion of papers based on the search period, language, document type, and incomplete details are the limitations of this research and open the scope of potential future research. The findings will help existing researchers in this field as well as new researchers interested in this field by clearly understanding trends and further research scopes. The study also offers actional recommendations and practices to policymakers. Practices, and entrepreneurs for the attainment of global sustainability goals. The novelty and originality of this study rely on a thorough literature review that describes the state of the art in the innovation ecosystem and circular economy.

Federated learning-based natural language processing: a systematic literature review

Federated learning (FL) is a decentralized machine learning (ML) framework that allows models to be trained without sharing the participants’ local data. FL thus preserves privacy better than centralized machine learning. Since textual data (such as clinical records, posts in social networks, or search queries) often contain personal information, many natural language processing (NLP) tasks dealing with such data have shifted from the centralized to the FL setting. However, FL is not free from issues, including convergence and security vulnerabilities (due to unreliable or poisoned data introduced into the model), communication and computation bottlenecks, and even privacy attacks orchestrated by honest-but-curious servers. In this paper, we present a systematic literature review (SLR) of NLP applications in FL with a special focus on FL issues and the solutions proposed so far. Our review surveys 36 recent papers published in relevant venues, which are systematically analyzed and compared from multiple perspectives. As a result of the survey, we also identify the most outstanding challenges in the area.

Impact of World Cerebral Palsy Day on Public Interest in Brazil: Evidence from Internet Search Data

ABSTRACT Aim This study investigated the impact of the World Cerebral Palsy Day (WCPD) campaign on the public interest using Google Trends Analysis data in Brazil. Methods Google Trends was used to collect Relative Search Volume (RSV) data for “cerebral palsy” from 2004 to 2011 (control years) and 2012 to 2022 (WCPD years). RSV during the 4 weeks around WCPD (period of interest) was compared with the rest of the year (control period) in each timeframe. Regional RSV, search queries, and main topics were also investigated. Results RSV increased by 62.22% from pre-campaign to campaign period. During the WCPD years, a 21.36% RSV increase occurred in campaign weeks, with an average difference of 12.16 (95% CI: 1.74, 22.58); notably in in the last five years in the southeast 9.47 (95% CI: 2.93, 16.01) and south 8.66 (95% CI: 1.66, 15.66) macro-regions. Conclusion The campaign has fulfilled its role, but targeting more vulnerable areas could further amplify its impact.

A new way of search query like knowledge graph and its interpretability

A new way of search query like knowledge graph and its interpretability

A practical vehicular‐fog‐based privacy‐preserving attribute‐based keyword search system using potential games

AbstractThe growth of fog computing drives Internet of Things users to outsource data for services like storage and sharing. To ensure secure and efficient data search, extensive research has been conducted on searchable encryption, specifically attribute‐based keyword search (ABKS). ABKS facilitates precise access control over encrypted data while maintaining search functionality. However, delegating complex search operations to fog nodes introduces challenges like latency limitations, compromised search results, and unauthorized occupation of server resources. In this manuscript, we propose a Time‐Efficient Data Retrieval (TEDR) scheme to address these challenges. Our scheme incorporates two access policies, allowing data owners (DOs) and data users (DUs) to control data access and search queries. TEDR enables fog nodes to verify search queries before decryption, avoiding unnecessary decryption for invalid queries. We introduce a decentralized query submitting framework to allocate search queries among fog nodes, addressing redundancy concerns and latency issues. We analyze the decentralized query submitting game and classify it as a potential game, making it suitable for decentralized decision‐making. We evaluate TEDR performance on real‐world datasets, demonstrating practicality and efficiency. Our scheme reduces computational and communication costs by approximately 30% compared to existing methods.

Indexing for Fine Arts Monographs: A Comparison Between User Queries and Controlled Vocabularies

ABSTRACTPrior research suggested the empirical approach to indexing for fine arts based on users' search queries. This study examined the characteristics of user queries submitted to the library catalog of a fine arts academy, and compared users' search terms with controlled vocabularies to assess their levels of matching. Based on search‐log analysis, this study identified eight categories of user queries and 10 subcategories, indicating that the information needs of the fine arts community are diverse in language and culture and focus on named entities, disciplines, and various types and levels of fine arts. The mapping between user queries and controlled vocabularies found that around 20% of user search terms were non‐matching. Based on the findings, this study provided suggestions for improving the search functions of library catalogs and enhancing controlled vocabularies to support the retrieval of fine arts monographs.

An Ontology Based Web Crawler with a Near-Duplicate Detection System to Improve the Performance of a Web Crawler

Purpose: The aim of the study is to examine how an ontology-based web crawler with a near-duplicate detection system improves the performance of a web crawler. Methodology: The experiment was carried out using secondary data from a sample web site which was used since crawling is an endless process. Using these two approaches, the ontology web crawler would search for relevant searches according to the search query of the user while the near-duplicate detection system would eliminate redundant data. Findings: It was observed that ontology web crawler performed better and faster than a normal crawler. It takes less execution time to search the web than other web crawlers. This is due to the fact that web documents are being filtered by the ontology web crawler such that only relevant web documents are retrieved according to the search query of the user. The relevant documents are further filtered by a near-duplicate detection system by removing web pages that are duplicates of each other and also remove near-duplicate web documents. This further reduces the number of web pages retrieved by the web crawler. This model saves on storage space because of the reduced number of web pages retrieved as it takes care of irrelevant and redundant web pages searched. Unique Contribution to Theory, Practice and Policy: The study recommends that the model can be improved to be dynamic by adding new relations that is the crawler should search for web pages related to the search even if they don’t contain the keywords searched. Domains and concepts should be added when visiting new web pages. Standardization of weights needs to be done because as of now experts assign weights to terms according to the area of expertise and knowledge.

Evaluating the readability of online patient information for cholangiocarcinoma.

264 Background: The digital age has facilitated the dissemination of free comprehensive information regarding the diagnosis and treatment of various cancers. Especially for rarer cancers, such as cholangiocarcinoma, patients heavily rely on online patient information (OPI) as a primary source of information. However, the usefulness of these websites is dependent on the readability of the information. The National Institute of Health (NIH) recommends OPI have a readability level of grade 8 or lower. We sought to assess the readability of OPI for cholangiocarcinoma. Methods: We utilized Google to identify the top ten websites utilizing the search query "cholangiocarcinoma". The readability score of each website was defined as the median of ten popular standardized readability scales - Flesch–Kincaid, Gunning Fog, Coleman–Liau, Simple Measure of Gobbledygook, Automated Readability Index, FORCAST, Raygor, Fry Graph, and New Dale Chall. These readability scales were obtained using www.readable.com. Group comparisons of readability score were performed for affiliation (i.e., academic, non-profit, or government) and website content (i.e., treatment modalities, complications, clinical trials, supportive care, and survivorship). Statistical analysis was done in RStudio. Pairwise comparisons were performed using the Wilcoxon rank sum test. Results: A total of 10 websites were analyzed with 4/10 (40%) affiliated with academic institutions, 3/10 (30%) affiliated with non-profit organization, and 3/10 (30%) affiliated with government websites. The median readability score for all 10 websites was 9.95 (IQR 9.15-10.75). The majority of the websites (9/10, 90%) websites had readability scores that did not satisfy the NIH recommendation for readability. Readability scores did not differ significantly between academic, government, and nonprofit websites (p=0.68). Readability also did not differ by website content. The correlation between word count and readability score was not statistically significant (r = 0.03, p = 0.92). Conclusions: We performed a first of its kind analysis to assess the readability of OPI for cholangiocarcinoma. The median readability of OPI for cholangiocarcinoma was at a 10th grade level and the majority of the websites did not satisfy the NIH guideline for readability. Surprisingly, government websites were not simpler to read than their academic and nonprofit counterparts. Our study underscores the need to develop OPI for cholangiocarcinoma that is easily readable for patients and their caregivers. Organizations publishing OPI may use readability scores to ensure their content is accessible for the general population.

Climate warming effects on temperature structure in lentic waters: A bibliometric analysis from the recent 20 years

Global climate warming and intensified summer heatwaves have exacerbated thermal stratification in inland lakes and reservoirs, leading to increased issues of deep-water hypoxia and harmful algae blooms. This study aims to systematically review the developments, barriers, and future directions of thermal phenology in freshwaters under warming conditions through a visualized meta-analysis. According to the Web of Science Core Collection database, we retrieved 3262 articles published between January 1, 2000, and December 31, 2023, using an advanced search query that included terms related to global warming, temperature stratification, and freshwater bodies. The data was then analyzed via bibliometric visualization tools to create comprehensive visual maps, highlighting research hotspots and development trends. Key findings include a significant upward trend on this topic, in the annual number of published articles post-2015, in which China and the USA are leading in the publication output. Keyword co-occurrence analysis identified climate change, global warming, and temperature as central themes, with specific environmental issues linking to lake eutrophication and runoff being prominent as well. The study also delves into the collaboration networks among researchers, institutions, and countries, revealing strong international partnerships primarily between China, the USA, and European nations. Based on the analysis, we recommend future research should focus on integrating machine learning and advanced modeling techniques to better predict and mitigate the impacts of climate warming on thermal dynamics of inland waters. By upscaling the research from traditionally local (or regional) to global perspective, our work is vital, not just for science, but also for management of the aquatic systems under rapidly changing climate conditions.

How Sarcopenia, Muscle Mass, Strength, and Performance Relate to Non-Alcoholic Fatty Liver Disease: A Systematic Review

Background and aim: Metabolic dysfunction-associated steatotic liver disease (MASLD), previously known as non-alcoholic fatty liver disease (NAFLD) affects up to 25% of the population and causes significant morbidity and mortality. Sarcopenia, the loss of muscle strength and quantity/quality, shares multiple pathways with MASLD, pointing to their possible association. In this systematic review, we aimed to describe the association between low muscle mass and/or strength and/or performance and the presence, development, or severity of MASLD/NAFLD. Methods: A search was performed in PubMed, Web of Science, Scopus, and LILACS, on 16 October 2020, for relevant studies, using a comprehensive search query and following PRISMA guidelines for systematic review conduction and reporting. Results: The search yielded 1042 results, of which 42 full papers and 11 poster abstracts were included. NAFLD was determined by imaging (n = 27), non-invasive tests (n = 13), liver biopsy (n = 11), or transient elastography (n = 2), and its severity by liver biopsy (n = 10), non-invasive tests (n = 7), transient elastography (n = 7), or imaging (n = 4). Muscle mass was, in most cases, adjusted for weight (n = 25), body mass index (BMI; n = 13), or height (n = 10). Most studies showed an association of low muscle mass and low strength with NAFLD presence and severity. However, the association of muscle mass with NAFLD was present when muscle mass was adjusted for weight or BMI but not when it was adjusted for height. A meta-analysis was not possible due to high heterogeneity in the methods of assessment of muscle parameters and NAFLD. Conclusions: This systematic review suggests the need for standardization of methods in this discipline. The issuing of specific guidance regarding sarcopenia in NAFLD would be of importance.

Harnessing AI for Enhanced Searching in Digital Libraries: Transforming Research Practices

The application of Artificial Intelligence (AI) technologies in digital libraries is changing the paradigm in which users search and interact with digital content. This article is focused on how the use of AI technologies improves the search in digital libraries, demonstrating the benefits of such technologies in overcoming the challenges associated with formulation of query, information retrieval, and end user usage. Most search engine systems face the problems of user queries, which are inherent in online searches. Many user queries are vague, imprecise or ambiguous with regards to the context and as a result lead to less than satisfactory search results. AI technologies such as Natural Language Processing (NLP) and Machine Learning (ML) solve this challenge effectively as they enhance comprehension of queries, contextual understanding, and relevance of the results returned. With the capabilities of NLP incorporated in search engines, search queries which are in the form of natural language can be understood and executed which leads to efficiency in targeting the information needed. Comprehensive restructuring of standard information systems is used by computer learning algorithms for searches by inference and instills usage in users. This study also examines the place of AI in query formulation and the different ways it can be employed; fully automated, semi-automated and/or manually so that the users are less faced with the challenges that are usually present during the query formulation process. Such increases in the level of difficulty and scope of information retrieval suggest that search AI improved the scenario.