Query Terms Research Articles

Hypopharyngeal Reconstruction: Possibilities, Outcomes, and Updates for Improving the Human Health for Quality of Life.

Hypopharyngeal carcinoma is usually present at late stages, necessitating an aggressive line of management consisting of surgical procedures, chemotherapy, and radiation therapy, depending on the case. Practitioners tend to support total laryngectomies or total esophagostomies for most cases of hypopharyngeal carcinoma. The extensive procedures needed will most probably require, depending on the residual defect, a follow-up reconstructive procedure that might require utilizing flaps. Types of reconstructive methods and types of grafts or flaps used could be divided into a multitude of categories depending on the magnitude, shape, extension, and whether the underlying defect that is being reconstructed is circumferential or not. These reconstructive procedures are aimed at improving the quality of life, improving the aesthetic outcome, and restoring the functionality of the pharyngoesophageal segment. When it comes to hypopharyngeal cancer, the most common kind is squamous cell carcinoma (SCC), which has the worst prognosis of all the head and neck malignancies. Overall, the 5-year survival rate remains low, despite recent advancements in diagnostic imaging, radiation, and chemotherapy, as well as enhanced surgical methods and techniques. Hypopharyngeal malignancies are more probable than other tumors to present with advanced primary illness, with nodal metastasis a distinct possibility. The size and amount of local dissemination of the original carcinoma, as well as the extent of involvement of regional lymph nodes, are the most critical factors in predicting prognosis. Hypopharyngeal cancers are more likely than other head and neck cancers to manifest with distant metastases at the time of diagnosis. The appearance of second primary tumors, as well as the development of distant metastases, is a contributing factor to poor survival rate. Imaging techniques such as computed tomography (CT) and magnetic resonance imaging (MRI) with contrast remain the gold standard for evaluating hypopharyngeal carcinoma in the early stages. In most cases, imaging leads to an increase in the tumor stage at the time of presentation. Objectives. The main objectives are to review the research published about flaps, outline the optimum situations that will dictate the usage of a few of the most often used flaps for the rebuilding of the hypopharyngeal segment defects, and outline some of the complications associated with reconstruction. Methods. The processing was carried out with the title-specific search of the PubMed database using the query terms “hypopharyngeal carcinoma” and “reconstruction” to identify the most relevant articles without restricting publication dates. Information about the types of defects and methods of reconstruction was extracted from the reviewed articles. Two books were also reviewed, which were Regional and Free Flaps for Head and Neck Reconstruction (second edition) and Head and Neck Reconstruction: A Defect-Oriented Approach. Conclusion. Deciding the appropriate approach to a case should be individualized and should depend on the capabilities of the center, the defect's size and status, and lastly, the surgeon's training. The use of interpretation in the diagnosis of flaps can offer the best results in restoring functionality and vascularity and might also offer improved cosmesis.

A pilot study on development and feasibility of the \u2018MyEducation: CABG application\u2019 for patients undergoing coronary artery bypass graft (CABG) surgery

BackgroundPatients scheduled for coronary artery bypass graft (CABG) surgery tend to have persistent symptoms of anxiety and depression. Course of hospital stay post-CABG procedure has become increasingly shorter over the last few decades. This pilot study was conducted to develop and test feasibility of MyEducation: CABG application as a learning tool to reduce anxiety and depression levels among patients undergoing CABG Surgery.MethodsThis study was quasi-experimental in design. Forty-five patients scheduled for CABG surgery were recruited via consecutive sampling from a Tertiary Referral Centre at Kuala Lumpur, Malaysia. MyEducation:CABG application (Web-based education application) was administered among the intervention group (N = 23); while the control group (N = 22) underwent standard care. Web-based education application were implemented by nurses at admission and prior to discharge. Patients were assisted in terms of queries and concerns, upon which corresponding information and support was provided. Sociodemographic data were obtained from patients, prior to administration of Hospital Anxiety and Depression Scale which was used to measure levels of anxiety and depression. The educational application was used to obtain satisfaction rating among intervention group. These measures were administered upon admission, on discharge and one-month post-discharge.ResultsMean anxiety and depression scores among the intervention group were lower compared to the control. This was significant for anxiety upon admission, on discharge and one-month post-discharge (p < 0.05). Reduced mean depression scores was only significant at one month post-discharge (p < 0.05). Intervention group were generally satisfied with design, content and usability of the application.ConclusionsUtilisation of MyEducation: CABG application as an educational platform were associated with reduced anxiety and depression among CABG patients, which probably explains positive user satisfaction levels reported. Hence, the study recommends implementation of this application among larger sample as a way to support patient scheduled for CABG aside, with further possibility of preventing complications.

English

BACKGROUND We intend to evaluate the accuracy of in vivo conventional and digital methods in generating complete-arch dental models for measuring tooth dimension. METHODS Search was conducted through an electronic database in Medline, Cochrane Library, ResearchGate, PubMed and Google Scholar using query terms such as intraoral scanning; digital impression; accuracy in vivo full arch digital impression; analog impression; accuracy conventional impression; in vivo intraoral scanning; alginate impression; accuracy of plaster models; digital models; and complete arch accuracy. The outcomes were the accuracy of teeth dimension measurements in plaster models in comparison to digitized and digital models in vivo. RESULTS Nine studies matched the inclusion criteria. Two papers compared teeth measurements through plaster models and direct digital models, four papers compared plaster models and digitized models, one paper compared plaster models, digitized and direct digital models, one paper compared four different methods of direct intraoral, plaster models, direct digital and digitized models and one study reported teeth measurements from directly measured intraoral, plaster models, and direct digital models. Neither digital nor plaster models could be considered to replicate exactly the dentition, however it was agreed by the authors that intraoral scanning could represent the intraoral situation more accurately on digital models due to the lesser procedural steps, hence fewer source of error. CONCLUSIONS According to the results of the present review, although there were slight differences in readings of the measurements made on all the methods, the differences were neither statistically nor clinically significant and it is acceptable in clinical application. This review was registered in PROSPERO at CRD42020208662. KEY WORDS Intraoral Scanning, Digital Impression, Analog Impression, Digitized Model, Digital Model, Digital Impression Accuracy, Stl files, Conventional Impression Accuracy, Tooth Dimension Accuracy, Digital Model Accuracy

Requirements of a data storage infrastructure for effective land administration systems: case study of Victoria, Australia

ABSTRACT Land administration systems are being modernised to streamline the cadastral data lodgement. However, in many jurisdictions, cadastral data are still stored as a flat file. This method of data storage has significant limitations in terms of effective access, management, query, and analysis of cadastral data. Therefore, this study elicited the requirements and proposed an approach to automate the cadastral data storage. The proposed approach was successfully implemented within the land registry organisation in Victoria, Australia and the database management system was rigorously tested. The outcomes can potentially contribute to the implementation of a similar data storage infrastructure in other jurisdictions.

Prediction of Age-Adjusted Mortality From Stroke in Japanese Prefectures: Ecological Study Using Search Engine Queries.

BackgroundStroke is a major cause of death and the need for nursing care in Japan, with large regional disparities.ObjectiveThe purpose of this study was to clarify the association between stroke-related information retrieval behavior and age-adjusted mortality in each prefecture in Japan.MethodsAge-adjusted mortality from stroke and aging rates were obtained from publicly available Japanese government statistics. A total of 9476 abstracts of Japanese articles related to symptoms and signs of stroke were identified in Ichushi-Web, a Japanese web-based database of biomedical articles, and 100 highly frequent words (hereafter referred to as the Stroke 100) were extracted. Using data from 2014 to 2019, a random forest analysis was carried out using the age-adjusted mortality from stroke in 47 prefectures as the outcome variable and the standardized retrieval numbers of the Stroke 100 words in the log data of Yahoo! JAPAN Search as predictive variables. Regression analysis was performed using a generalized linear mixed model (GLMM) with the number of standardized searches for Stroke 100 words with high importance scores in the random forest model as the predictive variable. In the GLMM, the aging rate and data year were used as control variables, and the random slope of data year and random intercept were calculated by prefecture.ResultsThe mean age-adjusted mortality from stroke was 28.07 (SD 4.55) deaths per 100,000 for all prefectures in all data years. The accuracy score of the random forest analysis was 89.94%, the average error was 2.79 degrees, and the mean squared error was 13.57 degrees. The following 9 variables with high importance scores in the random forest analysis were selected as predictive variables for the regression analysis: male, age, hospitalization, enforcement, progress, stroke, abnormal, use, and change. As a result of the regression analysis with GLMM, the standardized partial regression coefficients (β) and 95% confidence intervals showed that the following internet search terms were significantly associated with age-adjusted mortality from stroke: male (β=−5.83, 95% CI −8.67 to −3.29), age (β=−5.83, 95% CI −8.67 to −3.29), hospitalization (β=−5.83, 95% CI −8.67 to −3.29), and abnormal (β=3.83, 95% CI 1.14 to 6.56).ConclusionsStroke-related search behavior was associated with age-adjusted mortality from stroke in each prefecture in Japan. Query terms that were strongly associated with age-adjusted mortality rates of stroke suggested the possibility that individual characteristics, such as sex and age, have an impact on stroke-associated mortality and that it is important to receive medical care early after stroke onset. Further studies on the criteria and timing of alerting are needed by monitoring information-seeking behavior to identify queries that are strongly associated with stroke mortality.

Black-Box Audio Adversarial Attack Using Particle Swarm Optimization

The development of artificial neural networks and artificial intelligence has helped to address problems and improve services in various fields, such as autonomous driving, image classification, medical diagnosis, and speech recognition. However, this technology has raised security threats that are different from existing ones. Recent studies have shown that artificial neural networks can easily malfunction by adversarial examples. The adversarial examples operate the neural network model as intended by the adversary. In particular, adversarial examples targeting speech recognition models is an area that has been actively studied in recent years. Existing studies have focused more on white-box methods. However, most speech recognition services are provided online and involve black-box, making it difficult or impossible for adversaries to attack. Black-box attacks have several challenges. Typically, they have a low success rate and a high risk of detection. In particular, previously proposed genetic algorithm (GA)-based attacks are at a high risk of detection because they require numerous queries. Therefore, we propose an adversarial attack system using particle swarm optimization (PSO) algorithms to address these problems. The proposed system uses adversarial candidates as particles to obtain adversarial examples through iterative optimization. PSO-based adversarial attacks are more efficient in queries and have a higher attack success rate than the adversarial methods using GAs. In particular, our key function is that temporary particle generation maximizes query efficiency to reduce detection risk and prevent wastage of system resources. On average, our system exhibits 96% attack success rates with 1416.17 queries, indicating that is 71.41% and 8% better in terms of query and success rates than existing GA-based attacks, respectively.

Deep Neural Network and Pseudo Relevance Feedback Based Query Expansion

The neural network has attracted researchers immensely in the last couple of years due to its wide applications in various areas such as Data mining, Natural language processing, Image processing, and Information retrieval etc. Word embedding has been applied by many researchers for Information retrieval tasks. In this paper word embedding-based skip-gram model has been developed for the query expansion task. Vocabulary terms are obtained from the top “k” initially retrieved documents using the Pseudo relevance feedback model and then they are trained using the skip-gram model to find the expansion terms for the user query. The performance of the model based on mean average precision is 0.3176. The proposed model compares with other existing models. An improvement of 6.61%, 6.93%, and 9.07% on MAP value is observed compare to the Original query, BM25 model, and query expansion with the Chi-Square model respectively. The proposed model also retrieves 84, 25, and 81 additional relevant documents compare to the original query, query expansion with Chi-Square model, and BM25 model respectively and thus improves the recall value also. The per query analysis reveals that the proposed model performs well in 30, 36, and 30 queries compare to the original query, query expansion with Chi-square model, and BM25 model respectively.

Correlating &lt;i&gt;pre-search&lt;/i&gt; and &lt;i&gt;in-search&lt;/i&gt; context to predict search intent for exploratory search

Modern information systems are expected to respond to a wide variety of information needs from users with diverse goals. The topical dimension ('what' the user is searching for) of these information needs is well studied; however, the intent dimension ('why' the user is searching) has received relatively less attention. Traditionally, the intent is an immediate reason, purpose, or goal that motivates the user search, and captured in search contexts (pre-search, in-search, pro-search). An ideal information system would be able to use. This article proposed a novel intent estimation strategy, based on the intuition that captured intent proactively extracts potential results. Captured pre-search context adapts query term proximities within matched results beside document-terms statistics and pseudo-relevance feedback with user-relevance feedback for in-search. The assessment asserts the superior performance of the proposed strategy over equivalent on trade-offs, e.g., novelty, diversity (coverage, topicality), retrieval (precision, recall, F-measure) and exploitation vs. exploration.

Analyzing Newspaper Articles for Text-Related Data for Finding Vulnerable Posts Over the Internet That Are Linked to Terrorist Activities

One of the most critical activities of revealing terrorism-related information is classifying online documents.The internet provides consumers with a variety of useful knowledge, and the volume of web material is increasingly growing. This makes finding potentially hazardous records incredibly difficult. To define the contents, merely extracting keywords from records is inadequate. Many methods have been studied so far to develop automatic document classification systems, they are mainly computational and knowledge-based approaches. due to the complexities of natural languages, these approaches do not provide sufficient results. To fix this shortcoming, we given approach of structure dependent on the WordNet hierarchy and the frequency of n-gram data that employs word similarity. Using four different queries terms from four different regions, this approach was checked for the NY Times articles that were sampled. Our suggested approach successfully removes background words and phrases from the document recognizes connected to terrorism texts, according to experimental findings.

I Know What You Need: Investigating Document Retrieval Effectiveness with Partial Session Contexts

Reducing user effort in finding relevant information is one of the key objectives of search systems. Existing approaches have been shown to effectively exploit the context from the current search session of users for automatically suggesting queries to reduce their search efforts. However, these approaches do not accomplish the end goal of a search system—that of retrieving a set of potentially relevant documents for the evolving information need during a search session. This article takes the problem of query prediction one step further by investigating the problem of contextual recommendation within a search session. More specifically, given the partial context information of a session in the form of a small number of queries, we investigate how a search system can effectively predict the documents that a user would have been presented with had he continued the search session by submitting subsequent queries. To address the problem, we propose a model of contextual recommendation that seeks to capture the underlying semantics of information need transitions of a current user’s search context. This model leverages information from a number of past interactions of other users with similar interactions from an existing search log. To identify similar interactions, as a novel contribution, we propose an embedding approach that jointly learns representations of both individual query terms and also those of queries (in their entirety) from a search log data by leveraging session-level containment relationships. Our experiments conducted on a large query log, namely the AOL, demonstrate that using a joint embedding of queries and their terms within our proposed framework of document retrieval outperforms a number of text-only and sequence modeling based baselines.

Knowledge Management in the Federal Company for Radioactive Waste Disposal (BGE)

Abstract. With completion of the restructuring of the nuclear waste disposal landscape in Germany, the competences for construction and running of repositories for radioactive waste products are since 2017 with the Federal Company for Radioactive Waste Disposal (BGE). The BGE has over 50 years of experience in the disposal of nuclear waste in Germany. Over a period of several decades substantial effort was put into the research and developmental work on nuclear waste disposal in Germany, which led to a considerable state of knowledge, both thematically and quantitatively; however, so far no consistent overview exists on the various works and therefore on the total state of knowledge, as these works only partially exist or existed in the very different organizations (diffuse and local) within the nuclear waste disposal landscape. The emerging loss of specialist knowledge in Germany caused by the withdrawal from nuclear energy and the recession of national mining activities is doing the rest. This imminent loss of knowledge must be taken into consideration as a critical factor, especially considering the age structure within the BGE and the simultaneously occurring processes on selection of a site for disposal of highly radioactive waste products. For this reason, in the division of research and development/knowledge management (R&amp;amp;D/KM) an independent department for knowledge management was created in order to establish an infrastructure for knowledge management and a connection between knowledge management platforms and knowledge carriers in the company. In addition to the collection and distribution of the available knowledge, current results from research and development are also to be fed into the “learning organization”. For this purpose, knowledge management provides a digital information basis, into which current results from research and development are also entered as a knowledge store. This knowledge store consisting of over 14 000 documents, contains mostly research reports and scientific publications, which are concerned with diverse topics for the final disposal of radioactive waste products in geological formations. The total stock of internal company documents available can be accessed with a browser-based text analysis software. Intelligent search algorithms render the textual contents accessible, combine them with synonyms and dictionaries deposited in the system and make the resulting hits of the search queries available for the user in order of importance in summarized and full text versions. Using specific query terms, as known from search machines or library servers, this software analyzes the available documents of the digital information basis and in a brief summary of results, in addition to a brief description of the contents, the naming of relevant keywords, the identification of sources, compilers, institutions, knowledge carriers and an extended optimized information analysis of hits, also provides the possibility to retrieve the complete document and the download. The research options can, in addition to the queries via the established internet search engines also be combined with queries via the incorporated information or databank catalogue of national and international scientific institutions or libraries, which are concerned with research programs relevant for repositories. In order to further increase and optimize the information possibilities for employees of the BGE, a variety of knowledge landscapes has been implemented in the intranet, which enable specific queries on topics, expert information, institution, country and persons. Using these tools a first essential target of knowledge management, the collection, availability and distribution of written down knowledge in the company has been achieved. For the development of person-related and group-related knowledge the department of knowledge management is compiling concepts, which can only be implemented together and jointly with them, as they border on certain interfaces in the organization/company. This particularly concerns measures that fall into the areas of human resources, such as personnel management and personnel strategies or in the area of the particular project; keyword lessons learnt.

A query expansion method based on topic modeling and DBpedia features

Query Expansion (QE) is a method used for improving Information Retrieval (IR) by adding the terms that are almost selected from feedback documents, and similar to the user query terms. But, due to the very small average number of query keywords; it is sometimes difficult to detect the context around the user query, and expand the query accordingly, especially when it contains ambiguous terms(i.e. polysemy terms). To this end, Linked Open Data (LOD) sources may be exploited. Yet, most attributes from linked data are multi-valued which makes a system unable to determine the right one(s) to use for expansion. And few other attributes are single-valued but too long and noisy to use directly. To deal with the previous issues, integration of the topic modeling process has been proposed to predict the latent semantic attribute-topics to use for expansion. This approach reconstructs candidate documents for a given query using distribution technique Bose-Einstein statistics (Bo1) and DBpedia attributes. The Latent Dirichlet Allocation(LDA) based topic models are then generated by considering these documents and the relevant expansion terms are then determined. The proposed method has been evaluated using the AP dataset collection, and the experiments revealed significant improvements according to the retrieval results using the distribution technique Bo1. Also, the proposed “LDA-LinkedBo1” approach outperformed DBpedia association based approaches in terms of MRR@N.

Keyword aware influential community search in large attributed graphs

Influential community search (ICS) on a graph finds a closely connected group of vertices having a dominance over other groups of vertices. The ICS has many applications in recommendations, event organization, and so on. In this paper, we introduce a new variant of ICS, namely keyword-aware influential community query (KICQ), that finds the communities with the highest influential scores and whose keywords match with the query terms (a set of keywords) and predicates (AND or OR). It is challenging to find such communities from a large network as the traditional pre-computation approach is not applicable with the change of query terms at every instance of the search. To solve this problem, we design two efficient algorithms: (i) a branch-and-bound approach that exploits the bounds computed from already explored communities to prune the search space, and (ii) a novel index based approach that hierarchically organizes sub-communities and keywords with associated bounds to quickly identify the desired communities. We propose a new influence measure for a community that considers both the cohesiveness and influence of the community and eliminates the need for specifying values of internal parameters of a network. We present detailed experiments and a case study to demonstrate the effectiveness and efficiency of the proposed approaches.

Attention-based Unsupervised Keyphrase Extraction and Phrase Graph for COVID-19 Medical Literature Retrieval

Searching, reading, and finding information from the massive medical text collections are challenging. A typical biomedical search engine is not feasible to navigate each article to find critical information or keyphrases. Moreover, few tools provide a visualization of the relevant phrases to the query. However, there is a need to extract the keyphrases from each document for indexing and efficient search. The transformer-based neural networks—BERT has been used for various natural language processing tasks. The built-in self-attention mechanism can capture the associations between words and phrases in a sentence. This research investigates whether the self-attentions can be utilized to extract keyphrases from a document in an unsupervised manner and identify relevancy between phrases to construct a query relevancy phrase graph to visualize the search corpus phrases on their relevancy and importance. The comparison with six baseline methods shows that the self-attention-based unsupervised keyphrase extraction works well on a medical literature dataset. This unsupervised keyphrase extraction model can also be applied to other text data. The query relevancy graph model is applied to the COVID-19 literature dataset and to demonstrate that the attention-based phrase graph can successfully identify the medical phrases relevant to the query terms.

Top 100 Most Cited Neurologic and Neurosurgical Articles on COVID-19: A Bibliometric Analysis

ObjectiveWith the exponential growth of literature on coronavirus disease 2019 (COVID-19), we aimed to identify and characterize the 100 most cited COVID-19–related articles in neurology and neurosurgery.MethodsIn March 2021, we performed a title-specific search of the Scopus database using (“neurology” or “neurologic” or “neurosurgery” or “neurosurgical”) and “COVID” as our search query term without date restrictions. The top 100 most cited English-language articles were obtained and reviewed.ResultsOur search yielded 9648 articles published from December 2019 to March 2021. Bibliometric analysis of the top 100 articles found that the most cited article had a citation count of 1741 and was the first to report on the detailed neurologic manifestations of the disease; Neurology had the most number of publications; the majority of the primary authors were neurologists, but 35% were from nonneuroscience specialties; the United States, Italy, the United Kingdom, China, and Germany were the top contributors, with a combined total of 77%; most of the publications were correspondence or editorial articles; and most articles discussed the neurologic manifestations and complications of patients with COVID-19.ConclusionsThis study identified the top 100 most cited neurologic or neurosurgical COVID-19–related articles published to date. This list can be used to identify high-impact studies that will help health care practitioners in clinical decision making and researchers in navigating key areas of study and guiding future research.

119 Acute Stroke, Thrombolytics and Litigation: Reasons Physicians Get Sued

To investigate the reasons for litigation surrounding the use of alteplase in the setting of acute ischemic stroke. The authors examined cases from a legal online database, Westlaw, that took place from 1993 to 2020 regarding alleged complaints for whether or not thrombolytic treatment was given for acute stroke. The query terms that were used for this paper were “tissue plasminogen activator,” “tPA,” “alteplase,” “thrombolytic therapy,” and “acute stroke.” From the initial search, cases that did not include acute ischemic stroke or the use of alteplase were excluded, as were suits that had to do with other issues surrounding the stroke care but not involving the issue of thrombolysis specifically. Setting: online legal database. Type of participants: publicly available cases (study cleared by institutional IRB). The initial search yielded 1364 cases. In the final dataset, 33% involved the hospital as defendants, 19% involved the emergency physicians as defendants, 11% involved internal medicine physicians as defendants, (6%) involved nurses as defendants, (3%) involved a neurologist as the defendant, (3%) involved a radiologist as the defendant, and (3%) involved a gynecologist as the defendant. Six percent of the cases did not clearly specify each defendant involved and were thus classified as “various physicians” in Table 1. Thirty-six percent involved non-healthcare providers as the principal defendants but also often saw subsidiary physicians or hospital staff being sued. There was no clear trend in the number of cases over time. In over sixty percent of cases, the plaintiff claimed that the physician failed to offer or treat the patient with tPA, and of those cases, more than 25% were a result of a failure to timely diagnose the stroke, and 14% were a result of the hospital or physician violating the Emergency Medical Treatment and Labor Act (EMTALA). The plaintiff claimed in 31% of the cases that the physician delayed in administering tPA to the patient as a consequence of various reasons. In all cases, the plaintiffs sued for issues stemming from either failure to give tPA or a delay in giving tPA. Out of the cases that had a definitive outcome, the plaintiff won in 1/3 cases. It is more common for patients to sue physicians for not administering tPA in a timely fashion or at any point. Thus, tPA should not be withheld or delayed in patients who meet criteria, and physicians should not fear litigation in these situations.

#StopAsianHate: Archiving and Analyzing Twitter Discourse in the Wake of the 2021 Atlanta Spa Shootings

AbstractOn March 16, 2021, six Asian women were killed in Atlanta, US, possibly out of racist motivations. This tragic event, now known as the 2021 Atlanta Spa Shootings, precipitated a massive increase in the volume of counter‐anti‐Asian declarations and discussion on social media platforms such as Twitter. In a pilot study to chronicle and profile public opinions, social movements and patterns in the global Twitter discourse we scraped the Twitter API using the query term “StopAsianHate”, obtaining more than 5.5 million tweets and their metadata. By using social movement analytical frameworks to analyze traffic peaks and the use of hashtags, we identified a set of more than 300 frequently used hashtags that can serve as specific query words in future archival ingest activities, as well as the dimensions of and current problems with this social movement. This suggests the utility of this approach for both archiving applications and social‐political analyses of emerging topics and concerns.

Is Google Trends a reliable way to determine digital dental epidemiology?

Aim: Google Trends, which allows Internet users to interact with and search data, can provide in-depth information about new phenomena regarding population and health-related behavior and is a tool that can be accessed free of charge. With the widespread use of dental implants in almost every country in the world today, an increase has also been reported in the prevalence of peri-implantitis (PP), which is a peri-implant disease. The aim of this study is to determine whether the rates of PP that were obtained from previous studies on this disease are in line with the data obtained using Google Trends.&#x0D; Methodology: Using observational, ecological research, we searched Google Trends for the following query terms: peri implantitis + periimplantitis, to obtain the volume of this Internet search query. The queries were searched within Spain (ES), Germany (DE), the Netherlands (NL), the United Kingdom (UK), and Turkey from January 2010 to December 2019.&#x0D; Results: An examination of the search results for “peri-implantitis + peri-implantitis” on Google Trends found that the largest numbers of searches for these words were made from the country of ES, and the smallest numbers were made from Turkey. It took two years to make forecasts based on the results, and the study determined that there has been a change in the trends in countries that were searched for these words. Also, the results obtained in previous studies for the prevalence of peri-implantitis were not similar to the data obtained from Google Trends.&#x0D; Conclusion: We concluded in this study that Google Trends is not a reliable tool for dental epidemiology.&#x0D; &#x0D; How to cite this article: Üner DD, İzol BS. Is Google Trends a reliable way to determine digital dental epidemiology? Int Dent Res 2021;11(Suppl.1):38-46. https://doi.org/10.5577/intdentres.2021.vol11.suppl1.7&#x0D; &#x0D; Linguistic Revision: The English in this manuscript has been checked by at least two professional editors, both native speakers of English.

Leptospirosis at human-animal-environment interfaces in Latin-America: drivers, prevention, and control measures

Leptospirosis is one of the main re-emerging zoonotic diseases with a worldwide distribution, mainly in regions with hot or tropical climates. It is caused by the spirochetal bacteria of the genus Leptospira, which, in their pathogenic life forms, causes asymptomatic to severe infections in humans and animals. Animal-to-human transmission occurs most frequently in occupationally exposed groups and in travelers. However, leptospirosis also represents a disease associated with poverty and low sanitation environments, in rural and urban communities, in close contact with companion animals and livestock. A search was performed in PubMed, Scopus, Web of Science and Google scholar databases using query terms related to Leptospira/leptospirosis, reported during 1990-2020. This review focuses on the description of biologic and socioenvironmental drivers that influence the occurrence of the disease in humans. Leptospirosis is found at the human-animal-environment interfaces, and represents a challenge for public and animal health, and food production; hence, improving the strategies for prevention control and surveillance in Latin-American countries is critical, as one of the regions most affected by the disease.

Quantitative Evaluation of PACS Query/Retrieve Capabilities.

The PACS (Picture Archiving and Communication System) market is heterogenous with dozens of PACS providers having deployed installations in healthcare facilities. The DICOM® query and retrieve interfaces provided by PACS have multiple variations, related to the implemented SOP (Service Object Pair) Classes, transfer syntaxes, extended negotiations, matching attributes, and matching types. These variations can make integration of a new DICOM® consumer with a PACS complex and time consuming. Even if most of PACS products provide a DICOM® conformance statement with a description of its query/retrieve capabilities, there is no collective analysis describing the various PACS query and retrieve capabilities and variations. Also, application developers and healthcare facilities lack a method to evaluate the PACS capabilities and classify its functionalities. GE designed a method to evaluate the PACS capabilities in terms of query retrieve functionalities. Our aim is to analyze several PACS in test and production environments, using our method to provide the DICOM® object consumers a macroscopic knowledge of query/retrieve capabilities of PACS functionalities and its different variations. Our evaluation can also be used by PACS and VNA (Vendor Neutral Archive) developers to evaluate their query/retrieve capabilities, for quality improvement purpose.