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Objective: Our study aimed to evaluate the effectiveness of virtual reality (VR) intervention for pain and anxiety relief during outpatient hysteroscopy. Methods: Various databases were searched for available clinical trials from inception until June 2023. We selected randomized controlled trials (RCTs) that compared virtual reality intervention versus standard care among women undergoing outpatient hysteroscopy. We used Revman software to perform our meta-analysis. The primary outcome was the pain score during the procedure. The secondary outcomes were anxiety during the procedure and pain post-procedure. The Visual Analog Scale (VAS) was used to assess pain and anxiety. Results: Six RCTs were retrieved, involving a total of 457 patients. Virtual reality was associated with a significant reduction in pain score during the procedure in comparison with the control group (MD = − 1.43, 95% CI [− 1.69, − 1.16], p < 0.001). In addition, there was a significant decrease in anxiety during the procedure among the virtual reality group compared to the control group (p = 0.01). The pain score post-procedure significantly decreased within the virtual reality group (MD = − 1.52, 95% CI [− 1.78, − 1.26], p < 0.001). Conclusions: Virtual reality technology is a simple, feasible, and effective intervention for reducing pain and anxiety during outpatient hysteroscopy. More trials are required to confirm our findings. [ABSTRACT FROM AUTHOR]

This paper studies the spatial group-by query over complex polygons. Given a set of spatial points and a set of polygons, the spatial group-by query returns the number of points that lie within the boundaries of each polygon. Groups are selected from a set of non-overlapping complex polygons, typically in the order of thousands, while the input is a large-scale dataset that contains hundreds of millions or even billions of spatial points. This problem is challenging because real polygons (like counties, cities, postal codes, voting regions, etc.) are described by very complex boundaries. We propose a highly-parallelized query processing framework to efficiently compute the spatial group-by query on highly skewed spatial data. We also propose an effective query optimizer that adaptively assigns the appropriate processing scheme based on the query polygons. Our experimental evaluation with real data and queries has shown significant superiority over all existing techniques. [ABSTRACT FROM AUTHOR]

Introduction: To evaluate the safety and effectiveness of insulin glargine 300 U/mL (Gla-300) in people with type 2 diabetes mellitus (T2DM) in the Gulf region who fast during Ramadan. Methods: ORION was a real-world, prospective, observational study in people with T2DM treated with Gla-300 during pre-Ramadan, Ramadan, and post-Ramadan periods. This subgroup analysis included 222 participants from the Gulf region (Kuwait, Saudi Arabia, United Arab Emirates, and Qatar). The primary endpoint was the percentage of participants experiencing severe and/or symptomatic documented hypoglycemia (self-monitored plasma glucose [SMPG] ≤ 70 mg/dL) during Ramadan. Changes in glycated hemoglobin (HbA1c), fasting plasma glucose (FPG), SMPG, body weight, insulin dose, and adverse events (AEs) were also evaluated. Results: The primary endpoint was reported in one (0.5%) participant during Ramadan. The incidence rate of symptomatic documented hypoglycemia (SMPG ≤ 70 mg/dL) decreased from the pre-Ramadan (3.2%) to Ramadan period (0.5%), and no severe hypoglycemia events were reported during the study. Reductions were observed in HbA1c (mean ± standard deviation: − 0.51 ± 0.95% [− 5.5 ± 10.4 mmol/mol]), FPG (− 13.9 ± 47.5 mg/dL), and SMPG (− 6.1 ± 27.1 mg/dL). No significant changes were observed in body weight or Gla-300 dose. AEs were reported in 11 (5.0%) participants. Conclusion: In a real-world setting in the Gulf region, Gla-300 treatment in people with T2DM during Ramadan was associated with a low incidence of hypoglycemia and improved glycemic control. Trial Registration: CTRI/2019/02/017636. [ABSTRACT FROM AUTHOR]

This paper presents GeoTrend+; a system approach to support scalable local trend discovery on recent microblogs, e.g., tweets, comments, online reviews, and check-ins, that come in real time. GeoTrend+ discovers top-k trending keywords in arbitrary spatial regions from recent microblogs that continuously arrive with high rates and a significant portion has uncertain geolocations. GeoTrend+ distinguishes itself from existing techniques in different aspects: (1) Discovering trends in arbitrary spatial regions, e.g., city blocks. (2) Considering both exact geolocations, e.g., accurate latitude/longitude coordinates, and uncertain geolocations, e.g., district-level or city-level, that represents a significant portion of past years microblogs. (3) Promoting recent microblogs as first-class citizens and optimizes different components to digest a continuous flow of fast data in main-memory while removing old data efficiently. (4) Providing various main-memory optimization techniques that are able to distinguish useful from useless data to effectively utilize tight memory resources while maintaining accurate query results on relatively large amounts of data. (5) Supporting various trending measures that effectively capture trending items under a variety of definitions that suit different applications. GeoTrend+ limits its scope to real-time data that is posted during the last T time units. To support its queries efficiently, GeoTrend+ employs an in-memory spatial index that is able to efficiently digest incoming data and expire data that is beyond the last T time units. The index also materializes top-k keywords in different spatial regions so that incoming queries can be processed with low latency. In peak times, the main-memory optimization techniques are employed to shed less important data to sustain high query accuracy with limited memory resources. Experimental results based on real data and queries show the scalability of GeoTrend+ to support high arrival rates and low query response time, and at least 90+% query accuracy even under limited memory resources. [ABSTRACT FROM AUTHOR]

Microblogs data is the microlength user-generated data that is posted on the web, e.g., tweets, online reviews, comments on news and social media. It has gained considerable attention in recent years due to its widespread popularity, rich content, and value in several societal applications. Nowadays, microblogs applications span a wide spectrum of interests including targeted advertising, market reports, news delivery, political campaigns, rescue services, and public health. Consequently, major research efforts have been spent to manage, analyze, and visualize microblogs to support different applications. This paper gives a comprehensive review of major research and system work in microblogs data management. The paper reviews core components that enable large-scale querying and indexing for microblogs data. A dedicated part gives particular focus for discussing system-level issues and on-going effort on supporting microblogs through the rising wave of big data systems. In addition, we review the major research topics that exploit these core data management components to provide innovative and effective analysis and visualization for microblogs, such as event detection, recommendations, automatic geotagging, and user queries. Throughout the different parts, we highlight the challenges, innovations, and future opportunities in microblogs data research. [ABSTRACT FROM AUTHOR]