Background: Virtual reality (VR) and augmented reality (AR) have recently become popular research themes. However, there are no published bibliometric reports that have analyzed the corresponding scientific literature in relation to the application of these technologies in medicine. Objective: We used a bibliometric approach to identify and analyze the scientific literature on VR and AR research in medicine, revealing the popular research topics, key authors, scientific institutions, countries, and journals. We further aimed to capture and describe the themes and medical conditions most commonly investigated by VR and AR research. Methods: The Web of Science electronic database was searched to identify relevant papers on VR research in medicine. Basic publication and citation data were acquired using the “Analyze” and “Create Citation Report” functions of the database. Complete bibliographic data were exported to VOSviewer and Bibliometrix, dedicated bibliometric software packages, for further analyses. Visualization maps were generated to illustrate the recurring keywords and words mentioned in the titles and abstracts. Results: The analysis was based on data from 8399 papers. Major research themes were diagnostic and surgical procedures, as well as rehabilitation. Commonly studied medical conditions were pain, stroke, anxiety, depression, fear, cancer, and neurodegenerative disorders. Overall, contributions to the literature were globally distributed with heaviest contributions from the United States and United Kingdom. Studies from more clinically related research areas such as surgery, psychology, neurosciences, and rehabilitation had higher average numbers of citations than studies from computer sciences and engineering. Conclusions: The conducted bibliometric analysis unequivocally reveals the versatile emerging applications of VR and AR in medicine. With the further maturation of the technology and improved accessibility in countries where VR and AR research is strong, we expect it to have a marked impact on clinical practice and in the life of patients.

Background: Social media has been extensively used for the communication of health-related information and consecutively for the potential spread of medical misinformation. Conventional systematic reviews have been published on this topic to identify original articles and to summarize their methodological approaches and themes. A bibliometric study could complement their findings, for instance, by evaluating the geographical distribution of the publications and determining if they were well cited and disseminated in high-impact journals. Objective: The aim of this study was to perform a bibliometric analysis of the current literature to discover the prevalent trends and topics related to medical misinformation on social media. Methods: The Web of Science Core Collection electronic database was accessed to identify relevant papers with the following search string: ALL=(misinformati* OR “wrong informati*” OR disinformati* OR “misleading informati*” OR “fake news*”) AND ALL=(medic* OR illness* OR disease* OR health* OR pharma* OR drug* OR therap*) AND ALL=(“social media*” OR Facebook* OR Twitter* OR Instagram* OR YouTube* OR Weibo* OR Whatsapp* OR Reddit* OR TikTok* OR WeChat*). Full records were exported to a bibliometric software, VOSviewer, to link bibliographic information with citation data. Term and keyword maps were created to illustrate recurring terms and keywords. Results: Based on an analysis of 529 papers on medical and health-related misinformation on social media, we found that the most popularly investigated social media platforms were Twitter (n=90), YouTube (n=67), and Facebook (n=57). Articles targeting these 3 platforms had higher citations per paper (>13.7) than articles covering other social media platforms (Instagram, Weibo, WhatsApp, Reddit, and WeChat; citations per paper <8.7). Moreover, social media platform–specific papers accounted for 44.1% (233/529) of all identified publications. Investigations on these platforms had different foci. Twitter-based research explored cyberchondria and hypochondriasis, YouTube-based research explored tobacco smoking, and Facebook-based research studied vaccine hesitancy related to autism. COVID-19 was a common topic investigated across all platforms. Overall, the United States contributed to half of all identified papers, and 80% of the top 10 most productive institutions were based in this country. The identified papers were mostly published in journals of the categories public environmental and occupational health, communication, health care sciences services, medical informatics, and medicine general internal, with the top journal being the Journal of Medical Internet Research. Conclusions: There is a significant platform-specific topic preference for social media investigations on medical misinformation. With a large population of internet users from China, it may be reasonably expected that Weibo, WeChat, and TikTok (and its Chinese version Douyin) would be more investigated in future studies. Currently, these platforms present research gaps that leave their usage and information dissemination warranting further evaluation. Future studies should also include social platforms targeting non-English users to provide a wider global perspective.

Background: Bisulfite sequencing is commonly used to measure DNA methylation. Processing bisulfite sequencing data is often challenging owing to the computational demands of mapping a low-complexity, asymmetrical library and the lack of a unified processing toolset to produce an analysis-ready methylation matrix from read alignments. To address these shortcomings, we have developed BiSulfite Bolt (BSBolt), a fast and scalable bisulfite sequencing analysis platform. BSBolt performs a pre-alignment sequencing read assessment step to improve efficiency when handling asymmetrical bisulfite sequencing libraries. Findings: We evaluated BSBolt against simulated and real bisulfite sequencing libraries. We found that BSBolt provides accurate and fast bisulfite sequencing alignments and methylation calls. We also compared BSBolt to several existing bisulfite alignment tools and found BSBolt outperforms Bismark, BSSeeker2, BISCUIT, and BWA-Meth based on alignment accuracy and methylation calling accuracy. Conclusion: BSBolt offers streamlined processing of bisulfite sequencing data through an integrated toolset that offers support for simulation, alignment, methylation calling, and data aggregation. BSBolt is implemented as a Python package and command line utility for flexibility when building informatics pipelines. BSBolt is available at https://github.com/NuttyLogic/BSBolt under an MIT license.

Introduction Structural reorganisation of the synovium with expansion of fibroblast-like synoviocytes (FLS) and influx of immune cells is a hallmark of rheumatoid arthritis (RA). Activated FLS are increasingly recognised as a critical component driving synovial tissue remodelling by interacting with immune cells resulting in distinct synovial pathotypes of RA. Methods Automated high-content fluorescence microscopy of co-cultured cytokine-activated FLS and autologous peripheral CD4⁺ T cells from patients with RA was established to quantify cell–cell interactions. Phenotypic profiling of cytokine-treated FLS and co-cultured T cells was done by flow cytometry and RNA-Seq, which were integrated with publicly available transcriptomic data from patients with different histological synovial pathotypes. Computational prediction and knock-down experiments were performed in FLS to identify adhesion molecules for cell–cell interaction. Results Cytokine stimulation, especially with TNF-α, led to enhanced FLS-T cell interaction resulting in cell-cell contact-dependent activation, proliferation and differentiation of T cells. Signatures of cytokine-activated FLS were significantly enriched in RA synovial tissues defined as lymphoid-rich or leucocyte-rich pathotypes, with the most prominent effects for TNF-α. FLS cytokine signatures correlated with the number of infiltrating CD4⁺ T cells in synovial tissue of patients with RA. Ligand-receptor pair interaction analysis identified ICAM1 on FLS as an important mediator in TNF-mediated FLS-T cell interaction. Both, ICAM1 and its receptors were overexpressed in TNF-treated FLS and co-cultured T cells. Knock-down of ICAM1 in FLS resulted in reduced TNF-mediated FLS-T cell interaction. Conclusion Our study highlights the role of cytokine-activated FLS in orchestrating inflammation-associated synovial pathotypes providing novel insights into disease mechanisms of RA.

Diabetes mellitus type 2 (T2DM) is associated with oxidative stress which in turn can lead to DNA damage. The aim of the present study was to analyze oxidative stress, DNA damage and DNA repair in regard to hyperglycemic state and diabetes duration. Female T2DM patients (n = 146) were enrolled in the MIKRODIAB study and allocated in two groups regarding their glycated hemoglobin (HbA1c) level (HbA1c≤7.5%, n = 74; HbA1c>7.5%, n = 72). In addition, tertiles according to diabetes duration (DD) were created (DDI = 6.94±3.1 y, n = 49; DDII = 13.35±1.1 y, n = 48; DDIII = 22.90±7.3 y, n = 49). Oxidative stress parameters, including ferric reducing ability potential, malondialdehyde, oxidized and reduced glutathione, reduced thiols, oxidized LDL and F2-Isoprostane as well as the activity of antioxidant enzymes superoxide dismutase, catalase and glutathione peroxidase were measured. Damage to DNA was analyzed in peripheral blood mononuclear cells and whole blood with single cell gel electrophoresis. DNA base excision repair capacity was tested with the modified comet repair assay. Additionally, mRNA expressions of nine genes related to base excision repair were analyzed in a subset of 46 matched individuals. No significant differences in oxidative stress parameters, antioxidant enzyme activities, damage to DNA and base excision repair capacity, neither between a HbA1c cut off />7.5%, nor between diabetes duration was found. A significant up-regulation in mRNA expression was found for APEX1, LIG3 and XRCC1 in patients with >7.5% HbA1c. Additionally, we observed higher total cholesterol, LDL-cholesterol, LDL/HDL-cholesterol, triglycerides, Framingham risk score, systolic blood pressure, BMI and lower HDL-cholesterol in the hyperglycemic group. BMI, blood pressure and blood lipid status were worse in hyperglycemic individuals. However, no major disparities regarding oxidative stress, damage to DNA and DNA repair were present which might be due to good medical treatment with regular health checks in T2DM patients in Austria.