A growing body of literature on the 2019 novel coronavirus (SARS-CoV-2) is becoming available, but a synthesis of available data has not been conducted. We performed a scoping review of currently available clinical, epidemiological, laboratory, and chest imaging data related to the SARS-CoV-2 infection. We searched MEDLINE, Cochrane CENTRAL, EMBASE, Scopus and LILACS from 01 January 2019 to 24 February 2020. Study selection, data extraction and risk of bias assessment were performed by two independent reviewers. Qualitative synthesis and meta-analysis were conducted using the clinical and laboratory data, and random-effects models were applied to estimate pooled results. A total of 61 studies were included (59,254 patients). The most common disease-related symptoms were fever (82%, 95% confidence interval (CI) 56%–99%; n = 4410), cough (61%, 95% CI 39%–81%; n = 3985), muscle aches and/or fatigue (36%, 95% CI 18%–55%; n = 3778), dyspnea (26%, 95% CI 12%–41%; n = 3700), headache in 12% (95% CI 4%–23%, n = 3598 patients), sore throat in 10% (95% CI 5%–17%, n = 1387) and gastrointestinal symptoms in 9% (95% CI 3%–17%, n = 1744). Laboratory findings were described in a lower number of patients and revealed lymphopenia (0.93 × 10⁹/L, 95% CI 0.83–1.03 × 10⁹/L, n = 464) and abnormal C-reactive protein (33.72 mg/dL, 95% CI 21.54–45.91 mg/dL; n = 1637). Radiological findings varied, but mostly described ground-glass opacities and consolidation. Data on treatment options were limited. All-cause mortality was 0.3% (95% CI 0.0%–1.0%; n = 53,631). Epidemiological studies showed that mortality was higher in males and elderly patients. The majority of reported clinical symptoms and laboratory findings related to SARS-CoV-2 infection are non-specific. Clinical suspicion, accompanied by a relevant epidemiological history, should be followed by early imaging and virological assay.

The development of artificial intelligence (AI) in dentistry requires large and well-annotated datasets. However, the availability of public dental imaging datasets remains unclear. This study aimed to provide a comprehensive overview of all publicly available dental imaging datasets to address this gap and support AI development. This observational study searched all publicly available dataset resources (academic databases, preprints, and AI challenges), focusing on datasets/articles from 2020 to 2023, with PubMed searches extending back to 2011. We comprehensively searched for dental AI datasets containing images (intraoral photos, scans, radiographs, etc.) using relevant keywords. We included datasets of >50 images obtained from publicly available sources. We extracted dataset characteristics, patient demographics, country of origin, dataset size, ethical clearance, image details, FAIRness metrics, and metadata completeness. We screened 131,028 records and extracted 16 unique dental imaging datasets. The datasets were obtained from Kaggle (18.8%), GitHub, Google, Mendeley, PubMed, Zenodo (each 12.5%), Grand-Challenge, OSF, and arXiv (each 6.25%). The primary focus was tooth segmentation (62.5%) and labeling (56.2%). Panoramic radiography was the most common imaging modality (58.8%). Of the 13 countries, China contributed the most images (2,413). Of the datasets, 75% contained annotations, whereas the methods used to establish labels were often unclear and inconsistent. Only 31.2% of the datasets reported ethical approval, and 56.25% did not specify a license. Most data were obtained from dental clinics (50%). Intraoral radiographs had the highest findability score in the FAIR assessment, whereas cone-beam computed tomography datasets scored the lowest in all categories. These findings revealed a scarcity of publicly available imaging dental data and inconsistent metadata reporting. To promote the development of robust, equitable, and generalizable AI tools for dental diagnostics, treatment, and research, efforts are needed to address data scarcity, increase diversity, mandate metadata completeness, and ensure FAIRness in AI dental imaging research.

Background: Coronavirus disease (COVID-19) vaccine-related side effects have a determinant role in the public decision regarding vaccination. Therefore, this study has been designed to actively monitor the safety and effectiveness of COVID-19 vaccines globally. Methods: A multi-country, three-phase study including a cross-sectional survey to test for the short-term side effects of COVID-19 vaccines among target population groups. In the second phase, we will monitor the booster doses’ side effects, while in the third phase, the long-term safety and effectiveness will be investigated. A validated, self-administered questionnaire will be used to collect data from the target population; Results: The study protocol has been registered at ClinicalTrials.gov, with the identifier NCT04834869. Conclusions: CoVaST is the first independent study aiming to monitor the side effects of COVID-19 vaccines following booster doses, and the long-term safety and effectiveness of said vaccines.

According to the FAIR principles, data produced by scientific research should be findable, accessible, interoperable, and reusable—for instance, to be used in machine learning algorithms. However, to date, there is no estimate of the quantity or quality of dental research data evaluated via the FAIR principles. We aimed to determine the availability of open data in dental research and to assess compliance with the FAIR principles (or FAIRness) of shared dental research data. We downloaded all available articles published in PubMed-indexed dental journals from 2016 to 2021 as open access from Europe PubMed Central. In addition, we took a random sample of 500 dental articles that were not open access through Europe PubMed Central. We assessed data sharing in the articles and compliance of shared data to the FAIR principles programmatically. Results showed that of 7,509 investigated articles, 112 (1.5%) shared data. The average (SD) level of compliance with the FAIR metrics was 32.6% (31.9%). The average for each metric was as follows: findability, 3.4 (2.7) of 7; accessibility, 1.0 (1.0) of 3; interoperability, 1.1 (1.2) of 4; and reusability, 2.4 (2.6) of 10. No considerable changes in data sharing or quality of shared data occurred over the years. Our findings indicated that dental researchers rarely shared data, and when they did share, the FAIR quality was suboptimal. Machine learning algorithms could understand 1% of available dental research data. These undermine the reproducibility of dental research and hinder gaining the knowledge that can be gleaned from machine learning algorithms and applications.

There are currently no integrated data on the trend of dental caries amongst distinct age groups in Iran. We aimed to assess the national and subnational trend of dental caries of permanent teeth in Iran from 1990 to 2017. A literature search about dental caries and the decayed-missing-filled teeth index (DMFT) was performed in PubMed, Web of Science, Scopus, and 3 national databases (in Persian). All eligible national oral health surveys in these 28 years were included. We categorised and aggregated the DMFT values and their components based on age (5-year-based groups from 5 to 9 to 60+ years), sex, year, and province. The data for missing spots were estimated using the spatiotemporal Bayesian hierarchical model. We used the bootstrap method in multilevel models to predict the uncertainty interval (UI) of the modelled results. Nationally, the all-ages mean DMFT increased by nearly 58.0% (6.8 [95% UI, 4.1-10.5] in 1990 to 10.8 [95% UI, 7.5-14.5] in 2017). Decayed teeth (DT) and missing teeth (MT) rose by 84.5% and 31.6% during this period, respectively. Filled teeth (FT) showed almost a 2.6-fold increase in the same period from 0.6 (95% UI, 0.01-1.6) in 1990 to 1.7 (95% UI, 0.6-2.8) in 2017. The proportion of DT and FT continuously increased in both sexes. In 2017, the highest DT, MT, and FT were estimated in the 25-29 (4.9 [95% UI, 2.5-7.2]), 60+ (21.5 [95% UI, 17.5-25.4]), and 35-39 (2.6 [95% UI, 1.3-4.0]) year age groups. Caries of permanent dentition levies a growing burden on the Iranian population. Considering the continuous increase in caries during the 1990-2017 period, Iranian policymakers should pay heed to these findings and react more proactively to mitigate this perpetuating issue. Implementing nationwide interventions such as sugar consumption management should be encouraged to achieve sustainable outcomes in this regards.

We assessed adherence to five transparency practices—data sharing, code sharing, conflict of interest disclosure, funding disclosure, and protocol registration—in articles in dental journals. We searched and exported the full text of all research articles from PubMed‐indexed dental journals available in the Europe PubMed Central database until the end of 2021. We programmatically assessed their adherence to the five transparency practices using a validated and automated tool. Journal‐ and article‐related information was retrieved from ScimagoJR and Journal Citation Reports. Of all 329,784 articles published in PubMed‐indexed dental journals, 10,659 (3.2%) were available to download. Of those, 77% included a conflict of interest disclosure, and 62% included a funding disclosure. Seven percent of the articles had a registered protocol. Data sharing (2.0%) and code sharing (0.1%) were rarer. Sixteen percent of articles did not adhere to any of the five transparency practices, 29% adhered to one, 48% adhered to two, 7.0% adhered to three, 0.3% adhered to four, and no article adhered to all five practices. Adherence to transparency practices increased over time; however, data and code sharing especially remained rare. Coordinated efforts involving all stakeholders are needed to change current transparency practices in dental research.