Adverse Events and Safety Profile of the COVID-19 Vaccines in Adolescents: Safety Monitoring for Adverse Events Using Real-World Data

A COVID-19 vaccine BNT162b2 (Pfizer-BioNTech) has recently been authorized for adolescents in the US. However, the impact of adverse events on adolescents after vaccination has not been fully investigated. To assess the safety of the COVID-19 vaccine in adolescents, the incidence of adverse events (AEs) in adolescents and adults was compared after vaccination. We included 6304 adolescents (68.14 per 100,000 people) who reported adverse events using vaccine adverse event reporting system (VAERS) data from 10 May 2021 to 30 September 2021. The mean age was 13.6 ± 1.1 years and women (52.7%) outnumbered men. We analyzed severe and common adverse events in response to the COVID-19 vaccine among 6304 adolescents (68.14 per 100,000 people; 52% female; mean age, 13.6 ± 1.1 years). The risk of myocarditis or pericarditis among adolescents was significantly higher in men than in women (OR = 6.61, 95% CI = 4.43 to 9.88; p < 0.001), with a higher frequency after the second dose of the vaccine (OR = 8.52, 95% CI = 5.79 to 12.54; p < 0.001). In addition, severe adverse events such as multisystem inflammatory syndromes, where the incidence rate per 100,000 people was 0.11 (n = 10), and the relative risk was 244.3 (95% CI = 31.27 to 1908.38; p < 0.001), were significantly higher in adolescents than in adults. The risk of the inflammatory response to the COVID-19 vaccine, including myocarditis, pericarditis, or multisystem inflammatory syndromes, was significantly higher in men than in women, with a higher frequency in adolescents than in adults. The inflammation-related AEs may require close monitoring and management in adolescents

Synthesis of a New Glycoconjugate with Di-ᴅ-Psicose Anhydride Structure

Demand for healthy diets has led researchers to explore new saccharide as sucrose alternatives. ᴅ-Psicose, the C-3 epimer of ᴅ-fructose, has a similar sweetness intensity to sucrose but contributes fewer calories. This study proposes a disaccharide with a stable structure derived from ᴅ-psicose. The compound with a spiro-tricyclic core was generated at 32% conversion via caramelization of ᴅ-psicose under acidic anhydrous conditions. The compound was identified by high-resolution mass spectrometry and multi-dimensional nuclear magnetic resonance (NMR). The molecular formula was established as C12H20O10 from the molecular weight of m/z 324.1055. Twelve signals were observed by the 13C NMR spectrum. This compound, denoted di-ᴅ-psicose anhydride (DPA), exhibited a lower water solubility (40 g/L) and higher thermal stability (peak temperature = 194.7 °C) than that of ᴅ-psicose (peak temperature = 126.5 °C). The quantitatively evaluated metal ion scavenging ability of DPA was the best in magnesium (average 98.6 ± 1.1%). This synthesis methodology can provide disaccharides with high stability-reducing heavy metals

Natural Language Processing for Assessing Quality Indicators in Free-Text Colonoscopy and Pathology Reports: Development and Usability Study

BackgroundManual data extraction of colonoscopy quality indicators is time and labor intensive. Natural language processing (NLP), a computer-based linguistics technique, can automate the extraction of important clinical information, such as adverse events, from unstructured free-text reports. NLP information extraction can facilitate the optimization of clinical work by helping to improve quality control and patient management. ObjectiveWe developed an NLP pipeline to analyze free-text colonoscopy and pathology reports and evaluated its ability to automatically assess adenoma detection rate (ADR), sessile serrated lesion detection rate (SDR), and postcolonoscopy surveillance intervals. MethodsThe NLP tool for extracting colonoscopy quality indicators was developed using a data set of 2000 screening colonoscopy reports from a single health care system, with an associated 1425 pathology reports. The NLP system was then tested on a data set of 1000 colonoscopy reports and its performance was compared with that of 5 human annotators. Additionally, data from 54,562 colonoscopies performed between 2010 and 2019 were analyzed using the NLP pipeline. ResultsThe NLP pipeline achieved an overall accuracy of 0.99-1.00 for identifying polyp subtypes, 0.99-1.00 for identifying the anatomical location of polyps, and 0.98 for counting the number of neoplastic polyps. The NLP pipeline achieved performance similar to clinical experts for assessing ADR, SDR, and surveillance intervals. NLP analysis of a 10-year colonoscopy data set identified great individual variance in colonoscopy quality indicators among 25 endoscopists. ConclusionsThe NLP pipeline could accurately extract information from colonoscopy and pathology reports and demonstrated clinical efficacy for assessing ADR, SDR, and surveillance intervals in these reports. Implementation of the system enabled automated analysis and feedback on quality indicators, which could motivate endoscopists to improve the quality of their performance and improve clinical decision-making in colorectal cancer screening programs