RESUMO
BACKGROUND: Diagnosing chronic exertional compartment syndrome (CECS) is still a challenge. An increase in intramuscular pressure during and following exercise is accepted as the diagnostic standard. However, neither the methods used nor the interpretation of the obtained results are sufficiently standardized. METHODS: In the present pilot study, the metabolic state of CECS patients was investigated using microdialysis. We hypothesized that there was no difference in intramuscular concentrations of glucose, lactate, glutamate, and glycerol before and after exercise (H10) or between patients suffering from CECS and healthy control subjects (H20). This study was designed as an explorative case-control study (level of evidence III). Twelve patients suffering from CECS of the lower leg and six matched asymptomatic control subjects underwent microdialysis in the anterior (n = 7) or deep posterior compartment (n = 11) of the leg. Following ultrasound-guided insertion of the microdialysis catheters, 10-minute fractions of the dialysates were collected first during rest and then following fatigue- or pain-induced discontinuation of exercise. Dialysates were analysed for lactate, glucose, glutamate, and glycerol concentrations 6 × 10 min before and 6 × 10 min after exercise. RESULTS: Exercise-induced increases in lactate, glutamate, and glycerol concentrations were detected in both CECS patients and control subjects (all p < 0.001). No differences between CECS patients and control subjects were found by comparing the intramuscular glucose, lactate, glutamate, and glycerol concentrations at rest and following exercise (all p > 0.05). CONCLUSIONS: We found exercise-induced increases in the lactate, glutamate, and glycerol levels in skeletal muscle. However, the metabolic changes did not differentiate CECS patients from healthy subjects. TRIAL REGISTRATION: The registration trial number is DRKS00021589 on DRKS. 'Retrospectively registered'. Date of registration: April 4, 2020.
RESUMO
BACKGROUND: Wikipedia is an important source of medical information for both patients and medical professionals. Given its wide reach, improving the quality, completeness, and accessibility of medical information on Wikipedia could have a positive impact on global health. OBJECTIVE: We created a prototypical implementation of an automated system for keeping drug-drug interaction (DDI) information in Wikipedia up to date with current evidence about clinically significant drug interactions. Our work is based on Wikidata, a novel, graph-based database backend of Wikipedia currently in development. METHODS: We set up an automated process for integrating data from the Office of the National Coordinator for Health Information Technology (ONC) high priority DDI list into Wikidata. We set up exemplary implementations demonstrating how the DDI data we introduced into Wikidata could be displayed in Wikipedia articles in diverse languages. Finally, we conducted a pilot analysis to explore if adding the ONC high priority data would substantially enhance the information currently available on Wikipedia. RESULTS: We derived 1150 unique interactions from the ONC high priority list. Integration of the potential DDI data from Wikidata into Wikipedia articles proved to be straightforward and yielded useful results. We found that even though the majority of current English Wikipedia articles about pharmaceuticals contained sections detailing contraindications, only a small fraction of articles explicitly mentioned interaction partners from the ONC high priority list. For 91.30% (1050/1150) of the interaction pairs we tested, none of the 2 articles corresponding to the interacting substances explicitly mentioned the interaction partner. For 7.21% (83/1150) of the pairs, only 1 of the 2 associated Wikipedia articles mentioned the interaction partner; for only 1.48% (17/1150) of the pairs, both articles contained explicit mentions of the interaction partner. CONCLUSIONS: Our prototype demonstrated that automated updating of medical content in Wikipedia through Wikidata is a viable option, albeit further refinements and community-wide consensus building are required before integration into public Wikipedia is possible. A long-term endeavor to improve the medical information in Wikipedia through structured data representation and automated workflows might lead to a significant improvement of the quality of medical information in one of the world's most popular Web resources.
