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Electronic access to multiple data types, from generic information on biological systems at different functional and cellular levels to high-throughput molecular data from human patients, is a prerequisite of successful systems medicine research. However, scientists often encounter technical and conceptual difficulties that forestall the efficient and effective use of these resources. We summarize and discuss some of these obstacles, and suggest ways to avoid or evade them.The methodological gap between data capturing and data analysis is huge in human medical research. Primary data producers often do not fully apprehend the scientific value of their data, whereas data analysts maybe ignorant of the circumstances under which the data were collected. Therefore, the provision of easy-to-use data access tools not only helps to improve data quality on the part of the data producers but also is likely to foster an informed dialogue with the data analysts.We propose a means to integrate phenotypic data, questionnaire data and microbiome data with a user-friendly Systems Medicine toolbox embedded into i2b2/tranSMART. Our approach is exemplified by the integration of a basic outlier detection tool and a more advanced microbiome analysis (alpha diversity) script. Continuous discussion with clinicians, data managers, biostatisticians and systems medicine experts should serve to enrich even further the functionality of toolboxes like ours, being geared to be used by 'informed non-experts' but at the same time attuned to existing, more sophisticated analysis tools.
Assuntos
Inflamação , Pesquisa Biomédica , Humanos , Análise de SistemasRESUMO
Aim: SARS-CoV-2 hospital clusters are a challenge for healthcare systems. There is an increased risk of infection for both healthcare workers (HCWs) and patients; cluster countermeasures are also a drain on resources for the wards affected. We analysed to which extent characteristics and dynamics of SARS-CoV-2 clusters varied throughout the pandemic at a German university hospital. Methods: Patient and/or HCW clusters from 10/2020 to 04/2022 were included in the study and grouped by virus variant into i.) clusters comprised of the presumably predominant wild-type, Alpha or Delta (WAD) SARS-COV-2 variants, and ii.) clusters comprised predominantly of Omicron subtype cases. The two groups were compared for specific characteristics and dynamics. Results: Forty-two SARS-CoV-2 clusters and 528 cases were analysed. Twenty-one clusters and 297 cases were attributed to the WAD and 21 clusters and 231 cases to the Omicron group. There were no significant differences in median size (8 vs. 8 cases, p=0.94) or median duration (14 vs. 12 days; p=0.48), nor in the percentage of HCWs involved (46.8% vs. 50.2%; p=0.48). Patients in the WAD group were older (median 75 vs. 68 years of age; p≤0.05). The median time from cluster onset to case onset was significantly shorter for the Omicron group (median 6 vs. 11 days; p≤0.05). Conclusions: Omicron clusters exhibited a more rapid dynamic, forcing all parties involved to adapt to the increased workload. Compared to excessive community case counts, constant Omicron cluster-affiliated case counts and stable cluster characteristics suggest an improved compliance with IPC countermeasures.
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OBJECTIVES: To review the risk of airborne infections in schools and evaluate the effect of intervention measures reported in field studies. BACKGROUND: Schools are part of a country's critical infrastructure. Good infection prevention measures are essential for reducing the risk of infection in schools as much as possible, since these are places where many individuals spend a great deal of time together every weekday in a small area where airborne pathogens can spread quickly. Appropriate ventilation can reduce the indoor concentration of airborne pathogens and reduce the risk of infection. METHODS: A systematic search of the literature was conducted in the databases Embase, MEDLINE, and ScienceDirect using keywords such as school, classroom, ventilation, carbon dioxide (CO2) concentration, SARS-CoV-2, and airborne transmission. The primary endpoint of the studies selected was the risk of airborne infection or CO2 concentration as a surrogate parameter. Studies were grouped according to the study type. RESULTS: We identified 30 studies that met the inclusion criteria, six of them intervention studies. When specific ventilation strategies were lacking in schools being investigated, CO2 concentrations were often above the recommended maximum values. Improving ventilation lowered the CO2 concentration, resulting in a lower risk of airborne infections. CONCLUSIONS: The ventilation in many schools is not adequate to guarantee good indoor air quality. Ventilation is an important measure for reducing the risk of airborne infections in schools. The most important effect is to reduce the time of residence of pathogens in the classrooms.