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A precise clinical and radiological description of disease for each patient was recorded.
We conducted separate regression models to estimate the incident relative risk for stroke and ischemic heart disease for each category compared with the reference categories "sales" (occupation) and "wholesale and retail" (industry).
We compared different protocols of systemic gene delivery and monitored the progression of heart and muscle disease for each group of injected animals.
Recorded information includes patient demographics and native origin, age of onset, X-ray and pathologic confirmation of diagnosis, and stage of the disease for each case of oesophageal and cardiac cancer.
TP53 immunohistochemical (IHC) staining intensity has been previously associated with a more aggressive disease [19], [20], and we included this variable when we performed logistic regression predicting invasive disease, for each of the loci individually, dichotomizing the methylation extent at the median, and controlling for other potential confounders.
The clinical signs of gill disease for each sampling point detailed in hrs from the start of the experiment were: (a) 2 hrs lamellae with multi-focal areas of epithelial sloughing, necrosis and haemorrhage; (b) 6 hrs increased lamellar epithelial sloughing, oedema and necrosis.
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Such data are needed to further understand the links between temperature variations and disease abundance for each disease type and each coral reef region.
Although the one-by-one analysis of diseases for each gene could be helpful for studying chemical-induced diseases, a systematic enrichment analysis based on all interacting genes/proteins could provide overall effects that are more easily interpretable.
Instead of analysis of enriched diseases from all interacting genes, ChemProt [3] and HExpoChem [4] focused on analyzing diseases for each chemical-interacting gene/protein based on protein protein interactions.
One of the most serious diseases for each crop was surveyed.
The top five diseases for each outcome category can be seen to correspond with the diseases with the highest RRRs.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com