Additional file 3: of Association between inflammation and systolic blood pressure in RA compared to patients without RA

Figure S3. The relationship between C-reactive protein levels (CRP) and systolic blood pressure with 95% confidence intervals, in the RA outpatient population and the general population (NHANES) with trimming of extreme measurements of CRP (< 0.5% and > 99.5%). RA outpatient population CRP range 0.20–92.40 mg/L; NHANES CRP range 0.02–4.22 mg/L. RA, rheumatoid arthritis; NHANES, National Health and Nutrition Examination Survey. (PDF 476 kb

Additional file 5: of Association between inflammation and systolic blood pressure in RA compared to patients without RA

Table S1. Association between change in C-reactive protein (CRP) (per 10 mg/L) and change in diastolic blood pressure (DBP), pulse pressure (PP), and mean arterial pressure (MAP) (per mmHg) in patients with rheumatoid arthritis with significant changes in inflammation. (DOCX 17 kb

Additional file 4: of Association between inflammation and systolic blood pressure in RA compared to patients without RA

Figure S4. The relationship between C-reactive protein levels (CRP) and systolic blood pressure with 95% confidence intervals, in the non-RA outpatient population and general population (NHANES) with trimming of extreme measurements of CRP (< 0.5% and > 99.5%). Non-RA outpatient population CRP range 0.10–142.20 mg/L; NHANES CRP range 0.02–4.22 mg/L. RA, rheumatoid arthritis; NHANES, National Health and Nutrition Examination Survey. (PDF 471 kb

Additional file 2: of Association between inflammation and systolic blood pressure in RA compared to patients without RA

Figure S2. The relationship between C-reactive protein levels (CRP) and diastolic blood pressure (A), pulse pressure (B), and mean arterial pressure (C) with 95% confidence intervals, in the non-RA outpatient population and the general population (NHANES). RA, rheumatoid arthritis; NHANES, National Health and Nutrition Examination Survey. (PDF 1361 kb

Additional file 1: Figure S1. of Identification of subjects with polycystic ovary syndrome using electronic health records

Datamart calibration. The circles represent A) the initial broad datamart identified using codified data, B) the second refined datamart in which electronic notes with the words polycystic ovary syndrome or PCOS were found, and C) patients from the entire Research Population Data Registry database, without codified exclusion criteria. The overlap represents patients that were found using both codified data and with a PCOS term in the note (AXB) or patients with a PCOS term in the note and without exclusion criteria (BXC). Of note, patients without exclusion criteria are also found in A and AXB, but are not shown here for clarity. The numbers in the orange circles represent the number of charts with a confirmed PCOS diagnosis over the total number of charts reviewed by an expert (CKW) and the percentage confirmed. The white box indicates the patients with evaluable charts who were not included in the broad definition datamart (no codified terms identified) but who did have a PCOS term in their note and were included in the refined datamart. Table S1. ICD 9 codes for diagnoses and procedures and laboratory values used for inclusion and exclusion in the broad PCOS datamart. Patients were all female, 18-74 years of age (current), with any of the listed parameters measured at Massachusetts General Hospital or Brigham and Women’s Hospital. Table S2. Inclusion and exclusion criteria used to create the second refined PCOS datamart. Patients were all female, 18-40 years of age at first identification of any listed parameter from records at Massachusetts General Hospital or Brigham and Women’s Hospital. (DOCX 36 kb

Comorbidities of ASD in younger (0–17 years) vs older (18–34 years).

<p>All the comorbidities' prevalence were significantly different (p<0.0001 by Chi square) <i>except</i> for bowel disorders, epilepsy, autoimmune disorders (excluding IBD and DM1) and sleep disorders.</p

Characteristics of the ASD population studied.

<p>Counts given for patients in the pediatric and general hospital(s), and their sum. All counts are for patients under age 35.</p

Proportions of morbidity in the subpopulation with ASD and that of the hospital population.

<p>Confidence interval shown is the 95^th for the difference in the proportions. The columns 2,3 describe the proportions for all ages, columns 4,5 ages 0–17 and columns 6,7 ages 18–34).</p

Examples of Meta-Resources for Computational Biology.

<p>Summary comparing <i>iTools</i> to other similar meta-resources environments for archival and retrieval of software tools for computational biology.</p

A schematic and dynamic integration of <i>iTools</i> resources demonstrating interoperability of multi-disciplinary tools via graphical workflow environments.

<p>The three nodes with dash-boundaries on the <i>left</i> demonstrate schematically the integration of some computational biology tools. The graphical workflow on the <i>right</i> depicts the practical means of using <i>iTools</i> meta-data to construct module descriptions and generate multidisciplinary and heterogeneous data analysis protocols.</p