Demographic and Patient Characteristics comparing Breast Cancer Cases and Matched Controls, Nationwide Inpatient Sample, 2006–2009^*⁺.

<p>*Percentages may not add up to 100% due to rounding.</p><p>⁺Cell values may not add up to total due to missing data. There were 1012 cases and 1014 controls with missing data on race/ethnicity, and 92 cases and 104 controls with missing data on residential income.</p><p>Demographic and Patient Characteristics comparing Breast Cancer Cases and Matched Controls, Nationwide Inpatient Sample, 2006–2009^{<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0129169#t001fn001" target="_blank">*</a>}<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0129169#t001fn002" target="_blank">⁺</a>.</p

Association between in-hospital mortality and clinically diagnosed anxiety and depression among breast cancer cases, Nationwide inpatient sample, 2006–2009⁺.

<p>¹Adjusted for age at admission, race/ethnicity and stage</p><p>²Adjusted for age at admission, race/ethnicity, stage, residential income, region, insurance</p><p>³Adjusted for age at admission, race/ethnicity, stage, residential income, region, insurance, LOS, COM</p><p>*** p-value <0.001;</p><p>** p-value <0.01,</p><p>* p-value <0.05</p><p>⁺Cell values may not add up to total due to missing data. There were a total of 13,743 cases with missing information on race/ethnicity; 113 cases with missing information on depression, anxiety and stage at diagnosis, insurance, and region; and 1571 cases with missing data on residential income</p><p>Association between in-hospital mortality and clinically diagnosed anxiety and depression among breast cancer cases, Nationwide inpatient sample, 2006–2009<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0129169#t003fn007" target="_blank">⁺</a>.</p

Association between breast cancer diagnosis and clinically diagnosed anxiety and depression among matched cases and controls, Nationwide inpatient sample, 2006–2009⁺.

<p>¹Adjusted for race/ethnicity</p><p>²Adjusted for race/ethnicity, residential income, insurance and residential region</p><p>*** p-value <0.001;</p><p>** p-value <0.01,</p><p>* p-value <0.05</p><p>⁺Cell values may not add up to total due to missing data. There were 1012 cases and 1014 controls with missing data on race/ethnicity, and 92 cases and 104 controls with missing data on residential income.</p><p>Association between breast cancer diagnosis and clinically diagnosed anxiety and depression among matched cases and controls, Nationwide inpatient sample, 2006–2009<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0129169#t002fn006" target="_blank">⁺</a>.</p

Descriptive statistics and baseline differences.

<p>Descriptive statistics and baseline differences.</p

Alexithymia as moderator: Mean number of telephone contacts with general practitioner (GP) (Error bars: 95%CI) from baseline to follow-up of high and low alexithymic women in the EWI and neutral writing control group.

<p>Alexithymia as moderator: Mean number of telephone contacts with general practitioner (GP) (Error bars: 95%CI) from baseline to follow-up of high and low alexithymic women in the EWI and neutral writing control group.</p

CONSORT study flow diagram.

<p>CONSORT study flow diagram.</p

Writing topic as moderator: Mean number of telephone contacts with general practitioner (GP) (Error bars: 95%CI) from baseline to follow-up of EWI participants writing about their own cancer, EWI participants writing about other topics, and neutral writing controls.

<p>Writing topic as moderator: Mean number of telephone contacts with general practitioner (GP) (Error bars: 95%CI) from baseline to follow-up of EWI participants writing about their own cancer, EWI participants writing about other topics, and neutral writing controls.</p

Results from MLMs (baseline, 3- and 9 month follow-up) (Time × Group) of physical symptoms (PHQ), number of GP visits, and number of GP telephone contacts (2a), and results of moderation analyses with repressive coping (2b), social constraints (SCS-C) (2bc), rumination (ECQ) (2d), alexithymia (TAS-20) (2e), and writing topic (own cancer vs. other topic vs. neutral writing) included as moderators.

<p>Results from MLMs (baseline, 3- and 9 month follow-up) (Time × Group) of physical symptoms (PHQ), number of GP visits, and number of GP telephone contacts (2a), and results of moderation analyses with repressive coping (2b), social constraints (SCS-C) (2bc), rumination (ECQ) (2d), alexithymia (TAS-20) (2e), and writing topic (own cancer vs. other topic vs. neutral writing) included as moderators.</p

An Extensive Targeted Proteomic Analysis of Disease-Related Protein Biomarkers in Urine from Healthy Donors

<div><p>The analysis of protein biomarkers in urine is expected to lead to advances in a variety of clinical settings. Several characteristics of urine including a low-protein matrix, ease of testing and a demonstrated proteomic stability offer distinct advantages over current widely used biofluids, serum and plasma. Improvements in our understanding of the urine proteome and in methods used in its evaluation will facilitate the clinical development of urinary protein biomarkers. Multiplexed bead-based immunoassays were utilized to evaluate 211 proteins in urines from 103 healthy donors. An additional 25 healthy donors provided serial urine samples over the course of two days in order to assess temporal variation in selected biomarkers. Nearly one-third of the evaluated biomarkers were detected in urine at levels greater than 1ng/ml, representing a diverse panel of proteins with respect to structure, function and biological role. The presence of several biomarkers in urine was confirmed by western blot. Several methods of data normalization were employed to assess impact on biomarker variability. A complex pattern of correlations with urine creatinine, albumin and beta-2-microglobulin was observed indicating the presence of highly specific mechanisms of renal filtration. Further investigation of the urinary protein biomarkers identified in this preliminary study along with a consideration of the underlying proteomic trends suggested by these findings should lead to an improved capability to identify candidate biomarkers for clinical development.</p></div

Effect of several normalization methods on the population variability of urine proteins.

<p>Urines obtained from 103 healthy donors were evaluated for levels of 211 proteins using multiplexed bead-based immunoassays. Coefficients of variation (CV) were determined for each of the 211 urine proteins based on absolute and normalized values. Correlation between the two sets of values was evaluated using Pearson's test of correlation. Normalized values were calculated by dividing absolute biomarker concentration by the level of several urine parameters: <b>A</b>. urine creatinine (UCr); <b>B</b>. urine albumin; <b>C</b>. urine total protein; <b>D</b>. ratio of urine albumin to urine creatinin (ACR); <b>E</b>. β-2-microglobulin.</p