Multi-functional chitosan copolymer modified nanocrystals as oral andrographolide delivery systems for enhanced bioavailability and anti-inflammatory efficacy

Modifying nanocrystals with functional materials have been common strategy to enlarge the enhancing ability on oral absorption via nanocrystals; however, whether the functional materials have played their full enhancing ability in oral absorption is still unknown. In this study, we synthetized a novel chitosan-based copolymer (the copolymer of sodium dodecyl sulfate (SDS), chitosan (CS) and D-α-Tocopherol polyethylene glycol 1000 succinate, SDS-CS-TPGS), and modified nanocrystals with this copolymer, aiming to enhance the oral absorption of polymer andrographolide (ADR). In real-time distribution study, we found the distribution of ADR, SDS, CS and TPGS varies in gastrointestinal tract, while the distribution of ADR and SDS-CS-TPGS was similar, revealing the SDS-CS-TPGS could able to participate in the absorption process of andrographolide timely. To explore the oral absorption enhancing ability of SDS-CS-TPGS, we prepared a series of nanocrystals modified with different materials and explored their pharmacokinetic performances on SD rats. The results showed the nanocrystals modified with SDS-CS-TPGS (S-C-TANs) exhibited the highest bioavailability, which could enhance the AUC0-∞ of ADR from 1.291 mg/L*h to 5.275 mg/L*h (enhanced for about 4.09-folds). The enhanced anti- inflammatory efficacy was also found on ICR mice by employing ear swelling rate, TNF-α, IL-1β and IL-6 and pharmacodynamic index. These results indicated that modified with synthesized copolymer containing different functional stabilizers is an efficient strategy to enlarge the enhancing ability on oral absorption of nanocrystals.</p

The use of automated Ki67 analysis to predict Oncotype DX risk-of-recurrence categories in early-stage breast cancer

<div><p>Ki67 is a commonly used marker of cancer cell proliferation, and has significant prognostic value in breast cancer. In spite of its clinical importance, assessment of Ki67 remains a challenge, as current manual scoring methods have high inter- and intra-user variability. A major reason for this variability is selection bias, in that different observers will score different regions of the same tumor. Here, we developed an automated Ki67 scoring method that eliminates selection bias, by using whole-slide analysis to identify and score the tumor regions with the highest proliferative rates. The Ki67 indices calculated using this method were highly concordant with manual scoring by a pathologist (Pearson’s r = 0.909) and between users (Pearson’s r = 0.984). We assessed the clinical validity of this method by scoring Ki67 from 328 whole-slide sections of resected early-stage, hormone receptor-positive, human epidermal growth factor receptor 2-negative breast cancer. All patients had Oncotype DX testing performed (Genomic Health) and available Recurrence Scores. High Ki67 indices correlated significantly with several clinico-pathological correlates, including higher tumor grade (1 versus 3, P<0.001), higher mitotic score (1 versus 3, P<0.001), and lower Allred scores for estrogen and progesterone receptors (P = 0.002, 0.008). High Ki67 indices were also significantly correlated with higher Oncotype DX risk-of-recurrence group (low versus high, P<0.001). Ki67 index was the major contributor to a machine learning model which, when trained solely on clinico-pathological data and Ki67 scores, identified Oncotype DX high- and low-risk patients with 97% accuracy, 98% sensitivity and 80% specificity. Automated scoring of Ki67 can thus successfully address issues of consistency, reproducibility and accuracy, in a manner that integrates readily into the workflow of a pathology laboratory. Furthermore, automated Ki67 scores contribute significantly to models that predict risk of recurrence in breast cancer.</p></div

Outline of Random Forest training and evaluation workflow.

<p>Outline of Random Forest training and evaluation workflow.</p

Contribution of individual variables to the accuracy of the respective Random Forest models, as assessed by increases in mean squared error for models created without each variable.

<p>Graphs represent models after 1,000 cycles of validation trained with both clinico-pathological data and Oncotype DX expression data for ER, PgR and HER2 (A) or with clinico-pathological data alone (B). Error bars represent standard deviation from the mean. ER intensity, estrogen receptor staining intensity; ER score, estrogen receptor expression score (immunohistochemistry); PR intensity, progesterone receptor staining intensity; PR score, progesterone receptor expression score (immunohistochemistry); ODX ER, Oncotype DX estrogen receptor gene expression score; ODX HER2, Oncotype DX HER2 expression score; ODX PR, Oncotype DX progesterone receptor gene expression score; Tumor_arch, tumor differentiation score; Tumor_nuc_grade, tumor nuclear grade.</p

Summary performance of the Random Forest models predicting Oncotype DX risk groups.

<p>Recurrence Scores were predicted using the clinico-pathological variables listed in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0188983#pone.0188983.s004" target="_blank">S2 Table</a> alone (pRS), or using the <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0188983#pone.0188983.s004" target="_blank">S2 Table</a> variables in addition to gene expression scores for ER, PgR and HER2 that were included in the official Oncotype DX reports (pRS_odx). Evaluation of performance of the Random Forest models was based on the extent to which the models correctly predicted, or failed to predict, each patient’s actual low- or high-risk Oncotype DX category. Values represent the mean outcomes ± standard deviations over 1,000 testing iterations.</p

Clinico-pathological characteristics of patients in the study cohort.

<p>Clinico-pathological characteristics of patients in the study cohort.</p

Assessment of correlation between hot-spot Ki67 index and Oncotype DX scores.

<p>(A) Plot showing association between Ki67 index and Oncotype DX low-risk (blue), intermediate-risk (green) and high-risk (red) groupings. Pearson’s r = 0.5533; P<0.001. (B) Plot showing association between Ki67 and Oncotype DX low- and high-risk groupings. Pearson’s r = 0.684; P<0.001.</p

Assessment of interaction between Ki67 indices and clinico-pathological variables.

<p>Assessment of interaction between Ki67 indices and clinico-pathological variables.</p

Overview of the automated image analysis workflow.

<p>An H&E of the tumor specimen was manually annotated by a pathologist (A). Annotations were transferred to the matching Ki67-stained slide, and segmented into tiles (B). Ki67-positive and -negative nuclei in each tile (C) were identified and counted by the analysis algorithm, which colored them green and blue, respectively (D).</p

Association of high sensitivity C-reactive protein and abdominal aortic aneurysm: a meta-analysis and systematic review

<p><b>Objective:</b> To evaluate the association of high sensitivity C-reactive protein (hsCRP) with the presence of abdominal aortic aneurysm (AAA).</p> <p><b>Methods:</b> Medline, Cochrane, Embase, and Google Scholar databases were searched until 22 June 2016 using the keywords predictive factors, biomarkers, abdominal aortic aneurysm, prediction, high sensitivity C-reactive protein, and hsCRP. Prospective studies, retrospective studies, and cohort studies were included.</p> <p><b>Results:</b> Twelve case–control studies were included in the meta-analysis with a total of 8345 patients (1977 in the AAA group and 6368 in the control group). The pooled results showed that AAA patients had higher hsCRP value than the control group (difference in means = 1.827, 95% CI = 0.010 to 3.645, <i>p</i> = .049). Subgroup analysis found AAA patients with medium or small aortic diameter (<50 mm) had higher hsCRP plasma levels than the control group (difference in means = 1.301, 95% CI = 0.821 to 1.781, <i>p</i> < .001). In patients with large aortic diameter (≥50 mm), no difference was observed in hsCRP levels between the AAA and control groups (difference in means = 1.769, 95% CI = −1.387 to 4.925, <i>p</i> = .272). Multi-regression analysis found the difference in means of hsCRP plasma levels between AAA and control groups decreased as aortic diameter increased (slope = −0.04, <i>p</i> < .001), suggesting that hsCRP levels may be inversely associated with increasing aneurysm size.</p> <p><b>Conclusions:</b> Our findings suggest that hsCRP levels may possibly be used as a diagnostic biomarker for AAA patients with medium or small aortic diameter but not for AAA patients with large aortic diameter. The correlation between serum hsCRP level and AAA aneurysm is not conclusive due to the small number of included articles and between-study heterogeneity.</p