Downregulation of monocyte miRNAs: implications for immune dysfunction and disease severity in drug-resistant tuberculosis

Monocyte miRNAs govern both protective and pathological responses during tuberculosis (TB) through their differential expression and emerged as potent targets for biomarker discovery and host-directed therapeutics. Thus, this study examined the miRNA profile of sorted monocytes across the TB disease spectrum [drug-resistant TB (DR-TB), drug-sensitive TB (DS-TB), and latent TB] and in healthy individuals (HC) to understand the underlying pathophysiology and their regulatory mechanism

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

Analytical validation of CanAssist-Breast: an immunohistochemistry based prognostic test for hormone receptor positive breast cancer patients

Abstract Background CanAssist-Breast is an immunohistochemistry based test that predicts risk of distant recurrence in early-stage hormone receptor positive breast cancer patients within first five years of diagnosis. Immunohistochemistry gradings for 5 biomarkers (CD44, ABCC4, ABCC11, N-Cadherin and pan-Cadherins) and 3 clinical parameters (tumor size, tumor grade and node status) of 298 patient cohort were used to develop a machine learning based statistical algorithm. The algorithm generates a risk score based on which patients are stratified into two groups, low- or high-risk for recurrence. The aim of the current study is to demonstrate the analytical performance with respect to repeatability and reproducibility of CanAssist-Breast. Methods All potential sources of variation in CanAssist-Breast testing involving operator, run and observer that could affect the immunohistochemistry performance were tested using appropriate statistical analysis methods for each of the CanAssist-Breast biomarkers using a total 309 samples. The cumulative effect of these variations in the immunohistochemistry gradings on the generation of CanAssist-Breast risk score and risk category were also evaluated. Intra-class Correlation Coefficient, Bland Altman plots and pair-wise agreement were performed to establish concordance on IHC gradings, risk score and risk categorization respectively. Results CanAssist-Breast test exhibited high levels of concordance on immunohistochemistry gradings for all biomarkers with Intra-class Correlation Coefficient of ≥0.75 across all reproducibility and repeatability experiments. Bland-Altman plots demonstrated that agreement on risk scores between the comparators was within acceptable limits. We also observed > 90% agreement on risk categorization (low- or high-risk) across all variables tested. Conclusions The extensive analytical validation data for the CanAssist-Breast test, evaluating immunohistochemistry performance, risk score generation and risk categorization showed excellent agreement across variables, demonstrating that the test is robust

Clinical validation of an immunohistochemistry‐based CanAssist‐Breast test for distant recurrence prediction in hormone receptor‐positive breast cancer patients

Abstract CanAssist‐Breast (CAB) is an immunohistochemistry (IHC)‐based prognostic test for early‐stage Hormone Receptor (HR+)‐positive breast cancer patients. CAB uses a Support Vector Machine (SVM) trained algorithm which utilizes expression levels of five biomarkers (CD44, ABCC4, ABCC11, N‐Cadherin, and Pan‐Cadherin) and three clinical parameters such as tumor size, grade, and node status as inputs to generate a risk score and categorizes patients as low‐ or high‐risk for distant recurrence within 5 years of diagnosis. In this study, we present clinical validation of CAB. CAB was validated using a retrospective cohort of 857 patients. All patients were treated either with endocrine therapy or chemoendocrine therapy. Risk categorization by CAB was analyzed by calculating Distant Metastasis‐Free Survival (DMFS) and recurrence rates using Kaplan‐Meier survival curves. Multivariate analysis was performed to calculate Hazard ratios (HR) for CAB high‐risk vs low‐risk patients. The results showed that Distant Metastasis‐Free Survival (DMFS) was significantly different (P‐0.002) between low‐ (DMFS: 95%) and high‐risk (DMFS: 80%) categories in the endocrine therapy treated alone subgroup (n = 195) as well as in the total cohort (n = 857, low‐risk DMFS: 95%, high‐risk DMFS: 84%, P 74% of high Ki‐67 and IHC4 score intermediate‐risk zone patients into low‐risk category. Overall the data suggest that CAB can effectively predict risk of distant recurrence with clear dichotomous high‐ or low‐risk categorization