Genomic screening methodology not requiring barcoding: Single nucleotide polymorphism-based, mixed-cell screening (SMICS).

Although high-throughput, cancer cell-line screening is a time-honored, important tool for anti-cancer drug development, this process involves the testing of each, individual drug in each, individual cell-line. Despite the availability of robotic liquid handling systems, this process remains a time-consuming and costly investment. The Broad Institute developed a new method called Profiling Relative Inhibition Simultaneously in Mixtures (PRISM) to screen a mixture of barcoded, tumor cell-lines. Although this methodology significantly improved the efficiency of screening large numbers of cell-lines, the barcoding process itself was tedious that requires gene transfection and subsequent selection of stable cell-lines. In this study, we developed a new, genomic approach for screening multiple cancer cell-lines using endogenous "tags" that did not require prior barcoding: single nucleotide polymorphism-based, mixed-cell screening (SMICS). The code for SMICS is available at https://github.com/MarkeyBBSRF/SMICS.

Bayesian kinetic modeling for tracer-based metabolomic data.

BACKGROUND: Stable Isotope Resolved Metabolomics (SIRM) is a new biological approach that uses stable isotope tracers such as uniformly [Formula: see text]-enriched glucose ([Formula: see text]-Glc) to trace metabolic pathways or networks at the atomic level in complex biological systems. Non-steady-state kinetic modeling based on SIRM data uses sets of simultaneous ordinary differential equations (ODEs) to quantitatively characterize the dynamic behavior of metabolic networks. It has been increasingly used to understand the regulation of normal metabolism and dysregulation in the development of diseases. However, fitting a kinetic model is challenging because there are usually multiple sets of parameter values that fit the data equally well, especially for large-scale kinetic models. In addition, there is a lack of statistically rigorous methods to compare kinetic model parameters between different experimental groups. RESULTS: We propose a new Bayesian statistical framework to enhance parameter estimation and hypothesis testing for non-steady-state kinetic modeling of SIRM data. For estimating kinetic model parameters, we leverage the prior distribution not only to allow incorporation of experts' knowledge but also to provide robust parameter estimation. We also introduce a shrinkage approach for borrowing information across the ensemble of metabolites to stably estimate the variance of an individual isotopomer. In addition, we use a component-wise adaptive Metropolis algorithm with delayed rejection to perform efficient Monte Carlo sampling of the posterior distribution over high-dimensional parameter space. For comparing kinetic model parameters between experimental groups, we propose a new reparameterization method that converts the complex hypothesis testing problem into a more tractable parameter estimation problem. We also propose an inference procedure based on credible interval and credible value. Our method is freely available for academic use at https://github.com/xuzhang0131/MCMCFlux . CONCLUSIONS: Our new Bayesian framework provides robust estimation of kinetic model parameters and enables rigorous comparison of model parameters between experimental groups. Simulation studies and application to a lung cancer study demonstrate that our framework performs well for non-steady-state kinetic modeling of SIRM data.

Visceral fat-specific regulation of plasminogen activator inhibitor-1 in aged septic mice.

Elevated plasma levels of plasminogen activator inhibitor-1 (PAI-1) are documented in patients with sepsis and levels positively correlate with disease severity and mortality. Our previous work demonstrated that visceral adipose tissues (VAT) are a major source of PAI-1, especially in the aged (murine endotoxemia), that circulating PAI-1 protein levels match the trajectory of PAI-1 transcript levels in VAT (clinical sepsis), and that PAI-1 in both VAT and plasma are positively associated with acute kidney injury (AKI) in septic patients. In the current study utilizing preclinical sepsis models, PAI-1 tissue distribution was examined and cellular sources, as well as mechanisms mediating PAI-1 induction in VAT, were identified. In aged mice with sepsis, PAI-1 gene expression was significantly higher in VAT than in other major organs. VAT PAI-1 gene expression correlated with PAI-1 protein levels in both VAT and plasma. Moreover, VAT and plasma levels of PAI-1 were positively associated with AKI markers, modeling our previous clinical data. Using explant cultures of VAT, we determined that PAI-1 is secreted robustly in response to recombinant transforming growth factor ß (TGFß) and tumor necrosis factor α (TNFα) treatment; however, neutralization was effective only for TNFα indicating that TGFß is not an endogenous modulator of PAI-1. Within VAT, TNFα was localized to neutrophils and macrophages. PAI-1 protein levels were fourfold higher in stromal vascular fraction (SVF) cells compared with mature adipocytes, and among SVF cells, both immune and nonimmune compartments expressed PAI-1 in a similar fashion. PAI-1 was localized predominantly to macrophages within the immune compartment and preadipocytes and endothelial cells within the nonimmune compartment. Collectively, these results indicate that induction and secretion of PAI-1 from VAT is facilitated by a complex interaction among immune and nonimmune cells. As circulating PAI-1 contributes to AKI in sepsis, understanding PAI-1 regulation in VAT could yield novel strategies for reducing systemic consequences of PAI-1 overproduction.

Acute Kidney Injury Associated with Area under the Curve versus Trough Monitoring of Vancomycin in Obese Patients.

Vancomycin is a first-line agent used in the treatment of methicillin-resistant Staphylococcus aureus; however, vancomycin is associated with acute kidney injury (AKI). Previous literature demonstrates decreased incidence of AKI using 24-h area under the concentration-time curve (AUC24) monitoring, but its safety is unknown in obese populations. Patients ≥18 years, with body mass indices (BMI) ≥30 kg/m2, admitted between August 2015 and July 2017 or October 2017 and September 2019, who received vancomycin for ≥72 h and had level(s) drawn within 96 h of initiation were included. The primary outcome was incidence of AKI. Secondary outcomes included inpatient mortality rate, median inpatient length of stay, median vancomycin trough concentration, and median vancomycin AUC24. AKI was identified using the highest serum creatinine value compared with the value immediately prior to vancomycin initiation based on Kidney Disease Improving Global Outcomes (KDIGO) criteria. Overall, 1,024 patients met inclusion criteria, with 142 out of 626 patients in the trough group and 65 out of 398 patients in the AUC24 group meeting criteria for AKI (22.7% versus 16.3%, P = 0.008). Logistic regression of the data to account for confounding factors maintained significance for the reduction in incidence of AKI with AUC24 monitoring compared to trough monitoring (P = 0.010). Monitoring of vancomycin with AUC24 was associated with a decreased risk of AKI when compared with trough monitoring in obese patients.

MEScan: a powerful statistical framework for genome-scale mutual exclusivity analysis of cancer mutations.

MOTIVATION: Cancer somatic driver mutations associated with genes within a pathway often show a mutually exclusive pattern across a cohort of patients. This mutually exclusive mutational signal has been frequently used to distinguish driver from passenger mutations and to investigate relationships among driver mutations. Current methods for de novo discovery of mutually exclusive mutational patterns are limited because the heterogeneity in background mutation rate can confound mutational patterns, and the presence of highly mutated genes can lead to spurious patterns. In addition, most methods only focus on a limited number of pre-selected genes and are unable to perform genome-wide analysis due to computational inefficiency. RESULTS: We introduce a statistical framework, MEScan, for accurate and efficient mutual exclusivity analysis at the genomic scale. Our framework contains a fast and powerful statistical test for mutual exclusivity with adjustment of the background mutation rate and impact of highly mutated genes, and a multi-step procedure for genome-wide screening with the control of false discovery rate. We demonstrate that MEScan more accurately identifies mutually exclusive gene sets than existing methods and is at least two orders of magnitude faster than most methods. By applying MEScan to data from four different cancer types and pan-cancer, we have identified several biologically meaningful mutually exclusive gene sets. AVAILABILITY AND IMPLEMENTATION: MEScan is available as an R package at https://github.com/MarkeyBBSRF/MEScan. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Assessment of a modified renal angina index for AKI prediction in critically ill adults.

BACKGROUND: The renal angina index (RAI) is a useful tool for risk stratification of acute kidney injury (AKI) in critically ill children. We evaluated the performance of a modified adult RAI (mRAI) for the risk stratification of AKI in critically ill adults. METHODS: We used two independent intensive care unit (ICU) cohorts: 13 965 adult patients from the University of Kentucky (UKY) and 4789 from University of Texas Southwestern (UTSW). The mRAI included: diabetes, presence of sepsis, mechanical ventilation, pressor/inotrope use, percentage change in serum creatinine (SCr) in reference to admission SCr (ΔSCr) and fluid overload percentage within the first day of ICU admission. The primary outcome was AKI Stage ≥2 at Days 2-7. Performance and reclassification metrics were determined for the mRAI score compared with ΔSCr alone. RESULTS: The mRAI score outperformed ΔSCr and readjusted probabilities to predict AKI Stage ≥2 at Days 2-7: C-statistic: UKY 0.781 versus 0.708 [integrated discrimination improvement (IDI) 2.2%] and UTSW 0.766 versus 0.696 (IDI 1.8%) (P < 0.001 for both). In the UKY cohort, only 3.3% of patients with mRAI score <10 had the AKI event, while 16.4% of patients with mRAI score of ≥10 had the AKI event (negative predictive value 96.8%). Similar findings were observed in the UTSW cohort as part of external validation. CONCLUSIONS: In critically ill adults, the adult mRAI score determined within the first day of ICU admission outperformed changes in SCr for the prediction of AKI Stage ≥2 at Days 2-7 of ICU stay. The mRAI is a feasible tool for AKI risk stratification in adult patients in the ICU.

A strategy to identify event specific hospitalizations in large health claims databases.

BACKGROUND: Health insurance claims data offer a unique opportunity to study disease distribution on a large scale. Challenges arise in the process of accurately analyzing these raw data. One important challenge to overcome is the accurate classification of study outcomes. For example, using claims data, there is no clear way of classifying hospitalizations due to a specific event. This is because of the inherent disjointedness and lack of context that typically come with raw claims data. METHODS: In this paper, we propose a framework for classifying hospitalizations due to a specific event. We then tested this framework in a private health insurance claims database (Symphony) with approximately 4 million US adults who tested positive with COVID-19 between March and December 2020. Our claims specific COVID-19 related hospitalizations proportion is then compared to nationally reported rates from the Centers for Disease Control by age. RESULTS: Across all ages (18 +) the total percentage of Symphony patients who met our definition of hospitalized due to COVID-19 was 7.3% which was similar to the CDC's estimate of 7.5%. By age group, defined by the CDC, our estimates vs. the CDC's estimates were 18-49: 2.7% vs. 3%, 50-64: 8.2% vs. 9.2%, and 65 + : 14.6% vs. 28.1%. CONCLUSIONS: The proposed methodology is a rigorous way to define event specific hospitalizations in claims data. This methodology can be extended to many different types of events and used on a variety of different types of claims databases.

Cerium dioxide, a Jekyll and Hyde nanomaterial, can increase basal and decrease elevated inflammation and oxidative stress.

It was hypothesized that the catalyst nanoceria can increase inflammation/oxidative stress from the basal and reduce it from the elevated state. Macrophages clear nanoceria. To test the hypothesis, M0 (non-polarized), M1- (classically activated, pro-inflammatory), and M2-like (alternatively activated, regulatory phenotype) RAW 264.7 macrophages were nanoceria exposed. Inflammatory responses were quantified by IL-1ß level, arginase activity, and RT-qPCR and metabolic changes and oxidative stress by the mito and glycolysis stress tests (MST and GST). Morphology was determined by light microscopy, macrophage phenotype marker expression, and a novel three-dimensional immunohistochemical method. Nanoceria blocked IL-1ß and arginase effects, increased M0 cell OCR and GST toward the M2 phenotype and altered multiple M1- and M2-like cell endpoints toward the M0 level. M1-like cells had greater volume and less circularity/roundness. M2-like cells had greater volume than M0 macrophages. The results are overall consistent with the hypothesis.

Use of Wearable Sensors to Assess the Effects of Performing a Cognitive Task on Sensory Integration of Balance in Healthy Individuals.

This study investigated the effects of performing a cognitive task on the sensory integration of balance in healthy individuals. Ten subjects (five F/five M; 21.5 ± 2.17 years; 69.9 ± 3.4 inches; 155.6 ± 26.1 lbs; Caucasian), without known balance issues, performed the modified Clinical Test of Sensory Interaction of Balance (mCTSIB) with and without a cognitive task. The cognitive task involved counting down in threes from a randomly assigned number between 95 and 100. Postural sway area and postural sway jerk were assessed through the use of inertial sensors placed around the subjects' lower lumbar region. Each subject performed four trials for the four conditions of the mCTSIB: eyes open firm (EOFirm), eyes closed firm (ECFirm), eyes open foam (EOFoam), and eyes closed foam (ECFoam). We tested the effect of performing a cognitive task on the sensory integration of balance. We hypothesized that sensory cognitive interaction would be more apparent for more complex conditions and would be better assessed with postural sway jerk compared to postural sway area measure. With the addition of a cognitive task for the mCTSIB: (1) postural sway area increased in the baseline condition, i.e., EOFirm (p < 0.05), but did not increase in the most difficult condition, i.e., ECFoam; (2) postural sway jerk increased in all conditions of the mCTSIB (p < 0.05); (3) cognitive performance did not deteriorate across conditions of the mCTSIB. Postural sway jerk was shown to be a more sensitive measure in detecting the effect of a cognitive task on sensory integration for postural control. Overall, inertial sensors can be used to reliably assess postural sway differences related to sensory−cognitive integration.

Understanding the groups of care transition strategies used by U.S. hospitals: an application of factor analytic and latent class methods.

BACKGROUND: After activation of the Hospital Readmission Reduction Program (HRRP) in 2012, hospitals nationwide experimented broadly with the implementation of Transitional Care (TC) strategies to reduce hospital readmissions. Although numerous evidence-based TC models exist, they are often adapted to local contexts, rendering large-scale evaluation difficult. Little systematic evidence exists about prevailing implementation patterns of TC strategies among hospitals, nor which strategies in which combinations are most effective at improving patient outcomes. We aimed to identify and define combinations of TC strategies, or groups of transitional care activities, implemented among a large and diverse cohort of U.S. hospitals, with the ultimate goal of evaluating their comparative effectiveness. METHODS: We collected implementation data for 13 TC strategies through a nationwide, web-based survey of representatives from short-term acute-care and critical access hospitals (N = 370) and obtained Medicare claims data for patients discharged from participating hospitals. TC strategies were grouped separately through factor analysis and latent class analysis. RESULTS: We observed 348 variations in how hospitals implemented 13 TC strategies, highlighting the diversity of hospitals' TC strategy implementation. Factor analysis resulted in five overlapping groups of TC strategies, including those characterized by 1) medication reconciliation, 2) shared decision making, 3) identifying high risk patients, 4) care plan, and 5) cross-setting information exchange. We determined that the groups suggested by factor analysis results provided a more logical grouping. Further, groups of TC strategies based on factor analysis performed better than the ones based on latent class analysis in detecting differences in 30-day readmission trends. CONCLUSIONS: U.S. hospitals uniquely combine TC strategies in ways that require further evaluation. Factor analysis provides a logical method for grouping such strategies for comparative effectiveness analysis when the groups are dependent. Our findings provide hospitals and health systems 1) information about what groups of TC strategies are commonly being implemented by hospitals, 2) strengths associated with the factor analysis approach for classifying these groups, and ultimately, 3) information upon which comparative effectiveness trials can be designed. Our results further reveal promising targets for comparative effectiveness analyses, including groups incorporating cross-setting information exchange.

Gingival tissue autophagy pathway gene expression profiles in periodontitis and aging.

OBJECTIVE: We hypothesized that autophagy-related genes will be differentially expressed in periodontitis, suggesting an impaired gingival autophagic response associated with disease. BACKGROUND: Autophagy is a cellular physiologic mechanism to maintain tissue homeostasis, while deficient autophagic responses increase inflammation and susceptibility to infection. METHODS: Rhesus monkeys [<3 years to 23 years of age (n = 34)] were examined for periodontal health and naturally occurring periodontitis. Gingival tissues samples were obtained from healthy or diseased sites, total RNA was isolated, and the Rhesus Gene Chip 1.0 ST (Affymetrix) was used for gene expression analysis of 150 autophagy-related genes. RESULTS: Comparison of expression levels with adult healthy tissues demonstrated a rather limited number of individual genes that were significantly different across the age-groups. In contrast, with periodontitis in the adults and aged animals, about 15% of the genes were significantly increased or decreased. The differences were reflected in the mTOR complex (5/12), ULK1/ATG1 complex (5/9), PI3K complex (5/21), ATG9 complex (2/7), ATG12 conjugation/LC3 lipidation (7/22), and lysosome fusion/vesicle degradation [LF/VD (5/10)] activities within the broader autophagic pathway. The genes most greatly altered in gingival tissues of naturally occurring periodontitis were identified in the ATG12 and LF/VD pathways that approximated 50% of the genes in each of those categories. While healthy gingival aging did not appear to reflect altered autophagy gene expression, substantial differences were noted with periodontitis irrespective of the age of the animals. Future studies into the role of autophagy in periodontitis and could offer potential new therapeutic strategies to prevent and/or treat periodontal disease.

Vedolizumab Dose Escalation Improves Therapeutic Response in a Subset of Patients with Ulcerative Colitis.

BACKGROUND: The Gemini trial failed to detect a significant difference in response rate for patients with ulcerative colitis (UC) randomized to standard (every 8 week) vedolizumab dosing vs escalated (every 4 week) dosing. Subsequent real-world data imply the Gemini trial design may have obscured a benefit of escalated dosing. AIMS: We investigated outcomes after vedolizumab dose escalation for patients with UC. We also explored potential clinical predictors of dose escalation requirement. METHODS: In this retrospective study, we included patients with UC who received vedolizumab between 1/2017-1/2019. We compared rates of clinical response (decrease in partial Mayo score by ≥ 2) and remission (partial Mayo < 2) for standard vs escalated dosing. RESULTS: Among the 90 patients reviewed, 52 achieved and maintained remission on standard dosing. The average time to remission with standard dosing was 33.3 ± 6.6 weeks. After an average of 56.3 ± 7.4 weeks standard dosing, 24 patients (22 "partial responders" and 2 "non-responders") were dose-escalated. Of the 22 "partial responders" dose-escalated, 10 (45%) achieved remission, 10 (45%) achieved further improvement. Neither "non-responder" demonstrated further clinical benefit. Prior anti-tumor necrosis factor (anti-TNF) biologic exposure predicted dose escalation requirement (p = 0.008). Patients requiring dose escalation had more severe disease at baseline as measured by both full Mayo (p = 0.009) and partial Mayo scores (p = 0.01). CONCLUSIONS: We show dose escalation benefited patients with UC who exhibit a "partial response" to standard dosing. Early vedolizumab dose escalation should be considered in both patients with severe disease and those with prior anti-TNF experience.

Improving evidence-based grouping of transitional care strategies in hospital implementation using statistical tools and expert review.

BACKGROUND: As health systems transition to value-based care, improving transitional care (TC) remains a priority. Hospitals implementing evidence-based TC models often adapt them to local contexts. However, limited research has evaluated which groups of TC strategies, or transitional care activities, commonly implemented by hospitals correspond with improved patient outcomes. In order to identify TC strategy groups for evaluation, we applied a data-driven approach informed by literature review and expert opinion. METHODS: Based on a review of evidence-based TC models and the literature, focus groups with patients and family caregivers identifying what matters most to them during care transitions, and expert review, the Project ACHIEVE team identified 22 TC strategies to evaluate. Patient exposure to TC strategies was measured through a hospital survey (N = 42) and prospective survey of patients discharged from those hospitals (N = 8080). To define groups of TC strategies for evaluation, we performed a multistep process including: using ACHIEVE'S prior retrospective analysis; performing exploratory factor analysis, latent class analysis, and finite mixture model analysis on hospital and patient survey data; and confirming results through expert review. Machine learning (e.g., random forest) was performed using patient claims data to explore the predictive influence of individual strategies, strategy groups, and key covariates on 30-day hospital readmissions. RESULTS: The methodological approach identified five groups of TC strategies that were commonly delivered as a bundle by hospitals: 1) Patient Communication and Care Management, 2) Hospital-Based Trust, Plain Language, and Coordination, 3) Home-Based Trust, Plain language, and Coordination, 4) Patient/Family Caregiver Assessment and Information Exchange Among Providers, and 5) Assessment and Teach Back. Each TC strategy group comprises three to six, non-mutually exclusive TC strategies (i.e., some strategies are in multiple TC strategy groups). Results from random forest analyses revealed that TC strategies patients reported receiving were more important in predicting readmissions than TC strategies that hospitals reported delivering, and that other key co-variates, such as patient comorbidities, were the most important variables. CONCLUSION: Sophisticated statistical tools can help identify underlying patterns of hospitals' TC efforts. Using such tools, this study identified five groups of TC strategies that have potential to improve patient outcomes.

Identifying factors impacting the efficacy of postmastectomy radiotherapy in patients with early-stage breast cancer and one to two positive lymph nodes.

INTRODUCTION: In women with T1-2 breast cancer and one to two positive axillary lymph nodes (LN) at low risk for recurrence, postmastectomy radiation therapy (PMRT) may provide insufficient benefit to justify its toxicity. This study evaluated the interaction of factors associated with overall survival (OS) after PMRT in these patients. METHODS: The National Cancer Database was queried for women with T1-2 breast cancer undergoing mastectomy with one to two positive LN identified on lymphadenectomy. Patients were grouped according to number of positive LN and then stratified by PMRT use. Differences in OS were evaluated. RESULTS: Multivariable modeling demonstrated an interaction effect of age on the efficacy of PMRT. In patients more than or equal to 60 years old, PMRT was associated with improved survival when adjusting for age and tumor grade in patients with 1 to 2 positive LN (risk ratio = 0.62, 95% confidence interval = 0.40-0.93, P = .018). In patients less than 60 years old, tumor size and grade, but not PMRT, were associated with improved OS. CONCLUSION: For women with T1-2 breast cancer and one to two positive LN, PMRT's association with OS is influenced by age, tumor grade, and number of positive LN. PMRT appears to be associated with improvements in OS in older patients, but not younger patients, regardless of tumor size or nodal status.

The Effect of Personal Protective Equipment on Firefighter Occupational Performance.

Lesniak, AY, Bergstrom, HC, Clasey, JL, Stromberg, AJ, and Abel, MG. The effect of personal protective equipment on firefighter occupational performance. J Strength Cond Res 34(8): 2165-2172, 2020-The purpose of this study was to evaluate the effects of load carriage (LC) and LC plus respirator use (LC + self-contained breathing apparatus [SCBA]) on firefighters' work capacity to enhance our understanding of occupational demands. Twenty-one male structural firefighter recruits (age: 28.6 ± 4.3 years; height: 178.6 ± 7.2 cm; body mass: 94.1 ± 15.4 kg; body fat: 22.9 ± 6.1%) participated. Occupational performance was assessed by time to complete a simulated fire ground test (SFGT). After 2 familiarization trials, recruits performed the following SFGT conditions in a randomized order: PT (physical training clothes), LC only, and LC + SCBA. To describe within-group differences between SFGT conditions, relative difference scores were calculated as follows: % difference = ([experimental trial outcome - PT trial outcome]/PT trial outcome) × 100. Statistical differences between conditions were assessed with repeated-measures analysis of variance. The level of significance was set p < 0.01. Time to complete the LC + SCBA trial (345.9 ± 43.7 seconds; p < 0.001) and LC-only trial (331.2 ± 39.3 seconds; p < 0.001) were significantly greater than the PT trial (241.0 ± 33.3 seconds). Post-SFGT rating of perceived exertion was higher in the LC + SCBA trial (6.7 ± 1.7) and LC trial (6.4 ± 1.5) compared with the PT trial (4.7 ± 1.8; p < 0.001). Heart rate and lactate measures were similar across conditions (p = 0.488; p = 0.287). Personal protective equipment (PPE) significantly decreases the work capacity and increases the perceived effort of occupational tasks. Thus, these findings describe the additional physical demands produced by PPE and indicate that performance of firefighting tasks in an unloaded condition does not reflect work capacity in a bona fide condition.

A probabilistic method for leveraging functional annotations to enhance estimation of the temporal order of pathway mutations during carcinogenesis.

BACKGROUND: Cancer arises through accumulation of somatically acquired genetic mutations. An important question is to delineate the temporal order of somatic mutations during carcinogenesis, which contributes to better understanding of cancer biology and facilitates identification of new therapeutic targets. Although a number of statistical and computational methods have been proposed to estimate the temporal order of mutations, they do not account for the differences in the functional impacts of mutations and thus are likely to be obscured by the presence of passenger mutations that do not contribute to cancer progression. In addition, many methods infer the order of mutations at the gene level, which have limited power due to the low mutation rate in most genes. RESULTS: In this paper, we develop a Probabilistic Approach for estimating the Temporal Order of Pathway mutations by leveraging functional Annotations of mutations (PATOPA). PATOPA infers the order of mutations at the pathway level, wherein it uses a probabilistic method to characterize the likelihood of mutational events from different pathways occurring in a certain order. The functional impact of each mutation is incorporated to weigh more on a mutation that is more integral to tumor development. A maximum likelihood method is used to estimate parameters and infer the probability of one pathway being mutated prior to another. Simulation studies and analysis of whole exome sequencing data from The Cancer Genome Atlas (TCGA) demonstrate that PATOPA is able to accurately estimate the temporal order of pathway mutations and provides new biological insights on carcinogenesis of colorectal and lung cancers. CONCLUSIONS: PATOPA provides a useful tool to estimate temporal order of mutations at the pathway level while leveraging functional annotations of mutations.

SDA: a semi-parametric differential abundance analysis method for metabolomics and proteomics data.

BACKGROUND: Identifying differentially abundant features between different experimental groups is a common goal for many metabolomics and proteomics studies. However, analyzing data from mass spectrometry (MS) is difficult because the data may not be normally distributed and there is often a large fraction of zero values. Although several statistical methods have been proposed, they either require the data normality assumption or are inefficient. RESULTS: We propose a new semi-parametric differential abundance analysis (SDA) method for metabolomics and proteomics data from MS. The method considers a two-part model, a logistic regression for the zero proportion and a semi-parametric log-linear model for the possibly non-normally distributed non-zero values, to characterize data from each feature. A kernel-smoothed likelihood method is developed to estimate model coefficients and a likelihood ratio test is constructed for differential abundant analysis. The method has been implemented into an R package, SDAMS, which is available at https://www.bioconductor.org/packages/release/bioc/html/SDAMS.html . CONCLUSION: By introducing the two-part semi-parametric model, SDA is able to handle both non-normally distributed data and large fraction of zero values in a MS dataset. It also allows for adjustment of covariates. Simulations and real data analyses demonstrate that SDA outperforms existing methods.

Novel magnetic core-shell nanoparticles for the removal of polychlorinated biphenyls from contaminated water sources.

In this work, we developed novel core-shell nanoparticle systems with magnetic core and polymer shell via atom transfer radical polymerization for use as high affinity nanoadsorbents for organic contaminants in water and wastewater treatment. Polyphenolic-based moieties, curcumin multiacrylate (CMA) and quercetin multiacrylate (QMA), were incorporated into poly(ethylene glycol) (PEG) based polymeric shells to create high affinity binding sites for the capture of polychlorinated biphenyls (PCBs) as a model pollutant. The resulting magnetic nanoparticles (MNPs) were characterized by Fourier transform infrared spectroscopy (FTIR), thermogravimetric analysis (TGA), transmission electron microscopy (TEM), X-ray diffraction (XRD), dynamic light scattering (DLS), and UV-visible spectroscopy. The affinity of these novel materials for PCB 126 was evaluated and fitted to the nonlinear Langmuir model to determine binding affinities (KD). The KD values obtained were: PEG MNPs (8.42 nM) < IO MNPs (8.23nM) < QMA MNPs (5.88 nM) < CMA MNPs (2.72 nM), demonstrating that the presence of polyphenolic-based moieties enhanced PCB 126 binding affinity, which is hypothesized to be a result of π - π stacking interactions. These values are lower that KD values for activated carbon, providing strong evidence that these novel core-shell nanoparticles have a promising application as nanoadsorbents for specific organic contaminants offering a cost effective alternative to current remediation approaches.

Chondrogenic differentiation potential of adult and fetal equine cell types.

OBJECTIVE: To determine the chondrogenic potential of cells derived from interzone tissue, the normal progenitor of articular cartilage during fetal development, compared to that of adult bone marrow-derived and adipose-derived mesenchymal cell isolates. The objective of this study was to compare the chondrogenic potential of fetal musculoskeletal progenitor cells to adult cell types, which are currently used therapeutically to facilitate joint cartilage repair in equine clinical practice. The hypothesis tested was that cells derived from interzone tissue have a chondrogenic potential that exceeds that of adult bone marrow-derived and adipose-derived mesenchymal cell isolates. STUDY DESIGN: In vitro study. ANIMALS: Six young adult horses (15-17 months of age) and 6 equine fetuses aged 45-46 days of gestation. METHODS: Three-dimensional pellet cultures were established under chondrogenic conditions with fresh, primary cells isolated from adult (articular cartilage, bone marrow, adipose, dermis) and fetal (interzone, skeletal anlagen cartilage, dermis) tissues. Cellular morphology, pellet architecture, and proteoglycan synthesis were assessed in the pellet cultures. Steady state levels of ACAN (aggrecan core protein), COL2A1 (collagen type II), and COL1A1 (collagen type I) messenger RNA (mRNA) were compared among these cell types as pellet cultures and monolayer cultures. RESULTS: Adult articular chondrocytes, fetal interzone cells, and fetal anlage cells generated the largest pellets under these chondrogenic culture conditions. Pellets derived from adult articular chondrocytes and fetal anlage cells had the highest scores on a neocartilage grading scale. Fetal anlage and adult articular chondrocyte pellets had low steady-state levels of COL1A mRNA but high COL2A1 expression. Anlage chondrocyte pellets also had the highest expression of ACAN. CONCLUSION: Adult articular chondrocytes, fetal interzone cells, and fetal anlage chondrocytes exhibited the highest chondrogenic potential. In this study, adult adipose-derived cells exhibited very limited chondrogenesis, and bone marrow-derived cells had limited and variable chondrogenic potential. CLINICAL SIGNIFICANCE: Additional investigation of the high chondrogenic potential of fetal interzone cells and anlage chondrocytes to advance cell-based therapies in diarthrodial joints is warranted.