In-silico Modulation of the Irinotecan Release from a Functionalized MCM-41 Support

The release rate of a drug molecule from a porous support depends on a large number of factors, including support characteristics, surface functionalization (procedure and linker type), drug features, biological receptor fluid characteristics, and release conditions. Model-based (in-silico) modulation of the release rate through influential parameters can help in designing an optimized delivery system for a specific drug action. To prevent biased predictions, a dynamic mechanism-based model was adopted, by including kinetic terms related to surface adsorption-desorption, diffusion in pores, and external diffusion of the drug to the body fluid. Exemplification is made for the case of a functionalized silica MCM-41 support with a tunable pore size distribution and functionalization possibilities with hydrophobic (triethoxyvinylsilane, VTES) or hydrophilic (3-aminopropyl triethoxysilane, APTES) linkers. Variation of several structural parameters, referring to the average pore size, initial drug load, and linker proportion on a bi-functionalized support, pointed out the strong nonlinear relationships between the process variables and the release rate

The curvHDR Method for Gating Flow Cytometry Samples

Motivation: High-throughput flow cytometry experiments produce hundreds of large multivariate samples of cellular characteristics. These samples require specialized processing to obtain clinically meaningful measurements. A major component of this processing is a form of cell subsetting known as gating. Manual gating is time-consuming and subjective. Good automatic and semi-automatic gating algorithms are very beneficial to high-throughput flow cytometry. Results: We develop a statistical procedure, named curvHDR, for automatic and semi-automatic gating. The method combines the notions of significant high negative curvature regions and highest density regions and has the ability to adapt well to human-perceived gates. The underlying principles apply to dimension of arbitrary size, although we focus on dimensions up to three. Accompanying software, compatible with contemporary flow cytometry informatics, is developed. Availability: Software for Bioconductor within R is available

Generalized Confidence Intervals and Fiducial Intervals for Some Epidemiological Measures.

For binary outcome data from epidemiological studies, this article investigates the interval estimation of several measures of interest in the absence or presence of categorical covariates. When covariates are present, the logistic regression model as well as the log-binomial model are investigated. The measures considered include the common odds ratio (OR) from several studies, the number needed to treat (NNT), and the prevalence ratio. For each parameter, confidence intervals are constructed using the concepts of generalized pivotal quantities and fiducial quantities. Numerical results show that the confidence intervals so obtained exhibit satisfactory performance in terms of maintaining the coverage probabilities even when the sample sizes are not large. An appealing feature of the proposed solutions is that they are not based on maximization of the likelihood, and hence are free from convergence issues associated with the numerical calculation of the maximum likelihood estimators, especially in the context of the log-binomial model. The results are illustrated with a number of examples. The overall conclusion is that the proposed methodologies based on generalized pivotal quantities and fiducial quantities provide an accurate and unified approach for the interval estimation of the various epidemiological measures in the context of binary outcome data with or without covariates

Serum cartilage oligomeric matrix protein and clinical signs and symptoms of potential pre-radiographic hip and knee pathology

OBJECTIVE: To examine the cross-sectional relationship between serum cartilage oligomeric matrix protein (COMP) and hip and knee clinical signs and symptoms in a sample of adults without radiographic hip or knee osteoarthritis (OA). DESIGN: A total of 145 persons with available sera and no evidence of radiographic hip or knee OA (Kellgren-Lawrence grade 0) were randomly selected from the Caucasian participants of the Johnston County Osteoarthritis Project. COMP was quantified by a competitive ELISA assay with a monoclonal antibody 17-C10. Hip and knee clinical signs and symptoms were assessed by physical examination and interview, and their associations with Ln COMP analysed with general linear models. RESULTS: After adjustment for age, gender, body mass index (BMI), and other symptomatic joints, mean Ln COMP was statistically significantly higher among persons with hip-related clinical signs (P=0.018), among those with hip-related symptoms (P=0.046), and among individuals meeting American College of Rheumatology clinical criteria for hip OA (P=0.021). There were no statistically significant associations between any of the knee-related clinical signs and symptoms and Ln COMP. CONCLUSION: Serum COMP may be useful as a biomarker of pre-radiographic hip joint pathology; its utility as a biomarker of pre-radiographic knee joint pathology is unclear

Identification of an urinary metabolite profile associated with osteoarthritis

OBJECTIVE: Osteoarthritis (OA) is one of the most common diseases among the elderly. The main characteristic is the progressive destruction of articular cartilage. We lack quantitative and sensitive biomarkers for OA to detect changes in the joints in an early stage of the disease. In this study, we investigated whether a urinary metabolite profile could be found that could serve as a diagnostic biomarker for OA in humans. We also compared the profile we obtained previously in the guinea pig spontaneous OA model. METHODS: Urine samples of 92 participants (47 non-OA controls and 45 individuals with radiographic OA of the knees or hips) were selected from the Johnston County Osteoarthritis Project (North Carolina, USA). Participants ranged in age from 60 to 84 years. Samples were measured by 1H nuclear magnetic resonance spectroscopy (NMR) with subsequent principal component discriminant analysis and partial least squares regression analysis. RESULTS: Differences were observed between urine NMR spectra of OA cases and controls (P<0.001 for both male and female subjects). A metabolite profile could be determined which was strongly associated with OA. This profile largely resembled the profile previously identified for guinea pigs with OA (approximately 40 out of the approximately 125 signals of the human profile were present in the guinea pig profile as well). A correlation was found between the metabolite profile and radiographic OA severity (R2 = 0.82 (male); R2 = 0.93 (female)). CONCLUSION: This study showed that a urine metabolite profile may serve as a novel discriminating biomarker of OA

A computational framework to emulate the human perspective in flow cytometric data analysis

Background: In recent years, intense research efforts have focused on developing methods for automated flow cytometric data analysis. However, while designing such applications, little or no attention has been paid to the human perspective that is absolutely central to the manual gating process of identifying and characterizing cell populations. In particular, the assumption of many common techniques that cell populations could be modeled reliably with pre-specified distributions may not hold true in real-life samples, which can have populations of arbitrary shapes and considerable inter-sample variation. &lt;p/&gt;Results: To address this, we developed a new framework flowScape for emulating certain key aspects of the human perspective in analyzing flow data, which we implemented in multiple steps. First, flowScape begins with creating a mathematically rigorous map of the high-dimensional flow data landscape based on dense and sparse regions defined by relative concentrations of events around modes. In the second step, these modal clusters are connected with a global hierarchical structure. This representation allows flowScape to perform ridgeline analysis for both traversing the landscape and isolating cell populations at different levels of resolution. Finally, we extended manual gating with a new capacity for constructing templates that can identify target populations in terms of their relative parameters, as opposed to the more commonly used absolute or physical parameters. This allows flowScape to apply such templates in batch mode for detecting the corresponding populations in a flexible, sample-specific manner. We also demonstrated different applications of our framework to flow data analysis and show its superiority over other analytical methods. &lt;p/&gt;Conclusions: The human perspective, built on top of intuition and experience, is a very important component of flow cytometric data analysis. By emulating some of its approaches and extending these with automation and rigor, flowScape provides a flexible and robust framework for computational cytomics

A grid-tied PV-fuel cell multilevel inverter under PQ open-loop control scheme

Power generating entities’ connection to utility grids requires power converters to achieve high efficiency and low injected current harmonic distortion. The control of the power converter plays a crucial role in the grid-tied power converter’s performance. Various control techniques for grid-tied inverters ranging from classical to intelligent are introduced in several exist. Evaluating the current state and trend in grid-tied power inverters and related control methods, research shows that most works in this area focus on grid integration using the close-loop and other advanced control approaches. This is because these control methods are preferred since they provide adequate performance in case of uncertainties in the system. This investigation can aprove that PQ open-loop control technique can operate sufficiently and cost-effectively in grid-tied renewable and alternative power systems under normal operating conditions. Hence, this paper aims to assess the performance of a centralized single-stage grid-tied three-level diode clamped inverter connected to a PV-Fuel cell unit. An active and reactive power open-loop control scheme is employed to operate the inverter and achieves a current harmonic distortion below 5%. The system comprises a 150 kW/700 V PV, a 150 kW/1400 V fuel cell, a 265 kW multilevel inverter operating at a rated voltage of 415 V, and an LCL filter. Two operating scenarios are adopted to investigate the system’s responses further. In the first scenario, a local load of 509.2 kW is supplied from the PV-fuel cell inverter. The load also receives the grid’s power to meet the demand as the PV-fuel cell inverter provides only 265 kW. Whereas in the other scenario, the PV-fuel cell unit provides power to supply a local load while transporting the surplus to the grid. The results reveal the developed model’s good performance with a current harmonic distortion of 0.33%