Arsenic trioxide promotes mitochondrial DNA mutation and cell apoptosis in primary APL cells and NB4 cell line

This study aimed to investigate the effects of arsenic trioxide (As2O3) on the mitochondrial DNA (mtDNA) of acute promyelocytic leukemia (APL) cells. The NB4 cell line was treated with 2.0 μmol/L As2O3in vitro, and the primary APL cells were treated with 2.0 μmol/L As2O3in vitro and 0.16 mg kg-1 d-1 As2O3in vivo. The mitochondrial DNA of all the cells above was amplified by PCR, directly sequenced and analyzed by Sequence Navigatore and Factura software. The apoptosis rates were assayed by flow cytometry. Mitochondrial DNA mutation in the D-loop region was found in NB4 and APL cells before As2O3 use, but the mutation spots were remarkably increased after As2O3 treatment, which was positively correlated to the rates of cellular apoptosis, the correlation coefficient: rNB4-As2O3=0.973818, and rAPL-As2O3=0.934703. The mutation types include transition, transversion, codon insertion or deletion, and the mutation spots in all samples were not constant and regular. It is revealed that As2O3 aggravates mtDNA mutation in the D-loop region of acute promyelocytic leukemia cells both in vitro and in vivo. Mitochondrial DNA might be one of the targets of As2O3 in APL treatment.

Statistical power calculation and sample size determination for environmental studies with data below detection limits

Power calculation and sample size determination are critical in designing environmental monitoring programs. The traditional approach based on comparing the mean values may become statistically inappropriate and even invalid when substantial proportions of the response values are below the detection limits or censored because strong distributional assumptions have to be made on the censored observations when implementing the traditional procedures. In this paper, we propose a quantile methodology that is robust to outliers and can also handle data with a substantial proportion of below-detection-limit observations without the need of imputing the censored values. As a demonstration, we applied the methods to a nutrient monitoring project, which is a part of the Perth Long-Term Ocean Outlet Monitoring Program. In this example, the sample size required by our quantile methodology is, in fact, smaller than that by the traditional t-test, illustrating the merit of our method.

Estimation for the general sample selection models

Consider a general regression model with an arbitrary and unknown link function and a stochastic selection variable that determines whether the outcome variable is observable or missing. The paper proposes U-statistics that are based on kernel functions as estimators for the directions of the parameter vectors in the link function and the selection equation, and shows that these estimators are consistent and asymptotically normal.

A Bayesian decision approach for sample size determination in phase II trials

Stallard (1998, Biometrics 54, 279-294) recently used Bayesian decision theory for sample-size determination in phase II trials. His design maximizes the expected financial gains in the development of a new treatment. However, it results in a very high probability (0.65) of recommending an ineffective treatment for phase III testing. On the other hand, the expected gain using his design is more than 10 times that of a design that tightly controls the false positive error (Thall and Simon, 1994, Biometrics 50, 337-349). Stallard's design maximizes the expected gain per phase II trial, but it does not maximize the rate of gain or total gain for a fixed length of time because the rate of gain depends on the proportion: of treatments forwarding to the phase III study. We suggest maximizing the rate of gain, and the resulting optimal one-stage design becomes twice as efficient as Stallard's one-stage design. Furthermore, the new design has a probability of only 0.12 of passing an ineffective treatment to phase III study.

Parental divorce and adjustment in adulthood: Findings from a community sample

The current study examines the link between the experience of divorce in childhood and several indices of adjustment in adulthood in a large community sample of women. Results replicated previous research on the long-term correlation between parental divorce and depression and divorce in adulthood. Results further suggested that parental divorce was associated with a wide range of early risk factors, life course patterns, and several indices of adult adjustment. Regression analyses indicated that the long-term correlation between parental divorce and depression in adulthood is explained by quality of parent-child and parental marital relations (in childhood), concurrent levels of stressful life events and social support, and cohabitation. The long-term association between parental divorce and experiencing a divorce in adulthood was partly mediated through quality of parent-child relations, teenage pregnancy, leaving home before 18 years, and educational attainment.

Family type and depression in pregnancy: Factors mediating risk in a community sample

The rate of severe depression among women in single-parent and biological families and in a variety of stepfamilies was examined in a large community sample of 13,088 pregnant women in the United Kingdom. Compared with women in biological families and published population rates, women in single-parent families and step-families reported significantly elevated rates of depression. Family-type differences in several risk factors were examined, including cohabiting (vs. married) status, relationship history, and socioeconomic and psychosocial risks, such as crowding, social support, and stressful life events. Family-type differences in depression were mediated partly by differences in social support, stressful life events, and crowding, but a main effect of family type in predicting depression remained after statistically controlling for these risks.

Sampling Methods for Exploring Between Subject Variability in Cardiac Electrophysiology Experiments

Between-subject and within-subject variability is ubiquitous in biology and physiology and understanding and dealing with this is one of the biggest challenges in medicine. At the same time it is difficult to investigate this variability by experiments alone. A recent modelling and simulation approach, known as population of models (POM), allows this exploration to take place by building a mathematical model consisting of multiple parameter sets calibrated against experimental data. However, finding such sets within a high-dimensional parameter space of complex electrophysiological models is computationally challenging. By placing the POM approach within a statistical framework, we develop a novel and efficient algorithm based on sequential Monte Carlo (SMC). We compare the SMC approach with Latin hypercube sampling (LHS), a method commonly adopted in the literature for obtaining the POM, in terms of efficiency and output variability in the presence of a drug block through an in-depth investigation via the Beeler-Reuter cardiac electrophysiological model. We show improved efficiency via SMC and that it produces similar responses to LHS when making out-of-sample predictions in the presence of a simulated drug block.

Epithelial-mesenchymal axis in head and neck cancer cell lines

Objectives: Head and neck squamous cell carcinoma (HNSCC) is a heterogeneous tumour type which necessitates multiple invitro models to attain an appreciation of its multiple subtypes. The phenomenon of epithelial-mesenchymal transition (EMT) isimportant to the development of a metastatic cancer cell phenotype being relevant to the ability of cancer cells to intravasate intovasculature and to invade tissues. The role of EMT in human papilloma virus (HPV) positive HNSCC is not well understood. Thispaper aims to characterize seven HNSCC cell lines (FaDu, SCC-25, SCC-15, CAL27, RPMI2650) including two new HPV-16positive HNSCC cell lines (UD-SCC2, 93-VU-147T) for their epithelial and mesenchymal properties. Materials and methods: A panel of HNSCC cell lines from multiple head and neck anatomical sites were profiled for basalexpression of epithelial and mesenchymal characteristics at mRNA, protein and functional levels (proliferative, migratory andinvasive properties). Furthermore, 3D spheroid forming capabilities were investigated. Results: We found that the HPV-16 positive cell line, in particular UD-SCC2 demonstrated a more invasive and mesenchymalphenotype at the molecular and functional levels suggesting HPV infection may mediate some of these cellular properties.Moreover, HPV-negative cell lines were not strictly epithelial presenting with a dynamic range of expression. Conclusions: This study presents the molecular and phenotypic diversity of HNSCC cell lines. It highlights the need formore studies in this field and a scoring system where HNSCC cell lines are ranked according to their respective epithelial andmesenchymal nature. This data will be useful to anyone modelling HNSCC behaviour, providing a molecular context which willenable them to decipher cell phenotypes and to develop therapies which block EMT progression.

Adipocytes promote prostate cancer stem cell self-renewal through amplification of the cholecystokinin autocrine loop

Obesity has long been linked with prostate cancer progression, although the underlying mechanism is still largely unknown. Here, we report that adipocytes promote the enrichment of prostate cancer stem cells (CSCs) through a vicious cycle of autocrine amplification. In the presence of adipocytes, prostate cancer cells actively secrete the peptide hormone cholecystokinin (CCK), which not only stimulates prostate CSC self-renewal, but also induces cathepsin B (CTSB) production of the adipocytes. In return, CTSB facilitates further CCK secretion by the cancer cells. More importantly, inactivation of CCK receptor not only suppresses CTSB secretion by the adipocytes, but also synergizes the inhibitory effect of CTSB inhibitor on adipocyte-promoted prostate CSC self-renewal. In summary, we have uncovered a novel mechanism underlying the mutual interplay between adipocytes and prostate CSCs, which may help explaining the role of adipocytes in prostate cancer progression and provide opportunities for effective intervention.

A dosimetric retrospective planning study comparing volumetric arc therapy (VMAT) and stereotactic body radiotherapy (SBRT) treatment plans for non-small cell lung cancer (NSCLC)

Purpose A retrospective planning study comparing volumetric arc therapy (VMAT) and stereotactic body radiotherapy (SBRT) treatment plans for non-small cell lung cancer (NSCLC). Methods and materials Five randomly selected early stage lung cancer patients were included in the study. For each patient, four plans were created: the SBRT plan and three VMAT plans using different optimisation methodologies. A total of 20 different plans were evaluated. The dose parameters of dose conformity results and the target dose constraints results were compared for these plans. Results The mean planning target volume (PTV) for all the plans (SBRT and VMAT) was 18·3 cm3, with a range from 15·6 to 20·1 cm3. The maximum dose tolerance to 1 cc of all the plans was within 140% (84 Gy) of the prescribed dose, and 95% of the PTV of all the plans received 100% of the prescribed dose (60 Gy). In all the plans, 99% of the PTV received a dose >90% of the prescribed dose, and the mean dose in all the plans ranged from 67 to 72 Gy. The planning target dose conformity for the SBRT and the VMAT (0°, 15° collimator single arc plans and dual arc) plans showed the tightness of the prescription isodose conformity to the target. Conclusions SBRT and VMAT are radiotherapy approaches that increase doses to small tumour targets without increasing doses to the organs at risk. Although VMAT offers an alternative to SBRT for NSCLC and the potential advantage of VMAT is the reduced treatment times over SBRT, the statistical results show that there was no significant difference between the SBRT and VMAT optimised plans in terms of dose conformity and organ-at-risk sparing.

Feasibility of an in-patient exercise intervention for children undergoing haematopoietic stem cell transplant

With improving survival rates following HSCT in children, QOL and management of short- and long-term effects need to be considered. Exercise may help mitigate fatigue and declines in fitness and strength. The aims of this study were to assess the feasibility of an inpatient exercise intervention for children undergoing HSCT and observe the changes in physical and psychological health. Fourteen patients were recruited, mean age 10 yr. A 6MWT, isometric upper and lower body strength, balance, fatigue, and QOL were assessed prior to Tx and six wk post-Tx. A supervised exercise program was offered five days per week during the inpatient period and feasibility assessed through uptake rate. The study had 100% program completion and 60% uptake rate of exercise sessions. The mean (±s.d.) weekly activity was 117.5 (±79.3) minutes. Younger children performed significantly more minutes of exercise than adolescents. At reassessment, strength and fatigue were stabilized while aerobic fitness and balance decreased. QOL revealed a non-statistical trend towards improvement. No exercise-related adverse events were reported. A supervised inpatient exercise program is safe and feasible, with potential physiological and psychosocial benefits.

Multi-Disciplinary Design and Simulation of a Fuel Cell Engine for a UAV

This report describes a methodology for the design and coupling of a proton exchange membrane (PEM) Fuel Cell to an Unmanned Aerial Vehicle (UAV). The report summarizes existing work in the field, the type of UAV and the mission requirements, design the fuel cell system, simulation environment, and compares endurance and range to when the aircraft is fitted with a conventional internal combustion engine (ICE).

Estimating cell diffusivity and cell proliferation rate by interpreting IncuCyte ZOOM™ assay data using the Fisher-Kolmogorov model

Background: Standard methods for quantifying IncuCyte ZOOM™ assays involve measurements that quantify how rapidly the initially-vacant area becomes re-colonised with cells as a function of time. Unfortunately, these measurements give no insight into the details of the cellular-level mechanisms acting to close the initially-vacant area. We provide an alternative method enabling us to quantify the role of cell motility and cell proliferation separately. To achieve this we calibrate standard data available from IncuCyte ZOOM™ images to the solution of the Fisher-Kolmogorov model. Results: The Fisher-Kolmogorov model is a reaction-diffusion equation that has been used to describe collective cell spreading driven by cell migration, characterised by a cell diffusivity, D, and carrying capacity limited proliferation with proliferation rate, λ, and carrying capacity density, K. By analysing temporal changes in cell density in several subregions located well-behind the initial position of the leading edge we estimate λ and K. Given these estimates, we then apply automatic leading edge detection algorithms to the images produced by the IncuCyte ZOOM™ assay and match this data with a numerical solution of the Fisher-Kolmogorov equation to provide an estimate of D. We demonstrate this method by applying it to interpret a suite of IncuCyte ZOOM™ assays using PC-3 prostate cancer cells and obtain estimates of D, λ and K. Comparing estimates of D, λ and K for a control assay with estimates of D, λ and K for assays where epidermal growth factor (EGF) is applied in varying concentrations confirms that EGF enhances the rate of scratch closure and that this stimulation is driven by an increase in D and λ, whereas K is relatively unaffected by EGF. Conclusions: Our approach for estimating D, λ and K from an IncuCyte ZOOM™ assay provides more detail about cellular-level behaviour than standard methods for analysing these assays. In particular, our approach can be used to quantify the balance of cell migration and cell proliferation and, as we demonstrate, allow us to quantify how the addition of growth factors affects these processes individually.

Modelling the movement of interacting cell populations: A moment dynamics approach

Mathematical models describing the movement of multiple interacting subpopulations are relevant to many biological and ecological processes. Standard mean-field partial differential equation descriptions of these processes suffer from the limitation that they implicitly neglect to incorporate the impact of spatial correlations and clustering. To overcome this, we derive a moment dynamics description of a discrete stochastic process which describes the spreading of distinct interacting subpopulations. In particular, we motivate our model by mimicking the geometry of two typical cell biology experiments. Comparing the performance of the moment dynamics model with a traditional mean-field model confirms that the moment dynamics approach always outperforms the traditional mean-field approach. To provide more general insight we summarise the performance of the moment dynamics model and the traditional mean-field model over a wide range of parameter regimes. These results help distinguish between those situations where spatial correlation effects are sufficiently strong, such that a moment dynamics model is required, from other situations where spatial correlation effects are sufficiently weak, such that a traditional mean-field model is adequate.

Spectral analysis of pair-correlation bandwidth: Application to cell biology images

Images from cell biology experiments often indicate the presence of cell clustering, which can provide insight into the mechanisms driving the collective cell behaviour. Pair-correlation functions provide quantitative information about the presence, or absence, of clustering in a spatial distribution of cells. This is because the pair-correlation function describes the ratio of the abundance of pairs of cells, separated by a particular distance, relative to a randomly distributed reference population. Pair-correlation functions are often presented as a kernel density estimate where the frequency of pairs of objects are grouped using a particular bandwidth (or bin width), Δ>0. The choice of bandwidth has a dramatic impact: choosing Δ too large produces a pair-correlation function that contains insufficient information, whereas choosing Δ too small produces a pair-correlation signal dominated by fluctuations. Presently, there is little guidance available regarding how to make an objective choice of Δ. We present a new technique to choose Δ by analysing the power spectrum of the discrete Fourier transform of the pair-correlation function. Using synthetic simulation data, we confirm that our approach allows us to objectively choose Δ such that the appropriately binned pair-correlation function captures known features in uniform and clustered synthetic images. We also apply our technique to images from two different cell biology assays. The first assay corresponds to an approximately uniform distribution of cells, while the second assay involves a time series of images of a cell population which forms aggregates over time. The appropriately binned pair-correlation function allows us to make quantitative inferences about the average aggregate size, as well as quantifying how the average aggregate size changes with time.