Hydrogel-Forming Microneedles Prepared from ‘‘Super Swelling’’ Polymers Combined with Lyophilised Wafers for Transdermal Drug Delivery

We describe, for the first time, hydrogel-forming microneedle arrays prepared from "super swelling" polymeric compositions. We produced a microneedle formulation with enhanced swelling capabilities from aqueous blends containing 20% w/w Gantrez S-97, 7.5% w/w PEG 10,000 and 3% w/w Na2CO3 and utilised a drug reservoir of a lyophilised wafer-like design. These microneedle-lyophilised wafer compositions were robust and effectively penetrated skin, swelling extensively, but being removed intact. In in vitro delivery experiments across excised neonatal porcine skin, approximately 44 mg of the model high dose small molecule drug ibuprofen sodium was delivered in 24 h, equating to 37% of the loading in the lyophilised reservoir. The super swelling microneedles delivered approximately 1.24 mg of the model protein ovalbumin over 24 h, equivalent to a delivery efficiency of approximately 49%. The integrated microneedle-lyophilised wafer delivery system produced a progressive increase in plasma concentrations of ibuprofen sodium in rats over 6 h, with a maximal concentration of approximately 179 µg/ml achieved in this time. The plasma concentration had fallen to 71±6.7 µg/ml by 24 h. Ovalbumin levels peaked in rat plasma after only 1 hour at 42.36±17.01 ng/ml. Ovalbumin plasma levels then remained almost constant up to 6 h, dropping somewhat at 24 h, when 23.61±4.84 ng/ml was detected. This work represents a significant advancement on conventional microneedle systems, which are presently only suitable for bolus delivery of very potent drugs and vaccines. Once fully developed, such technology may greatly expand the range of drugs that can be delivered transdermally, to the benefit of patients and industry. Accordingly, we are currently progressing towards clinical evaluations with a range of candidate molecules.

Root Cause Analysis by a Combined Sparse Classification and Monte Carlo Approach

Classification methods with embedded feature selection capability are very appealing for the analysis of complex processes since they allow the analysis of root causes even when the number of input variables is high. In this work, we investigate the performance of three techniques for classification within a Monte Carlo strategy with the aim of root cause analysis. We consider the naive bayes classifier and the logistic regression model with two different implementations for controlling model complexity, namely, a LASSO-like implementation with a L1 norm regularization and a fully Bayesian implementation of the logistic model, the so called relevance vector machine. Several challenges can arise when estimating such models mainly linked to the characteristics of the data: a large number of input variables, high correlation among subsets of variables, the situation where the number of variables is higher than the number of available data points and the case of unbalanced datasets. Using an ecological and a semiconductor manufacturing dataset, we show advantages and drawbacks of each method, highlighting the superior performance in term of classification accuracy for the relevance vector machine with respect to the other classifiers. Moreover, we show how the combination of the proposed techniques and the Monte Carlo approach can be used to get more robust insights into the problem under analysis when faced with challenging modelling conditions.

Spindle assembly checkpoint protein expression correlates with cellular proliferation and shorter time to recurrence in ovarian cancer.

Ovarian carcinoma (OC) is the most lethal of the gynecological malignancies, often presenting at an advanced stage. Treatment is hampered by high levels of drug resistance. The taxanes are microtubule stabilizing agents, used as first-line agents in the treatment of OC that exert their apoptotic effects through the spindle assembly checkpoint. BUB1-related protein kinase (BUBR1) and mitotic arrest deficient 2 (MAD2), essential spindle assembly checkpoint components, play a key role in response to taxanes. BUBR1, MAD2, and Ki-67 were assessed on an OC tissue microarray platform representing 72 OC tumors of varying histologic subtypes. Sixty-one of these patients received paclitaxel and platinum agents combined; 11 received platinum alone. Overall survival was available for all 72 patients, whereas recurrence-free survival (RFS) was available for 66 patients. Increased BUBR1 expression was seen in serous carcinomas, compared with other histologies (P = .03). Increased BUBR1 was significantly associated with tumors of advanced stage (P = .05). Increased MAD2 and BUBR1 expression also correlated with increased cellular proliferation (P < .0002 and P = .02, respectively). Reduced MAD2 nuclear intensity was associated with a shorter RFS (P = .03), in ovarian tumors of differing histologic subtype (n = 66). In this subgroup, for those women who received paclitaxel and platinum agents combined (n = 57), reduced MAD2 intensity also identified women with a shorter RFS (P < .007). For the entire cohort of patients, irrespective of histologic subtype or treatment, MAD2 nuclear intensity retained independent significance in a multivariate model, with tumors showing reduced nuclear MAD2 intensity identifying patients with a poorer RFS (P = .05).

Control of biofilm growth through photodynamic treatments combined with chemical inhibitors: in vitro evaluation methods

The rock/atmosphere interface is inhabited by a complex microbial community including bacteria, algae and fungi. These communities are prominent biodeterioration agents and remarkably influence the status of stone monuments and buildings. Deeper comprehension of natural biodeterioration processes on stone surfaces has brought about a concept of complex microbial communities referred to as "subaerial biofilms". The practical implications of biofilm formation are that control strategies must be devised both for testing the susceptibility of the organisms within the biofilm and treating the established biofilm. Model multi-species biofilms associated with mineral surfaces that are frequently refractory to conventional treatment have been used as test targets. A combination of scanning microscopy with image analysis was applied along with traditional cultivation methods and fluorescent activity stains. Such a polyphasic approach allowed a comprehensive quantitative evaluation of the biofilm status and development. Effective treatment strategies incorporating chemical and physical agents have been demonstrated to prevent biofilm growth in vitro. Model biofilm growth on inorganic support was significantly reduced by a combination of PDT and biocides

An analysis of stage performance in automotive turbocharger centrifugal compressors

As the concept of engine downsizing becomes ever more integrated into automotive powertrain development strategies, so too does the pressure on turbocharger manufacturers to deliver improvements in map width and a reduction in the mass flow rate at which compressor surge occurs. A consequence of this development is the increasing importance of recirculating flows, both in the impeller inlet and outlet domains, on stage performance.
The current study seeks to evaluate the impact of the inclusion of impeller inlet recirculation on a meanline centrifugal compressor design tool. Using a combination of extensive test data, single passage CFD predictions, and 1-D analysis it is demonstrated how the addition of inlet recirculation modelling impacts upon stage performance close to the surge line. It is also demonstrated that, in its current configuration, the accuracy of the 1-D model prediction diminishes significantly with increasing blade tip speed.
Having ascertained that the existing model requires further work, an evaluation of the vaneless diffuser modelling method currently employed within the existing 1-D model is undertaken. The comparison of the predicted static pressure recovery coefficient with test data demonstrated the inherent inadequacies in the resulting prediction, in terms of both magnitude and variation with flow rate. A simplified alternative method based on an equivalent friction coefficient is also presented that, with further development, could provide a significantly improved stage performance prediction.

NO Oxidation on Platinum Group Metals Oxides:First Principles Calculations Combined with Microkinetic Analysis

By combining density functional theory calculation and microkinetic analysis, NO oxidation on the platinum group metal oxides (PtO(2), IrO(2), OsO(2)) is investigated, aiming at shedding light on the activities of metal oxides and exploring the activity variations of metal oxides compared to their corresponding metals. A microkinetic model, taking into account the possible low diffusion of surface species on metal oxide surfaces, is proposed for NO oxidation. The resultant turnover frequencies of NO oxidation show that under the typical experimental condition, T = 600 K, p(O2) = 0.1 atm, p(NO) = 3 x 10(-4) atm, p(NO2) = 1.7 x 10(-4) atm; (i) IrO(2)(110) exhibits higher activity than PtO(2)(110) and OsO(2)(110), and (ii) compared to the corresponding metallic Pt, Ir, and Os, the activity of PtO(2) to catalyze NO oxidation is lower, but interestingly IrO(2) and OsO(2) exhibit higher activities. The reasons for the activity differences between the metals and oxides are addressed. Moreover, other possible reaction pathways of NO oxidation on PtO(2)(110), involving O(2) molecule (NO + O(2) -> OONO) and lattice bridge-O(2c), are also found to give low activities. The origin of the Pt catalyst deactivation is also discussed.

An integrated approach for real-time model-based state-of-charge estimation of lithium-ion batteries

Lithium-ion batteries have been widely adopted in electric vehicles (EVs), and accurate state of charge (SOC) estimation is of paramount importance for the EV battery management system. Though a number of methods have been proposed, the SOC estimation for Lithium-ion batteries, such as LiFePo4 battery, however, faces two key challenges: the flat open circuit voltage (OCV) vs SOC relationship for some SOC ranges and the hysteresis effect. To address these problems, an integrated approach for real-time model-based SOC estimation of Lithium-ion batteries is proposed in this paper. Firstly, an auto-regression model is adopted to reproduce the battery terminal behaviour, combined with a non-linear complementary model to capture the hysteresis effect. The model parameters, including linear parameters and non-linear parameters, are optimized off-line using a hybrid optimization method that combines a meta-heuristic method (i.e., the teaching learning based optimization method) and the least square method. Secondly, using the trained model, two real-time model-based SOC estimation methods are presented, one based on the real-time battery OCV regression model achieved through weighted recursive least square method, and the other based on the state estimation using the extended Kalman filter method (EKF). To tackle the problem caused by the flat OCV-vs-SOC segments when the OCV-based SOC estimation method is adopted, a method combining the coulombic counting and the OCV-based method is proposed. Finally, modelling results and SOC estimation results are presented and analysed using the data collected from LiFePo4 battery cell. The results confirmed the effectiveness of the proposed approach, in particular the joint-EKF method.

Resistivity and Induced Polarization Monitoring of Biogas combined with Microbial Ecology on a Brown field Site

The accumulation of biogenic greenhouse gases (methane, carbon dioxide) in organic sediments is an important factor in the redevelopment and risk management of many brownfield sites. Good practice with brownfield site characterization requires the identification of free-gas phases and pathways that allow its migration and release at the ground surface. Gas pockets trapped in the subsurface have contrasting properties with the surrounding porous media that favor their detection using geophysical methods. We have developed a case study in which pockets of gas were intercepted with multilevel monitoring wells, and their lateral continuity was monitored over time using resistivity. We have developed a novel interpretation procedure based on Archie’s law to evaluate changes in water and gas content with respect to a mean background medium. We have used induced polarization data to account for errors in applying Archie’s law due to the contribution of surface conductivity effects. Mosaics defined by changes in water saturation allowed the recognition of gas migration and groundwater infiltration routes and the association of gas and groundwater fluxes. The inference on flux patterns was analyzed by taking into account pressure measurements in trapped gas reservoirs and by metagenomic analysis of the microbiological content, which was retrieved from suspended sediments in groundwater sampled in multilevel monitoring wells. A conceptual model combining physical and microbiological subsurface processes suggested that biogas trapped at depth may have the ability to quickly travel to the surface.

Two-Stage Orthogonal Least Squares Methods for Neural Network Construction

A number of neural networks can be formulated as the linear-in-the-parameters models. Training such networks can be transformed to a model selection problem where a compact model is selected from all the candidates using subset selection algorithms. Forward selection methods are popular fast subset selection approaches. However, they may only produce suboptimal models and can be trapped into a local minimum. More recently, a two-stage fast recursive algorithm (TSFRA) combining forward selection and backward model refinement has been proposed to improve the compactness and generalization performance of the model. This paper proposes unified two-stage orthogonal least squares methods instead of the fast recursive-based methods. In contrast to the TSFRA, this paper derives a new simplified relationship between the forward and the backward stages to avoid repetitive computations using the inherent orthogonal properties of the least squares methods. Furthermore, a new term exchanging scheme for backward model refinement is introduced to reduce computational demand. Finally, given the error reduction ratio criterion, effective and efficient forward and backward subset selection procedures are proposed. Extensive examples are presented to demonstrate the improved model compactness constructed by the proposed technique in comparison with some popular methods.

Solubility parameter based screening methods for early stage formulation development of itraconazole amorphous solid dispersions

Objectives: This article uses conventional and newly extended solubility parameter (δ) methods to identify polymeric materials capable of forming amorphous dispersions with itraconazole (itz). Methods: Combinations of itz and Soluplus, Eudragit E PO (EPO), Kollidon 17PF (17PF) or Kollidon VA64 (VA64) were prepared as amorphous solid dispersions using quench cooling and hot melt extrusion. Storage stability was evaluated under a range of conditions using differential scanning calorimetry and powder X-ray diffraction. Key findings: The rank order of itz miscibility with polymers using both conventional and novel δ-based approaches was 17PF > VA64 > Soluplus > EPO, and the application of the Flory–Huggins lattice model to itz–excipient binary systems corroborated the findings. The solid-state characterisation analyses of the formulations manufactured by melt extrusion correlated well with pre-formulation screening. Long-term storage studies showed that the physical stability of 17PF/vitamin E TPGS–itz was poor compared with Soluplus and VA64 formulations, and for EPO/itz systems variation in stability may be observed depending on the preparation method. Conclusion: Results have demonstrated that although δ-based screening may be useful in predicting the initial state of amorphous solid dispersions, assessment of the physical behaviour of the formulations at relevant temperatures may be more appropriate for the successful development of commercially acceptable amorphous drug products.

The Coxian phase-type distribution as a contribution to the multilevel model of in-hospital mortality

This paper presents multilevel models that utilize the Coxian phase-type distribution in order to be able to include a survival component in the model. The approach is demonstrated by modeling patient length of stay and in-hospital mortality in geriatric wards in Italy. The multilevel model is used to provide a means of controlling for the existence of possible intra-ward correlations, which may make patients within a hospital more alike in terms of experienced outcome than patients coming from different hospitals, everything else being equal. Within this multilevel model we introduce the use of the Coxian phase-type distribution to create a covariate that represents patient length of stay or stage (of hospital care). Results demonstrate that the use of the multilevel model for representing the in-patient mortality is successful and further enhanced by the inclusion of the Coxian phase-type distribution variable (stage covariate).

Prognoses and improvement for head and neck cancers diagnosed in Europe in early 2000s: The EUROCARE-5 a population-based study.

BACKGROUND: Head and neck (H&N) cancers are a heterogeneous group of malignancies, affecting various sites, with different prognoses. The aims of this study are to analyse survival for patients with H&N cancers in relation to tumour location, to assess the change in survival between European countries, and to investigate whether survival improved over time.
METHODS: We analysed about 250,000 H&N cancer cases from 86 cancer registries (CRs). Relative survival (RS) was estimated by sex, age, country and stage. We described survival time trends over 1999-2007, using the period approach. Model based survival estimates of relative excess risks (RERs) of death were also provided by country, after adjusting for sex, age and sub-site.
RESULTS: Five-year RS was the poorest for hypopharynx (25%) and the highest for larynx (59%). Outcome was significantly better in female than in male patients. In Europe, age-standardised 5-year survival remained stable from 1999-2001 to 2005-2007 for laryngeal cancer, while it increased for all the other H&N cancers. Five-year age-standardised RS was low in Eastern countries, 47% for larynx and 28% for all the other H&N cancers combined, and high in Ireland and the United Kingdom (UK), and Northern Europe (62% and 46%). Adjustment for sub-site narrowed the difference between countries. Fifty-four percent of patients was diagnosed at advanced stage (regional or metastatic). Five-year RS for localised cases ranged between 42% (hypopharynx) and 74% (larynx).
CONCLUSIONS: This study shows survival progresses during the study period. However, slightly more than half of patients were diagnosed with regional or metastatic disease at diagnosis. Early diagnosis and timely start of treatment are crucial to reduce the European gap to further improve H&N cancers outcome.

Plastin Polymorphisms Predict Gender- and Stage-Specific Colon Cancer Recurrence after Adjuvant Chemotherapy

Tumor recurrence after curative resection remains a major problem in patients with locally advanced colorectal cancer treated with adjuvant chemotherapy. Genetic single-nucleotide polymorphisms (SNP) may serve as useful molecular markers to predict clinical outcomes in these patients and identify targets for future drug development. Recent in vitro and in vivo studies have demonstrated that the plastin genes PLS3 and LCP1 are overexpressed in colon cancer cells and play an important role in tumor cell invasion, adhesion, and migration. Hence, we hypothesized that functional genetic variations of plastin may have direct effects on the progression and prognosis of locally advanced colorectal cancer. We tested whether functional tagging polymorphisms of PLS3 and LCP1 predict time to tumor recurrence (TTR) in 732 patients (training set, 234; validation set, 498) with stage II/III colorectal cancer. The PLS3 rs11342 and LCP1 rs4941543 polymorphisms were associated with a significantly increased risk for recurrence in the training set. PLS3 rs6643869 showed a consistent association with TTR in the training and validation set, when stratified by gender and tumor location. Female patients with the PLS3 rs6643869 AA genotype had the shortest median TTR compared with those with any G allele in the training set [1.7 vs. 9.4 years; HR, 2.84; 95% confidence interval (CI), 1.32-6.1; P = 0.005] and validation set (3.3 vs. 13.7 years; HR, 2.07; 95% CI, 1.09-3.91; P = 0.021). Our findings suggest that several SNPs of the PLS3 and LCP1 genes could serve as gender- and/or stage-specific molecular predictors of tumor recurrence in stage II/III patients with colorectal cancer as well as potential therapeutic targets.

Cancer dormancy: a model of early dissemination and late cancer recurrence

Cancer dormancy is a stage in tumor progression in which residual disease remains occult and asymptomatic for a prolonged period of time. Dormant tumor cells can be present as one of the earliest stages in tumor development, as well as a stage in micrometastases, and/or minimal residual disease left after an apparently successful treatment of the primary tumor. The general mechanisms that regulate the transition of disseminated tumor cells that have lain dormant into a proliferative state remain largely unknown. However, regulation of the growth from dormant tumor cells may be explained in part through the interaction of the tumor cell with its microenvironment, limitations in the blood supply, or an active immune system. An understanding of the regulatory machinery of these processes is essential for identifying early cancer biomarkers and could provide a rationale for the development of novel agents to target dormant tumor cells. This review focuses on the different signaling models responsible for early cancer dissemination and tumor recurrence that are involved in dormancy pathways.

Effective Teaching and Learning of Science in the Outdoors: A Blended Model

This paper reports on an innovative Continuing Professional Development (CPD) programme which addressed transition issues and issues with conducting outdoor work and attitudes towards science through ‘Shared Learning' days between elementary and middle school transition classes. Teachers supported each other to overcome issues with conducting outdoor work and contributed their expertise from their educational stage. The project utilised a blended CPD approach of workshops, coteaching and in-class support and was based upon a wealth earlier successful CPD programmes to result in a sound theoretical framework.
The outcomes were measured using a thorough mixed-methods approach. This paper will report on the achieved outcomes with effective outdoor learning as the vehicle to overcome identified issues and key challenges for policy development.