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Hydrogels are homogeneous three-dimensional polymeric networks capable of holding large amounts of water and are widely used in topical formulations. Herein, the physicomechanical, rheological, bioadhesive, and drug-release properties of hydrogels containing hydroxypropyl methylcellulose (HPMC) and polyvinylpyrrolidone (PVP) were examined, and the intermolecular interactions between the polymers were explored. A three-level factorial design was used to form HPMC–PVP binary hydrogels. The physicomechanical properties of the binary hydrogels alongside the homopolymeric HPMC hydrogels were characterized using a texture analyzer. Rheological properties of the gels were studied using a cone and plate rheometer. The bioadhesiveness of selected binary hydrogels was tested on porcine skin. Hydrophilic benzophenone-4 was loaded into both homopolymeric and binary gels, and drug-release profiles were investigated over 24 h at 33 °C. Fourier transform infrared spectroscopy (FTIR) was used to understand the inter-molecular drug–gel interactions. Factorial design analysis supported the dominant role of the HPMC in determining the gel properties, rather than the PVP, with the effect of both polymer concentrations being non-linear. The addition of PVP to the HPMC gels improved adhesiveness without significantly affecting other properties such as hardness, shear-thinning feature, and viscosity, thereby improving bioadhesiveness for sustained skin retention without negatively impacting cosmetic acceptability or ease of use. The release of benzophenone-4 in the HPMC hydrogels followed zero-order kinetics, with benzophenone-4 release being significantly retarded by the presence of PVP, likely due to intermolecular interactions between the drug and the PVP polymer, as confirmed by the FTIR. The HPMC–PVP binary hydrogels demonstrate strong bioadhesiveness resulting from the addition of PVP with desirable shear-thinning properties that allow the formulation to have extended skin-retention times. The developed HPMC–PVP binary hydrogel is a promising sustained-release platform for topical drug delivery.

Abstract Bisphosphonates (BP) are considered a treatment option for osteoarthritis (OA) due to reduction of OA-induced microtrauma in the bone marrow, stabilization of subchondral bone (SB) layer and pain reduction. The effects of high-dose alendronate (ALN) treatment on SB and articular cartilage after destabilization of the medial meniscus (DMM) or Sham surgery of male C57Bl/6J mice were analyzed. We performed serum analysis; histology and immunohistochemistry to assess the severity of OA and a possible pain symptomatology. Subsequently, the ratio of bone volume to total volume (BV/TV), epiphyseal trabecular morphology and the bone mineral density (BMD) was analyzed by nanoCT. Serum analysis revealed a reduction of ADAMTS5 level. The histological evaluation displayed no protective effect of ALN-treatment on cartilage erosion. NanoCT-analysis of the medial epiphysis revealed an increase of BV/TV in ALN-treated mice. Only the DMM group had significantly higher SB volume accompanied by decreased subchondral bone surface. Furthermore Nano-CT analysis revealed an increase in trabecular density and number, a decreased BMD and reduced osteophyte formation in the ALN mice. ALN treatment affected bone micro-architecture by reducing osteophytosis with simultaneous increasing subchondral bone plate thickness, trabecular thickness and BMD. Accordingly, ALN cannot be considered as a potential treatment strategy in general, however in a subgroup of patients with high bone turnover in an early-stage of OA, ALN might be an option when applied during a restricted time frame.

High penetration of distributed energy resources (DERs) changes the flows in power grids causing thermal congestions which are managed by real-time corrective topology switching. It is crucial to consider voltage stability margin (VSM) as a constraint when modifying grid topology. However, it is nontrivial to exhaustively search using AC power flow (ACPF) for all control actions with desired VSM. Sensitivity methods are used to solve this issue of “power flow-free VSM estimation” to screen candidate control actions. However, due to the volatile nature of DERs, sensitivity methods do not perform well near nonlinear operating regions which is overcome by solving ACPF. Here, we propose a new VSM estimation method that performs well at nonlinear operating regions without solving ACPF. We achieve this by formulating the learning of graph neural networks like the matrix-free power flow algorithms. We empirically demonstrate how this similarity bypasses the inaccuracy issues and performs well on unseen operating conditions and topologies without further re-training. The effectiveness is demonstrated on a power network with realistic load and generation profiles, various generation mix, and large control actions. The benefits are showcased in terms of speed, reliability to identify insecure controls, and adaptability to unseen scenarios and grid topologies.

This paper deals with the derivation of dynamic equivalents of active distribution networks, hosting inverter-based generators as well as static and motor loads. Equivalents are reduced-order models for use in dynamic simulations of the transmission system. They are of the grey-box types and their parameters are identified from large-disturbance Monte-Carlo simulations accounting for model uncertainty. After presenting an overview of the identification method at a single operating point, the paper deals with the update the equivalent when the operating conditions of the distribution network change. A procedure identifies the parameters to update, hence avoiding a complete new identification. Besides illustrative examples, two sets of simulation results are reported. First, the accuracy of the equivalent is validated in a long-term voltage instability scenario. Second, a larger-scale application is presented, with numerous instances of the equivalent attached to the model of the IEEE Nordic transmission test system. This combined model is used to assess the impact on short- and long-term voltage stability of the inverter-based generators with fast and slow controls.

The world-wide drive to decrease greenhouse gases in the atmosphere is rapidly changing the mix of fuel resources that are used to generate electricity. The percentage of fossil fuels, especially coal, is declining while the percentage of renewable resources, like wind and solar, is increasing. The generation mix has always depended on the availability of different fuels in different regions, like the availability of hydro or the acceptability of nuclear power, and the recent changes also depend on this geography, like the radiance of the sun or the intensity of the wind. Although these changes will lead to different architectures in the evolving power grid, some trends are already being felt.

The biopharmaceutical market is dominated by monoclonal antibodies, the majority of which are produced in Chinese hamster ovary (CHO) cell lines. Intense cell engineering, in combination with optimization of various process parameters results in increasing product titers. To enable further improvements in manufacturing processes, detailed information about how certain parameters affect cellular mechanisms in the production cells, and thereby also the expressed drug substance, is required. Therefore, in this study the effects of commonly applied changes in bioprocessing parameters on an anti-IL8 IgG1 producing CHO DP-12 cell line were investigated on the level of host cell proteome expression combined with product quality assessment of the expressed IgG1 monoclonal antibody. Applying shifts in temperature, pH and dissolved oxygen concentration, respectively, resulted in altered productivity and product quality. Furthermore, analysis of the cells using two-dimensional liquid chromatography-mass spectrometry employing tandem mass tag based isotopic quantitation and synchronous precursor selection-MS3 detection revealed substantial changes in the protein expression profiles of CHO cells. Pathway analysis indicated that applied bioprocessing conditions resulted in differential activation of oxidative phosphorylation. Additionally, activation of ERK5 and TNFR1 signaling suggested an affected cell cycle. Moreover, in-depth product characterization by means of charge variant analysis, peptide mapping, as well as structural and functional analysis, revealed posttranslational and structural changes in the expressed drug substance. Taken together, the present study allows the conclusion that, in anti-IL8 IgG1 producing CHO DP-12 cells, an improved energy metabolism achieved by lowering the cell culture pH is favorable when aiming towards high antibody production rates while maintaining product quality.

ObjectivesTo explore weight perceptions in a large, nationally representative sample of older adults, and the extent to which they differ according to age and perceived health status.SettingEngland.Participants5240 men and women (≥50 years old) participating in the English Longitudinal Study of Ageing (2016/2017).Main outcome measuresWeight perception was self-reported as too heavy, too light or about right.ResultsThe majority of older adults endorsed a weight perception that matched their (objectively measured) body mass index (BMI) classification. However, 1 in 10 (9.9%) older adults classified by BMI as normal weight (18.5–24.9 kg/m2) felt too light, with women at the upper end of the older age spectrum (OR=1.04, 95% CI 1.01 to 1.09), and men (OR=3.70, 95% CI 1.88 to 7.28) and women (OR=2.61, 95% CI 1.27 to 5.35) in poorer health more likely to do so. Almost half (44.8%) of older adults classified as overweight (25–29.9 kg/m2) and 1 in 10 (10.3%) classified as obese (≥30 kg/m2) felt about the right weight, with this observed more frequently among men and women at the upper end of the older age spectrum (OR range 1.04–1.06).ConclusionOlder adults’ perceptions of their own weight generally correspond with traditional BMI cut-offs for normal weight, overweight and obesity. However, a substantial minority ‘underestimate’ their weight status, with those at the upper end of the age spectrum and those in poorer health more likely to do so.

IntroductionAs cancer treatments may impact on fertility, a high priority for young patients with breast cancer is access to evidence-based, personalised information for them and their healthcare providers to guide treatment and fertility-related decisions prior to cancer treatment. Current tools to predict fertility outcomes after breast cancer treatments are imprecise and do not offer individualised prediction. To address the gap, we are developing a novel personalised infertility risk prediction tool (FoRECAsT) for premenopausal patients with breast cancer that considers current reproductive status, planned chemotherapy and adjuvant endocrine therapy to determine likely post-treatment infertility. The aim of this study is to explore the feasibility of implementing this FoRECAsT tool into clinical practice by exploring the barriers and facilitators of its use among patients and healthcare providers.Methods and analysisA cross-sectional exploratory study is being conducted using semistructured in-depth telephone interviews with 15–20 participants each from the following groups: (1) premenopausal patients with breast cancer younger than 40, diagnosed within last 5 years, (2) breast surgeons, (3) breast medical oncologists, (4) breast care nurses (5) fertility specialists and (6) fertility preservation nurses. Patients with breast cancer are being recruited from the joint Breast Service of three affiliated institutions of Victorian Comprehensive Cancer Centre in Melbourne, Australia—Peter MacCallum Cancer Centre, Royal Melbourne Hospital and Royal Women’s Hospital, and clinicians are being recruited from across Australia. Interviews are being audio recorded, transcribed verbatim and imported into qualitative data analysis software to facilitate data management and analyses.Ethics and disseminationThe study protocol has been approved by Melbourne Health Human Research Ethics Committee, Australia (HREC number: 2017.163). Confidentiality and privacy are maintained at every stage of the study. Findings will be disseminated through peer-reviewed scholarly and scientific journals, national and international conference presentations, social media, broadcast media, print media, internet and various community/stakeholder engagement activities.

For transient stability analysis of a multi-machine power system, the Extended Equal Area Criterion (EEAC) method applies the classic Equal Area Criterion (EAC) concept to an approximate One Machine Infinite Bus (OMIB) equivalent of the system to find the critical clearing angle. The system-critical clearing time can then be obtained by numerical integration of OMIB equations. The EEAC method was proposed in the 1980s and 1990s as a substitute for time-domain simulation for Transmission System Operators (TSOs) to provide fast, transient stability analysis with the limited computational power available those days. To ensure the secure operation of the power system, TSOs have to identify and prevent potential critical scenarios through offline analyses of a few dangerous ones. These days, due to increased uncertainties in electrical power systems, the number of these critical scenarios is increasing, substantially, calling for fast, transient stability analysis techniques once more. Among them, the EEAC is a unique approach that provides not only valuable information, but also a graphical representation of system dynamics. This paper revisits the EEAC but from a modern, functional point of view. First, the definition of the OMIB model of a multi-machine power system is redrawn in its general form. To achieve fast, transient stability analysis, EEAC relies on approximate models of the true OMIB model. These approximations are clarified, and the EAC concept is redefined with a general definition for instability, and its conditions. Based on the defined conditions and definitions, functions are developed for each EEAC building block, which are later put out together to provide a full-resolution, functional scheme. This functional scheme not only covers the previous literature on the subject, but also allows to introduce several possible new EEAC approaches and provides a detailed description of their implementation procedure. A number of approaches are applied to the French EHV network, and the approximations are examined.

A large part of metabolomics research relies on experiments involving mouse models, which are usually 6 to 20 weeks of age. However, in this age range mice undergo dramatic developmental changes. Even small age differences may lead to different metabolomes, which in turn could increase inter-sample variability and impair the reproducibility and comparability of metabolomics results. In order to learn more about the variability of the murine plasma metabolome, we analyzed male and female C57BL/6J, C57BL/6NTac, 129S1/SvImJ, and C3HeB/FeJ mice at 6, 10, 14, and 20 weeks of age, using targeted metabolomics (BIOCRATES AbsoluteIDQ™ p150 Kit). Our analysis revealed high variability of the murine plasma metabolome during adolescence and early adulthood. A general age range with minimal variability, and thus a stable metabolome, could not be identified. Age-related metabolomic changes as well as the metabolite profiles at specific ages differed markedly between mouse strains. This observation illustrates the fact that the developmental timing in mice is strain specific. We therefore stress the importance of deliberate strain choice, as well as consistency and precise documentation of animal age, in metabolomics studies.