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Context: Several liquid- and tissue-based biomarker tests (LTBTs) are available to inform the need for prostate biopsies and treatment of localised prostate cancer (PCa) through risk stratification, but translation into routine practice requires evidence of their clinical utility and economic impact. Objective: To review and summarise the health economic evidence on the ability of LTBTs to inform decisions on prostate biopsies and treatment of localised PCa through risk stratification. Evidence acquisition: A systematic search was performed in the EMBASE, MEDLINE, Health Technology Assessment, and National Health Service Health Economic Evaluation databases. Eligible publications were those presenting health economic evaluations of an LTBT to select individuals for biopsy or risk-stratify PCa patients for treatment. Data on the study objectives, context, methodology, clinical utility, and outcomes were extracted and summarised. Evidence synthesis: Of the 22 studies included, 14 were focused on test-informed biopsies and eight on treatment selection. Most studies performed cost-effectiveness analyses (n = 7), followed by costing (n = 4) or budget impact analyses (n = 3). Most (18 of 22) studies concluded that biomarker tests could decrease health care costs or would be cost-effective. However, downstream consequences and long-term outcomes were typically not included in studies that evaluated LTBT to inform biopsies. Long-term effectiveness was modelled by linking evidence from different sources instead of using data from prospective studies. Conclusions: Although studies concluded that LTBTs would probably be cost-saving or -effective, the strength of this evidence is disputable because of concerns around the validity and transparency of the assumptions made. This warrants prospective interventional trials to inform health economic analyses to ensure collection of direct evidence of clinical outcomes based on LTBT use. Patient summary: We reviewed studies that evaluated whether blood, urine, and tissue tests can reduce the health and economic burden of prostate cancer. Results indicate that these tests could be cost-effective, but clinical studies of long-term outcomes are needed to confirm the findings.

Abstract Background In oncology, Whole Genome Sequencing (WGS) is not yet widely implemented due to uncertainties such as the required infrastructure and expertise, costs and reimbursements, and unknown pan-cancer clinical utility. Therefore, this study aimed to investigate possible future developments facilitating or impeding the use of WGS as a molecular diagnostic in oncology through scenario drafting. Methods A four-step process was adopted for scenario drafting. First, the literature was searched for barriers and facilitators related to the implementation of WGS. Second, they were prioritized by international experts, and third, combined into coherent scenarios. Fourth, the scenarios were implemented in an online survey and their likelihood of taking place within 5 years was elicited from another group of experts. Based on the minimum, maximum, and most likely (mode) parameters, individual Program Evaluation and Review Technique (PERT) probability density functions were determined. Subsequently, individual opinions were aggregated by performing unweighted linear pooling, from which summary statistics were extracted and reported. Results Sixty-two unique barriers and facilitators were extracted from 70 articles. Price, clinical utility, and turnaround time of WGS were ranked as the most important aspects. Nine scenarios were developed and scored on likelihood by 18 experts. The scenario about introducing WGS as a clinical diagnostic with a lower price, shorter turnaround time, and improved degree of actionability, scored the highest likelihood (median: 68.3%). Scenarios with low likelihoods and strong consensus were about better treatment responses to more actionable targets (26.1%), and the effect of centralizing WGS (24.1%). Conclusions Based on current expert opinions, the implementation of WGS as a clinical diagnostic in oncology is heavily dependent on the price, clinical utility (both in terms of identifying actionable targets as in adding sufficient value in subsequent treatment), and turnaround time. These aspects and the optimal way of service provision are the main drivers for the implementation of WGS and should be focused on in further research. More knowledge regarding these factors is needed to inform strategic decision making regarding the implementation of WGS, which warrants support from all relevant stakeholders.

Abstract Background Current international consensus is that ‘early’ referral to palliative care services improves cancer patient and family carer outcomes. In practice, however, these referrals are not routine. An approach which directly addresses identified barriers to early integration of palliative care is required. This protocol details a trial of a standardized model of early palliative care (Care Plus) introduced at key defined, disease-specific times or transition points in the illness for people with cancer. Introduced as a ‘whole of system’ practice change for identified advanced cancers, the key outcomes of interest are population health service use change. The aims of the study are to examine the effect of Care Plus implementation on (1) acute hospitalisation days in the last 3 months of life; (2) timeliness of access to palliative care; (3) quality and (4) costs of end of life care; and (5) the acceptability of services for people with advanced cancer. Methods Multi-site stepped wedge implementation trial testing usual care (control) versus Care Plus (practice change). The design stipulates ‘control’ periods when usual care is observed, and the process of implementing Care Plus which includes phases of planning, engagement, practice change and evaluation. During the practice change phase, all patients with targeted advanced cancers reaching the transition point will, by default, receive Care Plus. Health service utilization and unit costs before and after implementation will be collated from hospital records, and state and national health service administrative datasets. Qualitative data from patients, consumers and clinicians before and after practice change will be gathered through interviews and focus groups. Discussion The study outcomes will detail the impact and acceptability of the standardized integration of palliative care as a practice change, including recommendations for ongoing sustainability and broader implementation. Trial registration Australian New Zealand Clinical Trials Registry ACTRN 12619001703190 . Registered 04 December 2019.

Abstract A new method to compute the target photometric variables of non-imaging optical systems is presented. The method is based on the phase space representation of each surface that forms the optical system. All surfaces can be modeled as detectors of the incident light and emitters of the reflected light. Moreover, we assume that the source can only emit light and the target can only receive light. Therefore, one phase space is taken into account for the source and one for the target. For the other surfaces both the source and target phase spaces are considered. The output intensity is computed from the rays that leave the source and hit the target. We implement the method for two-dimensional optical systems, and we compare the new method with Monte Carlo (MC) ray tracing. This paper is a proof of principle. Therefore, we present the results for systems formed by straight lines which are all located in the same medium. Numerical results show that the intensity found with the ray mapping method equals the exact intensity. Accuracy and speed advantages of several orders are observed with the new method.

Abstract Background Dynamic treatment regimens (DTRs) formalise the multi-stage and dynamic decision problems that clinicians often face when treating chronic or progressive medical conditions. Compared to randomised controlled trials, using observational data to optimise DTRs may allow a wider range of treatments to be evaluated at a lower cost. This review aimed to provide an overview of how DTRs are optimised with observational data in practice. Methods Using the PubMed database, a scoping review of studies in which DTRs were optimised using observational data was performed in October 2020. Data extracted from eligible articles included target medical condition, source and type of data, statistical methods, and translational relevance of the included studies. Results From 209 PubMed abstracts, 37 full-text articles were identified, and a further 26 were screened from the reference lists, totalling 63 articles for inclusion in a narrative data synthesis. Observational DTR models are a recent development and their application has been concentrated in a few medical areas, primarily HIV/AIDS (27, 43%), followed by cancer (8, 13%), and diabetes (6, 10%). There was substantial variation in the scope, intent, complexity, and quality between the included studies. Statistical methods that were used included inverse-probability weighting (26, 41%), the parametric G-formula (16, 25%), Q-learning (10, 16%), G-estimation (4, 6%), targeted maximum likelihood/minimum loss-based estimation (4, 6%), regret regression (3, 5%), and other less common approaches (10, 16%). Notably, studies that were primarily intended to address real-world clinical questions (18, 29%) tended to use inverse-probability weighting and the parametric G-formula, relatively well-established methods, along with a large amount of data. Studies focused on methodological developments (45, 71%) tended to be more complicated and included a demonstrative real-world application only. Conclusions As chronic and progressive conditions become more common, the need will grow for personalised treatments and methods to estimate the effects of DTRs. Observational DTR studies will be necessary, but so far their use to inform clinical practice has been limited. Focusing on simple DTRs, collecting large and rich clinical datasets, and fostering tight partnerships between content experts and data analysts may result in more clinically relevant observational DTR studies.

Abstract Members of the solute carrier (SLC) transporter protein family are increasingly recognized as therapeutic drug targets. The majority of drug screening assays for SLCs are based on the uptake of radiolabeled or fluorescent substrates. Thus, these approaches often have limitations that compromise on throughput or the physiological environment of the SLC. In this study, we report a novel application of an impedance-based biosensor, xCELLigence, to investigate dopamine transporter (DAT) activity via substrate-induced activation of G protein-coupled receptors (GPCRs). The resulting assay, which is coined the ‘transporter activity through receptor activation’ (TRACT) assay, is based on the hypothesis that DAT-mediated removal of extracellular dopamine directly affects the ability of dopamine to activate cognate membrane-bound GPCRs. In two human cell lines with heterologous DAT expression, dopamine-induced GPCR signaling was attenuated. Pharmacological inhibition or the absence of DAT restored the apparent potency of dopamine for GPCR activation. The inhibitory potencies for DAT inhibitors GBR12909 (pIC50 = 6.2, 6.6) and cocaine (pIC50 = 6.3) were in line with values from reported orthogonal transport assays. Conclusively, this study demonstrates the novel use of label-free whole-cell biosensors to investigate DAT activity using GPCR activation as a readout. This holds promise for other SLCs that share their substrate with a GPCR.

Background and AimSodium-glucose cotransporter-2 inhibitors (SGLT2i) induce less weight loss than expected. This may be explained by SGLT2i-induced alterations in central reward and satiety circuits, contributing to increased appetite and food intake. This hyperphagia may be specific to high-calorie foods. Glucagon-like peptide-1 receptor agonists (GLP-1RA) are associated with lower preferences for high-calorie foods, and with decreased activation in areas regulating satiety and reward in response to high-calorie food pictures, which may reflect this lower preference for energy-dense foods. To optimize treatment, we need a better understanding of how intake is controlled, and how [(un)healthy] food choices are made. The aim of the study was to investigate the effects of dapagliflozin, exenatide, and their combination on brain activation in response to low-calorie food pictures.MethodsWe performed an exploratory analysis of a larger, 16-week, double-blind, randomized, placebo-controlled trial. Sixty-eight subjects with obesity and type 2 diabetes were randomized to dapagliflozin, exenatide, dapagliflozin plus exenatide, or double placebo. Using functional MRI, the effects of treatments on brain responses to low-calorie food pictures were assessed after 10 days and 16 weeks.ResultsDapagliflozin versus placebo decreased activity in response to low-calorie food pictures, in the caudate nucleus, insula, and amygdala after 10 days, and in the insula after 16 weeks. Exenatide versus placebo increased activation in the putamen in response to low-calorie food pictures after 10 days, but not after 16 weeks. Dapagliflozin plus exenatide versus placebo had no effect on brain responses, but after 10 days dapagliflozin plus exenatide versus dapagliflozin increased activity in the insula and amygdala in response to low-calorie food pictures.ConclusionDapagliflozin decreased activation in response to low-calorie food pictures, which may reflect a specific decreased preference for low-calorie foods, in combination with the previously found increased activation in response to high-calorie foods, which may reflect a specific preference for high-calorie foods, and may hamper SGLT2i-induced weight loss. Exenatide treatment increased activation in response to low-calorie foods. Combination treatment may lead to more favorable brain responses to low-calorie food cues, as we observed that the dapagliflozin-induced decreased response to low-calorie food pictures had disappeared.

Excitatory amino acid transporters (EAAT/SLC1) mediate Na+-dependent uptake of extracellular glutamate and are potential drug targets for neurological disorders. Conventional methods to assess glutamate transport in vitro are based on radiolabels, fluorescent dyes or electrophysiology, which potentially compromise the cell’s physiology and are generally less suited for primary drug screens. Here, we describe a novel label-free method to assess human EAAT function in living cells, i.e., without the use of chemical modifications to the substrate or cellular environment. In adherent HEK293 cells overexpressing EAAT1, stimulation with glutamate or aspartate induced cell spreading, which was detected in real-time using an impedance-based biosensor. This change in cell morphology was prevented in the presence of the Na+/K+-ATPase inhibitor ouabain and EAAT inhibitors, which suggests the substrate-induced response was ion-dependent and transporter-specific. A mechanistic explanation for the phenotypic response was substantiated by actin cytoskeleton remodeling and changes in the intracellular levels of the osmolyte taurine, which suggests that the response involves cell swelling. In addition, substrate-induced cellular responses were observed for cells expressing other EAAT subtypes, as well as in a breast cancer cell line (MDA-MB-468) with endogenous EAAT1 expression. These findings allowed the development of a label-free high-throughput screening assay, which could be beneficial in early drug discovery for EAATs and holds potential for the study of other transport proteins that modulate cell shape.

Among animals, natural selection has resulted in a broad array of behavioural strategies to maintain core body temperature in a relatively narrow range. One important temperature regulation strategy is social thermoregulation, which is often done by warming the body together with conspecifics. The literature suggests that the same selection pressures that apply to other animals also apply to humans, producing individual differences in the tendency to socially thermoregulate. We wanted to investigate whether differences in social thermoregulation desires extend to other personality factors in a sample of French students. We conducted an exploratory, hypothesis-generating cross-sectional project to examine associations between thermoregulation and personality. We used conditional random forests in a training segment of our dataset to identify clusters of variables most likely to be shaped by individual differences to thermoregulate. We used the resulting clusters to fit hypothesis-generating mediation models. After we replicated the relationships in two datasets, personality was not related to social thermoregulation desires, with the exception of attachment avoidance. Attachment avoidance in turn predicted loneliness. This mediation proved robust across all three datasets. As our cross-sectional studies allow limited causal inferences, we suggest investing into prospective studies to understand whether and how social thermoregulation shapes attachment avoidance early in life and loneliness later in life. We also recommend replication of the current relationships in other climates, countries, and age groups.

The Concise Guide to PHARMACOLOGY 2015/16 provides concise overviews of the key properties of over 1750 human drug targets with their pharmacology, plus links to an open access knowledgebase of drug targets and their ligands (www.guidetopharmacology.org), which provides more detailed views of target and ligand properties. The full contents can be found at http://onlinelibrary.wiley.com/doi/10.1111/bph.13347/full. This compilation of the major pharmacological targets is divided into eight areas of focus: G protein-coupled receptors, ligand-gated ion channels, voltage-gated ion channels, other ion channels, nuclear hormone receptors, catalytic receptors, enzymes and transporters. These are presented with nomenclature guidance and summary information on the best available pharmacological tools, alongside key references and suggestions for further reading. The Concise Guide is published in landscape format in order to facilitate comparison of related targets. It is a condensed version of material contemporary to late 2015, which is presented in greater detail and constantly updated on the website www.guidetopharmacology.org, superseding data presented in the previous Guides to Receptors & Channels and the Concise Guide to PHARMACOLOGY 2013/14. It is produced in conjunction with NC-IUPHAR and provides the official IUPHAR classification and nomenclature for human drug targets, where appropriate. It consolidates information previously curated and displayed separately in IUPHAR-DB and GRAC and provides a permanent, citable, point-in-time record that will survive database updates.

Abstract The development of drugs is often hampered due to off-target interactions leading to adverse effects. Therefore, computational methods to assess the selectivity of ligands are of high interest. Currently, selectivity is often deduced from bioactivity predictions of a ligand for multiple targets (individual machine learning models). Here we show that modeling selectivity directly, by using the affinity difference between two drug targets as output value, leads to more accurate selectivity predictions. We test multiple approaches on a dataset consisting of ligands for the A1 and A2A adenosine receptors (among others classification, regression, and we define different selectivity classes). Finally, we present a regression model that predicts selectivity between these two drug targets by directly training on the difference in bioactivity, modeling the selectivity-window. The quality of this model was good as shown by the performances for fivefold cross-validation: ROC A1AR-selective 0.88 ± 0.04 and ROC A2AAR-selective 0.80 ± 0.07. To increase the accuracy of this selectivity model even further, inactive compounds were identified and removed prior to selectivity prediction by a combination of statistical models and structure-based docking. As a result, selectivity between the A1 and A2A adenosine receptors was predicted effectively using the selectivity-window model. The approach presented here can be readily applied to other selectivity cases.

We present a novel approach to minimize aberrations in imaging systems. The energy distributions at the source and target of an optical system play a crucial role in designing freeform surfaces through illumination optics methodologies. We quantify the on-axis and off-axis aberrations using a merit function that depends on the energy distributions. The minimization of the merit function yields optimal energy distributions, which subsequently enable us to design freeform reflector surfaces that cause the least aberrations. We validate our method by testing it for two configurations, a single-reflector system with a parallel source to a near-field target, and a double-reflector system with a parallel source to a point target.

We present a novel approach to computing reflectors with a scattering surface in illumination optics. A scattering model governed by a Fredholm integral equation is derived. Solving this integral relation yields a virtual specular target distribution, which we insert into a Monge-Ampère least-squares numerical solver to get a scattering reflector that yields the desired illumination.

Concatenated backward ray mapping is an alternative for ray tracing in 2D. It is based on the phase space descrip tion of an optical system. Phase space is the set of position and direction coordinates of rays intersecting an optical line. The original algorithm is limited to optical systems consisting of only straight line segments; we extend it to accommodate curved segments. The algorithm is applied to the compound parabolic concentrator, a standard optical system that collects parallel light and reshapes it to a focused beam. We compare the accuracy and speed of the extended algorithm to the original algorithm and Monte Carlo ray tracing. The results show that the extended algorithm outperforms both methods.

We present a novel approach of modelling surface light scattering in the context of two-dimensional reflector design, relying on energy conservation and optimal transport theory. For isotropic scattering in cylindrically or rotationally symmetric systems with in-plane scattering, the scattered light distribution can be expressed as a convolution between a scattering function, which characterises the optical properties of the surface, and a specular light distribution. Deconvolving this expression allows for traditional specular reflector design procedures to be used, whilst accounting for scattering. This approach thus constitutes solving the inverse problem of light scattering, allowing for direct computation of the reflector surface, without the need for design iterations.

Expert groups argue to raise the recommended daily allowance for protein in older adults from 0.8 to 1.2 g/kg/day to prevent undernutrition. However, protein is thought to increase satiety, possibly through effects on gut microbiota and central appetite regulation. If true, raising daily protein intake may work counterproductively. In a randomized controlled trial, we evaluated the effects of dietary advice aimed at increasing protein intake to 1.2 g/kg adjusted body weight/day (g/kg aBW/day) on appetite and gut microbiota in 90 community-dwelling older adults with habitual protein intake intervention = 47, Ncontrol = 43). Food intake was determined by 24-h dietary recalls and gut microbiota by 16S rRNA sequencing. Functional magnetic resonance imaging (fMRI) scans were performed in a subgroup of 48 participants to evaluate central nervous system responses to food-related stimuli. Both groups had mean baseline protein intake of 0.8 ± 0.2 g/kg aBW/day. At 6 months’ follow-up this increased to 1.2 ± 0.2 g/kg aBW/day for the intervention group and 0.9 ± 0.2 g/kg aBW/day for the control group. Microbiota composition was not affected, nor were appetite or brain activity in response to food-related stimuli. Increasing protein intake in older adults to 1.2 g/kg aBW/day does not negatively impact the gut microbiota or suppress appetite.