Gareth Davies

Bio: Gareth Davies is an academic researcher from Swansea University. The author has contributed to research in topics: Medicine & Open innovation. The author has an hindex of 9, co-authored 26 publications receiving 509 citations.

“Depression” increases “craving” for sweet rewards in animal and human models of depression and craving

Abstract: This study consisted of two experiments, one in rats and one in human volunteers, that used the identical progressive ratio (PR) operant procedure. In both experiments, responding was reinforced under a progressively increasing work requirement, and different groups of subjects received reinforcers that varied in sweetness. In experiment 1, rats were subjected to chronic mild stress, a well-validated animal model of depression. Performance under the PR schedule increased in subjects reinforced with conventional precision pellets (which contain 10% sucrose) or very sweet pellets, but not in subjects reinforced with sugar-free pellets. In experiment 2, volunteers were subjected to a depressive musical mood induction. Performance under the PR schedule increased in subjects reinforced with chocolate buttons, but not in subjects reinforced with with buttons made from the relatively unpalatable chocolate substitute carob. In experiment 2, depressive mood induction also increased chocolate craving, as measured by a novel questionnaire, and there were significant correlations between chocolate craving and chocolate-reinforced PR performance. These results suggest that performance under the PR schedule provides a measure of craving rather than reward, and that craving for sweet rewards is increased by depressive mood induction in both animal and human models. Implications for the interpretation of pharmacological studies using the PR procedure are also discussed.

Prediction models for diagnosis and prognosis of covid-19: systematic review and critical appraisal

Abstract: Objective To review and appraise the validity and usefulness of published and preprint reports of prediction models for diagnosing coronavirus disease 2019 (covid-19) in patients with suspected infection, for prognosis of patients with covid-19, and for detecting people in the general population at increased risk of covid-19 infection or being admitted to hospital with the disease. Design Living systematic review and critical appraisal by the COVID-PRECISE (Precise Risk Estimation to optimise covid-19 Care for Infected or Suspected patients in diverse sEttings) group. Data sources PubMed and Embase through Ovid, up to 1 July 2020, supplemented with arXiv, medRxiv, and bioRxiv up to 5 May 2020. Study selection Studies that developed or validated a multivariable covid-19 related prediction model. Data extraction At least two authors independently extracted data using the CHARMS (critical appraisal and data extraction for systematic reviews of prediction modelling studies) checklist; risk of bias was assessed using PROBAST (prediction model risk of bias assessment tool). Results 37 421 titles were screened, and 169 studies describing 232 prediction models were included. The review identified seven models for identifying people at risk in the general population; 118 diagnostic models for detecting covid-19 (75 were based on medical imaging, 10 to diagnose disease severity); and 107 prognostic models for predicting mortality risk, progression to severe disease, intensive care unit admission, ventilation, intubation, or length of hospital stay. The most frequent types of predictors included in the covid-19 prediction models are vital signs, age, comorbidities, and image features. Flu-like symptoms are frequently predictive in diagnostic models, while sex, C reactive protein, and lymphocyte counts are frequent prognostic factors. Reported C index estimates from the strongest form of validation available per model ranged from 0.71 to 0.99 in prediction models for the general population, from 0.65 to more than 0.99 in diagnostic models, and from 0.54 to 0.99 in prognostic models. All models were rated at high or unclear risk of bias, mostly because of non-representative selection of control patients, exclusion of patients who had not experienced the event of interest by the end of the study, high risk of model overfitting, and unclear reporting. Many models did not include a description of the target population (n=27, 12%) or care setting (n=75, 32%), and only 11 (5%) were externally validated by a calibration plot. The Jehi diagnostic model and the 4C mortality score were identified as promising models. Conclusion Prediction models for covid-19 are quickly entering the academic literature to support medical decision making at a time when they are urgently needed. This review indicates that almost all pubished prediction models are poorly reported, and at high risk of bias such that their reported predictive performance is probably optimistic. However, we have identified two (one diagnostic and one prognostic) promising models that should soon be validated in multiple cohorts, preferably through collaborative efforts and data sharing to also allow an investigation of the stability and heterogeneity in their performance across populations and settings. Details on all reviewed models are publicly available at https://www.covprecise.org/. Methodological guidance as provided in this paper should be followed because unreliable predictions could cause more harm than benefit in guiding clinical decisions. Finally, prediction model authors should adhere to the TRIPOD (transparent reporting of a multivariable prediction model for individual prognosis or diagnosis) reporting guideline. Systematic review registration Protocol https://osf.io/ehc47/, registration https://osf.io/wy245. Readers’ note This article is a living systematic review that will be updated to reflect emerging evidence. Updates may occur for up to two years from the date of original publication. This version is update 3 of the original article published on 7 April 2020 (BMJ 2020;369:m1328). Previous updates can be found as data supplements (https://www.bmj.com/content/369/bmj.m1328/related#datasupp). When citing this paper please consider adding the update number and date of access for clarity.

Validity, reliability and utility of the chronic mild stress model of depression: a 10-year review and evaluation

Abstract: This paper evaluates the validity, reliability and utility of the chronic mild stress (CMS) model of depression. In the CMS model, rats or mice are exposed sequentially, over a period of weeks, to a variety of mild stressors, and the measure most commonly used to track the effects is a decrease in consumption of a palatable sweet solution. The model has good predictive validity (behavioural changes are reversed by chronic treatment with a wide variety of antidepressants), face validity (almost all demonstrable symptoms of depression have been demonstrated), and construct validity (CMS causes a generalized decrease in responsiveness to rewards, comparable to anhedonia, the core symptom of the melancholic subtype of major depressive disorder). Overall, the CMS procedure appears to be at least as valid as any other animal model of depression. The procedure does, however, have two major drawbacks. One is the practical difficulty of carrying out CMS experiments, which are labour intensive, demanding of space, and of long duration. The other is that, while the procedure operates reliably in many laboratories, it can be difficult to establish, for reasons which remain unclear. However, once established, the CMS model can be used to study problems that are extremely difficult to address by other means.

Chronic Mild Stress (CMS) Revisited: Consistency and Behavioural-Neurobiological Concordance in the Effects of CMS

Abstract: The chronic mild stress (CMS) model of depression has high validity but has in the past been criticized for being difficult to replicate. However, a large number of recent publications have confirmed

An Agenda for a Reformed Cohesion Policy A place-based approach to meeting European Union challenges and expectations

Abstract: EU regional policy is an investment policy. It supports job creation, competitiveness, economic growth, improved quality of life and sustainable development. These investments support the delivery of the Europe 2020 strategy. The present paper analysis two strategically different options of EU regional policy: place-neutral versus place-based policies for economic development. Our results suggest that in many EU regions the place-neutral policies may no be the best policy response to facing new challenges posed by deeper economic integration and globalisation.