Nottingham Prognostic Index Plus (NPI+): a modern clinical decision making tool in breast cancer

Current management of breast cancer (BC) relies on risk stratification based on well-defined clinicopathologic factors. Global gene expression profiling studies have demonstrated that BC comprises distinct molecular classes with clinical relevance. In this study, we hypothesized that molecular features of BC are a key driver of tumour behaviour and when coupled with a novel and bespoke application of established clinicopathologic prognostic variables, can predict both clinical outcome and relevant therapeutic options more accurately than existing methods. In the current study, a comprehensive panel of biomarkers with relevance to BC was applied to a large and well-characterised series of BC, using immunohistochemistry and different multivariate clustering techniques, to identify the key molecular classes. Subsequently, each class was further stratified using a set of well-defined prognostic clinicopathologic variables. These variables were combined in formulae to prognostically stratify different molecular classes, collectively known as the Nottingham Prognostic Index Plus (NPI+). NPI+ was then used to predict outcome in the different molecular classes with.Seven core molecular classes were identified using a selective panel of 10 biomarkers. Incorporation of clinicopathologic variables in a second stage analysis resulted in identification of distinct prognostic groups within each molecular class (NPI+). Outcome analysis showed that using the bespoke NPI formulae for each biological breast cancer class provides improved patient outcome stratification superior to the traditional NPI. This study provides proof-of-principle evidence for the use of NPI+ in supporting improved individualised clinical decision making

Cosmological model with interactions in the dark sector

A cosmological model is proposed for the current Universe consisted of non-interacting baryonic matter and interacting dark components. The dark energy and dark matter are coupled through their effective barotropic indexes, which are considered as functions of the ratio between their energy densities. It is investigated two cases where the ratio is asymptotically stable and their parameters are adjusted by considering best fits to Hubble function data. It is shown that the deceleration parameter, the densities parameters, and the luminosity distance have the correct behavior which is expected for a viable present scenario of the Universe.Comment: 6 pages, 8 figure

Fitting the integrated Spectral Energy Distributions of Galaxies

Fitting the spectral energy distributions (SEDs) of galaxies is an almost universally used technique that has matured significantly in the last decade. Model predictions and fitting procedures have improved significantly over this time, attempting to keep up with the vastly increased volume and quality of available data. We review here the field of SED fitting, describing the modelling of ultraviolet to infrared galaxy SEDs, the creation of multiwavelength data sets, and the methods used to fit model SEDs to observed galaxy data sets. We touch upon the achievements and challenges in the major ingredients of SED fitting, with a special emphasis on describing the interplay between the quality of the available data, the quality of the available models, and the best fitting technique to use in order to obtain a realistic measurement as well as realistic uncertainties. We conclude that SED fitting can be used effectively to derive a range of physical properties of galaxies, such as redshift, stellar masses, star formation rates, dust masses, and metallicities, with care taken not to over-interpret the available data. Yet there still exist many issues such as estimating the age of the oldest stars in a galaxy, finer details ofdust properties and dust-star geometry, and the influences of poorly understood, luminous stellar types and phases. The challenge for the coming years will be to improve both the models and the observational data sets to resolve these uncertainties. The present review will be made available on an interactive, moderated web page (sedfitting.org), where the community can access and change the text. The intention is to expand the text and keep it up to date over the coming years.Comment: 54 pages, 26 figures, Accepted for publication in Astrophysics & Space Scienc

TRY plant trait database – enhanced coverage and open access

Plant traits—the morphological, anatomical, physiological, biochemical and phenological characteristics of plants—determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait‐based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits—almost complete coverage for ‘plant growth form’. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait–environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives

Whole-genome sequencing reveals host factors underlying critical COVID-19

Critical COVID-19 is caused by immune-mediated inflammatory lung injury. Host genetic variation influences the development of illness requiring critical care1 or hospitalization2,3,4 after infection with SARS-CoV-2. The GenOMICC (Genetics of Mortality in Critical Care) study enables the comparison of genomes from individuals who are critically ill with those of population controls to find underlying disease mechanisms. Here we use whole-genome sequencing in 7,491 critically ill individuals compared with 48,400 controls to discover and replicate 23 independent variants that significantly predispose to critical COVID-19. We identify 16 new independent associations, including variants within genes that are involved in interferon signalling (IL10RB and PLSCR1), leucocyte differentiation (BCL11A) and blood-type antigen secretor status (FUT2). Using transcriptome-wide association and colocalization to infer the effect of gene expression on disease severity, we find evidence that implicates multiple genes—including reduced expression of a membrane flippase (ATP11A), and increased expression of a mucin (MUC1)—in critical disease. Mendelian randomization provides evidence in support of causal roles for myeloid cell adhesion molecules (SELE, ICAM5 and CD209) and the coagulation factor F8, all of which are potentially druggable targets. Our results are broadly consistent with a multi-component model of COVID-19 pathophysiology, in which at least two distinct mechanisms can predispose to life-threatening disease: failure to control viral replication; or an enhanced tendency towards pulmonary inflammation and intravascular coagulation. We show that comparison between cases of critical illness and population controls is highly efficient for the detection of therapeutically relevant mechanisms of disease

Efficacy of a training programme designed to teach cervical smear screeners to identify ocular surface squamous neoplasia using conjunctival impression cytology

Ocular surface squamous neoplasia (OSSN) is a recently proposed term introduced to encompass both intraepithelial neoplasia and invasive squamous cell carcinoma of the conjunctiva and cornea. A teaching programme incorporating at manual, slide sets, and an evaluation test was developed. The aim was to teach experienced cervical smear screeners to evaluate ocular surface specimens collected by conjunctival impression cytology, with a minimum of individual tuition. The use of the manual was well accepted and half of the original six candidates were able to master the new skill adequately within 8 h. It was considered that the differences between the cytology of OSSN and the equivalent lesions of the uterine cervix are sufficient to prevent some experienced screeners acquiring these skills rapidly

Nottingham Prognostic Index Plus: Validation of a clinical decision making tool in breast cancer in an independent series

The Nottingham Prognostic Index Plus (NPI+)is a clinical decision making tool in breast cancer (BC) that aims to provide improved patient outcome stratification superior to the traditional NPI. This study aimed to validate the NPI+ in an independent series of BC. Eight hundred and eighty five primary early stage BC cases from Edinburgh were semi-quantitatively assessed for 10 biomarkers [Estrogen Receptor (ER), Progesterone Receptor (PgR), cyto-keratin (CK) 5/6, CK7/8, epidermal growth factor receptor (EGFR), HER2, HER3, HER4, p53, and Mucin 1] using immunohistochemistry and classified into biological classes by fuzzy logic-derived algorithms previously developed in the Nottingham series. Subsequently, NPI+ Prognostic Groups (PGs) were assigned for each class using bespoke NPI-like formulae, previously developed in each NPI+ biological class of the Nottingham series, utilising clinicopathological parameters: number of positive nodes, pathological tumour size, stage, tubule formation, nuclear pleomorphism and mitotic counts. Biological classes and PGs were compared between the Edinburgh and Nottingham series using Cramer’s V and their role in patient outcome prediction using Kaplan–Meier curves and tested using Log Rank. The NPI+ biomarker panel classified the Edinburgh series into seven biological classes similar to the Nottingham series (p>0.01). The biological classes were significantly associated with patient outcome (p0.01). The good PGs were similarly validated in Luminal B, Basal p53 normal, HER2+/ER- tumours and the poor PG in the Luminal N class (p>0.01). Due to small patient numbers assigned to the remaining PGs, Luminal N, Luminal B, Basal p53 normal and HER2+/ER- classes could not be validated. This study demonstrates the reproducibility of NPI+ and confirmed its prognostic value in an independent cohort of primary BC. Further validation in large randomised controlled trial material is warranted

Attitudes toward children: Distinguishing affection and stress

Background: Adults' views and behaviors toward children can vary from being supportive to shockingly abusive, and there are significant unanswered questions about the psychological factors underpinning this variability. Objective; The present research examined the content of adults' attitudes toward children to address these questions. Method: Ten studies (N = 4702) identified the factor structure of adults' descriptions of babies, toddlers, and school-age children and examined how the resulting factors related to a range of external variables. Results: Two factors emerged - affection toward children and stress elicited by them - and this factor structure was invariant across the United Kingdom, the United States, and South Africa. Affection uniquely captures emotional approach tendencies, concern for others, and broad positivity in evaluations, experiences, motivations, and donation behavior. Stress relates to emotional instability, emotional avoidance, and concern about disruptions to a self-oriented, structured life. The factors also predict distinct experiences in a challenging situation - home-parenting during COVID-19 lockdown - with affection explaining greater enjoyment and stress explaining greater perceived difficulty. Affection further predicts mentally visualizing children as pleasant and confident, whereas stress predicts mentally visualizing children as less innocent. Conclusions: These findings offer fundamental new insights about social cognitive processes in adults that impact adult–child relationships and children's well-being