Changing population characteristics, effect-measure modification, and cancer risk factor identification

Epidemiologic studies have identified a number of lifestyle factors, e.g. diet, obesity, and use of certain medications, which affect risk of colon cancer. However, the magnitude and significance of risk factor-disease associations differ among studies. We propose that population trends of changing prevalence of risk factors explains some of the variability between studies when factors that change prevalence also modify the effect of other risk factors. We used data collected from population-based control who were selected as study participants for two time periods, 1991–1994 and 1997–2000, along with data from the literature, to examine changes in the population prevalence of aspirin and non-steroidal anti-inflammatory medication (NSAID) use, obesity, and hormone replacement therapy (HRT) over time. Data from a population-based colon cancer case-control study were used to estimate effect-measurement modification among these factors. Sizeable changes in aspirin use, HRT use, and the proportion of the population that is obese were observed between the 1980s and 2000. Use of NSAIDs interacted with BMI and HRT; HRT use interacted with body mass index (BMI). We estimate that as the prevalence of NSAIDs use changed from 10% to almost 50%, the colon cancer relative risk associated with BMI >30 would change from 1.3 to 1.9 because of the modifying effect of NSAIDs. Similarly, the relative risk estimated for BMI would increase as the prevalence of use of HRT among post-menopausal women increased. In conclusion, as population characteristics change over time, these changes may have an influence on relative risk estimates for colon cancer for other exposures because of effect-measure modification. The impact of population changes on comparability between epidemiologic studies can be kept to a minimum if investigators assess exposure-disease associations within strata of other exposures, and present results in a manner that allows comparisons across studies. Effect-measure modification is an important component of data analysis that should be evaluated to obtain a complete understanding of disease etiology

Nerve Agent Hydrolysis Activity Designed into a Human Drug Metabolism Enzyme

Organophosphorus (OP) nerve agents are potent suicide inhibitors of the essential neurotransmitter-regulating enzyme acetylcholinesterase. Due to their acute toxicity, there is significant interest in developing effective countermeasures to OP poisoning. Here we impart nerve agent hydrolysis activity into the human drug metabolism enzyme carboxylesterase 1. Using crystal structures of the target enzyme in complex with nerve agent as a guide, a pair of histidine and glutamic acid residues were designed proximal to the enzyme's native catalytic triad. The resultant variant protein demonstrated significantly increased rates of reactivation following exposure to sarin, soman, and cyclosarin. Importantly, the addition of these residues did not alter the high affinity binding of nerve agents to this protein. Thus, using two amino acid substitutions, a novel enzyme was created that efficiently converted a group of hemisubstrates, compounds that can start but not complete a reaction cycle, into bona fide substrates. Such approaches may lead to novel countermeasures for nerve agent poisoning

‘Most people have no idea what autism is’ : Unpacking autism disclosure using social media analysis

Autism disclosure can be a complicated decision that autistic people experience. Positive outcomes can include feelings of acceptance and support, but negative outcomes can include stigma and discrimination. Although a surge in research on this topic has led to more understanding around autism disclosure, the methodologies used may have limited who was contributing to the conversation and data. To overcome this, we analyzed 3 years (2020−2022) of social media data (Reddit and Twitter) as this was public information that did not rely on researcher data collection. Reflexive thematic analysis of 3121 posts led to the generation of four themes: People do not understand autism (with experiences related to employment, dating, healthcare and mental health), autistic people just want privacy and respect, autistic people can lead us forward and non-autistic people need to assume more responsibility. We discuss how autistic adults experience the impact of society’s lack of understanding of autism on a daily basis whether they disclose or not, and that it is everybody’s responsibility to challenge negative stereotypes and promote a more inclusive society

3C. 3-Ketosteroid receptors (version 2019.4) in the IUPHAR/BPS Guide to Pharmacology Database

Steroid hormone receptors (nomenclature as agreed by the NC-IUPHAR Subcommittee on Nuclear Hormone Receptors [65, 193]) are nuclear hormone receptors of the NR3 class, with endogenous agonists that may be divided into 3-hydroxysteroids (estrone and 17β-estradiol) and 3-ketosteroids (dihydrotestosterone [DHT], aldosterone, cortisol, corticosterone, progesterone and testosterone)

3C. 3-Ketosteroid receptors in GtoPdb v.2023.1

Steroid hormone receptors (nomenclature as agreed by the NC-IUPHAR Subcommittee on Nuclear Hormone Receptors [75, 218, 3]) are nuclear hormone receptors of the NR3 class, with endogenous agonists that may be divided into 3-hydroxysteroids (estrone and 17β-estradiol) and 3-ketosteroids (dihydrotestosterone [DHT], aldosterone, cortisol, corticosterone, progesterone and testosterone). For rodent GR and MR, the physiological ligand is corticosterone rather than cortisol

3C. 3-Ketosteroid receptors in GtoPdb v.2021.3

Steroid hormone receptors (nomenclature as agreed by the NC-IUPHAR Subcommittee on Nuclear Hormone Receptors [74, 215, 3]) are nuclear hormone receptors of the NR3 class, with endogenous agonists that may be divided into 3-hydroxysteroids (estrone and 17β-estradiol) and 3-ketosteroids (dihydrotestosterone [DHT], aldosterone, cortisol, corticosterone, progesterone and testosterone). For rodent GR and MR, the physiological ligand is corticosterone rather than cortisol

The evolution of imprinting: chromosomal mapping of orthologues of mammalian imprinted domains in monotreme and marsupial mammals.

BACKGROUND: The evolution of genomic imprinting, the parental-origin specific expression of genes, is the subject of much debate. There are several theories to account for how the mechanism evolved including the hypothesis that it was driven by the evolution of X-inactivation, or that it arose from an ancestrally imprinted chromosome. RESULTS: Here we demonstrate that mammalian orthologues of imprinted genes are dispersed amongst autosomes in both monotreme and marsupial karyotypes. CONCLUSION: These data, along with the similar distribution seen in birds, suggest that imprinted genes were not located on an ancestrally imprinted chromosome or associated with a sex chromosome. Our results suggest imprinting evolution was a stepwise, adaptive process, with each gene/cluster independently becoming imprinted as the need arose.RIGHTS : This article is licensed under the BioMed Central licence at http://www.biomedcentral.com/about/license which is similar to the 'Creative Commons Attribution Licence'. In brief you may : copy, distribute, and display the work; make derivative works; or make commercial use of the work - under the following conditions: the original author must be given credit; for any reuse or distribution, it must be made clear to others what the license terms of this work are

Combination of a mitogen‐activated protein kinase inhibitor with the tyrosine kinase inhibitor pacritinib combats cell adhesion‐based residual disease and prevents re‐expansion of FLT3 ‐ITD acute myeloid leukaemia

Minimal residual disease (MRD) in acute myeloid leukaemia (AML) poses a major challenge due to drug insensitivity and high risk of relapse. Intensification of chemotherapy and stem cell transplantation are often pivoted on MRD status. Relapse rates are high even with the integration of first‐generation FMS‐like tyrosine kinase 3 (FLT3) inhibitors in pre‐ and post‐transplant regimes and as maintenance in FLT3 ‐mutated AML. Pre‐clinical progress is hampered by the lack of suitable modelling of residual disease and post‐therapy relapse. In the present study, we investigated the nature of pro‐survival signalling in primary residual tyrosine kinase inhibitor (TKI)‐treated AML cells adherent to stroma and further determined their drug sensitivity in order to inform rational future therapy combinations. Using a primary human leukaemia‐human stroma model to mimic the cell–cell interactions occurring in patients, the ability of several TKIs in clinical use, to abrogate stroma‐driven leukaemic signalling was determined, and a synergistic combination with a mitogen‐activated protein kinase (MEK) inhibitor identified for potential therapeutic application in the MRD setting. The findings reveal a common mechanism of stroma‐mediated resistance that may be independent of mutational status but can be targeted through rational drug design, to eradicate MRD and reduce treatment‐related toxicity

A holistic review of the medical school admission process: examining correlates of academic underperformance

Background: Despite medical school admission committees’ best efforts, a handful of seemingly capable students invariably struggle during their first year of study. Yet, even as entrance criteria continue to broaden beyond cognitive qualifications, attention inevitably reverts back to such factors when seeking to understand these phenomena. Using a host of applicant, admission, and post-admission variables, the purpose of this inductive study, then, was to identify a constellation of student characteristics that, taken collectively, would be predictive of students at-risk of underperforming during the first year of medical school. In it, we hypothesize that a wider range of factors than previously recognized could conceivably play roles in understanding why students experience academic problems early in the medical educational continuum. Methods: The study sample consisted of the five most recent matriculant cohorts from a large, southeastern medical school (n=537). Independent variables reflected: 1) the personal demographics of applicants (e.g., age, gender); 2) academic criteria (e.g., undergraduate grade point averages [GPA], medical college admission test); 3) selection processes (e.g., entrance track, interview scores, committee votes); and 4) other indicators of personality and professionalism (e.g., Mayer-Salovey-Caruso Emotional Intelligence Test™ emotional intelligence scores, NEO PI-R™ personality profiles, and appearances before the Professional Code Committee [PCC]). The dependent variable, first-year underperformance, was defined as ANY action (repeat, conditionally advance, or dismiss) by the college's Student Progress and Promotions Committee (SPPC) in response to predefined academic criteria. This study protocol was approved by the local medical institutional review board (IRB). Results: Of the 537 students comprising the study sample, 61 (11.4%) met the specified criterion for academic underperformance. Significantly increased academic risks were identified among students who 1) had lower mean undergraduate science GPAs (OR=0.24, p=0.001); 2) entered medical school via an accelerated BS/MD track (OR=16.15, p=0.002); 3) were 31 years of age or older (OR=14.76, p=0.005); and 4) were non-unanimous admission committee admits (OR=0.53, p=0.042). Two dimensions of the NEO PI-R™ personality inventory, openness (+) and conscientiousness (−), were modestly but significantly correlated with academic underperformance. Only for the latter, however, were mean scores found to differ significantly between academic performers and underperformers. Finally, appearing before the college's PCC (OR=4.21, p=0.056) fell just short of statistical significance. Conclusions: Our review of various correlates across the matriculation process highlights the heterogeneity of factors underlying students’ underperformance during the first year of medical school and challenges medical educators to understand the complexity of predicting who, among admitted matriculants, may be at future academic risk

Using 3D Imaging and Machine Learning to Predict Liveweight and Carcass Characteristics of Live Finishing Beef Cattle

Selection of finishing beef cattle for slaughter and evaluation of performance is currently achieved through visual assessment and/or by weighing through a crush. Consequently, large numbers of cattle are not meeting target specification at the abattoir. Video imaging analysis (VIA) is increasingly used in abattoirs to grade carcasses with high accuracy. There is potential for three-dimensional (3D) imaging to be used on farm to predict carcass characteristics of live animals and to optimise slaughter selections. The objectives of this study were to predict liveweight (LW) and carcass characteristics of live animals using 3D imaging technology and machine learning algorithms (artificial neural networks). Three dimensional images and LW's were passively collected from finishing steer and heifer beef cattle of a variety of breeds pre-slaughter (either on farm or after entry to the abattoir lairage) using an automated camera system. Sixty potential predictor variables were automatically extracted from the live animal 3D images using bespoke algorithms; these variables included lengths, heights, widths, areas, volumes, and ratios and were used to develop predictive models for liveweight and carcass characteristics. Cold carcass weights (CCW) for each animal were provided by the abattoir. Saleable meat yield (SMY) and EUROP fat and conformation grades were also determined for each individual by VIA of half of the carcass. Performance of prediction models was assessed using R2 and RMSE parameters following regression of predicted and actual variables for LW (R2 = 0.7, RMSE = 42), CCW (R2 = 0.88, RMSE = 14) and SMY (R2 = 0.72, RMSE = 14). The models predicted EUROP fat and conformation grades with 54 and 55% accuracy (R2), respectively. This study demonstrated that 3D imaging coupled with machine learning analytics can be used to predict LW, SMY and traditional carcass characteristics of live animals. This system presents an opportunity to reduce a considerable inefficiency in beef production enterprises through autonomous monitoring of finishing cattle on the farm and marketing of animals at the optimal time