Risk Model-Based Lung Cancer Screening and Racial and Ethnic Disparities in the US

Importance The revised 2021 US Preventive Services Task Force (USPSTF) guidelines for lung cancer screening have been shown to reduce disparities in screening eligibility and performance between African American and White individuals vs the 2013 guidelines. However, potential disparities across other racial and ethnic groups in the US remain unknown. Risk model–based screening may reduce racial and ethnic disparities and improve screening performance, but neither validation of key risk prediction models nor their screening performance has been examined by race and ethnicity.Objective To validate and recalibrate the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial 2012 (PLCOm2012) model—a well-established risk prediction model based on a predominantly White population—across races and ethnicities in the US and evaluate racial and ethnic disparities and screening performance through risk-based screening using PLCOm2012 vs the USPSTF 2021 criteria.Design, Setting, and Participants In a population-based cohort design, the Multiethnic Cohort Study enrolled participants in 1993-1996, followed up through December 31, 2018. Data analysis was conducted from April 1, 2022, to May 19. 2023. A total of 105 261 adults with a smoking history were included.Exposures The 6-year lung cancer risk was calculated through recalibrated PLCOm2012 (ie, PLCOm2012-Update) and screening eligibility based on a 6-year risk threshold greater than or equal to 1.3%, yielding similar eligibility as the USPSTF 2021 guidelines.Outcomes Predictive accuracy, screening eligibility-incidence (E-I) ratio (ie, ratio of the number of eligible to incident cases), and screening performance (sensitivity, specificity, and number needed to screen to detect 1 lung cancer).Results Of 105 261 participants (60 011 [57.0%] men; mean [SD] age, 59.8 [8.7] years), consisting of 19 258 (18.3%) African American, 27 227 (25.9%) Japanese American, 21 383 (20.3%) Latino, 8368 (7.9%) Native Hawaiian/Other Pacific Islander, and 29 025 (27.6%) White individuals, 1464 (1.4%) developed lung cancer within 6 years from enrollment. The PLCOm2012-Update showed good predictive accuracy across races and ethnicities (area under the curve, 0.72-0.82). The USPSTF 2021 criteria yielded a large disparity among African American individuals, whose E-I ratio was 53% lower vs White individuals (E-I ratio: 9.5 vs 20.3; P < .001). Under the risk-based screening (PLCOm2012-Update 6-year risk ≥1.3%), the disparity between African American and White individuals was substantially reduced (E-I ratio: 15.9 vs 18.4; P < .001), with minimal disparities observed in persons of other minoritized groups, including Japanese American, Latino, and Native Hawaiian/Other Pacific Islander. Risk-based screening yielded superior overall and race and ethnicity–specific performance to the USPSTF 2021 criteria, with higher overall sensitivity (67.2% vs 57.7%) and lower number needed to screen (26 vs 30) at similar specificity (76.6%).Conclusions The findings of this cohort study suggest that risk-based lung cancer screening can reduce racial and ethnic disparities and improve screening performance across races and ethnicities vs the USPSTF 2021 criteria

How should performance in EBUS mediastinal staging in lung cancer be measured?

There has been a paradigm shift in mediastinal staging algorithms in non-small cell lung cancer over the last decade in the United Kingdom (UK). This has seen endoscopic nodal staging (predominantly endobronchial ultrasound, EBUS) almost replace surgical staging (predominantly mediastinoscopy) as the pathological staging procedure of first choic

Physiological and Biomechanical Responses of Highly Trained Distance Runners to Lower-Body Positive Pressure Treadmill Running

Background: As a way to train at faster running speeds, add training volume, prevent injury, or rehabilitate after an injury, lower-body positive pressure treadmills (LBPPT) have become increasingly commonplace among athletes. However, there are conflicting evidence and a paucity of data describing the physiological and biomechanical responses to LBPPT running in highly trained or elite caliber runners at the running speeds they habitually train at, which are considerably faster than those of recreational runners. Furthermore, data is lacking regarding female runners’ responses to LBPPT running. Therefore, this study was designed to evaluate the physiological and biomechanical responses to LBPPT running in highly trained male and female distance runners. Methods: Fifteen highly trained distance runners (seven male; eight female) completed a single running test composed of 4 × 9-min interval series at fixed percentages of body weight ranging from 0 to 30% body weight support (BWS) in 10% increments on LBPPT. The first interval was always conducted at 0% BWS; thereafter, intervals at 10, 20, and 30% BWS were conducted in random order. Each interval consisted of three stages of 3 min each, at velocities of 14.5, 16.1, and 17.7 km·h−1 for men and 12.9, 14.5, and 16.1 km·h−1 for women. Expired gases, ventilation, breathing frequency, heart rate (HR), rating of perceived exertion (RPE), and stride characteristics were measured during each running speed and BWS. Results: Male and female runners had similar physiological and biomechanical responses to running on LBPPT. Increasing BWS increased stride length (p \u3c 0.02) and flight duration (p \u3c 0.01) and decreased stride rate (p \u3c 0.01) and contact time (p \u3c 0.01) in small-large magnitudes. There was a large attenuation of oxygen consumption (VO2) relative to BWS (p \u3c 0.001), while there were trivial-moderate reductions in respiratory exchange ratio, minute ventilation, and respiratory frequency (p \u3e 0.05), and small-large effects on HR and RPE (p \u3c 0.01). There were trivial-small differences in VE, respiratory frequency, HR, and RPE for a given VO2 across various BWS (p \u3e 0.05). Conclusions: The results indicate the male and female distance runners have similar physiological and biomechanical responses to LBPPT running. Overall, the biomechanical changes during LBPPT running all contributed to less metabolic cost and corresponding physiological changes. Keywords: AlterG, Lower-body positive pressure, Body weight support, Anti-gravity, Running, Stride characteristics, Physiological characteristics, Metabolic demand, Oxygen demand, Oxygen cos

A Bayesian Framework to Account for Complex Non-Genetic Factors in Gene Expression Levels Greatly Increases Power in eQTL Studies

Gene expression measurements are influenced by a wide range of factors, such as the state of the cell, experimental conditions and variants in the sequence of regulatory regions. To understand the effect of a variable of interest, such as the genotype of a locus, it is important to account for variation that is due to confounding causes. Here, we present VBQTL, a probabilistic approach for mapping expression quantitative trait loci (eQTLs) that jointly models contributions from genotype as well as known and hidden confounding factors. VBQTL is implemented within an efficient and flexible inference framework, making it fast and tractable on large-scale problems. We compare the performance of VBQTL with alternative methods for dealing with confounding variability on eQTL mapping datasets from simulations, yeast, mouse, and human. Employing Bayesian complexity control and joint modelling is shown to result in more precise estimates of the contribution of different confounding factors resulting in additional associations to measured transcript levels compared to alternative approaches. We present a threefold larger collection of cis eQTLs than previously found in a whole-genome eQTL scan of an outbred human population. Altogether, 27% of the tested probes show a significant genetic association in cis, and we validate that the additional eQTLs are likely to be real by replicating them in different sets of individuals. Our method is the next step in the analysis of high-dimensional phenotype data, and its application has revealed insights into genetic regulation of gene expression by demonstrating more abundant cis-acting eQTLs in human than previously shown. Our software is freely available online at http://www.sanger.ac.uk/resources/software/peer/

Kernel-imbedded Gaussian processes for disease classification using microarray gene expression data

BACKGROUND: Designing appropriate machine learning methods for identifying genes that have a significant discriminating power for disease outcomes has become more and more important for our understanding of diseases at genomic level. Although many machine learning methods have been developed and applied to the area of microarray gene expression data analysis, the majority of them are based on linear models, which however are not necessarily appropriate for the underlying connection between the target disease and its associated explanatory genes. Linear model based methods usually also bring in false positive significant features more easily. Furthermore, linear model based algorithms often involve calculating the inverse of a matrix that is possibly singular when the number of potentially important genes is relatively large. This leads to problems of numerical instability. To overcome these limitations, a few non-linear methods have recently been introduced to the area. Many of the existing non-linear methods have a couple of critical problems, the model selection problem and the model parameter tuning problem, that remain unsolved or even untouched. In general, a unified framework that allows model parameters of both linear and non-linear models to be easily tuned is always preferred in real-world applications. Kernel-induced learning methods form a class of approaches that show promising potentials to achieve this goal. RESULTS: A hierarchical statistical model named kernel-imbedded Gaussian process (KIGP) is developed under a unified Bayesian framework for binary disease classification problems using microarray gene expression data. In particular, based on a probit regression setting, an adaptive algorithm with a cascading structure is designed to find the appropriate kernel, to discover the potentially significant genes, and to make the optimal class prediction accordingly. A Gibbs sampler is built as the core of the algorithm to make Bayesian inferences. Simulation studies showed that, even without any knowledge of the underlying generative model, the KIGP performed very close to the theoretical Bayesian bound not only in the case with a linear Bayesian classifier but also in the case with a very non-linear Bayesian classifier. This sheds light on its broader usability to microarray data analysis problems, especially to those that linear methods work awkwardly. The KIGP was also applied to four published microarray datasets, and the results showed that the KIGP performed better than or at least as well as any of the referred state-of-the-art methods did in all of these cases. CONCLUSION: Mathematically built on the kernel-induced feature space concept under a Bayesian framework, the KIGP method presented in this paper provides a unified machine learning approach to explore both the linear and the possibly non-linear underlying relationship between the target features of a given binary disease classification problem and the related explanatory gene expression data. More importantly, it incorporates the model parameter tuning into the framework. The model selection problem is addressed in the form of selecting a proper kernel type. The KIGP method also gives Bayesian probabilistic predictions for disease classification. These properties and features are beneficial to most real-world applications. The algorithm is naturally robust in numerical computation. The simulation studies and the published data studies demonstrated that the proposed KIGP performs satisfactorily and consistently

Social Modulation during Songbird Courtship Potentiates Midbrain Dopaminergic Neurons

Synaptic transmission onto dopaminergic neurons of the mammalian ventral tegmental area (VTA) can be potentiated by acute or chronic exposure to addictive drugs. Because rewarding behavior, such as social affiliation, can activate the same neural circuitry as addictive drugs, we tested whether the intense social interaction of songbird courtship may also potentiate VTA synaptic function. We recorded glutamatergic synaptic currents from VTA of male zebra finches who had experienced distinct social and behavioral conditions during the previous hour. The level of synaptic transmission to VTA neurons, as assayed by the ratio of α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) to N-methyl-D-aspartic acid (NMDA) glutamate receptor mediated synaptic currents, was increased after males sang to females, and also after they saw females without singing, but not after they sang while alone. Potentiation after female exposure alone did not appear to result from stress, as it was not blocked by inhibition of glucocorticoid receptors. This potentiation was restricted to synapses of dopaminergic projection neurons, and appeared to be expressed postsynaptically. This study supports a model in which VTA dopaminergic neurons are more strongly activated during singing used for courtship than during non-courtship singing, and thus can provide social context-dependent modulation to forebrain areas. More generally, these results demonstrate that an intense social encounter can trigger the same pathways of neuronal plasticity as addictive drugs

Limits on WWZ and WW\gamma couplings from p\bar{p}\to e\nu jj X events at \sqrt{s} = 1.8 TeV

We present limits on anomalous WWZ and WW-gamma couplings from a search for WW and WZ production in p-bar p collisions at sqrt(s)=1.8 TeV. We use p-bar p -> e-nu jjX events recorded with the D0 detector at the Fermilab Tevatron Collider during the 1992-1995 run. The data sample corresponds to an integrated luminosity of 96.0+-5.1 pb^(-1). Assuming identical WWZ and WW-gamma coupling parameters, the 95% CL limits on the CP-conserving couplings are -0.33<lambda<0.36 (Delta-kappa=0) and -0.43<Delta-kappa<0.59 (lambda=0), for a form factor scale Lambda = 2.0 TeV. Limits based on other assumptions are also presented.Comment: 11 pages, 2 figures, 2 table

Search for New Physics in e mu X Data at D0 Using Sleuth: A Quasi-Model-Independent Search Strategy for New Physics

We present a quasi-model-independent search for the physics responsible for electroweak symmetry breaking. We define final states to be studied, and construct a rule that identifies a set of relevant variables for any particular final state. A new algorithm ("Sleuth") searches for regions of excess in those variables and quantifies the significance of any detected excess. After demonstrating the sensitivity of the method, we apply it to the semi-inclusive channel e mu X collected in 108 pb^-1 of ppbar collisions at sqrt(s) = 1.8 TeV at the D0 experiment during 1992-1996 at the Fermilab Tevatron. We find no evidence of new high p_T physics in this sample.Comment: 23 pages, 12 figures. Submitted to Physical Review

Search For Heavy Pointlike Dirac Monopoles

We have searched for central production of a pair of photons with high transverse energies in $p\bar p$ collisions at $\sqrt{s} = 1.8$ TeV using $70 pb^{-1}$ of data collected with the D\O detector at the Fermilab Tevatron in 1994--1996. If they exist, virtual heavy pointlike Dirac monopoles could rescatter pairs of nearly real photons into this final state via a box diagram. We observe no excess of events above background, and set lower 95% C.L. limits of $610, 870, or 1580 GeV/c^2$ on the mass of a spin 0, 1/2, or 1 Dirac monopole.Comment: 12 pages, 4 figure

The Dijet Mass Spectrum and a Search for Quark Compositeness in bar{p}p Collisions at sqrt{s} = 1.8 TeV

Using the DZero detector at the 1.8 TeV pbarp Fermilab Tevatron collider, we have measured the inclusive dijet mass spectrum in the central pseudorapidity region |eta_jet| < 1.0 for dijet masses greater than 200 Gev/c^2. We have also measured the ratio of spectra sigma(|eta_jet| < 0.5)/sigma(0.5 < |eta_jet| < 1.0). The order alpha_s^3 QCD predictions are in good agreement with the data and we rule out models of quark compositeness with a contact interaction scale < 2.4 TeV at the 95% confidence level.Comment: 11 pages, 4 figures, 2 tables, submitted to Phys. Rev. Let