Detection of emphysema progression in alpha 1-antitrypsin deficiency using CT densitometry; Methodological advances

<p>Abstract</p> <p>Background</p> <p>Computer tomography (CT) densitometry is a potential tool for detecting the progression of emphysema but the optimum methodology is uncertain. The level of inspiration affects reproducibility but the ability to adjust for this variable is facilitated by whole lung scanning methods. However, emphysema is frequently localised to sub-regions of the lung and targeted densitometric sampling may be more informative than whole lung assessment.</p> <p>Methods</p> <p>Emphysema progression over a 2-year interval was assessed in 71 patients (alpha 1-antitrypsin deficiency with PiZ phenotype) with CT densitometry, using the 15^thpercentile point (Perc15) and voxel index (VI) -950 Hounsfield Units (HU) and -910 HU (VI -950 and -910) on whole lung, limited single slices, and apical, central and basal thirds. The relationship between whole lung densitometric progression (ΔCT) and change in CT-derived lung volume (ΔCT_Vol) was characterised, and adjustment for lung volume using statistical modelling was evaluated.</p> <p>Results</p> <p>CT densitometric progression was statistically significant for all methods. ΔCT correlated with ΔCT_Voland linear regression indicated that nearly one half of lung density loss was secondary to apparent hyperinflation. The most accurate measure was obtained using a random coefficient model to adjust for lung volume and the greatest progression was detected by targeted sampling of the middle third of the lung.</p> <p>Conclusion</p> <p>Progressive hyperinflation may contribute significantly to loss of lung density, but volume effects and absolute tissue loss can be identified by statistical modelling. Targeted sampling of the middle lung region using Perc15 appears to be the most robust measure of emphysema progression.</p

Quantitative Computed Tomography in COPD: Possibilities and Limitations

Chronic obstructive pulmonary disease (COPD) is a heterogeneous disease that is characterized by chronic airflow limitation. Unraveling of this heterogeneity is challenging but important, because it might enable more accurate diagnosis and treatment. Because spirometry cannot distinguish between the different contributing pathways of airflow limitation, and visual scoring is time-consuming and prone to observer variability, other techniques are sought to start this phenotyping process. Quantitative computed tomography (CT) is a promising technique, because current CT technology is able to quantify emphysema, air trapping, and large airway wall dimensions. This review focuses on CT quantification techniques of COPD disease components and their current status and role in phenotyping COPD

Psychophysiology in games

Psychophysiology is the study of the relationship between psychology and its physiological manifestations. That relationship is of particular importance for both game design and ultimately gameplaying. Players’ psychophysiology offers a gateway towards a better understanding of playing behavior and experience. That knowledge can, in turn, be beneficial for the player as it allows designers to make better games for them; either explicitly by altering the game during play or implicitly during the game design process. This chapter argues for the importance of physiology for the investigation of player affect in games, reviews the current state of the art in sensor technology and outlines the key phases for the application of psychophysiology in games.The work is supported, in part, by the EU-funded FP7 ICT iLearnRWproject (project no: 318803).peer-reviewe

Population-level risks of alcohol consumption by amount, geography, age, sex, and year: a systematic analysis for the Global Burden of Disease Study 2020

Background The health risks associated with moderate alcohol consumption continue to be debated. Small amounts of alcohol might lower the risk of some health outcomes but increase the risk of others, suggesting that the overall risk depends, in part, on background disease rates, which vary by region, age, sex, and year. Methods For this analysis, we constructed burden-weighted dose–response relative risk curves across 22 health outcomes to estimate the theoretical minimum risk exposure level (TMREL) and non-drinker equivalence (NDE), the consumption level at which the health risk is equivalent to that of a non-drinker, using disease rates from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2020 for 21 regions, including 204 countries and territories, by 5-year age group, sex, and year for individuals aged 15–95 years and older from 1990 to 2020. Based on the NDE, we quantified the population consuming harmful amounts of alcohol. Findings The burden-weighted relative risk curves for alcohol use varied by region and age. Among individuals aged 15–39 years in 2020, the TMREL varied between 0 (95% uncertainty interval 0–0) and 0·603 (0·400–1·00) standard drinks per day, and the NDE varied between 0·002 (0–0) and 1·75 (0·698–4·30) standard drinks per day. Among individuals aged 40 years and older, the burden-weighted relative risk curve was J-shaped for all regions, with a 2020 TMREL that ranged from 0·114 (0–0·403) to 1·87 (0·500–3·30) standard drinks per day and an NDE that ranged between 0·193 (0–0·900) and 6·94 (3·40–8·30) standard drinks per day. Among individuals consuming harmful amounts of alcohol in 2020, 59·1% (54·3–65·4) were aged 15–39 years and 76·9% (73·0–81·3) were male. Interpretation There is strong evidence to support recommendations on alcohol consumption varying by age and location. Stronger interventions, particularly those tailored towards younger individuals, are needed to reduce the substantial global health loss attributable to alcohol. Funding Bill & Melinda Gates Foundation

Controllability of structural brain networks

Cognitive function is driven by dynamic interactions between large-scale neural circuits or networks, enabling behaviour. However, fundamental principles constraining these dynamic network processes have remained elusive. Here we use tools from control and network theories to offer a mechanistic explanation for how the brain moves between cognitive states drawn from the network organization of white matter microstructure. Our results suggest that densely connected areas, particularly in the default mode system, facilitate the movement of the brain to many easily reachable states. Weakly connected areas, particularly in cognitive control systems, facilitate the movement of the brain to difficult-to-reach states. Areas located on the boundary between network communities, particularly in attentional control systems, facilitate the integration or segregation of diverse cognitive systems. Our results suggest that structural network differences between cognitive circuits dictate their distinct roles in controlling trajectories of brain network function