The relationship between cognitive reserve and neuroplasticity in older adults

This item is only available electronically.Background: Cognitive Reserve (CR) is suggested to explain the difference between the expected impact of levels of age-related neuropathology and the real deficits which people experience. Neuroplasticity is speculated to be the neurophysiological mechanism underlying the cognition-protective effects of CR; however, this has not previously been experimentally demonstrated. Aim: To identify whether neuroplasticity mediates the relationship between CR and cognitive ability. Method: 23 healthy older adults participated in this study, which comprised 3 brain stimulation sessions: (1) continuous theta-burst stimulation (cTBS) applied to left dorsolateral prefrontal cortex, (2) cTBS applied to left motor cortex, and (3) a sham session. Resting electroencephalography (EEG) was used to calculate change in the aperiodic slope of neural power spectra (a novel measure of neuroplasticity) following cTBS. Participants were also assessed with measures of CR (lifetime of experiences; crystallised intelligence) and cognitive ability (fluid intelligence; paired associates learning). Results: We induced a neuroplasticity-like effect in both of the active cTBS conditions. This was not observed in the sham condition. We did not observe a significant relationship between neuroplasticity and CR or cognitive ability. This meant mediational analysis was not justified. Conclusions: We successfully demonstrated that analysis of the aperiodic slope is an effective means of identifying neuroplasticity with EEG. While we did not identify a significant relationship between our neuroplasticity measure and CR, we recommend further studies investigate other forms of neuroplasticity. Continued investigation of the neurophysiology underlying CR may facilitate the development of early interventions which could reduce the prevalence of age-related cognitive impairment.Thesis (B.PsychSc(Hons)) -- University of Adelaide, School of Psychology, 202

Closing critical gaps to enable a circular plastics economy

The request of a preliminary design of a plastic pyoil separation unit has been requested, downstream from a pyrolysis unit, converting plastic into usable oil, called pyoil. This design is focused on removing trace impurities from the pyoil stream and separating it into four separate cuts: py gas, naphtha, gas oil, and heavy resid. The py gas is used as a downstream fuel and is a vapor. The naphtha and gas oil (also called the light cut and medium cut, respectively) is used downstream in a steam cracker to produce valuable ethylene. The process that has been designed contains a series of adsorption columns, standing 36 ft tall and 6.5 ft in diameter, using PuriCycle H and HP catalysts (adsorbents) to remove trace elements. After these elements are removed, the feed enters a 42 stage, 95 ft tall, 5.5 ft diameter single multi-cut distillation column, charged with the separation into the four aforementioned streams. Once the separation has been performed, the products are cooled and pumped into storage tanks. The medium cut is also used to pre-heat the feed stream to save energy costs.Economics play a large part in the feasibility of a preliminary design, and capital costs and variable and fixed operating costs have been calculated. Preliminary design estimates vary from -20% to +40%, and the calculated values reflect this. Capital costs are estimated at $3,255,000, fixed operating costs are$340,000 annually, and variable operating costs are $2,087,000 annually.For general safety, situations where power is lost, pressure increases, and controller failure have been evaluated. For power loss, the control valves have the appropriate orientation, there are pressure relief systems in place on vulnerable equipment, and there are alarms in place for controller failures in order to prevent extreme situations within the process.In order to close the quantity, quality, and affordability gaps, it is recommended to create collection centers in the community, where citizens can recycle their plastic and get paid. In addition, the installation of a drum separator is highly recommended. The improvements would serve to increase the amount of recyclables collected, reduce the cost of sorting the recyclables, and raise the quality of the recyclables so they could be used in the pyrolysis plant

The Relationship between Body Mass Index and Mammographic Density during a Premenopausal Weight Loss Intervention Study.

We evaluated the association between short-term change in body mass index (BMI) and breast density during a 1 year weight-loss intervention (Manchester, UK). We included 65 premenopausal women (35-45 years, ≥7 kg adult weight gain, family history of breast cancer). BMI and breast density (semi-automated area-based, automated volume-based) were measured at baseline, 1 year, and 2 years after study entry (1 year post intervention). Cross-sectional (between-women) and short-term change (within-women) associations between BMI and breast density were measured using repeated-measures correlation coefficients and multivariable linear mixed models. BMI was positively correlated with dense volume between-women (r = 0.41, 95%CI: 0.17, 0.61), but less so within-women (r = 0.08, 95%CI: -0.16, 0.28). There was little association with dense area (between-women r = -0.12, 95%CI: -0.38, 0.16; within-women r = 0.01, 95%CI: -0.24, 0.25). BMI and breast fat were positively correlated (volume: between r = 0.77, 95%CI: 0.69, 0.84, within r = 0.58, 95%CI: 0.36, 0.75; area: between r = 0.74, 95%CI: 0.63, 0.82, within r = 0.45, 95%CI: 0.23, 0.63). Multivariable models reported similar associations. Exploratory analysis suggested associations between BMI gain from 20 years and density measures (standard deviation change per +5 kg/m2 BMI: dense area: +0.61 (95%CI: 0.12, 1.09); fat volume: -0.31 (95%CI: -0.62, 0.00)). Short-term BMI change is likely to be positively associated with breast fat, but we found little association with dense tissue, although power was limited by small sample size

How do predisposing factors differ between delirium motor subtypes? A systematic review and meta-analysis

BACKGROUND: Delirium is a common neurocognitive disorder in hospitalised older adults with vast negative consequences. The predominant method of subtyping delirium is by motor activity profile into hypoactive, hyperactive and mixed groups. OBJECTIVE: This systematic review and meta-analysis investigated how predisposing factors differ between delirium motor subtypes. METHODS: Databases (Medline, PsycINFO, Embase) were systematically searched for studies reporting predisposing factors (prior to delirium) for delirium motor subtypes. A total of 61 studies met inclusion criteria (N = 14,407, mean age 73.63 years). Random-effects meta-analyses synthesised differences between delirium motor subtypes relative to 22 factors. RESULTS: Hypoactive cases were older, had poorer cognition and higher physical risk scores than hyperactive cases and were more likely to be women, living in care homes, taking more medications, with worse functional performance and history of cerebrovascular disease than all remaining subtypes. Hyperactive cases were younger than hypoactive and mixed subtypes and were more likely to be men, with better cognition and lower physical risk scores than all other subtypes. Those with no motor subtype (unable to be classified) were more likely to be women and have better functional performance. Effect sizes were small. CONCLUSIONS: Important differences in those who develop motor subtypes of delirium were shown prior to delirium occurrence. We provide robust quantitative evidence for a common clinical assumption that indices of frailty (institutional living, cognitive and functional impairment) are seen more in hypoactive patients. Motor subtypes should be measured across delirium research. Motor subtyping has great potential to improve the clinical risk assessment and management of delirium

Exploring the prediction performance for breast cancer risk based on volumetric mammographic density at different thresholds

Background The percentage of mammographic dense tissue (PD) defined by pixel value threshold is a well-established risk factor for breast cancer. Recently there has been some evidence to suggest that an increased threshold based on visual assessment could improve risk prediction. It is unknown, however, whether this also applies to volumetric density using digital raw mammograms. Method Two case-control studies nested within a screening cohort (ages of participants 46–73 years) from Manchester UK were used. In the first study (317 cases and 947 controls) cases were detected at the first screen; whereas in the second study (318 cases and 935 controls), cases were diagnosed after the initial mammogram. Volpara software was used to estimate dense tissue height at each pixel point, and from these, volumetric and area-based PD were computed at a range of thresholds. Volumetric and area-based PDs were evaluated using conditional logistic regression, and their predictive ability was assessed using the Akaike information criterion (AIC) and matched concordance index (mC). Results The best performing volumetric PD was based on a threshold of 5 mm of dense tissue height (which we refer to as VPD5), and the best areal PD was at a threshold level of 6 mm (which we refer to as APD6), using pooled data and in both studies separately. VPD5 showed a modest improvement in prediction performance compared to the original volumetric PD by Volpara with ΔAIC = 5.90 for the pooled data. APD6, on the other hand, shows much stronger evidence for better prediction performance, with ΔAIC = 14.52 for the pooled data, and mC increased slightly from 0.567 to 0.577. Conclusion These results suggest that imposing a 5 mm threshold on dense tissue height for volumetric PD could result in better prediction of cancer risk. There is stronger evidence that area-based density with a 6 mm threshold gives better prediction than the original volumetric density metric

A novel and fully automated mammographic texture analysis for risk prediction : results from two case-control studies

BACKGROUND: The percentage of mammographic dense tissue (PD) is an important risk factor for breast cancer, and there is some evidence that texture features may further improve predictive ability. However, relatively little work has assessed or validated textural feature algorithms using raw full field digital mammograms (FFDM). METHOD: A case-control study nested within a screening cohort (age 46-73 years) from Manchester UK was used to develop a texture feature risk score (264 cases diagnosed at the same time as mammogram of the contralateral breast, 787 controls) using the least absolute shrinkage and selection operator (LASSO) method for 112 features, and validated in a second case-control study from the same cohort but with cases diagnosed after the index mammogram (317 cases, 931 controls). Predictive ability was assessed using deviance and matched concordance index (mC). The ability to improve risk estimation beyond percent volumetric density (Volpara) was evaluated using conditional logistic regression. RESULTS: The strongest features identified in the training set were "sum average" based on the grey-level co-occurrence matrix at low image resolutions (original resolution 10.628 pixels per mm; downsized by factors of 16, 32 and 64), which had a better deviance and mC than volumetric PD. In the validation study, the risk score combining the three sum average features achieved a better deviance than volumetric PD (Deltachi2 = 10.55 or 6.95 if logarithm PD) and a similar mC to volumetric PD (0.58 and 0.57, respectively). The risk score added independent information to volumetric PD (Deltachi2 = 14.38, p = 0.0008). CONCLUSION: Textural features based on digital mammograms improve risk assessment beyond volumetric percentage density. The features and risk score developed need further investigation in other settings

The effects of computerised cognitive training on post-CABG delirium and cognitive change: A prospective randomised controlled trial

Background: Cognitive impairments, including delirium, are common after coronary artery bypass grafting (CABG). Improving cognition pre- and post-operatively using computerised cognitive training (CCT) may be an effective approach to improve cognitive outcomes in CABG patients. Objectives: Investigate the effect of remotely supervised CCT on cognitive outcomes, including delirium, in older adults undergoing CABG surgery. Methods: Thirty-six participants, were analysed in a single-blinded randomised controlled trial (CCT Intervention: n = 18, Control: n = 18). CCT was completed by the intervention group pre-operatively (every other day, 45–60-minute sessions until surgery) and post-operatively, beginning 1-month post-CABG (3 x 45–60-minute sessions/week for 12-weeks), while the control group maintained usual care plus weekly phone calls. Cognitive assessments were conducted pre- and post-operatively at multiple follow-ups (discharge, 4-months and 6-months). Post-operative delirium incidence was assessed daily until discharge. Cognitive change data were calculated at each follow-up for each cognitive test (Addenbrooke’s Cognitive Examination III and CANTAB; z-scored). Results: Adherence to the CCT intervention (completion of three pre-operative or 66% of post-operative sessions) was achieved in 68% of pre-CABG and 59% of post-CABG participants. There were no statistically significant effects of CCT on any cognitive outcome, including delirium incidence. Conclusion: Adherence to the CCT program was comparatively higher than previous feasibility studies, possibly due to the level of supervision and support provided (blend of face-to-face and home-based training, with support phone calls). Implementing CCT interventions both pre- and post-operatively is feasible in those undergoing CABG. No statistically significant benefits from the CCT interventions were identified for delirium or cognitive function post-CABG, likely due to the sample size available (study recruitment greatly impacted by COVID-19). It also may be the case that multimodal intervention would be more effective

The Relationship between Body Mass Index and Mammographic Density during a Premenopausal Weight Loss Intervention Study

From MDPI via Jisc Publications RouterHistory: accepted 2021-06-18, pub-electronic 2021-06-29Publication status: PublishedFunder: Cancer Research UK; Grant(s): C569/A16891, IS-BRC-1215-20007We evaluated the association between short-term change in body mass index (BMI) and breast density during a 1 year weight-loss intervention (Manchester, UK). We included 65 premenopausal women (35–45 years, ≥7 kg adult weight gain, family history of breast cancer). BMI and breast density (semi-automated area-based, automated volume-based) were measured at baseline, 1 year, and 2 years after study entry (1 year post intervention). Cross-sectional (between-women) and short-term change (within-women) associations between BMI and breast density were measured using repeated-measures correlation coefficients and multivariable linear mixed models. BMI was positively correlated with dense volume between-women (r = 0.41, 95%CI: 0.17, 0.61), but less so within-women (r = 0.08, 95%CI: −0.16, 0.28). There was little association with dense area (between-women r = −0.12, 95%CI: −0.38, 0.16; within-women r = 0.01, 95%CI: −0.24, 0.25). BMI and breast fat were positively correlated (volume: between r = 0.77, 95%CI: 0.69, 0.84, within r = 0.58, 95%CI: 0.36, 0.75; area: between r = 0.74, 95%CI: 0.63, 0.82, within r = 0.45, 95%CI: 0.23, 0.63). Multivariable models reported similar associations. Exploratory analysis suggested associations between BMI gain from 20 years and density measures (standard deviation change per +5 kg/m2 BMI: dense area: +0.61 (95%CI: 0.12, 1.09); fat volume: −0.31 (95%CI: −0.62, 0.00)). Short-term BMI change is likely to be positively associated with breast fat, but we found little association with dense tissue, although power was limited by small sample size

Visually assessed breast density, breast cancer risk and the importance of the craniocaudal view

Contains fulltext : 69403.pdf (publisher's version ) (Open Access)INTRODUCTION: Mammographic density is known to be a strong risk factor for breast cancer. A particularly strong association with risk has been observed when density is measured using interactive threshold software. This, however, is a labour-intensive process for large-scale studies. METHODS: Our aim was to determine the performance of visually assessed percent breast density as an indicator of breast cancer risk. We compared the effect on risk of density as measured with the mediolateral oblique view only versus that estimated as the average density from the mediolateral oblique view and the craniocaudal view. Density was assessed using a visual analogue scale in 10,048 screening mammograms, including 311 breast cancer cases diagnosed at that screening episode or within the following 6 years. RESULTS: Where only the mediolateral oblique view was available, there was a modest effect of breast density on risk with an odds ratio for the 76% to 100% density relative to 0% to 25% of 1.51 (95% confidence interval 0.71 to 3.18). When two views were available, there was a considerably stronger association, with the corresponding odds ratio being 6.77 (95% confidence interval 2.75 to 16.67). CONCLUSION: This indicates that a substantial amount of information on risk from percentage breast density is contained in the second view. It also suggests that visually assessed breast density has predictive potential for breast cancer risk comparable to that of density measured using the interactive threshold software when two views are available. This observation needs to be confirmed by studies applying the different measurement methods to the same individuals

Mammographic density change in a cohort of premenopausal women receiving tamoxifen for breast cancer prevention over 5 years.

BACKGROUND: A decrease in breast density due to tamoxifen preventive therapy might indicate greater benefit from the drug. It is not known whether mammographic density continues to decline after 1 year of therapy, or whether measures of breast density change are sufficiently stable for personalised recommendations. METHODS: Mammographic density was measured annually over up to 5 years in premenopausal women with no previous diagnosis of breast cancer but at increased risk of breast cancer attending a family-history clinic in Manchester, UK (baseline 2010-2013). Tamoxifen (20 mg/day) for prevention was prescribed for up to 5 years in one group; the other group did not receive tamoxifen and were matched by age. Fully automatic methods were used on mammograms over the 5-year follow-up: three area-based measures (NN-VAS, Stratus, Densitas) and one volumetric (Volpara). Additionally, percentage breast density at baseline and first follow-up mammograms was measured visually. The size of density declines at the first follow-up mammogram and thereafter was estimated using a linear mixed model adjusted for age and body mass index. The stability of density change at 1 year was assessed by evaluating mean squared error loss from predictions based on individual or mean density change at 1 year. RESULTS: Analysis used mammograms from 126 healthy premenopausal women before and as they received tamoxifen for prevention (median age 42 years) and 172 matched controls (median age 41 years), with median 3 years follow-up. There was a strong correlation between percentage density measures used on the same mammogram in both the tamoxifen and no tamoxifen groups (all correlation coeficients > 0.8). Tamoxifen reduced mean breast density in year 1 by approximately 17-25% of the inter-quartile range of four automated percentage density measures at baseline, and from year 2, it decreased further by approximately 2-7% per year. Predicting change at 2 years using individual change at 1 year was approximately 60-300% worse than using mean change at 1year. CONCLUSIONS: All measures showed a consistent and large average tamoxifen-induced change in density over the first year, and a continued decline thereafter. However, these measures of density change at 1 year were not stable on an individual basis