Billy Budd, The Caine Mutiny, and Fiedler's Contingency Model : an inferential analysis of leadership

The purpose of this study was to show that the characters in leadership positions (formal and informal) portrayed in Billy Budd and The Caine Mutiny performed predictably with respect to the parameters of the Fiedler Contingency Model of leadership effectiveness. Qualitative inferences concerning character motivations and actions were derived by using the Contingency Model as a baseline for analysis of various leadership behaviors. The two novels selected for this study provided realistic microcosms of bureaucratic organizations in situations which revealed both task-motivated and relationship-motivated leadership types

Hope, optimism and survival in a randomized trial of chemotherapy for metastatic colorectal cancer

Purpose: Psychological responses to cancer are widely believed to affect survival. We investigated associations between hope, optimism, anxiety, depression, health utility and survival in patients starting first line chemotherapy for metastatic colorectal cancer. Methods: 429 subjects with metastatic colorectal cancer in a randomised controlled trial of chemotherapy, completed baseline questionnaires assessing: hopefulness, optimism, anxiety and depression and health utility. Hazard ratios (HR) and P-values were calculated with Cox models for overall survival (OS) and progression-free survival (PFS) in univariable and multivariable analyses. Results: Median follow-up was 31 months. Univariable analyses showed that OS was associated negatively with depression (HR 2.04, P<0.001), and positively with health utility (HR 0.56, P<0.001) and hopefulness (HR 0.75, P=0.013). In multivariable analysis, OS was also associated negatively with depression (HR 1.72, P<0.001), and positively with health utility (HR 0.73, P=0.014), but not with optimism, anxiety or hopefulness. PFS was not associated with hope, optimism, anxiety or depression in any analyses. Conclusions: Depression and health utility, but not optimism, hope, or anxiety were associated with survival after controlling for known prognostic factors in patients with advanced colorectal cancer. Further research is required to understand the nature of the relationship between depression and survival. If a causal mechanism is identified, this may lead to interventional possibilities

Predicting sporadic Alzheimer's disease progression via inherited Alzheimer's disease‐informed machine‐learning

Introduction Developing cross‐validated multi‐biomarker models for the prediction of the rate of cognitive decline in Alzheimer's disease (AD) is a critical yet unmet clinical challenge. Methods We applied support vector regression to AD biomarkers derived from cerebrospinal fluid, structural magnetic resonance imaging (MRI), amyloid‐PET and fluorodeoxyglucose positron‐emission tomography (FDG‐PET) to predict rates of cognitive decline. Prediction models were trained in autosomal‐dominant Alzheimer's disease (ADAD, n = 121) and subsequently cross‐validated in sporadic prodromal AD (n = 216). The sample size needed to detect treatment effects when using model‐based risk enrichment was estimated. Results A model combining all biomarker modalities and established in ADAD predicted the 4‐year rate of decline in global cognition (R2 = 24%) and memory (R2 = 25%) in sporadic AD. Model‐based risk‐enrichment reduced the sample size required for detecting simulated intervention effects by 50%–75%. Discussion Our independently validated machine‐learning model predicted cognitive decline in sporadic prodromal AD and may substantially reduce sample size needed in clinical trials in AD

Different rates of cognitive decline in autosomal dominant and late-onset Alzheimer disease

As prevention trials advance with autosomal dominant Alzheimer disease (ADAD) participants, understanding the similarities and differences between ADAD and "sporadic" late-onset AD (LOAD) is critical to determine generalizability of findings between these cohorts. Cognitive trajectories of ADAD mutation carriers (MCs) and autopsy-confirmed LOAD individuals were compared to address this question. Longitudinal rates of change on cognitive measures were compared in ADAD MCs (n = 310) and autopsy-confirmed LOAD participants (n = 163) before and after symptom onset (estimated/observed). LOAD participants declined more rapidly in the presymptomatic (preclinical) period and performed more poorly at symptom onset than ADAD participants on a cognitive composite. After symptom onset, however, the younger ADAD MCs declined more rapidly. The similar but not identical cognitive trajectories (declining but at different rates) for ADAD and LOAD suggest common AD pathologies but with some differences

Variant-dependent heterogeneity in amyloid β burden in autosomal dominant Alzheimer's disease: cross-sectional and longitudinal analyses of an observational study

Background: Insights gained from studying individuals with autosomal dominant Alzheimer's disease have broadly influenced mechanistic hypotheses, biomarker development, and clinical trials in both sporadic and dominantly inherited Alzheimer's disease. Although pathogenic variants causing autosomal dominant Alzheimer's disease are highly penetrant, there is substantial heterogeneity in levels of amyloid β (Aβ) between individuals. We aimed to examine whether this heterogeneity is related to disease progression and to investigate the association with mutation location within PSEN1, PSEN2, or APP. Methods: We did cross-sectional and longitudinal analyses of data from the Dominantly Inherited Alzheimer's Network (DIAN) observational study, which enrols individuals from families affected by autosomal dominant Alzheimer's disease. 340 participants in the DIAN study who were aged 18 years or older, had a history of autosomal dominant Alzheimer's disease in their family, and who were enrolled between September, 2008, and June, 2019, were included in our analysis. 206 participants were carriers of pathogenic mutations in PSEN1, PSEN2, or APP, and 134 were non-carriers. 62 unique pathogenic variants were identified in the cohort and were grouped in two ways. First, we sorted variants in PSEN1, PSEN2, or APP by the affected protein domain. Second, we divided PSEN1 variants according to position before or after codon 200. We examined variant-dependent variability in Aβ biomarkers, specifically Pittsburgh-Compound-B PET (PiB-PET) signal, levels of CSF Aβ1-42 (Aβ42), and levels of Aβ1-40 (Aβ40). Findings: Cortical and striatal PiB-PET signal showed striking variant-dependent variability using both grouping approaches (p0·7), and CSF Aβ42 levels (codon-based grouping: p=0·49; domain-based grouping: p=0·095). Longitudinal PiB-PET signal also varied across codon-based groups, mirroring cross-sectional analyses. Interpretation: Autosomal dominant Alzheimer's disease pathogenic variants showed highly differential temporal and regional patterns of PiB-PET signal, despite similar functional progression. These findings suggest that although increased PiB-PET signal is generally seen in autosomal dominant Alzheimer's disease, higher levels of PiB-PET signal at an individual level might not reflect more severe or more advanced disease. Our results have high relevance for ongoing clinical trials in autosomal dominant Alzheimer's disease, including those using Aβ PET as a surrogate marker of disease progression. Additionally, and pertinent to both sporadic and autosomal dominant Alzheimer's disease, our results suggest that CSF and PET measures of Aβ levels are not interchangeable and might reflect different Aβ-driven pathobiological processes. Funding: National Institute on Aging, Doris Duke Charitable Foundation, German Center for Neurodegenerative Diseases, Japanese Agency for Medical Research and Development

Segregation of functional networks is associated with cognitive resilience in Alzheimer's disease

Cognitive resilience is an important modulating factor of cognitive decline in Alzheimer's disease, but the functional brain mechanisms that support cognitive resilience remain elusive. Given previous findings in normal aging, we tested the hypothesis that higher segregation of the brain's connectome into distinct functional networks represents a functional mechanism underlying cognitive resilience in Alzheimer's disease. Using resting-state functional MRI, we assessed both resting-state-fMRI global system segregation, i.e. the balance of between-network to within-network connectivity, and the alternate index of modularity Q as predictors of cognitive resilience. We performed all analyses in two independent samples for validation: First, we included 108 individuals with autosomal dominantly inherited Alzheimer's disease and 71 non-carrier controls. Second, we included 156 amyloid-PET positive subjects across the spectrum of sporadic Alzheimer's disease as well as 184 amyloid-negative controls. In the autosomal dominant Alzheimer's disease sample, disease severity was assessed by estimated years from symptom onset. In the sporadic Alzheimer's sample, disease stage was assessed by temporal-lobe tau-PET (i.e. composite across Braak stage I & III regions). In both samples, we tested whether the effect of disease severity on cognition was attenuated at higher levels of functional network segregation. For autosomal dominant Alzheimer's disease, we found higher fMRI-assessed system segregation to be associated with an attenuated effect of estimated years from symptom onset on global cognition (p = 0.007). Similarly, for sporadic Alzheimer's disease patients, higher fMRI-assessed system segregation was associated with less decrement in global cognition (p = 0.001) and episodic memory (p = 0.004) per unit increase of temporal lobe tau-PET. Confirmatory analyses using the alternate index of modularity Q revealed consistent results. In conclusion, higher segregation of functional connections into distinct large-scale networks supports cognitive resilience in Alzheimer's disease

Measurement of the plasma levels of antibodies against the polymorphic vaccine candidate apical membrane antigen 1 in a malaria-exposed population

<p>Abstract</p> <p>Background</p> <p>Establishing antibody correlates of protection against malaria in human field studies and clinical trials requires, amongst others, an accurate estimation of antibody levels. For polymorphic antigens such as apical membrane antigen 1 (AMA1), this may be confounded by the occurrence of a large number of allelic variants in nature.</p> <p>Methods</p> <p>To test this hypothesis, plasma antibody levels in an age-stratified cohort of naturally exposed children from a malaria-endemic area in Southern Ghana were determined by indirect ELISA. Titres against four single <it>Pf</it>AMA1 alleles were compared with those against three different allele mixtures presumed to have a wider repertoire of epitope specificities. Associations of antibody levels with the incidence of clinical malaria as well as with previous exposure to parasites were also examined.</p> <p>Results</p> <p>Antibody titres against <it>Pf</it>AMA1 alleles generally increased with age/exposure while antibody specificity for <it>Pf</it>AMA1 variants decreased, implying that younger children (≤ 5 years) elicit a more strain-specific antibody response compared to older children. Antibody titre measurements against the FVO and 3D7 AMA1 alleles gave the best titre estimates as these varied least in pair-wise comparisons with titres against all <it>Pf</it>AMA1 allele mixtures. There was no association between antibody levels against any capture antigen and either clinical malaria incidence or parasite density.</p> <p>Conclusions</p> <p>The current data shows that levels of naturally acquired antigen-specific antibodies, especially in infants and young children, are dependent on the antigenic allele used for measurement. This may be relevant to the interpretation of antibody titre data from measurements against single <it>Pf</it>AMA1 alleles, especially in studies involving infants and young children who have experienced fewer infections.</p

Location of pathogenic variants in PSEN1 impacts progression of cognitive, clinical, and neurodegenerative measures in autosomal-dominant Alzheimer's disease

Although pathogenic variants in PSEN1 leading to autosomal-dominant Alzheimer disease (ADAD) are highly penetrant, substantial interindividual variability in the rates of cognitive decline and biomarker change are observed in ADAD. We hypothesized that this interindividual variability may be associated with the location of the pathogenic variant within PSEN1. PSEN1 pathogenic variant carriers participating in the Dominantly Inherited Alzheimer Network (DIAN) observational study were grouped based on whether the underlying variant affects a transmembrane (TM) or cytoplasmic (CY) protein domain within PSEN1. CY and TM carriers and variant non-carriers (NC) who completed clinical evaluation, multimodal neuroimaging, and lumbar puncture for collection of cerebrospinal fluid (CSF) as part of their participation in DIAN were included in this study. Linear mixed effects models were used to determine differences in clinical, cognitive, and biomarker measures between the NC, TM, and CY groups. While both the CY and TM groups were found to have similarly elevated Aβ compared to NC, TM carriers had greater cognitive impairment, smaller hippocampal volume, and elevated phosphorylated tau levels across the spectrum of pre-symptomatic and symptomatic phases of disease as compared to CY, using both cross-sectional and longitudinal data. As distinct portions of PSEN1 are differentially involved in APP processing by γ-secretase and the generation of toxic β-amyloid species, these results have important implications for understanding the pathobiology of ADAD and accounting for a substantial portion of the interindividual heterogeneity in ongoing ADAD clinical trials

Modeling autosomal dominant Alzheimer's disease with machine learning

INTRODUCTION: Machine learning models were used to discover novel disease trajectories for autosomal dominant Alzheimer's disease. METHODS: Longitudinal structural magnetic resonance imaging, amyloid positron emission tomography (PET), and fluorodeoxyglucose PET were acquired in 131 mutation carriers and 74 non-carriers from the Dominantly Inherited Alzheimer Network; the groups were matched for age, education, sex, and apolipoprotein ε4 (APOE ε4). A deep neural network was trained to predict disease progression for each modality. Relief algorithms identified the strongest predictors of mutation status. RESULTS: The Relief algorithm identified the caudate, cingulate, and precuneus as the strongest predictors among all modalities. The model yielded accurate results for predicting future Pittsburgh compound B (R2  = 0.95), fluorodeoxyglucose (R2  = 0.93), and atrophy (R2  = 0.95) in mutation carriers compared to non-carriers. DISCUSSION: Results suggest a sigmoidal trajectory for amyloid, a biphasic response for metabolism, and a gradual decrease in volume, with disease progression primarily in subcortical, middle frontal, and posterior parietal regions