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Treatment of 41 Schistosoma haematobium-infected children, 5–16 years old, with the drug praziquantel induced a switch from a predominantly IgA-specific antibody response to a predominantly IgG1 response within 12 weeks. A cross-sectional survey suggests that the same switch occurs naturally, but over several years, as children age (n = 251). The switch may be driven by alterations in cytokine levels in response to the release of antigens by dead or damaged parasites. Adults are more resistant to schistosome infection than children, and the switch to an “adult” response suggests that praziquantel treatment may have an immunizing effect, with benefits extending beyond a transient reduction in levels of infection.

This thesis reports two immuno-epidemiological studies of cellular immune responses to Schistosoma haematobium infection in humans. The first study was a cross sectional infection study. The study cohort consisted of 59 Gambians made up of two distinct age groups: children (12-16 years old) and adults (25-88 years old). The study examined three hypotheses: 1) protection against infection is associated with a Th2-type immune response, 2) Th1 and Th2 responses are dichotomous options in individuals and 3) cytokine production is affected by cross-reactive antigen. The second, a re-infection study, was based in Zimbabwe. The study cohort consisted of 83 Zimbabwean children (6 to 15 years) recruited from two separate villages. One site had significantly lower prevalence of infection than the other, conferring an opportunity to examine the effects of transmission dynamics on the development of a protective immune response. The study addressed two major hypotheses: 1) an appropriate protective type of immune response develops faster in the high prevalence area compared to the low prevalence area and 2) individuals produce either IL-4 or IL-5 but not both. (Abstract shortened by ProQuest.)

Abstract Grass pollens have been identified as mediators of respiratory distress, capable of exacerbating respiratory diseases including epidemic thunderstorm asthma (ETSA). It is hypothesised that during thunderstorms, grass pollen grains swell to absorb atmospheric water, rupture, and release internal protein content to the atmosphere. The inhalation of atmospheric grass pollen proteins results in deadly ETSA events. We sought to identify the underlying cellular mechanisms that may contribute towards the severity of ETSA in temperate climates using Timothy grass (Phleum pratense). Respiratory cells exposed to Timothy grass pollen protein extract (PPE) caused cells to undergo hypoxia ultimately triggering the subcellular re-organisation of F-actin from the peri junctional belt to cytoplasmic fibre assembly traversing the cell body. This change in actin configuration coincided with the spatial reorganisation of microtubules and importantly, decreased cell compressibility specifically at the cell centre. Further to this, we find that the pollen-induced reorganisation of the actin cytoskeleton prompting secretion of the pro-inflammatory cytokine, interleukin-8. In addition, the loss of peri-junctional actin following exposure to pollen proteins was accompanied by the release of epithelial transmembrane protein, E-cadherin from cell–cell junctions resulting in a decrease in epithelial barrier integrity. We demonstrate that Timothy grass pollen regulates F-actin dynamics and E-cadherin localisation in respiratory cells to mediate cell–cell junctional integrity highlighting a possible molecular pathway underpinning ETSA events.

We used a data-driven Bayesian model to automatically identify distinct latent factors of overlapping atrophy patterns from voxel-wise structural MRIs of late-onset Alzheimer’s disease (AD) dementia patients. Our approach estimated the extent to which multiple distinct atrophy patterns were expressed within each participant rather than assuming that each participant expressed a single atrophy factor. The model revealed a temporal atrophy factor (medial temporal cortex, hippocampus, and amygdala), a subcortical atrophy factor (striatum, thalamus, and cerebellum), and a cortical atrophy factor (frontal, parietal, lateral temporal, and lateral occipital cortices). To explore the influence of each factor in early AD, atrophy factor compositions were inferred in beta-amyloid–positive (Aβ+) mild cognitively impaired (MCI) and cognitively normal (CN) participants. All three factors were associated with memory decline across the entire clinical spectrum, whereas the cortical factor was associated with executive function decline in Aβ+ MCI participants and AD dementia patients. Direct comparison between factors revealed that the temporal factor showed the strongest association with memory, whereas the cortical factor showed the strongest association with executive function. The subcortical factor was associated with the slowest decline for both memory and executive function compared with temporal and cortical factors. These results suggest that distinct patterns of atrophy influence decline across different cognitive domains. Quantification of this heterogeneity may enable the computation of individual-level predictions relevant for disease monitoring and customized therapies. Factor compositions of participants and code used in this article are publicly available for future research.

Accurate diagnosis of mild cognitive impairment (MCI) before conversion to Alzheimer’s disease (AD) is invaluable for patient treatment. Many works showed that MCI and AD affect functional and structural connections between brain regions as well as the shape of cortical regions. However, ‘shape connections’ between brain regions are rarely investigated -e.g., how morphological attributes such as cortical thickness and sulcal depth of a specific brain region change in relation to morphological attributes in other regions. To fill this gap, we unprecedentedly design morphological brain multiplexes for late MCI/AD classification. Specifically, we use structural T1-w MRI to define morphological brain networks, each quantifying similarity in morphology between different cortical regions for a specific cortical attribute. Then, we define a brain multiplex where each intra-layer represents the morphological connectivity network of a specific cortical attribute, and each inter-layer encodes the similarity between two consecutive intra-layers. A significant performance gain is achieved when using the multiplex architecture in comparison to other conventional network analysis architectures. We also leverage this architecture to discover morphological connectional biomarkers fingerprinting the difference between late MCI and AD stages, which included the right entorhinal cortex and right caudal middle frontal gyrus.

Cubosomes are dispersed nanostructured particles of cubic phase liquid crystal that have stimulated significant research interest because of their potential for application in controlled-release and drug delivery. Despite the interest, cubosomes can be difficult to fabricate and stabilize with current methods. Most of the current work is limited to liquid phase processes involving high shear dispersion of bulk cubic liquid crystalline material into sub-micron particles, limiting application flexibility. In this work, two types of dry powder cubosome precursors are produced by spray-drying: (1) starch-encapsulated monoolein is produced by spray-drying a dispersion of cubic liquid crystalline particles in an aqueous starch solution and (2) dextran-encapsulated monoolein is produced by spray-drying an emulsion formed by the ethanol-dextran-monoolein-water system. The encapsulants are used to decrease powder cohesion during drying and to act as a soluble colloidal stabilizer upon hydration of the powders. Both powders are shown to form (on average) 0.6μm colloidally-stable cubosomes upon addition to water. However, the starch powders have a broader particle size distribution than the dextran powders because of the relative ease of spraying emulsions versus dispersions. The developed processes enable the production of nanostructured cubosomes by end-users rather than just specialized researchers and allow tailoring of the surface state of the cubosomes for broader application.[PUBLICATION ABSTRACT]

Accurate prediction of Alzheimer’s disease (AD) is important for the early diagnosis and treatment of this condition. Mild cognitive impairment (MCI) is an early stage of AD. Therefore, patients with MCI who are at high risk of fully developing AD should be identified to accurately predict AD. However, the relationship between brain images and AD is difficult to construct because of the complex characteristics of neuroimaging data. To address this problem, we present a longitudinal measurement of MCI brain images and a hierarchical classification method for AD prediction. Longitudinal images obtained from individuals with MCI were investigated to acquire important information on the longitudinal changes, which can be used to classify MCI subjects as either MCI conversion (MCIc) or MCI non-conversion (MCInc) individuals. Moreover, a hierarchical framework was introduced to the classifier to manage high feature dimensionality issues and incorporate spatial information for improving the prediction accuracy. The proposed method was evaluated using 131 patients with MCI (70 MCIc and 61 MCInc) based on MRI scans taken at different time points. Results showed that the proposed method achieved 79.4% accuracy for the classification of MCIc versus MCInc, thereby demonstrating very promising performance for AD prediction.

Neuroimaging genetics is an emerging field that aims to identify the associations between genetic variants (e.g., single nucleotide polymorphisms (SNPs)) and quantitative traits (QTs) such as brain imaging phenotypes. In recent studies, in order to detect complex multi-SNP-multi-QT associations, bi-multivariate techniques such as various structured sparse canonical correlation analysis (SCCA) algorithms have been proposed and used in imaging genetics studies. However, associations between genetic markers and imaging QTs identified by existing bi-multivariate methods may not be all disease specific. To bridge this gap, we propose an analytical framework, based on three-way sparse canonical correlation analysis (T-SCCA), to explore the intrinsic associations among genetic markers, imaging QTs, and clinical scores of interest. We perform an empirical study using the Alzheimer’s Disease Neuroimaging Initiative (ADNI) cohort to discover the relationships among SNPs from AD risk gene APOE , imaging QTs extracted from structural magnetic resonance imaging scans, and cognitive and diagnostic outcomes. The proposed T-SCCA model not only outperforms the traditional SCCA method in terms of identifying strong associations, but also discovers robust outcome-relevant imaging genetic patterns, demonstrating its promise for improving disease-related mechanistic understanding.

The Clusterin ( CLU ) gene, also known as apolipoprotein J ( ApoJ ), is currently the third most associated late-onset Alzheimer’s disease (LOAD) risk gene. However, little was known about the possible effect of CLU genetic variants on AD pathology in brain. Here, we evaluated the interaction between 7 CLU SNPs (covering 95% of genetic variations) and the role of CLU in β-amyloid (Aβ) deposition, AD-related structure atrophy, abnormal glucose metabolism on neuroimaging and CSF markers to clarify the possible approach by that CLU impacts AD. Finally, four loci (rs11136000, rs1532278, rs2279590, rs7982) showed significant associations with the Aβ deposition at the baseline level while genotypes of rs9331888 (P = 0.042) increased Aβ deposition. Besides, rs9331888 was significantly associated with baseline volume of left hippocampus (P = 0.014). We then further validated the association with Aβ deposition in the AD, mild cognitive impairment (MCI), normal control (NC) sub-groups. The results in sub-groups confirmed the association between CLU genotypes and Aβ deposition further. Our findings revealed that CLU genotypes could probably modulate the cerebral the Aβ loads on imaging and volume of hippocampus. These findings raise the possibility that the biological effects of CLU may be relatively confined to neuroimaging trait and hence may offer clues to AD.

Background Plasma neurofilament light chain (NfL) is a blood biomarker of neurodegeneration, including Alzheimer’s disease. However, its usefulness may be influenced by common conditions in older adults, including amyloid-β (Aβ) deposition and cardiometabolic risk factors like hypertension, diabetes mellitus (DM), impaired kidney function, and obesity. This longitudinal observational study using the Alzheimer’s Disease Neuroimaging Initiative cohort investigated how these conditions influence the prognostic capacity of plasma NfL. Methods Non-demented participants (cognitively unimpaired or mild cognitive impairment) underwent repeated assessments including the Alzheimer’s Disease Assessment Scale-Cognitive subscale (ADAS-Cog) scores, hippocampal volumes, and white matter hyperintensity (WMH) volumes at 6- or 12-month intervals. Linear mixed-effect models were employed to examine the interaction between plasma NfL and various variables of interest, such as Aβ (evaluated using Florbetapir positron emission tomography), hypertension, DM, impaired kidney function, or obesity. Results Over a mean follow-up period of 62.5 months, participants with a mean age of 72.1 years ( n  = 720, 48.8% female) at baseline were observed. Higher plasma NfL levels at baseline were associated with steeper increases in ADAS-Cog scores and WMH volumes, and steeper decreases in hippocampal volumes over time (all p -values < 0.001). Notably, Aβ at baseline significantly enhanced the association between plasma NfL and longitudinal changes in ADAS-Cog scores ( p -value 0.005) and hippocampal volumes ( p -value 0.004). Regarding ADAS-Cog score and WMH volume, the impact of Aβ was more prominent in cognitively unimpaired than in mild cognitive impairment. Hypertension significantly heightened the association between plasma NfL and longitudinal changes in ADAS-Cog scores, hippocampal volumes, and WMH volumes (all p -values < 0.001). DM influenced the association between plasma NfL and changes in ADAS-Cog scores ( p -value < 0.001) without affecting hippocampal and WMH volumes. Impaired kidney function did not significantly alter the association between plasma NfL and longitudinal changes in any outcome variables. Obesity heightened the association between plasma NfL and changes in hippocampal volumes only ( p -value 0.026). Conclusion This study suggests that the prognostic capacity of plasma NfL may be amplified in individuals with Aβ or hypertension. This finding emphasizes the importance of considering these factors in the NfL-based prognostic model for neurodegeneration in non-demented older adults.