Multimodal Hippocampal Subfield Grading For Alzheimer’s Disease Classification

Numerous studies have proposed biomarkers based on magnetic resonance imaging (MRI) to detect and predict the risk of evolution toward Alzheimer’s disease (AD). Most of these methods have focused on the hippocampus, which is known to be one of the earliest structures impacted by the disease. To date, patch-based grading approaches provide among the best biomarkers based on the hippocampus. However, this structure is complex and is divided into different subfields, not equally impacted by AD. Former in-vivo imaging studies mainly investigated structural alterations of these subfields using volumetric measurements and microstructural modifications with mean diffusivity measurements. The aim of our work is to improve the current classification performances based on the hippocampus with a new multimodal patch-based framework combining structural and diffusivity MRI. The combination of these two MRI modalities enables the capture of subtle structural and microstructural alterations. Moreover, we propose to study the efficiency of this new framework applied to the hippocampal subfields. To this end, we compare the classification accuracy provided by the different hippocampal subfields using volume, mean diffusivity, and our novel multimodal patch-based grading framework combining structural and diffusion MRI. The experiments conducted in this work show that our new multimodal patch-based method applied to the whole hippocampus provides the most discriminating biomarker for advanced AD detection while our new framework applied into subiculum obtains the best results for AD prediction, improving by two percentage points the accuracy compared to the whole hippocampus

Spatial distribution of CD3- and CD8-positive lymphocytes as pretest for POLE wild-type in molecular subgroups of endometrial carcinoma.

INTRODUCTION Over the years, the molecular classification of endometrial carcinoma has evolved significantly. Both POLEmut and MMRdef cases share tumor biological similarities like high tumor mutational burden and induce strong lymphatic reactions. While therefore use case scenarios for pretesting with tumor-infiltrating lymphocytes to replace molecular analysis did not show promising results, such testing may be warranted in cases where an inverse prediction, such as that of POLEwt, is being considered. For that reason we used a spatial digital pathology method to quantitatively examine CD3+ and CD8+ immune infiltrates in comparison to conventional histopathological parameters, prognostics and as potential pretest before molecular analysis. METHODS We applied a four-color multiplex immunofluorescence assay for pan-cytokeratin, CD3, CD8, and DAPI on 252 endometrial carcinomas as testing and compared it to further 213 cases as validation cohort from a similar multiplexing assay. We quantitatively assessed immune infiltrates in microscopic distances within the carcinoma, in a close distance of 50 microns, and in more distant areas. RESULTS Regarding prognostics, high CD3+ and CD8+ densities in intra-tumoral and close subregions pointed toward a favorable outcome. However, TCGA subtyping outperforms prognostication of CD3 and CD8 based parameters. Different CD3+ and CD8+ densities were significantly associated with the TCGA subgroups, but not consistently for histopathological parameter. In the testing cohort, intra-tumoral densities of less than 50 intra-tumoral CD8+ cells/mm2 were the most suitable parameter to assume a POLEwt, irrespective of an MMRdef, NSMP or p53abn background. An application to the validation cohort corroborates these findings with an overall sensitivity of 95.5%. DISCUSSION Molecular confirmation of POLEmut cases remains the gold standard. Even if CD3+ and CD8+ cell densities appeared less prognostic than TCGA, low intra-tumoral CD8+ values predict a POLE wild-type at substantial percentage rates, but not vice versa. This inverse correlation might be useful to increase pretest probabilities in consecutive POLE testing. Molecular subtyping is currently not conducted in one-third of cases deemed low-risk based on conventional clinical and histopathological parameters. However, this percentage could potentially be increased to two-thirds by excluding sequencing of predicted POLE wild-type cases, which could be determined through precise quantification of intra-tumoral CD8+ cells

Retest variability and patient reliability indices of quantitative fundus autofluorescence in age-related macular degeneration: a MACUSTAR study report

This study aimed to determine the retest variability of quantitative fundus autofluorescence (QAF) in patients with and without age-related macular degeneration (AMD) and evaluate the predictive value of patient reliability indices on retest reliability. A total of 132 eyes from 68 patients were examined, including healthy individuals and those with various stages of AMD. Duplicate QAF imaging was conducted at baseline and 2 weeks later across six study sites. Intraclass correlation (ICC) analysis was used to evaluate the consistency of imaging, and mean opinion scores (MOS) of image quality were generated by two researchers. The contribution of MOS and other factors to retest variation was assessed using mixed-effect linear models. Additionally, a Random Forest Regressor was trained to evaluate the extent to which manual image grading of image quality could be replaced by automated assessment (inferred MOS). The results showed that ICC values were high for all QAF images, with slightly lower values in AMD-affected eyes. The average inter-day ICC was found to be 0.77 for QAF segments within the QAF8 ring and 0.74 for peripheral segments. Image quality was predicted with a mean absolute error of 0.27 on a 5-point scale, and of all evaluated reliability indices, MOS/inferred MOS proved most important. The findings suggest that QAF allows for reliable testing of autofluorescence levels at the posterior pole in patients with AMD in a multicenter, multioperator setting. Patient reliability indices could serve as eligibility criteria for clinical trials, helping identify patients with adequate retest reliability

Application of digital pathology-based advanced analytics of tumour microenvironment organisation to predict prognosis and therapeutic response.

In recent years, the application of advanced analytics, especially artificial intelligence (AI), to digital H&E images, and other histological image types, has begun to radically change how histological images are used in the clinic. Alongside the recognition that the tumour microenvironment (TME) has a profound impact on tumour phenotype, the technical development of highly multiplexed immunofluorescence platforms has enhanced the biological complexity that can be captured in the TME with high precision. AI has an increasingly powerful role in the recognition and quantitation of image features and the association of such features with clinically important outcomes, as occurs in distinct stages in conventional machine learning. Deep-learning algorithms are able to elucidate TME patterns inherent in the input data with minimum levels of human intelligence and, hence, have the potential to achieve clinically relevant predictions and discovery of important TME features. Furthermore, the diverse repertoire of deep-learning algorithms able to interrogate TME patterns extends beyond convolutional neural networks to include attention-based models, graph neural networks, and multimodal models. To date, AI models have largely been evaluated retrospectively, outside the well-established rigour of prospective clinical trials, in part because traditional clinical trial methodology may not always be suitable for the assessment of AI technology. However, to enable digital pathology-based advanced analytics to meaningfully impact clinical care, specific measures of 'added benefit' to the current standard of care and validation in a prospective setting are important. This will need to be accompanied by adequate measures of explainability and interpretability. Despite such challenges, the combination of expanding datasets, increased computational power, and the possibility of integration of pre-clinical experimental insights into model development means there is exciting potential for the future progress of these AI applications. © 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland

Defect and thickness inspection system for cast thin films using machine vision and full-field transmission densitometry

Quick mass production of homogeneous thin film material is required in paper, plastic, fabric, and thin film industries. Due to the high feed rates and small thicknesses, machine vision and other nondestructive evaluation techniques are used to ensure consistent, defect-free material by continuously assessing post-production quality. One of the fastest growing inspection areas is for 0.5-500 micrometer thick thin films, which are used for semiconductor wafers, amorphous photovoltaics, optical films, plastics, and organic and inorganic membranes. As a demonstration application, a prototype roll-feed imaging system has been designed to inspect high-temperature polymer electrolyte membrane (PEM), used for fuel cells, after being die cast onto a moving transparent substrate. The inspection system continuously detects thin film defects and classifies them with a neural network into categories of holes, bubbles, thinning, and gels, with a 1.2% false alarm rate, 7.1% escape rate, and classification accuracy of 96.1%. In slot die casting processes, defect types are indicative of a misbalance in the mass flow rate and web speed; so, based on the classified defects, the inspection system informs the operator of corrective adjustments to these manufacturing parameters. Thickness uniformity is also critical to membrane functionality, so a real-time, full-field transmission densitometer has been created to measure the bi-directional thickness profile of the semi-transparent PEM between 25-400 micrometers. The local thickness of the 75 mm x 100 mm imaged area is determined by converting the optical density of the sample to thickness with the Beer-Lambert law. The PEM extinction coefficient is determined to be 1.4 D/mm and the average thickness error is found to be 4.7%. Finally, the defect inspection and thickness profilometry systems are compiled into a specially-designed graphical user interface for intuitive real-time operation and visualization.M.S.Committee Chair: Tequila Harris; Committee Member: Levent Degertekin; Committee Member: Wayne Dale

Multiplex immunohistochemical analysis of granulomatous inflammation in lung tissue sections using a mouse model of M. avium infection

INTRODUCTION: Investigating mechanisms of how intracellular bacterial pathogens such as Mycobacterium. avium (M. avium) evade the host immune response and replicate within macrophages is crucial to devising rational targets for host-directed therapies (HDT) against these associated diseases. This studied utilized the congenic mouse strain B6.Sst1S, which contains the super-susceptibility to tuberculosis (TB) allele. Among murine models of TB, this strain uniquely replicates human disease because mice develop granulomas with central caseous necrosis. Utilizing a susceptible model for M. avium infection, this study investigated the effect of mycobacterial pathogenesis on altering macrophage phenotypes and T cells distribution in areas of pulmonary granulomatous inflammation. METHODS:12 formalin fixed paraffin embedded (FFPE) lung sections from M. avium infected B6.Sst1S and B6 mice were examined microscopically (12 weeks post infection (wpi) n=5, 16 wpi=7). A targeted histology approach was initiated by using MRI coordinates to dictate the depths at which formalin fixed paraffin embedded (FFPE) lung samples were sectioned. Since interpretation of MRI images displayed no evidence of 2 discrete necrotizing granulomas, lungs were cut at sections representative of diffuse pathology at 2 mm into FFPE blocks. Using the Opal MethodTM (Akoya Biosciences), 6- plex immunohistochemical staining was performed with Arginase-1 (Arg1), inducible nitric oxide synthase (iNOS), CD68, CD3, M. tuberculosis antigen (cross-reacts with M. avium) and DAPI to segment nuclei. Slides were digitized by a Vectra PolarisTM fluorescent whole slide scanner. Autofluorescence was removed by InFormTM, and image analysis (IA) was conducted using HaloTM IA software. Statistical analysis was conducted using GraphPad PrismTM 8.0. RESULTS: Sst1 mediated susceptibility was statistically evident at 16 wpi but not at 12 wpi. B6.Sst1S mice showed a statistically significant (P <0.05) increase in M. avium+ cell expression in the non-inoculated lung lobes, but not the inoculated lung lobes. Pulmonary lesions within the inoculated and non-inoculated lung lobes contain different immune signatures. The predominately primary lesions of the inoculated lung lobes were associated with increased CD3+, M. avium+, and iNOS+ cell levels. When controlling for level of infection, there was lower levels of CD3+ cells within granulomatous lesions of B6.Sst1S mice, especially in the non-inoculated lung lobe. Controlling for level of infection also revealed elevated iNOS+ M. avium- cell expression in B6 mice. We observed elevated Arg1+ cell expression near iNOS+ M. avium+ cells, and, qualitatively, around larger lesions. T cell proximity analysis was contradictory and offers lessons for future the development of future IA modules. CONCLUSIONS: Sst1 mediated susceptibility was evident at 16 wpi and predominately mediated through secondary, metastatic lesions. Sst1 mediated susceptibility was also associated with fewer supportive cells (T cells and iNOS+ M. avium- cells) within granulomatous lesions. Future studies are necessary to evaluate to what degree granulomatous lesion Arg1+ cell expression and CD3+ proximity correlate to susceptibility

Comparison of manual and automatic barcode detection in rough horticultural production systems

Automation of production in the nurseries of flower producing companies using barcode scanners have been attempted but with little success. Stationary laser barcode scanners which have been used for automation have failed due to the close proximity between the barcode and the scanner, and factors such as speed, angle of inclination of the barcode, damage to the barcode and dirt on the barcode. Furthermore, laser barcode scanners are still being used manually in the nurseries making work laborious and time consuming, thereby leading to reduced productivity. Therefore, an automated image-based barcode detection system to help solve the aforementioned problems was proposed. Experiments were conducted under different situations with clean and artificially soiled Code 128 barcodes in both the laboratory and under real production conditions in a flower producing company. The images were analyzed with a specific algorithm developed with the software tool Halcon. Overall the results from the company showed that the image-based system has a future prospect for automation in the nursery

A computational approach to motivated behaviour and apathy

The loss of motivation and goal-directed behaviour is characteristic of apathy. Across a wide range of neuropsychiatric disorders, including Huntington’s disease (HD), apathy is poorly understood, associated with significant morbidity, and is hard to treat. One of the challenges in understanding the neural basis of apathy is moving from phenomenology and behavioural dysfunction to neural circuits in a principled manner. The computational framework offers one such approach. I adopt this framework to better understand motivated behaviour and apathy in four complementary projects. At the heart of many apathy formulations is impaired self-initiation of goal-directed behaviour. An influential computational theory proposes that “opportunity cost”, the amount of reward we stand to lose by not taking actions per unit time, is a key variable in governing the timing of self-initiated behaviour. Using a novel task, I found that free-operant behaviour in healthy participants both in laboratory conditions and in online testing, conforms to predictions of this computational model. Furthermore, in both studies I found that in younger adults sensitivity to opportunity cost predicted behavioural apathy scores. Similar pilot results were found in a cohort of patients with HD. These data suggest that opportunity cost may be an important computational variable relevant for understanding a core feature of apathy – the timing of self-initiated behaviour. In my second project, I used a reinforcement learning paradigm to probe for early dysfunction in a cohort of HD gene carriers approximately 25 years from clinical onset. Based on empirical data and computational models of basal ganglia function I predicted that asymmetry in learning from gains and losses may be an early feature of carrying the HD gene. As predicted, in this task fMRI study, HD gene carriers demonstrated an exaggerated neural response to gains as compared to losses. Gene carriers also differed in the neural response to expected value suggesting that carrying the HD gene is associated with altered processing of valence and value decades from onset. Finally, based on neurocomputational models of basal ganglia pathway function, I tested the hypothesis that apathy in HD would be associated with the involvement of the direct pathway. Support for this hypothesis was found in two related projects. Firstly, using data from a large international HD cohort study, I found that apathy was associated with motor features of the disease thought to represent direct pathway involvement. Secondly, I tested this hypothesis in vivo using resting state fMRI data and a model of basal ganglia connectivity in a large peri-manifest HD cohort. In keeping with my predictions, whilst emerging motor signs were associated with changes in the indirect pathway, apathy scores were associated with connectivity changes in the direct pathway connectivity within my model. For patients with apathy across neuropsychiatry there is an urgent need to understand the neural basis of motivated behaviour in order to develop novel therapies. In this thesis, I have used a computational framework to develop and test a range of hypotheses to advance this understanding. In particular, I have focussed on the computational factors which drive us to self-initiate, their potential neural underpinnings and the relevance of these models for apathy in patients with HD. The data I present supports the hypothesis that opportunity cost and basal ganglia pathway connectivity may be two important components necessary to generate motivated behaviour and contribute to the development of apathy in HD