Foretelling pathology: The poetics of prognosis

This paper examines a number of French middle-brow novels, usually called at the time romans de murs, from the period 1880-1910. It shows how, in these stories, doctors are shown to foretell the course of narrative through the diagnosis of certain pathologies, especially psychosexual ones. These pathologies are thus represented as implacable narrative programmes. In effect, most of these novels renounce the standard fictional resources of intrigue and suspense in favour of the relentless working out of their initial prognosis. The authority of medical discourse is therefore not just confirmed and disseminated: it is elaborated as fatality in the very terms of the novel. Copyright © SAGE Publications.

Predictive gene lists for breast cancer prognosis: a topographic visualisation study

Background: The controversy surrounding the non-uniqueness of predictive gene lists (PGL) of small selected subsets of genes from very large potential candidates as available in DNA microarray experiments is now widely acknowledged 1. Many of these studies have focused on constructing discriminative semi-parametric models and as such are also subject to the issue of random correlations of sparse model selection in high dimensional spaces. In this work we outline a different approach based around an unsupervised patient-specific nonlinear topographic projection in predictive gene lists. Methods: We construct nonlinear topographic projection maps based on inter-patient gene-list relative dissimilarities. The Neuroscale, the Stochastic Neighbor Embedding(SNE) and the Locally Linear Embedding(LLE) techniques have been used to construct two-dimensional projective visualisation plots of 70 dimensional PGLs per patient, classifiers are also constructed to identify the prognosis indicator of each patient using the resulting projections from those visualisation techniques and investigate whether a-posteriori two prognosis groups are separable on the evidence of the gene lists. A literature-proposed predictive gene list for breast cancer is benchmarked against a separate gene list using the above methods. Generalisation ability is investigated by using the mapping capability of Neuroscale to visualise the follow-up study, but based on the projections derived from the original dataset. Results: The results indicate that small subsets of patient-specific PGLs have insufficient prognostic dissimilarity to permit a distinction between two prognosis patients. Uncertainty and diversity across multiple gene expressions prevents unambiguous or even confident patient grouping. Comparative projections across different PGLs provide similar results. Conclusion: The random correlation effect to an arbitrary outcome induced by small subset selection from very high dimensional interrelated gene expression profiles leads to an outcome with associated uncertainty. This continuum and uncertainty precludes any attempts at constructing discriminative classifiers. However a patient's gene expression profile could possibly be used in treatment planning, based on knowledge of other patients' responses. We conclude that many of the patients involved in such medical studies are intrinsically unclassifiable on the basis of provided PGL evidence. This additional category of 'unclassifiable' should be accommodated within medical decision support systems if serious errors and unnecessary adjuvant therapy are to be avoided.

A study of patient-specific prognosis of ovarian cancer

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Lupus prognosis:a clinical study

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Survival prognosis in ovarian cancer

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The Inflation Index Prognosis Based on the Method of Decision-Marking “Tree”

The description of the support system for marking decision in terms of prognosing the inflation level based on the multifactor dependence represented by the decision – marking “tree” is given in the paper. The interrelation of factors affecting the inflation level – economic, financial, political, socio-demographic ones, is considered. The perspectives for developing the method of decision – marking “tree”, and pointing out the so- called “narrow” spaces and further analysis of possible scenarios for inflation level prognosing in particular, are defined.

Multi-agent Systems in the Harvest Prognosis

The paper presents a case study of geo-monitoring a region consisting in the capturing and encoding of human expertise into a knowledge-based system. As soon as the maps have been processed, the data patterns are detected using knowledge-based agents for the harvest prognosis.

Incidentally Raised Cardiac Troponin I Has a Worse Prognosis in Older Patients Compared to Those with Normal Cardiac Troponin I and Patients with Acute Coronary Syndrome : A Cohort Study

Acknowledgement J.H.B.-S. was supported by the Norfolk and Norwich University Hospital (NNUH) Research and Development (R&D) research capability funds between July 2013 and December 2014.

Medium-term prognosis of an incident cohort of parkinsonian patients compared to controls

Funding This work was supported by Parkinson's UK (grant numbers G0502, G0914), BMA Doris Hillier Award, the BUPA Foundation, NHS Grampian Endowments, RS MacDonald Trust.

Immunotherapy for the treatment of poor prognosis cancers

Cancer is amongst the leading causes of death worldwide and the number one cause in the developed world. Every year there are close to 10 million cancer related deaths and this corresponds to hundreds of millions of euro in health care costs and lost productivity, placing a substantial drain on the economy. The efficacy of traditional treatment modalities for cancer therapy, such as surgery, radiotherapy and chemotherapy has plateaued, and while they are undoubtedly effective at prolonging patient lifespan, there is a high rate of adverse side effects and fatal reoccurrence. Currently, there is a huge amount of interest in the areas of cancer immunosurveillance and cancer immuno-editing, which explain some of the complex interactions between the host immune system and cancer. If left unchecked, cancerous malignancies have the ability to generate an immunosuppressive microenvironment, effectively shielding themselves from elimination and promoting tumour growth and progression. To overcome this, the potential of the immune system must be harnessed and the work undertaken in this thesis sought to contribute to this goal. Focus was placed on using novel therapies, combining tumour ablation with immune-modulating antibodies to maximise tumour elimination in an immune dependent manner, to overcome immunosuppression and promote immune activation. Chapter 2 focuses on the use of ECT as a method of tumour ablation and its effects on the immune system. ECT proved to be effective at inhibiting the tumour growth both in vitro and in vivo, and conferred significant survival advantages in both small and large animal models. More importantly, ECT proved to cause tumour death in an immune dependent manner, displaying the hallmarks of Immunogenic Cell Death, increases in immune cell infiltration and generating tumour-specific immune responses. Chapter 3 focuses on combining ECT with immune checkpoint blockade inhibitors; anti- CTLA-4 and anti-PD-1. Both combinations proved to be effective at inhibiting both primary and distal tumour growth, indicating the generation of tumour specific immune responses and prolonged animal survival. In addition, the treatments caused increases in the levels of certain intra-tumoural immune cell subsets and modulated the cytokine profile of treated animals in a way that was favourable overall. Chapter 4 focuses on the combining ECT with an anti-iCOS agonist antibody, capable of causing immune co-stimulation. This novel combinational therapy proved to be the most effective by far, with a high cure rate achieved across a number of different in vivo tumour models. Total regression was seen in both primary and distal tumours, as well as spontaneous metastases, with the tumour specific immune response generated conferring total protection to animals on tumour rechallenge. Overall the data presented here adds further insight into the area of cancer immunotherapy with some of the novel combinational therapies demonstrating substantial clinic potential.

Computational models for improved diagnosis and prognosis of stroke using robot-based biomarkers

Stroke is a leading cause of death and permanent disability worldwide, affecting millions of individuals. Traditional clinical scores for assessment of stroke-related impairments are inherently subjective and limited by inter-rater and intra-rater reliability, as well as floor and ceiling effects. In contrast, robotic technologies provide objective, highly repeatable tools for quantification of neurological impairments following stroke. KINARM is an exoskeleton robotic device that provides objective, reliable tools for assessment of sensorimotor, proprioceptive and cognitive brain function by means of a battery of behavioral tasks. As such, KINARM is particularly useful for assessment of neurological impairments following stroke. This thesis introduces a computational framework for assessment of neurological impairments using the data provided by KINARM. This is done by achieving two main objectives. First, to investigate how robotic measurements can be used to estimate current and future abilities to perform daily activities for subjects with stroke. We are able to predict clinical scores related to activities of daily living at present and future time points using a set of robotic biomarkers. The findings of this analysis provide a proof of principle that robotic evaluation can be an effective tool for clinical decision support and target-based rehabilitation therapy. The second main objective of this thesis is to address the emerging problem of long assessment time, which can potentially lead to fatigue when assessing subjects with stroke. To address this issue, we examine two time reduction strategies. The first strategy focuses on task selection, whereby KINARM tasks are arranged in a hierarchical structure so that an earlier task in the assessment procedure can be used to decide whether or not subsequent tasks should be performed. The second strategy focuses on time reduction on the longest two individual KINARM tasks. Both reduction strategies are shown to provide significant time savings, ranging from 30% to 90% using task selection and 50% using individual task reductions, thereby establishing a framework for reduction of assessment time on a broader set of KINARM tasks. All in all, findings of this thesis establish an improved platform for diagnosis and prognosis of stroke using robot-based biomarkers.

p.(L576P) -KIT mutation in GIST: Favorable prognosis and sensitive to imatinib?

Exon 11 KIT mutations are found in a majority of gastrointestinal stromal tumors (GIST) and are usually predictive of response to imatinib, a KIT, PDGFRA and ABL inhibitor. Exon 11 mutations with poor sensitivity to imatinib and poor outcome can be observed on rare occasions, including p.(L576P). In silico and in vitro studies suggested a decreased binding affinity for imatinib in p.(L576P) KIT mutations, thereby offering an explanation for their poor outcome and poor response to standard therapy. These observations were further corroborated with anecdotal case reports of refractoriness or non-durable response to imatinib therapy. However, we describe the favorable response to imatinib and outcome in 5 p.(L576P)-KIT mutant GIST patients treated at a tertiary sarcoma referral center. The sensitivity of p.(L576P)-KIT mutations to imatinib, and the prognostic impact of this mutation need to be further evaluated in a larger cohort. Based on our observations, p.(L576P) mutated GISTs should be treated with standard first line imatinib therapy.