Monitoring of lineage-specific chimaerism allows early prediction of response following donor lymphocyte infusions for relapsed chronic myeloid leukaemia

Donor lymphocyte infusions (DLI) have been shown to enhance the graft-versus-leukaemia (GVL) effect and induce haematological and molecular remission in patients with relapsed CML following allogeneic bone marrow transplantation (BMT). The potent donor cell-mediated cytolysis following DLI may lead to a short period of aplasia before the re-establishment of donor haematopoiesis. The absence of detectable donor cells in patients prior to DLI infusion may result in permanent aplasia in certain patients. We report on four patients who relapsed 1, 3, 6.5 and 7 years post-BMT for chronic phase CML and were treated with DLI from their original BMT donor. Polymorphic short tandem repeats (STRs) were used to assess haematological chimaerism both prior to and following DLI. At the time of relapse, STR-PCR indicated the presence of donor cells in all four patients, at levels ranging from 1-40%. A clinical and molecular response was seen in 4/4 patients following a short period of cytopenia and all patients remain in clinical remission with a follow-up of 2 months-3 years post-DLI. STR-PCR indicated that a response was occurring during the period of pancytopenia when metaphase analysis was unsuccessful. Lineage-specific analysis of the cellular response to DLI was monitored using STR-PCR of peripheral blood (PB) and bone marrow (BM) lymphocyte-enriched fractions and CD2-positive and -negative T cell fractions. In one patient BM and PB CD34-positive and -negative fractions were also assessed. A change in the ratio of donor:recipient cells in the PB lymphocyte fraction was the earliest molecular indication of an anti-leukaemic response. Subsequent conversion to donor chimaerism occurred in the other lineages and the granulocyte fraction was the last lineage to convert. In conclusion, lineage-specific STR-PCR permits detailed monitoring of subtle changes in donor/recipient cell dynamics in specific lineages following DLI during the crucial pancytopenic phase and may be a useful predictor of haematological response to DLI therapy.

Predicting conversion to dementia in a memory clinic: A standard clinical approach compared with an empirically defined clustering method (latent profile analysis) for mild cognitive impairment subtyping

Introduction
Mild cognitive impairment (MCI) has clinical value in its ability to predict later dementia. A better understanding of cognitive profiles can further help delineate who is most at risk of conversion to dementia. We aimed to (1) examine to what extent the usual MCI subtyping using core criteria corresponds to empirically defined clusters of patients (latent profile analysis [LPA] of continuous neuropsychological data) and (2) compare the two methods of subtyping memory clinic participants in their prediction of conversion to dementia.

Methods
Memory clinic participants (MCI, n = 139) and age-matched controls (n = 98) were recruited. Participants had a full cognitive assessment, and results were grouped (1) according to traditional MCI subtypes and (2) using LPA. MCI participants were followed over approximately 2 years after their initial assessment to monitor for conversion to dementia.

Results
Groups were well matched for age and education. Controls performed significantly better than MCI participants on all cognitive measures. With the traditional analysis, most MCI participants were in the amnestic multidomain subgroup (46.8%) and this group was most at risk of conversion to dementia (63%). From the LPA, a three-profile solution fit the data best. Profile 3 was the largest group (40.3%), the most cognitively impaired, and most at risk of conversion to dementia (68% of the group).

Discussion
LPA provides a useful adjunct in delineating MCI participants most at risk of conversion to dementia and adds confidence to standard categories of clinical inference.

Investigating clinical predictors of arteriovenous fistula functional patency in a European cohort

Background: Arteriovenous fistula (AVF) failure to mature (FTM) rates contribute to excessive dependence on central venous catheters for haemodialysis. Choosing the most appropriate vascular access site for an individual patient is guided largely by their age, co-morbidities and clinical examination. We investigated the clinical predictors of AVF FTM in a European cohort of patients and applied an existing clinical risk prediction model for AVF FTM to this population.
Methods: A prospective cohort study was designed that included all patients undergoing AVF creation between January 2009 and December 2014 in a single centre (Belfast City Hospital) who had a functional AVF outcome observed by March 2015.
Results: A total of 525 patients had a functional AVF outcome recorded and were included in the FTM analysis. In this cohort, 309 (59%) patients achieved functional AVF patency and 216 (41%) patients had FTM. Female gender [P < 0.001, odds ratio (OR) 2.03 (CI 1.37–3.02)] and lower-arm AVF [P < 0.001, OR 4.07 (CI 2.77–5.92)] were associated with AVF FTM. The Lok model did not predict FTM outcomes based on the associated risk stratification in our population.
Conclusions: In this European study, female gender was associated with twice the risk of AVF FTM and a lower-arm AVF with four times the risk of FTM. The FTM risk prediction model was not found to be discriminative in this population. Clinical risk factors for AVF FTM vary between populations;we would recommend that units investigate their own clinical predictors of FTM to maximize AVF functional patency and ultimately survival in dialysis patients. Clinical predictors of AVF FTM may not be sufficient on their own to improve vascular access functional patency rates.

Diagnosing pulmonary embolism in outpatients with clinical assessment, D-dimer measurement, venous ultrasound, and helical computed tomography: a multicenter management study.

PURPOSE: To evaluate a diagnostic strategy for pulmonary embolism that combined clinical assessment, plasma D-dimer measurement, lower limb venous ultrasonography, and helical computed tomography (CT). METHODS: A cohort of 965 consecutive patients presenting to the emergency departments of three general and teaching hospitals with clinically suspected pulmonary embolism underwent sequential noninvasive testing. Clinical probability was assessed by a prediction rule combined with implicit judgment. All patients were followed for 3 months. RESULTS: A normal D-dimer level (&lt;500 microg/L by a rapid enzyme-linked immunosorbent assay) ruled out venous thromboembolism in 280 patients (29%), and finding a deep vein thrombosis by ultrasonography established the diagnosis in 92 patients (9.5%). Helical CT was required in only 593 patients (61%) and showed pulmonary embolism in 124 patients (12.8%). Pulmonary embolism was considered ruled out in the 450 patients (46.6%) with a negative ultrasound and CT scan and a low-to-intermediate clinical probability. The 8 patients with a negative ultrasound and CT scan despite a high clinical probability proceeded to pulmonary angiography (positive: 2; negative: 6). Helical CT was inconclusive in 11 patients (pulmonary embolism: 4; no pulmonary embolism: 7). The overall prevalence of pulmonary embolism was 23%. Patients classified as not having pulmonary embolism were not anticoagulated during follow-up and had a 3-month thromboembolic risk of 1.0% (95% confidence interval: 0.5% to 2.1%). CONCLUSION: A noninvasive diagnostic strategy combining clinical assessment, D-dimer measurement, ultrasonography, and helical CT yielded a diagnosis in 99% of outpatients suspected of pulmonary embolism, and appeared to be safe, provided that CT was combined with ultrasonography to rule out the disease.

Risk prediction of prevalent diabetes in a Swiss population using a weighted genetic score--the CoLaus Study.

AIMS/HYPOTHESIS: Several susceptibility genes for type 2 diabetes have been discovered recently. Individually, these genes increase the disease risk only minimally. The goals of the present study were to determine, at the population level, the risk of diabetes in individuals who carry risk alleles within several susceptibility genes for the disease and the added value of this genetic information over the clinical predictors. METHODS: We constructed an additive genetic score using the most replicated single-nucleotide polymorphisms (SNPs) within 15 type 2 diabetes-susceptibility genes, weighting each SNP with its reported effect. We tested this score in the extensively phenotyped population-based cross-sectional CoLaus Study in Lausanne, Switzerland (n = 5,360), involving 356 diabetic individuals. RESULTS: The clinical predictors of prevalent diabetes were age, BMI, family history of diabetes, WHR, and triacylglycerol/HDL-cholesterol ratio. After adjustment for these variables, the risk of diabetes was 2.7 (95% CI 1.8-4.0, p = 0.000006) for individuals with a genetic score within the top quintile, compared with the bottom quintile. Adding the genetic score to the clinical covariates improved the area under the receiver operating characteristic curve slightly (from 0.86 to 0.87), yet significantly (p = 0.002). BMI was similar in these two extreme quintiles. CONCLUSIONS/INTERPRETATION: In this population, a simple weighted 15 SNP-based genetic score provides additional information over clinical predictors of prevalent diabetes. At this stage, however, the clinical benefit of this genetic information is limited.

Characterization and clinical evaluation of CD10+ stroma cells in the breast cancer microenvironment.

PURPOSE: There is growing evidence that interaction between stromal and tumor cells is pivotal in breast cancer progression and response to therapy. Based on earlier research suggesting that during breast cancer progression, striking changes occur in CD10(+) stromal cells, we aimed to better characterize this cell population and its clinical relevance. EXPERIMENTAL DESIGN: We developed a CD10(+) stroma gene expression signature (using HG U133 Plus 2.0) on the basis of the comparison of CD10 cells isolated from tumoral (n = 28) and normal (n = 3) breast tissue. We further characterized the CD10(+) cells by coculture experiments of representative breast cancer cell lines with the different CD10(+) stromal cell types (fibroblasts, myoepithelial, and mesenchymal stem cells). We then evaluated its clinical relevance in terms of in situ to invasive progression, invasive breast cancer prognosis, and prediction of efficacy of chemotherapy using publicly available data sets. RESULTS: This 12-gene CD10(+) stroma signature includes, among others, genes involved in matrix remodeling (MMP11, MMP13, and COL10A1) and genes related to osteoblast differentiation (periostin). The coculture experiments showed that all 3 CD10(+) cell types contribute to the CD10(+) stroma signature, although mesenchymal stem cells have the highest CD10(+) stroma signature score. Of interest, this signature showed an important role in differentiating in situ from invasive breast cancer, in prognosis of the HER2(+) subpopulation of breast cancer only, and potentially in nonresponse to chemotherapy for those patients. CONCLUSIONS: Our results highlight the importance of CD10(+) cells in breast cancer prognosis and efficacy of chemotherapy, particularly within the HER2(+) breast cancer disease.

Comparison of clinical and angiographic prognostic risk scores in elderly patients presenting with acute coronary syndrome and referred for percutaneous coronary intervention.

BACKGROUND: Multiple risk prediction models have been validated in all-age patients presenting with acute coronary syndrome (ACS) and treated with percutaneous coronary intervention (PCI); however, they have not been validated specifically in the elderly. METHODS: We calculated the GRACE (Global Registry of Acute Coronary Events) score, the logistic EuroSCORE, the AMIS (Acute Myocardial Infarction Swiss registry) score, and the SYNTAX (Synergy between Percutaneous Coronary Intervention with TAXUS and Cardiac Surgery) score in a consecutive series of 114 patients ≥75 years presenting with ACS and treated with PCI within 24 hours of hospital admission. Patients were stratified according to score tertiles and analysed retrospectively by comparing the lower/mid tertiles as an aggregate group with the higher tertile group. The primary endpoint was 30-day mortality. Secondary endpoints were the composite of death and major adverse cardiovascular events (MACE) at 30 days, and 1-year MACE-free survival. Model discrimination ability was assessed using the area under receiver operating characteristic curve (AUC). RESULTS: Thirty-day mortality was higher in the upper tertile compared with the aggregate lower/mid tertiles according to the logistic EuroSCORE (42% vs 5%; odds ratio [OR] = 14, 95% confidence interval [CI] = 4-48; p <0.001; AUC = 0.79), the GRACE score (40% vs 4%; OR = 17, 95% CI = 4-64; p <0.001; AUC = 0.80), the AMIS score (40% vs 4%; OR = 16, 95% CI = 4-63; p <0.001; AUC = 0.80), and the SYNTAX score (37% vs 5%; OR = 11, 95% CI = 3-37; p <0.001; AUC = 0.77). CONCLUSIONS: In elderly patients presenting with ACS and referred to PCI within 24 hours of admission, the GRACE score, the EuroSCORE, the AMIS score, and the SYNTAX score predicted 30 day mortality. The predictive value of clinical scores was improved by using them in combination.

Prediction of multiple infections after severe burn trauma: a prospective cohort study.

OBJECTIVE: To develop predictive models for early triage of burn patients based on hypersusceptibility to repeated infections. BACKGROUND: Infection remains a major cause of mortality and morbidity after severe trauma, demanding new strategies to combat infections. Models for infection prediction are lacking. METHODS: Secondary analysis of 459 burn patients (≥16 years old) with 20% or more total body surface area burns recruited from 6 US burn centers. We compared blood transcriptomes with a 180-hour cutoff on the injury-to-transcriptome interval of 47 patients (≤1 infection episode) to those of 66 hypersusceptible patients [multiple (≥2) infection episodes (MIE)]. We used LASSO regression to select biomarkers and multivariate logistic regression to built models, accuracy of which were assessed by area under receiver operating characteristic curve (AUROC) and cross-validation. RESULTS: Three predictive models were developed using covariates of (1) clinical characteristics; (2) expression profiles of 14 genomic probes; (3) combining (1) and (2). The genomic and clinical models were highly predictive of MIE status [AUROCGenomic = 0.946 (95% CI: 0.906-0.986); AUROCClinical = 0.864 (CI: 0.794-0.933); AUROCGenomic/AUROCClinical P = 0.044]. Combined model has an increased AUROCCombined of 0.967 (CI: 0.940-0.993) compared with the individual models (AUROCCombined/AUROCClinical P = 0.0069). Hypersusceptible patients show early alterations in immune-related signaling pathways, epigenetic modulation, and chromatin remodeling. CONCLUSIONS: Early triage of burn patients more susceptible to infections can be made using clinical characteristics and/or genomic signatures. Genomic signature suggests new insights into the pathophysiology of hypersusceptibility to infection may lead to novel potential therapeutic or prophylactic targets.

A Meta-Analysis of Trabecular Bone Score in Fracture Risk Prediction and Its Relationship to FRAX.

Trabecular bone score (TBS) is a gray-level textural index of bone microarchitecture derived from lumbar spine dual-energy X-ray absorptiometry (DXA) images. TBS is a bone mineral density (BMD)-independent predictor of fracture risk. The objective of this meta-analysis was to determine whether TBS predicted fracture risk independently of FRAX probability and to examine their combined performance by adjusting the FRAX probability for TBS. We utilized individual-level data from 17,809 men and women in 14 prospective population-based cohorts. Baseline evaluation included TBS and the FRAX risk variables, and outcomes during follow-up (mean 6.7 years) comprised major osteoporotic fractures. The association between TBS, FRAX probabilities, and the risk of fracture was examined using an extension of the Poisson regression model in each cohort and for each sex and expressed as the gradient of risk (GR; hazard ratio per 1 SD change in risk variable in direction of increased risk). FRAX probabilities were adjusted for TBS using an adjustment factor derived from an independent cohort (the Manitoba Bone Density Cohort). Overall, the GR of TBS for major osteoporotic fracture was 1.44 (95% confidence interval [CI] 1.35-1.53) when adjusted for age and time since baseline and was similar in men and women (p > 0.10). When additionally adjusted for FRAX 10-year probability of major osteoporotic fracture, TBS remained a significant, independent predictor for fracture (GR = 1.32, 95% CI 1.24-1.41). The adjustment of FRAX probability for TBS resulted in a small increase in the GR (1.76, 95% CI 1.65-1.87 versus 1.70, 95% CI 1.60-1.81). A smaller change in GR for hip fracture was observed (FRAX hip fracture probability GR 2.25 vs. 2.22). TBS is a significant predictor of fracture risk independently of FRAX. The findings support the use of TBS as a potential adjustment for FRAX probability, though the impact of the adjustment remains to be determined in the context of clinical assessment guidelines. © 2015 American Society for Bone and Mineral Research.

An investigation of the psychopathy construct and its (novel) correlates in non-clinical samples

A substantial research literature exists regarding the psychopathy construct in forensic populations, but more recently, the construct has been extended to non-clinical populations. The purpose of the present dissertation was to investigate the content and the correlates of the psychopathy construct, with a particular focus on addressing gaps and controversies in the literature. In Study 1, the role of low anxiety in psychopathy was investigated, as some authors have proposed that low anxiety is integral to the psychopathy construct. Participants (n = 346) responded to two self-report psychopathy scales, the SRP-III and the PPI-R, as well as measures of temperament, personality, and antisociality. Of particular interest was the PPI-R Stress Immunity sub scale, which represents low anxiety content. I t was found that Stress Immunity was not correlated with SRP-III psychopathy, nor did it share common personality or temperament correlates or contribute to the prediction of anti sociality. From Study 1, it was concluded that it was unlikely that low anxiety is a central feature of the psychopathy construct. In Study 2, the relationship between SRP-III psychopathy and Ability Emotional Intelligence (Le., Emotional Intelligence measured as an ability, rather than as a self-report personality trait-like characteristic) was investigated, to determine whether psychopathy is be s t seen as a syndrome characterized by emotional deficits or by the ability to skillfully manipulate and prey upon the others' emotions. A negative correlation between the two constructs was found, suggesting that psychopathy is best characterized by deficits in perceiving, facilitating, managing, and understanding emotions. In Study 3, sex differences in the sexual behavior (i.e., promiscuity, age of first sexual behaviors, extradyadic sexual relations) and appearance-related esteem (i.e., body shame,appearance anxiety, self-esteem) correlates of SRP-III psychopathy were investigated. The sexual behavior correlates of psychopathy were quite similar for men and women, but the esteem correlates were very different, such that high psychopathy in men was related to high esteem, whereas high psychopathy in women was generally related to low esteem. This sex difference was difficult to interpret in that it was not mediated by sexual behavior, suggesting that further exploration of this topic is warranted. Together, these three studies contribute to our understanding of non-clinical psychopathy, indicating that low anxiety is likely not part of the construct, that psychopathy is related to low levels of ability in Emotional Intelligence, and that psychopathy is an important predictor of behavior, ability, and beliefs and feelings about the self

Mechanism and Prediction of Post-Operative Atrial Fibrillation Based on Atrial Electrograms

La fibrillation auriculaire (FA) est une arythmie touchant les oreillettes. En FA, la contraction auriculaire est rapide et irrégulière. Le remplissage des ventricules devient incomplet, ce qui réduit le débit cardiaque. La FA peut entraîner des palpitations, des évanouissements, des douleurs thoraciques ou l’insuffisance cardiaque. Elle augmente aussi le risque d'accident vasculaire. Le pontage coronarien est une intervention chirurgicale réalisée pour restaurer le flux sanguin dans les cas de maladie coronarienne sévère. 10% à 65% des patients qui n'ont jamais subi de FA, en sont victime le plus souvent lors du deuxième ou troisième jour postopératoire. La FA est particulièrement fréquente après une chirurgie de la valve mitrale, survenant alors dans environ 64% des patients. L'apparition de la FA postopératoire est associée à une augmentation de la morbidité, de la durée et des coûts d'hospitalisation. Les mécanismes responsables de la FA postopératoire ne sont pas bien compris. L'identification des patients à haut risque de FA après un pontage coronarien serait utile pour sa prévention. Le présent projet est basé sur l'analyse d’électrogrammes cardiaques enregistrées chez les patients après pontage un aorte-coronaire. Le premier objectif de la recherche est d'étudier si les enregistrements affichent des changements typiques avant l'apparition de la FA. Le deuxième objectif est d'identifier des facteurs prédictifs permettant d’identifier les patients qui vont développer une FA. Les enregistrements ont été réalisés par l'équipe du Dr Pierre Pagé sur 137 patients traités par pontage coronarien. Trois électrodes unipolaires ont été suturées sur l'épicarde des oreillettes pour enregistrer en continu pendant les 4 premiers jours postopératoires. La première tâche était de développer un algorithme pour détecter et distinguer les activations auriculaires et ventriculaires sur chaque canal, et pour combiner les activations des trois canaux appartenant à un même événement cardiaque. L'algorithme a été développé et optimisé sur un premier ensemble de marqueurs, et sa performance évaluée sur un second ensemble. Un logiciel de validation a été développé pour préparer ces deux ensembles et pour corriger les détections sur tous les enregistrements qui ont été utilisés plus tard dans les analyses. Il a été complété par des outils pour former, étiqueter et valider les battements sinusaux normaux, les activations auriculaires et ventriculaires prématurées (PAA, PVA), ainsi que les épisodes d'arythmie. Les données cliniques préopératoires ont ensuite été analysées pour établir le risque préopératoire de FA. L’âge, le niveau de créatinine sérique et un diagnostic d'infarctus du myocarde se sont révélés être les plus importants facteurs de prédiction. Bien que le niveau du risque préopératoire puisse dans une certaine mesure prédire qui développera la FA, il n'était pas corrélé avec le temps de l'apparition de la FA postopératoire. Pour l'ensemble des patients ayant eu au moins un épisode de FA d’une durée de 10 minutes ou plus, les deux heures précédant la première FA prolongée ont été analysées. Cette première FA prolongée était toujours déclenchée par un PAA dont l’origine était le plus souvent sur l'oreillette gauche. Cependant, au cours des deux heures pré-FA, la distribution des PAA et de la fraction de ceux-ci provenant de l'oreillette gauche était large et inhomogène parmi les patients. Le nombre de PAA, la durée des arythmies transitoires, le rythme cardiaque sinusal, la portion basse fréquence de la variabilité du rythme cardiaque (LF portion) montraient des changements significatifs dans la dernière heure avant le début de la FA. La dernière étape consistait à comparer les patients avec et sans FA prolongée pour trouver des facteurs permettant de discriminer les deux groupes. Cinq types de modèles de régression logistique ont été comparés. Ils avaient une sensibilité, une spécificité et une courbe opérateur-receveur similaires, et tous avaient un niveau de prédiction des patients sans FA très faible. Une méthode de moyenne glissante a été proposée pour améliorer la discrimination, surtout pour les patients sans FA. Deux modèles ont été retenus, sélectionnés sur les critères de robustesse, de précision, et d’applicabilité. Autour 70% patients sans FA et 75% de patients avec FA ont été correctement identifiés dans la dernière heure avant la FA. Le taux de PAA, la fraction des PAA initiés dans l'oreillette gauche, le pNN50, le temps de conduction auriculo-ventriculaire, et la corrélation entre ce dernier et le rythme cardiaque étaient les variables de prédiction communes à ces deux modèles.

Development of a multi-gene PCR assay for the prediction of the response to hormone therapy in breast cancer

Deux tiers des cancers du sein expriment des récepteurs hormonaux ostrogéniques (tumeur ER-positive) et la croissance de ces tumeurs est stimulée par l’estrogène. Des traitements adjuvant avec des anti-estrogènes, tel que le Tamoxifen et les Inhibiteurs de l’Aromatase peuvent améliorer la survie des patientes atteinte de cancer du sein. Toutefois la thérapie hormonale n’est pas efficace dans toutes les tumeurs mammaires ER-positives. Les tumeurs peuvent présenter avec une résistance intrinsèque ou acquise au Tamoxifen. Présentement, c’est impossible de prédire quelle patiente va bénéficier ou non du Tamoxifen. Des études préliminaires du laboratoire de Dr. Mader, ont identifié le niveau d’expression de 20 gènes, qui peuvent prédire la réponse thérapeutique au Tamoxifen (survie sans récidive). Ces marqueurs, identifié en utilisant une analyse bioinformatique de bases de données publiques de profils d’expression des gènes, sont capables de discriminer quelles patientes vont mieux répondre au Tamoxifen. Le but principal de cette étude est de développer un outil de PCR qui peut évaluer le niveau d’expression de ces 20 gènes prédictif et de tester cette signature de 20 gènes dans une étude rétrospective, en utilisant des tumeurs de cancer du sein en bloc de paraffine, de patients avec une histoire médicale connue. Cet outil aurait donc un impact direct dans la pratique clinique. Des traitements futiles pourraient être éviter et l’indentification de tumeurs ER+ avec peu de chance de répondre à un traitement anti-estrogène amélioré. En conséquence, de la recherche plus appropriée pour les tumeurs résistantes au Tamoxifen, pourront se faire.

Statistical Machine Learning Techniques for the Prediction of Learning Disabilities in School-Age Children

Learning Disability (LD) is a general term that describes specific kinds of learning problems. It is a neurological condition that affects a child's brain and impairs his ability to carry out one or many specific tasks. The learning disabled children are neither slow nor mentally retarded. This disorder can make it problematic for a child to learn as quickly or in the same way as some child who isn't affected by a learning disability. An affected child can have normal or above average intelligence. They may have difficulty paying attention, with reading or letter recognition, or with mathematics. It does not mean that children who have learning disabilities are less intelligent. In fact, many children who have learning disabilities are more intelligent than an average child. Learning disabilities vary from child to child. One child with LD may not have the same kind of learning problems as another child with LD. There is no cure for learning disabilities and they are life-long. However, children with LD can be high achievers and can be taught ways to get around the learning disability. In this research work, data mining using machine learning techniques are used to analyze the symptoms of LD, establish interrelationships between them and evaluate the relative importance of these symptoms. To increase the diagnostic accuracy of learning disability prediction, a knowledge based tool based on statistical machine learning or data mining techniques, with high accuracy,according to the knowledge obtained from the clinical information, is proposed. The basic idea of the developed knowledge based tool is to increase the accuracy of the learning disability assessment and reduce the time used for the same. Different statistical machine learning techniques in data mining are used in the study. Identifying the important parameters of LD prediction using the data mining techniques, identifying the hidden relationship between the symptoms of LD and estimating the relative significance of each symptoms of LD are also the parts of the objectives of this research work. The developed tool has many advantages compared to the traditional methods of using check lists in determination of learning disabilities. For improving the performance of various classifiers, we developed some preprocessing methods for the LD prediction system. A new system based on fuzzy and rough set models are also developed for LD prediction. Here also the importance of pre-processing is studied. A Graphical User Interface (GUI) is designed for developing an integrated knowledge based tool for prediction of LD as well as its degree. The designed tool stores the details of the children in the student database and retrieves their LD report as and when required. The present study undoubtedly proves the effectiveness of the tool developed based on various machine learning techniques. It also identifies the important parameters of LD and accurately predicts the learning disability in school age children. This thesis makes several major contributions in technical, general and social areas. The results are found very beneficial to the parents, teachers and the institutions. They are able to diagnose the child’s problem at an early stage and can go for the proper treatments/counseling at the correct time so as to avoid the academic and social losses.

A prediction model for establishing the disability degree in adults with spinal cord injury: results based on WHO-DAS II perspective

Objective: To establish a prediction model of the degree of disability in adults with Spinal CordInjury (SCI ) based on the use of the WHO-DAS II . Methods: The disability degree was correlatedwith three variable groups: clinical, sociodemographic and those related with rehabilitation services.A model of multiple linear regression was built to predict disability. 45 people with sci exhibitingdiverse etiology, neurological level and completeness participated. Patients were older than 18 andthey had more than a six-month post-injury. The WHO-DAS II and the ASIA impairment scale(AIS ) were used. Results: Variables that evidenced a significant relationship with disability were thefollowing: occupational situation, type of affiliation to the public health care system, injury evolutiontime, neurological level, partial preservation zone, ais motor and sensory scores and number ofclinical complications during the last year. Complications significantly associated to disability werejoint pain, urinary infections, intestinal problems and autonomic disreflexia. None of the variablesrelated to rehabilitation services showed significant association with disability. The disability degreeexhibited significant differences in favor of the groups that received the following services: assistivedevices supply and vocational, job or educational counseling. Conclusions: The best predictiondisability model in adults with sci with more than six months post-injury was built with variablesof injury evolution time, AIS sensory score and injury-related unemployment.

The measurement of maternal plasma corticotropin-releasing factor (CRF) and CRF-binding protein improves the early prediction of preeclampsia

In the present study we measured maternal plasma concentrations of two placental neurohormones, corticotropin-releasing factor (CRF) and CRF-binding protein (CRF-BP), in 58 at-risk pregnant women consecutively enrolled between 28 and 29 wk of pregnancy to evaluate whether their evaluation may predict third trimester-onset preeclampsia ( PE). The statistical significance was assessed by t test. The cut-off points for defining altered CRF and CRF-BP levels for prediction of PE were chosen by receiving operator characteristics curve analysis, and the probability of developing PE was calculated for several combinations of hormone testing results. CRF and CRF-BP levels were significantly ( both P < 0.0001) higher and lower, respectively, in the patients (n = 20) who later developed PE than in those who did not present PE at follow-up. CRF at the cut-off 425.95 pmol/liter achieved a sensitivity of 94.8% and a specificity of 96.9%, whereas CRF-BP at the cut-off 125.8 nmol/liter combined a sensitivity of 92.5% and a specificity of 82.5% as single markers for prediction of PE. The probability of PE was 34.5% in the whole study population, 93.75% when both CRF and CRF-BP levels were changed, and 0% if both hormone markers were unaltered. The measurement of CRF and CRF-BP levels may add significant prognostic information for predicting PE in at-risk pregnant women.