Simultaneous prediction of acidity parameters (pH and titratable acidity) in Kefir using near infrared reflectance spectroscopy.

Acidity in terms of pH and titratable acids influences the texture and flavour of fermented dairy products, such as Kefir. However, the methods for determining pH and titratable acidity (TA) are time consuming. Near infrared (NIR) spectroscopy is a non-destructive method, which simultaneously predicts multiple traits from a single scan and can be used to predict pH and TA. The best pH NIR calibration model was obtained with no spectral pre-treatment applied, whereas smoothing was found to be the best pre-treatment to develop the TA calibration model. Using cross-validation, the prediction results were found acceptable for both pH and TA. With external validation, similar results were found for pH and TA, and both models were found to be acceptable for screening purposes.

Optimal Use of Treatment Modalities and Treatment Results of Osteosarcoma and Soft Tissue Sarcomas

Soft tissue sarcomas are malignant tumours of mesenchymal origin. Because of infiltrative growth pattern, simple enucleation of the tumour causes a high rate of local recurrence. Instead, these tumours should be resected with a rim of normal tissue around the tumour. Data on the adequate margin width are scarce. At Helsinki University Central Hospital (HUCH) a multidisciplinary treatment group started in 1987. Surgical resection with a wide margin (2.5 cm) is the primary aim. In case of narrower margin radiation therapy is necessary. The role of adjuvant chemotherapy remains unclear. Our aims were to study local control by the surgical margin and to develop a new prognostic tool to aid decision-making on which patients should receive adjuvant chemotherapy. Patients with soft tissue sarcoma of the extremity or the trunk wall referred to HUCH during 1987-2002 form material in Studies I and II. External validation material comes from the Lund university sarcoma registry. The smallest surgical margin of at least 2.5 centimetres yielded local control of 89 per cent at five years. Amputation rate was 9 per cent. The proposed prognostic model with necrosis, vascular invasion, size on a continuous scale, depth, location and grade worked well both in Helsinki material and in the validation material, and it also showed good calibration. Based on the present study, we recommend the smallest surgical margin of 2-3 centimetres in soft tissue sarcoma irrespective of grade. Improvement in local control was present but modest in margins wider than 1 centimetre. In cases where gaining a wider margin would lead to a considerable loss of function, smaller margin is to be considered combined to radiation therapy. Patients treated with inadequate margins should be offered radiation therapy irrespective of tumour grade. Our new prognostic model to estimate 10-year survival probability in patients with soft tissue sarcoma of the extremities or trunk wall showed good dicscrimination and calibration. For time being the prognostic model is available for scientific use and further validations. In the future, the model may aid in clinical decision-making. For operable osteosarcoma, neoadjuvant multidrug chemotherapy followed by delayed surgery and multidrug adjuvant chemotherapy is the treatment of choice. Overall survival rates at five years are approximately 75 per cent in modern trials with classical osteosarcoma. All patients diagnosed and reported to the Finnish Cancer Registry with osteosarcoma in Finland during 1971-2005 form the material in Studies III and IV. Limb-salvage rate increased from 23 per cent to 78 per cent during 1971-2005. The 10-year sarcoma-specific survival for the whole study population improved from 32 per cent to 62 per cent. It was 75 per cent for patients with a local high-grade osteosarcoma of the extremity diagnosed during 1991-2005. This study outlines the improved prognosis of osteosarcoma patients in Finland with modern chemotherapy. The 10-year survival rates are good also in an international scale. Nonetheless, their limb-salvage rate remains inferior to those seen for highly selected patient series. Overall, the centralisation of osteosarcoma treatment would most likely improve both survival and limb-salvage rates even further.

Prediction of Multi-Target Networks of Neuroprotective Compounds with Entropy Indices and Synthesis, Assay, and Theoretical Study of New Asymmetric 1,2-Rasagiline Carbamates

In a multi-target complex network, the links (L-ij) represent the interactions between the drug (d(i)) and the target (t(j)), characterized by different experimental measures (K-i, K-m, IC50, etc.) obtained in pharmacological assays under diverse boundary conditions (c(j)). In this work, we handle Shannon entropy measures for developing a model encompassing a multi-target network of neuroprotective/neurotoxic compounds reported in the CHEMBL database. The model predicts correctly >8300 experimental outcomes with Accuracy, Specificity, and Sensitivity above 80%-90% on training and external validation series. Indeed, the model can calculate different outcomes for >30 experimental measures in >400 different experimental protocolsin relation with >150 molecular and cellular targets on 11 different organisms (including human). Hereafter, we reported by the first time the synthesis, characterization, and experimental assays of a new series of chiral 1,2-rasagiline carbamate derivatives not reported in previous works. The experimental tests included: (1) assay in absence of neurotoxic agents; (2) in the presence of glutamate; and (3) in the presence of H2O2. Lastly, we used the new Assessing Links with Moving Averages (ALMA)-entropy model to predict possible outcomes for the new compounds in a high number of pharmacological tests not carried out experimentally.

Model selection for prognostic time-to-event gene signature discovery with applications in early breast cancer data

Model selection between competing models is a key consideration in the discovery of prognostic multigene signatures. The use of appropriate statistical performance measures as well as verification of biological significance of the signatures is imperative to maximise the chance of external validation of the generated signatures. Current approaches in time-to-event studies often use only a single measure of performance in model selection, such as logrank test p-values, or dichotomise the follow-up times at some phase of the study to facilitate signature discovery. In this study we improve the prognostic signature discovery process through the application of the multivariate partial Cox model combined with the concordance index, hazard ratio of predictions, independence from available clinical covariates and biological enrichment as measures of signature performance. The proposed framework was applied to discover prognostic multigene signatures from early breast cancer data. The partial Cox model combined with the multiple performance measures were used in both guiding the selection of the optimal panel of prognostic genes and prediction of risk within cross validation without dichotomising the follow-up times at any stage. The signatures were successfully externally cross validated in independent breast cancer datasets, yielding a hazard ratio of 2.55 [1.44, 4.51] for the top ranking signature.

Metabolic signature of lung cancer: a metabolomic study of human tissues and biofluids

This thesis reports the application of metabolomics to human tissues and biofluids (blood plasma and urine) to unveil the metabolic signature of primary lung cancer. In Chapter 1, a brief introduction on lung cancer epidemiology and pathogenesis, together with a review of the main metabolic dysregulations known to be associated with cancer, is presented. The metabolomics approach is also described, addressing the analytical and statistical methods employed, as well as the current state of the art on its application to clinical lung cancer studies. Chapter 2 provides the experimental details of this work, in regard to the subjects enrolled, sample collection and analysis, and data processing. In Chapter 3, the metabolic characterization of intact lung tissues (from 56 patients) by proton High Resolution Magic Angle Spinning (HRMAS) Nuclear Magnetic Resonance (NMR) spectroscopy is described. After careful assessment of acquisition conditions and thorough spectral assignment (over 50 metabolites identified), the metabolic profiles of tumour and adjacent control tissues were compared through multivariate analysis. The two tissue classes could be discriminated with 97% accuracy, with 13 metabolites significantly accounting for this discrimination: glucose and acetate (depleted in tumours), together with lactate, alanine, glutamate, GSH, taurine, creatine, phosphocholine, glycerophosphocholine, phosphoethanolamine, uracil nucleotides and peptides (increased in tumours). Some of these variations corroborated typical features of cancer metabolism (e.g., upregulated glycolysis and glutaminolysis), while others suggested less known pathways (e.g., antioxidant protection, protein degradation) to play important roles. Another major and novel finding described in this chapter was the dependence of this metabolic signature on tumour histological subtype. While main alterations in adenocarcinomas (AdC) related to phospholipid and protein metabolisms, squamous cell carcinomas (SqCC) were found to have stronger glycolytic and glutaminolytic profiles, making it possible to build a valid classification model to discriminate these two subtypes. Chapter 4 reports the NMR metabolomic study of blood plasma from over 100 patients and near 100 healthy controls, the multivariate model built having afforded a classification rate of 87%. The two groups were found to differ significantly in the levels of lactate, pyruvate, acetoacetate, LDL+VLDL lipoproteins and glycoproteins (increased in patients), together with glutamine, histidine, valine, methanol, HDL lipoproteins and two unassigned compounds (decreased in patients). Interestingly, these variations were detected from initial disease stages and the magnitude of some of them depended on the histological type, although not allowing AdC vs. SqCC discrimination. Moreover, it is shown in this chapter that age mismatch between control and cancer groups could not be ruled out as a possible confounding factor, and exploratory external validation afforded a classification rate of 85%. The NMR profiling of urine from lung cancer patients and healthy controls is presented in Chapter 5. Compared to plasma, the classification model built with urinary profiles resulted in a superior classification rate (97%). After careful assessment of possible bias from gender, age and smoking habits, a set of 19 metabolites was proposed to be cancer-related (out of which 3 were unknowns and 6 were partially identified as N-acetylated metabolites). As for plasma, these variations were detected regardless of disease stage and showed some dependency on histological subtype, the AdC vs. SqCC model built showing modest predictive power. In addition, preliminary external validation of the urine-based classification model afforded 100% sensitivity and 90% specificity, which are exciting results in terms of potential for future clinical application. Chapter 6 describes the analysis of urine from a subset of patients by a different profiling technique, namely, Ultra-Performance Liquid Chromatography coupled to Mass Spectrometry (UPLC-MS). Although the identification of discriminant metabolites was very limited, multivariate models showed high classification rate and predictive power, thus reinforcing the value of urine in the context of lung cancer diagnosis. Finally, the main conclusions of this thesis are presented in Chapter 7, highlighting the potential of integrated metabolomics of tissues and biofluids to improve current understanding of lung cancer altered metabolism and to reveal new marker profiles with diagnostic value.

Groundtruth system for underwater benchmarking

Oceans - San Diego, 2013

Évaluation de l'impact clinique et économique du développement d'un traitement pour la schizophrénie

Contexte : Les stratégies pharmacologiques pour traiter la schizophrénie reçoivent une attention croissante due au développement de nouvelles pharmacothérapies plus efficaces, mieux tolérées mais plus coûteuses. La schizophrénie est une maladie chronique présentant différents états spécifiques et définis par leur sévérité. Objectifs : Ce programme de recherche vise à: 1) Évaluer les facteurs associés au risque d'être dans un état spécifique de la schizophrénie, afin de construire les fonctions de risque de la modélisation du cours naturel de la schizophrénie; 2) Développer et valider un modèle de Markov avec microsimulations de Monte-Carlo, afin de simuler l'évolution naturelle des patients qui sont nouvellement diagnostiqués pour la schizophrénie, en fonction du profil individuel des facteurs de risque; 3) Estimer le coût direct de la schizophrénie (pour les soins de santé et autres non reliés aux soins de santé) dans la perspective gouvernementale et simuler l’impact clinique et économique du développement d’un traitement dans une cohorte de patients nouvellement diagnostiqués avec la schizophrénie, suivis pendant les cinq premières années post-diagnostic. Méthode : Pour le premier objectif de ce programme de recherche, un total de 14 320 patients nouvellement diagnostiqués avec la schizophrénie ont été identifiés dans les bases de données de la RAMQ et de Med-Echo. Les six états spécifiques de la schizophrénie ont été définis : le premier épisode (FE), l'état de dépendance faible (LDS), l’état de dépendance élevée (HDS), l’état stable (Stable), l’état de bien-être (Well) et l'état de décès (Death). Pour évaluer les facteurs associés au risque de se trouver dans chacun des états spécifiques de la schizophrénie, nous avons construit 4 fonctions de risque en se basant sur l'analyse de risque proportionnel de Cox pour des risques compétitifs. Pour le deuxième objectif, nous avons élaboré et validé un modèle de Markov avec microsimulations de Monte-Carlo intégrant les six états spécifiques de la schizophrénie. Dans le modèle, chaque sujet avait ses propres probabilités de transition entre les états spécifiques de la schizophrénie. Ces probabilités ont été estimées en utilisant la méthode de la fonction d'incidence cumulée. Pour le troisième objectif, nous avons utilisé le modèle de Markov développé précédemment. Ce modèle inclut les coûts directs de soins de santé, estimés en utilisant les bases de données de la Régie de l'assurance maladie du Québec et Med-Echo, et les coûts directs autres que pour les soins de santé, estimés à partir des enquêtes et publications de Statistique Canada. Résultats : Un total de 14 320 personnes nouvellement diagnostiquées avec la schizophrénie ont été identifiées dans la cohorte à l'étude. Le suivi moyen des sujets était de 4,4 (± 2,6) ans. Parmi les facteurs associés à l’évolution de la schizophrénie, on peut énumérer l’âge, le sexe, le traitement pour la schizophrénie et les comorbidités. Après une période de cinq ans, nos résultats montrent que 41% des patients seront considérés guéris, 13% seront dans un état stable et 3,4% seront décédés. Au cours des 5 premières années après le diagnostic de schizophrénie, le coût direct moyen de soins de santé et autres que les soins de santé a été estimé à 36 701 $ canadiens (CAN) (95% CI: 36 264-37 138). Le coût des soins de santé a représenté 56,2% du coût direct, le coût de l'aide sociale 34,6% et le coût associé à l’institutionnalisation dans les établissements de soins de longue durée 9,2%. Si un nouveau traitement était disponible et offrait une augmentation de 20% de l'efficacité thérapeutique, le coût direct des soins de santé et autres que les soins de santé pourrait être réduit jusqu’à 14,2%. Conclusion : Nous avons identifié des facteurs associés à l’évolution de la schizophrénie. Le modèle de Markov que nous avons développé est le premier modèle canadien intégrant des probabilités de transition ajustées pour le profil individuel des facteurs de risque, en utilisant des données réelles. Le modèle montre une bonne validité interne et externe. Nos résultats indiquent qu’un nouveau traitement pourrait éventuellement réduire les hospitalisations et le coût associé aux établissements de soins de longue durée, augmenter les chances des patients de retourner sur le marché du travail et ainsi contribuer à la réduction du coût de l'aide sociale.

ADRB2 and LEPR Gene Polymorphisms: Synergistic Effects on the Risk of Obesity in Japanese

The objective of the present study was to validate a recently reported synergistic effect between variants located in the leptin receptor (LEPR) gene and in the beta-2 adrenergic receptor (ADRB2) gene on the risk of overweight/obesity. We studied a middle-aged/ elderly sample of 4,193 nondiabetic Japanese subjects stratified according gender (1,911 women and 2,282 men). The LEPR Gln223Arg (rs1137101) variant as well as both ADRB2 Arg16Gly (rs1042713) and Gln27Glu (rs1042714) polymorphisms were analyzed. The primary outcome was the risk of overweight/obesity defined as BMI >= 25 kg/m(2), whereas secondary outcomes included the risk of a BMI >= 27 kg/m(2) and BMI as a continuous variable. None of the studied polymorphisms showed statistically significant individual effects, regardless of the group or phenotype studied. Haplotype analysis also did not disclose any associations of ADRB2 polymorphisms with BMI. However, dimensionality reduction-based models confirmed significant interactions among the investigated variants for BMI as a continuous variable as well as for the risk of obesity defined as BMI >= 27 kg/m(2). All disclosed interactions were found in men only. Our results provide external validation for a male specific ADRB2-LEPR interaction effect on the risk of overweight/obesity, but indicate that effect sizes associated with these interactions may be smaller in the population studied.

A quantum chemical study on a set of non-imidazole H(3) antihistamine molecules

Molecular orbital calculations were carried out on a set of 28 non-imidazole H(3) antihistamine compounds using the Hartree-Fock method in order to investigate the possible relationships between electronic structural properties and binding affinity for H3 receptors (pK(i)). It was observed that the frontier effective-for-reaction molecular orbital (FERMO) energies were better correlated with pK(i) values than highest occupied molecular orbital (HOMO) and lowest unoccupied molecular orbital (LUMO) energy values. Exploratory data analysis through hierarchical cluster (HCA) and principal component analysis (PCA) showed a separation of the compounds in two sets, one grouping the molecules with high pK(i) values, the other gathering low pK(i) value compounds. This separation was obtained with the use of the following descriptors: FERMO energies (epsilon(FERMO)), charges derived from the electrostatic potential on the nitrogen atom (N(1)), electronic density indexes for FERMO on the N(1) atom (Sigma((FERMO))c(i)(2)). and electrophilicity (omega`). These electronic descriptors were used to construct a quantitative structure-activity relationship (QSAR) model through the partial least-squares (PLS) method with three principal components. This model generated Q(2) = 0.88 and R(2) = 0.927 values obtained from a training set and external validation of 23 and 5 molecules, respectively. After the analysis of the PLS regression equation and the values for the selected electronic descriptors, it is suggested that high values of FERMO energies and of Sigma((FERMO))c(i)(2), together with low values of electrophilicity and pronounced negative charges on N(1) appear as desirable properties for the conception of new molecules which might have high binding affinity. 2010 Elsevier Inc. All rights reserved.

Design-related bias in hospital fall risk screening tool predictive accuracy evaluations: systematic review and meta-analysis.

Introduction: Fall risk screening tools are frequently used as a part of falls prevention programs in hospitals. Design-related bias in evaluations of tool predictive accuracy could lead to overoptimistic results, which would then contribute to program failure in practice.

Methods: A systematic review was undertaken. Two blind reviewers assessed the methodology of relevant publications into a four-point classification system adapted from multiple sources. The association between study design classification and reported results was examined using linear regression with clustering based on screening tool and robust variance estimates with point estimates of Youden Index (= sensitivity + specificity - 1) as the dependent variable. Meta-analysis was then performed pooling data from prospective studies.

Results: Thirty-five publications met inclusion criteria, containing 51 evaluations of fall risk screening tools. Twenty evaluations were classified as retrospective validation evaluations, 11 as prospective (temporal) validation evaluations, and 20 as prospective (external) validation evaluations. Retrospective evaluations had significantly higher Youden Indices (point estimate [95% confidence interval]: 0.22 [0.11, 0.33]). Pooled Youden Indices from prospective evaluations demonstrated the STRATIFY, Morse Falls Scale, and nursing staff clinical judgment to have comparable accuracy.

Discussion: Practitioners should exercise caution in comparing validity of fall risk assessment tools where the evaluation has been limited to retrospective classifications of methodology. Heterogeneity between studies indicates that the Morse Falls Scale and STRATIFY may still be useful in particular settings, but that widespread adoption of either is unlikely to generate benefits significantly greater than that of nursing staff clinical judgment.

Diagnosis of myalgic encephalomyelitis : where are we now?

Introduction: The World Health Organization has classified myalgic encephalomyelitis (ME) as a neurological disease since 1969 considering chronic fatigue syndrome (CFS) as a synonym used interchangeably for ME since 1969. ME and CFS are considered to be neuro-immune disorders, characterized by specific symptom profiles and a neuro-immune pathophysiology. However, there is controversy as to which criteria should be used to classify patients with “chronic fatigue syndrome.”

Areas covered: The Centers for Disease Control and Prevention (CDC) criteria consider chronic fatigue (CF) to be distinctive for CFS, whereas the International Consensus Criteria (ICC) stresses the presence of post-exertion malaise (PEM) as the hallmark feature of ME. These case definitions have not been subjected to rigorous external validation methods, for example, pattern recognition analyses, instead being based on clinical insights and consensus.

Expert opinion: Pattern recognition methods showed the existence of three qualitatively different categories: (a) CF, where CF evident, but not satisfying full CDC syndrome criteria. (b) CFS, satisfying CDC criteria but without PEM. (c) ME, where PEM is evident in CFS. Future research on this “chronic fatigue spectrum” should, therefore, use the abovementioned validated categories and novel tailored algorithms to classify patients into ME, CFS, or CF.

Model to predict inpatient mortality from information gathered at presentation to an emergency department: The Triage Information Mortality Model (TIMM)

OBJECTIVES: To derive and validate a mortality prediction model from information available at ED triage. METHODS: Multivariable logistic regression of variables from administrative datasets to predict inpatient mortality of patients admitted through an ED. Accuracy of the model was assessed using the receiver operating characteristic area under the curve (ROC-AUC) and calibration using the Hosmer-Lemeshow goodness of fit test. The model was derived, internally validated and externally validated. Derivation and internal validation were in a tertiary referral hospital and external validation was in an urban community hospital. RESULTS: The ROC-AUC for the derivation set was 0.859 (95% CI 0.856-0.865), for the internal validation set was 0.848 (95% CI 0.840-0.856) and for the external validation set was 0.837 (95% CI 0.823-0.851). Calibration assessed by the Hosmer-Lemeshow goodness of fit test was good. CONCLUSIONS: The model successfully predicts inpatient mortality from information available at the point of triage in the ED.

Colorectal preOperative surgical score (CrOSS) for mortality in major colorectal surgery

BACKGROUND: Colorectal surgery carries a significant mortality risk, with reported rates of 1-6% for elective surgery and up to 22% in the emergency setting. Both clinicians and patients will benefit from being able to predict the likelihood of death before surgery. Recently, we have described and validated two risk stratification models for colorectal surgery, the Barwon Health 2012 and Association Française de Chirurgie models. However, these models are not suitable for assessment at patient's bedside. The purpose of this study is to develop a simplified preoperative model capable of predicting mortality following colorectal surgery. METHODS: The new model is termed Colorectal preOperative Surgical Score (CrOSS). The development and internal validation of CrOSS was performed using a prospectively maintained colorectal database. External validation was performed using retrospective data. Univariate and multivariate analyses were performed in model development. Calibration and discrimination were used for model validation. RESULTS: There were 474 and 389 consecutive colorectal surgeries at Geelong Hospital and Western Hospital. Overall mortality rates were 5.16% and 1.03%, respectively. Significant predictors for mortality were as follows: age ≥70, urgent operation, albumin ≤30 g/L and congestive heart failure (receiver operating characteristic (ROC) = 0.870, calibration P-value = 0.937). The predicted risk of mortality was stratified according to the risk profile of 0.39-66.51%. When validated externally, CrOSS predicted mortality accurately (ROC = 0.847, calibration P-value = 0.199). CONCLUSIONS: A robust and simple preoperative model has been created to risk-stratify patients for colorectal surgery. This was successfully validated at another tertiary hospital.

Escherichia coli phylogenetic group determination and its application in the identification of the major animal source of fecal contamination

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Método não radiográfico para o diagnóstico do padrão esquelético classe II hiperdivergente

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)