Real-time adaptive models for the personalized prediction of glycemic profile in type 1 diabetes patients

Prediction of glycemic profile is an important task for both early recognition of hypoglycemia and enhancement of the control algorithms for optimization of insulin infusion rate. Adaptive models for glucose prediction and recognition of hypoglycemia based on statistical and artificial intelligence techniques are presented.

Genome-wide prediction of childhood asthma and related phenotypes in a longitudinal birth cohort

Childhood wheezing and asthma vary greatly in clinical presentation and time course. The extent to which phenotypic variation reflects heterogeneity in disease pathways is unclear.

Framingham risk score and alternatives for prediction of coronary heart disease in older adults

Background Guidelines for the prevention of coronary heart disease (CHD) recommend use of Framingham-based risk scores that were developed in white middle-aged populations. It remains unclear whether and how CHD risk prediction might be improved among older adults. We aimed to compare the prognostic performance of the Framingham risk score (FRS), directly and after recalibration, with refit functions derived from the present cohort, as well as to assess the utility of adding other routinely available risk parameters to FRS. Methods Among 2193 black and white older adults (mean age, 73.5 years) without pre-existing cardiovascular disease from the Health ABC cohort, we examined adjudicated CHD events, defined as incident myocardial infarction, CHD death, and hospitalization for angina or coronary revascularization. Results During 8-year follow-up, 351 participants experienced CHD events. The FRS poorly discriminated between persons who experienced CHD events vs. not (C-index: 0.577 in women; 0.583 in men) and underestimated absolute risk prediction by 51% in women and 8% in men. Recalibration of the FRS improved absolute risk prediction, particulary for women. For both genders, refitting these functions substantially improved absolute risk prediction, with similar discrimination to the FRS. Results did not differ between whites and blacks. The addition of lifestyle variables, waist circumference and creatinine did not improve risk prediction beyond risk factors of the FRS. Conclusions The FRS underestimates CHD risk in older adults, particularly in women, although traditional risk factors remain the best predictors of CHD. Re-estimated risk functions using these factors improve accurate estimation of absolute risk.

Basic symptoms and the prediction of first-episode psychosis

Recent focus on early detection and intervention in psychosis has renewed interest in subtle psychopathology beyond positive and negative symptoms. Such self-experienced sub-clinical disturbances are described in detail by the basic symptom concept. This review will give an introduction into the concept of basic symptoms and describe the development of the current instruments for their assessment, the Schizophrenia Proneness Instrument, Adult (SPI-A) and Child and Youth version (SPI-CY), as well as of the two at-risk criteria: the at-risk criterion Cognitive-Perceptive Basic Symptoms (COPER) and the high-risk criterion Cognitive Disturbances (COGDIS). Further, an overview of prospective studies using both or either basic symptom criteria and transition rates related to these will be given, and the potential benefit of combining ultra-high risk criteria, particularly attenuated psychotic symptoms, and basic symptom criteria will be discussed. Finally, their prevalence in psychosis patients, i.e. the sensitivity, as well as in general population samples will be described. It is concluded that both COPER and COGDIS are able to identify subjects at a high risk of developing psychosis. Further, they appear to be sufficiently frequent prior to onset of the first psychotic episode as well as sufficiently rare in persons of general population to be considered as valuable for an early detection of psychosis.

Prediction of psychosis in clinical high-risk patients by the Schizotypal Personality Questionnaire. Results of the EPOS project

OBJECTIVE: Schizotypal features indicate proneness to psychosis in the general population. It is also possible that they increase transition to psychosis (TTP) among clinical high-risk patients (CHR). Our aim was to investigate whether schizotypal features predict TTP in CHR patients. METHODS: In the EPOS (European Prediction of Psychosis Study) project, 245 young help-seeking CHR patients were prospectively followed for 18 months and their TTP was identified. At baseline, subjects were assessed with the Schizotypal Personality Questionnaire (SPQ). Associations between SPQ items and its subscales with the TTP were analysed in Cox regression analysis. RESULTS: The SPQ subscales and items describing ideas of reference and lack of close interpersonal relationships were found to correlate significantly with TTP. The co-occurrence of these features doubled the risk of TTP. CONCLUSIONS: Presence of ideas of reference and lack of close interpersonal relations increase the risk of full-blown psychosis among CHR patients. This co-occurrence makes the risk of psychosis very high.

Prediction of codeine toxicity in infants and their mothers using a novel combination of maternal genetic markers

Substantial variation exists in response to standard doses of codeine ranging from poor analgesia to life-threatening central nervous system (CNS) depression. We aimed to discover the genetic markers predictive of codeine toxicity by evaluating the associations between polymorphisms in cytochrome P450 2D6 (CYP2D6), UDP-glucuronosyltransferase 2B7 (UGT2B7), P-glycoprotein (ABCB1), mu-opioid receptor (OPRM1), and catechol O-methyltransferase (COMT) genes, which are involved in the codeine pathway, and the symptoms of CNS depression in 111 breastfeeding mothers using codeine and their infants. A genetic model combining the maternal risk genotypes in CYP2D6 and ABCB1 was significantly associated with the adverse outcomes in infants (odds ratio (OR) 2.68; 95% confidence interval (CI) 1.61-4.48; P(trend) = 0.0002) and their mothers (OR 2.74; 95% CI 1.55-4.84; P(trend) = 0.0005). A novel combination of the genetic and clinical factors predicted 87% of the infant and maternal CNS depression cases with a sensitivity of 80% and a specificity of 87%. Genetic markers can be used to improve the outcome of codeine therapy and are also probably important for other opioids sharing common biotransformation pathways.

Analysis and Prediction of Transient Opacity Spikes Using Dimensional Modeling

Dimensional modeling, GT-Power in particular, has been used for two related purposes-to quantify and understand the inaccuracies of transient engine flow estimates that cause transient smoke spikes and to improve empirical models of opacity or particulate matter used for engine calibration. It has been proposed by dimensional modeling that exhaust gas recirculation flow rate was significantly underestimated and volumetric efficiency was overestimated by the electronic control module during the turbocharger lag period of an electronically controlled heavy duty diesel engine. Factoring in cylinder-to-cylinder variation, it has been shown that the electronic control module estimated fuel-Oxygen ratio was lower than actual by up to 35% during the turbocharger lag period but within 2% of actual elsewhere, thus hindering fuel-Oxygen ratio limit-based smoke control. The dimensional modeling of transient flow was enabled with a new method of simulating transient data in which the manifold pressures and exhaust gas recirculation system flow resistance, characterized as a function of exhaust gas recirculation valve position at each measured transient data point, were replicated by quasi-static or transient simulation to predict engine flows. Dimensional modeling was also used to transform the engine operating parameter model input space to a more fundamental lower dimensional space so that a nearest neighbor approach could be used to predict smoke emissions. This new approach, intended for engine calibration and control modeling, was termed the "nonparametric reduced dimensionality" approach. It was used to predict federal test procedure cumulative particulate matter within 7% of measured value, based solely on steady-state training data. Very little correlation between the model inputs in the transformed space was observed as compared to the engine operating parameter space. This more uniform, smaller, shrunken model input space might explain how the nonparametric reduced dimensionality approach model could successfully predict federal test procedure emissions when roughly 40% of all transient points were classified as outliers as per the steady-state training data.

Prediction of whole-genome DNA-DNA similarity, determination of G+C content and phylogenetic analysis within the family Pasteurellaceae by multilocus sequence analysis (MLSA)

Genome predictions based on selected genes would be a very welcome approach for taxonomic studies, including DNA-DNA similarity, G+C content and representative phylogeny of bacteria. At present, DNA-DNA hybridizations are still considered the gold standard in species descriptions. However, this method is time-consuming and troublesome, and datasets can vary significantly between experiments as well as between laboratories. For the same reasons, full matrix hybridizations are rarely performed, weakening the significance of the results obtained. The authors established a universal sequencing approach for the three genes recN, rpoA and thdF for the Pasteurellaceae, and determined if the sequences could be used for predicting DNA-DNA relatedness within the family. The sequence-based similarity values calculated using a previously published formula proved most useful for species and genus separation, indicating that this method provides better resolution and no experimental variation compared to hybridization. By this method, cross-comparisons within the family over species and genus borders easily become possible. The three genes also serve as an indicator of the genome G+C content of a species. A mean divergence of around 1 % was observed from the classical method, which in itself has poor reproducibility. Finally, the three genes can be used alone or in combination with already-established 16S rRNA, rpoB and infB gene-sequencing strategies in a multisequence-based phylogeny for the family Pasteurellaceae. It is proposed to use the three sequences as a taxonomic tool, replacing DNA-DNA hybridization.

Importance of the sampled milk fraction for the prediction of total quarter somatic cell count

This study investigated the changes in somatic cell counts (SCC) in different fractions of milk, with special emphasis on the foremilk and cisternal milk fractions. Therefore, in Experiment 1, quarter milk samples were defined as strict foremilk (F), cisternal milk (C), first 400 g of alveolar milk (A1), and the remaining alveolar milk (A2). Experiment 2 included 6 foremilk fractions (F1 to F6), consisting of one hand-stripped milk jet each, and the remaining cisternal milk plus the entire alveolar milk (RM). In Experiment 1, changes during milking indicated the importance of the sampled milk fraction for measuring SCC because the decrease in the first 3 fractions (F, C, and A1) was enormous in milk with high total quarter SCC. The decline in SCC from F to C was 50% and was 80% from C to A1. Total quarter SCC presented a value of approximately 20% of SCC in F or 35% of SCC in C. Changes in milk with low or very low SCC were marginal during milking. Fractions F and C showed significant differences in SCC among different total SCC concentrations. These differences disappeared with the alveolar fractions A1 and A2. In Experiment 2, a more detailed investigation of foremilk fractions supported the findings of Experiment 1. A significant decline in the foremilk fractions even of F1 to F6 was observed in high-SCC milk at concentrations >350 x 10(3) cells/mL. Although one of these foremilk fractions presented only 0.1 to 0.2% of the total milk, the SCC was 2- to 3-fold greater than the total quarter milk SCC. Because the trait of interest (SCC) was measured directly by using the DeLaval cell counter (DCC), the quality of measurement was tested. Statistically interesting factors (repeatability, recovery rate, and potential matrix effects of milk) proved that the DCC is a useful tool for identifying the SCC of milk samples, and thus of grading udder health status. Generally, the DCC provides reliable results, but one must consider that SCC even in strict foremilk can differ dramatically from SCC in the total cisternal fraction, and thus also from SCC in the alveolar fraction.