PAPP-A y β-hCG libre como predictores de preeclampsia y bajo peso al nacer en una población latina

Introducción: La Preeclampsia ocurre entre el 2-7% de los embarazos. Previos estudios han sugerido la asociación entre los niveles alterados de PAPP-A y la β-hCG libre con el desarrollo de Preeclampsia (PE) y/o Bajo Peso al Nacer (BPN). Metodología: El diseño del estudio es de Prueba Diagnóstica con enfoque de casos y controles. Las mediciones séricas de PAPP-A y la β-hCG libre, fueron realizadas entre la semana 11-13.6 días durante 2 años. Resultados: La cohorte incluyó 399 pacientes, la incidencia de PE fue de 2,26% y de BPN fue de 14.54%. El punto de corte del percentil 10 fue MoM PAPP-A: 0,368293 y MoM β-hCG libre: 0,412268; la especificidad en PE leve fue de 90,5 y para BPN de 90. Los MoM de la β-hCG libre, la edad y el peso materno se comportan como factores de riesgo, mientras que mayores valores de MoM de la PAPP-A y mayor número de partos factores de protección. Para el BPEG severo la edad materna y la paridad se comportan como factores de riesgo, mientras que un aumento promedio de los valores de los MoM de la PAPP-A y la β-hCG libre, como factores de protección en el desarrollo de BPEG Severo. Conclusiones: Existe una relación significativa entre los valores alterados de PAPP-A y de β-hCG libre, valorados a la semana 11 a 13 con la incidencia de Preeclampsia y de Bajo Peso al nacer en fetos cromosómicamente normales, mostrando unos niveles significativamente más bajos a medida que aumentaba la severidad de la enfermedad.

focus lecture and activities link to videos & posters (Yr2)

Use across a series of lectures to introduce the module, focus on the topic of interdisciplinarity and get to know the group. Contains links to videos to watch

Predicting sense of community and participation by applying machine learning to open government data

Community capacity is used to monitor socio-economic development. It is composed of a number of dimensions, which can be measured to understand the possible issues in the implementation of a policy or the outcome of a project targeting a community. Measuring community capacity dimensions is usually expensive and time consuming, requiring locally organised surveys. Therefore, we investigate a technique to estimate them by applying the Random Forests algorithm on secondary open government data. This research focuses on the prediction of measures for two dimensions: sense of community and participation. The most important variables for this prediction were determined. The variables included in the datasets used to train the predictive models complied with two criteria: nationwide availability; sufficiently fine-grained geographic breakdown, i.e. neighbourhood level. The models explained 77% of the sense of community measures and 63% of participation. Due to the low geographic detail of the outcome measures available, further research is required to apply the predictive models to a neighbourhood level. The variables that were found to be more determinant for prediction were only partially in agreement with the factors that, according to the social science literature consulted, are the most influential for sense of community and participation. This finding should be further investigated from a social science perspective, in order to be understood in depth.

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.

Clinical prediction of occupational and non-specific low back pain

Non-specific Occupational Low Back Pain (NOLBP) is a health condition that generates a high absenteeism and disability. Due to multifactorial causes is difficult to determine accurate diagnosis and prognosis. The clinical prediction of NOLBP is identified as a series of models that integrate a multivariate analysis to determine early diagnosis, course, and occupational impact of this health condition. Objective: to identify predictor factors of NOLBP, and the type of material referred to in the scientific evidence and establish the scopes of the prediction. Materials and method: the title search was conducted in the databases PubMed, Science Direct, and Ebsco Springer, between1985 and 2012. The selected articles were classified through a bibliometric analysis allowing to define the most relevant ones. Results: 101 titles met the established criteria, but only 43 metthe purpose of the review. As for NOLBP prediction, the studies varied in relation to the factors for example: diagnosis, transition of lumbar pain from acute to chronic, absenteeism from work, disability and return to work. Conclusion: clinical prediction is considered as a strategic to determine course and prognostic of NOLBP, and to determine the characteristics that increase the risk of chronicity in workers with this health condition. Likewise, clinical prediction rules are tools that aim to facilitate decision making about the evaluation, diagnosis, prognosis and intervention for low back pain, which should incorporate risk factors of physical, psychological and social.

Talent development : exposing the weakest link. 'El desarrollo del talento : sobre el eslabón más débil'.

Resumen basado en el de la publicación. Monográfico con el título: 'Alumnos de altas capacidades : reflexiones sobre educación'

Prediction of human adaptation and performance in underwater environments

Resumen tomado de la publicaci??n

Psychometric prediction of penitentiary recidivism

Resumen tomado de la publicaci??n

Spanish version of the Link???s Perceived Devaluation and Discrimination Scale

Resumen tomado de la publicaci??n

Prediction of the bulking phenomenon in wastewater treatment plants

The control and prediction of wastewater treatment plants poses an important goal: to avoid breaking the environmental balance by always keeping the system in stable operating conditions. It is known that qualitative information — coming from microscopic examinations and subjective remarks — has a deep influence on the activated sludge process. In particular, on the total amount of effluent suspended solids, one of the measures of overall plant performance. The search for an input–output model of this variable and the prediction of sudden increases (bulking episodes) is thus a central concern to ensure the fulfillment of current discharge limitations. Unfortunately, the strong interrelation between variables, their heterogeneity and the very high amount of missing information makes the use of traditional techniques difficult, or even impossible. Through the combined use of several methods — rough set theory and artificial neural networks, mainly — reasonable prediction models are found, which also serve to show the different importance of variables and provide insight into the process dynamics

Prediction of postprandial blood glucose under intra-patient variability and uncertainty and its use in the design of insulin dosing strategies for type 1 diabetic patients

In this thesis I propose a novel method to estimate the dose and injection-to-meal time for low-risk intensive insulin therapy. This dosage-aid system uses an optimization algorithm to determine the insulin dose and injection-to-meal time that minimizes the risk of postprandial hyper- and hypoglycaemia in type 1 diabetic patients. To this end, the algorithm applies a methodology that quantifies the risk of experiencing different grades of hypo- or hyperglycaemia in the postprandial state induced by insulin therapy according to an individual patient’s parameters. This methodology is based on modal interval analysis (MIA). Applying MIA, the postprandial glucose level is predicted with consideration of intra-patient variability and other sources of uncertainty. A worst-case approach is then used to calculate the risk index. In this way, a safer prediction of possible hyper- and hypoglycaemic episodes induced by the insulin therapy tested can be calculated in terms of these uncertainties.