Serum tests, liver stiffness and artificial neural networks for diagnosing cirrhosis and portal hypertension

BACKGROUND The diagnostic performance of biochemical scores and artificial neural network models for portal hypertension and cirrhosis is not well established. AIMS To assess diagnostic accuracy of six serum scores, artificial neural networks and liver stiffness measured by transient elastography, for diagnosing cirrhosis, clinically significant portal hypertension and oesophageal varices. METHODS 202 consecutive compensated patients requiring liver biopsy and hepatic venous pressure gradient measurement were included. Several serum tests (alone and combined into scores) and liver stiffness were measured. Artificial neural networks containing or not liver stiffness as input variable were also created. RESULTS The best non-invasive method for diagnosing cirrhosis, portal hypertension and oesophageal varices was liver stiffness (C-statistics=0.93, 0.94, and 0.90, respectively). Among serum tests/scores the best for diagnosing cirrhosis and portal hypertension and oesophageal varices were, respectively, Fibrosis-4, and Lok score. Artificial neural networks including liver stiffness had high diagnostic performance for cirrhosis, portal hypertension and oesophageal varices (accuracy>80%), but were not statistically superior to liver stiffness alone. CONCLUSIONS Liver stiffness was the best non-invasive method to assess the presence of cirrhosis, portal hypertension and oesophageal varices. The use of artificial neural networks integrating different non-invasive tests did not increase the diagnostic accuracy of liver stiffness alone.

New algorithms for automated phylogenetic reconstruction using artificial intelligence and data mining techniques

Academic and industrial research in the late 90s have brought about an exponential explosion of DNA sequence data. Automated expert systems are being created to help biologists to extract patterns, trends and links from this ever-deepening ocean of information. Two such systems aimed on retrieving and subsequently utilizing phylogenetically relevant information have been developed in this dissertation, the major objective of which was to automate the often difficult and confusing phylogenetic reconstruction process. ^ Popular phylogenetic reconstruction methods, such as distance-based methods, attempt to find an optimal tree topology (that reflects the relationships among related sequences and their evolutionary history) by searching through the topology space. Various compromises between the fast (but incomplete) and exhaustive (but computationally prohibitive) search heuristics have been suggested. An intelligent compromise algorithm that relies on a flexible “beam” search principle from the Artificial Intelligence domain and uses the pre-computed local topology reliability information to adjust the beam search space continuously is described in the second chapter of this dissertation. ^ However, sometimes even a (virtually) complete distance-based method is inferior to the significantly more elaborate (and computationally expensive) maximum likelihood (ML) method. In fact, depending on the nature of the sequence data in question either method might prove to be superior. Therefore, it is difficult (even for an expert) to tell a priori which phylogenetic reconstruction method—distance-based, ML or maybe maximum parsimony (MP)—should be chosen for any particular data set. ^ A number of factors, often hidden, influence the performance of a method. For example, it is generally understood that for a phylogenetically “difficult” data set more sophisticated methods (e.g., ML) tend to be more effective and thus should be chosen. However, it is the interplay of many factors that one needs to consider in order to avoid choosing an inferior method (potentially a costly mistake, both in terms of computational expenses and in terms of reconstruction accuracy.) ^ Chapter III of this dissertation details a phylogenetic reconstruction expert system that selects a superior proper method automatically. It uses a classifier (a Decision Tree-inducing algorithm) to map a new data set to the proper phylogenetic reconstruction method. ^

Grapholita molesta : caracterización de una cría artificial

Grapholita molesta (Busck) es considerada plaga principal del duraznero en Mendoza. Sus larvas han sido criadas, en condiciones de laboratorio, con dietas naturales por diversos autores. Los objetivos fueron: 1. poner a punto la cría artificial de la especie; 2. diseñar una jaula para el apareamiento y la oviposición de los adultos; 3. evaluar la eficiencia de la jaula y la dieta larvaria mediante grados día, ciclo biológico, peso de pupas, recuperación de huevoadulto, fecundidad, viabilidad y longevidad. La cría artificial de una especie constituye una herramienta para profundizar sus conocimientos bioetoecológicos y, en consecuencia, aplicarlos en su control. En 1996 se fundó una cría con larvas salvajes alimentadas con manzanas verdes pequeñas del cv. Granny Smith. Los adultos se desarrollaron en una jaula especialmente diseñada. En el ciclo biológico, la recuperación huevo-adulto y la fecundidad se obtuvieron valores superiores a los citados por otros autores pero no ocurrió lo mismo con las otras variables. La cría artificial de Grapholita molesta (Busck) se logró desarrollar por 37 generaciones y la alta fecundidad obtenida es una clave fundamental en el éxito de su mantenimiento.

Evaluación de los efectos de la composición de la dieta artificial para la cría de Diatraea saccharalis (Lepidoptera: Crambidae) y Cotesia flavipes (Hymenoptera: Braconidae)

El control biológico aumentativo de Diatrae saccharalis Fabricius (Lepidoptera: Crambidae) requiere la cría masiva del parasitoide Cotesia flavipes Cameron (Braconidae: Microgastrinae) y por ello, es necesario el desarrollo de dietas artificiales eficientes. El objetivo fue examinar los efectos de distintos tipos de dieta sobre parámetros biológicos de D. saccharalis y su impacto en la producción de cocones de C. flavipes. Se sembraron 46136 huevos de D. saccharalis en once combinaciones de dietas artificiales, con dos tipos de harinas y tres tipos de antibióticos. Los resultados mostraron que la composición de la dieta afectó los parámetros biológicos de ambas especies. La mayor eficiencia en la cría se obtuvo con el empleo de combinaciones de harina de poroto y ampicilina. Sin embargo, si se considera la relación entre costos de producción y parámetros biológicos, la dieta con harina de poroto, oxitetraciclina y estreptomicina resulta más adecuada para la cría masiva.