Early combination disease modifying antirheumatic drug treatment for rheumatoid arthritis

Most people presenting with rheumatoid arthritis today can expect to achieve disease suppression, can avoid or substantially delay joint damage and deformities, and can maintain a good quality of life. Optimal management requires early diagnosis and treatment, usually with combinations of conventional disease modifying antirheumatic drugs (DMARDs). If these do not effect remission, biological DMARDs may be beneficial. Lack of recognition of the early signs of rheumatoid arthritis, ignorance of the benefits of early application of modern treatment regimens, and avoidable delays in securing specialist appointments may hinder achievement of best outcomes for many patients. Triage for recognising possible early rheumatoid arthritis must begin in primary care settings with the following pattern of presentation as a guide: involvement of three or more joints; early-morning joint stiffness of greater than 30 minutes; or bilateral squeeze tenderness at metacarpophalangeal or metatarsophalangeal joints.

Paracetamol [acetaminophen]-induced gastrotoxicity: Revealed by induced hyperacidity in combination with acute or chronic inflammation

Paracetamol is regarded as a relatively safe drug in the gastro-duodenal region of humans but recent epidemiological investigations have suggested that at high doses there may be an increased risk of ulcers and bleeding. To investigate the possibility that inflammatory conditions and gastric acidity may play a role in potentiating development of gastric mucosal injury from paracetamol in rats (as noted previously with various non-steroidal anti-inflammatory drugs) we studied the gastric irritant effects of paracetamol and some phenolic and non-phenolic analgesics and antipyretics in rats with adjuvant or collagen II induced arthritis or zymosan-induced paw inflammation and given 1.0 ml hydrochloric acid (HCl) 0.1 M and/or an i. p. injection of the cholinomimetic, acetyl-β-methyl choline chloride 5.0 mg/kg. Gastric lesions were determined 2 h after oral administration of 100 or 250 mg/kg paracetamol or at therapeutically effective doses of the phenolic or non-phenolic analgesics/antipyretics. The results showed that gastric mucosal injury occurred with all these agents when given to animals that received all treatments so indicating there is an adverse synergy of these three factors, namely: (i) intrinsic disease; (ii) hyperacidity; and (iii) vagal stimulation for rapidly promoting gastric damage, both in the fundic as well as the antral mucosa, for producing gastric damage by paracetamol, as well as the other agents. Removing one of these three predisposing factors effectively blunts/abolishes expression of this paracetamol-induced gastrotoxity in rats. These three factors, without paracetamol, did not cause significant acute gastropathy.

Boosting the tree augmented naive bayes classifier

The Tree Augmented Naïve Bayes (TAN) classifier relaxes the sweeping independence assumptions of the Naïve Bayes approach by taking account of conditional probabilities. It does this in a limited sense, by incorporating the conditional probability of each attribute given the class and (at most) one other attribute. The method of boosting has previously proven very effective in improving the performance of Naïve Bayes classifiers and in this paper, we investigate its effectiveness on application to the TAN classifier.

Tuning pattern classifier parameters using a genetic algorithm with an application in mobile robotics

Support vector machines (SVMs) have recently emerged as a powerful technique for solving problems in pattern classification and regression. Best performance is obtained from the SVM its parameters have their values optimally set. In practice, good parameter settings are usually obtained by a lengthy process of trial and error. This paper describes the use of genetic algorithm to evolve these parameter settings for an application in mobile robotics.

Drug combination: pharmaceutical technologies applied to the manufacturing of combined dosage forms for oral administration

Lo scopo di questa ricerca di dottorato è stato lo studio di una forma di dosaggio flessibile e personalizzabile, indirizzata alle necessità individuali di ogni paziente, per il trattamento dell’iperplasia prostatica benigna. La terapia proposta prevede l’utilizzo di due farmaci, un alfa bloccante (farmaco A) e un inibitore delle 5- fosfodiesterasi (farmaco B) e, somministrati in una singola forma di dosaggio contenenti differenti dosi e combinazioni dei due farmaci. Lo sviluppo di un sistema di rilascio per la somministrazione orale di farmaco A e farmaco B è stato realizzato grazie alla tecnologia Dome Matrix. La tecnologia si basa sull’assemblaggio di moduli utilizzati come elementi di controllo del rilascio. L’assemblaggio dei moduli può essere ottenuto attraverso diverse configurazioni. Sono stati quindi realizzati sistemi assemblati in grado di galleggiare sul contenuto gastrico; la prolungata permanenza della forma farmaceutica nello stomaco favorisce la solubilizzazione dei due principi attivi che quindi potrebbero raggiungere il sito di assorbimento nel primo tratto intestinale già in dispersione molecolare, condizione ideale per essere assorbiti. La prima parte della ricerca è stata focalizzata sulla realizzazione di un sistema assemblato a rilascio modificato di farmaco A. Moduli contenenti diversi dosaggi di farmaco sono stati assemblati in varie configurazioni e dosi differenti per ottenere una forma di dosaggio flessibile, adattabile alle esigenze terapeutiche del paziente. La seconda parte del lavoro di tesi ha riguardato la realizzazione di un sistema assemblato, contenente entrambi i farmaci in associazione. L’ultima parte della ricerca è stata svolta presso la “University of Texas at Austin” sotto la supervisione del Professor Nicholas Peppas. Il lavoro svolto è stato focalizzato sullo studio delle caratteristiche di rigonfiamento dei singoli moduli di farmaco e dei loro sistemi assemblati; il comportamento di tali sistemi è stato investigato anche grazie all’utilizzo della tecnica di tomografia computerizzata a raggi X.

The n-tuple classifier: too good to ignore

The n-tuple pattern recognition method has been tested using a selection of 11 large data sets from the European Community StatLog project, so that the results could be compared with those reported for the 23 other algorithms the project tested. The results indicate that this ultra-fast memory-based method is a viable competitor with the others, which include optimisation-based neural network algorithms, even though the theory of memory-based neural computing is less highly developed in terms of statistical theory.

Benchmarking the n-tuple classifier with statlog dataset

The n-tuple recognition method was tested on 11 large real-world data sets and its performance compared to 23 other classification algorithms. On 7 of these, the results show no systematic performance gap between the n-tuple method and the others. Evidence was found to support a possible explanation for why the n-tuple method yields poor results for certain datasets. Preliminary empirical results of a study of the confidence interval (the difference between the two highest scores) are also reported. These suggest a counter-intuitive correlation between the confidence interval distribution and the overall classification performance of the system.

Good-turing estimation for the frequentist n-tuple classifier

We present results concerning the application of the Good-Turing (GT) estimation method to the frequentist n-tuple system. We show that the Good-Turing method can, to a certain extent rectify the Zero Frequency Problem by providing, within a formal framework, improved estimates of small tallies. We also show that it leads to better tuple system performance than Maximum Likelihood estimation (MLE). However, preliminary experimental results suggest that replacing zero tallies with an arbitrary constant close to zero before MLE yields better performance than that of GT system.