Latent tuberculosis infection : a diagnostic challenge : Tuberculin skin testing versus interferon-γ release assays

Trabalho Final do Curso de Mestrado Integrado em Medicina, Faculdade de Medicina, Universidade de Lisboa, 2014

Truck lane needs methodology: a heuristic approach to solve a five option discrete network design problem. Interim report.

Texas State Department of Highways and Public Transportation, Transportation Planning Division, Austin

mecA locus diversity in methicillin-resistant Staphylococcus aureus isolates in Brisbane, Australia, and the development of a novel diagnostic procedure for the Western Samoan phage pattern clone

An emerging public health phenomenon is the increasing incidence of methicillin-resistant Staphylococcus aureus (MRSA) infections that are acquired outside of health care facilities. One lineage of community-acquired MRSA (CA-MRSA) is known as the Western Samoan phage pattern (WSPP) clone. The central aim of this study was to develop an efficient genotyping procedure for the identification of WSPP isolates. The approach taken was to make use of the highly variable region downstream of mecA in combination with a single nucleotide polymorphism (SNP) defined by the S. aureus multilocus sequence typing (MLST) database. The premise was that a combinatorial genotyping method that interrogated both a highly variable region and the genomic backbone would deliver a high degree of informative power relative to the number of genetic polymorphisms-interrogated. Thirty-five MRSA isolates were used for this study, and their gene contents and order downstream of mecA were determined. The CA-MRSA isolates were found to contain a truncated mecA downstream region consisting of mecA-HVR-IS431 mec-dcs-Ins117, and a PCR-based method for identifying this structure was developed. The hospital-acquired isolates were found to contain eight different mecA downstream regions, three of which were novel. The Minimum SNPs computer software program was used to mine the S. aureus MLST database, and the arcC 2726 polymorph was identified as 82% discriminatory for ST-30. A real-time PCR assay was developed to interrogate this SNP. We found that the assay for the truncated mecA downstream region in combination with the interrogation of arcC position 272 provided an unambiguous identification of WSPP isolates.

Genomewide linkage study in 1,176 affected sister pair families identifies a significant susceptibility locus for endometriosis on chromosome 10q26

Endometriosis is a common gynecological disease that affects up to 10% of women in their reproductive years. It causes pelvic pain, severe dysmenorrhea, and subfertility. The disease is defined as the presence of tissue resembling endometrium in sites outside the uterus. Its cause remains uncertain despite 150 years of hypothesis-driven research, and thus the therapeutic options are limited. Disease predisposition is inherited as a complex genetic trait, which provides an alternative route to understanding the disease. We seek to identify susceptibility loci, using a positional-cloning approach that starts with linkage analysis to identify genomic regions likely to harbor these genes. We conducted a linkage study of 1,176 families ( 931 from an Australian group and 245 from a U. K. group), each with at least two members-mainly affected sister pairs-with surgically diagnosed disease. We have identified a region of significant linkage on chromosome 10q26 ( maximum LOD score [MLS] of 3.09; genomewide P = .047) and another region of suggestive linkage on chromosome 20p13 MLS p 2.09). Minor peaks with MLS > 1.0) were found on chromosomes 2, 6, 7, 8, 12, 14, 15, and 17. This is the first report of linkage to a major locus for endometriosis. The findings will facilitate discovery of novel positional genetic variants that influence the risk of developing this debilitating disease. Greater understanding of the aberrant cellular and molecular mechanisms involved in the etiology and pathophysiology of endometriosis should lead to better diagnostic methods and targeted treatments.

A simple implementation of a normal mixture approach to differential gene expression in multiclass microarrays

Motivation: An important problem in microarray experiments is the detection of genes that are differentially expressed in a given number of classes. We provide a straightforward and easily implemented method for estimating the posterior probability that an individual gene is null. The problem can be expressed in a two-component mixture framework, using an empirical Bayes approach. Current methods of implementing this approach either have some limitations due to the minimal assumptions made or with more specific assumptions are computationally intensive. Results: By converting to a z-score the value of the test statistic used to test the significance of each gene, we propose a simple two-component normal mixture that models adequately the distribution of this score. The usefulness of our approach is demonstrated on three real datasets.

An Incremental EM-based Learning Approach for On-Line Prediction of Hospital Resource Utilization

Objective: Inpatient length of stay (LOS) is an important measure of hospital activity, health care resource consumption, and patient acuity. This research work aims at developing an incremental expectation maximization (EM) based learning approach on mixture of experts (ME) system for on-line prediction of LOS. The use of a batchmode learning process in most existing artificial neural networks to predict LOS is unrealistic, as the data become available over time and their pattern change dynamically. In contrast, an on-line process is capable of providing an output whenever a new datum becomes available. This on-the-spot information is therefore more useful and practical for making decisions, especially when one deals with a tremendous amount of data. Methods and material: The proposed approach is illustrated using a real example of gastroenteritis LOS data. The data set was extracted from a retrospective cohort study on all infants born in 1995-1997 and their subsequent admissions for gastroenteritis. The total number of admissions in this data set was n = 692. Linked hospitalization records of the cohort were retrieved retrospectively to derive the outcome measure, patient demographics, and associated co-morbidities information. A comparative study of the incremental learning and the batch-mode learning algorithms is considered. The performances of the learning algorithms are compared based on the mean absolute difference (MAD) between the predictions and the actual LOS, and the proportion of predictions with MAD < 1 day (Prop(MAD < 1)). The significance of the comparison is assessed through a regression analysis. Results: The incremental learning algorithm provides better on-line prediction of LOS when the system has gained sufficient training from more examples (MAD = 1.77 days and Prop(MAD < 1) = 54.3%), compared to that using the batch-mode learning. The regression analysis indicates a significant decrease of MAD (p-value = 0.063) and a significant (p-value = 0.044) increase of Prop(MAD

Arthritic disease suppression and cartilage protection with glycosaminoglycan polypeptide complexes (Peptacans) derived from the cartilage extracellular matrix: A novel approach to therapy

Molecular fragments of cartilage are antigenic and can stimulate an autoimmune response. Oral administration of type II collagen prevents disease onset in animal models of arthritis but the effects of other matrix components have not been reported. We evaluated glycosaminoglycan polypeptides (GAG-P) and matrix proteins (CaP) from cartilage for a) mitigating disease activity in rats with collagen-induced arthritis (CIA) and adjuvant-induced arthritis (AIA) and b) stimulating proteoglycan (PG) synthesis by chondrocytes in-vitro. CIA and AIA were established in Wistar rats using standard methods. Agents were administered orally (10–200 mg/kg), either for seven days prior to disease induction (toleragenic protocol), or continuously for 15 days after injecting the arthritigen (prophylactic protocol). Joint swelling and arthritis scores were determined on day 15. Histological sections of joint tissues were assessed post-necropsy. In chondrocyte cultures, CaP + / − interleukin-1 stimulated PG biosynthesis. CaP was also active in preventing arthritis onset at 3.3, 10 or 20 mg/kg in the rat CIA model using the toleragenic protocol. It was only active at 20 and 200 mg/kg in the CIA prophylactic protocol. GAG-P was active in the CIA toleragenic protocol at 20 mg/kg but chondroitin sulfate and glucosamine hydrochloride or glucosamine sulfate were all inactive. The efficacy of CaP in the rat AIA model was less than in the CIA model. These findings lead us to suggest that oral CaP could be used as a disease-modifying anti-arthritic drug.

A core vocabulary approach for management of inconsistent speech disorder

Developmental speech disorder is accounted for by theories derived from psychology, psycholinguistics, linguistics and medicine, with researchers developing assessment protocols that reflect their theoretical perspective. How theory and data analyses lead to different therapy approaches, however, is sometimes unclear. Here, we present a case management plan for a 7 year old boy with unintelligible speech. Assessment data were analysed to address seven case management questions regarding need for intervention, service delivery, differential diagnosis, intervention goals, generalization of therapeutic gains, discharge criteria and evaluation of efficacy. Jarrod was diagnosed as having inconsistent speech disorder that required intervention. He pronounced 88% of words differently when asked to name each word in the 25 word inconsistency test of the Diagnostic Evaluation of Articulation and Phonology three times, each trial separated by another activity. Other standardized assessments supported the diagnosis of inconsistent speech disorder that, according to previous research, is associated with a deficit in phonological assembly. Core vocabulary intervention was chosen as the most appropriate therapy technique. Its nature and a possible protocol for implementation is described.

A Bayesian state space modelling approach to probabilistic quantitative precipitation forecasting

The generation of very short range forecasts of precipitation in the 0-6 h time window is traditionally referred to as nowcasting. Most existing nowcasting systems essentially extrapolate radar observations in some manner, however, very few systems account for the uncertainties involved. Thus deterministic forecast are produced, which have a limited use when decisions must be made, since they have no measure of confidence or spread of the forecast. This paper develops a Bayesian state space modelling framework for quantitative precipitation nowcasting which is probabilistic from conception. The model treats the observations (radar) as noisy realisations of the underlying true precipitation process, recognising that this process can never be completely known, and thus must be represented probabilistically. In the model presented here the dynamics of the precipitation are dominated by advection, so this is a probabilistic extrapolation forecast. The model is designed in such a way as to minimise the computational burden, while maintaining a full, joint representation of the probability density function of the precipitation process. The update and evolution equations avoid the need to sample, thus only one model needs be run as opposed to the more traditional ensemble route. It is shown that the model works well on both simulated and real data, but that further work is required before the model can be used operationally. © 2004 Elsevier B.V. All rights reserved.

Combinatorial approach to multi-substituted 1,4-Benzodiazepines as novel non-peptide CCK-antagonists

For the drug discovery process, a library of 168 multisubstituted 1,4-benzodiazepines were prepared by a 5-step solid phase combinatorial approach. Substituents were varied in the 3,5, 7 and 8-position on the benzodiazepine scaffold. The combinatorial library was evaluated in a CCK radiolabelled binding assay and CCKA (alimentary) and CCKB (brain) selective lead structures were discovered. The template of CCKA selective 1,4-benzodiazepin-2-ones bearing the tryptophan moiety was chemically modified by selective alkylation and acylation reactions. These studies provided a series of Asperlicin naturally analogues. The fully optimised Asperlicin related compound possessed a similar CCKA activity as the natural occuring compound. 3-Alkylated 1,4-benzodiazepines with selectivity towards the CCKB receptor subtype were optimised on A) the lipophilic side chain and B) the 2-aminophenyl-ketone moiety, together with some stereochemical changes. A C3 unit in the 3-position of 1,4-benzodiazepines possessed a CCKB activity within the nanomolar range. Further SAR optimisation on the N1-position by selective alkylation resulted in an improved CCKB binding with potentially decreased activity on the GABAA/benzodiazepine receptor complex. The in vivo studies revealed two N1-alkylated compounds containing unsaturated alkyl groups with anxiolytic properties. Alternative chemical approaches have been developed, including a route that is suitable for scale up of the desired target molecule in order to provide sufficient quantities for further in vivo evaluation.

Shaping acoustic fields as a toolset for microfluidic manipulations in diagnostic technologies

Ultrasonics offers the possibility of developing sophisticated fluid manipulation tools in lab-on-a-chip technologies. Here we demonstrate the ability to shape ultrasonic fields by using phononic lattices, patterned on a disposable chip, to carry out the complex sequence of fluidic manipulations required to detect the rodent malaria parasite Plasmodium berghei in blood. To illustrate the different tools that are available to us, we used acoustic fields to produce the required rotational vortices that mechanically lyse both the red blood cells and the parasitic cells present in a drop of blood. This procedure was followed by the amplification of parasitic genomic sequences using different acoustic fields and frequencies to heat the sample and perform a real-time PCR amplification. The system does not require the use of lytic reagents nor enrichment steps, making it suitable for further integration into lab-on-a-chip point-of-care devices. This acoustic sample preparation and PCR enables us to detect ca. 30 parasites in a microliter-sized blood sample, which is the same order of magnitude in sensitivity as lab-based PCR tests. Unlike other lab-on-a-chip methods, where the sample moves through channels, here we use our ability to shape the acoustic fields in a frequency-dependent manner to provide different analytical functions. The methods also provide a clear route toward the integration of PCR to detect pathogens in a single handheld system.

An efficient route to highly organized, tunable macroporous-mesoporous alumina

(Figure Presented) Organized macroporous-mesoporous alumina can be obtained via a dual-templating approach. Monodispersed polystyrene beads promote macropore formation, while a P123 surfactant templating agent drives the formation of ordered hexagonal mesopores throughout the alumina framework. These well-defined pore networks coexist over a wide range of temperatures and macropore sizes. © 2009 American Chemical Society.