The diagnostic interview for psychoses (DIP): development, reliability and applications

Background. We describe the development, reliability and applications of the Diagnostic Interview for Psychoses (DIP), a comprehensive interview schedule for psychotic disorders. Method. The DIP is intended for use by interviewers with a clinical background and was designed to occupy the middle ground between fully structured, lay-administered schedules, and semi-structured., psychiatrist-administered interviews. It encompasses four main domains: (a) demographic data; (b) social functioning and disability; (c) a diagnostic module comprising symptoms, signs and past history ratings; and (d) patterns of service utilization Lind patient-perceived need for services. It generates diagnoses according to several sets of criteria using the OPCRIT computerized diagnostic algorithm and can be administered either on-screen or in a hard-copy format. Results. The DIP proved easy to use and was well accepted in the field. For the diagnostic module, inter-rater reliability was assessed on 20 cases rated by 24 clinicians: good reliability was demonstrated for both ICD-10 and DSM-III-R diagnoses. Seven cases were interviewed 2-11 weeks apart to determine test-retest reliability, with pairwise agreement of 0.8-1.0 for most items. Diagnostic validity was assessed in 10 cases, interviewed with the DIP and using the SCAN as 'gold standard': in nine cases clinical diagnoses were in agreement. Conclusions. The DIP is suitable for use in large-scale epidemiological studies of psychotic disorders. as well as in smaller Studies where time is at a premium. While the diagnostic module stands on its own, the full DIP schedule, covering demography, social functioning and service utilization makes it a versatile multi-purpose tool.

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.

The development of a research agenda for substance use disorders diagnosis in the Diagnostic and Statistical Manual of Mental Disorders, fifth edition (DSM-V)

Aims paper describes the background to the establishment of the Substance Use Disorders Workgroup, which was charged with developing the research agenda for the development of the next edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM). It summarizes 18 articles that were commissioned to inform that process. Methods A preliminary list of research topics, developed at the DSM-V Launch Conference in 2004, led to the identification of subjects that were subject to formal presentations and detailed discussion at the Substance Use Disorders Conference in February 2005. Results The 18 articles presented in this supplement examine: (1) categorical versus dimensional diagnoses; (2) the neurobiological basis of substance use disorders; (3) social and cultural perspectives; (4) the crosswalk between DSM-IV and the International Classification of Diseases Tenth Revision (ICD-10); (5) comorbidity of substance use disorders and mental health disorders; (6) subtypes of disorders; (7) issues in adolescence; (8) substance-specific criteria; (9) the place of non-substance addictive disorders; and (10) the available research resources. Conclusions In the final paper a broadly based research agenda for the development of diagnostic concepts and criteria for substance use disorders is presented.

Physical enhancement of transdermal drug application: is delivery technology keeping up with pharmaceutical development?

Advances in molecular biology have given us a wide range of protein and peptide-based drugs that are unsuitable for oral delivery because of their high degree of first-pass metabolism. Though parenteral delivery is the obvious answer, for the successful development of commercial chronic and self-administration usage formulations it is not the ideal choice. Transdermal delivery is emerging as the biggest application target for these agents, however, the skin is extremely efficient at keeping out such large molecular weight compounds and therapeutic levels are never going to be realistically achieved by passive absorption. Physical enhancement mechanisms including: iontophoresis, electroporation, ultrasound, photomechanical waves, microneedles and jet-propelled particles are emerging as solutions to this topical delivery dilemma. Adding proteins and peptides to the list of other large molecular weight drugs with insufficient passive transdermal fluxes to be therapeutically useful, we have a collection of pharmacological agents waiting for efficient delivery methods to be introduced. This article reviews the current state of physical transdermal delivery technology, assesses the pros and cons of each technique and summarises the evidence-base of their drug delivery capabilities.

From complexity to category: Responding to diagnostic uncertainties of autistic spectrum disorders

Objective: Recent data from Education Queensland has identified rising numbers of children receiving diagnoses of autistic spectrum disorder (ASD). Faced with funding diagnostic pressures, in clinical situations that are complex and inherently uncertain, it is possible that specialists err on the side of a positive diagnosis. This study examines the extent to which possible overinclusion of ASD diagnosis may exist in the presence of uncertainty and factors potentially related to this practice in Queensland. Methods: Using anonymous self-report, all Queensland child psychiatrists and paediatricians who see paediatric patients with development/behavioural problems were surveyed and asked whether they had ever specified an ASD diagnosis in the presence of diagnostic uncertainty. Using logistic regression, elicited responses to the diagnostic uncertainty questions were related to other clinical- and practice-related characteristics. Results: Overall, 58% of surveyed psychiatrists and paediatricians indicated that, in the face of diagnostic uncertainty, they had erred on the side of providing an ASD diagnosis for educational ascertainment and 36% of clinicians had provided an autism diagnosis for Carer's Allowance when Centrelink diagnostic specifications had not been met. Conclusion: In the absence of definitive biological markers, ASD remains a behavioural diagnosis that is often complex and uncertain. In response to systems that demand a categorical diagnostic response, specialists are providing ASD diagnoses, even when uncertain. The motivation for this practice appears to be a clinical risk/benefit analysis of what will achieve the best outcomes for children. It is likely that these practices will continue unless systems change eligibility to funding based on functional impairment rather than medical diagnostic categories.

GANN: Genetic algorithm neural networks for the detection of conserved combinations of features in DNA

Background: The multitude of motif detection algorithms developed to date have largely focused on the detection of patterns in primary sequence. Since sequence-dependent DNA structure and flexibility may also play a role in protein-DNA interactions, the simultaneous exploration of sequence-and structure-based hypotheses about the composition of binding sites and the ordering of features in a regulatory region should be considered as well. The consideration of structural features requires the development of new detection tools that can deal with data types other than primary sequence. Results: GANN ( available at http://bioinformatics.org.au/gann) is a machine learning tool for the detection of conserved features in DNA. The software suite contains programs to extract different regions of genomic DNA from flat files and convert these sequences to indices that reflect sequence and structural composition or the presence of specific protein binding sites. The machine learning component allows the classification of different types of sequences based on subsamples of these indices, and can identify the best combinations of indices and machine learning architecture for sequence discrimination. Another key feature of GANN is the replicated splitting of data into training and test sets, and the implementation of negative controls. In validation experiments, GANN successfully merged important sequence and structural features to yield good predictive models for synthetic and real regulatory regions. Conclusion: GANN is a flexible tool that can search through large sets of sequence and structural feature combinations to identify those that best characterize a set of sequences.

Development of an oligonucleotide-based SNP detection method on lateral flow strips using hexapet tages

Detection of point mutations or single nucleotide polymorphisms (SNPs) is important in relation to disease susceptibility or detection in pathogens of mutations determining drug resistance or host range. There is an emergent need for rapid detection methods amenable to point-of-care applications. The purpose of this study was to reduce to practice a novel method for SNP detection and to demonstrate that this technology can be used downstream of nucleic acid amplification. The authors used a model system to develop an oligonucleotide-based SNP detection system on nitrocellulose lateral flow strips. To optimize the assay they used cloned sequences of the herpes simplex virus-1 (HSV-1) DNA polymerase gene into which they introduced a point mutation. The assay system uses chimeric polymerase chain reaction (PCR) primers that incorporate hexameric repeat tags ("hexapet tags"). The chimeric sequences allow capture of amplified products to predefined positions on a lateral flow strip. These "hexapet" sequences have minimal cross-reactivity and allow specific hybridization-based capture of the PCR products at room temperature onto lateral flow strips that have been striped with complementary hexapet tags. The allele-specific amplification was carried out with both mutant and wild-type primer sets present in the PCR mix ("competitive" format). The resulting PCR products carried a hexapet tag that corresponded with either a wild-type or mutant sequence. The lateral flow strips are dropped into the PCR reaction tube, and mutant sequence and wild-type sequences diffuse along the strip and are captured at the corresponding position on the strip. A red line indicative of a positive reaction is visible after 1 minute. Unlike other systems that require separate reactions and strips for each target sequence, this system allows multiplex PCR reactions and multiplex detection on a single strip or other suitable substrates. Unambiguous visual discrimination of a point mutation under room temperature hybridization conditions was achieved with this model system in 10 minutes after PCR. The authors have developed a capture-based hybridization method for the detection and discrimination of HSV-1 DNA polymerase genes that contain a single nucleotide change. It has been demonstrated that the hexapet oligonucleotides can be adapted for hybridization on the lateral flow strip platform for discrimination of SNPs. This is the first step in demonstrating SNP detection on lateral flow using the hexapet oligonucleotide capture system. It is anticipated that this novel system can be widely used in point-of-care settings.

The empirical basis of substance use disorders diagnosis: research recommendations for the Diagnostic and Statistical Manual of Mental Disorders, fifth edition (DSM-V)

Aims This paper presents the recommendations, developed from a 3-year consultation process, for a program of research to underpin the development of diagnostic concepts and criteria in the Substance Use Disorders section of the Diagnostic and Statistical Manual of Mental Disorders (DSM) and potentially the relevant section of the next revision of the International Classification of Diseases (ICD). Methods A preliminary list of research topics was developed at the DSM-V Launch Conference in 2004. This led to the presentation of articles on these topics at a specific Substance Use Disorders Conference in February 2005, at the end of which a preliminary list of research questions was developed. This was further refined through an iterative process involving conference participants over the following year. Results Research questions have been placed into four categories: (1) questions that could be addressed immediately through secondary analyses of existing data sets; (2) items likely to require position papers to propose criteria or more focused questions with a view to subsequent analyses of existing data sets; (3) issues that could be proposed for literature reviews, but with a lower probability that these might progress to a data analytic phase; and (4) suggestions or comments that might not require immediate action, but that could be considered by the DSM-V and ICD 11 revision committees as part of their deliberations. Conclusions A broadly based research agenda for the development of diagnostic concepts and criteria for substance use disorders is presented.

A gradient descent algorithm for minimizing amino acid coupling reactions when synthesizing cyclic-peptide libraries

Combinatorial chemistry has become an invaluable tool in medicinal chemistry for the identification of new drug leads. For example, libraries of predetermined sequences and head-to-tail cyclized peptides are routinely synthesized in our laboratory using the IRORI approach. Such libraries are used as molecular toolkits that enable the development of pharmacophores that define activity and specificity at receptor targets. These libraries can be quite large and difficult to handle, due to physical and chemical constraints imposed by their size. Therefore, smaller sub-libraries are often targeted for synthesis. The number of coupling reactions required can be greatly reduced if the peptides having common amino acids are grouped into the same sub-library (batching). This paper describes a schedule optimizer to minimize the number of coupling reactions by rotating and aligning sequences while simultaneously batching. The gradient descent method thereby reduces the number of coupling reactions required for synthesizing cyclic peptide libraries. We show that the algorithm results in a 75% reduction in the number of coupling reactions for a typical cyclic peptide library.

Development of a DNA-based method for detection and identification of Phytophthora species

Phytophthora diseases cause major losses to agricultural and horticultural production in Australia and worldwide. Most Phytophthora diseases are soilborne and difficult to control, making disease prevention an important component of many disease management strategies. Detection and identification of the causal agent, therefore, is an essential part of effective disease management. This paper describes the development and validation of a DNA-based diagnostic assay that can detect and identify 27 different Phytophthora species. We have designed PCR primers that are specific to the genus Phytophthora. The resulting amplicon after PCR is subjected to digestion by restriction enzymes to yield a specific restriction pattern or fingerprint unique to each species. The restriction patterns are compared with a key comprising restriction patterns of type specimens or representative isolates of 27 different Phytophthora species. A number of fundamental issues, such as genetic diversity within and among species which underpin the development and validation of DNA-based diagnostic assays, are addressed in this paper.

White and gray matter development in human fetal, newborn and pediatric brains

Brain anatomy is characterized by dramatic growth from the end of the second trimester through the neonatal stage. The characterization of normal axonal growth of the white matter tracts has not been well-documented to date and could provide important clues to understanding the extensive inhomogeneity of white matter injuries in cerebral palsy (CP) patients. However, anatomical studies of human brain development during this period are surprisingly scarce and histology-based atlases have become available only recently. Diffusion tensor magnetic resonance imaging (DTMRI) can reveal detailed anatomy of white matter. We acquired diffusion tensor images (DTI) of postmortem fetal brain samples and in vivo neonates and children. Neural structures were annotated in two-dimensional (2D) slices, segmented, measured, and reconstructed three-dimensionally (3D). The growth status of various white matter tracts was evaluated on cross-sections at 19-20 gestational weeks, and compared with 0-month-old neonates and 5- to 6-year-old children. Limbic, commissural, association, and projection white matter tracts and gray matter structures were illustrated in 3D and quantitatively characterized to assess their dynamic changes. The overall pattern of the time courses for the development of different white matter is that limbic fibers develop first and association fibers last and commissural and projection fibers are forming from anterior to posterior part of the brain. The resultant DTNIRI-based 3D human brain data will be a valuable resource for human brain developmental study and will provide reference standards for diagnostic radiology of premature newborns. (c) 2006 Elsevier Inc. All rights reserved.

Multilevel modelling for inference of genetic regulatory networks

Time-course experiments with microarrays are often used to study dynamic biological systems and genetic regulatory networks (GRNs) that model how genes influence each other in cell-level development of organisms. The inference for GRNs provides important insights into the fundamental biological processes such as growth and is useful in disease diagnosis and genomic drug design. Due to the experimental design, multilevel data hierarchies are often present in time-course gene expression data. Most existing methods, however, ignore the dependency of the expression measurements over time and the correlation among gene expression profiles. Such independence assumptions violate regulatory interactions and can result in overlooking certain important subject effects and lead to spurious inference for regulatory networks or mechanisms. In this paper, a multilevel mixed-effects model is adopted to incorporate data hierarchies in the analysis of time-course data, where temporal and subject effects are both assumed to be random. The method starts with the clustering of genes by fitting the mixture model within the multilevel random-effects model framework using the expectation-maximization (EM) algorithm. The network of regulatory interactions is then determined by searching for regulatory control elements (activators and inhibitors) shared by the clusters of co-expressed genes, based on a time-lagged correlation coefficients measurement. The method is applied to two real time-course datasets from the budding yeast (Saccharomyces cerevisiae) genome. It is shown that the proposed method provides clusters of cell-cycle regulated genes that are supported by existing gene function annotations, and hence enables inference on regulatory interactions for the genetic network.