Surface EMG recordings and pain-related disorders: A diagnostic framework

Surface electromyography provides a very valuable set of information when used diagnostically with pain-related disorders. Unfortunately, most researchers and clinicians limit their investigation to only one level of diagnostic information available concerning the neuromuscular system. This article develops and encourages the clinician/researcher to consider three levels of diagnostic information: emotional, organ-related, and postural aspects. The theoretical background and diagnostic procedures used to investigate each is presented. Differential treatment considerations, given differential diagnostic findings, are discussed.     

Validation of computerized diagnostic information in a clinical database from a national equine clinic network

Background  

Computerized diagnostic information offers potential for epidemiological research; however data accuracy must be addressed. The principal aim of this study was to evaluate the completeness and correctness of diagnostic information in a computerized equine clinical database compared to corresponding hand written veterinary clinical records, used as gold standard, and to assess factors related to correctness. Further, the aim was to investigate completeness (epidemiologic sensitivity), correctness (positive predictive value), specificity and prevalence for diagnoses for four body systems and correctness for affected limb information for four joint diseases.     

Gaucher''s disease: morphological findings in a case studied with fine needle aspiration

Fine needle aspiration (FNA) is a diagnostic tool whose inexpensiveness, simplicity and innocuousness have led it to be increasingly accepted in daily medical practice. This method, which is useful for the study of lesions that are accessible by radiological exploration or palpation, provides information about the cytological aspects of punctured lesions. Sometimes, such information can be extended to the histological area through study of cell blocks, true microbiopsies, which are of great diagnostic use. This was the technique used in the case described in the present report.     

Bayesian latent class models for identifying canine visceral leishmaniosis using diagnostic tests in the absence of a gold standard

BackgroundLike many infectious diseases, there is no practical gold standard for diagnosing clinical visceral leishmaniasis (VL). Latent class modeling has been proposed to estimate a latent gold standard for identifying disease. These proposed models for VL have leveraged information from diagnostic tests with dichotomous serological and PCR assays, but have not employed continuous diagnostic test information.Methods/Principal findingsIn this paper, we employ Bayesian latent class models to improve the identification of canine visceral leishmaniasis using the dichotomous PCR assay and the Dual Path Platform (DPP) serology test. The DPP test has historically been used as a dichotomous assay, but can also yield numerical information via the DPP reader. Using data collected from a cohort of hunting dogs across the United States, which were identified as having either negative or symptomatic disease, we evaluate the impact of including numerical DPP reader information as a proxy for immune response. We find that inclusion of DPP reader information allows us to illustrate changes in immune response as a function of age.Conclusions/SignificanceUtilization of continuous DPP reader information can improve the correct discrimination between individuals that are negative for disease and those with clinical VL. These models provide a promising avenue for diagnostic testing in contexts with multiple, imperfect diagnostic tests. Specifically, they can easily be applied to human visceral leishmaniasis when diagnostic test results are available. Also, appropriate diagnosis of canine visceral leishmaniasis has important consequences for curtailing spread of disease to humans.     

SNP array analysis in constitutional and cancer genome diagnostics--copy number variants, genotyping and quality control

Array-based comparative genomic hybridization analysis of genomic DNA was first applied in postnatal diagnosis for patients with intellectual disability (ID) and/or congenital anomalies (CA). Genome-wide single-nucleotide polymorphism (SNP) array analysis was subsequently implemented as the first line diagnostic test for ID/CA patients in our laboratory in 2009, because its diagnostic yield is significantly higher than that of routine cytogenetic analysis. In addition to the detection of copy number variations, the genotype information obtained with SNP array analysis enables the detection of stretches of homozygosity and thereby the possible identification of recessive disease genes, mosaic aneuploidy, or uniparental disomy. Patient-parent (trio) information analysis is used to screen for the presence of any form of uniparental disomy in the patient and can determine the parental origin of a de novo copy number variation. Moreover, the outcome of a genotype analysis is used as a final quality control by ruling out potential sample mismatches due to non-paternity or sample mix-up. SNP array analysis is now also used in our laboratory for patients with disorders for which locus heterogeneity is known (homozygosity pre-screening), in prenatal diagnosis in case of structural ultrasound anomalies, and for patients with leukemia. In this report, we summarize our array findings and experiences in the various diagnostic applications and demonstrate the power of a SNP-based array platform for molecular karyotyping, because it not only significantly improves the diagnostic yield in both constitutional and cancer genome diagnostics, but it also enhances the quality of the diagnostic laboratory workflow.     

A Fault Diagnosis Methodology for Gear Pump Based on EEMD and Bayesian Network

This paper proposes a fault diagnosis methodology for a gear pump based on the ensemble empirical mode decomposition (EEMD) method and the Bayesian network. Essentially, the presented scheme is a multi-source information fusion based methodology. Compared with the conventional fault diagnosis with only EEMD, the proposed method is able to take advantage of all useful information besides sensor signals. The presented diagnostic Bayesian network consists of a fault layer, a fault feature layer and a multi-source information layer. Vibration signals from sensor measurement are decomposed by the EEMD method and the energy of intrinsic mode functions (IMFs) are calculated as fault features. These features are added into the fault feature layer in the Bayesian network. The other sources of useful information are added to the information layer. The generalized three-layer Bayesian network can be developed by fully incorporating faults and fault symptoms as well as other useful information such as naked eye inspection and maintenance records. Therefore, diagnostic accuracy and capacity can be improved. The proposed methodology is applied to the fault diagnosis of a gear pump and the structure and parameters of the Bayesian network is established. Compared with artificial neural network and support vector machine classification algorithms, the proposed model has the best diagnostic performance when sensor data is used only. A case study has demonstrated that some information from human observation or system repair records is very helpful to the fault diagnosis. It is effective and efficient in diagnosing faults based on uncertain, incomplete information.     

SPECT and MRT in the diagnosis of myocarditis of mild and moderate severity]

Technetium-99mWBC SPECT and contrast material enhanced MRI were used for the diagnostics of mild myocarditis. The use of labeled leukocytes and paramagnetic contrast agent allow visualizing inflammation of myocardium and assessing the efficacy of treatment. 49 patients aged 14 to 49 years old were studied. Case presentation demonstrates high information and diagnostic value of used methods.     

Phevor Combines Multiple Biomedical Ontologies for Accurate Identification of Disease-Causing Alleles in Single Individuals and Small Nuclear Families

Phevor integrates phenotype, gene function, and disease information with personal genomic data for improved power to identify disease-causing alleles. Phevor works by combining knowledge resident in multiple biomedical ontologies with the outputs of variant-prioritization tools. It does so by using an algorithm that propagates information across and between ontologies. This process enables Phevor to accurately reprioritize potentially damaging alleles identified by variant-prioritization tools in light of gene function, disease, and phenotype knowledge. Phevor is especially useful for single-exome and family-trio-based diagnostic analyses, the most commonly occurring clinical scenarios and ones for which existing personal genome diagnostic tools are most inaccurate and underpowered. Here, we present a series of benchmark analyses illustrating Phevor’s performance characteristics. Also presented are three recent Utah Genome Project case studies in which Phevor was used to identify disease-causing alleles. Collectively, these results show that Phevor improves diagnostic accuracy not only for individuals presenting with established disease phenotypes but also for those with previously undescribed and atypical disease presentations. Importantly, Phevor is not limited to known diseases or known disease-causing alleles. As we demonstrate, Phevor can also use latent information in ontologies to discover genes and disease-causing alleles not previously associated with disease.     

Use of information on disease diagnoses from databases for animal health economic,welfare and food safety purposes: strengths and limitations of recordings

Many animal health, welfare and food safety databases include data on clinical and test-based disease diagnoses. However, the circumstances and constraints for establishing the diagnoses vary considerably among databases. Therefore results based on different databases are difficult to compare and compilation of data in order to perform meta-analysis is almost impossible. Nevertheless, diagnostic information collected either routinely or in research projects is valuable in cross comparisons between databases, but there is a need for improved transparency and documentation of the data and the performance characteristics of tests used to establish diagnoses. The objective of this paper is to outline the circumstances and constraints for recording of disease diagnoses in different types of databases, and to discuss these in the context of disease diagnoses when using them for additional purposes, including research. Finally some limitations and recommendations for use of data and for recording of diagnostic information in the future are given. It is concluded that many research questions have such a specific objective that investigators need to collect their own data. However, there are also examples, where a minimal amount of extra information or continued validation could make sufficient improvement of secondary data to be used for other purposes. Regardless, researchers should always carefully evaluate the opportunities and constraints when they decide to use secondary data. If the data in the existing databases are not sufficiently valid, researchers may have to collect their own data, but improved recording of diagnostic data may improve the usefulness of secondary diagnostic data in the future.     

Peptidomics for cancer diagnosis: present and future

Analysis of peptides in biological fluids by mass spectrometry holds promise of providing diagnostic and prognostic information for cancer and other diseases. Before this technology is used clinically, it is important to understand its advantages and limitations. These are summarized, along with proposals, on how to proceed in the future.     

Assessment of Diagnostic Skills in Specialist Examinations Should Include Lessons Learnt From Misdiagnosis

Background

Poor authenticity in high stake clinical exams adversely effects validity. We propose including known misleading diagnostic factors and contextual biases in the assessment of diagnostic skills amongst advanced specialty trainees. We hypothesise that this strategy offers a more realistic and critical assessment of diagnostic skill than strategies in which candidates are presented with directive, bias free information, allowing for assumptions which cannot be made in real life.

Methods

Eleven patient based practice clinical exam stations were presented to nine advanced ophthalmology trainees. Four patients had a history of misdiagnosis or near misdiagnosis of key ophthalmic findings, presumed to result from identifiable biases and misleading information. In those four stations, candidates were presented with authentic, file based information and were asked authentic questions, similar to those with which the patients presented. If the candidates were unsuccessful in identifying key findings, the questions were converted into directive questions about the same key findings (i.e. “examine the patient’s eyelids, what is your diagnosis?”), and the candidates re-assessed the patient and re-answered.

Results

Ninety-eight doctor-patient encounters took place. Of those, 35 encounters were analysed for the purpose of this study. In 63% of those encounters, key findings were missed when the question included authentic biases or misleading background information, but rephrasing the question to a directive exam format led to their correct identification (Fail converted to pass). Key findings were detected despite contextual biases or misleading background information in only 23% of encounters. In 14% the findings were missed with either question phrasing.

Conclusions

Presentation authentic questions provide a more realistic and less forgiving measure of diagnostic skills than directive exam questions. Given the prevalence of diagnostic errors and their importance to patient outcomes, known mechanisms contributing to diagnostic errors should be used as one of the assessment tools of advanced speciality trainees.     

Sensitivity and specificity of a monitoring test

The usefulness of a diagnostic test is generally assessed by calculating the sensitivity and specificity, or the predictive value positive and predictive value negative of the test. When subjects are monitored periodically for evidence of disease, these calculations must incorporate the varying amounts of information per individual. If in addition, the test results lie on a continuous scale, these quantities vary with the cutoff value (cutpoint) used to define a positive test. They are usually calculated for a spectrum of potential cutpoints in order to produce receiver-operator characteristic curves. In this paper we use a partial likelihood solution to the discrete logistic model in order to obtain estimates of the diagnostic test indices and to provide a significance test when the diagnostic test is administered repeatedly to individuals.     

Design of two-phase prevalence surveys of rare disorders

Two-phase medical surveys, in which a large sample is assessed with an inexpensive screening instrument and a subsample is selected for a more thorough diagnostic evaluation, appear to have great merit in the epidemiologic study of certain rare disorders. We present the optimal design of two-phase surveys when resources are fixed and when 100% of those screened positive in the first phase must be included in the second-phase evaluation. We go on to examine the relative efficiency of this two-phase design compared to a single-phase design in which all resources are used in a survey that employs the diagnostic evaluation. Given information on the accuracy of the screen and the prevalence of the disorder, the utility of the two-phase design depends on the relative cost of the screening to the diagnostic assessments.     

DIAGNOSTIC SELF‐TESTING: AUTONOMOUS CHOICES AND RELATIONAL RESPONSIBILITIES

Diagnostic self‐testing devices are being developed for many illnesses, chronic diseases and infections. These will be used in hospitals, at point‐of‐care facilities and at home. Designed to allow earlier detection of diseases, self‐testing diagnostic devices may improve disease prevention, slow the progression of disease and facilitate better treatment outcomes. These devices have the potential to benefit both the individual and society by enabling individuals to take a more proactive role in the maintenance of their health and by helping society improve health and reduce health costs. However, the full implications of future home‐based diagnostic technology for individuals and society remain unclear due to their novelty. We argue that the development of diagnostic tools, especially for home use, will heighten a number of ethical challenges. This paper will explore some of the ethical implications of home‐based self‐testing diagnostic devices for the autonomous and relational dimensions of the person. This will be facilitated by examining the impact of diagnostic devices for individual autonomy, for the delivery of accurate diagnosis and for the personal significance of the information for the user. The latter will be examined using Charles Taylor's view of personhood and his emphasis on human agency and interpretation. While the ethical issues are not necessarily new, the development of home‐based self‐testing diagnostic devices will make issues regarding autonomy, accuracy of information and personal significance more and more demanding. This will be the case particularly when an individual's autonomous choices come into conflict with the person's relational responsibilities.     

MiRNAs as potential biomarker of kidney diseases: A review

MicroRNAs (miRNAs) are 22 nucleotides short, non-coding and tissue-specific single-stranded RNA which modulates target gene expression. Presently, shreds of evidence confirmed that miRNAs play a key role in kidney pathophysiology. The objectives of the present review are to summarize new research data towards the latest developments in the potential use of miRNAs as a diagnostic biomarker for kidney diseases. This holistic information will update the existing knowledge of kidney disease biomarkers. “miRNA profile for Diabetic Kidney disease, Acute kidney injury, Renal fibrosis, hemodialysis, transplants, FSGS, IgAN, etc.” are the search keywords which have been used in this review. The search outcome gave an exciting insightful perception of miRNAs competence as a biomarker. Also it is observed that various samples as plasma, urine and biopsies were used for profiling the miRNA expression. The miRNAs were not only used for diagnostic biomarkers but also for therapeutic targets. Each kidney disease showed different miRNAs expression profile and few miRNAs quite common with some kidney diseases. miRNAs are simple and efficient diagnostic biomarkers for kidney diseases.     

Lectinomics I. Relevance of exogenous plant lectins in biomedical diagnostics

This review focuses on utilization of plant lectins as medical diagnostic reagents and tools. The lectin-related diagnostic is aimed at detection of several diseases connected to alteration of the glycosylation profiles of cells and at identification of microbial and viral agents in clinical microbiology. Certain lectins, proposed for or used as diagnostic tools could even recognize those cellular determinants, which are not detected by available antibodies. Broad information is presented on the lectinomics field, illustrating that lectin diagnostics might become practical alternative to antibody-based diagnostic products. In addition, the rising trend of lectin utilization in biomedical diagnostics might initiate a development of innovative methods based on better analytical technologies. Lectin microarray, a rapid and simple methodology, can be viewed as an example for such initiative. This technology could provide simple and efficient screening tools for analysis of glycosylation patterns in biological samples (cellular extracts, tissues and the whole cells), allowing thus personalized detection of changes associated with carbohydrate-related diseases.     

Comparison of the pick-and-smear and Saccomanno methods for sputum cytologic analysis

The two methods of preparing sputum specimens for cytologic study, the (fresh) pick-and-smear technique and the (blended) Saccomanno technique, were compared using 249 consecutive specimens. Two slides were prepared for each specimen by each technique. Of the specimens, 103 showed squamous metaplasia, carcinoma in situ or carcinoma. A semiquantitative rating system (0 to 4+) was used to determine the number of diagnostic cells for each method for those 103 cases. More diagnostic cells were found on the Saccomanno preparations (217) than on the fresh preparations (154). There were 121 diagnostic cells in the Saccomanno preparations versus 95 diagnostic cells in the fresh preparations from 63 squamous metaplasias; 7 versus 3 for the preparations from 5 carcinomas in situ; 64 versus 42 from 28 squamous cell carcinomas; 3 versus 1 from 1 large cell undiffernomas; and 12 diagnostic cells in Saccomanno preparations versus 5 in fresh preparations from 3 small cell cancers. Twelve squamous metaplasias, two carcinomas in situ, four squamous carcinomas, one adenocarcinoma and one small cell cancer had no diagnostic cells on the fresh preparations; four squamous metaplasias and one squamous carcinoma had no diagnostic cells on the Saccomanno preparations. More diagnostic information and fewer false-negative results were achieved with the Saccomanno technique.     

Discrimination strategies of humans and rhesus monkeys for complex visual displays

By learning to discriminate among visual stimuli, human observers can become experts at specific visual tasks. The same is true for Rhesus monkeys, the major animal model of human visual perception. Here, we systematically compare how humans and monkeys solve a simple visual task. We trained humans and monkeys to discriminate between the members of small natural-image sets. We employed the "Bubbles" procedure to determine the stimulus features used by the observers. On average, monkeys used image features drawn from a diagnostic region covering about 7% +/- 2% of the images. Humans were able to use image features drawn from a much larger diagnostic region covering on average 51% +/- 4% of the images. Similarly for the two species, however, about 2% of the image needed to be visible within the diagnostic region on any individual trial for correct performance. We characterize the low-level image properties of the diagnostic regions and discuss individual differences among the monkeys. Our results reveal that monkeys base their behavior on confined image patches and essentially ignore a large fraction of the visual input, whereas humans are able to gather visual information with greater flexibility from large image regions.     

Evaluation and optimization of diagnostic tests using receiver operating characteristic analysis and information theory

We describe a mathematical technique and an associated computer program for comparing, evaluating and optimizing diagnostic tests. The technique combines receiver operating characteristic (ROC) analysis with information theory and cost-benefit analysis to accomplish this. The program is menu driven and highly interactive; it generates 13 possible user-determined ASCII disk files which can be easily converted to graphs. These graphs allow the user to make detailed comparisons among various diagnostic tests for all values of disorder prevalence, and also provide guidelines for cut-off selection in order to optimize tests. These techniques are applied to three published studies of the enzyme screening assay for diagnosis of infection with the HIV virus. We show how graphs produced by this program can be used to compare and optimize these diagnostic tests. The program is written for an IBM-compatible microcomputer running on a DOS operating system.     

Dual echo steady state (dess) pulse sequence in the evaluation of brain cerebrospinal fluid flow disturbances

The authors present their experience with the Dual Echo Steady State pulse sequence used to evaluate brain cerebrospinal fluid (CSF) flow by low-field magnetic resonance imaging. Normal and pathologic CSF flow patterns are discussed. The capacities of the technique as an auxiliary tool to have additional diagnostic information via visualization of flows in the brain CSF pathways are shown.