Are material safety data sheets (MSDS) useful in the diagnosis and management of occupational contact dermatitis?

This study assesses both the success of medical practitioners in accessing hazardous substances' information from product manufacturers and the accuracy and clinical usefulness of Material Safety Data Sheets (MSDS) presented by workers with suspected occupational contact dermatitis (OCD). 00 consecutively presented MSDS were collected from 42 workers attending an occupational dermatology clinic. Product manufacturers were contacted to verify ingredients. MSDS were evaluated for compliance with the Australian criteria for listing of OCD relevant information (sensitizers present at a concentration > or =1%, irritants present at a concentration > or =20%), and for clinical usefulness. All sensitizers were checked for clinical relevance to the worker's dermatitis. Manufacturers supplied product constituents for 77/100 MSDS. 58 MSDS satisfied the Australian standard. 57/58 MSDS were deemed clinically useful. Irritants were listed for 19/23 MSDS and sensitizers were listed for 30/68 MSDS (P = 0.001). 3 MSDS contained sensitizers, which were clinically relevant to the presenting worker's dermatitis, 1 appropriately listed, 1 present at > or =1% but not listed, and 1 present at <1% in the product and therefore, not required to be listed. Sensitizers are frequently omitted from MSDS and clinicians are often unsuccessful in obtaining crucial information from manufacturers. MSDS are inadequate for the protection and diagnosis of workers with suspected OCD.

Inter-observer variation in the diagnosis of coronal articular fracture lines in the lunate facet of the distal radius

Background: Several studies support the use of CT for diagnosing coronal fractures of the distal radius but the inter-observer reliability of these observations is less well studied. We tested the null hypothesis that radiographs alone and the combination of radiographs and two-dimensional computed tomography scans (2DCT) have the same inter-observer variation for the diagnosis of coronal articular fracture lines in the distal radius. Methods: Using a web-based survey, 63 surgeons were randomized to evaluate 16 fractures of the distal radius on radiographs alone or radiographs and 2DCT for the presence or absence of a coronal fracture line of the lunate facet and, if present, the stability of the fracture. The kappa multirater measure was calculated to estimate agreement between observers. Results: The inter-observer variation in diagnosis of a coronal fracture line was fair with both radiographs and 2DCT, as was the diagnosis of instability of the volar lunate facet fracture when present. Conclusion: Two-dimensional computed tomography does not improve observer agreement on the diagnosis of coronal plane articular fracture lines in the lunate facet of the distal radius. © 2012 American Association for Hand Surgery.

Medical diagnosis by fuzzy standard additive model with wavelets

This paper proposes a combination of fuzzy standard additive model (SAM) with wavelet features for medical diagnosis. Wavelet transformation is used to reduce the dimension of high-dimensional datasets. This helps to improve the convergence speed of supervised learning process of the fuzzy SAM, which has a heavy computational burden in high-dimensional data. Fuzzy SAM becomes highly capable when deployed with wavelet features. This combination remarkably reduces its computational training burden. The performance of the proposed methodology is examined for two frequently used medical datasets: the lump breast cancer and heart disease. Experiments are deployed with a five-fold cross validation. Results demonstrate the superiority of the proposed method compared to other machine learning methods including probabilistic neural network, support vector machine, fuzzy ARTMAP, and adaptive neuro-fuzzy inference system. Faster convergence but higher accuracy shows a win-win solution of the proposed approach.

Poor agreement between self-reported diagnosis and bone mineral density results in the identification of osteoporosis.

Osteoporosis is a major health concern, estimated to affect millions worldwide. Bone mineral density (BMD) assessment is not practical for many large-scale epidemiological studies resulting in the reliance of self-report methods to ascertain diagnostic information. The aim of the study was to assess the validity of self-reported diagnosis of osteoporosis in a population-based study. This study examined data collected from 906 men and 843 women participating in the Geelong Osteoporosis Study. Osteoporosis was self-reported and compared against results of BMD scans of the hip and spine. Validity was examined by calculating sensitivity, specificity, positive predictive value, negative predictive value, and kappa statistic. Osteoporosis was self-reported by 118 (6.7%) participants and identified using BMD results for 64 (3.7%) participants. Specificity and negative predictive value were good (95.1% and 96.0%, respectively), whereas sensitivity and positive predictive value were poor (35.9% and 31.4%, respectively). The overall level of agreement (kappa) was 0.29. The results changed only slightly when we included participants with osteopenia and adult fracture as osteoporotic. Reliance on self-report methods to ascertain osteoporosis status is not recommended.

Validation of DNA methylation biomarkers for diagnosis of acute lymphoblastic leukemia

DNA methylation biomarkers capable of diagnosis and subtyping have been found for many cancers. Fifteen such markers have previously been identified for pediatric acute lymphoblastic leukemia (ALL). Validation of these markers is necessary to assess their clinical utility for molecular diagnostics. Substantial efficiencies could be achieved with these DNA methylation markers for disease tracking with potential to replace patient-specific genetic testing.

Motor fault detection and diagnosis using a hybrid FMM-CART model with online learning

In this paper, a hybrid online learning model that combines the fuzzy min-max (FMM) neural network and the Classification and Regression Tree (CART) for motor fault detection and diagnosis tasks is described. The hybrid model, known as FMM-CART, incorporates the advantages of both FMM and CART for undertaking data classification (with FMM) and rule extraction (with CART) problems. In particular, the CART model is enhanced with an importance predictor-based feature selection measure. To evaluate the effectiveness of the proposed online FMM-CART model, a series of experiments using publicly available data sets containing motor bearing faults is first conducted. The results (primarily prediction accuracy and model complexity) are analyzed and compared with those reported in the literature. Then, an experimental study on detecting imbalanced voltage supply of an induction motor using a laboratory-scale test rig is performed. In addition to producing accurate results, a set of rules in the form of a decision tree is extracted from FMM-CART to provide explanations for its predictions. The results positively demonstrate the usefulness of FMM-CART with online learning capabilities in tackling real-world motor fault detection and diagnosis tasks. © 2014 Springer Science+Business Media New York.

MicroRNAs: biogenesis, roles for carcinogenesis and as potential biomarkers for cancer diagnosis and prognosis

MicroRNAs (miRNAs) are short non-coding RNAs of 20-24 nucleotides that play important roles in carcinogenesis. Accordingly, miRNAs control numerous cancer-relevant biological events such as cell proliferation, cell cycle control, metabolism and apoptosis. In this review, we summarize the current knowledge and concepts concerning the biogenesis of miRNAs, miRNA roles in cancer and their potential as biomarkers for cancer diagnosis and prognosis including the regulation of key cancer-related pathways, such as cell cycle control and miRNA dysregulation. Moreover, microRNA molecules are already receiving the attention of world researchers as therapeutic targets and agents. Therefore, in-depth knowledge of microRNAs has the potential not only to identify their roles in cancer, but also to exploit them as potential biomarkers for cancer diagnosis and identify therapeutic targets for new drug discovery.