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PubMed2006

WormBase: methods for data mining and comparative genomics.

WormBase is a comprehensive repository for information on Caenorhabditis elegans and related nematodes. Although the primary web-based interface of WormBase (http:// www.wormbase.org/) is familiar to most C. elegans researchers, WormBase also offers powerful data-mining features for addressing questions of comparative genomics, genome structure, and evolution. In this chapter, we focus on data mining at WormBase through the use of flexible web interfaces, custom queries, and scripts. The intended audience includes users wishing to query the database beyond the confines of the web interface or fetch data en masse. No knowledge of programming is necessary or assumed, although users with intermediate skills in the Perl scripting language will be able to utilize additional data-mining approaches.

Animals↗

PubMed2004

Allele mining for stress tolerance genes in Oryza species and related germplasm.

Allele mining exploits the deoxyribonucleic acid (DNA) sequence of one genotype to isolate useful alleles from related genotypes. The international project to sequence the genome of Oryza sativa L cv. Nipponbare will make allele mining possible for all genes of rice and possibly related cereals. We used a rice calmodulin gene, a rice gene encoding a late embryogenesis-associated protein, and salt-inducible rice gene to optimize the polymerase chain reaction (PCR) for allele mining of stress tolerance genes on identified accessions of rice and related germplasm. Two sets of PCR primers were designed for each gene. Primers based on the 5' and 3' untranslated region of genes were found to be sufficiently conserved so as to be effective over the entire range of germplasm in rice for which the concept of allelism is applicable. However, the primers based on the adjacent amino (N) and carboxy (C) termini amplify additional loci.

3' Untranslated Regions↗

PubMedDate not supplied

The use of Data Mining in the categorization of patients with Azoospermia.

OBJECTIVE: Data Mining is a relatively new field of Medical Informatics. The aim of this study was to compare Data Mining diagnosis with clinical diagnosis by applying a Data Miner (DM) to a clinical dataset of infertile men with azoospermia. DESIGN: One hundred and forty-seven azoospermic men were clinically classified into four groups: a) obstructive azoospermia (n=63), b) non-obstructive azoospermia (n=71), c) hypergonadotropic hypogonadism (n=2), and d) hypogonadotropic hypogonadism (n=11). The DM (IBM's DB2/Intelligent Miner for Data 6.1) was asked to reproduce a four-cluster model. RESULTS: DM formed four groups of patients: a) eugonadal men with normal testicular volume and normal FSH levels (n=86), b) eugonadal men with significantly reduced testicular volume (median 6.5 cm3) and very high FSH levels (n=29), c) eugonadal men with moderately reduced testicular volume (median 14.5 cm3) and raised FSH levels (n=20), and d) hypogonadal men (n=12). Overall DM concordance rate in hypogonadal men was 92%, in obstructive azoospermia 73%, and in non-obstructive azoospermia 69%. CONCLUSIONS: Data Mining produces clinically meaningful results but different from those of the clinical diagnosis. It is possible that the use of large sets of structured and formalised data and continuous evaluation of DM results will generate a useful methodology for the Clinician.

Cohort Studies↗

PubMed2006

Bioinformatics and data mining in proteomics.

Proteomic studies involve the identification as well as qualitative and quantitative comparison of proteins expressed under different conditions, and elucidation of their properties and functions, usually in a large-scale, high-throughput format. The high dimensionality of data generated from these studies will require the development of improved bioinformatics tools and data-mining approaches for efficient and accurate data analysis of biological specimens from healthy and diseased individuals. Mining large proteomics data sets provides a better understanding of the complexities between the normal and abnormal cell proteome of various biological systems, including environmental hazards, infectious agents (bioterrorism) and cancers. This review will shed light on recent developments in bioinformatics and data-mining approaches, and their limitations when applied to proteomics data sets, in order to strengthen the interdependence between proteomic technologies and bioinformatics tools.

Computational Biology↗

PubMed2003

[Diagnosis of health conditions in a pan-mining community in the Tapajós River Basin, Itaituba, Par , Brazil, 1992].

The purpose of this article is to describe health conditions in a pan-mining community, in particular the interaction of mercury contamination with other health problems. This study was carried out in a panning mine located on Rato Creek, a tributary of the Tapajó s River Basin in the municipality of Itaituba, State of Par The study population consisted of 223 individuals. An epidemiological questionnaire was applied, medical treatment was given, and biological specimens were collected. Testing of fecal samples showed that 96.1% of the individuals had parasites and 66.4% were anemic. Prevalence of hepatitis B virus was 85.0%, and 11 individuals (6.0%) were virus carriers (HBsAg positive). Of 186 patients tested for malaria, 65 individuals (35.0%) had the infection, of whom 34 (52.3%) were asymptomatic. Prevalence of syphillis was 41.6%. Urine mercury levels were measured in 173 individuals. Some 16 (9.2%) of those tested had mercury levels between 10 and 19 ug/l, while 9 patients (5.2%) had levels above 20 ug/l. An overview of general health conditions showed a combination of problems in individuals, who lacked adequate diagnosis, in addition to a high level of self-medication, given the absence of health facilities. In addition, these individuals are threatened by critical levels of mercury contamination. In conclusion, this article gives a general overview of health conditions in these pan-mining communities, demonstrating the multicausal nature of health conditions in the Amazon region, a fact which should be considered when planning appropriate strategies for clinical treatment.

English Abstract↗

PubMed2005

Comparison of a partial life-cycle bioassay in artificial streams to a standard beaker bioassay to assess effects of metal mine effluent on Chironomus tentans.

A novel, partial life-cycle bioassay using Chironomus tentans in artificial streams was developed for evaluating the effects of metal mine effluent. The utility of this bioassay was compared to an existing beaker life-cycle bioassay under laboratory conditions. Chironomus tentans larvae were exposed to 45% (v/v) treated metal mine effluent from day 11 through hatching of the second generation. Response patterns were consistent between the two bioassays for hatching success and time to emergence but inconsistent for other endpoints. Significant effects of effluent were obtained for growth, survival, number of adults emerged, and number of eggs per egg case in the artificial stream bioassay but not in the beaker bioassay. Conversely, significant effects on sex ratio and number of egg cases per female were observed in the beaker bioassay but not in the artificial stream bioassay. These differences are believed to be a consequence of the number of organisms per replicate used in each bioassay, which results in a difference in statistical power. As a result, higher coefficients of variation and effects sizes were observed in the beaker bioassay relative to the artificial stream bioassay for almost all endpoints. These results provide evidence that the bioassay in artificial streams can be an effective tool for evaluating the effects of metal mine effluent on life-cycle endpoints in C. tentans.

Animals↗

PubMed2004

Heavy-metal pollution and its state in algae in Kakehashi river and Godani river at the foot of Ogoya mine, Ishikawa Prefecture.

Alga as Achnanthes minutissima among diatoms is a widely adaptable taxon on the state of an aquatic environment. In this study, it was found that diatom had a specific tolerance to heavy metals (Cu, Zn, Pb, and Cd etc.) in river water samples, because the diatom assemblage consisted of almost only Achnanthes minutissima in Kakehashi river and Godani river, which were polluted with waste water from Ogoya copper mine. The relationship between the concentrations of heavy metals (Cu, Zn, Pb and Cd) in river water and the attached substances (algae and silt etc.) and the relative abundances of diatom taxa were investigated in detail. The results indicated that the higher is the concentration of heavy metals in the river environment, the higher is only the relative abundances of Achnanthes minutissima. Thus, the taxon can be used as a bioindicator of heavy metal pollution. The relative rates of toxic chemical forms of copper in algae were 61 - 92% in the attached substances and 49-70% in the sediment on the river bed, respectively. Therefore, it was found that diatom as Achnanthes minutissima had a tolerance to heavy metals in river water, being able to live in such an environment. Since the water treated with calcium hydroxide from the deposition reservoir of Ogoya mine enters in Godani river, the river is polluted by heavy metals (Cu, Zn, Pb and Cd etc.). From the viewpoint of both biological and chemical analyses, Godani river is still polluted with heavy metals, because their concentrations in the river samples were very high. On the other hand, in Kakehashi river, the concentrations of heavy metals were very low and the distributions of some diatoms appeared in an unpolluted Nishimata river were observed. Therefore, Kakehashi river seems to be considerably recovered from heavy-metal pollution after closing the Ogoya mine.

Eukaryota↗

PubMed2006

Data mining in the US using the Vaccine Adverse Event Reporting System.

The US Vaccine Adverse Event Reporting System (VAERS), which is charged with vigilance for detecting vaccine-related safety issues, faces an increasingly complex immunisation environment. Since 1990, steady increases in vaccine licensing and distribution have resulted in increasing numbers of reports to VAERS. Prominent features of current reports include more routine vaccine co-administration and frequent reports of new postvaccination clinical syndromes. Data-mining methods, based on disproportionality analyses, are one strategy being pursued by VAERS researchers to increase the utility of its complex database. The types of analyses used include proportional reporting ratios, association rule discovery, and various 'historic limits' methods that compare observed versus expected event counts. The use of such strategies in VAERS has been primarily supplemental and retrospective. Signals for inactivated influenza, typhoid and tetanus toxoid-containing vaccines have been successfully identified. Concerns flagged through data mining should always be subject to clinical case review as a first evaluation step. Persistent issues should be subject to formal hypothesis testing in large linked databases or other controlled-study settings. Automated data-mining techniques for prospective use are currently undergoing development and evaluation within VAERS. Their use (as one signal-detection tool among many) by trained medical evaluators who are aware of system limitations is one legitimate approach to improving the ability of VAERS to generate vaccine-safety hypotheses. Such approaches are needed as more new vaccines continue to be licensed.

Adverse Drug Reaction Reporting Systems↗

PubMed1999

Observational studies as human experimentation: the uranium mining experience in the Navajo Nation (1947-66).

This article evaluates how an observational epidemiologic study of federal agencies in uranium miners became an experiment of opportunity for radiation effects. Navajo miners and communities suffered environmental exposures caused by the practices of uranium mining and milling in the Navajo reservation during the 1947 to 1966 period. A historical review of the state-of-the-art knowledge of the health effects of uranium mining and milling during the years prior to 1947 was conducted. Contemporary prevention and remediation practices also were assessed. An appraisal of the summary of findings of a comprehensive evaluation of radiation human experimentation conducted by the U.S. federal government in 1995-96 (ACHRE) demonstrates that uranium miners, including Navajo miners, were the single group that was put more seriously at risk of harm from radiation exposures, with inadequate disclosure and often with fatal consequences. Uranium miners were unwilling and unaware victims of human experimentation to evaluate the health effects of radiation. The failure of the State and U.S. Governments to issue regulations or demand installation of known mine-dust exposure control measures caused widespread environmental damage in the Navajo Nation.

Journal Article↗

PubMed2006

Linear data mining the Wichita clinical matrix suggests sleep and allostatic load involvement in chronic fatigue syndrome.

OBJECTIVES: To provide a mathematical introduction to the Wichita (KS, USA) clinical dataset, which is all of the nongenetic data (no microarray or single nucleotide polymorphism data) from the 2-day clinical evaluation, and show the preliminary findings and limitations, of popular, matrix algebra-based data mining techniques. METHODS: An initial matrix of 440 variables by 227 human subjects was reduced to 183 variables by 164 subjects. Variables were excluded that strongly correlated with chronic fatigue syndrome (CFS) case classification by design (for example, the multidimensional fatigue inventory [MFI] data), that were otherwise self reporting in nature and also tended to correlate strongly with CFS classification, or were sparse or nonvarying between case and control. Subjects were excluded if they did not clearly fall into well-defined CFS classifications, had comorbid depression with melancholic features, or other medical or psychiatric exclusions. The popular data mining techniques, principle components analysis (PCA) and linear discriminant analysis (LDA), were used to determine how well the data separated into groups. Two different feature selection methods helped identify the most discriminating parameters. RESULTS: Although purely biological features (variables) were found to separate CFS cases from controls, including many allostatic load and sleep-related variables, most parameters were not statistically significant individually. However, biological correlates of CFS, such as heart rate and heart rate variability, require further investigation. CONCLUSIONS: Feature selection of a limited number of variables from the purely biological dataset produced better separation between groups than a PCA of the entire dataset. Feature selection highlighted the importance of many of the allostatic load variables studied in more detail by Maloney and colleagues in this issue [1] , as well as some sleep-related variables. Nonetheless, matrix linear algebra-based data mining approaches appeared to be of limited utility when compared with more sophisticated nonlinear analyses on richer data types, such as those found in Maloney and colleagues [1] and Goertzel and colleagues [2] in this issue.

Adult↗

PubMedDate not supplied

Data mining and healthcare informatics.

OBJECTIVE: To acquaint members of the Academy with a relatively recent development in the area of data exploration and statistical analysis. METHODS: A review of the concepts and methods inherent in data-mining with a special emphasis on the those methods applicable to predictive modeling. RESULTS: Data-mining is demonstrated to be a useful tool for researchers in those circumstances where large amounts of information are available. CONCLUSIONS: With the advent and proliferation of on-line data collection, truly massive databases are now available to health care researchers. In that situation, data-mining methods yield some unique opportunities to researchers who wish to develop prediction models and to establish associations.

Database Management Systems↗

PubMed2006

Outcome of tissue sparing surgical intervention in mine blast limb injuries.

OBJECTIVE: To describe the pattern of mine blast limb injuries in civilian population of Kashmir, to evaluate the outcome of tissue sparing surgical intervention in these injuries and to determine the sensitivity of hand-held percutaneous Doppler for tissue viability. DESIGN: Descriptive study. PLACE AND DURATION OF STUDY: Combined Military Hospital, Muzaffarabad and Islamic International Medical College Hospitals at Rawalpindi/Islamabad from November 1997 to May 2005. PATIENTS AND METHODS: One hundred and three patients who sustained mine blast injuries to upper or lower limbs, along side the line of control between the Indian-held Kashmir and Azad Kashmir, regardless of age and gender, were included in this study. Patients who already had amputation after injury at some other place were excluded. All patients were initially managed in emergency and had more than one surgical intervention. Transcutaneous Doppler was used to evaluate the vascularity of the remaining tissue. All patients were operated under spinal or general anaesthesia and had repeated debridements followed by skin cover by split skin graft, full thickness skin graft or rotational flaps. Every patient received at least 5 days course of antibiotics and tetanus prophylaxis. Postoperative rehabilitation and follow-up was conducted for at least 6 months after discharge from the hospital. RESULTS: Mean age of victims in this study was 22 years. Out of 103 patients, 72 (69.9%) received initial wound care in the peripheral primary health care centre but were not amputated while 31 patients (30%) were just dressed and referred for further treatment at tertiary care hospitals. Eighty-five patients (82.5%), out of the total, had some sort of traumatic amputation at presentation due to the original injury. That included loss of limb below knee in 19 (18.45%) patients, at distal tibiofibular region in 13 (12.6%), mid tarsal amputations in 39(37.9%), and hemi foot amputation in 15 (14.6%) patients. Nine (8.7%) patients had losses of two or less than two toes, 1 (0.97%) patient had injury at mid palmer region, and 5 (4.9%) patients had 2 fingers traumatic amputation. Eighteen (17.5%) patients had soft tissue ( with or without bony injury) injury only without any actual traumatic amputation. Infection rate was 27% in patients who did not have wound care in the periphery. Those who had wound toilet in-the peripheral hospital had infection rate of 16%. Two patients developed fulminating multi-resistant progressive infection requiring below knee amputation. CONCLUSION: Conservative wound debridement and early skin coverage by different means and preserving maximum soft tissues and bone results in functionally and cosmetically better limb in victims of land mine.

Adolescent↗

PubMed1997

Data mining: a strategy for knowledge development and structure in nursing practice.

Data mining is an emerging technique used more widely by the business world than the world of nursing and health care. However, this strategy can be helpful for improving the quality of decision making by clinicians and health care administrators. This paper addresses the concepts and techniques of data mining that could be useful for practicing nurses as well as nurse administrators. Data mining can be an important tool for the development of nursing knowledge and knowledge structures. An example of the use of the technique in an inpatient setting is provided and insights from the process are discussed.

Cluster Analysis↗

PubMed1999

Increasing the diversity of medical data mining through distributed object technology.

Data mining has been recently experiencing a boom of interest from researchers and software producers. In medicine, however, its applications are still rather rare. In this paper, we argue that this is primarily due to the requirements of reproducibility of results and diversity of available data mining tools, both of which are crucially important for medical research. We propose to tackle the diversity requirement by means of distributed object technologies. The results presented here rely on our experience with medical data mining using the method GUHA and with further developing that method.

Artificial Intelligence↗

PubMed2001

[Subcutaneous inflammatory edema induced by MINE chemotherapy].

BACKGROUND: The MINE regimen (mitoguazone, ifosfamide, vinorelbine and etoposide) is a salvage chemotherapy for relapsed and refractory Hodgkin's disease. CASE REPORTS: We report the cases of a 16-year-old girl and a 17-year-old boy who had Hodgkin's disease and developed painful and massive subcutaneous inflammatory edema after MINE chemotherapy. Morphine was unable to control pain leading to major functional disability of joint movement. One patient had an elevated creatine kinase level, hypoalbuminemia, hypodermic and muscular edema at magnetic resonance imaging and diffuse hemorrhagic hypodermic edema at skin biopsy. The other patient was found to have only hypoalbuminemia. The clinical course was favorable in both cases within a few weeks, but with recurrent episodes of pain and localized areas of fat necrosis five months later in one case. DISCUSSION: This side effect of MINE chemotherapy - subcutaneous inflammatory edema, myalgia and skin pain - has not been described previously for the different components of the regimen. Three clinicopathological hypotheses could be put forward: capillary leak syndrome, panniculitis, toxic fasciitis. The causal drug remains undetermined, but the most likely would be vinorelbine because of the chronology of the eruptions during the first and last days of chemotherapy and because of the known vascular toxicity of vinorelbine which could explain a capillary leak syndrome.

Adolescent↗

PubMed2001

Mining association rules from clinical databases: an intelligent diagnostic process in healthcare.

Data mining is the process of discovering interesting knowledge, such as patterns, associations, changes, anomalies and significant structures, from large amounts of data stored in databases, data warehouses, or other information repositories. Mining Associations is one of the techniques involved in the process mentioned above and used in this paper. Association is the discovery of association relationships or correlations among a set of items. The algorithm that was implemented is a basic algorithm for mining association rules, known as a priori. In Healthcare, association rules are considered to be quite useful as they offer the possibility to conduct intelligent diagnosis and extract invaluable information and build important knowledge bases quickly and automatically. The problem of identifying new, unexpected and interesting patterns in medical databases in general, and diabetic data repositories in specific, is considered in this paper. We have applied the a priori algorithm to a database containing records of diabetic patients and attempted to extract association rules from the stored real parameters. The results indicate that the methodology followed may be of good value to the diagnostic procedure, especially when large data volumes are involved. The followed process and the implemented system offer an efficient and effective tool in the management of diabetes. Their clinical relevance and utility await the results of prospective clinical studies currently under investigation.

Algorithms↗

PubMed2001

[Clinical and statistical patterns of the hearing system lesions caused by explosive mine trauma].

Mine-explosive trauma has become the predominant type of pathology in the structure of surgical sanitary losses. In 70-75% of the cases it is combined with acoustic injury that requires the improvement of diagnosis and prediction of acoustic injuries at the stage of qualified medical care. In 772 casualties with mine-explosive trauma admitted to the different departments of Kabul Central Hospital the conditions of explosive injury were identified and statistical analysis of acoustic injury dependence on the above-mentioned conditions was conducted. The quantitative patterns obtained could be used in prognosis of the possible value and structure of acoubarotraumatic losses in the mine-explosive injuries.

Adult↗

PubMed2001

Using data mining to find fraud in HCFA health care claims.

Data mining can be/used to detect health care fraud and abuse through visualization of very large data sets to isolate new and unusual patterns of activity. Data mining has allowed better direction and use of health care fraud detection and investigative resources by recognizing and quantifying the underlying indicators of fraudulent claims, fraudulent providers, and fraudulent beneficiaries. A large amount of work must be performed prior to the actual data mining. These precursory tasks include: customer discussions, data extraction and cleaning, transformation of the database, and auditing (basic statistics and visualization of the information) of the data. This paper describes the tasks performed in support of a project for HCFA (Health Care Financing Administration).

Centers for Medicare and Medicaid Services, U.S.↗