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The Journal of the Royal College of... Apr 1977I have developed a system of classifying diseases in general practice based on headings relevant to general practice which are built up in a hierarchy. I believe this is...
I have developed a system of classifying diseases in general practice based on headings relevant to general practice which are built up in a hierarchy. I believe this is better than abbreviating the International Classification of Disease with the addition of individual symptoms.Ideally, a classification ought to include occupational diseases and psychosocial problems, and it must be accompanied by a terminology defining as precisely as possible the different categories based on criteria relevant to ordinary general practice. It should still follow the main divisions of the International Classification of Disease as far as possible.I believe that general practice will not progress any further unless it is prepared to loosen its traditional allegiance to hospitalorientated disease classifications, which are, for our purpose, inappropriately rigid.
Topics: Disease; Family Practice; Humans; Occupational Diseases
PubMed: 859155
DOI: No ID Found -
Medical Hypotheses May 1984Non-equilibrium thermodynamics, biochemistry and physiology can be associated to form a possible foundation for a theory of nutritional medicine. The theory predicts a...
Non-equilibrium thermodynamics, biochemistry and physiology can be associated to form a possible foundation for a theory of nutritional medicine. The theory predicts a new class of diseases. The relationship between this new class and cancer is discussed.
Topics: Disease; Enzymes; Humans; Models, Biological; Neoplasms; Nutrition Disorders; Pharmacology; Psychophysiologic Disorders; Stress, Physiological; Thermodynamics
PubMed: 6748992
DOI: 10.1016/0306-9877(84)90065-3 -
Bioengineered Jun 2022miRNA is a small endogenous RNA and an important regulator of gene expression. miR-4443 is abnormally expressed in 12 diseases including cancer. The expression of... (Review)
Review
miRNA is a small endogenous RNA and an important regulator of gene expression. miR-4443 is abnormally expressed in 12 diseases including cancer. The expression of miR-4443 is regulated by 3 upstream factors. miR-4443 has 12 downstream target genes. miR-4443 inhibits the expression of its target genes, thereby affecting the migration, proliferation, and invasion of pathological cells. miR-4443 participates in 4 signaling pathways and plays a role in the occurrence and development of several diseases. In addition, miR-4443 can also promote resistance to multiple drugs. Here, this article summarizes the aberrant expression of miR-4443 and its pathogenic molecular mechanisms in human diseases, which provides clues and directions for the follow-up research of miR-4443.
Topics: Humans; MicroRNAs; Disease; Neoplasms; Gene Expression Regulation
PubMed: 36250718
DOI: 10.1080/21655979.2022.2109807 -
American Journal of Epidemiology Oct 2017Genetic and environmental factors are both known to contribute to susceptibility to complex diseases. Therefore, the study of gene-environment interaction (G×E) has... (Review)
Review
Genetic and environmental factors are both known to contribute to susceptibility to complex diseases. Therefore, the study of gene-environment interaction (G×E) has been a focus of research for several years. In this article, select examples of G×E from the literature are described to highlight different approaches and underlying principles related to the success of these studies. These examples can be broadly categorized as studies of single metabolism genes, genes in complex metabolism pathways, ranges of exposure levels, functional approaches and model systems, and pharmacogenomics. Some studies illustrated the success of studying exposure metabolism for which candidate genes can be identified. Moreover, some G×E successes depended on the availability of high-quality exposure assessment and longitudinal measures, study populations with a wide range of exposure levels, and the inclusion of ethnically and geographically diverse populations. In several examples, large population sizes were required to detect G×Es. Other examples illustrated the impact of accurately defining scale of the interactions (i.e., additive or multiplicative). Last, model systems and functional approaches provided insights into G×E in several examples. Future studies may benefit from these lessons learned.
Topics: Biomedical Research; Disease; Environmental Exposure; Gene-Environment Interaction; Genetic Predisposition to Disease; Genetic Variation; Genome-Wide Association Study; Humans; Models, Biological
PubMed: 28978190
DOI: 10.1093/aje/kwx230 -
Der Internist May 2017Until the middle of the 20th century, clinical microbiology was limited to bacterial cultures enabling the detection of pathogenic microorganisms. Knowledge about the...
Until the middle of the 20th century, clinical microbiology was limited to bacterial cultures enabling the detection of pathogenic microorganisms. Knowledge about the mutual relationship between humans and microorganisms has increased slowly. With the introduction of culture-independent analysis methods, comprehensive cataloging of the human microbiome was possible for the first time. Since then, compositional changes in relation to diseases have been studied. The goals of the Human Microbiome Project and MetaHIT include comparative studies of healthy and diseased individuals. Numerous libraries on time- and location-dependent changes of the microbiota composition in human diseases have been created. However, a mathematical correlation does not equal biological or medical relevance. Future research needs to validate the hypotheses generated in these studies in functional experiments and evaluate their true impact on clinical practice.
Topics: Disease; Humans; Microbial Consortia
PubMed: 28357466
DOI: 10.1007/s00108-017-0224-1 -
Molecular BioSystems Jun 2014The identification of disease genes is very important not only to provide greater understanding of gene function and cellular mechanisms which drive human disease, but...
The identification of disease genes is very important not only to provide greater understanding of gene function and cellular mechanisms which drive human disease, but also to enhance human disease diagnosis and treatment. Recently, high-throughput techniques have been applied to detect dozens or even hundreds of candidate genes. However, experimental approaches to validate the many candidates are usually time-consuming, tedious and expensive, and sometimes lack reproducibility. Therefore, numerous theoretical and computational methods (e.g. network-based approaches) have been developed to prioritize candidate disease genes. Many network-based approaches implicitly utilize the observation that genes causing the same or similar diseases tend to correlate with each other in gene-protein relationship networks. Of these network approaches, the random walk with restart algorithm (RWR) is considered to be a state-of-the-art approach. To further improve the performance of RWR, we propose a novel method named ESFSC to identify disease-related genes, by enlarging the seed set according to the centrality of disease genes in a network and fusing information of the protein-protein interaction (PPI) network topological similarity and the gene expression correlation. The ESFSC algorithm restarts at all of the nodes in the seed set consisting of the known disease genes and their k-nearest neighbor nodes, then walks in the global network separately guided by the similarity transition matrix constructed with PPI network topological similarity properties and the correlational transition matrix constructed with the gene expression profiles. As a result, all the genes in the network are ranked by weighted fusing the above results of the RWR guided by two types of transition matrices. Comprehensive simulation results of the 10 diseases with 97 known disease genes collected from the Online Mendelian Inheritance in Man (OMIM) database show that ESFSC outperforms existing methods for prioritizing candidate disease genes. The top prediction results of Alzheimer's disease are consistent with previous literature reports.
Topics: Algorithms; Computational Biology; Databases, Genetic; Disease; Genetic Association Studies; Genetic Predisposition to Disease; Humans; Models, Genetic; Protein Interaction Maps
PubMed: 24695957
DOI: 10.1039/c3mb70588a -
Bioinformatics (Oxford, England) Nov 2010DisGeNET is a plugin for Cytoscape to query and analyze human gene-disease networks. DisGeNET allows user-friendly access to a new gene-disease database that we have...
UNLABELLED
DisGeNET is a plugin for Cytoscape to query and analyze human gene-disease networks. DisGeNET allows user-friendly access to a new gene-disease database that we have developed by integrating data from several public sources. DisGeNET permits queries restricted to (i) the original data source, (ii) the association type, (iii) the disease class or (iv) specific gene(s)/disease(s). It represents gene-disease associations in terms of bipartite graphs and provides gene centric and disease centric views of the data. It assists the user in the interpretation and exploration of the genetic basis of human diseases by a variety of built-in functions. Moreover, DisGeNET permits multicolouring of nodes (genes/diseases) according to standard disease classification for expedient visualization.
AVAILABILITY
DisGeNET is compatible with Cytoscape 2.6.3 and 2.7.0, please visit http://ibi.imim.es/DisGeNET/DisGeNETweb.html for installation guide, user tutorial and download.
Topics: Computational Biology; Databases, Genetic; Disease; Gene Regulatory Networks; Humans; Software
PubMed: 20861032
DOI: 10.1093/bioinformatics/btq538 -
The Journal of Medicine and Philosophy Apr 2008In a previous paper the concept of disease was fuzzy-logically analyzed and a sketch was given of a prototype resemblance theory of disease (Sadegh-Zadeh (2000). J. Med....
In a previous paper the concept of disease was fuzzy-logically analyzed and a sketch was given of a prototype resemblance theory of disease (Sadegh-Zadeh (2000). J. Med. Philos., 25:605-38). This theory is outlined in the present paper. It demonstrates what it means to say that the concept of disease is a nonclassical one and, therefore, not amenable to traditional methods of inquiry. The theory undertakes a reconstruction of disease as a category that in contradistinction to traditional views is not based on a set of common features of its members, that is individual diseases, but on a few best examples of the category, called its prototypes, and a similarity relationship such that a human condition is considered a disease if it resembles a prototype. It enables new approaches to resolving many of the stubborn problems associated with the concept of disease.
Topics: Cultural Characteristics; Disease; Fuzzy Logic; Humans; Philosophy, Medical; Terminology as Topic
PubMed: 18480497
DOI: 10.1093/jmp/jhn004 -
Journal of Biomedical Informatics May 2017Mining disease-specific associations from existing knowledge resources can be useful for building disease-specific ontologies and supporting knowledge-based...
OBJECTIVE
Mining disease-specific associations from existing knowledge resources can be useful for building disease-specific ontologies and supporting knowledge-based applications. Many association mining techniques have been exploited. However, the challenge remains when those extracted associations contained much noise. It is unreliable to determine the relevance of the association by simply setting up arbitrary cut-off points on multiple scores of relevance; and it would be expensive to ask human experts to manually review a large number of associations. We propose that machine-learning-based classification can be used to separate the signal from the noise, and to provide a feasible approach to create and maintain disease-specific vocabularies.
METHOD
We initially focused on disease-medication associations for the purpose of simplicity. For a disease of interest, we extracted potentially treatment-related drug concepts from biomedical literature citations and from a local clinical data repository. Each concept was associated with multiple measures of relevance (i.e., features) such as frequency of occurrence. For the machine purpose of learning, we formed nine datasets for three diseases with each disease having two single-source datasets and one from the combination of previous two datasets. All the datasets were labeled using existing reference standards. Thereafter, we conducted two experiments: (1) to test if adding features from the clinical data repository would improve the performance of classification achieved using features from the biomedical literature only, and (2) to determine if classifier(s) trained with known medication-disease data sets would be generalizable to new disease(s).
RESULTS
Simple logistic regression and LogitBoost were two classifiers identified as the preferred models separately for the biomedical-literature datasets and combined datasets. The performance of the classification using combined features provided significant improvement beyond that using biomedical-literature features alone (p-value<0.001). The performance of the classifier built from known diseases to predict associated concepts for new diseases showed no significant difference from the performance of the classifier built and tested using the new disease's dataset.
CONCLUSION
It is feasible to use classification approaches to automatically predict the relevance of a concept to a disease of interest. It is useful to combine features from disparate sources for the task of classification. Classifiers built from known diseases were generalizable to new diseases.
Topics: Biological Ontologies; Data Mining; Databases as Topic; Disease; Humans; Machine Learning; Periodicals as Topic; Publications
PubMed: 28435015
DOI: 10.1016/j.jbi.2017.04.014 -
Scientific Reports Sep 2023The critically endangered black rhinoceros (Diceros bicornis; black rhino) experiences extinction threats from poaching in-situ. The ex-situ population, which serves...
The critically endangered black rhinoceros (Diceros bicornis; black rhino) experiences extinction threats from poaching in-situ. The ex-situ population, which serves as a genetic reservoir against impending extinction threats, experiences its own threats to survival related to several disease syndromes not typically observed among their wild counterparts. We performed an untargeted metabolomic analysis of serum from 30 ex-situ housed black rhinos (Eastern black rhino, EBR, n = 14 animals; Southern black rhino, SBR, n = 16 animals) and analyzed differences in metabolite profiles between subspecies, sex, and health status (healthy n = 13 vs. diseased n = 14). Of the 636 metabolites detected, several were differentially (fold change > 1.5; p < 0.05) expressed between EBR vs. SBR (40 metabolites), female vs. male (36 metabolites), and healthy vs. diseased (22 metabolites). Results suggest dysregulation of propanoate, amino acid metabolism, and bile acid biosynthesis in the subspecies and sex comparisons. Assessment of healthy versus diseased rhinos indicates involvement of arachidonic acid metabolism, bile acid biosynthesis, and the pentose phosphate pathway in animals exhibiting inflammatory disease syndromes. This study represents the first systematic characterization of the circulating serum metabolome in the black rhinoceros. Findings further implicate mitochondrial and immune dysfunction as key contributors for the diverse disease syndromes reported in ex-situ managed black rhinos.
Topics: Female; Male; Animals; Syndrome; Metabolomics; Immune System Diseases; Perissodactyla; Bile Acids and Salts
PubMed: 37726331
DOI: 10.1038/s41598-023-41508-4