scholarly journals Linking worlds: a theoretical reflection on some preconditions for ethnographic collaborations in personalized medicine

2019 ◽  
pp. 165-190
Author(s):  
José Carlos Pinto Da Costa
Keyword(s):  
Personalized Medicine ◽  
Environmental Factors ◽  
Medical Care ◽  
Access To Healthcare ◽  
Pathogenic Potential ◽  
Patterns Of Use ◽  
Genetic And Environmental Factors ◽  
Object Of Study ◽  
Individual Scale ◽  
Theoretical Reflection

Precision, or personalized, medicine (PM) is a ground-breaking approach to medical care which aims to predict, prevent and treat diseases by studying, on an individual scale, the pathogenic potential of the association between genetic and environmental factors. As one of the most important outcomes of biotechnological research, PM is generated in the lab. Nonetheless, the impacts of PM will be observed outside of the lab, namely, on the modification of population’s patterns of use and access to healthcare. Taking PM as object of study, anthropologists are challenged to make a double reflection. The first consists in understanding which peculiarities an ethnography should have to grasp engineers’ and other experts’ underlying modes of knowing and doing inside de lab. The second, more analytical, consists in identifying the indicators revealed by that ethnography which may promote an interpretation of how these modes simultaneously mirror and resonate a given cultural will located both upstream and downstream the lab — from and to outside of it. The purpose of this paper is to reflect on the hypothesis stressing that an ethnographic collaboration might configure an effective way of doing this.

scholarly journals Personalized Medicine in Autoimmune Diseases

2021 ◽  
Vol 11 (11) ◽  
pp. 1181
Author(s):  
Roberto Díaz-Peña
Keyword(s):  
Personalized Medicine ◽  
Environmental Factors ◽  
Autoimmune Diseases ◽  
Multifactorial Disorders ◽  
Genetic And Environmental Factors

Autoimmune diseases are multifactorial disorders caused by both genetic and environmental factors and without a known cure [...]

Reductionist thinking and animal models in neuropsychiatric research

2019 ◽  
Vol 42 ◽  
Author(s):  
Nicole M. Baran
Keyword(s):  
Animal Models ◽  
Mental Disorders ◽  
Environmental Factors ◽  
Neuropsychiatric Disorders ◽  
Model Organisms ◽  
Developmental Genetic ◽  
Term Study ◽  
Genetic And Environmental Factors ◽  
Mechanisms Of Plasticity

AbstractReductionist thinking in neuroscience is manifest in the widespread use of animal models of neuropsychiatric disorders. Broader investigations of diverse behaviors in non-model organisms and longer-term study of the mechanisms of plasticity will yield fundamental insights into the neurobiological, developmental, genetic, and environmental factors contributing to the “massively multifactorial system networks” which go awry in mental disorders.

Questions on the role of biofilms for the adaptation of microorganisms to unfavorable environmental factors by the example of P. aeruginosa

2020 ◽  
Vol 99 (4) ◽  
pp. 379-383
Author(s):  
Vasily N. Afonyushkin ◽  
N. A. Donchenko ◽  
Ju. N. Kozlova ◽  
N. A. Davidova ◽  
V. Yu. Koptev ◽  
...  
Keyword(s):  
Antibiotic Resistance ◽  
Environmental Factors ◽  
Barrier Function ◽  
Infectious Agent ◽  
Antagonistic Activity ◽  
The Body ◽  
Main Function ◽  
Pathogenic Potential ◽  
The Face

Pseudomonas aeruginosa is a widely represented species of bacteria possessing of a pathogenic potential. This infectious agent is causing wound infections, fibrotic cystitis, fibrosing pneumonia, bacterial sepsis, etc. The microorganism is highly resistant to antiseptics, disinfectants, immune system responses of the body. The responses of a quorum sense of this kind of bacteria ensure the inclusion of many pathogenicity factors. The analysis of the scientific literature made it possible to formulate four questions concerning the role of biofilms for the adaptation of P. aeruginosa to adverse environmental factors: Is another person appears to be predominantly of a source an etiological agent or the source of P. aeruginosa infection in the environment? Does the formation of biofilms influence on the antibiotic resistance? How the antagonistic activity of microorganisms is realized in biofilm form? What is the main function of biofilms in the functioning of bacteria? A hypothesis has been put forward the effect of biofilms on the increase of antibiotic resistance of bacteria and, in particular, P. aeruginosa to be secondary in charcter. It is more likely a biofilmboth to fulfill the function of storing nutrients and provide topical competition in the face of food scarcity. In connection with the incompatibility of the molecular radii of most antibiotics and pores in biofilm, biofilm is doubtful to be capable of performing a barrier function for protecting against antibiotics. However, with respect to antibodies and immunocompetent cells, the barrier function is beyond doubt. The biofilm is more likely to fulfill the function of storing nutrients and providing topical competition in conditions of scarcity of food resources.

Artificial Intelligence for epigenetics: towards personalized medicine

2020 ◽  
Vol 27 ◽  
Author(s):  
Giulia De Riso ◽  
Sergio Cocozza
Keyword(s):  
Biological Sciences ◽  
Artificial Intelligence ◽  
Personalized Medicine ◽  
Molecular Mechanisms ◽  
Genomic Sequence ◽  
Health Care Interventions ◽  
Genome Wide ◽  
Genetic And Environmental Factors ◽  
Opening Up ◽  
Omic Data

: Epigenetics is a field of biological sciences focused on the study of reversible, heritable changes in gene function not due to modifications of the genomic sequence. These changes are the result of a complex cross-talk between several molecular mechanisms, that is in turn orchestrated by genetic and environmental factors. The epigenetic profile captures the unique regulatory landscape and the exposure to environmental stimuli of an individual. It thus constitutes a valuable reservoir of information for personalized medicine, which is aimed at customizing health-care interventions based on the unique characteristics of each individual. Nowadays, the complex milieu of epigenomic marks can be studied at the genome-wide level thanks to massive, highthroughput technologies. This new experimental approach is opening up new and interesting knowledge perspectives. However, the analysis of these complex omic data requires to face important analytic issues. Artificial Intelligence, and in particular Machine Learning, are emerging as powerful resources to decipher epigenomic data. In this review, we will first describe the most used ML approaches in epigenomics. We then will recapitulate some of the recent applications of ML to epigenomic analysis. Finally, we will provide some examples of how the ML approach to epigenetic data can be useful for personalized medicine.

The Association Between Chronic Disease and Psychological Distress: An Australian Twin Study

2020 ◽  
Vol 23 (6) ◽  
pp. 322-329
Author(s):  
Jessica Tyler ◽  
Janine Lam ◽  
Katrina Scurrah ◽  
Gillian Dite
Keyword(s):  
Psychological Distress ◽  
Chronic Disease ◽  
Environmental Factors ◽  
Twin Study ◽  
Disease Status ◽  
Adult Health ◽  
Linear Regression Models ◽  
Cross Sectional ◽  
Genetic And Environmental Factors ◽  
Distress Scale

AbstractThere is a commonly observed association between chronic disease and psychological distress, but many potential factors could confound this association. This study investigated the association using a powerful twin study design that can control for unmeasured confounders that are shared between twins, including genetic and environmental factors. We used twin-paired cross-sectional data from the Adult Health and Lifestyle Questionnaire collected by Twins Research Australia from 2014 to 2017. Linear regression models fitted using maximum likelihood estimations (MLE) were used to test the association between self-reported chronic disease status and psychological distress, measured by the Kessler Psychological Distress Scale (K6). When comparing between twin pairs, having any chronic disease was associated with a 1.29 increase in K6 (95% CI: 0.91, 1.66; p < .001). When comparing twins within a pair, having any chronic disease was associated with a 0.36 increase in K6 (95% CI: 0.002, 0.71; p = .049). This within-pair estimate is of most interest as comparing twins within a pair naturally controls for shared factors such as genes, age and shared lived experiences. Whereas the between-pair estimate does not. The weaker effect found within pairs tells us that genetic and environmental factors shared between twins confounds the relationship between chronic disease and psychological distress. This suggests that associations found in unrelated samples may show exaggerated estimates.

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