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Results for “Disease modelling”

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At least 37 records · Page 2Linked to original sources
PubMed1979

[Organ zinc content in an experimental animal disease model: dystrophy-dependent changes in the liver and skeletal muscles and effects of a mineral substitute during the recovery phase].

The zinc content of the liver and the thigh muscle of the rat were measured by means of atomic absorption spectrophotometry under different conditions: standard diet, carbohydrate diet, an experimental dystrophy model and its recovery period. The influence of a parental mineral substitution on the whole body and the organ weight was studied. Carbohydrate diet caused a lower rise of the whole body weight, a decrease of the zinc content of the liver and an increase of the zinc content of the thigh muscle. In dystrophy model, the liver weight decreased to one fifth of the controls. The zinc concentration of the liver increased markedly, the zinc content of the whole liver is equal to that of control animals. The thigh muscle showed an increase of the zinc content, too. The parenteral mineral application had no effect upon the whole body weight and the organ weights. Little influences on the zinc content of the skeletal muscle were seen. The zinc content of parenchymatous organs varies markedly, and it is influenced by diets and disease. In the dystrophy model, zinc is concentrated in atrophic organs in order to protect the organism against zinc deficiency.

Animals↗
PubMed2025

Hierarchical Multi-Label Classification With Gene-Environment Interactions in Disease Modeling.

In biomedical studies, gene-environment (G-E) interactions have been demonstrated to have important implications for analyzing disease outcomes beyond the main G and main E effects. Many approaches have been developed for G-E interaction analysis, yielding important findings. However, hierarchical multi-label classification, which provides insightful information on disease outcomes, remains unexplored in G-E analysis literature. Moreover, unlabeled data are commonly observed in practical settings but omitted by many existing methods of hierarchical multi-label classification. In this study, we consider a semi-supervised scenario and develop a novel approach for the two-layer hierarchical response with G-E interactions. A two-step penalized estimation is then proposed using an efficient expectation-maximization (EM) algorithm. Simulation shows that it has superior performance in classification and feature selection. The analysis of The Cancer Genome Atlas (TCGA) data on lung cancer demonstrates the practical utility of the proposed method. Overall, this study can fill the important knowledge gap in G-E interaction analysis by providing a widely applicable framework for hierarchical multi-label classification of complex disease outcomes.

Humans↗
PubMed2025

Poly(ADP-ribose) Polymerase 1 Deficiency Attenuates Amyloid Pathology, Neurodegeneration, and Cognitive Decline in a Familial Alzheimer's Disease Model.

Poly(ADP-ribose) (PAR) polymerase-1 (PARP1) has been implicated in DNA damage responses and neuroinflammation in Alzheimer's disease (AD), yet its role in amyloid-β (Aβ) pathology remains unclear. Here, we show that PARP1 activation drives Aβ pathology and neurodegeneration. Using a sensitive ELISA, we observed significantly elevated PAR levels in the cerebrospinal fluid (CSF) of patients with mild cognitive impairment (MCI) and AD compared to controls. In vitro, oligomeric Aβ1-42 activated PARP1 and induced DNA damage, while genetic or pharmacological inhibition of PARP1 conferred neuroprotection. In vivo, PARP1 knockout in the 5XFAD mouse model of amyloidosis led to reduced amyloid plaque burden, preserved synaptic and neuronal integrity, attenuated glial activation and neuroinflammation, and rescued cognitive deficits. Mechanistically, PARP1 deficiency decreased amyloid precursor protein (APP) and BACE1 levels, altered γ-secretase complex composition, and enhanced Aβ degradation via neprilysin. These findings position PARP1 as a critical mediator of Aβ toxicity and neurodegeneration, suggesting its inhibition as a promising therapeutic strategy for AD.

Alzheimer’s disease↗
PubMed2026

CRISPR/Cas9-mediated editing of ERCC6 in iPSCs: A disease model for Cockayne Syndrome type B.

Cockayne Syndrome type B (CSB) is caused by mutations in the ERCC6 gene, which encodes a key protein involved in transcription-coupled nucleotide excision repair (TC-NER) and chromatin remodeling. Deficiency in CSB leads to defective transcriptional recovery after DNA damage, oxidative stress accumulation, and progressive neurodegeneration. In this work, we generated a CRISPR/Cas9-engineered human induced pluripotent stem cell (iPSC) line, IUFi004-A-12, carrying a homozygous mutation in ERCC6 causing a premature stop codon in its 10th exon. The modified iPSCs displayed normal morphology, expressed pluripotency markers, and differentiated into all three germ layers. This model enables mechanistic studies of CSB dysfunction and facilitates therapeutic development for Cockayne Syndrome.

Humans↗
PubMedDate not supplied

[Dynamic psychiatric interpretation of a disease model of trichotillomania].

Every patient's "communal existence" allows to interpret deviations of behavior such as trichotillomania as a disturbance of interaction. When the child begins to wangle out of the dual union between mother and child it needs ways to delimitate itself, If the mother does not concede this demarcation (f. i. because she does not want to release the child from the symbiosis and therefore is "overprotecting") the child often becomes a victim of despair and, later on, of perplexity. At last it uses the own body as "Vis-a-vis". The accumulated aggressiveness is then worked off motorically by means of trichotillomania. In this way despair and perplexity in the field of interaction are passed on so to speak "atmospherically" to the mother, who in turn, having arranged treatment, transfers them on to the doctor. It is discussed how the area of irritation can be dissolved.

Aggression↗
PubMed1978

An analysis of the benefits of serial screening for breast cancer based upon a mathematical model of the disease.

A model of breast cancer is developed that consists of hypotheses about the age-specific incidence of the disease, the rate of disease progression, the tendency of the disease to be detected without benefit of regularly scheduled screening examinations, and prognosis related to the extent of disease at treatment. Parameters for the model are estimated from published data. The model is validated by comparing model predictions to data not used in parameterization. The model, under a variety of assumptions, is then used to analyze questions of interest about breast cancer screening strategies. These include the following: the benefits from screening with mammography and clinical examination as a function of the frequency and starting age of screening, the effect of different assumptions about radiation risks on the benefits of screening, the benefits from screening with mammography if yearly clinical examinations are performed, and the benefits from screening with mammography and clinical examination if self-examinations are performed.

Adult↗