ORCID Profile
0000-0002-4160-7875
Current Organisations
University of Sheffield
,
University of Bristol
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Publisher: Elsevier BV
Date: 06-2017
DOI: 10.1016/J.PLACENTA.2017.01.114
Abstract: Normal placental function is essential for optimal fetal growth. Transport of glucose from mother to fetus is critical for fetal nutrient demands and can be stored in the placenta as glycogen. However, the function of this glycogen deposition remains a matter of debate: It could be a source of fuel for the placenta itself or a storage reservoir for later use by the fetus in times of need. While the significance of placental glycogen remains elusive, mounting evidence indicates that altered glycogen metabolism and/or deposition accompanies many pregnancy complications that adversely affect fetal development. This review will summarize histological, biochemical and molecular evidence that glycogen accumulates in a) placentas from a variety of experimental rodent models of perturbed pregnancy, including maternal alcohol exposure, glucocorticoid exposure, dietary deficiencies and hypoxia and b) placentas from human pregnancies with complications including preecl sia, gestational diabetes mellitus and intrauterine growth restriction (IUGR). These pregnancies typically result in altered fetal growth, developmental abnormalities and/or disease outcomes in offspring. Collectively, this evidence suggests that changes in placental glycogen deposition is a common feature of pregnancy complications, particularly those associated with altered fetal growth.
Publisher: Wiley
Date: 10-08-2023
DOI: 10.1002/ALZ.13412
Abstract: Artificial intelligence (AI) and neuroimaging offer new opportunities for diagnosis and prognosis of dementia. We systematically reviewed studies reporting AI for neuroimaging in diagnosis and/or prognosis of cognitive neurodegenerative diseases. A total of 255 studies were identified. Most studies relied on the Alzheimer's Disease Neuroimaging Initiative dataset. Algorithmic classifiers were the most commonly used AI method (48%) and discriminative models performed best for differentiating Alzheimer's disease from controls. The accuracy of algorithms varied with the patient cohort, imaging modalities, and stratifiers used. Few studies performed validation in an independent cohort. The literature has several methodological limitations including lack of sufficient algorithm development descriptions and standard definitions. We make recommendations to improve model validation including addressing key clinical questions, providing sufficient description of AI methods and validating findings in independent datasets. Collaborative approaches between experts in AI and medicine will help achieve the promising potential of AI tools in practice. There has been a rapid expansion in the use of machine learning for diagnosis and prognosis in neurodegenerative disease Most studies (71%) relied on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset with no other in idual dataset used more than five times There has been a recent rise in the use of more complex discriminative models (e.g., neural networks) that performed better than other classifiers for classification of AD vs healthy controls We make recommendations to address methodological considerations, addressing key clinical questions, and validation We also make recommendations for the field more broadly to standardize outcome measures, address gaps in the literature, and monitor sources of bias
Publisher: Springer Science and Business Media LLC
Date: 04-04-2019
DOI: 10.1007/S00415-019-09260-W
Abstract: Impairments in activities of daily living (ADL) are a criterion for Alzheimer's disease (AD) dementia. However, ADL gradually decline in AD, impacting on advanced (a-ADL, complex interpersonal or social functioning), instrumental (IADL, maintaining life in community), and finally basic functions (BADL, activities related to physiological and self-maintenance needs). Information and communication technologies (ICT) have become an increasingly important aspect of daily functioning. Yet, the links of ADL, ICT, and neuropathology of AD dementia are poorly understood. Such knowledge is critical as it can provide biomarker evidence of functional decline in AD. ADL were evaluated with the Technology-Activities of Daily Living Questionnaire (T-ADLQ) in 33 patients with AD and 30 controls. ADL were ided in BADL, IADL, and a-ADL. The three domain subscores were covaried against gray matter atrophy via voxel-based morphometry. Our results showed that three domain subscores of ADL correlate with several brain structures, with a varying degree of overlap between them. BADL score correlated mostly with frontal atrophy, IADL with more widespread frontal, temporal and occipital atrophy and a-ADL with occipital and temporal atrophy. Finally, ICT subscale was associated with atrophy in the precuneus. The association between ADL domains and neurodegeneration in AD follows a traceable neuropathological pathway which involves different neural networks. This the first evidence of ADL phenotypes in AD characterised by specific patterns of functional decline and well-defined neuropathological changes. The identification of such phenotypes can yield functional biomarkers for dementias such as AD.
Location: United Kingdom of Great Britain and Northern Ireland
Location: United Kingdom of Great Britain and Northern Ireland
No related grants have been discovered for Carlos Muñoz-Neira.