D
Daniel Hill
Researcher at University College London
Publications - 7
Citations - 44
Daniel Hill is an academic researcher from University College London. The author has contributed to research in topics: Retinal & Medicine. The author has an hindex of 1, co-authored 3 publications receiving 16 citations.
Papers
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Journal ArticleDOI
Curcumin: Novel Treatment in Neonatal Hypoxic-Ischemic Brain Injury.
Eridan Rocha-Ferreira,Eridan Rocha-Ferreira,Claudia Sisa,Sarah Bright,Tessa Fautz,Michael E. Harris,Ingrid Contreras Riquelme,Chinedu Agwu,Tugce Kurulday,Beenaben Mistry,Daniel Hill,Sigrun Lange,Mariya Hristova +12 more
TL;DR: Curcumin is suggested as a potent neuroprotective agent with potential for the treatment of Hypoxic-ischemic encephalopathy, suggesting that the protective effects of curcumin on the neonatal brain following HI are most likely due to cur cumin’s anti-inflammatory and antioxidant properties, as seen in the reduced glial and iNOS activity.
Journal ArticleDOI
Automated characterisation of microglia in ageing mice using image processing and supervised machine learning algorithms
Soyoung Choi,Daniel Hill,Li Guo,Richard Nicholas,Dimitrios Papadopoulos,M Francesca Cordeiro +5 more
TL;DR: In this paper , a machine learning approach was developed to facilitate automatic classification of images of retinal microglial cells as one of five morphotypes, using a support vector machine (SVM).
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Retinal Changes in Transgenic Mouse Models of Alzheimer's Disease.
TL;DR: In this paper, a review of retinal changes in various transgenic AD mouse models with retinal imaging and immunohistochemical analysis as well as therapeutic effects in those models is presented.
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Neuroprotective Strategies in Glaucoma - Translation to Clinical Trials
TL;DR: Neuroprotection in glaucoma is any medical treatment by which decline in visual function can be slowed or prevented by supporting the health and survival of neural cells, independent of lowering of intraocular pressure (IOP) as mentioned in this paper.
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A proposed theoretical framework for retinal biomarkers
TL;DR: A new approach to retinal biomarkers that describes cause and effect graphically in a Bayesian network is presented, which allows a more complete assessment of how well a biomarker might reflect the brain, and how data from right and left eyes can be used to rule out poor biomarker candidates.