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1.
Brain ; 145(3): 1069-1078, 2022 04 29.
Article in English | MEDLINE | ID: mdl-34919645

ABSTRACT

The TDP-43 type C pathological form of frontotemporal lobar degeneration is characterized by the presence of immunoreactive TDP-43 short and long dystrophic neurites, neuronal cytoplasmic inclusions, neuronal loss and gliosis and the absence of neuronal intranuclear inclusions. Frontotemporal lobar degeneration-TDP-type C cases are commonly associated with the semantic variant of primary progressive aphasia or behavioural variant frontotemporal dementia. Here, we provide detailed characterization of regional distributions of pathological TDP-43 and neuronal loss and gliosis in cortical and subcortical regions in 10 TDP-type C cases and investigate the relationship between inclusions and neuronal loss and gliosis. Specimens were obtained from the first 10 TDP-type C cases accessioned from the Northwestern Alzheimer's Disease Research Center (semantic variant of primary progressive aphasia, n = 7; behavioural variant frontotemporal dementia, n = 3). A total of 42 cortical (majority bilateral) and subcortical regions were immunostained with a phosphorylated TDP-43 antibody and/or stained with haematoxylin-eosin. Regions were evaluated for atrophy, and for long dystrophic neurites, short dystrophic neurites, neuronal cytoplasmic inclusions, and neuronal loss and gliosis using a semiquantitative 5-point scale. We calculated a 'neuron-to-inclusion' score (TDP-type C mean score - neuronal loss and gliosis mean score) for each region per case to assess the relationship between TDP-type C inclusions and neuronal loss and gliosis. Primary progressive aphasia cases demonstrated leftward asymmetry of cortical atrophy consistent with the aphasic phenotype. We also observed abundant inclusions and neurodegeneration in both cortical and subcortical regions, with certain subcortical regions emerging as particularly vulnerable to dystrophic neurites (e.g. amygdala, caudate and putamen). Interestingly, linear mixed models showed that regions with lowest TDP-type C pathology had high neuronal dropout, and conversely, regions with abundant pathology displayed relatively preserved neuronal densities (P < 0.05). This inverse relationship between the extent of TDP-positive inclusions and neuronal loss may reflect a process whereby inclusions disappear as their associated neurons are lost. Together, these findings offer insight into the putative substrates of neurodegeneration in unique dementia syndromes.


Subject(s)
Aphasia, Primary Progressive , Frontotemporal Dementia , Frontotemporal Lobar Degeneration , Nervous System Malformations , Aphasia, Primary Progressive/pathology , Atrophy , Autopsy , DNA-Binding Proteins/genetics , Frontotemporal Dementia/pathology , Frontotemporal Lobar Degeneration/pathology , Gliosis , Humans
3.
J Glaucoma ; 23(4): 258-61, 2014.
Article in English | MEDLINE | ID: mdl-22955016

ABSTRACT

Pericardium is a biomaterial widely used for covering the outflow tubes of glaucoma drainage devices. Recently, glycerol preserved cornea has been introduced as an alternative that offers durability and improved cosmesis because of its clarity. We retrospectively reviewed 262 patients in the University of Alabama Birmingham Glaucoma Service who underwent shunt procedures using either cornea tissue or pericardium to cover the tube. The primary outcome measure was the number of erosions of the covering material. Nine out of 101 (8.9%) patients in the pericardium covered group experienced an erosion compared with 3 out of 161 (1.9%) in the cornea covered group. A significant difference was reached with P=0.0125. Median follow-up was 440 days for the cornea group and 331 days for the pericardium group. The type of glaucoma (primary open-angle glaucoma vs. secondary glaucoma) was not associated with the risk of erosion (odds ratio, 0.501; 95% confidence interval, 0.204-1.234). The median time to exposure was 252 days in the pericardium group and 440 days in the cornea group (P=0.0017).


Subject(s)
Corneal Transplantation , Glaucoma Drainage Implants , Glaucoma, Open-Angle/surgery , Glycerol , Organ Preservation Solutions , Pericardium/transplantation , Prosthesis Implantation/methods , Aged , Female , Humans , Male , Postoperative Complications/prevention & control , Retrospective Studies , Sclera/surgery , Surgical Wound Dehiscence/prevention & control , Suture Techniques , Tissue Preservation , Treatment Outcome
4.
Am Nat ; 172(5): 712-25, 2008 Nov.
Article in English | MEDLINE | ID: mdl-18840071

ABSTRACT

Variation in the temporal pattern of vulnerability can provide important insights into predator-prey relationships and the evolution of antipredator behavior. We illustrate these points with a system that has coyotes (Canis latrans) as a predator and two species of congeneric deer (Odocoileus spp.) as prey. The deer employ different antipredator tactics (aggressive defense vs. flight) that result in contrasting patterns of age-dependent vulnerability in their probability of being captured when encountered by coyotes. We use long-term survival data and a simple mathematical model to show that (1) species differences in age-dependent vulnerability are reflected in seasonal predation rates and (2) seasonal variation in prey vulnerability and predator hunt activity, which can be associated with the availability of alternative prey, interact to shape seasonal and annual predation rates for each prey species. Shifting hunt activity from summer to winter, or vice versa, alleviated annual mortality on one species and focused it on the other. Our results indicate that seasonal variation in prey vulnerability and hunt activity interact to influence the impact that a predator has on any particular type of prey. Furthermore, these results indicate that seasonal variation in predation pressure is an important selection pressure shaping prey defenses.


Subject(s)
Aging/physiology , Coyotes/physiology , Deer/physiology , Predatory Behavior/physiology , Aggression/physiology , Animals , Escape Reaction/physiology , Female , Male , Models, Biological , Population Dynamics , Seasons , Time Factors
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