RESUMO
Amyotrophic Lateral Sclerosis (ALS) is characterised by a loss of motor neurons in the brain and spinal cord that is preceded by early-stage changes in synapses that may be associated with TAR-DNA-Binding Protein 43 (TDP-43) pathology. Cellular inclusions of hyperphosphorylated TDP-43 (pTDP-43) are a key hallmark of neurodegenerative diseases such ALS. However, there has been little characterisation of the synaptic expression of TDP-43 inside subpopulations of spinal cord synapses. This study utilises a range of high-resolution and super-resolution microscopy techniques with immunolabelling, as well as an aptamer-based TDP-43 labelling strategy visualised with single-molecule localisation microscopy, to characterise and quantify the presence of pTDP-43 in populations of excitatory synapses near where motor neurons reside in the lateral ventral horn of the mouse lumbar spinal cord. We observe that TDP-43 is expressed in approximately half of spinal cord synapses as nanoscale clusters. Synaptic TDP-43 clusters are found most abundantly at synapses associated with VGLUT1-positive presynaptic terminals, compared to VGLUT2-associated synapses. Our nanoscopy techniques showed no difference in the subsynaptic expression of pTDP-43 in the ALS mouse model, SOD1G93a, compared to healthy controls, despite prominent structural deficits in VGLUT1-associated synapses in SOD1G93a mice. This research characterises the basic synaptic expression of TDP-43 with nanoscale precision and provides a framework with which to investigate the potential relationship between TDP-43 pathology and synaptic pathology in neurodegenerative diseases.
RESUMO
BACKGROUND: CARING is a screening tool developed to identify patients who have a high likelihood of death in 1 year. AIM: This study sought to validate a modified CARING tool (termed PREDICT) using a population of patients presenting to the Emergency Department. SETTING/PARTICIPANTS: In total, 1000 patients aged over 55 years who were admitted to hospital via the Emergency Department between January and June 2009 were eligible for inclusion in this study. DESIGN: Data on the six prognostic indicators comprising PREDICT were obtained retrospectively from patient records. One-year mortality data were obtained from the State Death Registry. Weights were applied to each PREDICT criterion, and its final score ranged from 0 to 44. Receiver operator characteristic analyses and diagnostic accuracy statistics were used to assess the accuracy of PREDICT in identifying 1-year mortality. RESULTS: The sample comprised 976 patients with a median (interquartile range) age of 71 years (62-81 years) and a 1-year mortality of 23.4%. In total, 50% had ≥1 PREDICT criteria with a 1-year mortality of 40.4%. Receiver operator characteristic analysis gave an area under the curve of 0.86 (95% confidence interval: 0.83-0.89). Using a cut-off of 13 points, PREDICT had a 95.3% (95% confidence interval: 93.6-96.6) specificity and 53.9% (95% confidence interval: 47.5-60.3) sensitivity for predicting 1-year mortality. PREDICT was simpler than the CARING criteria and identified 158 patients per 1000 admitted who could benefit from advance care planning. CONCLUSION: PREDICT was successfully applied to the Australian healthcare system with findings similar to the original CARING study conducted in the United States. This tool could improve end-of-life care by identifying who should have advance care planning or an advance healthcare directive.
