Tripartite Relationship Among Synaptic, Amyloid, and Tau Proteins: An In Vivo and Postmortem Study

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Temporal order of clinical and biomarker changes in familial frontotemporal dementia

Data availability: The datasets analyzed for the current study reflect collaborative efforts of two research consortia: ALLFTD and GENFI. Each consortium provides clinical data access based on established policies for data use: processes for request are available for review at allftd.org/data for ALLFTD data and by emailing [email protected]. Certain data elements from both consortia (for example raw MRI images) may be restricted due to the potential for identifiability in the context of the sensitive nature of the genetic data. The deidentified combined dataset will be available for request through the FTD Prevention Initiative in 2023 (https://www.thefpi.org/).Code availability: Custom R code is available at https://doi.org/10.5281/zenodo.6687486.Copyright © The Author(s). Unlike familial Alzheimer’s disease, we have been unable to accurately predict symptom onset in presymptomatic familial frontotemporal dementia (f-FTD) mutation carriers, which is a major hurdle to designing disease prevention trials. We developed multimodal models for f-FTD disease progression and estimated clinical trial sample sizes in C9orf72, GRN and MAPT mutation carriers. Models included longitudinal clinical and neuropsychological scores, regional brain volumes and plasma neurofilament light chain (NfL) in 796 carriers and 412 noncarrier controls. We found that the temporal ordering of clinical and biomarker progression differed by genotype. In prevention-trial simulations using model-based patient selection, atrophy and NfL were the best endpoints, whereas clinical measures were potential endpoints in early symptomatic trials. f-FTD prevention trials are feasible but will likely require global recruitment efforts. These disease progression models will facilitate the planning of f-FTD clinical trials, including the selection of optimal endpoints and enrollment criteria to maximize power to detect treatment effects.Data collection and dissemination of the data presented in this paper were supported by the ALLFTD Consortium (U19: AG063911, funded by the National Institute on Aging and the National Institute of Neurological Diseases and Stroke) and the former ARTFL and LEFFTDS Consortia (ARTFL: U54 NS092089, funded by the National Institute of Neurological Diseases and Stroke and National Center for Advancing Translational Sciences; LEFFTDS: U01 AG045390, funded by the National Institute on Aging and the National Institute of Neurological Diseases and Stroke). The manuscript was reviewed by the ALLFTD Executive Committee for scientific content. The authors acknowledge the invaluable contributions of the study participants and families as well as the assistance of the support staffs at each of the participating sites. This work is also supported by the Association for Frontotemporal Degeneration (including the FTD Biomarkers Initiative), the Bluefield Project to Cure FTD, Larry L. Hillblom Foundation (2018-A-025-FEL (A.M.S.)), the National Institutes of Health (AG038791 (A.L.B.), AG032306 (H.J.R.), AG016976 (W.K.), AG062677 (Ron C. Peterson), AG019724 (B.L.M.), AG058233 (Suzee E. Lee), AG072122 (Walter Kukull), P30 AG062422 (B.L.M.), K12 HD001459 (N.G.), K23AG061253 (A.M.S.), AG062422 (RCP), K24AG045333 (H.J.R.)) and the Rainwater Charitable Foundation. Samples from the National Centralized Repository for Alzheimer Disease and Related Dementias (NCRAD), which receives government support under a cooperative agreement grant (U24 AG021886 (T.F.)) awarded by the National Institute on Aging (NIA), were used in this study. This work was also supported by Medical Research Council UK GENFI grant MR/M023664/1 (J.D.R.), the Bluefield Project, the National Institute for Health Research including awards to Cambridge and UCL Biomedical Research Centres and a JPND GENFI-PROX grant (2019–02248). Several authors of this publication are members of the European Reference Network for Rare Neurologic Diseases, project 739510. J.D.R. and L.L.R. are also supported by the National Institute for Health and Care Research (NIHR) UCL/H Biomedical Research Centre, the Leonard Wolfson Experimental Neurology Centre Clinical Research Facility and the UK Dementia Research Institute, which receives its funding from UK DRI Ltd, funded by the UK Medical Research Council, Alzheimer’s Society and Alzheimer’s Research UK. J.D.R. is also supported by the Miriam Marks Brain Research UK Senior Fellowship and has received funding from an MRC Clinician Scientist Fellowship (MR/M008525/1) and the NIHR Rare Disease Translational Research Collaboration (BRC149/NS/MH). M.B. is supported by a Fellowship award from the Alzheimer’s Society, UK (AS-JF-19a-004-517). RC and C.G. are supported by a Frontotemporal Dementia Research Studentships in Memory of David Blechner funded through The National Brain Appeal (RCN 290173). J.B.R. is supported by NIHR Cambridge Biomedical Research Centre (BRC-1215-20014; the views expressed are those of the authors and not necessarily those of the NIHR or the Department of Health and Social Care), the Wellcome Trust (220258), the Cambridge Centre for Parkinson-plus and the Medical Research Council (SUAG/092 G116768); I.L.B. is supported by ANR-PRTS PREV-DemAls, PHRC PREDICT-PGRN, and several authors of this publication are members of the European Reference Network for Rare Neurological Diseases (project 739510). J.L. is funded by the Deutsche Forschungsgemeinschaft (German Research Foundation) under Germany’s Excellence Strategy within the framework of the Munich Cluster for Systems Neurology (EXC 2145 SyNergy – ID 390857198). R.S.-V. was funded at the Hospital Clinic de Barcelona by Instituto de Salud Carlos III, Spain (grant code PI20/00448 to RSV) and Fundació Marató TV3, Spain (grant code 20143810 to R.S.-V.). M.M. was, in part, funded by the UK Medical Research Council, the Italian Ministry of Health and the Canadian Institutes of Health Research as part of a Centres of Excellence in Neurodegeneration grant, by Canadian Institutes of Health Research operating grants (MOP- 371851 and PJT-175242) and by funding from the Weston Brain Institute. R.L. is supported by the Canadian Institutes of Health Research and the Chaire de Recherche sur les Aphasies Primaires Progressives Fondation Famille Lemaire. C.G. is supported by the Swedish Frontotemporal Dementia Initiative Schörling Foundation, Swedish Research Council, JPND Prefrontals, 2015–02926,2018–02754, Swedish Alzheimer Foundation, Swedish Brain Foundation, Karolinska Institutet Doctoral Funding, KI Strat-Neuro, Swedish Dementia Foundation, and Stockholm County Council ALF/Region Stockholm. J.L. is supported by Germany’s Excellence Strategy within the framework of the Munich Cluster for Systems Neurology (German Research Foundation, EXC 2145 Synergy 390857198). The Dementia Research Centre is supported by Alzheimer’s Research UK, Alzheimer’s Society, Brain Research UK, and The Wolfson Foundation. This work was supported by the National Institute for Health Research UCL/H Biomedical Research Centre, the Leonard Wolfson Experimental Neurology Centre Clinical Research Facility and the UK Dementia Research Institute, which receives its funding from UK DRI Ltd, funded by the UK Medical Research Council, Alzheimer’s Society, and Alzheimer’s Research UK

XPS–SIMS Surface Characterization of Aluminovanadate Oxide Catalyst Precursors Co-Precipitated at Different pH: Effect of Calcination

X-ray photoelectron spectroscopy and time-offlight secondary ion mass spectrometry were employed in a comparative study of the surface physical and chemical state of aluminovanadate oxide catalyst precursors (V-Al- O), which were precipitated in the range of pH from 5.5 to 10, after drying and calcination. Core-level photoelectron spectra, X-ray induced Auger and valence band spectra of the samples were measured so as to quantitatively evaluate the surface concentrations of the catalyst components. The binding energy shifts of the respective O 1s, V 2p and Al 2p lines were determined as a function of pH and analyzed in terms of the initial state effect related to the atomic charge and Madelung potential. The surface of the catalysts was composed of aluminum hydroxide/oxyhydroxide and of dispersed vanadium oxide species. Increasing pH was found to result in a monotonic variation of the elemental surface composition, modification of the valence band, progressive hydroxylation of the surface and increasing dispersion of vanadium oxide species. Increasing pH was also accompanied by an increase in the abundance of V4? species, specific surface area and reducibility. Calcination in air at 500 °C gave rise to surface segregation of vanadium, changes in the valence band and partial dehydroxylation. The structural transformations in vanadium oxide species and aluminium hydroxide support and their interaction were accompanied by an increasing abundance of V-O-Al bonds. The net result of the restructuring was a decrease in the specific surface area and reducibility of the calcined catalysts. The enhancement of the catalytic activity in propane oxidative dehydrogenation demonstrated by V-Al-O samples with increasing precipitation pH and after calcination was in good correlation with a growing population of the V4? states and increasing nucleophilicity of oxygen sites. © Springer Science+Business Media, LLC 2012.SCOPUS: ar.jinfo:eu-repo/semantics/publishe

Effect of surgical delay on outcome in hip fracture patients: a retrospective multivariate analysis of 192 patients

Previous studies have not demonstrated consistent results on the effect of surgical delay on outcome. This study investigated the association between the delay to surgery and the development of postoperative complications, length of hospital stay (LOS) and one-year mortality. Patients that underwent surgery for a hip fracture in a two-year period were included in a retrospective study. Uni- and multivariate regression analysis was performed in 192 hip fracture patients. There was a trend towards fewer postoperative complications (P = 0.064; multivariate regression, MR) and shorter LOS (P = 0.088; MR) in patients with a delay of less than one day to surgery. No association between surgical delay and one-year mortality was found in the population as a whole (P = 0.632; univariate regression, UR). Delay to surgery beyond one day was associated with an increased risk of infectious complications (P = 0.004; MR). In ASA I and II class patients, operation beyond one day from admission was associated with an increased risk of one-year mortality (P = 0.03; MR) and more postoperative infectious complications (P = 0.02; MR). The trends towards fewer complications and shorter LOS suggest that early surgery (within one day from admission) is beneficial for hip fracture patients who are able to undergo an operation

Analysis of surgical intervention populations using generic surgical process models.

International audiencePURPOSE: According to differences in patient characteristics, surgical performance, or used surgical technological resources, surgical interventions have high variability. No methods for the generation and comparison of statistical 'mean' surgical procedures are available. The convenience of these models is to provide increased evidence for clinical, technical, and administrative decision-making. METHODS: Based on several measurements of patient individual surgical treatments, we present a method of how to calculate a statistical 'mean' intervention model, called generic Surgical Process Model (gSPM), from a number of interventions. In a proof-of-concept study, we show how statistical 'mean' procedure courses can be computed and how differences between several of these models can be quantified. Patient individual surgical treatments of 102 cataract interventions from eye surgery were allocated to an ambulatory or inpatient sample, and the gSPMs for each of the samples were computed. Both treatment strategies are exemplary compared for the interventional phase Capsulorhexis. RESULTS: Statistical differences between the gSPMs of ambulatory and inpatient procedures of performance times for surgical activities and activity sequences were identified. Furthermore, the work flow that corresponds to the general recommended clinical treatment was recovered out of the individual Surgical Process Models. CONCLUSION: The computation of gSPMs is a new approach in medical engineering and medical informatics. It supports increased evidence, e.g. for the application of alternative surgical strategies, investments for surgical technology, optimization protocols, or surgical education. Furthermore, this may be applicable in more technical research fields, as well, such as the development of surgical workflow management systems for the operating room of the future