Building Protein Domain Based Composite Biobricks for Mammalian Expression Systems

The purpose of this RFC is to describe a method that allows the design of protein domain based parts, starting with gene centered information and translate these informations into BBF RFC 25 compatible part. The method is designed to be used in mammalian expression systems

Pattern and degree of individual brain atrophy predicts dementia onset in dominantly inherited Alzheimer\u27s disease

Introduction: Asymptomatic and mildly symptomatic dominantly inherited Alzheimer\u27s disease mutation carriers (DIAD-MC) are ideal candidates for preventative treatment trials aimed at delaying or preventing dementia onset. Brain atrophy is an early feature of DIAD-MC and could help predict risk for dementia during trial enrollment. Methods: We created a dementia risk score by entering standardized gray-matter volumes from 231 DIAD-MC into a logistic regression to classify participants with and without dementia. The score\u27s predictive utility was assessed using Cox models and receiver operating curves on a separate group of 65 DIAD-MC followed longitudinally. Results: Our risk score separated asymptomatic versus demented DIAD-MC with 96.4% (standard error = 0.02) and predicted conversion to dementia at next visit (hazard ratio = 1.32, 95% confidence interval [CI: 1.15, 1.49]) and within 2 years (area under the curve = 90.3%, 95% CI [82.3%-98.2%]) and improved prediction beyond established methods based on familial age of onset. Discussion: Individualized risk scores based on brain atrophy could be useful for establishing enrollment criteria and stratifying DIAD-MC participants for prevention trials

Diagnostic Utility of Measuring Cerebral Atrophy in the Behavioral Variant of Frontotemporal Dementia and Association With Clinical Deterioration

Can widely available measures of atrophy on magnetic resonance imaging increase diagnostic certainty of underlying frontotemporal lobar degeneration (FTLD) and estimate clinical deterioration in the behavioral variant of frontotemporal dementia (bvFTD)? This diagnostic/prognostic study investigated the clinical utility of 5 validated visual atrophy scales (VAS) and the Magnetic Resonance Parkinsonism Index. When combined, VAS showed excellent diagnostic performance for differentiating between bvFTD with high and low confidence of FTLD and for the estimation of longitudinal clinical deterioration, whereas the Magnetic Resonance Parkinsonism Index was increased in bvFTD with underlying 4-repeat tauopathies. These findings suggest that, in bvFTD, VAS can be used to increase diagnostic certainty of underlying FTLD and estimate longitudinal clinical deterioration. This diagnostic/prognostic study assesses the utility of 6 visual atrophy scales and the Magnetic Resonance Parkinsonism Index in patients with behavioral variant frontotemporal dementia to distinguish those with high vs low confidence of frontotemporal lobar degeneration. The presence of atrophy on magnetic resonance imaging can support the diagnosis of the behavioral variant of frontotemporal dementia (bvFTD), but reproducible measurements are lacking. To assess the diagnostic and prognostic utility of 6 visual atrophy scales (VAS) and the Magnetic Resonance Parkinsonism Index (MRPI). In this diagnostic/prognostic study, data from 235 patients with bvFTD and 225 age- and magnetic resonance imaging-matched control individuals from 3 centers were collected from December 1, 1998, to September 30, 2019. One hundred twenty-one participants with bvFTD had high confidence of frontotemporal lobar degeneration (FTLD) (bvFTD-HC), and 19 had low confidence of FTLD (bvFTD-LC). Blinded clinicians applied 6 previously validated VAS, and the MRPI was calculated with a fully automated approach. Cortical thickness and subcortical volumes were also measured for comparison. Data were analyzed from February 1 to June 30, 2020. The main outcomes of this study were bvFTD-HC or a neuropathological diagnosis of 4-repeat (4R) tauopathy and the clinical deterioration rate (assessed by longitudinal measurements of Clinical Dementia Rating Sum of Boxes). Measures of cerebral atrophy included VAS scores, the bvFTD atrophy score (sum of VAS scores in orbitofrontal, anterior cingulate, anterior temporal, medial temporal lobe, and frontal insula regions), the MRPI, and other computerized quantifications of cortical and subcortical volumes. The areas under the receiver operating characteristic curve (AUROC) were calculated for the differentiation of participants with bvFTD-HC and bvFTD-LC and controls. Linear mixed models were used to evaluate the ability of atrophy measures to estimate longitudinal clinical deterioration. Of the 460 included participants, 296 (64.3%) were men, and the mean (SD) age was 62.6 (11.4) years. The accuracy of the bvFTD atrophy score for the differentiation of bvFTD-HC from controls (AUROC, 0.930; 95% CI, 0.903-0.957) and bvFTD-HC from bvFTD-LC (AUROC, 0.880; 95% CI, 0.787-0.972) was comparable to computerized measures (AUROC, 0.973 [95% CI, 0.954-0.993] and 0.898 [95% CI, 0.834-0.962], respectively). The MRPI was increased in patients with bvFTD and underlying 4R tauopathies compared with other FTLD subtypes (14.1 [2.0] vs 11.2 [2.6] points; P < .001). Higher bvFTD atrophy scores were associated with faster clinical deterioration in bvFTD (1.86-point change in Clinical Dementia Rating Sum of Boxes score per bvFTD atrophy score increase per year; 95% CI, 0.99-2.73; P < .001). Based on these study findings, in bvFTD, VAS increased the diagnostic certainty of underlying FTLD, and the MRPI showed potential for the detection of participants with underlying 4R tauopathies. These widely available measures of atrophy can also be useful to estimate longitudinal clinical deterioration

Pattern and degree of individual brain atrophy predicts dementia onset in dominantly inherited Alzheimer's disease

Introduction: Asymptomatic and mildly symptomatic dominantly inherited Alzheimer's disease mutation carriers (DIAD-MC) are ideal candidates for preventative treatment trials aimed at delaying or preventing dementia onset. Brain atrophy is an early feature of DIAD-MC and could help predict risk for dementia during trial enrollment. Methods: We created a dementia risk score by entering standardized gray-matter volumes from 231 DIAD-MC into a logistic regression to classify participants with and without dementia. The score's predictive utility was assessed using Cox models and receiver operating curves on a separate group of 65 DIAD-MC followed longitudinally. Results: Our risk score separated asymptomatic versus demented DIAD-MC with 96.4% (standard error = 0.02) and predicted conversion to dementia at next visit (hazard ratio = 1.32, 95% confidence interval [CI: 1.15, 1.49]) and within 2 years (area under the curve = 90.3%, 95% CI [82.3%–98.2%]) and improved prediction beyond established methods based on familial age of onset. Discussion: Individualized risk scores based on brain atrophy could be useful for establishing enrollment criteria and stratifying DIAD-MC participants for prevention trials.Fil: Keret, Ophir. University of California; Estados UnidosFil: Staffaroni, Adam M.. University of California; Estados UnidosFil: Ringman, John M.. University of Southern California; Estados UnidosFil: Cobigo, Yann. University of California; Estados UnidosFil: Goh, Sheng Yang M.. University of California; Estados UnidosFil: Wolf, Amy. University of California; Estados UnidosFil: Allen, Isabel Elaine. University of California; Estados UnidosFil: Salloway, Stephen. Brown University; Estados UnidosFil: Chhatwal, Jasmeer. Harvard Medical School; Estados UnidosFil: Brickman, Adam M.. Columbia University; Estados UnidosFil: Reyes Dumeyer, Dolly. Columbia University; Estados UnidosFil: Bateman, Randal J.. University of Washington; Estados UnidosFil: Benzinger, Tammie L.S.. University of Washington; Estados UnidosFil: Morris, John C.. University of Washington; Estados UnidosFil: Ances, Beau M.. University of Washington; Estados UnidosFil: Joseph Mathurin, Nelly. University of Washington; Estados UnidosFil: Perrin, Richard J.. University of Washington; Estados UnidosFil: Gordon, Brian A.. University of Washington; Estados UnidosFil: Levin, Johannes. German Center for Neurodegenerative Diseases; Alemania. Ludwig Maximilians Universitat; AlemaniaFil: Vöglein, Jonathan. Ludwig Maximilians Universitat; Alemania. German Center for Neurodegenerative Diseases; AlemaniaFil: Jucker, Mathias. German Center for Neurodegenerative Diseases; Alemania. Eberhard Karls Universität Tübingen; AlemaniaFil: la Fougère, Christian. Eberhard Karls Universität Tübingen; Alemania. German Center for Neurodegenerative Diseases; AlemaniaFil: Martins, Ralph N.. Cooperative Research Centres Australia; Australia. University of Western Australia; Australia. Edith Cowan University; Australia. Australian Alzheimer's Research Foundation; Australia. Macquarie University; AustraliaFil: Sohrabi, Hamid R.. University of Western Australia; Australia. Macquarie University; Australia. Australian Alzheimer's Research Foundation; Australia. Cooperative Research Centres Australia; Australia. Edith Cowan University; AustraliaFil: Taddei, Kevin. Australian Alzheimer's Research Foundation; Australia. Edith Cowan University; AustraliaFil: Villemagne, Victor L.. Austin Health; AustraliaFil: Schofield, Peter R.. Neuroscience Research Australia; Australia. Unsw Medicine; AustraliaFil: Brooks, William S.. Neuroscience Research Australia; Australia. Unsw Medicine; AustraliaFil: Fulham, Michael. Royal Prince Alfred Hospital; AustraliaFil: Masters, Colin L.. University of Melbourne; AustraliaFil: Allegri, Ricardo Francisco. Fundación para la Lucha contra las Enfermedades Neurológicas de la Infancia. Instituto de Neurociencias - Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Neurociencias; Argentin

Pattern and degree of individual brain atrophy predicts dementia onset in dominantly inherited Alzheimer\u27s disease

Introduction: Asymptomatic and mildly symptomatic dominantly inherited Alzheimer\u27s disease mutation carriers (DIAD-MC) are ideal candidates for preventative treatment trials aimed at delaying or preventing dementia onset. Brain atrophy is an early feature of DIAD-MC and could help predict risk for dementia during trial enrollment. Methods: We created a dementia risk score by entering standardized gray-matter volumes from 231 DIAD-MC into a logistic regression to classify participants with and without dementia. The score\u27s predictive utility was assessed using Cox models and receiver operating curves on a separate group of 65 DIAD-MC followed longitudinally. Results: Our risk score separated asymptomatic versus demented DIAD-MC with 96.4% (standard error = 0.02) and predicted conversion to dementia at next visit (hazard ratio = 1.32, 95% confidence interval [CI: 1.15, 1.49]) and within 2 years (area under the curve = 90.3%, 95% CI [82.3%-98.2%]) and improved prediction beyond established methods based on familial age of onset. Discussion: Individualized risk scores based on brain atrophy could be useful for establishing enrollment criteria and stratifying DIAD-MC participants for prevention trials

Pattern and degree of individual brain atrophy predicts dementia onset in dominantly inherited Alzheimer's disease

Introduction: Asymptomatic and mildly symptomatic dominantly inherited Alzheimer's disease mutation carriers (DIAD-MC) are ideal candidates for preventative treatment trials aimed at delaying or preventing dementia onset. Brain atrophy is an early feature of DIAD-MC and could help predict risk for dementia during trial enrollment. Methods: We created a dementia risk score by entering standardized gray-matter volumes from 231 DIAD-MC into a logistic regression to classify participants with and without dementia. The score's predictive utility was assessed using Cox models and receiver operating curves on a separate group of 65 DIAD-MC followed longitudinally. Results: Our risk score separated asymptomatic versus demented DIAD-MC with 96.4% (standard error = 0.02) and predicted conversion to dementia at next visit (hazard ratio = 1.32, 95% confidence interval [CI: 1.15, 1.49]) and within 2 years (area under the curve = 90.3%, 95% CI [82.3%-98.2%]) and improved prediction beyond established methods based on familial age of onset. Discussion: Individualized risk scores based on brain atrophy could be useful for establishing enrollment criteria and stratifying DIAD-MC participants for prevention trials