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Record W7146564911

Hereditary spastic paraplegia : a novel mutation and expansion of the phenotype variability in SPG10

2014· article· en· W7146564911 on OpenAlexfundno aff
Laura Carosi, Temistocle Lo Giudice, Martina Di Lullo, Federica Lombardi, Carla Babalini, Fabrizio Gaudiello, Girolama Alessandra Marfia, Roberto Figari Massa, Toshitaka Kawarai, Antonio Orlacchio

Bibliographic record

VenueInstitutional Repositories DataBase (IRDB) · 2014
Typearticle
Languageen
FieldNeuroscience
TopicHereditary Neurological Disorders
Canadian institutionsnot available
FundersCanada Excellence Research Chairs, Government of Canada
KeywordsHereditary spastic paraplegiaSpasticityGenetic heterogeneitySpasticMutationPeripheral neuropathyNeurologyNeurological disorderParaplegia
DOInot available

Abstract

fetched live from OpenAlex

INTRODUCTION Hereditary spastic paraplegias (HSPs) are a group of disorders characterized by slow progressive weakness and spasticity of the lower limbs. HSPs have been divided into pure and complicated forms, depending on the absence or presence of additional neurological or non-neurological features. To date, 72 loci and 55 spastic paraplegia genes (SPGs) have been identified. SPG10 is caused by mutations in the KIF5A gene encoding neuron-specific kinesin heavy chain 5A (NK-HC5A), a member of the kinesin-1 family of motor proteins. In mammals, NK-HC5A is necessary for the anterograde axonal transport of neurofilament subunits, and it has a role in the transport of other anterograde cargoes, such as membrane vesicles. SPG10 is an autosomal dominant HSP (ADHSP), accounting for about 10% of the complicated forms. Peripheral neuropathy and cognitive impairment are the most common additional clinical features. This report describes a novel KIF5A genetic defect in a large Italian ADHSP family with exclusive clinical features. Methods The currently living family members were examined by movement disorder specialists (TLG, GAM, RM and AO). Neurological assessments, including the Spastic Paraplegia Rating Scale (SPRS) Mental Deterioration Battery (MDB) electrophysiology of peripheral nerves, and neuroimaging analyses, were carried out. After informed consent was given, genomic DNA was extracted from peripheral lymphocytes. Genetic analyses were conducted as described in online supplementary data, including linkage studies at the currently known ADHSP loci, PCR-direct sequencing, PCR-restriction fragment length polymorphism (PCR-RFLP) assay, and in silico analysis. Results The RM 551 family is composed of a three-generation kindred with AD inheritance. Eleven individuals were diagnosed as ‘certainly affected’ and classified as having complicated HSP.1 At examination, the age range of the affected individuals was 19–77 years (mean±SD=40.4±17.9 years). Spastic paraparesis was the primary symptom in the clinical course of each patient; age at onset ranged between 12 and 55 years (mean±SD=32.2±15.0 years). The decrease of age at onset between successive generations ranged from 4 to 39 years per generation. The disease was slowly progressive and urinary urgency was a common symptom. Electroneurography of both motor and sensory nerves, as well as electromyography, were normal. MDB did not show cognitive impairment. Other clinical features associated with SPG10 were absent. Interestingly, this variant form of HSP was associated in all affected individuals of the family with varicose veins (VV) of the legs, as well as bilateral Dupuytren's disease (DD) at various stages, according to Clinical-Etiology-Anatomy-Pathophysiology (CEAP) and Tubiana classifications, respectively (table 1 and online supplementary figure S1). DD and VV were absent in all members of the family who did not have gait difficulty. The environmental associations of DD, such as alcohol consumption, tobacco exposure, manual activities, retractile capsulitis, epilepsy, diabetes, HIV and dyslipidemia, were absent in all patients. Obesity and lower limb deep vein thrombosis examined by venous ultrasonography were also absent, suggesting a primary form of VV.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.009
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.746
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.023
GPT teacher head0.246
Teacher spread0.223 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2014
Admission routes1
Has abstractyes

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