Results 191 to 200 of about 25,306 (274)

Atypical Parkinsonism with Pathological Dopamine Transporter Imaging in Neuronal Ceroid Lipofuscinosis Type 5. [PDF]

open access: yesMov Disord Clin Pract, 2022
Lange LM   +6 more
europepmc   +1 more source

Greater locus coeruleus vulnerability in atypical clinicopathologic forms of Alzheimer's disease

open access: yesAlzheimer's &Dementia, Volume 22, Issue 2, February 2026.
Abstract INTRODUCTION The locus coeruleus (LC) degenerates early in Alzheimer's disease (AD). However, the extent of rostrocaudal degeneration across clinicopathologic heterogeneity remains underexplored in AD. METHODS Using digital pathology, we quantified LC neuronal density and area at three neuroanatomic levels in a large AD series.
Sara Rose Dunlop   +16 more
wiley   +1 more source

Ethnic Differences in Atypical Parkinsonism-is South Asian PSP Different? [PDF]

open access: yesMov Disord Clin Pract
Balint B   +8 more
europepmc   +1 more source

Urinary tract infection‐related delirium in Alzheimer's disease and related dementias: Clinical challenges and translational opportunities

open access: yesAlzheimer's &Dementia, Volume 22, Issue 2, February 2026.
Abstract Alzheimer's disease and related dementias (ADRD) affects millions of patients worldwide and is a leading cause of morbidity in older adults. Patients with ADRD are particularly susceptible to developing urinary tract infection (UTI) and UTI‐related delirium, creating a self‐perpetuating cycle in which ADRD increases vulnerability to infection ...
Sarah Kim   +3 more
wiley   +1 more source

Novel SLC9A6 Variation in Female Carriers With Intellectual Disability and Atypical Parkinsonism. [PDF]

open access: yesNeurol Genet, 2022
Nan H   +7 more
europepmc   +1 more source

Research priorities in limb and task-specific dystonias [PDF]

open access: yes, 2017
Altenmüller, Eckart   +13 more
core   +2 more sources

Machine learning methods for predicting adverse drug events: A systematic review

open access: yesBritish Journal of Clinical Pharmacology, Volume 92, Issue 2, Page 422-444, February 2026.
Abstract Predicting adverse drug events (ADEs) in outpatient settings is crucial for improving medication safety, identifying high‐risk patients and reducing health‐care costs. While traditional methods struggle with the complexity of health‐care data, machine learning (ML) models offer improved prediction capabilities; however, their effectiveness in ...
Niaz Chalabianloo   +8 more
wiley   +1 more source

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