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Purpose: To increase the awareness of longus colli tendinitis (LCT) among spine specialists and to present a practical overview of diagnostic and treatment options, so that unnecessary interventions are avoided. Five sample cases from a German spine center will also be presented. Methods: Literature review and case series. A PubMed search was performed in May 2015, and the articles found were reviewed for clinical presentation, investigations, and treatment. The frequency of publication of LCT cases and the specialty of journals were also noted. Recent cases treated in our institution were also reviewed. The clinical findings, investigations, and therapeutic interventions were summarized. Results: The PubMed search from May 2015 found 104 articles, published over 51 years, on the topic of LCT. Only four were published in spine journals. A review of this literature yielded a total of 242 cases. The classic clinical triad included neck pain, limitation of movements, and swallowing complaints. C-reactive Protein (CRP) values were available in 21 cases (mean 23.66 mg/dL). A contrast-enhanced computed tomography (CT) scan was the best diagnostic modality. LCT is usually a self-limiting condition, but non-steroidal anti-inflammatory drugs (NSAIDs) may help alleviate discomfort. Five cases of LCT were diagnosed and treated in our center over the past three years. Conclusions: LCT, which is uncommon and has non-specific symptoms, is often referred to spine centers. Spine specialists should be aware of its clinical presentation and radiographic findings in order to avoid unnecessary interventions. The condition is self-limiting and can be treated conservatively.

Background: Amyloid-β positivity (Aβ+) based on PET imaging is part of the enrollment criteria for many of the clinical trials of Alzheimer's disease (AD), particularly in trials for amyloid-targeted therapy. Predicting Aβ positivity prior to PET imaging can decrease unnecessary patient burden and costs of running these trials. Objective: The aim of this study was to evaluate the performance of a machine learning model in estimating the individual risk of Aβ+ based on gold-standard of PET imaging. Methods: We used data from an amnestic mild cognitive impairment (aMCI) subset of the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort to develop and validate the models. The predictors of Aβ status included demographic and ApoE4 status in all models plus a combination of neuropsychological tests (NP), MRI volumetrics, and cerebrospinal fluid (CSF) biomarkers. Results: The models that included NP and MRI measures separately showed an area under the receiver operating characteristics (AUC) of 0.74 and 0.72, respectively. However, using NP and