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Objective: It is unknown whether delay in diagnosis affects morbidity reportedly in paraneoplastic syndromes (PNS). We aimed to explore various aspects of PNS, including prevalence, clinical characteristics, diagnostic criteria, and treatment outcomes. Methods: We studied n-PNS diagnosis between 2016 to 2023, and included only patients with positive onconeural antibodies, who developed cancer, and exhibited a recognizable PNS phenotype. Results: We identified 12 patients with positive Abs and co-occurring cancer, most prevalent PNS antibodies included anti-GAD65, anti-Recoverin and anti-Yo. The most common phenotypes were limbic encephalitis (n=5, 42%) and encephalomyelitis (n=4,33%). Cancer preceded neurological presentation in 6 cases. Among the 6 patients who initially presented with n-PNS, median time from neurological presentation to oncologic diagnosis was 73 days, as five of them (83%) were diagnosed with cancer during oncological evaluation prompted by the PNS diagnosis or suspicion. Lymphoma was the most frequent cancer (n=3, 25%), followed by lung cancer (n=2, 17%), and ovarian cancer (n=2, 17%). Among patients who received immunotherapy as n-PNS treatment (n=9, 75%), steroids were a part of the management at 78% (n=7). Another immunotherapy used included plasmapheresis (n=5, 55%) and steroid sparing immunosuppressant (n=2, 29%). Four (33%) patients had short term therapeutic benefit with improvement or stabilization at mRS ≤ 4. Median Disability-adjusted life years (DALYs), as disease burden value, was 13 years. Death occurred in 9 of the 12 patients, with most cases deaths attributed to cancer progression. Compering to the expected median survival by type and stage of tumor, from 9 deceased patients, 56% (n=5) died younger than expected. Median survival was 410 days (range 29-2738 days), and 152 days since the appearance of n-PNS (range 8-1434 days). There were no differences in survival between patients who initially presented with n-PNS versus cancer (p=0.39). Conclusion: In up to 8 years of follow up, there was no difference in mortality among patients who presented initially n-PNS. There was a significant decline in the quality of life, most face substantial disability and functional impairment long term. [ABSTRACT FROM AUTHOR]

Background and purpose: Advanced analysis of electroencephalography (EEG) data has become an essential tool in brain research. Based solely on resting state EEG signals, a data‐driven, predictive and explanatory approach is presented to discriminate painful from non‐painful diabetic polyneuropathy (DPN) patients. Methods: Three minutes long, 64 electrode resting‐state recordings were obtained from 180 DPN patients. The analysis consisted of a mixture of traditional, explanatory and machine learning analyses. First, the 10 functional bivariate connections best differentiating between painful and non‐painful patients in each EEG band were identified and the relevant receiver operating characteristic was calculated. Later, those connections were correlated with selected clinical parameters. Results: Predictive analysis indicated that theta and beta bands contain most of the information required for discrimination between painful and non‐painful polyneuropathy patients, with area under the receiver operating characteristic curve values of 0.93 for theta and 0.89 for beta bands. Assessing statistical differences between the average magnitude of functional connectivity values and clinical pain parameters revealed that painful DPN patients had significantly higher cortical functional connectivity than non‐painful ones (p = 0.008 for theta and p = 0.001 for alpha bands). Moreover, intra‐band analysis of individual significant functional connections revealed a positive correlation with average reported pain in the previous 3 months in all frequency bands. Conclusions: Resting state EEG functional connectivity can serve as a highly accurate biomarker for the presence or absence of pain in DPN patients. This highlights the importance of the brain, in addition to the peripheral lesions, in generating the clinical pain picture. This tool can probably be extended to other pain syndromes. [ABSTRACT FROM AUTHOR]