Sundowning means the emergence or worsening of neuropsychiatric symptoms (NPS) in the late afternoon or early evening. This syndrome has been recognized since a long time in the field of dementing illnesses and is well known among most of health-care providers involved in the assistance of people with dementia. Indeed, it represents a common manifestation among persons with dementia and is associated with several adverse outcomes (such as institutionalization, faster cognitive worsening, and greater caregiver burden). Its occurrence and phenotypic characteristics may be influenced by diverse neurobiological, psychosocial, and environmental determinants. Moreover, it may pose diagnostic challenges in relation to other common causes of behavioral disruptions. Beside these considerations, this phenomenon has so far drawn limited clinical and scientific interest compared to other specific NPS occurring in dementias, as indicated by the lack of commonly agreed definitions, specific screening/assessment tools, and robust estimates on its prevalence. Accordingly, no randomized controlled trial specifically investigating the effectiveness of pharmacological and non-pharmacological strategies in managing this condition among demented patients has been yet conducted. In the present narrative review, we present and discuss available evidence concerning sundowning occurring in people with dementia. A special focus is given to its definitions, pathophysiological determinants, and clinical relevance, as well as to the clinical and therapeutic approaches required for its management in the daily practice.

Background: Monoclonal antibodies (mAbs) are currently among the most investigated targets for potential disease-modifying therapies in Alzheimer’s disease (AD). Objective: Our objectives were to identify all registered trials investigating mAbs in MCI due to AD or AD at any stage, retrieve available published and unpublished data from all registered trials, and analyze data on safety and efficacy outcomes. Methods: A systematic search of all registered trials on ClinicalTrials.gov and EUCT was performed. Available results were searched on both platforms and on PubMed, ISI Web of Knowledge, and The Cochrane Library. Results: Overall, 101 studies were identified on 27 mAbs. Results were available for 50 trials investigating 12 mAbs. For 18 trials, data were available from both published and unpublished sources, for 21 trials only from published sources, and for 11 trials only from unpublished sources. Meta-analyses of amyloid-related imaging abnormalities (ARIA) events showed overall risk ratios of 10.65 for ARIA-E and of 1.75 for ARIA-H. The meta-analysis of PET-SUVR showed an overall significant effect of mAbs in reducing amyloid (SMD –0.88), but when considering clinical efficacy, data on CDR-SB showed that treated patients had a statistically significant but clinically non-relevant lower worsening (SMD –0.04). Conclusion: Our results suggest that the risk-benefit profile of mAbs remains unclear. Research should focus on clarifying the effect of amyloid on cognitive decline, providing data on treatment response rate, and accounting for minimal clinically important difference. Research on mAbs should also investigate the possible long-term impact of ARIA events, including potential factors predicting their onset.

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Background: Elevated total tau (tTau), 181-phosphorylated phosphorylated tau (pTau), and low amyloid-β42 (Aβ42) in cerebrospinal fluid (CSF) represent a diagnostic biomarker for Alzheimer’s disease (AD).Objective: The goal was to determine the overall accuracy of CSF Aβ42, tTau, pTau, and the Aβ42/total tau index (ATI) in a non-research, clinical setting for the diagnosis of AD.Methods: From medical records in 1,016 patients that had CSF studies for dementia over a 12-year period (2005 to 2017), we calculated the sensitivity and specificity of CSF Aβ42, tTau, and pTau and the ATI in relation to the final clinical diagnosis.Results:Compared with non-demented patients and patients with other dementias or mild cognitive impairment (MCI), the sensitivity and specificity of the recommended ATI and pTau cut-offs (ATI < 1.0 and pTau >61 pg/ml) for the diagnosis of AD were 0.88 and 0.72, respectively. Similar results were obtained comparing AD with non-demented patients only (0.88, 0.82) and AD with other types of dementia (0.81, 0.77). A subgroup of patients with presumed normal pressure hydrocephalus (n = 154) were biopsied at the time of shunt placement. Using the pathological manifestations of AD as the standard, the sensitivity was 0.83 while the specificity was 0.72.Conclusions: In a non-research setting, CSF biomarkers for AD showed a high sensitivity in accordance with previous studies, but modest specificity differentiating AD from other types of dementia or MCI. This study of unselected patients provides a valid and realistic assessment of the diagnostic accuracy of these CSF biomarkers in clinical practice.

BackgroundNeuroimaging differentiation of glioblastoma, primary central nervous system lymphoma (PCNSL) and solitary brain metastasis (BM) remains challenging in specific cases showing similar appearances or atypical features. Overall, advanced MRI protocols have high diagnostic reliability, but their limited worldwide availability, coupled with the overlapping of specific neuroimaging features among tumor subgroups, represent significant drawbacks and entail disparities in the planning and management of these oncological patients.ObjectiveTo evaluate the classification performance metrics of a deep learning algorithm trained on T1-weighted gadolinium-enhanced (T1Gd) MRI scans of glioblastomas, atypical PCNSLs and BMs.Materials and MethodsWe enrolled 121 patients (glioblastoma: n=47; PCNSL: n=37; BM: n=37) who had undergone preoperative T1Gd-MRI and histopathological confirmation. Each lesion was segmented, and all ROIs were exported in a DICOM dataset. The patient cohort was then split in a training and hold-out test sets following a 70/30 ratio. A Resnet101 model, a deep neural network (DNN), was trained on the training set and validated on the hold-out test set to differentiate glioblastomas, PCNSLs and BMs on T1Gd-MRI scans.ResultsThe DNN achieved optimal classification performance in distinguishing PCNSLs (AUC: 0.98; 95%CI: 0.95 - 1.00) and glioblastomas (AUC: 0.90; 95%CI: 0.81 - 0.97) and moderate ability in differentiating BMs (AUC: 0.81; 95%CI: 0.70 - 0.95). This performance may allow clinicians to correctly identify patients eligible for lesion biopsy or surgical resection.ConclusionWe trained and internally validated a deep learning model able to reliably differentiate ambiguous cases of PCNSLs, glioblastoma and BMs by means of T1Gd-MRI. The proposed predictive model may provide a low-cost, easily-accessible and high-speed decision-making support for eligibility to diagnostic brain biopsy or maximal tumor resection in atypical cases.