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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identify all registered trials investigating mAbs in MCI due to AD or AD at any stage, retrieve available published and unpublished data from all registered trials, and analyze data on safety and efficacy outcomes.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 For 18 trials, data were available from both published and unpublished sources, for 21 trials only from published sources, and for 11 trials only from unpublished sources.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 (MD –0.15). 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.

(1) Background: Neuroimaging differentiation of glioblastoma, primary central nervous system lymphoma (PCNSL) and solitary brain metastasis (BM) represents a diagnostic and therapeutic challenge in neurosurgical practice, expanding the burden of care and exposing patients to additional risks related to further invasive procedures and treatment delays. In addition, atypical cases and overlapping features have not been entirely addressed by modern diagnostic research. The aim of this study was to validate a previously designed and internally validated ResNet101 deep learning model to differentiate glioblastomas, PCNSLs and BMs. (2) Methods: We enrolled 126 patients (glioblastoma: n = 64; PCNSL: n = 27; BM: n = 35) with preoperative T1Gd-MRI scans and histopathological confirmation. Each lesion was segmented, and all regions of interest were exported in a DICOM dataset. A pre-trained ResNet101 deep neural network model implemented in a previous work on 121 patients was externally validated on the current cohort to differentiate glioblastomas, PCNSLs and BMs on T1Gd-MRI scans. (3) Results: The model achieved optimal classification performance in distinguishing PCNSLs (AUC: 0.73; 95%CI: 0.62–0.85), glioblastomas (AUC: 0.78; 95%CI: 0.71–0.87) and moderate to low ability in differentiating BMs (AUC: 0.63; 95%CI: 0.52–0.76). The performance of expert neuro-radiologists on conventional plus advanced MR imaging, assessed by retrospectively reviewing the diagnostic reports of the selected cohort of patients, was found superior in accuracy for BMs (89.69%) and not inferior for PCNSL (82.90%) and glioblastomas (84.09%). (4) Conclusions: We investigated whether the previously published deep learning model was generalizable to an external population recruited at a different institution—this validation confirmed the consistency of the model and laid the groundwork for future clinical applications in brain tumour classification. This artificial intelligence-based model might represent a valuable educational resource and, if largely replicated on prospective data, help physicians differentiate glioblastomas, PCNSL and solitary BMs, especially in settings with limited resources.

We aimed to explore the awareness and preparedness of dementia caregivers and people with mild cognitive deficits on how to prevent COVID-19 infection and cope with the indirect consequences of the pandemic. A total of 139 patient-caregiver dyads received a telephone survey and 109 completed the survey. The majority of respondents reported having a moderate-to-good knowledge of the typical manifestations of COVID-19. Conversely, only few of them were informed of the atypical presentations and on how to recognize emergency warning signs. Filling the knowledge gaps on COVID-19 in the most vulnerable people may represent a significant resource to tackle the pandemic.

More than 500,000 dementia cases can be estimated among migrants living in Europe. There is the need to collect "real world" data on the preparedness of healthcare services to support the inclusion of migrants in the public health response to dementia. The present study aimed (i) to estimate the number of migrants referred to Italian memory clinics (Centers for Cognitive Disorders and Dementia [CCDDs]) and (ii) to identify possible barriers and resources for the provision of diversity-sensitive care.A survey of all Italian CCDDs was conducted between December 2020 and April 2021. An online questionnaire was developed to obtain information on the number of migrants referred to Italian CCDDs in 2019, the challenges encountered in the diagnostic approach, and possible facilitators in the provision of care.Overall, 343 of the 570 contacted CCDDs completed the survey questionnaire (response rate: 60.2%). Nearly 4527 migrants were referred to these services in 2019. Migrants accounted for a median 1.1% (IQR: 0.9%-2.8%) of overall CCDD referrals. More than one-third of respondents reported that the number of migrants referred to their facilities had increased in the last 5 years. The overall quality of the migrants' cognitive assessment was deemed to be very poor or insufficient in most cases. A minority of CCDDs had translated information material on dementia and reported the possibility to contact cultural mediators and interpreters.A relevant number of migrants are being referred to Italian CCDDs that are still not adequately prepared to deliver diversity-sensitive care and support.

Background: Late neurocognitive sequelae are common among long-term brain tumour survivors, resulting in significantly worse quality of life. Cognitive rehabilitation through specific APP/software for PC/tablets represents an innovative intervention spreading in recent years. In this study, we aim to review the current evidence and trends regarding these innovative approaches. Methods: A systematic literature review was performed. Inclusion criteria were: (i) Studies recruiting patients diagnosed with any brain tumour before 21 years of age; (ii) studies assessing the role of digital interventions on cognitive outcomes. Case reports, case series, reviews, letters, conference proceedings, abstracts, and editorials were excluded. Results: Overall, nine studies were included; 152 patients (67.8% males) with brain tumours underwent a digital intervention. The mean age at diagnosis and the intervention enrolment ranged from 4.9 to 9.4 years and 11.1 to 13.3 years, respectively. The computer-based software interventions employed were: Cogmed, Captain’s Log, Fast ForWord, and Nintendo Wii. Most of these studies assessed the effects of cognitive training on working memory, attention, and performance in daily living activities. Conclusions: The studies suggest that this type of intervention improves cognitive functions, such as working memory, attention, and processing speed. However, some studies revealed only transient positive effects with a significant number of dropouts during follow-up. Trials with greater sample sizes are warranted. Motivating families and children to complete cognitive interventions could significantly improve cognitive outcomes and quality of life.

Background Artificial Intelligence (AI) and Machine Learning (ML) augment decision-making processes and productivity by supporting surgeons over a range of clinical activities: from diagnosis and preoperative planning to intraoperative surgical assistance. We reviewed the literature to identify current AI platforms applied to neurosurgical perioperative and intraoperative settings and describe their role in multiple subspecialties. Methods A systematic review of the literature was conducted following the PRISMA guidelines. PubMed, EMBASE, and Scopus databases were searched from inception to December 31, 2020. Original articles were included if they: presented AI platforms implemented in perioperative, intraoperative settings and reported ML models' performance metrics. Due to the heterogeneity in neurosurgical applications, a qualitative synthesis was deemed appropriate. The risk of bias and applicability of predicted outcomes were assessed using the PROBAST tool. Results 41 articles were included. All studies evaluated a supervised learning algorithm. A total of 10 ML models were described; the most frequent were neural networks (n = 15) and tree-based models (n = 13). Overall, the risk of bias was medium-high, but applicability was considered positive for all studies. Articles were grouped into 4 categories according to the subspecialty of interest: neuro-oncology, spine, functional and other. For each category, different prediction tasks were identified. Conclusions In this review, we summarize the state-of-art applications of AI for the intraoperative augmentation of neurosurgical workflows across multiple subspecialties. ML models may boost surgical team performances by reducing human errors and providing patient-tailored surgical plans, but further and higher-quality studies need to be conducted.

Background Despite advances in endoscopic transnasal transsphenoidal surgery (ETNS) for pituitary adenomas (PAs), cerebrospinal fluid (CSF) leakage remains a life-threatening complication predisposing to major morbidity and mortality. In the current study we developed a supervised ML model able to predict the risk of intraoperative CSF leakage by comparing different machine learning (ML) methods and explaining the functioning and the rationale of the best performing algorithm. Methods A retrospective cohort of 238 patients treated via E-TNS for PAs was selected. A customized pipeline of several ML models was programmed and trained; the best five models were tested on a hold-out test and the best classifier was then prospectively validated on a cohort of 35 recently treated patients. Results Intraoperative CSF leak occurred in 54 (22,6%) of 238 patients. The most important risk's predictors were: non secreting status, older age, x-, y- and z-axes diameters, ostedural invasiveness, volume, ICD and R-ratio. The random forest (RF) classifier outperformed other models, with an AUC of 0.84, high sensitivity (86%) and specificity (88%). Positive predictive value and negative predictive value were 88% and 80% respectively. F1 score was 0.84. Prospective validation confirmed outstanding performance metrics: AUC (0,81), sensitivity (83%), specificity (79%), negative predictive value (95%) and F1 score (0,75). Conclusions The RF classifier showed the best performance across all models selected. RF models might predict surgical outcomes in heterogeneous multimorbid and fragile populations outperforming classical statistical analyses and other ML models (SVM, ANN etc.), improving patient management and reducing preventable morbidity and additional costs.

Introduction Physical activity has been included in the list of twelve modifiable risk factors for dementia, despite conflicting results from observational and controlled studies. In particular it is not clear whether physical inactivity near the time of dementia diagnosis is a consequence or cause of dementia. We review all available studies reporting the possible association between having engaged in PA before 60 years of age and the risk of dementia. Evidence acquisition We performed a systematic review based on the methodology reported in the Cochrane handbook for systematic reviews and following the PRISMA statement. Bibliographic searches were carried out on the databases PubMed, ISI Web of Science and the Cochrane Database of Systematic Reviews. Further references were retrieved from published systematic reviews on the same topic. Included studies were assessed using the Newcastle Ottawa scale. Evidence synthesis The bibliographic search yielded 1381 records. A total of 11 studies were included. Three of the included studies were case control studies, while the remaining 8 were cohort studies The overall quality of included studies was high. However, clinical criteria for the diagnosis of dementia, criteria to define and measure and PA and time-reference of exposure were shoemteero sgteundeieosu sc,o wnsitidh ering specific age range of exposure, and other reports dealing with more generic "adult age". Conclusions This review suggests that there is insufficient evidence to conclude whether PA in early life may affect the incidence of dementia in later life. Studies in this field are very complicated and recognizing the impact of PA in early life given all the confounding factors is very difficult. Further studies are warranted. In these studies, it will be crucial to define the type, quantity and intensity of PA as well as to stratify analysis by sex, cultures and social classes.

Introduction The progressive ageing of the population is one of the main socio-demographic phenomena, taking place at a global level. Several recent population-based studies conducted worldwide suggest that the age-specific risk of dementia may be changing in some countries and areas. Evidence acquisition This systematic review was performed using the methodology proposed by the Cochrane handbook for systematic reviews and reported following the PRISMA statement. A structured bibliographic search was performed on the databases PubMed, ISI Web of Science and the Cochrane Database of Systematic Reviews. All included studies were qualitatively assessed using the Methodological Evaluation of Observational REsearch (MORE). Evidence synthesis The bibliographic search yielded 2394 records. Three more articles were retrieved from other sources. A total of 10 studies were included, 5 reported data on a possible reduction in the prevalence of dementia, and 5 reported data on a possible reduction in its incidence. Discussion and conclusions The present systematic review focused on the recent observations of a possible decrease in the frequency of dementia and cognitive impairment in some Western countries (US, UK, Sweden, the Netherlands, France, Iceland) between 1977 and 2014. The included studies have a high heterogeneity in terms of the clinical criteria used to diagnose dementia, and of the criteria used to define the clinical condition preceding dementia, such as isolated cognitive impairment. Moreover, the methodological quality with which they were conducted was also heterogeneous, with scores ranging from 1 to 7 using the MORE tool.

The implementation of effective interventions for neuropsychiatric symptoms (NPS) is perceived as one of the most pressing research priorities in the field of dementia and one of the main unmet needs from the perspective of affected individuals and their caregivers and relatives. Nevertheless, to date, only a relatively marginal part of dementia research has focused on NPS. This study aimed to describe and discuss the state of the art concerning the identification and development of new pharmacological treatments for NPS in dementia. A review of 320 ongoing phase 1, 2, 3, and 4 protocols registered in the clinicaltrials.gov database was performed. All the trials enrolling patients with dementia were selected. Only studies adopting clinical measures of NPS frequency and/or severity as primary outcome were retained and analyzed. Overall, only a minority of ongoing phase 1, 2, 3 and 4 protocols on dementia (i.e., 9.0%) is primarily targeting NPS. Most of these studies are adopting a placebo-controlled parallel assignment design, testing oral compounds, and targeting specific NPS (mostly agitation and/or aggression). A total of 3,445 subjects with dementia will tentatively be recruited in these trials. The methodologies adopted in these studies, the characteristics of the tested interventions, the eligibility criteria, and the operational definitions of NPS are presented and discussed. The relevance of NPS is not yet matched by an adequate research effort. The current tendency at privileging disease-modifying approaches and other symptoms of dementia and the methodological complexity of studying NPS are still substantially contributing to the gap between research activities and clinical needs.

The present study aimed to describe and discuss the state of the art of biomarker use in ongoing Alzheimer’s disease (AD) research. A review of 222 ongoing phase 1, 2, 3, and 4 protocols registered in the clinicaltrials.gov database was performed. All the trials (i) enrolling subjects with clinical disturbances and/or preclinical diagnoses falling within the AD continuum; and (ii) testing the efficacy and/or safety/tolerability of a therapeutic intervention, were analyzed. The use of biomarkers of amyloid deposition, tau pathology, and neurodegeneration among the eligibility criteria and/or study outcomes was assessed. Overall, 58.2% of ongoing interventional studies on AD adopt candidate biomarkers. They are mostly adopted by studies at the preliminary stages of the drug development process to explore the safety profile of novel therapies, and to provide evidence of target engagement and disease-modifying properties. The biologically supported selection of participants is mostly based on biomarkers of amyloid deposition, whereas the use of biomarkers as study outcomes mostly relies on markers of neurodegeneration. Biomarkers play an important role in the design and conduction of research protocols targeting AD. Nevertheless, their clinical validity, utility, and cost-effectiveness in the “real world” remain to be clarified.

Background The construct of mild cognitive impairment (MCI) is triggering growing clinical and research interest. The detection of MCI may be affected by diverse ethno-cultural determinants possibly influencing the personal and social perception of the individual cognitive functioning as well as the reliability of objective cognitive assessment. These challenges may acquire special relevance in subjects with a migration background and composing ethnic minority groups. Objective The present study is aimed at providing an estimate of the number of MCI cases occurring in the migrant population living in the extended European Union (EU) in 2018. Methods The number of MCI cases in older migrants living in Europe and in each of the 32 considered countries was estimated by multiplying the number of migrants, provided by Eurostat, with the age-specific prevalence rates, derived by the harmonized data produced by the COSMIC collaboration and based on different operational definitions of MCI. Results Nearly 686,000 cases of MCI were estimated in the extended EU by applying age-specific prevalence rates based on the International Working Group criteria. Higher figures were obtained when the Clinical Dementia Rating- and the Mini Mental State Examination-based criteria were applied. The proportion of MCI cases in migrant subjects ranged from 1.1% (Romania) to 54.1% (Liechtenstein) (median: 8.4%; IQR: 4.7%-14.2%). Conclusions MCI represents and will increasingly constitute a relevant issue in the migrant population living in Europe. The present data reinforce the need of developing approaches and models of care that may be diversity-sensitive and inclusive for a culturally variegated population.

INTRODUCTION: Residents in facilities such as nursing homes (NHs) are particularly vulnerable to Coronavirus disease 2019 (COVID-19). A national survey was carried out to collect information on the spreading and impact of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in nursing homes, and on how suspected and/or confirmed cases were managed. We carried out a survey between 25 March 2020 and 5 May 2020. MATERIALS AND METHODS: All Italian nursing homes either public or providing services both privately and within the NHS were included in the study. An on-line questionnaire was sent to 3292 nursing homes across all Italian regions. Nursing homes were also contacted by telephone to provide assistance in completing the questionnaire. RESULTS: A total of 1356 nursing homes voluntarily participated to the survey, hosting a total of 100,806 residents. Overall, 9154 residents died due to any cause from February 1 to the time when the questionnaire was completed (from March 25 to May 5). Of these, 7.4% had COVID-19 and 33.8% had flu-like symptoms, corresponding to a cumulative incidence of 0.7 and 3.1, respectively. Lack of personnel, difficulty in transferring patients to hospital or other facility, isolating residents with COVID-19, number of beds and geographical area were the main factor positively associated to the presence of COVID-19 in nursing homes. DISCUSSION: This survey showed the dissemination and impact of SARS-CoV-2 infection in Italian nursing homes and on how older and potentially chronically ill people residing in these long-term care facilities were managed.

Background and aims: Many clinical trials on potentially disease-modifying drugs are focused to mild cognitive impairment (MCI) prodromal-to-Alzheimer's disease. The MCI population actually includes patients with higher risk of progression to AD. Since the new drugs, if effective, will carry both elevated unit costs and not marginal side effects, they should be administered selectively to high-risk subjects. The Italian INTERCEPTOR project, promoted by the Italian Medicine Agency and the Italian Ministry of Health, is a multicenter, interventional, nontherapeutic cohort study in subjects with MCI, with the primary aim of identifying biomarkers that better predict the conversion to AD after 3 years of follow-up. Methods: A sample of 500 subjects with MCI was planned to be enrolled. The biomarkers under investigation obtained by the analysis of MMSE, CSF, FDG-PET, FCRST, APOE4, EEG, Volumetric MRI. Multivariate prediction model will provide the predictive performance of each biomarker and combinations. In applying biomarkers, three scenarios are considered: 1) use of cut-offs indicated by experts 2) defining new optimal cut-offs for the specific population 3) use of values in a continuous form in order to evaluate all predictive information. Finally, nomograms will be defined for use in clinical practice. Results: Due to COVID-19, the target sample size was not reached. The enrolment was closed on October 31st 2020. Overall 498 patients were screened, 362 recruited into the study and 17 dropped out. Conclusions: The findings will support the diagnostic path redefinition to identify those patients in the early stage eligible to prescription for disease-modifying medications.

Deep-Brain Stimulation (DBS) is an important treatment option for the management of Parkinson’s disease (PD) and is a common symptomatic treatment. However, an increasing number of studies have examined the biological processes to assess if DBS can also modify the natural history of PD by acting on its pathophysiological mechanisms. Relevant literature published up to November 2020 was systematically searched on databases such as PubMed, ISI Web of Knowledge, Academic Search Index, and Science Citation Index. The following predefined inclusion criteria were applied to the full-text versions of the selected articles: i) recruiting and monitoring of PD subjects that were previously treated with DBS and ii) investigating the electrophysiological, biochemical, epigenetic, or neuroimaging effects of DBS. Studies focusing exclusively on motor and clinical changes were excluded. Reviews, case reports, studies on animal models, and computational studies were also not considered. Out of 2,960 records screened, 43 studies met the inclusion criteria. Only three studies described a potential disease-modifying effect of DBS. However, a wide heterogeneity was observed in the investigated biomarkers, and the design and methodological issues of several studies limited their ability to find potential disease-modifying features. Specifically, 60.4% of the trials followed-up subjects for no more than 1 year from the surgical intervention, and 67.4% observed patients with PD only once after DBS. Moreover, 64.2% of the studies enrolled late-stage PD patients. Most of the studies (88.4%) reported that DBS only had a symptomatic effect, with several of them showing some limitations in the study design and recruitment of patients. Further studies using shared biomarkers are encouraged to assess if and how DBS might affect the progression of PD. Based on the existing preclinical literature, prospective clinical trials examining the course of PD in early-stage patients are needed.