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The classical BCR::ABL-negative myeloproliferative neoplasms (MPN) include Polycythemia Vera (PV), Essential Thrombocytemia (ET), and Primary Myelofibrosis (PMF). They are acquired clonal disorders of the hematopoietic stem cells (HSC) leading to hyperplasia of one or several myeloid lineages. MPN are caused by three main recurrent mutations, JAK2V617F and mutations in the calreticulin (CALR) and the thrombopoietin receptor (MPL) genes. Here, we review the general diagnosis, the complications, and the management of MPN. Second, we explain the physiopathology of the natural disease development and its regulation, which contributes to MPN heterogeneity. Thirdly, we describe the new paradigm of the MPN development highlighting the early origin of driver mutations decades before the onset of symptoms and the consequence on early detection of MPN cases in the general population for early diagnosis and better medical management. Finally, we present the interferon alpha (IFNα) therapy as a potential early disease-modifying drug after reporting its good hematological and molecular efficacies in ET, PV and early MF in clinical trials as well as its mechanism of action in pre-clinical studies. As a result, we may expect that, in the future, MPN patients will be diagnosed very early during the course of disease and that new selective therapies under development, such as IFNα, JAK2V617F inhibitors and CALRmut monoclonal antibodies, would be able to intercept the mutated clones.

Abstract Background The Genetic Frontotemporal Initiative Staging Group has proposed clinical criteria for the diagnosis of prodromal frontotemporal dementia (FTD), termed mild cognitive and/or behavioral and/or motor impairment (MCBMI). The objective of the study was to validate the proposed research criteria for MCBMI-FTD in a cohort of genetically confirmed FTD cases against healthy controls. Methods A total of 398 participants were enrolled, 117 of whom were carriers of an FTD pathogenic variant with mild clinical symptoms, while 281 were non-carrier family members (healthy controls (HC)). A subgroup of patients underwent blood neurofilament light (NfL) levels and anterior cingulate atrophy assessment. Results The core clinical criteria correctly classified MCBMI vs HC with an AUC of 0.79 (p < 0.001), while the addition of either blood NfL or anterior cingulate atrophy significantly increased the AUC to 0.84 and 0.82, respectively (p < 0.001). The addition of both markers further increased the AUC to 0.90 (p < 0.001). Conclusions The proposed MCBMI criteria showed very good classification accuracy for identifying the prodromal stage of FTD.

Abstract INTRODUCTION We aimed to expand the range of the frontotemporal dementia (FTD) phenotypes assessed by the Clinical Dementia Rating Dementia Staging Instrument plus National Alzheimer's Coordinating Center Behavior and Language Domains (CDR plus NACC FTLD). METHODS Neuropsychiatric and motor domains were added to the standard CDR plus NACC FTLD generating a new CDR plus NACC FTLD‐NM scale. This was assessed in 522 mutation carriers and 310 mutation‐negative controls from the Genetic Frontotemporal dementia Initiative (GENFI). RESULTS The new scale led to higher global severity scores than the CDR plus NACC FTLD: 1.4% of participants were now considered prodromal rather than asymptomatic, while 1.3% were now considered symptomatic rather than asymptomatic or prodromal. No participants with a clinical diagnosis of an FTD spectrum disorder were classified as asymptomatic using the new scales. DISCUSSION Adding new domains to the CDR plus NACC FTLD leads to a scale that encompasses the wider phenotypic spectrum of FTD with further work needed to validate its use more widely. Highlights The new Clinical Dementia Rating Dementia Staging Instrument plus National Alzheimer's Coordinating Center Behavior and Language Domains neuropsychiatric and motor (CDR plus NACC FTLD‐NM) rating scale was significantly positively correlated with the original CDR plus NACC FTLD and negatively correlated with the FTD Rating Scale (FRS). No participants with a clinical diagnosis in the frontotemporal dementia spectrum were classified as asymptomatic with the new CDR plus NACC FTLD‐NM rating scale. Individuals had higher global severity scores with the addition of the neuropsychiatric and motor domains. A receiver operating characteristic analysis of symptomatic diagnosis showed nominally higher areas under the curve for the new scales.

With the arrival of disease-modifying drugs, neurodegenerative diseases will require an accurate diagnosis for optimal treatment. Convolutional neural networks are powerful deep learning techniques that can provide great help to physicians in image analysis. The purpose of this study is to introduce and validate a 3D neural network for classification of Alzheimer's disease (AD), frontotemporal dementia (FTD) or cognitively normal (CN) subjects based on brain glucose metabolism. Retrospective [18F]-FDG-PET scans of 199 CE, 192 FTD and 200 CN subjects were collected from our local database, Alzheimer's disease and frontotemporal lobar degeneration neuroimaging initiatives. Training and test sets were created using randomization on a 90 %-10 % basis, and training of a 3D VGG16-like neural network was performed using data augmentation and cross-validation. Performance was compared to clinical interpretation by three specialists in the independent test set. Regions determining classification were identified in an occlusion experiment and Gradient-weighted Class Activation Mapping. Test set subjects were age- and sex-matched across categories. The model achieved an overall 89.8 % accuracy in predicting the class of test scans. Areas under the ROC curves were 93.3 % for AD, 95.3 % for FTD, and 99.9 % for CN. The physicians' consensus showed a 69.5 % accuracy, and there was substantial agreement between them (kappa = 0.61, 95 % CI: 0.49–0.73). To our knowledge, this is the first study to introduce a deep learning model able to discriminate AD and FTD based on [18F]-FDG PET scans, and to isolate CN subjects with excellent accuracy. These initial results are promising and hint at the potential for generalization to data from other centers.