From Uterus to Brain: An Update on Epidemiology, Clinical Features, and Treatment of Brain Metastases From Gestational Trophoblastic Neoplasia

In this review, we provide the state of the art about brain metastases (BMs) from gestational trophoblastic neoplasia (GTN), a rare condition. Data concerning the epidemiology, clinical presentation, innovations in therapeutic modalities, and outcomes of GTN BMs are comprehensively presented with particular attention to the role of radiotherapy, neurosurgery, and the most recent chemotherapy regimens. Good response rates have been achieved thanks to multi-agent chemotherapy, but brain involvement by GTNs entails significant risks for patients’ health since sudden and extensive intracranial hemorrhages are possible. Moreover, despite the evolution of treatment protocols, a small proportion of these patients ultimately develops a resistant disease. To tackle this unmet clinical need, immunotherapy has been recently proposed. The role of this novel option for this subset of patients as well as the achieved results so far are also discussed

Is there a place for immune checkpoint inhibitors in vulvar neoplasms? A state of the art review

Vulvar cancer (VC) is a rare neoplasm, usually arising in postmenopausal women, although human papilloma virus (HPV)-associated VC usually develop in younger women. Incidences of VCs are rising in many countries. Surgery is the cornerstone of early-stage VC management, whereas therapies for advanced VC are multimodal and not standardized, combining chemotherapy and radiotherapy to avoid exenterative surgery. Randomized controlled trials (RCTs) are scarce due to the rarity of the disease and prognosis has not improved. Hence, new therapies are needed to improve the outcomes of these patients. In recent years, improved knowledge regarding the crosstalk between neoplastic and tumor cells has allowed researchers to develop a novel therapeutic approach exploiting these molecular interactions. Both the innate and adaptive immune systems play a key role in anti-tumor immunesurveillance. Immune checkpoint inhibitors (ICIs) have demonstrated efficacy in multiple tumor types, improving survival rates and disease outcomes. In some gynecologic cancers (e.g., cervical cancer), many studies are showing promising results and a growing interest is emerging about the potential use of ICIs in VC. The aim of this manuscript is to summarize the latest developments in the field of VC immunoncology, to present the role of state-of-the-art ICIs in VC management and to discuss new potential immunotherapeutic approaches

CD36 deficiency attenuates experimental mycobacterial infection

<p>Abstract</p> <p>Background</p> <p>Members of the CD36 scavenger receptor family have been implicated as sensors of microbial products that mediate phagocytosis and inflammation in response to a broad range of pathogens. We investigated the role of CD36 in host response to mycobacterial infection.</p> <p>Methods</p> <p>Experimental <it>Mycobacterium bovis </it>Bacillus Calmette-Guérin (BCG) infection in <it>Cd36^+/+</it>and <it>Cd36^-/-</it>mice, and <it>in vitro </it>co-cultivation of <it>M. tuberculosis</it>, BCG and <it>M. marinum </it>with <it>Cd36^+/+</it>and <it>Cd36^-/-</it>murine macrophages.</p> <p>Results</p> <p>Using an <it>in vivo </it>model of BCG infection in <it>Cd36^+/+</it>and <it>Cd36^-/-</it>mice, we found that mycobacterial burden in liver and spleen is reduced (83% lower peak splenic colony forming units, p < 0.001), as well as the density of granulomas, and circulating tumor necrosis factor (TNF) levels in <it>Cd36^-/-</it>animals. Intracellular growth of all three mycobacterial species was reduced in <it>Cd36^-/-</it>relative to wild type <it>Cd36^+/+</it>macrophages <it>in vitro</it>. This difference was not attributable to alterations in mycobacterial uptake, macrophage viability, rate of macrophage apoptosis, production of reactive oxygen and/or nitrogen species, TNF or interleukin-10. Using an <it>in vitro </it>model designed to recapitulate cellular events implicated in mycobacterial infection and dissemination <it>in vivo </it>(i.e., phagocytosis of apoptotic macrophages containing mycobacteria), we demonstrated reduced recovery of viable mycobacteria within <it>Cd36^-/-</it>macrophages.</p> <p>Conclusions</p> <p>Together, these data indicate that CD36 deficiency confers resistance to mycobacterial infection. This observation is best explained by reduced intracellular survival of mycobacteria in the <it>Cd36^-/-</it>macrophage and a role for CD36 in the cellular events involved in granuloma formation that promote early bacterial expansion and dissemination.</p

Model Selection for Degree-corrected Block Models

The proliferation of models for networks raises challenging problems of model selection: the data are sparse and globally dependent, and models are typically high-dimensional and have large numbers of latent variables. Together, these issues mean that the usual model-selection criteria do not work properly for networks. We illustrate these challenges, and show one way to resolve them, by considering the key network-analysis problem of dividing a graph into communities or blocks of nodes with homogeneous patterns of links to the rest of the network. The standard tool for doing this is the stochastic block model, under which the probability of a link between two nodes is a function solely of the blocks to which they belong. This imposes a homogeneous degree distribution within each block; this can be unrealistic, so degree-corrected block models add a parameter for each node, modulating its over-all degree. The choice between ordinary and degree-corrected block models matters because they make very different inferences about communities. We present the first principled and tractable approach to model selection between standard and degree-corrected block models, based on new large-graph asymptotics for the distribution of log-likelihood ratios under the stochastic block model, finding substantial departures from classical results for sparse graphs. We also develop linear-time approximations for log-likelihoods under both the stochastic block model and the degree-corrected model, using belief propagation. Applications to simulated and real networks show excellent agreement with our approximations. Our results thus both solve the practical problem of deciding on degree correction, and point to a general approach to model selection in network analysis

Association of Cholinergic Basal Forebrain Volume and Functional Connectivity with Markers of Inflammatory Response in the Alzheimer's Disease Spectrum

BACKGROUND: Inflammation has been described as a key pathogenic event in Alzheimer's disease (AD), downstream of amyloid and tau pathology. Preclinical and clinical data suggest that the cholinergic basal forebrain may moderate inflammatory response to different pathologies. OBJECTIVE: To study the association of cholinergic basal forebrain volume and functional connectivity with measures of neuroinflammation in people from the AD spectrum. METHODS: We studied 261 cases from the DELCODE cohort, including people with subjective cognitive decline, mild cognitive impairment, AD dementia, first degree relatives, and healthy controls. Using Bayesian ANCOVA, we tested associations of MRI indices of cholinergic basal forebrain volume and functional connectivity with cerebrospinal fluid (CSF) levels of sTREM2 as a marker of microglia activation, and serum levels of complement C3. Using Bayesian elastic net regression, we determined associations between basal forebrain measures and a large inflammation marker panel from CSF and serum. RESULTS: We found anecdotal to moderate evidence in favor of the absence of an effect of basal forebrain volume and functional connectivity on CSF sTREM2 and serum C3 levels both in Aβ42/ptau-positive and negative cases. Bayesian elastic net regression identified several CSF and serum markers of inflammation that were associated with basal forebrain volume and functional connectivity. The effect sizes were moderate to small. CONCLUSION: Our data-driven analyses generate the hypothesis that cholinergic basal forebrain may be involved in the neuroinflammation response to Aβ42 and phospho-tau pathology in people from the AD spectrum. This hypothesis needs to be tested in independent samples

Gaussian Process-based prediction of memory performance and biomarker status in ageing and Alzheimer's disease-A systematic model evaluation

Neuroimaging markers based on Magnetic Resonance Imaging (MRI) combined with various other measures (such as genetic covariates, biomarkers, vascular risk factors, neuropsychological tests etc.) might provide useful predictions of clinical outcomes during the progression towards Alzheimer's disease (AD). The use of multiple features in predictive frameworks for clinical outcomes has become increasingly prevalent in AD research. However, many studies do not focus on systematically and accurately evaluating combinations of multiple input features. Hence, the aim of the present work is to explore and assess optimal combinations of various features for MR-based prediction of (1) cognitive status and (2) biomarker positivity with a multi kernel learning Gaussian process framework. The explored features and parameters included (A) combinations of brain tissues, modulation, smoothing, and image resolution;(B) incorporating demographics & clinical covariates;(C) the impact of the size of the training data set;(D) the influence of dimensionality reduction and the choice of kernel types. The approach was tested in a large German cohort including 959 subjects from the multicentric longitudinal study of cognitive impairment and dementia (DELCODE). Our evaluation suggests the best prediction of memory performance was obtained for a combination of neuroimaging markers, demographics, genetic information (ApoE4) and CSF biomarkers explaining 57% of outcome variance in out-of sample predictions. The highest performance for A 42/40 status classification was achieved for a combination of demographics, ApoE4, and a memory score while usage of structural MRI further improved the classification of individual patient's pTau status