The Mediterranean Diet in Pregnancy: Implications for Maternal Brain Morphometry in a Secondary Analysis of the IMPACT BCN Randomized Clinical Trial.

INTRODUCTION: A Mediterranean diet has positive effects on the brain in mid-older adults; however, there is scarce information on pregnant individuals. We aimed to evaluate the effect of a structured Mediterranean diet intervention on the cortical structure of the maternal brain during pregnancy. METHODS: This study was a secondary analysis of the IMPACT BCN, a randomized clinical trial with 1221 high-risk pregnant women randomly allocated into three groups at 19-23 weeks of gestation: Mediterranean diet intervention, a mindfulness-based stress reduction program, or usual care. Maternal brain magnetic resonance imaging was performed during the third trimester of pregnancy in a random subgroup of participants. For this study, data from the Mediterranean diet and usual groups were analyzed. Maternal dietary intake, adherence to the Mediterranean diet and metabolite biomarkers were evaluated using a food frequency questionnaire, a 17-item dietary screener and plasma/urine samples, respectively. RESULTS: The cluster-wise analysis showed that the Mediterranean diet group participants (n = 34) had significantly larger surface areas in the right precuneus (90%CI: <0.0001-0.0004, p < 0.001) and left superior parietal (90%CI: 0.026-0.033, p = 0.03) lobules compared to the usual care group participants (n = 37). A larger right precuneus area was associated with high improvements in adherence to the Mediterranean diet, a high intake of walnuts and high concentrations of urinary hydroxytyrosol. A larger left superior parietal area was associated with a high intake of walnuts and high concentrations of urinary hydroxytyrosol. CONCLUSIONS: The promotion of a Mediterranean diet during pregnancy has a significant effect on maternal brain structure.

Interception of vertically approaching objects: temporal recruitment of the internal model of gravity and contribution of optical information.

Introduction: Recent views posit that precise control of the interceptive timing can be achieved by combining on-line processing of visual information with predictions based on prior experience. Indeed, for interception of free-falling objects under gravity's effects, experimental evidence shows that time-to-contact predictions can be derived from an internal gravity representation in the vestibular cortex. However, whether the internal gravity model is fully engaged at the target motion outset or reinforced by visual motion processing at later stages of motion is not yet clear. Moreover, there is no conclusive evidence about the relative contribution of internalized gravity and optical information in determining the time-to-contact estimates. Methods: We sought to gain insight on this issue by asking 32 participants to intercept free falling objects approaching directly from above in virtual reality. Object motion had durations comprised between 800 and 1100 ms and it could be either congruent with gravity (1 g accelerated motion) or not (constant velocity or -1 g decelerated motion). We analyzed accuracy and precision of the interceptive responses, and fitted them to Bayesian regression models, which included predictors related to the recruitment of a priori gravity information at different times during the target motion, as well as based on available optical information. Results: Consistent with the use of internalized gravity information, interception accuracy and precision were significantly higher with 1 g motion. Moreover, Bayesian regression indicated that interceptive responses were predicted very closely by assuming engagement of the gravity prior 450 ms after the motion onset, and that adding a predictor related to on-line processing of optical information improved only slightly the model predictive power. Discussion: Thus, engagement of a priori gravity information depended critically on the processing of the first 450 ms of visual motion information, exerting a predominant influence on the interceptive timing, compared to continuously available optical information. Finally, these results may support a parallel processing scheme for the control of interceptive timing.

Preliminary Evidence of the Differential Expression of Human Endogenous Retroviruses in Kawasaki Disease and SARS-CoV-2-Associated Multisystem Inflammatory Syndrome in Children.

Multisystem inflammatory syndrome in children (MIS-C) is a postinfectious sequela of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), with some clinical features overlapping with Kawasaki disease (KD). Our research group and others have highlighted that the spike protein of SARS-CoV-2 can trigger the activation of human endogenous retroviruses (HERVs), which in turn induces inflammatory and immune reactions, suggesting HERVs as contributing factors in COVID-19 immunopathology. With the aim to identify new factors involved in the processes underlying KD and MIS-C, we analysed the transcriptional levels of HERVs, HERV-related genes, and immune mediators in children during the acute and subacute phases compared with COVID-19 paediatric patients and healthy controls. The results showed higher levels of HERV-W, HERV-K, Syn-1, and ASCT-1/2 in KD, MIS-C, and COV patients, while higher levels of Syn-2 and MFSD2A were found only in MIS-C patients. Moreover, KD and MIS-C shared the dysregulation of several inflammatory and regulatory cytokines. Interestingly, in MIS-C patients, negative correlations have been found between HERV-W and IL-10 and between Syn-2 and IL-10, while positive correlations have been found between HERV-K and IL-10. In addition, HERV-W expression positively correlated with the C-reactive protein. This pilot study supports the role of HERVs in inflammatory diseases, suggesting their interplay with the immune system in this setting. The elevated expression of Syn-2 and MFSD2A seems to be a distinctive trait of MIS-C patients, allowing to distinguish them from KD ones. The understanding of pathological mechanisms can lead to the best available treatment for these two diseases, limiting complications and serious outcomes.

Multishell diffusion MRI reveals whole-brain white matter changes in HIV.

Diffusion tensor imaging (DTI) and diffusion kurtosis imaging (DKI) have been previously used to explore white matter related to human immunodeficiency virus (HIV) infection. While DTI and DKI suffer from low specificity, the Combined Hindered and Restricted Model of Diffusion (CHARMED) provides additional microstructural specificity. We used these three models to evaluate microstructural differences between 35 HIV-positive patients without neurological impairment and 20 healthy controls who underwent diffusion-weighted imaging using three b-values. While significant group effects were found in all diffusion metrics, CHARMED and DKI analyses uncovered wider involvement (80% vs. 20%) of all white matter tracts in HIV infection compared with DTI. In restricted fraction (FR) analysis, we found significant differences in the left corticospinal tract, middle cerebellar peduncle, right inferior cerebellar peduncle, right corticospinal tract, splenium of the corpus callosum, left superior cerebellar peduncle, left superior cerebellar peduncle, pontine crossing tract, left posterior limb of the internal capsule, and left/right medial lemniscus. These are involved in language, motor, equilibrium, behavior, and proprioception, supporting the functional integration that is frequently impaired in HIV-positivity. Additionally, we employed a machine learning algorithm (XGBoost) to discriminate HIV-positive patients from healthy controls using DTI and CHARMED metrics on an ROIwise basis, and unique contributions to this discrimination were examined using Shapley Explanation values. The CHARMED and DKI estimates produced the best performance. Our results suggest that biophysical multishell imaging, combining additional sensitivity and built-in specificity, provides further information about the brain microstructural changes in multimodal areas involved in attentive, emotional and memory networks often impaired in HIV patients.

Expression profile of HERVs and inflammatory mediators detected in nasal mucosa as a predictive biomarker of COVID-19 severity.

Introduction: Our research group and others demonstrated the implication of the human endogenous retroviruses (HERVs) in SARS-CoV-2 infection and their association with disease progression, suggesting HERVs as contributing factors in COVID-19 immunopathology. To identify early predictive biomarkers of the COVID-19 severity, we analyzed the expression of HERVs and inflammatory mediators in SARS-CoV-2-positive and -negative nasopharyngeal/oropharyngeal swabs with respect to biochemical parameters and clinical outcome. Methods: Residuals of swab samples (20 SARS-CoV-2-negative and 43 SARS-CoV-2-positive) were collected during the first wave of the pandemic and expression levels of HERVs and inflammatory mediators were analyzed by qRT-Real time PCR. Results: The results obtained show that infection with SARS-CoV-2 resulted in a general increase in the expression of HERVs and mediators of the immune response. In particular, SARS-CoV-2 infection is associated with increased expression of HERV-K and HERV-W, IL-1ß, IL-6, IL-17, TNF-α, MCP-1, INF-γ, TLR-3, and TLR-7, while lower levels of IL-10, IFN-α, IFN-ß, and TLR-4 were found in individuals who underwent hospitalization. Moreover, higher expression of HERV-W, IL-1ß, IL-6, IFN-α, and IFN-ß reflected the respiratory outcome of patients during hospitalization. Interestingly, a machine learning model was able to classify hospitalized vs not hospitalized patients with good accuracy based on the expression levels of HERV-K, HERV-W, IL-6, TNF-a, TLR-3, TLR-7, and the N gene of SARS-CoV-2. These latest biomarkers also correlated with parameters of coagulation and inflammation. Discussion: Overall, the present results suggest HERVs as contributing elements in COVID-19 and early genomic biomarkers to predict COVID-19 severity and disease outcome.

Dynomics: A Novel and Promising Approach for Improved Breast Cancer Prognosis Prediction.

Traditional imaging techniques for breast cancer (BC) diagnosis and prediction, such as X-rays and magnetic resonance imaging (MRI), demonstrate varying sensitivity and specificity due to clinical and technological factors. Consequently, positron emission tomography (PET), capable of detecting abnormal metabolic activity, has emerged as a more effective tool, providing critical quantitative and qualitative tumor-related metabolic information. This study leverages a public clinical dataset of dynamic 18F-Fluorothymidine (FLT) PET scans from BC patients, extending conventional static radiomics methods to the time domain-termed as 'Dynomics'. Radiomic features were extracted from both static and dynamic PET images on lesion and reference tissue masks. The extracted features were used to train an XGBoost model for classifying tumor versus reference tissue and complete versus partial responders to neoadjuvant chemotherapy. The results underscored the superiority of dynamic and static radiomics over standard PET imaging, achieving accuracy of 94% in tumor tissue classification. Notably, in predicting BC prognosis, dynomics delivered the highest performance, achieving accuracy of 86%, thereby outperforming both static radiomics and standard PET data. This study illustrates the enhanced clinical utility of dynomics in yielding more precise and reliable information for BC diagnosis and prognosis, paving the way for improved treatment strategies.

Analyzing the sensitivity of quantitative 3D MRI of longitudinal relaxation at very low field in Gd-doped phantoms.

PURPOSE: Recently, new MRI systems working at magnetic field below 10 mT (Very and Ultra Low Field regime) have been developed, showing improved T1-contrast in projected 2D maps (i.e. images without slice selection). Moving from projected 2D to 3D maps is not trivial due to the low SNR of such devices. This work aimed to demonstrate the ability and the sensitivity of a VLF-MRI scanner operating at 8.9 mT in quantitatively obtaining 3D longitudinal relaxation rate (R1) maps and distinguishing between voxels intensities. We used phantoms consisting of vessels doped with different Gadolinium (Gd)-based Contrast Agent (CA) concentrations, providing a set of various R1 values. As CA, we used a commercial compound (MultiHance®, gadobenate dimeglumine) routinely used in clinical MRI. METHODS: 3D R1 maps and T1-weighted MR images were analysed to identify each vessel. R1 maps were further processed by an automatic clustering analysis to evaluate the sensitivity at the single-voxel level. Results obtained at 8.9 mT were compared with commercial scanners operating at 0.2 T, 1.5 T, and 3 T. RESULTS: VLF R1 maps offered a higher sensitivity in distinguishing the different CA concentrations and an improved contrast compared to higher fields. Moreover, the high sensitivity of 3D quantitative VLF-MRI allowed an effective clustering of the 3D map values, assessing their reliability at the single voxel level. Conversely, in all fields, T1-weighted images were less reliable, even at higher CA concentrations. CONCLUSION: In summary, with few excitations and an isotropic voxel size of 3 mm, VLF-MRI 3D quantitative mapping showed a sensitivity better than 2.7 s-1 corresponding to a concentration difference of 0.17 mM of MultiHance in copper sulfate doped water, and improved contrast compared to higher fields. Based on these results, future studies should characterize R1 contrast at VLF, also with other CA, in the living tissues.

Heritability of human "directed" functional connectome.

INTRODUCTION: The functional connectivity patterns in the brain are highly heritable; however, it is unclear how genetic factors influence the directionality of such "information flows." Studying the "directionality" of the brain functional connectivity and assessing how heritability modulates it can improve our understanding of the human connectome. METHODS: Here, we investigated the heritability of "directed" functional connections using a state-space formulation of Granger causality (GC), in conjunction with blind deconvolution methods accounting for local variability in the hemodynamic response function. Such GC implementation is ideal to explore the directionality of functional interactions across a large number of networks. Resting-state functional magnetic resonance imaging data were drawn from the Human Connectome Project (total n = 898 participants). To add robustness to our findings, the dataset was randomly split into a "discovery" and a "replication" sample (each with n = 449 participants). The two cohorts were carefully matched in terms of demographic variables and other confounding factors (e.g., education). The effect of shared environment was also modeled. RESULTS: The parieto- and prefronto-cerebellar, parieto-prefrontal, and posterior-cingulate to hippocampus connections showed the highest and most replicable heritability effects with little influence by shared environment. In contrast, shared environmental factors significantly affected the visuo-parietal and sensory-motor directed connectivity. CONCLUSION: We suggest a robust role of heritability in influencing the directed connectivity of some cortico-subcortical circuits implicated in cognition. Further studies, for example using task-based fMRI and GC, are warranted to confirm the asymmetric effects of genetic factors on the functional connectivity within cognitive networks and their role in supporting executive functions and learning.

An Interpretable Machine Learning Model to Predict Cortical Atrophy in Multiple Sclerosis.

To date, the relationship between central hallmarks of multiple sclerosis (MS), such as white matter (WM)/cortical demyelinated lesions and cortical gray matter atrophy, remains unclear. We investigated the interplay between cortical atrophy and individual lesion-type patterns that have recently emerged as new radiological markers of MS disease progression. We employed a machine learning model to predict mean cortical thinning in whole-brain and single hemispheres in 150 cortical regions using demographic and lesion-related characteristics, evaluated via an ultrahigh field (7 Tesla) MRI. We found that (i) volume and rimless (i.e., without a "rim" of iron-laden immune cells) WM lesions, patient age, and volume of intracortical lesions have the most predictive power; (ii) WM lesions are more important for prediction when their load is small, while cortical lesion load becomes more important as it increases; (iii) WM lesions play a greater role in the progression of atrophy during the latest stages of the disease. Our results highlight the intricacy of MS pathology across the whole brain. In turn, this calls for multivariate statistical analyses and mechanistic modeling techniques to understand the etiopathogenesis of lesions.

Predicting the cytotoxicity of nanomaterials through explainable, extreme gradient boosting.

Nanoparticles (NPs) are a wide class of materials currently used in several industrial and biomedical applications. Due to their small size (1-100 nm), NPs can easily enter the human body, inducing tissue damage. NP toxicity depends on physical and chemical NP properties (e.g., size, charge and surface area) in ways and magnitudes that are still unknown. We assess the average as well as the individual importance of NP atomic descriptors, along with chemical properties and experimental conditions, in determining cytotoxicity endpoints for several nanomaterials. We employ a multicenter cytotoxicity nanomaterial database (12 different materials with first and second dimensions ranging between 2.70 and 81.2 nm and between 4.10 and 4048 nm, respectively). We develop a regressor model based on extreme gradient boosting with hyperparameter optimization. We employ Shapley additive explanations to obtain good cytotoxicity prediction performance. Model performances are quantified as statistically significant Spearman correlations between the true and predicted values, ranging from 0.5 to 0.7. Our results show that i) size in situ and surface areas larger than 200 nm and 50 m2/g, respectively, ii) primary particles smaller than 20 nm; iii) irregular (i.e., not spherical) shapes and iv) positive Z-potentials contribute the most to the prediction of NP cytotoxicity, especially if lactate dehydrogenase (LDH) assays are employed for short experimental times. These results were moderately stable across toxicity endpoints, although some degree of variability emerged across dose quantification methods, confirming the complexity of nano-bio interactions and the need for large, systematic experimental characterization to reach a safer-by-design approach.

Regulation of P-glycoprotein and Breast Cancer Resistance Protein Expression Induced by Focused Ultrasound-Mediated Blood-Brain Barrier Disruption: A Pilot Study.

The blood-brain barrier (BBB) controls brain homeostasis; it is formed by vascular endothelial cells that are physically connected by tight junctions (TJs). The BBB expresses efflux transporters such as P-glycoprotein (P-gp) and breast cancer resistance protein (BCRP), which limit the passage of substrate molecules from blood circulation to the brain. Focused ultrasound (FUS) with microbubbles can create a local and reversible detachment of the TJs. However, very little is known about the effect of FUS on the expression of efflux transporters. We investigated the in vivo effects of moderate acoustic pressures on both P-gp and BCRP expression for up to two weeks after sonication. Magnetic resonance-guided FUS was applied in the striatum of 12 rats. P-gp and BCRP expression were determined by immunohistochemistry at 1, 3, 7, and 14 days postFUS. Our results indicate that FUS-induced BBB opening is capable of (i) decreasing P-gp expression up to 3 days after sonication in both the treated and in the contralateral brain regions and is capable of (ii) overexpressing BCRP up to 7 days after FUS in the sonicated regions only. Our findings may help improve FUS-aided drug delivery strategies by considering both the mechanical effect on the TJs and the regulation of P-gp and BCRP.

Invasive aspergillosis in relapsed/refractory acute myeloid leukaemia patients: Results from SEIFEM 2016-B survey.

BACKGROUND: In patients with relapsed/refractory acute myeloid leukaemia (R/R AML) who received salvage chemotherapy, limited and not updated studies explored the incidence of invasive aspergillosis (IA) and the role of antifungal prophylaxis (AP). The aims of this multicentre retrospective 'SEIFEM 2016-B' study were as follows: (1) to evaluate the current rate and the outcome of proven/probable IA and (2) to assess the efficacy of AP, in a large 'real life' series of patient with R/R AML submitted to salvage chemotherapy. RESULTS: Of 2250 R/R AML patients, a total of 74 cases of IA (5.1%) were recorded as follows: 10 (0.7%) proven and 64 (4.3%) probable. Information about AP were available in 73/74 (99%) patients. Fifty-eight (79%) breakthrough infections occurred, mainly during AP with posaconazole [25 (43%)]. The patients who received AP during salvage chemotherapy showed a benefit from antifungal therapy (AT) than patients who did not received AP [43 (86%) vs 7 (14%); p < .033]. In a multivariate analysis, AP and absence of severe mucositis had a significant favourable effect on overall response rate. CONCLUSION: Our data demonstrated that the incidence of IA during the salvage chemotherapy is similar to the past. Nevertheless, the attributable mortality rate (AMR) appears to be lower than that previously reported in R/R AML. Further prospective studies should be performed to confirm our preliminary observation and understand and the why a decreased AMR is reported in this setting of high-risk patients.

Classification of real-world pathological phonocardiograms through multi-instance learning.

Heart auscultation is an inexpensive and fundamental technique to effectively to diagnose cardiovascular disease. However, due to relatively high human error rates even when auscultation is performed by an experienced physician, and due to the not universal availability of qualified personnel e.g. in developing countries, a large body of research is attempting to develop automated, computational tools for detecting abnormalities in heart sounds. The large heterogeneity of achievable data quality and devices, the variety o possible heart pathologies, and a generally poor signal-to-noise ratio make this problem extremely challenging. We present an accurate classification strategy for diagnosing heart sounds based on 1) automatic heart phase segmentation, 2) state-of-the art filters drawn from the filed of speech synthesis (mel-frequency cepstral representation), and 3) an ad-hoc multi-branch, multi-instance artificial neural network based on convolutional layers and fully connected neuronal ensembles which separately learns from each heart phase, hence leveraging their different physiological significance. We demonstrate that it is possible to train our architecture to reach very high performances, e.g. an AUC of 0.87 or a sensitivity of 0.97. Our machine-learning-based tool could be employed for heart sound classification, especially as a screening tool in a variety of situations including telemedicine applications.

Cortical and phase rim lesions on 7 T MRI as markers of multiple sclerosis disease progression.

In multiple sclerosis, individual lesion-type patterns on magnetic resonance imaging might be valuable for predicting clinical outcome and monitoring treatment effects. Neuropathological and imaging studies consistently show that cortical lesions contribute to disease progression. The presence of chronic active white matter lesions harbouring a paramagnetic rim on susceptibility-weighted magnetic resonance imaging has also been associated with an aggressive form of multiple sclerosis. It is, however, still uncertain how these two types of lesions relate to each other, or which one plays a greater role in disability progression. In this prospective, longitudinal study in 100 multiple sclerosis patients (74 relapsing-remitting, 26 secondary progressive), we used ultra-high field 7-T susceptibility imaging to characterize cortical and rim lesion presence and evolution. Clinical evaluations were obtained over a mean period of 3.2 years in 71 patients, 46 of which had a follow-up magnetic resonance imaging. At baseline, cortical and rim lesions were identified in 96% and 63% of patients, respectively. Rim lesion prevalence was similar across disease stages. Patients with rim lesions had higher cortical and overall white matter lesion load than subjects without rim lesions (P = 0.018-0.05). Altogether, cortical lesions increased by both count and volume (P = 0.004) over time, while rim lesions expanded their volume (P = 0.023) whilst lacking new rim lesions; rimless white matter lesions increased their count but decreased their volume (P = 0.016). We used a modern machine learning algorithm based on extreme gradient boosting techniques to assess the cumulative power as well as the individual importance of cortical and rim lesion types in predicting disease stage and disability progression, alongside with more traditional imaging markers. The most influential imaging features that discriminated between multiple sclerosis stages (area under the curve±standard deviation = 0.82 ± 0.08) included, as expected, the normalized white matter and thalamic volume, white matter lesion volume, but also leukocortical lesion volume. Subarachnoid cerebrospinal fluid and leukocortical lesion volumes, along with rim lesion volume were the most important predictors of Expanded Disability Status Scale progression (area under the curve±standard deviation = 0.69 ± 0.12). Taken together, these results indicate that while cortical lesions are extremely frequent in multiple sclerosis, rim lesion development occurs only in a subset of patients. Both, however, persist over time and relate to disease progression. Their combined assessment is needed to improve the ability of identifying multiple sclerosis patients at risk of progressing disease.

A novel multi-branch architecture for state of the art robust detection of pathological phonocardiograms.

Heart auscultation is an inexpensive and fundamental technique to effectively diagnose cardiovascular disease. However, due to relatively high human error rates even when auscultation is performed by an experienced physician, and due to the not universal availability of qualified personnel, e.g. in developing countries, many efforts are made worldwide to propose computational tools for detecting abnormalities in heart sounds. The large heterogeneity of achievable data quality and devices, the variety of possible heart pathologies, and a generally poor signal-to-noise ratio make this problem very challenging. We present an accurate classification strategy for diagnosing heart sounds based on (1) automatic heart phase segmentation, (2) state-of-the art filters drawn from the field of speech synthesis (mel-frequency cepstral representation) and (3) an ad hoc multi-branch, multi-instance artificial neural network based on convolutional layers and fully connected neuronal ensembles which separately learns from each heart phase hence implicitly leveraging their different physiological significance. We demonstrate that it is possible to train our architecture to reach very high performances, e.g. an area under the curve of 0.87 or a sensitivity of 0.97. Our machine-learning-based tool could be employed for heartsound classification, especially as a screening tool in a variety of situations including telemedicine applications. This article is part of the theme issue 'Advanced computation in cardiovascular physiology: new challenges and opportunities'.

Radiomics in breast cancer classification and prediction.

Breast Cancer (BC) is the common form of cancer in women. Its diagnosis and screening are usually performed through different imaging modalities such as mammography, magnetic resonance imaging and ultrasound. However, mammography and ultrasound-imaging techniques have limited sensitivity and specificity both in identifying lesions and in differentiating malign from benign lesions, especially in presence of dense breast parenchyma. Due to the higher resolution of magnetic resonance images, MRI represents the method with the higher specificity and sensitivity among all the available tools, in both lesions' identification and diagnosis. However, especially for diagnosis, even MRI has limitations that are only partially solved if combined with mammography. Unfortunately, due to the limits of all these imaging tools, in order to have a certain diagnosis, patients often receive painful and costly bioptics procedures. In this context, several computational approaches have been developed to increase sensitivity, while maintaining the same specificity, in BC diagnosis and screening. Amongst these, radiomics has been increasingly gaining ground in oncology to improve cancer diagnosis, prognosis and treatment. Radiomics derives multiple quantitative features from single or multiple medical imaging modalities, highlighting image traits which are not visible to the naked eye and hence significantly augmenting the discriminatory and predictive potential of medical imaging. This review article aims to summarize the state of the art in radiomics-based BC research. The dominating evidence extracted from the literature points towards a high potential of radiomics in disentangling malignant from benign breast lesions, classifying BC types and grades and also in predicting treatment response and recurrence risk. In the era of personalized medicine, radiomics has the potential to improve diagnosis, prognosis, prediction, monitoring, image-based intervention, and assessment of therapeutic response in BC.

Deep computational pathology in breast cancer.

Deep Learning (DL) algorithms are a set of techniques that exploit large and/or complex real-world datasets for cross-domain and cross-discipline prediction and classification tasks. DL architectures excel in computer vision tasks, and in particular image processing and interpretation. This has prompted a wave of disruptingly innovative applications in medical imaging, where DL strategies have the potential to vastly outperform human experts. This is particularly relevant in the context of histopathology, where whole slide imaging (WSI) of stained tissue in conjuction with DL algorithms for their interpretation, selection and cancer staging are beginning to play an ever increasing role in supporting human operators in visual assessments. This has the potential to reduce everyday workload as well as to increase precision and reproducibility across observers, centers, staining techniques and even pathologies. In this paper we introduce the most common DL architectures used in image analysis, with a focus on histopathological image analysis in general and in breast histology in particular. We briefly review how, state-of-art DL architectures compare to human performance on across a number of critical tasks such as mitotic count, tubules analysis and nuclear pleomorphism analysis. Also, the development of DL algorithms specialized to pathology images have been enormously fueled by a number of world-wide challenges based on large, multicentric image databases which are now publicly available. In turn, this has allowed most recent efforts to shift more and more towards semi-supervised learning methods, which provide greater flexibility and applicability. We also review all major repositories of manually labelled pathology images in breast cancer and provide an in-depth discussion of the challenges specific to training DL architectures to interpret WSI data, as well as a review of the state-of-the-art methods for interpretation of images generated from immunohistochemical analysis of breast lesions. We finally discuss the future challenges and opportunities which the adoption of DL paradigms is most likely to pose in the field of pathology for breast cancer detection, diagnosis, staging and prognosis. This review is intended as a comprehensive stepping stone into the field of modern computational pathology for a transdisciplinary readership across technical and medical disciplines.

Recent Advances on Ultrasound Contrast Agents for Blood-Brain Barrier Opening with Focused Ultrasound.

The blood-brain barrier is the primary obstacle to efficient intracerebral drug delivery. Focused ultrasound, in conjunction with microbubbles, is a targeted and non-invasive way to disrupt the blood-brain barrier. Many commercially available ultrasound contrast agents and agents specifically designed for therapeutic purposes have been investigated in ultrasound-mediated blood-brain barrier opening studies. The new generation of sono-sensitive agents, such as liquid-core droplets, can also potentially disrupt the blood-brain barrier after their ultrasound-induced vaporization. In this review, we describe the different compositions of agents used for ultrasound-mediated blood-brain barrier opening in recent studies, and we discuss the challenges of the past five years related to the optimal formulation of agents.

Frequency dependent functional brain reorganization in anesthesia is specific to drug concentration.

The differential effects of general anesthesia on brain activity in terms of drug selection, concentration and combination remain to be elucidated. Using fMRI, it has been shown that increasing doses of sevoflurane is associated with progressive breakdown in brain functional connectivity, while EEG studies have shown that higher activity in the delta band is associated with unconsciousness. Despite these promising results, the band- specific neural substrates of brain changes which occur during sevoflurane anesthesia have not yet been investigated. To this end, we employ high-density EEG-based brain connectivity estimates and graph theoretical analysis in a protocol of progressive sevoflurane administration (conditions: baseline, 1.1%, 2.1%, 2.8%, recovery), both at a global (whole-brain) and at a local (sensor-specific) level in 12 healthy subjects (7 males, mean age 25 ± 4.7 years). We show a statistically significant dependence of global strength, clustering coefficient and efficiency on sevoflurane concentration in the slow delta, beta 1 and beta 2 bands. Interestingly, high and low-frequency bands behaved in an opposite manner as a function of condition. We also found significant band*condition interactive effects in clustering coefficient, efficiency and strength both on local and global scales.

Ultrasound neuromodulation: mechanisms and the potential of multimodal stimulation for neuronal function assessment.

Focused ultrasound (FUS) neuromodulation has shown that mechanical waves can interact with cell membranes and mechanosensitive ion channels, causing changes in neuronal activity. However, the thorough understanding of the mechanisms involved in these interactions are hindered by different experimental conditions for a variety of animal scales and models. While the lack of complete understanding of FUS neuromodulation mechanisms does not impede benefiting from the current known advantages and potential of this technique, a precise characterization of its mechanisms of action and their dependence on experimental setup (e.g., tuning acoustic parameters and characterizing safety ranges) has the potential to exponentially improve its efficacy as well as spatial and functional selectivity. This could potentially reach the cell type specificity typical of other, more invasive techniques e.g., opto- and chemogenetics or at least orientation-specific selectivity afforded by transcranial magnetic stimulation. Here, the mechanisms and their potential overlap are reviewed along with discussions on the potential insights into mechanisms that magnetic resonance imaging sequences along with a multimodal stimulation approach involving electrical, magnetic, chemical, light, and mechanical stimuli can provide.