Identifying the joint signature of brain atrophy and gene variant scores in Alzheimer's Disease.

The joint modeling of genetic data and brain imaging information allows for determining the pathophysiological pathways of neurodegenerative diseases such as Alzheimer's disease (AD). This task has typically been approached using mass-univariate methods that rely on a complete set of Single Nucleotide Polymorphisms (SNPs) to assess their association with selected image-derived phenotypes (IDPs). However, such methods are prone to multiple comparisons bias and, most importantly, fail to account for potential cross-feature interactions, resulting in insufficient detection of significant associations. Ways to overcome these limitations while reducing the number of traits aim at conveying genetic information at the gene level and capturing the integrated genetic effects of a set of genetic variants, rather than looking at each SNP individually. Their associations with brain IDPs are still largely unexplored in the current literature, though they can uncover new potential genetic determinants for brain modulations in the AD continuum. In this work, we explored an explainable multivariate model to analyze the genetic basis of the grey matter modulations, relying on the AD Neuroimaging Initiative (ADNI) phase 3 dataset. Cortical thicknesses and subcortical volumes derived from T1-weighted Magnetic Resonance were considered to describe the imaging phenotypes. At the same time the genetic counterpart was represented by gene variant scores extracted by the Sequence Kernel Association Test (SKAT) filtering model. Moreover, transcriptomic analysis was carried on to assess the expression of the resulting genes in the main brain structures as a form of validation. Results highlighted meaningful genotype-phenotype interactionsas defined by three latent components showing a significant difference in the projection scores between patients and controls. Among the significant associations, the model highlighted EPHX1 and BCAS1 gene variant scores involved in neurodegenerative and myelination processes, hence relevant for AD. In particular, the first was associated with decreased subcortical volumes and the second with decreasedtemporal lobe thickness. Noteworthy, BCAS1 is particularly expressed in the dentate gyrus. Overall, the proposed approach allowed capturing genotype-phenotype interactions in a restricted study cohort that was confirmed by transcriptomic analysis, offering insights into the underlying mechanisms of neurodegeneration in AD in line with previous findings and suggesting new potential disease biomarkers.

Unraveling the MRI-Based Microstructural Signatures Behind Primary Progressive and Relapsing-Remitting Multiple Sclerosis Phenotypes.

BACKGROUND: The mechanisms driving primary progressive and relapsing-remitting multiple sclerosis (PPMS/RRMS) phenotypes are unknown. Magnetic resonance imaging (MRI) studies support the involvement of gray matter (GM) in the degeneration, highlighting its damage as an early feature of both phenotypes. However, the role of GM microstructure is unclear, calling for new methods for its decryption. PURPOSE: To investigate the morphometric and microstructural GM differences between PPMS and RRMS to characterize GM tissue degeneration using MRI. STUDY TYPE: Prospective cross-sectional study. SUBJECTS: Forty-five PPMS (26 females) and 45 RRMS (32 females) patients. FIELD STRENGTH/SEQUENCE: 3T scanner. Three-dimensional (3D) fast field echo T1-weighted (T1-w), 3D turbo spin echo (TSE) T2-w, 3D TSE fluid-attenuated inversion recovery, and spin echo-echo planar imaging diffusion MRI (dMRI). ASSESSMENT: T1-w and dMRI data were employed for providing information about morphometric and microstructural features, respectively. For dMRI, both diffusion tensor imaging and 3D simple harmonics oscillator based reconstruction and estimation models were used for feature extraction from a predefined set of regions. A support vector machine (SVM) was used to perform patients' classification relying on all these measures. STATISTICAL TESTS: Differences between MS phenotypes were investigated using the analysis of covariance and statistical tests (P < 0.05 was considered statistically significant). RESULTS: All the dMRI indices showed significant microstructural alterations between the considered MS phenotypes, for example, the mode and the median of the return to the plane probability in the hippocampus. Conversely, thalamic volume was the only morphometric feature significantly different between the two MS groups. Ten of the 12 features retained by the selection process as discriminative across the two MS groups regarded the hippocampus. The SVM classifier using these selected features reached an accuracy of 70% and a precision of 69%. DATA CONCLUSION: We provided evidence in support of the ability of dMRI to discriminate between PPMS and RRMS, as well as highlight the central role of the hippocampus. LEVEL OF EVIDENCE: 2 TECHNICAL EFFICACY STAGE: 3.

Development of a microarray-based tool to characterize vaginal bacterial fluctuations and application to a novel antibiotic treatment for bacterial vaginosis.

The healthy vaginal microbiota is generally dominated by lactobacilli that confer antimicrobial protection and play a crucial role in health. Bacterial vaginosis (BV) is the most prevalent lower genital tract infection in women in reproductive age and is characterized by a shift in the relative abundances of Lactobacillus spp. to a greater abundance of strictly anaerobic bacteria. In this study, we designed a new phylogenetic microarray-based tool (VaginArray) that includes 17 probe sets specific for the most representative bacterial groups of the human vaginal ecosystem. This tool was implemented using the ligase detection reaction-universal array (LDR-UA) approach. The entire probe set properly recognized the specific targets and showed an overall sensitivity of 6 to 12 ng per probe. The VaginArray was applied to assess the efficacy of rifaximin vaginal tablets for the treatment of BV, analyzing the vaginal bacterial communities of 22 BV-affected women treated with rifaximin vaginal tablets at a dosage of 25 mg/day for 5 days. Our results showed the ability of rifaximin to reduce the growth of various BV-related bacteria (Atopobium vaginae, Prevotella, Megasphaera, Mobiluncus, and Sneathia spp.), with the highest antibiotic susceptibility for A. vaginae and Sneathia spp. Moreover, we observed an increase of Lactobacillus crispatus levels in the subset of women who maintained remission after 1 month of therapy, opening new perspectives for the treatment of BV.

Rifaximin modulates the vaginal microbiome and metabolome in women affected by bacterial vaginosis.

Bacterial vaginosis (BV) is a common vaginal disorder characterized by the decrease of lactobacilli and overgrowth of Gardnerella vaginalis and resident anaerobic vaginal bacteria. In the present work, the effects of rifaximin vaginal tablets on vaginal microbiota and metabolome of women affected by BV were investigated by combining quantitative PCR and a metabolomic approach based on (1)H nuclear magnetic resonance. To highlight the general trends of the bacterial communities and metabolomic profiles in response to the antibiotic/placebo therapy, a multivariate statistical strategy was set up based on the trajectories traced by vaginal samples in a principal component analysis space. Our data demonstrated the efficacy of rifaximin in restoring a health-like condition in terms of both bacterial communities and metabolomic features. In particular, rifaximin treatment was significantly associated with an increase in the lactobacillus/BV-related bacteria ratio, as well as with an increase in lactic acid concentration and a decrease of a pool of metabolites typically produced by BV-related bacteria (acetic acid, succinate, short-chain fatty acids, and biogenic amines). Among the tested dosages of rifaximin (100 and 25 mg for 5 days and 100 mg for 2 days), 25 mg for 5 days was found to be the most effective.

XAI-Based Assessment of the AMURA Model for Detecting Amyloid-ß and Tau Microstructural Signatures in Alzheimer's Disease.

Brain microstructural changes already occur in the earliest phases of Alzheimer's disease (AD) as evidenced in diffusion magnetic resonance imaging (dMRI) literature. This study investigates the potential of the novel dMRI Apparent Measures Using Reduced Acquisitions (AMURA) as imaging markers for capturing such tissue modifications.Tract-based spatial statistics (TBSS) and support vector machines (SVMs) based on different measures were exploited to distinguish between amyloid-beta/tau negative (A[Formula: see text]-/tau-) and A[Formula: see text]+/tau+ or A[Formula: see text]+/tau- subjects. Moreover, eXplainable Artificial Intelligence (XAI) was used to highlight the most influential features in the SVMs classifications and to validate the results by seeing the explanations' recurrence across different methods.TBSS analysis revealed significant differences between A[Formula: see text]-/tau- and other groups in line with the literature. The best SVM classification performance reached an accuracy of 0.73 by using advanced measures compared to more standard ones. Moreover, the explainability analysis suggested the results' stability and the central role of the cingulum to show early sign of AD.By relying on SVM classification and XAI interpretation of the outcomes, AMURA indices can be considered viable markers for amyloid and tau pathology. Clinical impact: This pre-clinical research revealed AMURA indices as viable imaging markers for timely AD diagnosis by acquiring clinically feasible dMR images, with advantages compared to more invasive methods employed nowadays.

An interpretable generative multimodal neuroimaging-genomics framework for decoding Alzheimer's disease.

Alzheimer's disease (AD) is the most prevalent form of dementia, affecting millions worldwide with a progressive decline in cognitive abilities. The AD continuum encompasses a prodormal stage known as Mild Cognitive Impairment (MCI), where patients may either progress to AD (MCIc) or remain stable (MCInc). Understanding the underlying mechanisms of AD requires complementary analysis derived from different data sources, leading to the development of multimodal deep learning models. In this study, we leveraged structural and functional Magnetic Resonance Imaging (sMRI/fMRI) to investigate the disease-induced grey matter and functional network connectivity changes. Moreover, considering AD's strong genetic component, we introduce Single Nucleotide Polymorphisms (SNPs) as a third channel. Given such diverse inputs, missing one or more modalities is a typical concern of multimodal methods. We hence propose a novel deep learning based classification framework where generative module employing Cycle Generative Adversarial Networks (cGAN) was adopted to impute missing data within the latent space. Additionally, we adopted an Explainable Artificial Intelligence (XAI) method, Integrated Gradients (IG), to extract input features relevance, enhancing our understanding of the learned representations. Two critical tasks were addressed: AD detection and MCI conversion prediction. Experimental results showed that our framework was able to reach the state-of-the-art in the classification of CN vs AD reaching an average test accuracy of 0.926 ± 0.02. For the MCInc vs MCIc task, we achieved an average prediction accuracy of 0.711 ± 0.01 using the pre-trained model for CN and AD. The interpretability analysis revealed that the classification performance was led by significant grey matter modulations in cortical and subcortical brain areas well known for their association with AD. Moreover, impairments in sensory-motor and visual resting state network connectivity along the disease continuum, as well as mutations in SNPs defining biological processes linked to amyloid-beta and cholesterol formation clearance and regulation, were identified as contributors to the achieved performance. Overall, our integrative deep learning approach shows promise for AD detection and MCI prediction, while shading light on important biological insights.

Proteome profiles of vaginal fluids from women affected by bacterial vaginosis and healthy controls: outcomes of rifaximin treatment.

OBJECTIVES: This study was designed to characterize the proteome of vaginal fluid (VF) from women with bacterial vaginosis (BV) in comparison with that from healthy women, and to evaluate the effect exerted by rifaximin vaginal tablets. METHODS: Women with BV (n = 39) and matched healthy controls (n = 41) were included in the study. BV patients were distributed among four groups receiving different doses of rifaximin. Vaginal rinsings were collected at the screening visit from all the participants and at a follow-up visit from BV-affected women. The VF proteome was analysed by tandem mass spectrometry using an Orbitrap mass analyser. RESULTS: A large number of human proteins were differentially expressed in women with BV in comparison with healthy women (n = 118) and in BV-affected women treated with rifaximin (n = 284). In both comparisons, a high proportion of the dysregulated proteins (∼20%) were involved in the innate immune response. Twenty-one of 24 proteins increased in abundance in women with BV versus healthy women and 31/59 proteins decreased after rifaximin treatment, suggesting a general reduction of the immune response resulting from the therapy. Major changes in protein abundance were found following treatment with 25 mg of rifaximin once daily for 5 days. CONCLUSIONS: BV is associated with a massive change in the VF proteome, mainly regarding the abundance of proteins involved in the innate immune response. Rifaximin at a dosage of 25 mg for 5 days modulated the vaginal proteome, counteracting the alterations associated with the BV condition.

Intrinsic Excitability in Layer IV-VI Anterior Insula to Basolateral Amygdala Projection Neurons Correlates with the Confidence of Taste Valence Encoding.

Avoiding potentially harmful, and consuming safe food is crucial for the survival of living organisms. However, the perceived valence of sensory information can change following conflicting experiences. Pleasurability and aversiveness are two crucial parameters defining the perceived valence of a taste and can be impacted by novelty. Importantly, the ability of a given taste to serve as the conditioned stimulus (CS) in conditioned taste aversion (CTA) is dependent on its valence. Activity in anterior insula (aIC) Layer IV-VI pyramidal neurons projecting to the basolateral amygdala (BLA) is correlated with and necessary for CTA learning and retrieval, as well as the expression of neophobia toward novel tastants, but not learning taste familiarity. Yet, the cellular mechanisms underlying the updating of taste valence representation in this specific pathway are poorly understood. Here, using retrograde viral tracing and whole-cell patch-clamp electrophysiology in trained mice, we demonstrate that the intrinsic properties of deep-lying Layer IV-VI, but not superficial Layer I-III aIC-BLA neurons, are differentially modulated by both novelty and valence, reflecting the subjective predictability of taste valence arising from prior experience. These correlative changes in the profile of intrinsic properties of LIV-VI aIC-BLA neurons were detectable following both simple taste experiences, as well as following memory retrieval, extinction learning, and reinstatement.

eXplainable AI Allows Predicting Upper Limb Rehabilitation Outcomes in Sub-Acute Stroke Patients.

While stroke is one of the leading causes of disability, the prediction of upper limb (UL) functional recovery following rehabilitation is still unsatisfactory, hampered by the clinical complexity of post-stroke impairment. Predictive models leading to accurate estimates while revealing which features contribute most to the predictions are the key to unveil the mechanisms subserving the post-intervention recovery, prompting a new focus on individualized treatments and precision medicine in stroke. Machine learning (ML) and explainable artificial intelligence (XAI) are emerging as the enabling technology in different fields, being promising tools also in clinics. In this study, we had the twofold goal of evaluating whether ML can allow deriving accurate predictions of UL recovery in sub-acute patients, and disentangling the contribution of the variables shaping the outcomes. To do so, Random Forest equipped with four XAI methods was applied to interpret the results and assess the feature relevance and their consensus. Our results revealed increased performance when using ML compared to conventional statistical approaches. Moreover, the features deemed as the most relevant were concordant across the XAI methods, suggesting good stability of the results. In particular, the baseline motor impairment as measured by simple clinical scales had the largest impact, as expected. Our findings highlight the core role of ML not only for accurately predicting the individual outcome scores after rehabilitation, but also for making ML results interpretable when associated to XAI methods. This provides clinicians with robust predictions and reliable explanations that are key factors in therapeutic planning/monitoring of stroke patients.

Efficacy of rifaximin vaginal tablets in treatment of bacterial vaginosis: a molecular characterization of the vaginal microbiota.

Bacterial vaginosis (BV) is a common vaginal disorder characterized by an alteration of the vaginal bacterial morphotypes, associated with sexually transmitted infections and adverse pregnancy outcomes. The purpose of the present study was to evaluate the impact of different doses of rifaximin vaginal tablets (100 mg/day for 5 days, 25 mg/day for 5 days, and 100 mg/day for 2 days) on the vaginal microbiota of 102 European patients with BV enrolled in a multicenter, double-blind, randomized, placebo-controlled study. An integrated molecular approach based on quantitative PCR (qPCR) and PCR-denaturing gradient gel electrophoresis (PCR-DGGE) was used to investigate the effects of vaginal tablets containing the antibiotic. An increase in members of the genus Lactobacillus and a decrease in the BV-related bacterial groups after the antibiotic treatment were demonstrated by qPCR. PCR-DGGE profiles confirmed the capability of rifaximin to modulate the composition of the vaginal microbial communities and to reduce their complexity. This molecular analysis supported the clinical observation that rifaximin at 25 mg/day for 5 days represents an effective treatment to be used in future pivotal studies for the treatment of BV.

Dietary supplementation with probiotics during late pregnancy: outcome on vaginal microbiota and cytokine secretion.

BACKGROUND: The vaginal microbiota of healthy women consists of a wide variety of anaerobic and aerobic bacterial genera and species dominated by the genus Lactobacillus. The activity of lactobacilli helps to maintain the natural healthy balance of the vaginal microbiota. This role is particularly important during pregnancy because vaginal dismicrobism is one of the most important mechanisms for preterm birth and perinatal complications. In the present study, we characterized the impact of a dietary supplementation with the probiotic VSL#3, a mixture of Lactobacillus, Bifidobacterium and Streptococcus strains, on the vaginal microbiota and immunological profiles of healthy women during late pregnancy. RESULTS: An association between the oral intake of the probiotic VSL#3 and changes in the composition of the vaginal microbiota of pregnant women was revealed by PCR-DGGE population profiling. Despite no significant changes were found in the amounts of the principal vaginal bacterial populations in women administered with VSL#3, qPCR results suggested a potential role of the probiotic product in counteracting the decrease of Bifidobacterium and the increase of Atopobium, that occurred in control women during late pregnancy. The modulation of the vaginal microbiota was associated with significant changes in some vaginal cytokines. In particular, the decrease of the anti-inflammatory cytokines IL-4 and IL-10 was observed only in control women but not in women supplemented with VSL#3. In addition, the probiotic consumption induced the decrease of the pro-inflammatory chemokine Eotaxin, suggesting a potential anti-inflammatory effect on the vaginal immunity. CONCLUSION: Dietary supplementation with the probiotic VSL#3 during the last trimester of pregnancy was associated to a modulation of the vaginal microbiota and cytokine secretion, with potential implications in preventing preterm birth. TRIAL REGISTRATION: ClinicalTrials.gov NCT01367470.

Perturbation of Cortical Excitability in a Conditional Model of PCDH19 Disorder.

PCDH19 epilepsy (DEE9) is an X-linked syndrome associated with cognitive and behavioral disturbances. Since heterozygous females are affected, while mutant males are spared, it is likely that DEE9 pathogenesis is related to disturbed cell-to-cell communication associated with mosaicism. However, the effects of mosaic PCDH19 expression on cortical networks are unknown. We mimicked the pathology of DEE9 by introducing a patch of mosaic protein expression in one hemisphere of the cortex of conditional PCDH19 knockout mice one day after birth. In the contralateral area, PCDH19 expression was unaffected, thus providing an internal control. In this model, we characterized the physiology of the disrupted network using local field recordings and two photon Ca2+ imaging in urethane anesthetized mice. We found transient episodes of hyperexcitability in the form of brief hypersynchronous spikes or bursts of field potential oscillations in the 9-25 Hz range. Furthermore, we observed a strong disruption of slow wave activity, a crucial component of NREM sleep. This phenotype was present also when PCDH19 loss occurred in adult mice, demonstrating that PCDH19 exerts a function on cortical circuitry outside of early development. Our results indicate that a focal mosaic mutation of PCDH19 disrupts cortical networks and broaden our understanding of DEE9.

Microstructural MRI Correlates of Cognitive Impairment in Multiple Sclerosis: The Role of Deep Gray Matter.

Although cognitive impairment (CI) is frequently observed in people with multiple sclerosis (pwMS), its pathogenesis is still controversial. Conflicting results emerged concerning the role of microstructural gray matter (GM) damage especially when involving the deep GM structures. In this study, we aimed at evaluating whether differences in cortical and deep GM structures between apparently cognitively normal (ACN) and CI pwMS (36 subjects in total) are present, using an extensive set of diffusion MRI (dMRI) indices and conventional morphometry measures. The results revealed increased anisotropy and restriction over several deep GM structures in CI compared with ACN pwMS, while no changes in volume were present in the same areas. Conversely, reduced anisotropy/restriction values were detected in cortical regions, mostly the pericalcarine cortex and precuneus, combined with reduced thickness of the superior frontal gyrus and insula. Most of the dMRI metrics but none of the morphometric indices correlated with the Symbol Digit Modality Test. These results suggest that deep GM microstructural damage can be a strong anatomical substrate of CI in pwMS and might allow identifying pwMS at higher risk of developing CI.

Impact of a synbiotic food on the gut microbial ecology and metabolic profiles.

BACKGROUND: The human gut harbors a diverse community of microorganisms which serve numerous important functions for the host wellbeing. Functional foods are commonly used to modulate the composition of the gut microbiota contributing to the maintenance of the host health or prevention of disease. In the present study, we characterized the impact of one month intake of a synbiotic food, containing fructooligosaccharides and the probiotic strains Lactobacillus helveticus Bar13 and Bifidobacterium longum Bar33, on the gut microbiota composition and metabolic profiles of 20 healthy subjects. RESULTS: The synbiotic food did not modify the overall structure of the gut microbiome, as indicated by Polymerase Chain Reaction-Denaturing Gradient Gel Electrophoresis (PCR-DGGE). The ability of the probiotic L. helveticus and B. longum strains to pass through the gastrointestinal tract was hypothesized on the basis of real-time PCR data. In spite of a stable microbiota, the intake of the synbiotic food resulted in a shift of the fecal metabolic profiles, highlighted by the Gas Chromatography Mass Spectrometry Solid Phase Micro-Extraction (GC-MS/SPME) analysis. The extent of short chain fatty acids (SCFA), ketones, carbon disulfide and methyl acetate was significantly affected by the synbiotic food consumption. Furthermore, the Canonical discriminant Analysis of Principal coordinates (CAP) of GC-MS/SPME profiles allowed a separation of the stool samples recovered before and after the consumption of the functional food. CONCLUSION: In this study we investigated the global impact of a dietary intervention on the gut ecology and metabolism in healthy humans. We demonstrated that the intake of a synbiotic food leads to a modulation of the gut metabolic activities with a maintenance of the gut biostructure. In particular, the significant increase of SCFA, ketones, carbon disulfide and methyl acetate following the feeding period suggests potential health promoting effects of the synbiotic food.

Modelling genetic mosaicism of neurodevelopmental disorders in vivo by a Cre-amplifying fluorescent reporter.

Genetic mosaicism, a condition in which an organ includes cells with different genotypes, is frequently present in monogenic diseases of the central nervous system caused by the random inactivation of the X-chromosome, in the case of X-linked pathologies, or by somatic mutations affecting a subset of neurons. The comprehension of the mechanisms of these diseases and of the cell-autonomous effects of specific mutations requires the generation of sparse mosaic models, in which the genotype of each neuron is univocally identified by the expression of a fluorescent protein in vivo. Here, we show a dual-color reporter system that, when expressed in a floxed mouse line for a target gene, leads to the creation of mosaics with tunable degree. We demonstrate the generation of a knockout mosaic of the autism/epilepsy related gene PTEN in which the genotype of each neuron is reliably identified, and the neuronal phenotype is accurately characterized by two-photon microscopy.

Mucoadhesive chitosan/gelatin films for buccal delivery of propranolol hydrochloride.

The aim of this work was to develop and characterize chitosan/gelatin films as innovative mucoadhesive system for buccal delivery of propranolol hydrochloride. FT-IR and TGA analysis confirmed the interaction between chitosan and gelatin. The presence of higher chitosan amounts in chitosan/gelatin films allowed the lowest percent water-uptake ability (235.1±5.3%) and the highest in vivo residence time in the buccal cavity (240±13min). Moreover, the presence of mannitol in the formulation allowed 80% drug permeation through porcine buccal mucosa in 5h. This behaviour suggests that the application of four and two films containing 5mg of propranolol hydrochloride could be suitable for achieving the proposed daily dose for hypertension and atrial fibrillation treatment, respectively. Another interesting aspect of chitosan/gelatin films was their compatibility with buccal microflora in the absence of drug and their ability to determine growth inhibition for pathogen bacteria, but not for probiotic species, when loaded with drug.

Development of microparticulate systems for intestinal delivery of Lactobacillus acidophilus and Bifidobacterium lactis.

In the present study intestinal delivery systems resistant to gastric juice, loaded with the probiotic bacteria Lactobacillus acidophilus LA14 and Bifidobacterium lactis BI07, were produced by the polyelectrolyte complexation. First, beads were prepared by the traditional extrusion method and nine formulations were developed using alginate as main carrier and the biopolymer, xanthan gum (XG), as hydrophilic retardant polymer or the cellulose derivative, cellulose acetate phthalate (CAP), as gastro-resistant polymer. The results showed that the incorporation of the 0.5% (w/v) of XG or the 1% (w/v) of CAP within the 3% (w/v) of alginate solution increased the survival of the probiotic bacteria in acid conditions from 63% of the freeze-dried bacteria up to 76%. Subsequently, these formula was used to prepare smaller microcapsules by means of an atomization device. Despite of the high viscosity of the biomass suspension, the spraying system produced spherical and non-aggregated microcapsules able to survive in harsh condition better than beads: the survival of the probiotic bacteria after acid incubation was 91%. The performance of the microcapsules in simulated gastric fluid (SGF) containing pepsin and in gut medium (GM) containing bile salts was excellent (viability>95%). Furthermore, the viability of probiotic bacteria was maintained after an incubation of 24h in GM. Finally, stability tests performed at 5 degrees C highlighted a bacterial viability of about 82% and 70% after 6 and 9 months, respectively.