Predicting conversion from mild cognitive impairment to Alzheimer's disease: a multimodal approach.

Successively predicting whether mild cognitive impairment patients will progress to Alzheimer's disease is of significant clinical relevance. This ability may provide information that can be leveraged by emerging intervention approaches and thus mitigate some of the negative effects of the disease. Neuroimaging biomarkers have gained some attention in recent years and may be useful in predicting the conversion of mild cognitive impairment to Alzheimer's disease. We implemented a novel multi-modal approach that allowed us to evaluate the potential of different imaging modalities, both alone and in different degrees of combinations, in predicting the conversion to Alzheimer's disease of mild cognitive impairment patients. We applied this approach to the imaging data from the Alzheimer's Disease Neuroimaging Initiative that is a multi-modal imaging dataset comprised of MRI, Fluorodeoxyglucose PET, Florbetapir PET and diffusion tensor imaging. We included a total of 480 mild cognitive impairment patients that were split into two groups: converted and stable. Imaging data were segmented into atlas-based regions of interest, from which relevant features were extracted for the different imaging modalities and used to construct machine-learning models to classify mild cognitive impairment patients into converted or stable, using each of the different imaging modalities independently. The models were then combined, using a simple weight fusion ensemble strategy, to evaluate the complementarity of different imaging modalities and their contribution to the prediction accuracy of the models. The single-modality findings revealed that the model, utilizing features extracted from Florbetapir PET, demonstrated the highest performance with a balanced accuracy of 83.51%. Concerning multi-modality models, not all combinations enhanced mild cognitive impairment conversion prediction. Notably, the combination of MRI with Fluorodeoxyglucose PET emerged as the most promising, exhibiting an overall improvement in predictive capabilities, achieving a balanced accuracy of 78.43%. This indicates synergy and complementarity between the two imaging modalities in predicting mild cognitive impairment conversion. These findings suggest that ß-amyloid accumulation provides robust predictive capabilities, while the combination of multiple imaging modalities has the potential to surpass certain single-modality approaches. Exploring modality-specific biomarkers, we identified the brainstem as a sensitive biomarker for both MRI and Fluorodeoxyglucose PET modalities, implicating its involvement in early Alzheimer's pathology. Notably, the corpus callosum and adjacent cortical regions emerged as potential biomarkers, warranting further study into their role in the early stages of Alzheimer's disease.

Activity of the Di-Substituted Urea-Derived Compound I-17 in Leishmania In Vitro Infections.

Protein synthesis has been a very rich target for developing drugs to control prokaryotic and eukaryotic pathogens. Despite the development of new drug formulations, treating human cutaneous and visceral Leishmaniasis still needs significant improvements due to the considerable side effects and low adherence associated with the current treatment regimen. In this work, we show that the di-substituted urea-derived compounds I-17 and 3m are effective in inhibiting the promastigote growth of different Leishmania species and reducing the macrophage intracellular load of amastigotes of the Leishmania (L.) amazonensis and L. major species, in addition to exhibiting low macrophage cytotoxicity. We also show a potential immunomodulatory effect of I-17 and 3m in infected macrophages, which exhibited increased expression of inducible Nitric Oxide Synthase (NOS2) and production of Nitric Oxide (NO). Our data indicate that I-17, 3m, and their analogs may be helpful in developing new drugs for treating leishmaniasis.

Parametric fMRI analysis of videos of variable arousal levels reveals different dorsal vs ventral activation preferences between autism and controls.

Atypical sensory processing is now considered a ubiquitous feature of autism spectrum disorder (ASD) and is responsible for the atypical sensory-based behaviours seen in these individuals. Specifically, emotional arousal is a critical ASD target since it comprises emotion regulation and sensory processing, two core aspects of autism. So, in this project, we used task-based fMRI and a well-catalogued dataset of videos with variable arousal levels to characterize the sensory processing of emotional arousal content in ASD and typically developed controls. Our analysis revealed a difference in the secondary attention network where ASD individuals showed a clear yet lateralized preference to the dorsal attention network, whereas the neurotypical individuals preferred the ventral attention network.

Assessing Arousal Through Multimodal Biosignals: A Preliminary Approach.

The increase in Autism Spectrum Disorder (ASD) prevalence estimates over the last decades has driven a quest to develop new forms of rehabilitation that can be accessible to a larger part of this population. These rehabilitation approaches often take the form of computer games that are blind to the user's emotional state, which compromises their efficacy. In this study, a set of physiological signals were acquired in simultaneous with functional Magnetic Resonance Imaging (fMRI) with the future prospect of combining both kinds of data to create models capable of assessing the true emotional state of their users based on physiological response as a measure of autonomic nervous system, having as ground truth the activity of targeted brain regions. This paper describes an initial approach, focusing on the information contained on the physiological signals alone. A total of 35 features were extracted from biosignals' segments and subsequently used for automatic classification of arousal state (High Arousal vs. Low Arousal). The suboptimal results, although some extracted features present statistically significant differences, underline the challenging nature of our proposal and the added obstacles of recording physiological signals in the magnetic resonance environment. Further exploration of the measured signals is needed to gather a bigger number of discriminative features that can improve classification outcomes.

Combined Structural MR and Diffusion Tensor Imaging Classify the Presence of Alzheimer's Disease With the Same Performance as MR Combined With Amyloid Positron Emission Tomography: A Data Integration Approach.

Background: In recent years, classification frameworks using imaging data have shown that multimodal classification methods perform favorably over the use of a single imaging modality for the diagnosis of Alzheimer's Disease. The currently used clinical approach often emphasizes the use of qualitative MRI and/or PET data for clinical diagnosis. Based on the hypothesis that classification of isolated imaging modalities is not predictive of their respective value in combined approaches, we investigate whether the combination of T1 Weighted MRI and diffusion tensor imaging (DTI) can yield an equivalent performance as the combination of quantitative structural MRI (sMRI) with amyloid-PET. Methods: We parcellated the brain into regions of interest (ROI) following different anatomical labeling atlases. For each region of interest different metrics were extracted from the different imaging modalities (sMRI, PiB-PET, and DTI) to be used as features. Thereafter, the feature sets were reduced using an embedded-based feature selection method. The final reduced sets were then used as input in support vector machine (SVM) classifiers. Three different base classifiers were created, one for each imaging modality, and validated using internal (n = 41) and external data from the ADNI initiative (n = 330 for sMRI, n = 148 for DTI and n = 55 for PiB-PET) sources. Finally, the classifiers were ensembled using a weighted method in order to evaluate the performance of different combinations. Results: For the base classifiers the following performance levels were found: sMRI-based classifier (accuracy, 92%; specificity, 97% and sensitivity, 87%), PiB-PET (accuracy, 91%; specificity, 89%; and sensitivity, 92%) and the lowest performance was attained with DTI (accuracy, 80%; specificity, 76%; and sensitivity, 82%). From the multimodal approaches, when integrating two modalities, the following results were observed: sMRI+PiB-PET (accuracy, 98%; specificity, 98%; and sensitivity, 99%), sMRI+DTI (accuracy, 97%; specificity, 99%; and sensitivity, 94%) and PiB-PET+DTI (accuracy, 91%; specificity, 90%; and sensitivity, 93%). Finally, the combination of all imaging modalities yielded an accuracy of 98%, specificity of 97% and sensitivity of 99%. Conclusion: Although DTI in isolation shows relatively poor performance, when combined with structural MR, it showed a surprising classification performance which was comparable to MR combined with amyloid PET. These results are consistent with the notion that white matter changes are also important in Alzheimer's Disease.

Nutritional Conditions Modulate C. neoformans Extracellular Vesicles' Capacity to Elicit Host Immune Response.

Cryptococcus neoformans is a human pathogenic fungus that mainly afflicts immunocompromised patients. One of its virulence strategies is the production of extracellular vesicles (EVs), containing cargo with immunomodulatory properties. We evaluated EV's characteristics produced by capsular and acapsular strains of C. neoformans (B3501 and ΔCap67, respectively) growing in nutritionally poor or rich media and co-cultures with bone marrow-derived macrophages or dendritic cells from C57BL/6 mice. EVs produced under a poor nutritional condition displayed a larger hydrodynamic size, contained more virulence compounds, and induced a more robust inflammatory pattern than those produced in a rich nutritional medium, independently of strain. We treated infected mice with EVs produced in the rich medium, and the EVs inhibited more genes related to the inflammasome than untreated infected mice. These findings suggest that the EVs participate in the pathogenic processes that result in the dissemination of C. neoformans. Thus, these results highlight the versatility of EVs' properties during infection by C. neoformans in different tissues and support ongoing efforts to harness EVs to prevent and treat cryptococcosis.

Antibacterial Polyketide Heterodimers from Pyrenacantha kaurabassana Tubers.

Two heterodimers comprising anthraquinone and methylbenzoisocoumarin moieties (1 and 2) were isolated, together with emodin and physcion from the tubers of Pyrenacantha kaurabassana. The structures of 1 and 2 were established by NMR spectroscopy, including the analysis of a 2D INADEQUATE spectrum. On the basis of the data obtained, the structures that were previously proposed in the literature for these compounds were revised. Compounds 1 and 2 showed antibacterial activity against three different strains of Staphylococcus aureus. Compound 2 also showed bactericidal activity against Helicobacter pylori.