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Abstract Impaired clearance of amyloid β (Aβ) in late-onset Alzheimer’s disease (AD) affects disease progression. The role of peripheral monocytes in Aβ clearance from the central nervous system (CNS) is unclear. We use a flow cytometry assay to identify Aβ-binding monocytes in blood, validated by confocal microscopy, Western blotting, and mass spectrometry. Flow cytometry immunophenotyping and correlation with AD biomarkers are studied in 150 participants from the AIBL study. We also examine monocytes in human cerebrospinal fluid (CSF) and their migration in an APP/PS1 mouse model. The assay reveals macrophage-like Aβ-binding monocytes with high phagocytic potential in both the periphery and CNS. We find lower surface Aβ levels in mild cognitive impairment (MCI) and AD-dementia patients compared to cognitively unimpaired individuals. Monocyte infiltration from blood to CSF and migration from CNS to peripheral lymph nodes and blood are observed. Here we show that Aβ-binding monocytes may play a role in CNS Aβ clearance, suggesting their potential as a biomarker for AD diagnosis and monitoring.

Résumé: Cet article vise à sillonner les stratégies argumentatives déployées dans les propos d’Emmanuel Macron. Notre objectif principal est d’analyser les stratégies argumentatives et examiner les techniques spécifiques utilisées par ce président français. Nous voulons aussi comprendre l’effet contextuel autrement dit comment le contexte influe sur le choix des stratégies argumentatives de Macron, et évaluer l'effet potentiel de ces stratégies mises en œuvre sur la perception de l'auditoire, en particulier sur des questions de solidarité internationale et de soutien à l'Ukraine. Ces objectifs contribueraient à mieux comprendre la rhétorique politique de Macron dans des contextes internationaux délicats. Le corpus d’étude sera La prise de parole d'Emmanuel Macron après la conférence de soutien à l'Ukraine diffusée sur la chaine « BFMTV » le 26 février 2024. Mots-clés : Argumentation, stratégie, discours politique, prise de parole.

Abstract Background Ferroptosis is a form of regulated cell death characterised by lipid peroxidation as the terminal endpoint and a requirement for iron. Although it protects against cancer and infection, ferroptosis is also implicated in causing neuronal death in degenerative diseases of the central nervous system (CNS). The precise role for ferroptosis in causing neuronal death is yet to be fully resolved. Methods To elucidate the role of ferroptosis in neuronal death we utilised co-culture and conditioned medium transfer experiments involving microglia, astrocytes and neurones. We ratified clinical significance of our cell culture findings via assessment of human CNS tissue from cases of the fatal, paralysing neurodegenerative condition of amyotrophic lateral sclerosis (ALS). We utilised the SOD1G37R mouse model of ALS and a CNS-permeant ferroptosis inhibitor to verify pharmacological significance in vivo. Results We found that sublethal ferroptotic stress selectively affecting microglia triggers an inflammatory cascade that results in non-cell autonomous neuronal death. Central to this cascade is the conversion of astrocytes to a neurotoxic state. We show that spinal cord tissue from human cases of ALS exhibits a signature of ferroptosis that encompasses atomic, molecular and biochemical features. Further, we show the molecular correlation between ferroptosis and neurotoxic astrocytes evident in human ALS-affected spinal cord is recapitulated in the SOD1G37R mouse model where treatment with a CNS-permeant ferroptosis inhibitor, CuII(atsm), ameliorated these markers and was neuroprotective. Conclusions By showing that microglia responding to sublethal ferroptotic stress culminates in non-cell autonomous neuronal death, our results implicate microglial ferroptotic stress as a rectifiable cause of neuronal death in neurodegenerative disease. As ferroptosis is currently primarily regarded as an intrinsic cell death phenomenon, these results introduce an entirely new pathophysiological role for ferroptosis in disease. Graphical Abstract

Background/Objectives: RNA polymerase (NS5B), serves as a crucial target for pharmaceutical interventions aimed at combating the hepatitis C virus (HCV), which poses significant health challenges worldwide. The present research endeavors to explore and implement a variety of advanced molecular modeling techniques that aim to create and identify innovative and highly effective inhibitors that specifically target the RNA polymerase enzyme. Methods: In this study, a QSAR investigation was carried out on a set of thirty-eight isothiazole derivatives targeting NS5B inhibition and thus hepatitis C virus (HCV) treatment. The research methodology made use of various statistical techniques including multiple linear regression (MLR) and artificial neural networks (ANNs) to develop satisfactory models in terms of internal and external validation parameters, indicating their reliability in predicting the activity of new inhibitors. Accordingly, a series of potent NS5B inhibitors is designed, and their inhibitory potential is confirmed through molecular docking simulations. Results: These simulations showed that the interactions between these inhibitors and the active site 221 binding pocket of the NS5B protein are hydrophobic and hydrogen bond interactions, as well as carbon–hydrogen bonds and electrostatic interactions. Additionally, these newly formulated compounds displayed favorable ADMET characteristics, with molecular dynamics investigations revealing a stable energetic state and dynamic equilibrium. Conclusions: Our work highlights the importance of NS5B inhibition for the treatment of HCV.

Plasmodium falciparum dihydrofolate reductase (PfDHFR) is a promising therapeutic target for antimalarial interventions. Diaminodihydrotriazine derivatives have demonstrated their efficacy as antimalarial agents by targeting PfDHFR inhibition. In this study, we used a multifaceted approach including 3D-QSAR, in silico pharmacokinetic/ADMET evaluation, molecular docking techniques, and molecular dynamics simulation to explore a series of diaminodihydrotriazine compounds, to identify new antimalarial drug candidates featuring the diaminodihydrotriazine scaffold. Using comparative molecular similarity indices analysis (CoMSIA), we built a robust 3D-QSAR model exhibiting favorable statistical parameters, with Q2 and R2 values of 0.553 and 0.981, respectively. External validation using a test set yielded an R2pre value of 0.787, confirming the model's predictive ability. In addition, we performed comprehensive ADMET and drug similarity profile analyses for potent designed compounds, yielding promising results. Molecular docking studies further complemented our investigation facilitating the elucidation of optimal interaction modes between these inhibitors and their target receptor. Additionally, molecular dynamics simulation was conducted for the promising compound 8a. This study not only provides valuable insights into the design of new antimalarial agents but also highlights the importance of computational methods for accelerating drug discovery processes, particularly in the context of infectious disease control.

Neuropathic pain syndrome has a profoundly negative and agonizing impact on the lives of the individuals it afflicts. In order to find an effective treatment for this condition, extensive and thorough scientific studies have demonstrated that the σ1 receptor serves as an exceptional target for therapeutic compounds. The 3D-QSAR studies were constructed using the technique of comparative molecular similarity indice analysis (CoMSIA). The outcomes of these studies demonstrated the reliability of CoMSIA model (with R2 train value of 0.96 and Q2 value of 0.54) in accurately predicting the activity of various compounds. By assimilating the valuable insights gathered from the field contributors of the 3D-QSAR models and conducting molecular docking studies on the highly potent compound C48, a total of sixteen new compounds were successfully designed to exhibit enhanced efficacy against neuropathic pain. In addition to the comprehensive 3D-QSAR analysis, the newly synthesized compounds were subjected to an absorption, distribution, metabolism, excretion, and toxicity evaluation. This evaluation aimed to assess the pharmacokinetic and toxicological properties of the compounds, providing valuable insight for future in vitro investigations. Calculations DFT of the new compounds mol2, mol3, and mol4, including the analysis of their molecular properties, geometric optimization, frontier molecular orbital (FMOs), density states (DOS), and energy evaluation, In order to compare these compounds with the reference ligand 61 w, their respective properties were thoroughly investigated. The most stable orientations for the compounds Mol2, Mol4, and Mol3 were determined to be the ones that yielded the highest stability and efficiency. The significant advancements made in this study, should serve as a strong motivation for future in vitro investigations on these compounds.

The primary aim of the current investigation is to contribute to SARS-CoV-2 research by identifying potential lead compounds for clinical applications, with a specific focus on inhibitors targeting the main protease (Mpro). In this research, molecular docking analysis was conducted using the software molecular operating environmental (MOE) to evaluate the potency of bioactive compounds sourced from medicinal plants as inhibitors of SARS- -CoV-2 Mpro. Among 118 natural compounds with anti-HIV characteristics, the top seven candidates (h3, h84, h85, h87, h90, h108 and h110), were identified based on their superior binding energies with comparison to the reference ligand N3. These selected compounds exhibited binding affinities of –33.996, –35.336, –32.615, –32.154, –33.452, –31.903 and –40.360 kJ mol-1, respectively. To further refine our shortlist of potential candidates for human application, we examined the drug-likeness, and the pharmaceutical attributes of these compounds using the SwissADME web server. Among them, only two compounds, namely h85 and h87, demonstrated favorable pharmacological properties suitable for human administration. These two compounds were subsequently shortlisted for further investigation. To explore the conformational stability of ligands within the Mpro active site, we performed molecular dynamics (MD) simulations. These simulations showed reliable and steady trajectories, supported by analyses of root-mean-square-fluctuation (RMSF) and root- -mean-square deviation (RMSD). These findings and favorable molecular properties as well as interaction profiles suggest that these two lead compounds may be promising SARS-CoV-2 therapeutic candidates. They present exciting starting points for further drug design.

Audio description is a technique that was first introduced in the early 1970s by the American professor Gregory Thomas Frazier. Its aim is to describe the visual elements of a cinematographic or audiovisual work for blind or visually impaired individuals, providing them with essential information to understand the piece, including decors, characters, actions, and the actors' gestures. This technique has found success in the field of cinema. This success holds promising perspectives in the field of education, particularly in classes designed for blind and visually impaired students, as well as in inclusive classrooms with diverse needs. In this context, our contribution seeks to present and discuss the results of an integration experiment of audio description conducted with approximately twenty students from the Alaouite Organization for the Promotion of the Blind in Morocco, in the Fès-Meknès region. The objective of this experiment is to enhance pedagogical presentation and develop the visual, linguistic, and artistic capacities of students in inclusive classes, especially those who are blind or visually impaired. The project will be detaily presented, including its implementation and the key benefits it offers for the advancement of learning in Moroccan inclusive classrooms.

Merging Density Functional Theory (DFT) with the Quantitative Structure-Activity Relationship (2D/3D-QSAR) modeling represents a promising avenue for exploring antibacterial activity and discovering potential drugs effective against both gram-positive and gram-negative microorganisms. In this study, we employed this integrated approach to investigate a newly synthesized and promising class of 1,3,4-oxadiazole derivatives renowned for their high performance as antibacterial agents. To ensure an accurate characterization and thorough description of the targeted biological activity, we systematically evaluated various DFT functionals to precisely predict the geometrical and electronic descriptors of the investigated compounds. These descriptors were crucial for developing and validating the proposed 2D/3D-QSAR models. Our results reveal that the introduction of a donor group enhances the antibacterial activity of the derivatives. The analysis of molecular descriptors underscores the positive impact of this modification on the compounds' efficacy against bacteria. Additionally, our experimental compounds exhibit favorable characteristics concerning oral bioavailability, a crucial aspect in drug development. The identification of robust correlations between antibacterial activity and specific descriptors was achieved by conducting an extensive analysis that encompassed multiple linear regression (MLR), Random Forest (RF), and Artificial Neural Networks (ANN). Subsequently, a partial least squares (PLS) model was employed to construct 3D-QSAR models based on the Comparative Molecular Field Analysis (CoMFA) and Comparative Similarity Indices Analysis (CoMSIA) descriptors. Validation of the output models was performed using leave-one-out and bootstrapping strategies. The resultant 2D/3D-QSAR models demonstrated a high correlation between experimental and predicted activity values. Leveraging these models, the developed MLR model, expressed as pMIC = -12.704 + 0.260 logP – 6.104 10-03 SAG – 51.385 qN33, serves as a valuable tool for predicting antibacterial activity. we indicate that the machine learning methods, Artificial Neural Networks (ANN) and Random Forest (RF), outperform traditional models in accurately predicting antibacterial activity. We designed ten novel molecules, subjecting them to molecular docking and molecular dynamics simulations to predict their optimal postures when docked in the target and gain insights into the formed interactions. Additionally, Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET) analysis was applied to discern the potential behavior of these novel compounds in the human body. As a result, the newly suggested chemical, X4, X10, exhibited robust inhibitory potential against gram-negative microbes.

Flavonoids have been the subject of several studies for many years, particularly due to their high antioxidant activity. However, understanding the structure–activity relationships (SAR) of flavonoids is crucial for optimizing their properties and designing new derivatives with enhanced activities. In this study, we employed Quantitative Structure-Activity Relationship (QSAR) methods to analyze a group of 31 flavonoids with known biological activity. The Gaussian program was used to calculate the molecular descriptors. Using statistical modeling techniques, such as multiple linear regression, we developed QSAR models to correlate the molecular descriptors with the activity values. The models were rigorously validated using appropriate procedures to ensure their reliability and predictive power with a correlation coefficient R2pred = 0.86, and an absolute average relative error (AARE pred) of 0.06 for the test set.

In this article, we study the growth of meromorphic solutions of linear delay-differential equation of the form [\ \sum_{i=0}^{n}\sum_{j=0}^{m}A_{ij}(z)f^{(j)}(z+c_{i})=F(z),/] where Aij(z) (i = 0, 1, ..., n, j = 0, 1, ..., m, n, m ∈ N) and F(z) are meromorphic of finite logarithmic order, ci(i = 0, ..., n) are distinct non-zero complex constants. We extend those results obtained recently by Chen and Zheng, Bellaama and Belaïdi to the logarithmic lower order.

Plant extracts and essential oils are promising new sources of non-toxic alternatives to chemicals substances. They are used for their antimicrobial properties against plant diseases of fungal origin, against bacterial, and those of virus origin. This study was carried out to study the antifungal activity of Artemisia herba-alba, Foeniculum vulgare and Citrus sinensis essential oils against Fusarium oxysporum f. sp. albedinis. Essential oil was extracted by steam distillation. Antifungal activity of essential oil was investigated by macro-broth method of dilution by a minimal inhibitory concentration (MIC) assay against this pathogen. The yield of essential oil obtained by steam distillation of Foeniculum vulgare samples was 2.31% greater than that obtained from Citrus sinensis sample which was 1.8%, followed by Artemisia herba alba samples 1.22%. Regarding antifungal activity, the results revealed a better inhibitory activity of Artemisia herba alba against the tested strainsat the lowest LC50 values (0.1 µl/ml). On the other hand, Foeniculum vulgare, Artemisia herba alba and Citrus sinensis essential oils show similar MICs of mycelial growth against this pathogen. The value of the MIC and CMF is greater than 50 µl/ml for the three essential oils.

In this study, we use complementary and sensitive experimental techniques XPS (X-rays Photoelectron Spectroscopy), AES (Auger Electron Spectroscopy, REELS (Reflection Electron Energy-Loss Spectroscopy) and PL (photoluminescence) to investigate and compare the chemical, structure, electronic and optical properties of Un-doped ZnO (UZO) and Indium-doped ZnO (IZO) (4% In; 6% In) thin films. Spray method is used for the growth of these thin films on Si substrate. A treatment process UHV (Ultra-High -Vacuum: Ar+ sputtering followed by checked successive heating until 650°C) is performed. XPS and AES results allow to confirm the clean state of samples and the incorporation of indium into the ZnO matrix to form chemical species of (In-O-Zn) type. The recorded REELS spectra at different primary energies and the PL measurements justify that the UHV treatment plays an important role to improve the physical structure of IZO (6% In).

Obstructive sleep apnea syndrome (OSAS) is associated with chronic intermittent hypoxia (cIH) that causes disturbances in glucose and lipid metabolism. Animals exposed to cIH show lower body weight and food intake, but the protein-energy metabolism has never been investigated. Here, to address the gap, we studied the impact of cIH on nutritional status in rats. A total of 24 male Wistar rats were randomized into 3 groups (n = 8): a control group (Ctrl), a cIH group (cIH) exposed to cIH (30 s 21–30 s 5% fraction of inspired oxygen, 8 h per day, for 14 days), and a pair-fed group (PF) exposed to normoxia with food intake adjusted to the intake of the cIH group rats with anorexia. Body weight and food intake were measured throughout the study. After 14 days, the rats were euthanized, the organs were collected, weighed, and the liver, intestine mucosa, and muscles were snap-frozen to measure total protein content. Food intake was decreased in the cIH group. Body weight was significantly lower in the cIH group only (−11%, p < 0.05). Thymus and liver weight as well as EDL protein content tended to be lower in the cIH group than in the Ctrl and PF groups. Jejunum and ileum mucosa protein contents were lower in the cIH group compared to the PF group. cIH causes a slight impairment of nutritional status and immunity. This pre-clinical work argues for greater consideration of malnutrition in care for OSAS patients. Further studies are warranted to devise an adequate nutritional strategy.