Michel-Edwar Mickael

Background: Anxiety and depressive symptoms are common, and dietary patterns have been proposed as a modifiable influence on them. The majority of the evidence rests on composite adherence indices such as the Mediterranean Diet Adherence Screener (MEDAS), which sums its components as if they all contributed equally and addresses depression more often than anxiety. Whether individual dietary components relate to symptoms independently of the composite index has rarely been tested, and rarely alongside social media use, a further exposure proposed to shape both diet and mood. Methods: An anonymous online survey in English, Spanish, and Russian was completed by 115 adults aged 18 to 65 years with no previous diagnosis of mental illness, comprising a 14-item modified Mediterranean Diet Score (mMEDS), the Hospital Anxiety and Depression Scale (HADS), and questions on daily time spent on Instagram, Facebook, and Twitter/X. Four primary hypotheses tested the associations of olive oil intake and red and processed meat intake with each HADS subscale; all other comparisons were exploratory. The Benjamini–Hochberg correction was applied within each family. Effect sizes are reported with bootstrap 95% confidence intervals. The primary comparisons were additionally adjusted for sex, age band, and educational level. Results: All four primary estimates lay in the hypothesized direction. Higher olive oil intake was associated with lower anxiety scores ( r = −0.249, 95% CI − 0.448 to −0.050, p = 0.021), and restricted red and processed meat intake was associated with lower anxiety and depression scores after adjustment ( β = −2.12, p = 0.021; β = −1.62, p = 0.032). None survived correction for multiple testing. The mMEDS total did not appear to be related to either subscale ( ρ = −0.045 and −0.141), while item-level associations differed in direction across food groups. Conclusion: Component-level signals appeared where the composite showed none, which may indicate that summing adherence items can obscure relationships running in opposite directions between foods. For studies using MEDAS-type indices, testing components alongside the total score could be worthwhile. Olive oil quantity and red and processed meat intake may be candidates for confirmatory work.

Introduction: Leukocyte infiltration across the blood-brain barrier contributes to many neuroinflammatory disorders, yet the physical mechanisms that determine how immune cells cross the barrier remain incompletely understood. In particular, it is unclear how leukocytes choose between paracellular migration through endothelial junctions and transcellular migration through the endothelial cell body. This question is especially relevant during central nervous system inflammation, where different T-lymphocyte subsets, including Th1 and Th17 cells, appear to preferentially use distinct migration routes. Previous studies have examined individual mechanical forces or upstream biochemical signaling pathways, but these approaches have not provided a unified framework that considers the energetic contributions of both the leukocyte and the deforming endothelium. Methods: To address this, we developed a two-route energy minimization model that incorporates an anisotropic, fiber-reinforced hyperelastic model, junctional unzipping, Helfrich membrane bending, caveolar vesicle fusion, and cytokine-dependent signaling. Model parameters were optimized, and route switching was evaluated using Sobol global sensitivity analysis to determine which specific mechanical and signaling parameters independently drive route switching rather than merely scaling overall energy magnitude. Results: The model reproduced the experimental validation conditions used in this study and identified distinct regimes of mechanical control. Under IFN-γ-dominant conditions, route selection was influenced primarily by ICAM-1 adhesion kinetics. Under IL-17-dominant conditions, control shifted toward PECAM-1-related physical properties and their nonlinear interactions, which accounted for 76% of the variance in route switching. Discussion: These exploratory findings suggest that leukocyte transmigration is shaped by local biochemical conditions together with the mechanical properties of the endothelial barrier, rather than by a single fixed migration program. Mapping the associated energetic landscape may help clarify the physical factors governing immune-cell entry into the central nervous system and provide a framework for studying mechanisms that regulate leukocyte trafficking during neuroinflammation.

Several social media platforms provide a route for communication about scientific topics. X, formerly known as Twitter, has been shown to offer researchers an opportunity to exchange scientific information with audiences extending well beyond the research community. In recent years another platform, Bluesky, has emerged and appears to encourage scientific discourse, and a wave of migration of researchers from X to Bluesky has been documented in survey data, poll responses and altmetric records. Whether scientific claims are described differently on the two platforms has not been fully studied. We collected 8,697 Bluesky posts and 3,48,900 X posts mentioning the term science over a matched observation window, restricted in both cases to English-language text written by the posting account. A combination of data analysis and natural language processing tools was then used to compare the discourse on the two platforms, quantifying disciplinary composition, field concentration, cross-disciplinary reference and linguistic register, with effect sizes reported alongside every test and correction applied for multiple comparisons. The two platforms carried posts across a similar range of science-related topics, including health and medicine, biology and life sciences, and engineering and technology. However, posts on X were easier to read (Flesch–Kincaid grade 9.07 against 10.02), addressed the reader more directly (second-person pronouns 0.0153 against 0.0093) and were less complex (complex word density 0.092 against 0.108). Posts on Bluesky were longer (30.6 against 28.7 words; 15.98 against 14.54 words per sentence), more concentrated within single disciplines (mean field isolation 0.6882 against 0.6200), higher in first-person pronoun use (0.0255 against 0.0208) and more often classified as academic (16.6% against 12.7%). These patterns are consistent with greater peer-oriented scientific discourse on Bluesky and greater audience-oriented dissemination on X, although audience composition was not measured directly.

Diabetes mellitus is a pervasive global health crisis, with type 2 diabetes (T2D) representing the majority of cases. While conventional pharmacological therapies exist, suboptimal efficacy, adverse effects, and inadequate glycemic control in a substantial proportion of patients highlight the need for complementary strategies. There is growing scientific and clinical interest in plant-based functional foods, including Ribes nigrum L. (blackcurrant), which is rich in bioactive phytochemicals such as anthocyanins, flavonols, and phenolic acids. This review evaluates the antidiabetic and metabolic regulatory potential of blackcurrant, focusing on its multi-target mechanisms of action. Available experimental evidence suggests that blackcurrants may influence glucose metabolism through several mechanisms, including inhibition of carbohydrate-digesting enzymes (α-amylase and α-glucosidase), modulation of intestinal glucose transporters (SGLT1 and GLUT2), and potential modulation of insulin sensitivity involving AMPK signaling and GLUT4 translocation. In addition, blackcurrant constituents may stimulate glucagon-like peptide-1 (GLP-1) secretion, modulate gut microbiota composition, and exert antioxidant and anti-inflammatory effects that counteract key drivers of insulin resistance and β-cell dysfunction. Supporting human studies indicate that blackcurrant consumption has been associated with attenuation of postprandial glycemic and insulinemic responses and modest improvements in cardiometabolic biomarkers, although findings remain limited by study size, duration, and formulation heterogeneity. Overall, Ribes nigrum represents a promising dietary adjunct for the prevention and management of T2D through multi-target metabolic regulation.

Cervical cancer remains a major global health challenge, particularly in low- and middle-income countries, where access to screening, vaccination, and timely treatment may be limited. Cervical cytology has played an important historical role in prevention, but it is labor-intensive, time-consuming, and subject to observer variability and limited sensitivity. In many contemporary screening programs, HPV testing is now used as the primary screening test, while cytology is used mainly for the triage of HPV-positive women. In recent years, artificial intelligence (AI), particularly deep learning (DL), has shown considerable potential in medical image analysis and computer-aided diagnosis. This review summarizes current applications of AI in cervical cytology and related diagnostic workflows, including automated and assisted slide screening, liquid-based cytology, the triage of equivocal or HPV-positive cases, and colposcopy support. Across these settings, AI-assisted systems may improve efficiency, standardization, and diagnostic consistency, and may reduce workload in resource-constrained environments. However, the evidence is heterogeneous, and important challenges remain, including the need for large and diverse datasets, prospective validation, regulatory approval, digital infrastructure, workflow integration, and the resolution of ethical and legal issues. AI should therefore be regarded as a promising adjunct to human expertise rather than a replacement in cervical cytology and related clinical diagnostic pathways.

The immune system’s evolution is crucial for its role in fighting pathogens and involvement in autoimmune and neurodegenerative diseases. The GFI1 gene family plays a role in the regulation of the function of immune cells, including neutrophils and CD4 + T cells. GFI1 consists of two members, GFI1A and GFI1B. GFI1A is vital for myeloid and lymphoid differentiation, while GFI1B is crucial for generating red blood cells and platelets. Both genes share a repressor SNAG domain and C2H2 zinc finger domains. However, the full relationship between their structure and function remains unclear. We aimed to decipher the relationship between structural evolution and novel functionalization in the GFI1 gene family. We employed a comprehensive phylogenetic approach that integrated tree construction, ancestral state reconstruction, positive selection analysis, motif mining, and non-homology-based functional prediction to trace GFI1 family evolutionary history over 700 million years. Our analysis revealed that the GFI1 gene family originated from a single ancestral gene in early metazoans and underwent multiple lineage-specific duplication events in invertebrates, jawless vertebrates, and jawed vertebrates, indicating adaptive diversification across evolutionary lineages, albeit without evidence of significant positive selection. We identified new motifs in the less-characterized middle regions, such as the SPOP-binding motif in GFI1A, potentially regulating cytokine production in CD4 + T cells, and the FEDFW motif, possibly involved in neutrophil recruitment. These motifs are unique to GFI1A in higher vertebrates. In GFI1B, we discovered a unique EPLRP motif, a separase cleavage site linked to sister chromatid separation. Our results indicate that GFI1 has evolved new functions to adapt to the complexity of the vertebrate immune system.

Severe motor impairments such as amyotrophic lateral sclerosis and locked-in syndrome lead to partial or complete loss of speech, severely restricting communication as voluntary motor control deteriorates. In this study, we developed a non-invasive, wearable EEG-based brain–computer interface that reconstructs coherent natural language sentences by decoding linguistic components directly from neural activity. EEG data were collected from 20 healthy volunteers using two bilateral temporal electrodes (T7/T8) sampled at 128 Hz, with signals segmented into 4-second windows (512 samples) and normalized to a [−1, 1] range. Participants silently generated 6 pronouns, 80 verbs, and 217 object nouns across six languages under controlled cognitive tasks. Separate 1D convolutional neural networks were trained to classify pronouns and to regress verbs and nouns into 300-dimensional FastText semantic embeddings using cosine similarity loss, with data splits of 70/30 or 80/20 for training and validation and augmentation applied exclusively to training data. Pronoun decoding achieved over 60% training accuracy but showed reduced generalization (validation AUC ≈ 0.55), reflecting the context-dependent nature of indexical language, while verb and noun models demonstrated stable convergence over 200–300 epochs and successfully mapped EEG features into semantic space. A Siamese network integrated decoded components into a shared embedding space to ensure semantic coherence prior to sentence generation, enabling the production of grammatically correct and contextually appropriate sentences aligned with experimental conditions such as hunger and thirst. Validtaion cohort of 10 individuals was used to predict their thoughts related to hunger, while fasting. 80% accuracy was achieved. These findings demonstrate that bilateral temporal EEG signals alone are sufficient to recover structured linguistic intent when combined with similarity-based semantic modeling, advancing a scalable, non-invasive communication framework for clinical translation.

Introduction: Human behavior is significantly influenced by emotions, with negative sentiments such as fear and anxiety driving various coping mechanisms, including cognitive behavioral therapy (CBT), dietary changes, and medication use. Social media platforms like X (formerly Twitter) offer valuable insights into these behaviors due to their real-time, user-generated content. While previous research has explored general sentiment on X (formerly Twitter), there has been limited focus on the reasons behind negative sentiments and the coping strategies employed, particularly in relation to brain health. Methods: We analyzed 390,000 X-posts tagged with #brain and #health, categorizing them into positive, negative, and neutral sentiments. We then investigate the use of CBT techniques, dietary adjustments, and specific medications across these sentiments. Results: Our findings reveal distinct patterns in how negative and positive sentiments are expressed and managed on social media. Negative sentiments are often linked to serious health concerns, such as COVID-19 and brain inflammation, and exhibit various cognitive distortions. These X-posts also mention coping strategies like using medications such as lorazepam and simvastatin, or consuming comfort foods like pizza. In contrast, positive sentiments emphasize resilience and improvement, with mentions of mindfulness, supplements, and medications like doxycycline and pregabalin. The study also highlights the risk of disseminating information about dietary and drug supplements that may not be suitable for public use due to serious side effects, such as Chaga mushrooms, which, despite being associated with positive sentiment, are known to cause renal failure in certain cases. Conclusion: Overall, the study profiles the use of positive and negative brain health sentiment of X, which underscores both the advantages and risks of using X (formerly Twitter) as a platform for sharing brain health-related information.

Aim: This manuscript summarizes the key scientific and practical outcomes of the #DHPSP2024 digital networking event, focusing on emerging trends in digital health technologies, innovations in patient safety, and their implications for improving healthcare delivery. Methods: The #DHPSP2024 event was held from June 18 to 20, 2024, on X (formerly Twitter) and LinkedIn, connecting professionals and stakeholders in digital health and patient safety from different sectors. Data from posts on X and LinkedIn were analyzed for geographical distribution, engagement metrics (impressions, likes, shares), top hashtags, and frequently used terms. A qualitative analysis of the central themes and key online messaging discussions of the network event was also conducted. Results: On X, 2,329 posts by 179 participants from 38 countries generated over 231,000 impressions, with the most activity in Austria, China, and India. LinkedIn engagement included 3,475 likes, 217 comments, and 2,030 shares. Both platforms highlighted core themes such as digital health, patient safety, treatment quality, research on natural compounds, and interdisciplinary collaboration. Online messaging discussions emphasized technologies like telemedicine and artificial intelligence as critical tools for enhancing care delivery and patient safety. Participants also promoted special issues of scientific journals and explored collaborative research opportunities. Conclusions: The #DHPSP2024 event underscored the pivotal role of digital technologies in transforming healthcare, particularly in improving the quality and safety of interventions. The findings demonstrate how digital networking events, grounded in open innovation, foster global research communities, accelerate knowledge exchange, and support the integration of clinically relevant digital solutions. The strong engagement reflects growing interest in leveraging digital platforms to advance health outcomes and professional development. Overall, the event contributed to greater visibility of ongoing research, encouraged interdisciplinary cooperation, and may positively influence both the adoption of innovations in healthcare practice and the dissemination of scientific knowledge.