Faculty of Medicine and Pharmacy of Rabat

The clustering of neurotransmitter receptors at appropriate postsynaptic sites is essential for controlling synaptic transmission. While most known mechanisms involve receptor binding with cytoplasmic scaffolds, recent evidence highlights the importance of extracellular interactions that directly target receptors. Using Caenorhabditis elegans, we identified a trans-synaptic complex that involves RIG-5 and ZIG-8, two adhesion molecules of the immunoglobulin (Ig) superfamily and orthologous to Drosophila DIPs and Dprs, and mammalian IgLONs. Our results show that RIG-5 and ZIG-8 are anchored in the pre- and postsynaptic membranes, respectively, and interact in vivo via their first Ig domains. Furthermore, ZIG-8 directly binds a α7-like acetylcholine receptor (AChR), known as ACR-16, via a cis-interaction between its Ig2 domain and the base of the extracellular AChR domain. This study provides direct evidence that trans-synaptic IgLON interactions can organize neurochemical synapses and suggests that the IgLONs may directly interact with ionotropic receptors in the mammalian nervous system. Synaptic extracellular proteins increasingly emerge as key organizers of neurotransmitter receptors. Here, authors show that an IgLON family cell adhesion molecule directly traps a nicotinic receptor through extracellular interactions.

Essential oils (EOs) are complex mixtures of volatile metabolites with strong biological properties. Their multi-target mechanisms confer rapid bactericidal activity, lowering the likelihood of resistance development compared to antibiotics. This review examines bacterial responses to EO-induced damage, including adaptation, tolerance, resistance, cross‑adaptation, and collateral sensitivity, which are often overlooked in existing reviews of EOs’ biological efficacy. Additionally, it links EO-adaptation to food, including application methods, biofilms, and interactions with food components.

Immune-mediated congenital heart block (CHB) is a rare but severe manifestation of neonatal lupus, resulting from transplacental transfer of maternal anti-SSA/Ro and anti-SSB/La antibodies. It is associated with significant fetal and neonatal morbidity and mortality and may reveal previously undiagnosed maternal autoimmune disease. We report the case of a 34-year-old multigravida woman with no known systemic disease, referred at 29 weeks of gestation for suspected fetal arrhythmia. Fetal echocardiography revealed complete atrioventricular block associated with myocardial hypertrophy and pericardial effusion. Maternal immunologic screening demonstrated high titers of anti-SSA/Ro and anti-SSB/La antibodies, leading to the diagnosis of systemic lupus erythematosus. Despite multidisciplinary management and antenatal planning, the neonate developed severe postnatal bradyarrhythmia and heart failure, resulting in death on day 4 of life. This case highlights the pathogenic mechanisms, diagnostic challenges, and therapeutic strategies of immune-mediated CHB. Early detection through serial fetal echocardiography and maternal antibody screening is essential, as established complete CHB remains largely irreversible. Preventive therapy with hydroxychloroquine has emerged as the most effective strategy to reduce recurrence risk by more than 50% in subsequent pregnancies, based on recent prospective trials. Immune-mediated CHB may be the first manifestation of maternal autoimmune disease. Systematic immunologic screening, close fetal surveillance, and preventive treatment with hydroxychloroquine are crucial to optimize maternal and neonatal outcomes.

With the emergence of Large Language Models (LLMs), numerous use cases have arisen in the medical field, particularly in generating summaries for consultation transcriptions and extensive medical reports. A major concern is that these summaries may omit critical information from the original input, potentially jeopardizing the decision-making process. This issue of omission is distinct from hallucination, which involves generating incorrect or fabricated facts. To address omissions, this paper introduces a dataset designed to evaluate such issues and proposes a frugal approach called EmbedKDECheck for detecting omissions in LLM-generated texts. The dataset, created in French, has been validated by medical experts to ensure it accurately represents real-world scenarios in the medical field. The objective is to develop a reference-free (black-box) method that can evaluate the reliability of summaries or reports without requiring significant computational resources, relying only on input and output. Unlike methods that rely on embeddings derived from the LLM itself, our approach uses embeddings generated by a third-party, lightweight NLP model based on a combination of FastText and Word2Vec. These embeddings are then combined with anomaly detection models to identify omissions effectively, making the method well-suited for resource-constrained environments. EmbedKDECheck was benchmarked against black-box state-of-the-art frameworks and models, including SelfCheckGPT, ChainPoll, and G-Eval, which leverage GPT. Results demonstrated its satisfactory performance in detecting omissions in LLM-generated summaries. This work advances frugal methodologies for evaluating the reliability of LLM-generated texts, with significant potential to improve the safety and accuracy of medical decision support systems in surgery and other healthcare domains.

Respiratory diseases represent a major cause of morbidity and mortality worldwide, highlighting the need for rapid and accurate diagnostic tools. This article introduces an innovative method for the automatic detection of wheezes based on a convolutional neural network (CNN) model. By leveraging a database of respiratory sound recordings and advanced feature extraction techniques such as Mel-frequency cepstral coefficients (MFCC), our system achieves a 93