Identification of early symptoms of endometriosis through the analysis of online social networks: A social media study.

Objective: Endometriosis is a complex full-body inflammation disease with an average time to diagnosis of 7-10 years. Social networks give opportunity to patient to openly discuss about their condition, share experiences, and seek advice. Thus, data from social media may provide insightful data about patient's experience. This study aimed at applying a text-mining approach to online social networks in order to identify early signs associated with endometriosis. Methods: An automated exploration technique of online forums was performed to extract posts. After a cleaning step of the built corpus, we retrieved all symptoms evoked by women, and connected them to the MedDRA dictionary. Then, temporal markers allowed targeting only the earliest symptoms. The latter were those evoked near a marker of precocity. A co-occurrence approach was further applied to better account for the context of evocations. Results: Results were visualised using the graph-oriented database Neo4j. We collected 7148 discussions threads and 78,905 posts from 10 French forums. We extracted 41 groups of contextualised symptoms, including 20 groups of early symptoms associated with endometriosis. Among these groups of early symptoms, 13 were found to portray already known signs of endometriosis. The remaining 7 clusters of early symptoms were limb oedema, muscle pain, neuralgia, haematuria, vaginal itching, altered general condition (i.e. dizziness, fatigue, nausea) and hot flush. Conclusion: We pointed out some additional symptoms of endometriosis qualified as early symptoms, which can serve as a screening tool for prevention and/or treatment purpose. The present findings offer an opportunity for further exploration of early biological processes triggering this disease.

Medical students' contribution to research; the scientific output of medical theses held in Moroccan medical schools during the last decade (2011-2021).

BACKGROUND: In order to obtain their medical degree, medical students in Morocco are required to carry out a research project and provide a thesis outlining its methodology and findings. However, little is known about the scientific output of these theses. The aim of the present study was to explore and evaluate the characteristics and publication patterns in scientific-indexed journals of medical theses written by medical students in Morocco. METHODS: Data was extracted from registered theses between 2011 and 2021 in four medical schools that have an open-source documents archiving platform. Publication of these theses was assessed in 2022 using a search strategy in three indexed databases; Pubmed, Scopus and Web of science. RESULTS: 9807 theses were registered between 2011 and 2021, 41% of them in the faculty of medicine of Rabat. 99.1% of these theses were written in French, 61.7% were reporting a retrospective case series, and 38.9% of them covered surgical disciplines. 83 (0.8%) of the registered theses were published in a scientific-indexed journal, and half of the papers (49.4%) was written in French. The graduate student was the paper's lead author in 54.2% of the papers. The articles driven from the theses were published after a mean delay of 1.49 ± 1.34 years and the targeted journals had a mean SJR score of 0.69 ± 1.21. The total number of detected citations of the 83 published papers was 216. CONCLUSION: The publication rate of Moroccan medical theses is very low when compared to other countries, which leads to question the real benefit of this time and resources consuming educational activity.

Exploration of the core protein network under endometriosis symptomatology using a computational approach.

Background: Endometriosis is defined by implantation and invasive growth of endometrial tissue in extra-uterine locations causing heterogeneous symptoms, and a unique clinical picture for each patient. Understanding the complex biological mechanisms underlying these symptoms and the protein networks involved may be useful for early diagnosis and identification of pharmacological targets. Methods: In the present study, we combined three approaches (i) a text-mining analysis to perform a systematic search of proteins over existing literature, (ii) a functional enrichment analysis to identify the biological pathways in which proteins are most involved, and (iii) a protein-protein interaction (PPI) network to identify which proteins modulate the most strongly the symptomatology of endometriosis. Results: Two hundred seventy-eight proteins associated with endometriosis symptomatology in the scientific literature were extracted. Thirty-five proteins were selected according to degree and betweenness scores criteria. The most enriched biological pathways associated with these symptoms were (i) Interleukin-4 and Interleukin-13 signaling (p = 1.11 x 10-16), (ii) Signaling by Interleukins (p = 1.11 x 10-16), (iii) Cytokine signaling in Immune system (p = 1.11 x 10-16), and (iv) Interleukin-10 signaling (p = 5.66 x 10-15). Conclusion: Our study identified some key proteins with the ability to modulate endometriosis symptomatology. Our findings indicate that both pro- and anti-inflammatory biological pathways may play important roles in the symptomatology of endometriosis. This approach represents a genuine systemic method that may complement traditional experimental studies. The current data can be used to identify promising biomarkers for early diagnosis and potential therapeutic targets.

A Computational Exploration of the Molecular Network Associated to Neuroinflammation in Alzheimer's Disease.

Neuroinflammation, as defined by the presence of classically activated microglia, is thought to play a key role in numerous neurodegenerative disorders such as Alzheimer's disease. While modulating neuroinflammation could prove beneficial against neurodegeneration, identifying its most relevant biological processes and pharmacological targets remains highly challenging. In the present study, we combined text-mining, functional enrichment and protein-level functional interaction analyses to 1) identify the proteins significantly associated to neuroinflammation in Alzheimer's disease over the scientific literature, 2) distinguish the key proteins most likely to control the neuroinflammatory processes significantly associated to Alzheimer's disease, 3) identify their regulatory microRNAs among those dysregulated in Alzheimer's disease and 4) assess their pharmacological targetability. 94 proteins were found to be significantly associated to neuroinflammation in Alzheimer's disease over the scientific literature and IL4, IL10 and IL13 signaling as well as TLR-mediated MyD88- and TRAF6-dependent responses were their most significantly enriched biological processes. IL10, TLR4, IL6, AKT1, CRP, IL4, CXCL8, TNF-alpha, ITGAM, CCL2 and NOS3 were identified as the most potent regulators of the functional interaction network formed by these immune processes. These key proteins were indexed to be regulated by 63 microRNAs dysregulated in Alzheimer's disease, 13 long non-coding RNAs and targetable by 55 small molecules and 8 protein-based therapeutics. In conclusion, our study identifies eleven key proteins with the highest ability to control neuroinflammatory processes significantly associated to Alzheimer's disease, as well as pharmacological compounds with single or pleiotropic actions acting on them. As such, it may facilitate the prioritization of diagnostic and target-engagement biomarkers as well as the development of effective therapeutic strategies against neuroinflammation in Alzheimer's disease.

Protein network exploration prioritizes targets for modulating neuroinflammation in Parkinson's disease.

Parkinson's disease is a progressive neurodegenerative disease associated with a loss of dopaminergic neurons in the substantia nigra of the brain. Neuroinflammation, another hallmark of the disease, is thought to play an important role in the neurodegenerative process. While mitigating neuroinflammation could prove beneficial for Parkinson's disease, identifying the most relevant biological processes and pharmacological targets as well as drugs to modulate them remains highly challenging. The present study aimed to better understand the protein network behind neuroinflammation in Parkinson's disease and to prioritize possible targets for its pharmacological modulation. We first used text-mining to systematically collect the proteins significantly associated to Parkinson's disease neuroinflammation over the scientific literature. The functional interaction network formed by these proteins was then analyzed by integrating functional enrichment, network topology analysis and drug-protein interaction analysis. We identified 57 proteins significantly associated to neuroinflammation in Parkinson's disease. Toll-like Receptor Cascades as well as Interleukin 4, Interleukin 10 and Interleukin 13 signaling appeared as the most significantly enriched biological processes. Protein network analysis using STRING and CentiScaPe identified 8 proteins with the highest ability to control these biological processes underlying neuroinflammation, namely caspase 1, heme oxygenase 1, interleukin 1beta, interleukin 4, interleukin 6, interleukin 10, tumor necrosis factor alpha and toll-like receptor 4. These key proteins were indexed to be targetable by a total of 38 drugs including 27 small compounds 11 protein-based therapies. In conclusion, our study highlights key proteins in Parkinson's disease neuroinflammation as well as pharmacological compounds acting on them. As such, it may facilitate the prioritization of biomarkers for the development of diagnostic, target-engagement assessment and therapeutic tools against Parkinson's disease.