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Background: Accurate classification of host phenotypes from microbiome data is crucial for advancing microbiome-based therapies, with machine learning offering effective solutions. However, the complexity of the gut microbiome, data sparsity, compositionality, and population-specificity present significant challenges. Microbiome data transformations can alleviate some of the aforementioned challenges, but their usage in machine learning tasks has largely been unexplored.

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Crisdesalazine alleviates inflammation in an experimental autoimmune encephalomyelitis multiple sclerosis mouse model by regulating the immune system.

BMC Neurosci

January 2025

Laboratory of Veterinary Internal Medicine, Department of Clinical Veterinary Science, College of Veterinary Medicine, Seoul National University, Seoul, 08826, Republic of Korea.

Microglia/macrophages participate in the development of and recovery from experimental autoimmune encephalomyelitis (EAE), and the macrophage M1 (pro-inflammatory)/M2 (anti-inflammatory) phase transition is involved in EAE disease progression. We evaluated the efficacy of crisdesalazine (a novel microsomal prostaglandin E2 synthase-1 inhibitor) in an EAE model, including its immune-regulating potency in lipopolysaccharide-stimulated macrophages, and its neuroprotective effects in a macrophage-neuronal co-culture system. Crisdesalazine significantly alleviated clinical symptoms, inhibited inflammatory cell infiltration and demyelination in the spinal cord, and altered the phase of microglial/macrophage and regulatory T cells.

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Background: The rarity that is inherent in rare disease (RD) often means that patients and parents of children with RDs feel uniquely isolated and therefore are unprepared or unsupported in their care. To overcome this isolation, many within the RD community turn to the internet, and social media groups in particular, to gather useful information about their RDs. While previous research has shown that social media support groups are helpful for those affected by RDs, it is unclear what these groups are particularly useful or helpful for patients and parents of children with RDs.

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Part 2 explores the transformative potential of artificial intelligence (AI) in addressing the complexities of headache disorders through innovative approaches, including digital twin models, wearable healthcare technologies and biosensors, and AI-driven drug discovery. Digital twins, as dynamic digital representations of patients, offer opportunities for personalized headache management by integrating diverse datasets such as neuroimaging, multiomics, and wearable sensor data to advance headache research, optimize treatment, and enable virtual trials. In addition, AI-driven wearable devices equipped with next-generation biosensors combined with multi-agent chatbots could enable real-time physiological and biochemical monitoring, diagnosing, facilitating early headache attack forecasting and prevention, disease tracking, and personalized interventions.

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Article Synopsis
  • Smart wearables are essential for health monitoring and assisting the elderly or individuals with disabilities, but current machine learning methods face high resource demands and limited scalability.
  • This research introduces a new behavior detection approach that combines multi-source sensing with logical reasoning, aiming to streamline the process of behavior recognition.
  • The developed system achieves over 90% accuracy in recognizing 11 daily activities while significantly reducing the need for extensive training data compared to traditional machine learning methods.
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