Immunology is a knowledge area of paramount importance in life sciences and health care professional education with diverse applications, as well as for a general public understanding of issues related to vaccination. However, many concepts are complex and difficult to understand based only on conventional classes or static images. The use of tools, such as educational software, may enhance the learning of dynamic molecular phenomena that occur in our bodies. Virtual Immunology is a software that aims to facilitate the learning of certain complex immunology concepts. Herein, we present the “Antigen-antibody interactions” module that was used and evaluated by 127 students and 3 teachers from medical schools from 2 universities, 1 public and 1 private, both in the state of Rio de Janeiro, Brazil. The pretest/posttest research design was used to assess student learning in a randomized sample. To evaluate user perceptions concerning software quality, 14 statements were analyzed using a Likert scale. Results indicate suitable evaluations from both students and teachers concerning the “Antigen-antibody module” as an auxiliary tool in immunology teaching. The software was well rated as an educational resource since it allows dynamically viewing immunological phenomena. In addition, its ease of use and immunological process visualization were the best-evaluated parameters by the students, who recommended this software module as an auxiliary learning tool. The use of the evaluated software may motivate students and aid in the understanding of immunology-related concepts, becoming a complementary tool that may enhance the teaching-learning process.

Graph neural networks (GNNs) are deep learning algorithms that process graph-structured data and are suitable for applications such as social networks, physical models, financial markets, and molecular predictions. Bibliometrics, a tool for tracking research evolution, identifying milestones, and assessing current research, can help identify emerging trends. This study aims to map GNN applications, research directions, and key contributors. An analysis of 40,741 GNN-related publications from the Web Science Core Collection reveals a rising trend in GNN publications, especially since 2018. Computer Science, Engineering, and Telecommunications play significant roles in GNN research, with a focus on deep learning, graph convolutional networks, neural networks, convolutional neural networks, and machine learning. China and the USA combined account for 76.4% of the publications. Chinese universities concentrate on graph convolutional networks, deep learning, feature extraction, and task analysis, whereas American universities focus on machine learning and deep learning. The study also highlights the importance of Chemistry, Physics, Mathematics, Imaging Science & Photographic Technology, and Computer Science in their respective knowledge communities. In conclusion, the bibliometric analysis provides an overview of GNN research, showing growing interest and applications across various disciplines, and highlighting the potential of GNNs in solving complex problems and the need for continued research and collaboration.

P2X7 is a purinergic receptor involved in important physiological functions and pathological processes, such as inflammation, neurodegeneration, and pain. Despite its relevance, there is no selective antagonist useful in the treatment of diseases related to this receptor. In this context, research for a selective, safe, and potent antagonist compound that can be used in clinical therapy has been growing. In this work, we evaluated the potential antagonistic activity of three fungal extracts, namely, Vishniacozyma victoriae, Metschnikowia australis, and Ascomycota sp., which were discovered in a high‐throughput screening campaign to search for new antagonists for P2X7R from natural products. First, the IC₅₀ values of these fungal extracts were determined in J774.G8 (murine macrophage cell line) and U937 (human monocyte cell line) cells through dye uptake assays. The IC₅₀ values of V. victoriae were 2.6 and 0.92 μg/mL, M. australis has IC₅₀ values of 3.8 and 1.5 μg/mL, and Ascomycota sp. showed values of 2.1 and 0.67 μg/mL in J774.G8 and U937 cells, respectively. These extracts also significantly inhibited propidium iodide and Lucifer yellow uptake via P2X7R pore, P2X7R currents in electrophysiology, IL‐1β release, and the production of oxide nitric and reactive oxygen species. The extracts did not cause cytotoxicity within a period of 24 h. The results showed the promising antagonistic activity of these extracts toward P2X7R, thereby indicating that they can be future candidates for phytomedicines with potential clinical applicability.

P2X7 is an ATP-activated purinergic receptor implicated in pro-inflammatory responses. It is associated with the development of several diseases, including inflammatory and neurodegenerative conditions. Although several P2X7 receptor antagonists have recently been reported in the literature, none of them is approved for clinical use. However, the structure of the known antagonists can serve as a scaffold for discovering effective compounds in clinical therapy. This study aimed to propose an improved virtual screening methodology for the identification of novel potential P2X7 receptor antagonists from natural products through the combination of shape-based and docking approaches. First, a shape-based screening was performed based on the structure of JNJ-47965567, a P2X7 antagonist, using two natural product compound databases, MEGx (~5.8 × 10³ compounds) and NATx (~32 × 10³ compounds). Then, the compounds selected by the proposed shape-based model, with Shape–Tanimoto score values ranging between 0.624 and 0.799, were filtered for drug-like properties. Finally, the compounds that met the drug-like filter criteria were docked into the P2X7 allosteric binding site, using the docking programs GOLD and DockThor. The docking poses with the best score values were submitted to careful visual inspection of the P2X7 allosteric binding site. Based on our established visual inspection criteria, four compounds from the MEGx database and four from the NATx database were finally selected as potential P2X7 receptor antagonists. The selected compounds are structurally different from known P2X7 antagonists, have drug-like properties, and are predicted to interact with key P2X7 allosteric binding pocket residues, including F88, F92, F95, F103, M105, F108, Y295, Y298, and I310. Therefore, the combination of shape-based screening and docking approaches proposed in our study has proven useful in selecting potential novel P2X7 antagonist candidates from natural-product-derived compounds databases. This approach could also be useful for selecting potential inhibitors/antagonists of other receptors and/or biological targets.

Poorly designed preclinical studies may compromise human health due to erroneous conclusions regarding treatment effects in addition to contributing to experimental irreproducibility and wasted resources. Randomization is one of the crucial steps to enhance scientific rigor and is a commonly recognized bias-reducing instrument that increases the reliability and reproduction of studies involving animals (even with syngeneic animals). This procedure should be considered when planning a study and reported during data publication. In this context, this work aimed to highlight the importance of adopting quality measures in preclinical trials, with an emphasis on animal randomization. The ‘Mouse Randomization’ app was developed to help researchers estimate an adequate sample size to obtain significant statistical power, ensuring the ethical use of animals. This app is freely available on the internet to carry out animal randomization and calculate sample sizes for in vivo experiments. We believe that this brief discussion about animal randomization could raise awareness among researchers on how to improve the quality of preclinical research, increasing reproducibility and avoiding animal misuse.

The purinergic receptor P2X7, cloned in the 1990s, is distinctive in that, beyond the initial 14 opening of a cationic channel at lower ATP concentrations, sustained stimulation leads to the formation 15 of a membrane pore permeable to molecules up to 900 Da (Alves et al., 2014). P2X7 receptor activation 16 triggers pro-inflammatory pathways implicated in neurodegeneration, chronic inflammation, and pain, 17 affecting millions of people worldwide (da Silva Ferreira et al., 2019)