Metastases are the main cause of cancer-related mortality in breast cancer. Although significant progress has been made in the field of tumor metastasis, the exact molecular mechanisms involved in tumor metastasis are still unclear. Here, we report that ATOH8-V1, a novel isoform of ATOH8, is highly expressed in breast cancer and is a negative prognostic indicator of survival for patients. Forced expression of ATOH8-V1 dramatically enhances, while silencing of ATOH8-V1 decreases the metastasis of breast cancer cell lines. Moreover, ATOH8-V1 directly binds to the RhoC promoter and stimulates the expression of RhoC, which in turn enhances the metastasis of breast cancer. Altogether, our data demonstrate that ATOH8-V1 is a novel pro-metastatic factor that enhances cancer metastasis, suggesting that ATOH8-V1 is a potential therapeutic target for treatment of metastatic cancers.

Non-tuberculous mycobacterial (NTM) infection is usually observed in patients with immunosuppressive conditions. It may also cause unregulated immune responses. While there have been increasing numbers of reported tuberculosis-related HPS (haemophagocytic syndrome), HPS caused by NTM infection is still very rarely reported. We report a previously healthy 21-year-old Chinese female with fever, night sweats and fatigue, in whom HPS was diagnosed according to the HLH-2004 criteria. Mycobacterium intracellulare was cultured from her peripheral blood. After treatment with corticosteroid, clarithromycin, rifampicin, ethambutol and amikacin, the patient finally recovered. We also reviewed relevant publications on NTM infection complicated with HPS and found 11 cases, including ours. Clinical presentations, diagnoses and prognoses were analysed and summarized to deepen our understanding of this rare condition. Most reported NTM-related cases were caused by disseminated infection. The lack of localized symptoms might add to the difficulty involved in making the right diagnosis. While it usually takes time to obtain tissue or blood culture results, granuloma in a bone marrow biopsy might be an early indicator of possible mycobacterial infection. Although treatment varied, the overall prognosis of NTM-related HPS was promising.

Artificial intelligence (AI) has revolutionized many fields, and its potential in healthcare has been increasingly recognized. Based on diverse data sources such as imaging, laboratory tests, medical records, and electrophysiological data, diagnostic AI has witnessed rapid development in recent years. A comprehensive understanding of the development status, contributing factors, and their relationships in the application of AI to medical diagnostics is essential to further promote its use in clinical practice. In this study, we conducted a bibliometric analysis to explore the evolution of task-specific to general-purpose AI for medical diagnostics. We used the Web of Science database to search for relevant articles published between 2010 and 2023, and applied VOSviewer, the R package Bibliometrix, and CiteSpace to analyze collaborative networks and keywords. Our analysis revealed that the field of AI in medical diagnostics has experienced rapid growth in recent years, with a focus on tasks such as image analysis, disease prediction, and decision support. Collaborative networks were observed among researchers and institutions, indicating a trend of global cooperation in this field. Additionally, we identified several key factors contributing to the development of AI in medical diagnostics, including data quality, algorithm design, and computational power. Challenges to progress in the field include model explainability, robustness, and equality, which will require multi-stakeholder, interdisciplinary collaboration to tackle. Our study provides a holistic understanding of the path from task-specific, mono-modal AI toward general-purpose, multimodal AI for medical diagnostics. With the continuous improvement of AI technology and the accumulation of medical data, we believe that AI will play a greater role in medical diagnostics in the future.

Background: Artificial intelligence (AI) is increasingly being integrated into medical education. As AI technologies continue to evolve, they are expected to enable more sophisticated student tutoring, performance evaluation, and reforms of curricula. However, medical education entities have been ill-prepared to embrace this technological revolution, and there is anxiety concerning its potential harm to the community. Objective: To explore research trends in the field and identify future directions for AI-enabled medical education, we conducted a systematic bibliometric analysis focusing on temporal trajectories in the field. Methods: Documents were collected from the Web of Science and Scopus databases covering the period from 2000 to 2024. A multistep search strategy combining information retrieval, a definitive journal list, and cocitation analysis was used to identify relevant publications. Journal and author impact were assessed using both publication and citation metrics. Research trends and hot spots were examined through citation burst detection, frequency analysis, and co-occurrence networks, with a color gradient used to indicate the average occurrence year of keywords. The citation lineage structure of the field was evaluated using a k-means clustering-based analysis of cocitation networks to trace influential references. Results: Our analysis revealed a significant increase in publications since 2021, with foundational works emerging as early as 2019. Influential journals in this domain included JMIR Medical Education, Anatomical Sciences Education, and Medical Education. The evolving research trajectory exhibited a shift from conventional computer-assisted learning tools toward generative AI platforms. Earlier applications of AI in medical education were predominantly concentrated at the undergraduate level, indicating substantial potential for expansion into graduate and continuing medical education. Furthermore, limited cocitation connections were observed between recent generative AI research and conventional medical AI studies, and investigations into medical students’ attitudes toward generative AI remain scarce. Conclusions: There are critical needs for (1) interdisciplinary studies that intentionally integrate generative AI with foundational medical AI work and (2) involving medical educators and students in AI development. Future research should focus on building theoretical frameworks and collaborative projects that connect these currently separate domains to foster a more cohesive knowledge base.

There is no common medical professionalism framework in China, mandating work to conceptualize professionalism from various perspectives. Studies on students viewpoints about medical professionalism are limited. Therefore, this study aimed to investigate how Chinese medical students perceive professionalism to provide a reference for future medical education reform and policy development. Fifty-four written reflections on medical professionalism were collected from first-year students of China 4 + 4 medical education program enrolled in 2020 to 2021 academic years. Essays were subjected to thematic analysis using NVivo 12. Three main themes emerged: inter-personal, intra-personal, and public professionalism. Students emphasized the importance of physician-patient relationships, proficiency of medical knowledge, and enthusiasm for promoting health-related issues. By contrast, teamwork and confidentiality were not considered essential aspects of professionalism. The medical professionalism framework articulated by students in China was roughly the same as in other countries. Where there were differences, these may have been due to the unique sociocultural environment. Future medical professionalism education should be adjusted according to students understanding of professionalism.