The impact of open access on citations, Pageviews, and downloads: a scientometric analysis in Postgraduate Medical Journal.

PURPOSE: The influence of Open Access (OA) on the citation impact of scholarly articles remains a topic of considerable debate. This study aims to elucidate the relationship between OA publication and citation metrics, as well as article visibility, within the context of the Postgraduate Medical Journal (PMJ). METHODS: We conducted a retrospective analysis of 373 articles published in PMJ between 2020 and 2021. Data on OA status, citations, page views, PDF downloads, and other relevant variables were extracted from Journal Citation Reports and PMJ's official website. Multivariable linear regression and other statistical analyses were used to assess the impact of OA on these metrics. RESULTS: OA articles (n = 78) demonstrated significantly higher citation counts, page views, and PDF downloads compared with subscription-based articles (n = 295). Specifically, OA articles showed a significant increase in citation frequency with a ß coefficient of 25.08 and a 95% CI of 17.168-32.992 (P < .001). Similarly, OA status was independently associated with increases in page views [ß = 288.636, 95%CI: 177.749-399.524, P < .001] and PDF downloads [ß = 118.966, 95%CI: 86.357-151.575, P < .001]. Strong correlations among total citations, page views, and PDF downloads were observed in both OA and subscription articles. CONCLUSION: The study highlights a significant and independent association of OA publishing with increased citation counts, page views, and PDF downloads in PMJ, suggesting that OA articles have broader reach and greater visibility. Further research, including randomized controlled studies across various journals, is needed to confirm these findings and explore the full impact of OA publishing.

New perspectives on cancer clinical research in the era of big data and machine learning.

In the 21st century, the development of medical science has entered the era of big data, and machine learning has become an essential tool for mining medical big data. The establishment of the SEER database has provided a wealth of epidemiological data for cancer clinical research, and the number of studies based on SEER and machine learning has been growing in recent years. This article reviews recent research based on SEER and machine learning and finds that the current focus of such studies is primarily on the development and validation of models using machine learning algorithms, with the main directions being lymph node metastasis prediction, distant metastasis prediction, and prognosis-related research. Compared to traditional models, machine learning algorithms have the advantage of stronger adaptability, but also suffer from disadvantages such as overfitting and poor interpretability, which need to be weighed in practical applications. At present, machine learning algorithms, as the foundation of artificial intelligence, have just begun to emerge in the field of cancer clinical research. The future development of oncology will enter a more precise era of cancer research, characterized by larger data, higher dimensions, and more frequent information exchange. Machine learning is bound to shine brightly in this field.

Corrigendum: The Mechanisms and Boundary Conditions of Drug Memory Reconsolidation.

[This corrects the article DOI: 10.3389/fnins.2021.717956.].

The Mechanisms and Boundary Conditions of Drug Memory Reconsolidation.

Drug addiction can be seen as a disorder of maladaptive learning characterized by relapse. Therefore, disrupting drug-related memories could be an approach to improving therapies for addiction. Pioneering studies over the last two decades have revealed that consolidated memories are not static, but can be reconsolidated after retrieval, thereby providing candidate pathways for the treatment of addiction. The limbic-corticostriatal system is known to play a vital role in encoding the drug memory engram. Specific structures within this system contribute differently to the process of memory reconsolidation, making it a potential target for preventing relapse. In addition, as molecular processes are also active during memory reconsolidation, amnestic agents can be used to attenuate drug memory. In this review, we focus primarily on the brain structures involved in storing the drug memory engram, as well as the molecular processes involved in drug memory reconsolidation. Notably, we describe reports regarding boundary conditions constraining the therapeutic potential of memory reconsolidation. Furthermore, we discuss the principles that could be employed to modify stored memories. Finally, we emphasize the challenge of reconsolidation-based strategies, but end with an optimistic view on the development of reconsolidation theory for drug relapse prevention.