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Background: It is now understood that T cells play a key role in the occurrence and development of psoriasis. Herein, a bibliometric analysis was conducted to summarize the content and trends of T cell-related research in psoriasis. Methods: A bibliometric analysis was conducted on publications pertaining to T cells in psoriasis between 2003 and 2022 retrieved from the Web of Science Core Collection (WoSCC) database using tools such as CiteSpace, the Bibliometrix R package, and VOSviewer. Results: The study included a total of 3595 articles authored by 14,188 individuals, including all coauthors in article bylines. The Laboratory for Investigative Dermatology at Rockefeller University, led by James G Krueger, has made significant contributions to this field through focusing on the pathogenesis of psoriasis and exploring the potential of using biological agents to treat psoriasis. Furthermore, targeted inhibitors have significantly impacted the treatment of psoriasis, with researchers focusing on small-molecule targeted drugs as a new area of research that could potentially replace biological agents. Conclusions: Research has established the efficacy and long-term safety of targeted inhibition of T cell-related targets. Deucravacitinib, a psoriasis treatment drug targeting TYK2 as an allosteric inhibitor, has attracted significant attention and raised high expectations.

The breast cosmetic outcome after breast conserving therapy is essential for evaluating breast treatment and determining patient’s remedy selection. This prompts the need of objective and efficient methods for breast cosmesis evaluations. However, current evaluation methods rely on ratings from a small group of physicians or semi-automated pipelines, making the processes time-consuming and their results inconsistent. To solve the problem, in this study, we proposed: 1. a fully-automatic Machine Learning Breast Cosmetic evaluation algorithm leveraging the state-of-the-art Deep Learning algorithms for breast detection and contour annotation, 2. a novel set of Breast Cosmesis features, 3. a new Breast Cosmetic dataset consisting 3k+ images from three clinical trials with human annotations on both breast components and their cosmesis scores. We show our fully-automatic framework can achieve comparable performance to state-of-the-art without the need of human inputs, leading to a more objective, low-cost and scalable solution for breast cosmetic evaluation in breast cancer treatment.