This bibliometric study analyzed 1,261 publications on ovarian cancer care from 2000 to 2023, revealing key research clusters, international collaboration gaps, and emerging trends. The U.S. led in publications, with hotspots including quality of life, surgery, and paclitaxel therapy. Findings highlight the need for multidisciplinary approaches and stronger global cooperation to improve patient outcomes.

Promoting the acceptance of clinical studies about traditional Chinese medicine (TCM) interventions by the international community is a key strategy for internationalization of TCM. However, the complexities of TCM interventions – in terms of its theories, practice patterns, and components – pose significant challenges in designing and implementing clinical studies that are well accepted by the international community. This article summarized the current status of clinical studies about TCM interventions published in international journals, explored underlying barriers to promoting their global acceptance, and discussed potential strategies for future development.

In the paper published on Science of Traditional Chinese Medicine(STCM), the authors utilized synthetic biology technology to create the first artificial herbal cell (AHC) based on a traditional Chinese medicine (TCM) formulation: Compound Danshen Yeast 1.0. This breakthrough provides foundational technology for producing TCM formulations through one-step fermentation using simple carbon sources like glucose and ethanol—eliminating the need for wild harvesting or cultivation of medicinal herbs. By harnessing synthetic biology, the team reprogrammed a single yeast strain to simultaneously synthesize three classes of active ingredients—notoginsenosides (protopanaxadiol), tanshinone diterpenoid (miltiradiene), and borneol.

Decoding cosmic evolution depends on accurately predicting the complex chemical reactions in the harsh environment of space. Traditional methods for such predictions rely heavily on costly laboratory experiments or expert knowledge, both of which are resource-intensive and limited in scope. Recently, a research team developed an innovative AI tool that predicts astrochemical reactions with high accuracy and efficiency, demonstrating that deep learning techniques can successfully address data limitations in astrochemistry. Titled “A Two-Stage End-to-End Deep Learning Approach for Predicting Astrochemical Reactions,” this research was published May 15 in Intelligent Computing, a Science Partner Journal.