Chemotherapy, although the first line of defense in cancer treatment, is indiscriminate and is damaging to the entire body, which, ironically, worsens a patient’s chances of getting better from cancer. Doxorubicin is a widely used chemotherapy drug for breast cancer but has significant side effects due to its systemic delivery.

This study led by co-first authors, Viresh Krishnan Sukumar (N2CR PhD Scholarship student) and Dr Alex Tai Yee Kit, found that a 10-minute magnetic exposure increases doxorubicin uptake in breast cancer cells without harming the surrounding healthy cells. The study suggests a localized, non-invasive magnetic therapy to enhance doxorubicin efficacy with fewer side effects. This may ultimately allow for the lowering of systemic doxorubicin administration that could improve a patient’s outcome against cancer. This would be a potential game changer in cancer treatment.

Management literature on how interorganizational relationships influence firm performance has emphasized the importance of connections, positions, and cliques. We advance this literature by examining how network communities (membership in cohesive network structures) shape the performance of venture capital (VC) firms. We propose that community insiders affiliated with at least one network community will outperform outsiders. We also explicate the conditions under which the advantages of network community affiliations are likely to be muted. Specifically, we argue for the potential diseconomies of network community affiliations and the possibility of a substitutive relationship between network communities and institutional development. Leveraging recent advances in community-detection technology and longitudinal data on VC firms in China, we find support for our theoretical arguments. Analysis of mechanisms underlying our theoretical arguments reveals that the value of community affiliations comes from indirect connections within network communities. By integrating social network and institutional perspectives, this study highlights not only the promises but also the limits of relying on network community affiliations.

• Large language models (LLMs) like ChatGPT could have a role in narrowing the health information digital divide, democratising access to healthcare

• But evidence indicates that LLMs might exacerbate the digital disparity in health information access in low and middle income countries

• Most LLMs perform badly in low resources languages like Vietnamese, resulting in the dissemination of inaccurate health information and posing potential public health risks

• Coordinated effort from policy makers, research funding agencies, big technology corporations, the research community, healthcare practitioners, and linguistically underrepresented communities is crucial to bridge the gap in AI language inclusivity