Background: An inclusive academic environment is pivotal to ensure student well-being and a strong sense of belonging and authenticity. Specific attention for an inclusive learning environment is particularly important during a student’s transition to higher education. At Utrecht University’s Medical School, explorative interviews with students from minority groups indicated they did not always feel included during the orientation programme of their academic education. We, therefore, developed a bias awareness training with theoretical and practical components on diversity and inclusion for peer-mentors who are assigned to each first-year student at the start of university. Methods: At the end of the orientation programme, we investigated the effectiveness of the training for two consecutive years using two measurements. Firstly, we investigated the behavioural changes in the peer-mentors through a (self-reporting) questionnaire. Additionally, we measured the perceived inclusion of the first-year students, divided into belonging and authenticity, using a validated questionnaire. Results: Our results show that peer-mentors found the training useful and indicated it enabled them to create an inclusive atmosphere. Overall, students experienced a high level of inclusion during the orientation programme. After the first year, the bias training was adjusted based on the evaluations. This had a positive effect, as mentors felt they were significantly more able to provide an inclusive orientation in the second year of this study. In line with this, students experienced an increased level of authenticity specifically due to the peer-mentor in the second year as compared to the first. Conclusions: We conclude that training peer-mentors is an effective way to increase awareness and to ensure an inclusive atmosphere during the start of higher education.

The primary goal of pharmacology teaching is to prepare medical students to prescribe medications both safely and efficiently. At the Utrecht University Medical School, pharmacology is integrated into the three-year bachelor’s curriculum, primarily through large group sessions with limited interaction. A recent evaluation highlighted students’ appreciation for pharmacology teaching, but students admitted to attending these teaching moments unprepared, resulting in passive learning. To address this, team-based learning (TBL) was implemented to facilitate learning through interaction, critical thinking, problem solving and reflection through six steps, from superficial to deeper cognitive learning. This study, conducted over two academic years, assessed students’ perception and performance regarding TBL. Analysis of a digital questionnaire using a 5-point Likert scale showed high student satisfaction with TBL as a teaching methodology. However, confidence in pharmacology knowledge following TBL was moderate. TBL attendees outperformed non-attendees in pharmacology-related exam questions, indicating that TBL has a positive impact on student performance. We conclude that TBL is an engaging and effective method for pharmacology education, positively influencing student learning and performance. This method could be broadly applied for teaching pharmacology within the medical curriculum or other biomedical programs.

Antimicrobial resistance has become one of the major threats to human health. Therefore, there is a strong need for novel antimicrobials with new mechanisms of action. The kingdom of fungi is an excellent source of antimicrobials for this purpose because it encompasses countless fungal species that harbor unusual metabolic pathways. Previously, we have established a library of secondary metabolites from 10,207 strains of fungi. Here, we screened for antimicrobial activity of the library against seven pathogenic bacterial strains and investigated the identity of the active compounds using ethyl acetate extraction, activity-directed purification using HPLC fractionation and chemical analyses. We initially found 280 antimicrobial strains and subsequently identified 17 structurally distinct compounds from 26 strains upon further analysis. All but one of these compounds, berkchaetoazaphilone B (BAB), were known to have antimicrobial activity. Here, we studied the antimicrobial properties of BAB, and found that BAB affected energy metabolism in both prokaryotic and eukaryotic cells. We conclude that fungi are a rich source of chemically diverse secondary metabolites with antimicrobial activity.

Human immunodeficiency virus type 1 (HIV-1) is classified into different phylogenetic subtypes, with subtype C representing more than half of the novel infections globally. However, there are relatively few subtype C envelopes available for study. We amplified 18 unique env genes from 13 patients who were infected with subtype C HIV-1 in six African countries and in Scotland to create replication-competent viruses. These envelopes are phylogenetically diverse across the subtype C spectrum, and have on average more N-linked glycosylation sites and slightly longer variable loops than previously described C envelopes. We found that CCR3 coreceptor usage is less prevalent in subtype C than in subtype B viruses, and these envelopes have varied sensitivity to neutralization. The subtype C chimeric viruses generated in this study will be useful for evaluating the breadth of neutralizing antibodies and other entry inhibitors.

Scientists often use generics , that is, unquantified statements about whole categories of people or phenomena, when communicating research findings (e.g. “statins reduce cardiovascular events”). Large language models, such as ChatGPT, frequently adopt the same style when summarizing scientific texts. However, generics can prompt overgeneralizations, especially when they are interpreted differently across audiences. In a study comparing laypeople, scientists, and two leading large language models (ChatGPT-5 and DeepSeek), we found systematic differences in interpretation of generics. Compared with most scientists, laypeople judged scientific generics as more generalizable and credible, while large language models rated them even higher. These mismatches highlight significant risks for science communication. Scientists may use generics and incorrectly assume laypeople share their interpretation, while large language models may systematically overgeneralize scientific findings when summarizing research. Our findings underscore the need for greater attention to language choices in both human and large language model-mediated science communication.