Children who follow higher trajectories of body mass index (BMI) and fat mass index across childhood report greater body dissatisfaction at age 14, in both boys and girls, according to a new study published October 14, 2026, in the open access journal PLOS One by Dr. Madelaine Davies Kellock, a Research Fellow at University College London, UK, and colleagues.

Researchers studying how economic inequality became a durable feature of societies often point to factors such as agricultural intensification or the rise of the state, but a new study by Dongdong Li and colleagues suggests inequality can have much deeper roots. In their analysis of previously published mortuary data from more than 2,300 burials across 6,000 years of settlement in the Central Plain of China, Li et al. conclude that inequality was shaped by social-relational networks that long preceded the rise of the state. The differentiated social positions within these networks could not be explained by agricultural intensification or the size of the population, they found. But the networks did control an individual’s access to labor, resources, and symbolic capital such as ritual objects. The researchers reconstructed these networks in prehistoric China using isotopic, osteological, and zooarchaeological data from sites dated from 7000 to 1500 BCE. Burial wealth as well as signs of skeletal health and dietary variety were proxies for relational inequalities. Li et al. say that differential access to valued but scarce items initially produced unequal social-relational networks in the region, but that changes in subsistence, the rise of mobile wealth, and escalating violent conflict may have reorganized these networks to concentrate ritual, political, military, and technological control. Early states then “stabilized rather than created these pre-existing disparities by institutionalizing control over resources, violence, and network-based access,” the authors conclude.

In this issue of Science Translational Medicine, two Editorials and two Viewpoint articles dive into the latest challenges and opportunities in humanity’s fight against antimicrobial resistance. Highlighted topics include the grave danger posed by drug-resistant tuberculosis and the rollout of AI technologies to streamline the discovery of antibiotics.

In the first Editorial, Mark Woolhouse considers the paramount importance of a simple but fundamental concept in antibiotic therapy: “Prevention is better than cure.” He discusses the dire lack of access to frontline antibiotics in low- and middle-income countries, and stresses that new antibiotics will not solve the problem of drug resistance in the long term. Woolhouse argues that investing resources into preventative frameworks such as infection prevention and control (IPC) and water, sanitation, and hygiene (WASH) would improve the health of millions with only minimal risk, while also reducing demand for antibiotics and extending their lifespans.

In the second Editorial, Cesar Roque-Borda, João Perdigão, and Fernando Pavan sound the alarm against the emergence of strains of tuberculosis that can resist rifampicin, the backbone of tuberculosis care. They write about how these strains mark an inflection point in antimicrobial resistance, and highlight the urgent need for an effective tuberculosis vaccine. They also call for holistic control measures that integrate genomics and social interventions to combat poverty, malnutrition, and unequal access to care.

Despite these dangers, a Viewpoint from Gianmarco Gasperini and colleagues presents a plethora of new strategies to tackle the threat. These innovations include adoption of modernized commercial models, genomic surveillance, and more streamlined clinical trial designs, which could be integrated with rapidly advancing artificial intelligence platforms and international collaborations to safeguard global health.

Aarti Krishnan and colleagues focus further on how the rise of artificial intelligence is transforming the discovery and design of antibiotics. In their Viewpoint, they survey the latest advances in models built to discover small-molecule and peptide antimicrobials. However, these models also require data standards, validation pipelines, economic incentives, and regulatory frameworks to reach the clinic. “Without them, even the most sophisticated models will remain disconnected from the patients who urgently need new treatments,” the authors write.