A new study published in Substance Use & Addiction Journal found that state-level AUD estimates among women ranged from approximately 6-13 percent, with all New England states and several in the West and Midwest exceeding the national average of 8 percent among women. More than 40 percent of addiction treatment facilities did not offer women-specific treatment, and services targeted toward women—especially childcare—were disproportionately unavailable in many states that had higher-than-average estimated AUD prevalence among women. The study was funded by the National Institute on Alcohol Abuse and Alcoholism and is the first to explore state-level differences in AUD among women and the availability of women-specific services in treatment facilities.

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.

 

By parsing metabolic data from more than 18,000 participants in China, scientists have constructed “clock” models of human aging that can give clues about the risk of chronic diseases and mortality. The comprehensive study provides a treasure trove of data about aging’s impact on metabolism and yields clinically relevant metrics that could guide health monitoring and disease prevention efforts. Aging is a highly complicated process, and scientists have only begun to tease apart how the body’s molecular pathways shift as we get older. In recent years, researchers have sought to identify biomarkers of aging – such as epigenetic markers and changes in metabolic signatures – and integrate them into models called aging clocks. These models might help clinicians identify people who have a higher risk of chronic diseases and could inform early interventions. Now, Linlin Cao and colleagues present results from one of the most comprehensive studies to date of metabolic aging, involving 18,476 participants ranging from one to 96 years of age from six clinical centers in China. They profiled metabolites in plasma samples from the participants and linked 56 metabolites to age-associated shifts. Based on these data, the team derived a metric called metabolomic age acceleration (mAA), which correlated with a higher risk of fatty liver disease, hypertension, diabetes, and all-cause mortality as the cohort aged. The team also examined a disease cohort and uncovered a second metabolic metric called age deviation that was higher in patients with more severe and systemic disease. “Together, these findings support plasma metabolomics as a scalable and clinically interpretable framework for studies of disease prevention, early detection, healthy aging, and aging-related heterogeneity across the life course,” Cao et al. conclude. They add that their age prediction framework is available to the public via a web interface.

 

By applying mRNA technology and structural engineering, researchers have fashioned an advanced antibody therapy that can neutralize SARS-CoV-2 while constraining the virus’ ability to escape recognition. The therapy protected mice against infections with an immune-evasive Omicron variant, and provides a structural template for antiviral antibodies that can keep up with their constantly evolving viral targets. Although the COVID-19 pandemic is over, SARS-CoV-2 remains a threat to public health, and is especially dangerous for people with compromised immune systems who can’t fully benefit from vaccines. Antibody therapies are critical for these high-risk groups, as they can bind to and neutralize the virus without the need for vaccine-induced immunity. However, like other viruses, SARS-CoV-2 is constantly evolving – and variants quickly appeared that could evade the first generation of antibody therapies. This escape has also undermined the deployment of next-generation antibodies, so new approaches are needed. Here, Anna Wec and colleagues used high-throughput screening and isolated several antibodies from llamas that could neutralize the XBB.1.5 and JN.1 variants of SARS-CoV-2. The researchers engineered these lead candidates into more potent bispecific antibodies, which can target multiple sites on the same protein. Furthermore, they introduced structural modifications that allow multiple antibodies to be delivered in tandem via mRNA. This mRNA strategy maximizes the antibodies’ ability to target SARS-CoV-2 as it evolves, and could potentially lower the high costs and manufacturing complexity that have held back previous bispecific antibody therapies. The platform neutralized a broad range of historical and current SARS-CoV-2 variants in serum and protected mice against infections with XBB1.5. “This platform may have applications in other therapeutic areas in which broad antigen coverage, resistance to escape, or simultaneous engagement of multiple disease-associated targets is desirable,” Wec et al. write.

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.

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.