The gene-regulatory evolution of the human skeleton Yizhi Yan, Nadav Mishol, Katharina Lange, Zicong Zhang, Gal Bodek, Aya Kigel, Noam Priel, Nachshon Egyes, Omer Ronen, Itamar Nini, Liat Rotenstreich, Amit Philosoph, Sira Martinez, Silvia Beltramone, Rika Tsujikawa, Adi Rozenblatt, Lucas Esteban Wange, María Torralvo, Guy Hirsh, Yael Elboim, Sergey Viukov, Idan Korenfeld, Mythili Damal Kandadai, Océane Cluzeau, …David Gokhman

https://www.nature.com/articles/s41586-026-11053-x Skeletal modifications were central to human evolution, enabling adaptations for bipedalism, large cranial vaults and childbirth1. Despite their importance, the genetic changes that gave rise to the unique human form remain mostly unknown2. Here we systematically map the gene-regulatory changes that shaped human skeletal evolution. Using massively parallel reporter assays (MPRAs) in chondrocytes, we assayed 561,410 human-derived substitutions in promoters and enhancers, identifying 15,077 loci with human-specific regulatory activity. We then generated human–ape hybrid cells and differentiated them into osteochondral progenitors. Integrating the hybrid cells with MPRA measurements produced genome-wide atlases of human-specific changes in cis-regulatory expression, and the sequence variants that drive them. These atlases reveal an extensive rewiring of the extracellular matrix (ECM), including a marked suppression of glycosaminoglycan (GAG) biosynthesis, leading to an approximately three-to-fourfold reduction in joint GAG content in humans compared with non-human apes. We find that this human-specific shift bears signatures of selection, and is likely to be a key contributor to the exceptional susceptibility of humans to degenerative skeletal diseases3,4,5. Together, our results reveal a coordinated evolutionary remodelling of the human skeletal ECM, and establish a comprehensive framework for dissecting the genetic basis of human skeletal biology.

Micelles in cancer nanomedicine: targeted delivery, imaging, and therapy Amirhossein Rashnoodi [1]ORCID iD , Zaynab Seraj [1]ORCID iD , Niyoosha Zamani [1]ORCID iD , Bahareh Khalili Najafabad* [1]

https://www.academia.edu/3071-2521/2/3/10.20935/AcadDrug8458

Pharmacometric generative stochastic modeling of patient-reported outcome measures Kuteesa R. Bisaso* [1] , Karungi S. Bisaso [1] , Karyaburo R. Kadada [1] , Jackson K. Mukonzo [2] , Ene I. Ette [3]

https://www.academia.edu/3071-2521/2/2/10.20935/AcadDrug8393 Introduction: Patient-reported outcome measures (PROMs) capture the patient’s own perspective on their health, illness, and therapeutic effects on the illness. PROM data are inherently high-dimensional, discrete, and interdependent, challenging standard models that rely on restrictive assumptions and often collapse item-level information. This study, therefore, investigates the use of restricted Boltzmann machines (RBM) to jointly model, simulate, and interpret multidimensional PROM data within a pharmacometric framework. Materials and methods: A mixed-variate RBM was applied to longitudinal neuropsychological impairment data from 157 HIV-positive patients receiving efavirenz. The model jointly represented binary symptom items, an ordinal mini-mental state examination (MMSE) measure, efavirenz mid-dose concentrations, and clinical variables (CD4 cell count and viral load) using an energy-based formulation. Parameters were estimated via persistent contrastive divergence. Model performance was evaluated using reconstruction error, pseudolikelihood, free-energy stability, and predictive accuracy on a held-out dataset, alongside simulation-based diagnostics including visual predictive checks. The model was used to derive a variable importance ranking for all the PROM items, clinical variables, and drug concentrations. Results: The RBM captured joint dependencies across PROM items and covariates. On the held-out test set, the model demonstrated good conditional predictive performance, with log-loss ranging from 0.01 to 0.72, Brier scores from 0.00 to 0.26, and mean squared error (MSE) from 0.01 to 0.50. Excellent calibration was achieved at week 12 (t84), particularly for sleepwalking, tactile, and visual hallucinations. Visual predictive checks showed good agreement between observed and simulated symptom trajectories. Variable importance analysis indicated that mid-dose concentrations were not more predictive of post-baseline PROMs than clinical variables and baseline PROMs. Therapeutic drug monitoring simulations revealed limited impact of concentration capping on neuropsychological outcomes. Conclusions: Generative RBMs provide a flexible and minimally assumptive framework for pharmacometric PROM analysis, enabling joint modeling and simulation of complex symptom data. This approach complements traditional methods and supports the use of baseline clinical state over single exposure metrics for prediction. https://www.academia.edu/journals/academia-drug-development-and-pharmacotherapy/articles?source=journal-top-nav

GLP-1 drugs fail to help some people lose weight — scientists are on a quest for answers Understanding why some people see no benefits from potent anti-obesity medications could lead to new therapies and personalized weight-loss regimens. By Mariana Lenharo

https://www.nature.com/articles/d41586-026-03020-3?utm_source=Live+Audience&utm_campaign=5858217fe2-nature-briefing-daily-20260925&utm_medium=email&utm_term=0_-33f35e09ea-50432164 Around 10–15% of people taking popular GLP-1 drugs such as Wegovy lose little to no weight — and researchers don’t really know why. Solving this mystery might lead to new medications for people who don’t benefit from currently available drugs, and could make it possible to match individuals to treatments that are most likely to work for them.

Whole genome sequencing for Klebsiella pneumoniae in the Tunisian antimicrobial resistance surveillance system: micro-costing, pragmatic cost-effectiveness, and budget impact evaluation Dana Itania Send email to dana.itani@alumni.lshtm.ac.uk ∙ Kasim Allelb,c ∙ Sanaa Farjanid,k ∙ Hanen Smaouie ∙ Meriam Zribif ∙ Lamia Thabetg ∙ et al.

Whole genome sequencing for Klebsiella pneumoniae in the Tunisian antimicrobial resistance surveillance system: micro-costing, pragmatic cost-effectiveness, and budget impact evaluation Dana Itania Send email to dana.itani@alumni.lshtm.ac.uk ∙ Kasim Allelb,c ∙ Sanaa Farjanid,k ∙ Hanen Smaouie ∙ Meriam Zribif ∙ Lamia Thabetg ∙ et al. https://www.thelancet.com/journals/laneme/article/PIIS3050-5054(26)00005-0/fulltext?dgcid=hubspot_email_conferencealerts_gmi16&utm_campaign=conferencealerts&utm_medium=email&_hsenc=p2ANqtz-_w9lEQaNptxFCdrRsDnCz3SzqU1kOeUSWp1UkYZl803TSsBkQQ86GmLHxjAqqhMPNOMA5TjY-fxD8E1VPkL0SAR5d1ZQ&_hsmi=440685191&utm_content=440685191&utm_source=hs_email

High-grade gliomas show distinct biology across age and sex Proteogenomic analysis of brain tumors identified potential treatment targets and prognostic markers that differed across age, developmental stage, and sex. Written byBree Foster, PhD

High-grade gliomas show distinct biology across age and sex Proteogenomic analysis of brain tumors identified potential treatment targets and prognostic markers that differed across age, developmental stage, and sex. Written byBree Foster, PhD High-grade gliomas (HGGs) are highly aggressive primary brain tumors, with a 5-year survival rate below 10 percent. Recent genomic and epigenomic profiling studies have revealed key distinctions between adult HGG and pediatric HGG, suggesting they are biologically distinct diseases. Reflecting these differences, the 2021 WHO Classification of Tumors of the Central Nervous System made major changes to the classification of HGGs, separating pediatric and adult tumors into distinct categories. However, this leaves a less clearly defined group in between: adolescents and young adults (AYA), whose tumors can fall between the biological profiles of pediatric and adult disease. https://www.drugdiscoverynews.com/high-grade-gliomas-show-distinct-biology-across-age-and-sex-17544

Claude spots a mystery enzyme scientists overlooked Autonomous agents flagged a family of phage enzymes with CRISPR-like repeats, though what the system does remains unknown. Written byAndrea Corona

Claude spots a mystery enzyme scientists overlooked Autonomous agents flagged a family of phage enzymes with CRISPR-like repeats, though what the system does remains unknown. Written byAndrea Corona https://www.drugdiscoverynews.com/claude-spots-a-mystery-enzyme-scientists-overlooked-17549 AI has already changed how scientists predict protein structures and design new molecules. The next leap may be discovery itself, with agents that read raw biological data, spot what looks strange, and chase it down on their own. A new study offers an early glimpse of that future, and of what it could mean for the hunt for the next generation of therapeutic tools. Researchers at Anthropic reported that a team of autonomous large language model (LLM) agents surveyed reverse transcriptase (RT) genes across 1.9 billion metagenomic protein clusters and surfaced a new family of the enzymes, along with the repeat arrays that define it.