PubMed Health⌕ Search

Biomedical subjects

Yuxin Liu

Publications and source records attributed to Yuxin Liu.

3 recordsLinked to original sources
PubMed2026

Teclistamab versus lenalidomide-dexamethasone in high-risk smoldering multiple myeloma: a randomized phase 2 trial.

Teclistamab, a B cell maturation antigen-targeting bispecific antibody, has demonstrated substantial activity in relapsed multiple myeloma (MM), particularly in earlier lines of therapy, and may have higher efficacy in high-risk smoldering MM (HR-SMM) with a more functional immune system. In the randomized phase 2 ImmunoPRISM trial, we compared fixed-duration teclistamab with lenalidomide-dexamethasone (Rd) in HR-SMM. After a six-patient safety run-in, patients were randomized 2:1 to teclistamab or Rd. The primary endpoint was complete response (CR) rate. As of 26 May 2026, 59 patients were treated-45 received teclistamab and 14 received Rd. Teclistamab treatment induced a CR in 77.8% patients versus 0% with Rd, and minimal residual disease negativity at 10-5 in 82.2% patients. At a median follow-up of 24.5 months, 2-year progression-free survival was 92% with teclistamab versus 49% with Rd. Overall, response rates, duration of response and time to progression (TTP) were significantly improved in teclistamab compared to Rd. Toxicities in the teclistamab arm included grades 1-2 cytokine release syndrome, no neurotoxicity and no increase in grade 3 infections compared to Rd (20% versus 21%). No deaths occurred in either arm. Teclistamab represents a highly active immune-interception strategy for HR-SMM. ClinicalTrials.gov registration: NCT05469893 .

Journal Article↗
PubMed2026

Social disconnection integrates genetic and proteomic risks in suicidal ideation and depression.

Suicidal ideation (SI) and major depressive disorder (MDD) are complex psychiatric conditions arising from the interplay of genetic liability, molecular processes, and psychosocial factors. While these dimensions have been extensively studied in isolation, their joint contribution to SI and MDD remains unclear. This study integrates multi-modal data to elucidate these synergistic effects and develop robust models for individual-level risk stratification. Leveraging longitudinal multi-modal data from 13,085 UK Biobank participants, we integrated genomic, proteomic, and social connection profiles. We developed interpretable risk scores using a rigorous supervised machine learning framework encompassing diverse linear and ensemble classifiers. Permutation importance was employed to quantify feature contributions and derive transparent, weighted risk metrics across diverse classifiers. These scores were validated through association, interaction, and mediation analyses. Social connection-based risk scores significantly differentiated cases and controls across the two suicidal ideation phenotypes at 2017 and 2023 with cross-sectional analyses (AUCs: 0.70 - 0.73), outperforming proteomic-only models. Functional dimensions of social connection emerged as the most informative predictors. Longitudinal analyses revealed that social risk scores at baseline predicted suicidal ideation onset six years later, independent of demographic covariates. Interaction analyses demonstrated that polygenic risk for suicide attempt significantly interacted with both social and proteomic risk features in relation to depression. Structural equation models further confirmed that social disconnection acts as a key mediator linking genetic predisposition to MDD and SI. Social disconnection is a critical risk factor mediating the impact of genetic vulnerability on psychiatric outcomes. Integrating social, genetic, and molecular data supports a multilevel framework for risk stratification and highlights the potential of socially oriented interventions to mitigate biological risk.

Humans↗