Skip to main content
Artificial Intelligence

Treatment Decisions in Multiple Myeloma.

| Source: The New England journal of medicine

Revolutions in transplantation and targeted and immune therapies have transformed multiple myeloma from a disease with an associated survival of a few years into one for which functional cure is an emerging goal. This abundance of effective therapies has created clinical complexity. Here we provide a practical framework, anchored in trial evidence and informed by emerging biologic discoveries, for the navigation of treatment decisions across the disease spectrum. We outline how cytogenetic and g

Revolutions in transplantation and targeted and immune therapies have transformed multiple myeloma from a disease with an associated survival of a few years into one for which functional cure is an emerging goal. This abundance of effective therapies has created clinical complexity. Here we provide a practical framework, anchored in trial evidence and informed by emerging biologic discoveries, for the navigation of treatment decisions across the disease spectrum. We outline how cytogenetic and genomic risk stratification, functional fitness, and measurable residual disease status individualize therapy in newly diagnosed disease, in which quadruplet induction therapy is now standard and the role of autologous transplantation is being reevaluated. Regarding relapse, we address the sequencing of B-cell maturation antigen-directed chimeric antigen receptor (CAR) T cells, bispecific antibodies, and antibody-drug conjugates, emphasizing T-cell fitness and multiantigen targeting to counter exhaustion and antigen escape. We also consider early interception in high-risk smoldering myeloma. Throughout, we underscore that enrollment of patients in clinical trials should be considered in order to ensure continued progress.

Read the original source →

Related Stories

Artificial Intelligence

Protein resource allocation across subcellular compartments revealed by large-scale absolute quantitative proteomics in yeast.

Subcellular compartmentalization within complex membrane-bound organelles is a hallmark of eukaryal life, enabling metabolic specialization through spatial segregation. Here, we compiled a dataset encompassing 300 absolute quantitative proteomes, the largest to date, across two yeast species under diverse experimental conditions. Leveraging big data analysis, formula fitting, and machine learning, quantitative correlations among protein abundance, resource allocation, and cellular phenotypes wer

Continue reading
Artificial Intelligence

Quantifying ancient architecture: Stone-level machine learning reveals architectural variation and settlement growth at Shivta.

Built environments provide durable evidence for how ancient settlements were organized, expanded, and transformed over time. Yet quantitative approaches to documenting architectural variability are generally labor-intensive, limited to individual structures, or dependent on aggregate volumetric estimates. We present a scalable, machine-learning-based framework that shifts the unit of analysis to the individual building stone and combines two complementary datasets: low-altitude drone imagery of

Continue reading
Artificial Intelligence

Data-driven discovery and rapid, direct synthesis of MXenes.

MXenes are a rapidly expanding family of two-dimensional transition metal carbides and nitrides, yet scalable synthesis routes remain limited and historical reports suggest a broader, largely unexplored chemical space. We used machine learning-assisted database mining to uncover hidden multilayer MXenes. Screening reveals 33 previously synthesized but unrecognized MXene candidates. Guided by these findings, we synthesized seven multilayer MXenes using a rapid, scalable self-propagating high-temp

Continue reading
Artificial Intelligence

Intrinsic gestational timing governs human cerebellar development after preterm birth.

Intrinsic programs shape brain maturation; however, which of these programs are perturbed by prematurity as well as the molecular pathways involved remain unknown. The human cerebellum serves as a paradigm of extrauterine development-its accelerated growth and circuit formation align with the third trimester, a period disrupted by preterm delivery. Using multimodal datasets encompassing in vivo neuroimaging and neurodevelopmental outcomes with postmortem spatial-transcriptomic and histopathologi

Continue reading
Artificial Intelligence

CX3CL1 (fractalkine)-CX3CR1 interface drives CD8⁺ T cell-induced neurodegeneration in human brain organoids.

Immune-neuron interactions play a critical role in neuroinflammatory and neurodegenerative diseases, such as Alzheimer's disease (AD), but how CD8⁺ T cells contribute to brain pathology remains poorly understood. Amyloid-β (Aβ), a hallmark of AD, is known to trigger neuronal stress and inflammation. Here, we established a human cortical organoid (hCO) model to investigate CD8⁺ T cell responses to Aβ-induced neurodegenerative stress. Aβ exposure increased neu

Continue reading