Single-cell analysis of neoplastic plasma cells identifies myeloma pathobiology mediators and potential targets.

Multiple myeloma is a clonal plasma cell (PC) dyscrasia that arises from precursors and has been studied utilizing approaches focused on CD138 cells. By combining single-cell RNA sequencing (scRNA-seq) with scB-cell receptor sequencing (scBCR-seq), we differentiate monoclonal/neoplastic from polyclonal/normal PCs and find more dysregulated genes, especially in precursor patients, than we would have by analyzing bulk PCs. To determine whether this approach can identify oncogenes that contribute to disease pathobiology, mitotic arrest deficient-2 like-1 (MAD2L1) and S-adenosylmethionine synthase isoform type-2 (MAT2A) are validated as targets with drug-like molecules that suppress myeloma growth in preclinical models.

Multi-omics reveal immune microenvironment alterations in multiple myeloma and its precursor stages.

Tumor immune microenvironmental alterations occur early in multiple myeloma (MM) development. In this study, we aim to systematically characterize the tumor immune microenvironment (TME) and the tumor-immune interactions from precursor stages, i.e.

A systematic review and meta-analysis of artificial intelligence versus clinicians for skin cancer diagnosis.

Scientific research of artificial intelligence (AI) in dermatology has increased exponentially. The objective of this study was to perform a systematic review and meta-analysis to evaluate the performance of AI algorithms for skin cancer classification in comparison to clinicians with different levels of expertise. Based on PRISMA guidelines, 3 electronic databases (PubMed, Embase, and Cochrane Library) were screened for relevant articles up to August 2022.

A Risk Stratification System in Myeloma Patients with Autologous Stem Cell Transplantation.

Autologous stem cell transplantation (ASCT) has been a mainstay in myeloma treatment for over three decades, but patient prognosis post-ASCT varies significantly. In a retrospective study of 5259 patients with multiple myeloma (MM) at the University of Arkansas for Medical Sciences undergoing ASCT with a median 57-month follow-up, we divided the dataset into training (70%) and validation (30%) subsets. Employing univariable and multivariable Cox analyses, we systematically assessed 29 clinical variables, identifying crucial adverse prognostic factors, such as extended duration between MM diagnosis and ASCT, elevated serum ferritin, and reduced transferrin levels.