Novel machine learning technique further clarifies unrelated donor selection to optimize transplantation outcomes.

We investigated the impact of donor characteristics on outcomes in allogeneic hematopoietic cell transplantation (HCT) recipients using a novel machine learning approach, the Nonparametric Failure Time Bayesian Additive Regression Trees (NFT BART). NFT BART models were trained on data from 10 016 patients who underwent a first HLA-A, B, C, and DRB1 matched unrelated donor (MUD) HCT between 2016 and 2019, reported to the Center for International Blood and Marrow Transplant Research, then validated on an independent cohort of 1802 patients. The NFT BART models were adjusted based on recipient, disease, and transplant variables.

Effect on Travel Distance of a Statewide Regionalization Policy for Initial Breast Cancer Surgery.

Purpose: Reimbursement strategies to regionalize care can be effective for improving patient outcomes but may adversely affect access to care. We sought to determine the effect on travel distance for surgical treatment of a 2009 New York State (NYS) policy restricting Medicaid reimbursement for breast cancer surgery at low-volume hospitals.

Patients And Methods: From a linked data set merging the NYS tumor registry with hospital discharge data, we identified women younger than 65 years with stage I-III first breast tumors from pre- and post-policy periods.

Effects of housing stability and contemporary mortgage lending bias on breast cancer stage at diagnosis among older women in the United States.

Background: Interventions aimed at upstream factors contributing to late-stage diagnoses could reduce disparities and improve breast cancer outcomes. This study examines the association between measures of housing stability and contemporary mortgage lending bias on breast cancer stage at diagnosis among older women in the United States.

Methods: We studied 67,588 women aged 66-90 from the SEER-Medicare linked database (2010-2015).

Optimal Donor Selection Across Multiple Outcomes For Hematopoietic Stem Cell Transplantation By Bayesian Nonparametric Machine Learning.

Allogeneic hematopoietic cell transplantation (HCT) is one of the only curative treatment options for patients suffering from life-threatening hematologic malignancies; yet, the possible adverse complications can be serious even fatal. Matching between donor and recipient for 4 of the HLA genes is widely accepted and supported by the literature. However, among 8/8 allele matched unrelated donors, there is less agreement among centers and transplant physicians about how to prioritize donor characteristics like additional HLA loci (DPB1 and DQB1), donor sex/parity, CMV status, and age to optimize transplant outcomes.

Histidine-containing dipeptide supplementation improves delayed recall: a systematic review and meta-analysis.

Context: Histidine-containing dipeptides (carnosine, anserine, beta-alanine and others) are found in human muscle tissue and other organs like the brain. Data in rodents and humans indicate that administration of exogenous carnosine improved cognitive performance. However, RCTs results vary.