Embracing the mental health challenges of uterus transplant candidates.

Uterus transplantation (UTx) has emerged from clinical trials and is expected to become the standard of care for uterine factor infertility. Uterus transplant candidates historically have had to meet strict eligibility criteria to participate in clinical trials. Continued application of psychologic selection criteria from clinical trial may hinder the expansion of UTx.

With big data comes big responsibility: Strategies for utilizing aggregated, standardized, de-identified electronic health record data for research.

Electronic health records (EHRs), though they are maintained and utilized for clinical and billing purposes, may provide a wealth of information for research. Currently, sources are available that offer insight into the health histories of well over a quarter of a billion people. Their use, however, is fraught with hazards, including introduction or reinforcement of biases, clarity of disease definitions, protection of patient privacy, definitions of covariates or confounders, accuracy of medication usage compared with prescriptions, the need to introduce other data sources such as vaccination or death records and the ensuing potential for inaccuracy, duplicative records, and understanding and interpreting the outcomes of data queries.

Cryoneurolysis for management of post-mastectomy pain: a systematic review.

Aims: Post-mastectomy pain syndrome is a common postoperative complication that can impact patient quality of life and function. The aim of this systematic review is to evaluate the effectiveness of cryoneurolysis as an intervention for the management of post-mastectomy pain.

Methods: A prospero-registered systematic review was performed following PRISMA 2020 guidelines.

Antigen-independent single-cell circulating tumor cell detection using deep-learning-assisted biolasers.

Circulating tumor cells (CTCs) in the bloodstream are important biomarkers for clinical prognosis of cancers. Current CTC identification methods are based on immuno-labeling, which depends on the differential expression of specific antigens between the cancer cells and white blood cells. Here we present an antigen-independent CTC detection method utilizing a deep-learning-assisted single-cell biolaser.