Transuterine relocation of pregnant uterine horn segment in an exploratory rat model with implications for tubal ectopic pregnancy.

Ectopic pregnancy affects ~ 2% of pregnancies annually in the United States, with no current treatments allowing for the continuation of the pregnancy. Thus, this study sought to initiate an investigation into the potential design of a surgical technique, in an animal model, that could serve as a foundation for future research into the potential of relocating an ectopic embryo into the uterus at the human level. Female Long-Evans rats were randomly assigned to one of two groups: Embryo Relocation (ER; n = 12; underwent embryo relocation surgery) and Normal Pregnancy (NP; n = 12; carried a normal pregnancy; no surgery).

On the Dynamics of a Novel Liquid-Coupled Piezoelectric Micromachined Ultrasonic Transducer Designed to Have a Reduced Resonant Frequency and Enhanced Ultrasonic Reception Capabilities.

This paper introduces a novel design for a liquid-deployed Piezoelectric Micromachined Ultrasonic Transducer (PMUT). This design was specifically developed to resonate at a lower ultrasonic frequency than a PMUT with a circular, fully clamped diaphragm with the same diameter. Furthermore, the novel design was also optimised to enhance its ultrasonic radiation reception capabilities.

The expression patterns of different cell types and their interactions in the tumor microenvironment are predictive of breast cancer patient response to neoadjuvant chemotherapy.

The tumor microenvironment (TME) is a complex ecosystem of diverse cell types whose interactions govern tumor growth and clinical outcome. While the TME's impact on immunotherapy has been extensively studied, its role in chemotherapy response remains less explored. To address this, we developed DECODEM (DEcoupling Cell-type-specific Outcomes using DEconvolution and Machine learning), a generic computational framework leveraging cellular deconvolution of to associate the gene expression of individual cell types in the TME with clinical response.

A deep-learning framework to predict cancer treatment response from histopathology images through imputed transcriptomics.

Advances in artificial intelligence have paved the way for leveraging hematoxylin and eosin-stained tumor slides for precision oncology. We present ENLIGHT-DeepPT, an indirect two-step approach consisting of (1) DeepPT, a deep-learning framework that predicts genome-wide tumor mRNA expression from slides, and (2) ENLIGHT, which predicts response to targeted and immune therapies from the inferred expression values. We show that DeepPT successfully predicts transcriptomics in all 16 The Cancer Genome Atlas cohorts tested and generalizes well to two independent datasets.

Predictability of B cell clonal persistence and immunosurveillance in breast cancer.

B cells and T cells are important components of the adaptive immune system and mediate anticancer immunity. The T cell landscape in cancer is well characterized, but the contribution of B cells to anticancer immunosurveillance is less well explored. Here we show an integrative analysis of the B cell and T cell receptor repertoire from individuals with metastatic breast cancer and individuals with early breast cancer during neoadjuvant therapy.