Neural Network Water Model Based on the MB-Pol Many-Body Potential.

The MB-pol many-body potential accurately predicts many properties of water, including cluster, liquid phase, and vapor-liquid equilibrium properties, but its high computational cost can make applying it in large-scale simulations quite challenging. In order to address this limitation, we developed a "deep potential" neural network (DPMD) model based on the MB-pol potential for water. We find that a DPMD model trained on mostly liquid configurations yields a good description of the bulk liquid phase but severely underpredicts vapor-liquid coexistence densities.

Host genetic background regulates the capacity for anti-tumor antibody-dependent phagocytosis.

Background: Antitumor antibody, or targeted immunotherapy, has revolutionized cancer treatment and markedly improved patient outcomes. A prime example is the monoclonal antibody (mAb) trastuzumab, which targets human epidermal growth factor receptor 2 (HER2). However, like many targeted immunotherapies, only a subset of patients benefit from trastuzumab long-term.

A Deep Potential model for liquid-vapor equilibrium and cavitation rates of water.

Computational studies of liquid water and its phase transition into vapor have traditionally been performed using classical water models. Here, we utilize the Deep Potential methodology-a machine learning approach-to study this ubiquitous phase transition, starting from the phase diagram in the liquid-vapor coexistence regime. The machine learning model is trained on ab initio energies and forces based on the SCAN density functional, which has been previously shown to reproduce solid phases and other properties of water.

A diversity outbred F1 mouse model identifies host-intrinsic genetic regulators of response to immune checkpoint inhibitors.

Immune checkpoint inhibitors (ICI) have improved outcomes for a variety of malignancies; however, many patients fail to benefit. While tumor-intrinsic mechanisms are likely involved in therapy resistance, it is unclear to what extent host genetic background influences response. To investigate this, we utilized the Diversity Outbred (DO) and Collaborative Cross (CC) mouse models.

A deep potential model with long-range electrostatic interactions.

Machine learning models for the potential energy of multi-atomic systems, such as the deep potential (DP) model, make molecular simulations with the accuracy of quantum mechanical density functional theory possible at a cost only moderately higher than that of empirical force fields. However, the majority of these models lack explicit long-range interactions and fail to describe properties that derive from the Coulombic tail of the forces. To overcome this limitation, we extend the DP model by approximating the long-range electrostatic interaction between ions (nuclei + core electrons) and valence electrons with that of distributions of spherical Gaussian charges located at ionic and electronic sites.

Heterogeneous estrogen receptor expression in circulating tumor cells suggests diverse mechanisms of fulvestrant resistance.

Fulvestrant is a dose dependent selective estrogen receptor (ER) down-regulator (SERD) used in ER-positive metastatic breast cancer (MBC). Nearly all patients develop resistance. We performed molecular analysis of circulating tumor cells (CTC) to gain insight into fulvestrant resistance.

Significance of Circulating Tumor Cells in Metastatic Triple-Negative Breast Cancer Patients within a Randomized, Phase II Trial: TBCRC 019.

Purpose: Circulating tumor cells (CTC) are prognostic in metastatic breast cancer (MBC). We tested whether EpCAM-based capture system (CellSearch) is effective in patients with triple-negative (TN) MBC, and whether CTC apoptosis and clustering enhances the prognostic role of CTC.

Experimental Design: CTC enumeration and apoptosis were determined using the CXC CellSearch kit at baseline and days 15 and 29 in blood drawn from TN MBC patients who participated in a prospective randomized phase II trial of nanoparticle albumin-bound paclitaxel (nab-paclitaxel) with or without tigatuzumab.

Development of circulating tumor cell-endocrine therapy index in patients with hormone receptor-positive breast cancer.

Background: Endocrine therapy (ET) fails to induce a response in one half of patients with hormone receptor (HR)-positive metastatic breast cancer (MBC), and almost all will eventually become refractory to ET. Circulating tumor cells (CTC) are associated with worse prognosis in patients with MBC, but enumeration alone is insufficient to predict the absolute odds of benefit from any therapy, including ET. We developed a multiparameter CTC-Endocrine Therapy Index (CTC-ETI), which we hypothesize may predict resistance to ET in patients with HR-positive MBC.

Unveiling the rat urinary proteome with three complementary proteomics approaches.

Urine is a suitable biological fluid to look for markers of physiological and pathological processes, including renal and nonrenal diseases. In addition, it is an optimal body sample for diagnosis, because it is easily obtained without invasive procedures and can be sampled in large quantities at almost any time. Rats are frequently used as a model to study human diseases, and rat urine has been analyzed to search for disease biomarkers.

[Clinical proteomics and new biomarkers in biological fluids].

Protein analysis in biological fluids has been used for many years for diagnosis and monitoring of diseases. First it was quantification of total protein, afterwards the electrophoretic separation of proteins and later the quantification of specific proteins using immunoassays. These proteins are used as biological markers (biomarkers) of disease.

Tumor microvasculature targeting with dendrimer-entrapped gold nanoparticles.

Monodisperse dendrimer-entrapped gold nanoparticles (diameter = 3.0 nm) were prepared using G5 poly(amidoamine) (PAMAM) dendrimer functionalized with fluorescein isothiocyanate (FI) and Arg-Gly-Asp (RGD) peptide as template; in vitro targeting efficacy to integrin receptor expressing cells was confirmed by flow cytometry, confocal microscopy, and ICP-MS.