CD40-mediated immune cell activation enhances response to anti-PD-1 in murine intrahepatic cholangiocarcinoma.

BACKGROUND & AIMS: While cholangiocarcinomas (CCAs) commonly express programmed cell death 1 (PD-1) and its ligand (PD-L1), they respond poorly to immune checkpoint inhibitors (ICIs). We aimed to determine whether stimulating antigen-presenting cells, including macrophages and dendritic cells, using a CD40 agonist could improve this response. METHODS: We compared treatment responses in subcutaneous, orthotopic, and 2 plasmid-based murine intrahepatic CCA (iCCA) models. Mice were treated for 4 weeks with weekly IgG control, a CD40 agonistic antibody, anti-PD-1, or the combination of both (anti-CD40/PD-1). Flow cytometric (FACS) analysis of lymphocytes and myeloid cell populations (including activation status) was performed. We used dendritic cell knockout mice, and macrophage, CD4+ and CD8+ T cell depletion models to identify effector cells. Anti-CD40/PD-1 was combined with chemotherapy (gemcitabine/cisplatin) to test for improved therapeutic efficacy. RESULTS: In all 4 models, anti-PD-1 alone was minimally efficacious. Mice exhibited a moderate response to CD40 agonist monotherapy. Combination anti-CD40/PD-1 therapy led to a significantly greater reduction in tumor burden. FACS demonstrated increased number and activation of CD4+ and CD8+ T cells, natural killer cells, and myeloid cells in tumor and non-tumor liver tissue of tumor-bearing mice treated with anti-CD40/PD-1. Depletion of macrophages, dendritic cells, CD4+ T cells, or CD8+ T cells abrogated treatment efficacy. Combining anti-CD40/PD-1 with gemcitabine/cisplatin resulted in a significant survival benefit compared to gemcitabine/cisplatin alone. CONCLUSION: CD40-mediated activation of macrophages and dendritic cells in iCCA significantly enhances response to anti-PD-1 therapy. This regimen may enhance the efficacy of first-line chemotherapy. LAY SUMMARY: Checkpoint inhibition, a common form of immune therapy, is generally ineffective for the treatment of cholangiocarcinoma. These tumors suppress the infiltration and function of surrounding immune cells. Stimulating immune cells such as macrophages and dendritic cells via the CD40 receptor activates downstream immune cells and enhances the response to checkpoint inhibitors.

Risk Factors for Delta Hepatitis in a North American Cohort: Who Should Be Screened?

INTRODUCTION: The American Association for the Study of Liver Diseases recommends hepatitis D virus (HDV) screening in certain high-risk groups; however, the effectiveness is unknown. METHODS: A study of North American patients with hepatitis B (HBV) referred to the NIH was performed to identify risk factors associated with HDV infection. Active HDV was "confirmed" by serum HDV RNA or histologic HDV antigen staining. RESULTS: Six hundred fifty-two were studied, of which 91 were HDV "confirmed." Independent risk factors for HDV included: intravenous drug users, HBV-DNA <2,000 IU/mL, alanine aminotransferase >40 U/L, and HDV endemic country of origin. DISUSSION: North American patients with HBV and significant risk factors should be screened for HDV.

Machine Learning Assisted Classification of Aluminum Nitride Thin Film Stress via In-Situ Optical Emission Spectroscopy Data.

In this study, we submit a complex set of in-situ data collected by optical emission spectroscopy (OES) during the process of aluminum nitride (AlN) thin film. Changing the sputtering power and nitrogen(N2) flow rate, AlN film was deposited on Si substrate using a superior sputtering with a pulsed direct current (DC) method. The correlation between OES data and deposited film residual stress (tensile vs. compressive) associated with crystalline status by X-ray diffraction spectroscopy (XRD), scanning electron microscope (SEM), and transmission electron microscope (TEM) measurements were investigated and established throughout the machine learning exercise. An important answer to know is whether the stress of the processing film is compressive or tensile. To answer this question, we can access as many optical spectra data as we need, record the data to generate a library, and exploit principal component analysis (PCA) to reduce complexity from complex data. After preprocessing through PCA, we demonstrated that we could apply standard artificial neural networks (ANNs), and we could obtain a machine learning classification method to distinguish the stress types of the AlN thin films obtained by analyzing XRD results and correlating with TEM microstructures. Combining PCA with ANNs, an accurate method for in-situ stress prediction and classification was created to solve the semiconductor process problems related to film property on deposited films more efficiently. Therefore, methods for machine learning-assisted classification can be further extended and applied to other semiconductors or related research of interest in the future.

Instance theory predicts information theory: Episodic uncertainty as a determinant of keystroke dynamics.

How does prior experience shape skilled performance in structured environments? We use skilled typing of natural text to evaluate correspondence between performance (keystroke timing) and structure in the environment (letter uncertainty). We had ∼350 typists copy-type English text. We reproduced Ostry's (1983) analysis of interkeystroke interval as a function of letter position and word length, that showed prominent first-letter and midword slowing effects. We propose a novel account that letter position and word length effects on keystroke dynamics reflect informational uncertainty about letters in those locations, rather than resource limited planning/buffering processes. We computed positional uncertainty for letters in all positions of words from length one to nine using Google's n-gram database. We show that variance in interkeystroke interval by letter position and word length tracks natural variation in letter uncertainty. Finally, we provide a model showing how a general learning and memory process could acquire sensitivity to patterns of letter uncertainty in natural English. In doing so, we draw an equivalence between Logan's (1988) instance theory of automatization and Shannon's measure of entropy (H) from information theory. Instance theory's predictions for automatization as a function of experience follow exactly the uncertainty in the choice set being automatized. As a result, instance theory stands as a general process model explaining how context-specific experiences in a structured environment tune skilled performance. (PsycINFO Database Record (c) 2019 APA, all rights reserved).