Microrheology of Ionic Liquid Doped Mucus for an Efficient Delivery of Protein-Based Oral Drugs.

Developing protein-based drugs for oral administration is one of the most challenging aspects of research due to their low stability and inability to permeate through intestinal mucus barrier. Recent studies suggest that the ionic liquids (ILs) can combine with protein-based drugs to improve stability and mucus-penetration capabilities. However, the interactions among protein-based drugs, ILs, and mucin are rather unknown, which can play a pivotal role in such drug delivery.

T-cell immune checkpoint inhibition plus hypomethylation for locally advanced HER2-negative breast cancer: a phase 2 neoadjuvant window trial of decitabine and pembrolizumab followed by standard neoadjuvant chemotherapy.

Background: Higher levels of tumor-infiltrating lymphocytes (TILs) in breast cancers are associated with increased likelihood of pathologic complete response (pCR) to chemotherapy. DNA methyltransferase inhibitors (DNMTi) can augment immune responses to cancers, decreasing myeloid-derived suppressor cells (MDSCs) and increasing T lymphocyte responsiveness. We have shown that the DNMTi decitabine augments the effectiveness of immunotherapy using murine triple-negative breast cancer (TNBC) models.

Sodium-Glucose Cotransporter-2 Inhibitors and Arrhythmias: A Meta-Analysis of 38 Randomized Controlled Trials.

Background: Sodium-glucose cotransporter-2 inhibitors (SGLT2i) have shown promising results in reducing hospitalizations from heart failure (HF) and cardiovascular mortality. However, their effect on arrhythmia and sudden cardiac death (SCD) is not well established.

Objectives: The authors sought to evaluate the association between SGLT2i and the risk of arrhythmias and SCD in patients with type 2 diabetes mellitus, HF, or chronic kidney disease.

The external validity of machine learning-based prediction scores from hematological parameters of COVID-19: A study using hospital records from Brazil, Italy, and Western Europe.

The unprecedented worldwide pandemic caused by COVID-19 has motivated several research groups to develop machine-learning based approaches that aim to automate the diagnosis or screening of COVID-19, in large-scale. The gold standard for COVID-19 detection, quantitative-Real-Time-Polymerase-Chain-Reaction (qRT-PCR), is expensive and time-consuming. Alternatively, haematology-based detections were fast and near-accurate, although those were less explored.