Aspirin-triggered resolvin D1 modulates pulmonary and neurological inflammation in an IL-22 knock-out organic dust exposure mouse model.

Agriculture dust contains many organic immunogenic compounds, and organic dust exposure is strongly associated with the development of immune-mediated chronic pulmonary diseases such as chronic obstructive pulmonary disease (COPD). Chronic organic dust exposure from agriculture sources induces chronic lung inflammatory diseases and organic dust exposure has recently been linked to an increased risk of developing dementia. The cytokine interleukin-22 (IL-22) has been established as an important mediator in the resolution and repair of lung tissues.

Safety and efficacy of mesenchymal stromal cells mitochondria transplantation as a cell-free therapy for osteoarthritis.

Objective: The inflammatory responses from synovial fibroblasts and macrophages and the mitochondrial dysfunction in chondrocytes lead to oxidative stress, disrupt extracellular matrix (ECM) homeostasis, and accelerate the deterioration process of articular cartilage in osteoarthritis (OA). In recent years, it has been proposed that mesenchymal stromal cells (MSC) transfer their functional mitochondria to damaged cells in response to cellular stress, becoming one of the mechanisms underpinning their therapeutic effects. Therefore, we hypothesize that a novel cell-free treatment for OA could involve direct mitochondria transplantation, restoring both cellular and mitochondrial homeostasis.

I don't know about you, but I'm feeling IL-22.

Defense of the human body against damaging and pathogenic insults is a heavily regulated affair. A primary mechanism of defense at sites of insult are soluble mediators whose defensive maneuvers increase barrier integrity and promote pro-reparative and resolution processes. IL-22 is a cytokine in the IL-10 cytokine family that has garnered increased attention in recent years due to its intimate link in promoting resolution of inflammatory insults, while simultaneously being over expressed in certain fibrotic and chronic inflammatory-skewed diseases.

Transformer-based deep learning model for the diagnosis of suspected lung cancer in primary care based on electronic health record data.

Background: Due to its late stage of diagnosis lung cancer is the commonest cause of death from cancer in the UK. Existing epidemiological risk models in clinical usage, which have Positive Predictive Values (PPV) of less than 10%, do not consider the temporal relations expressed in sequential electronic health record (EHR) data. We aimed to build a model for lung cancer early detection in primary care using machine learning with deep 'transformer' models on EHR data to learn from these complex sequential 'care pathways'.

Hyperspectral unmixing for Raman spectroscopy via physics-constrained autoencoders.

Raman spectroscopy is widely used across scientific domains to characterize the chemical composition of samples in a nondestructive, label-free manner. Many applications entail the unmixing of signals from mixtures of molecular species to identify the individual components present and their proportions, yet conventional methods for chemometrics often struggle with complex mixture scenarios encountered in practice. Here, we develop hyperspectral unmixing algorithms based on autoencoder neural networks, and we systematically validate them using both synthetic and experimental benchmark datasets created in-house.