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.

ANTIPASTI: Interpretable prediction of antibody binding affinity exploiting normal modes and deep learning.

The high binding affinity of antibodies toward their cognate targets is key to eliciting effective immune responses, as well as to the use of antibodies as research and therapeutic tools. Here, we propose ANTIPASTI, a convolutional neural network model that achieves state-of-the-art performance in the prediction of antibody binding affinity using as input a representation of antibody-antigen structures in terms of normal mode correlation maps derived from elastic network models. This representation captures not only structural features but energetic patterns of local and global residue fluctuations.

WOMAC, Kujala Score, and Knee Injury and Osteoarthritis Outcome Score for Quality of Life Thresholds for Predicting Increased and Decreased Likelihood of Failure to Improve Quality of Life After Total Knee Replacement.

Background Improvement in quality of life is the primary goal following total knee arthroplasty (TKA). Patient-reported outcome measures (PROMs) have become the standard for evaluating TKA results, capturing the patient's perspective. However, PROMs face challenges such as inconsistent presurgery data collection and ambiguity in determining clinical significance.