Predicting amyotrophic lateral sclerosis (ALS) progression with machine learning.

Objective: To predict ALS progression with varying observation and prediction window lengths, using machine learning (ML).

Methods: We used demographic, clinical, and laboratory parameters from 5030 patients in the Pooled Resource Open-Access ALS Clinical Trials (PRO-ACT) database to model ALS disease progression as fast (at least 1.5 points decline in ALS Functional Rating Scale-Revised (ALSFRS-R) per month) or non-fast, using Extreme Gradient Boosting (XGBoost) and Bayesian Long Short Term Memory (BLSTM).

Describing and characterising variability in ALS disease progression.

Background, Objectives: Decrease in the revised ALS Functional Rating Scale (ALSFRS-R) score is currently the most widely used measure of disease progression. However, it does not sufficiently encompass the heterogeneity of ALS. We describe a measure of variability in ALSFRS-R scores and demonstrate its utility in disease characterization.

Photodynamic augmentation of oncolytic virus therapy for central nervous system malignancies.

Oncolytic viruses (OVs) have emerged as a clinical therapeutic modality potentially effective for cancers that evade conventional therapies, including central nervous system malignancies. Rationally designed combinatorial strategies can augment the efficacy of OVs by boosting tumor-selective cytotoxicity and modulating the tumor microenvironment (TME). Photodynamic therapy (PDT) of cancer not only mediates direct neoplastic cell death but also primes the TME to sensitize the tumor to secondary therapies, allowing for the combination of two potentially synergistic therapies with broader targets.