Institute For Respiratory Health

To systematically review and analyze the effects of photobiomodulation on oral mucositis, xerostomia, salivary flow rates, oral pain, and quality of life in patients with head and neck cancer.

A systematic search of six databases was conducted up to April 2025. Eligible randomized controlled trials (RCTs) examined the effects of photobiomodulation on the specified outcomes in patients with head and neck cancer. Pooled estimates (risk ratio [RR] and standardized mean difference [SMD]) were calculated using a three-level mixed-effects meta-analysis.

Thirty-three articles describing 30 RCTs ( n  = 1748) were included. Photobiomodulation significantly reduced the risk of severe oral mucositis (RR 0.46, 95% CI: 0.29–0.71, P  < 0.001) and severe oral pain (RR 0.35, 95% CI: 0.23–0.53, P  < 0.001) compared with controls. Significant improvements were also observed for salivary flow rate (SMD 0.75, 95% CI: 0.03–1.46, P  = 0.044). No significant effects were observed on xerostomia (SMD −0.07, 95% CI: −0.47–0.33, P  = 0.646) and quality of life (SMD 1.06, 95% CI: −0.03–2.14, P  = 0.060). The benefits were consistent across different photobiomodulation protocols for oral mucositis and quality of life.

Photobiomodulation appears to be a promising supportive care intervention, reducing the risk of severe mucositis and oral pain, while potentially improving salivary flow rates and preserving quality of life in patients with head and neck cancer. However, the certainty of the evidence ranged from very low to low and most included studies presented a high risk of bias; therefore, these findings should be interpreted with caution.

Idiopathic pulmonary fibrosis (IPF) is a serious respiratory disease with a poor prognosis and limited treatments. IPF is characterized by accumulation of extracellular matrix, destruction of gas exchange units, and decline in lung function. The pathogenesis and underlying mechanisms are poorly understood although a combination of environmental and genetic factors is implicated. Epigenetic modifications represent a facet of gene regulation likely important to key pathways including transforming growth factor (TGF-β1) signaling, fibroblast-to-myofibroblast differentiation, epithelial-to-mesenchymal transition, cellular aging, and apoptosis. Methylation, acetylation, noncoding RNAs, telomere length, and G-quadruplexes have all been investigated with varying levels of progress but are certainly potential targets for drug development. This review discusses the underlying epigenetic biology of IPF, associated clinical applications, and potential novel treatments.

Objective. To investigate the use of reinforcement learning (RL) algorithms to optimise complex combination cancer therapies. The RL algorithm investigated the effect of varying the radiotherapy (RT) dose in each fraction when administered in conjunction with the immune checkpoint inhibitors (ICIs) anti-PD-1 and anti-CTLA-4. Approach. Data were available for BALB/c mice inoculated with a syngeneic mesothelioma tumour on the flank, treated with combination RT and ICI with tumour growth subsequently measured. A deep Q-network (DQN) and a double DQN were trained using a mechanistic model fitted to the mesothelioma volumes to simulate the dynamics of the tumour microenvironment. Two reward functions were created for the RL algorithm to optimise: the first only considered tumour cell killing, while the second penalised treatment schedules with higher total RT dose. Comparison with experimental results was via the tumour control probability (TCP). Main Results. All the TCPs obtained with the RL algorithm exceeded the TCPs obtained with the same mechanistic model when only 1 or 2 fractions of RT were administered. However, the baseline schedule of 2 Gy per fraction outperformed the treatment schedules generated by RL. Significance. This study highlights the potential for RL to explore the vast solution space of possible treatment schedules, conceivably at the individual patient level.