Correction: Ariaee et al. The Degree of Inulin Polymerization Is Important for Short-Term Amelioration of High-Fat Diet (HFD)-Induced Metabolic Dysfunction and Gut Microbiota Dysbiosis in Rats. 2024, , 1039.

In the original publication [...

The impact of fibre and prebiotic interventions on outcomes in cancer and haematopoietic stem cell transplantation: A systematic review.

Background & Aims: Cancer therapy is associated with a range of toxicities that severely impact patient well-being and a range of clinical outcomes. Dietary fibre/prebiotics characteristically improve the gastrointestinal microenvironment, which consequently elicits beneficial downstream effects that could be relevant to the prevention and management of treatment-related toxicities. Despite the compelling theoretical scientific rationale there has been limited effort to synthesise the available evidence to conclude such scientific underpinning to the clinical use of fibre/prebiotics in cancer patients.

DPYD genotype-guided dose personalisation for fluoropyrimidine-based chemotherapy prescribing in solid organ cancer patients in Australia: GeneScreen 5-FU study protocol.

Background: Fluoropyrimidine (FP) chemotherapies are commonly prescribed for upper and lower gastrointestinal, breast and head and neck malignancies. Over 16,000 people with cancer require FP chemotherapies per annum in Australia. Between 10 and 40% patients experience grade 3-4 (≥ G3) toxicities that require hospital-based management ± intensive care admission.

Credit where it's due: Recognising lived experience in research authorship.

Objective: To identify current practice and preferences about whether and how to acknowledge authors' lived experience when authors contribute their lived experience expertise to research outputs in the context of health and healthcare.

Methods: Surveys to people with lived experience and to academic researchers who had conducted research together (via consultation, partnership or lived-experience-led).

Results: Responses from 40 academic researchers and 36 lived experience contributors were included.

Improving Cardiovascular Disease Prediction With Machine Learning Using Mental Health Data: A Prospective UK Biobank Study.

Background: Robust and accurate prediction of cardiovascular disease (CVD) risk facilitates early intervention to benefit patients. The intricate relationship between mental health disorders and CVD is widely recognized. However, existing models often overlook psychological factors, relying on a limited set of clinical and lifestyle parameters, or being developed on restricted population subsets.