Hospital food service quality improvement questionnaire (HFSQIQ): Development, translation and validation of a questionnaire.

The hospital food service department provides meals to staff, patients, and their caregivers while adhering to dietary therapy guidelines and promoting nutritional wellness. High-quality food service plays a pivotal role in offering inpatients nourishing meals that promote physical and mental well-being, aiding their recovery and overall health during their hospitalisation. This study aimed to develop and validate a tool for measuring and evaluating hospital food operations using the Total Quality Management approach. A literature review, in-depth interviews with food service employees, and a peer-review process were conducted to identify the domains and items for the questionnaire. A “Hospital Food Service Quality Improvement Questionnaire” (HFSQIQ) with 61 items in six domains was developed and the content validation was performed by seven experts. The questionnaire was translated into Malay, and the internal consistency of the HFSQIQ was examined using Cronbach’s alpha. Resultantly, the HFSQIQ depicted high validity and reliability, with a high I-CVI and Kappa index rating for most items and a Cronbach alpha value of 0.97 and 0.98 for the importance and performance scales, respectively. In conclusion, the HFSQIQ is a useful tool for evaluating and improving the quality of hospital food service operations

Genetic mutations linked to Parkinson's disease differentially control nucleolar activity in pre-symptomatic mouse models

Genetic mutations underlying neurodegenerative disorders impair ribosomal DNA (rDNA) transcription suggesting that nucleolar dysfunction could be a novel pathomechanism in polyglutamine diseases and in certain forms of amyotrophic lateral sclerosis/frontotemporal dementia. Here, we investigated nucleolar activity in pre-symptomatic digenic models of Parkinson's disease (PD) that model the multifactorial aetiology of this disease. To this end, we analysed a novel mouse model mildly overexpressing mutant human alpha-synuclein (hA53T-SNCA) in a PTEN-induced kinase 1 (PINK1/ PARK6) knockout background and mutant mice lacking both DJ-1 (also known as PARK7) and PINK1. We showed that overexpressed hA53T-SNCA localizes to the nucleolus. Moreover, these mutants show a progressive reduction of rDNA transcription linked to a reduced mouse lifespan. By contrast, rDNA transcription is preserved in DJ-1/PINK1 double knockout (DKO) mice. mRNA levels of the nucleolar transcription initiation factor 1A (TIF-IA, also known as RRN3) decrease in the substantia nigra of individuals with PD. Because loss of TIF-IA, as a tool to mimic nucleolar stress, increases oxidative stress and because DJ-1 and PINK1 mutations result in higher vulnerability to oxidative stress, we further explored the synergism between these PD-associated genes and impaired nucleolar function. By the conditional ablation of TIF-IA, we blocked ribosomal RNA (rRNA) synthesis in adult dopaminergic neurons in a DJ-1/PINK1 DKO background. However, the early phenotype of these triple knockout mice was similar to those mice exclusively lacking TIF-IA. These data sustain a model in which loss of DJ-1 and PINK1 does not impair nucleolar activity in a pre-symptomatic stage. This is the first study to analyse nucleolar function in digenic PD models. We can conclude that, at least in these models, the nucleolus is not as severely disrupted as previously shown in DA neurons from PD patients and neurotoxin-based PD mouse models. The results also show that the early increase in rDNA transcription and nucleolar integrity may represent specific homeostatic responses in these digenic pre-symptomatic PD models.Peer reviewe

Elevated circulating lactate levels and widespread expression of its cognate receptor, hydroxycarboxylic acid receptor 1 (HCAR1), in ovarian cancer

Augmented glycolysis in cancer cells is a process required for growth and development. The Warburg effect provides evidence of increased glycolysis and lactic acid fermentation in cancer cells. The lactate end-product of glycolysis is receiving growing traction for its role as a cell signalling molecule. Ovarian cancer (OvCa) is also characterised by altered glucose metabolism. We aim to explore circulating lactate levels in patients with high-grade serous OvCa (HGSOC) and to elucidate the expression of the lactate receptor hydroxycarboxylic acid receptor 1 (HCAR1) in OvCa. HCAR1 expression was detected in patient biopsy cores using immunohistochemistry, while lactate was measured from whole blood with a Biosen-C line clinic measuring system. We noted significantly elevated lactate levels in OvCa patients (4.3 ± 1.9 mmol/L) compared with healthy controls (1.4 ± 0.6 mmol/L; < 0.0001), with an AUC of 0.96. The HCAR1 gene is overexpressed in OvCa compared to healthy controls ( < 0.001). Using an OvCa tissue microarray (>75% expression in 100 patients), high protein expression was also recorded across all epithelial OvCa subtypes and ovarian normal adjacent tissue (NAT). Lactate monitoring is a simple, cost-efficient test that can offer point-of-care results. Our data suggest that the potential of circulating lactate as a screening biomarker in OvCa merits further research attention

BLOOM: A 176B-Parameter Open-Access Multilingual Language Model

Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning. To facilitate future research and applications using LLMs, we publicly release our models and code under the Responsible AI License