Cell-Penetrating Milk-Derived Peptides with a Non-Inflammatory Profile

Milk-derived peptides are known to confer anti-inflammatory effects. We hypothesised that milk-derived cell-penetrating peptides might modulate inflammation in useful ways. Using computational techniques, we identified and synthesised peptides from the milk protein Alpha-S1-casein that were predicted to be cell-penetrating using a machine learning predictor. We modified the interpretation of the prediction results to consider the effects of histidine. Peptides were then selected for testing to determine their cell penetrability and anti-inflammatory effects using HeLa cells and J774.2 mouse macrophage cell lines. The selected peptides all showed cell penetrating behaviour, as judged using confocal microscopy of fluorescently labelled peptides. None of the peptides had an effect on either the NF-κB transcription factor or TNFα and IL-1β secretion. Thus, the identified milk-derived sequences have the ability to be internalised into the cell without affecting cell homeostatic mechanisms such as NF-κB activation. These peptides are worthy of further investigation for other potential bioactivities or as a naturally derived carrier to promote the cellular internalisation of other active peptides.

The ethical matrix as a method for involving people living with disease and the wider public (PPI) in near-term artificial intelligence research

INTRODUCTION

The rapid pace of research in the field of Artificial Intelligence in medicine has associated risks for near-term AI. Ethical considerations of the use of AI in medicine remain a subject of much debate. Concurrently, the Involvement of People living with disease and the Public (PPI) in research is becoming mandatory in the EU and UK. The goal of this research was to elucidate the important values for our relevant stakeholders: People with MS, Radiologists, neurologists, Registered Healthcare Practitioners and Computer Scientists concerning AI in radiology and synthesize these in an ethical matrix.

METHODS

An ethical matrix workshop co-designed with a patient expert. The workshop yielded a survey which was disseminated to the professional societies of the relevant stakeholders. Quantitative data were analysed using the Pingouin 0.53 python package. Qualitative data were examined with word frequency analysis and analysed for themes with grounded theory with a patient expert.

RESULTS

184 participants were recruited, (54, 60, 17, 12, 41 respectively). There were significant (p < 0.00001) differences in age, gender and ethnicity between groups. Key themes emerging from our results were the importance fast and accurate results, explanations over model performance and the significance of maintaining personal connections and choice. These themes were used to construct the ethical matrix.

CONCLUSION

The ethical matrix is a useful tool for PPI and stakeholder engagement with particular advantages for near-term AI in the pandemic era.

IMPLICATIONS FOR PRACTICE

We have produced an ethical matrix that allows for the inclusion of stakeholder opinion in medical AI research design.

Tradeoff of semantic relatedness and degree of overlearning: differential effects on metamemory and on long-term retention

We investigated people's recall and recognition of, and Judgments of Learning (JOLs) and Feelings of Knowing (FOKs) about: (a) pairs of related words that were learned to a criterion of two correct recalls (criterion-learned/related) versus (b) pairs of unrelated words that were learned to a criterion of eight correct recalls (overlearned/unrelated). Recall, FOK on unrecalled targets, and recognition were tested at either (between-subjects variable) two or six weeks after learning. In Experiment 1, subjects JOLs were greater in magnitude for criterion-learned/related items than for overlearned/unrelated items, and they predicted that recall would be the same after a 2-week retention interval as after a 6-week retention interval (between-subjects prediction). In contrast, however, subsequent recall was greater on the 2-week retention test than on the 6-week retention test and was greater for the overlearned/unrelated items than for the criterion-learned/related items; also, subjects' FOKs (and recognition performance) were greater in magnitude for the overlearned/unrelated items than for the criterion-learned/related items. Experiment 2 revealed that the overweighting of the importance of relatedness disappears from JOLs when those JOLs occur one day after the acquisition session. These findings imply that the information tracked by metacognitive monitoring judgments is different for JOLs than for FOKs, with the JOLs (relative to FOKs) based more on semantic relatedness and less on the degree of learning during acquisition. Also, subjects' JOLs are not particularly good at accurately forecasting their eventual level of recall on long-term retention tests that occur several weeks after acquisition.