Modeling the influence of attitudes, trust, and beliefs on endoscopists' acceptance of artificial intelligence applications in medical practice.

Introduction: The potential for deployment of Artificial Intelligence (AI) technologies in various fields of medicine is vast, yet acceptance of AI amongst clinicians has been patchy. This research therefore examines the role of antecedents, namely trust, attitude, and beliefs in driving AI acceptance in clinical practice. Methods: We utilized online surveys to gather data from clinicians in the field of gastroenterology. Results: A total of 164 participants responded to the survey. Participants had a mean age of 44.49 (SD = 9.65). Most participants were male (n = 116, 70.30%) and specialized in gastroenterology (n = 153, 92.73%). Based on the results collected, we proposed and tested a model of AI acceptance in medical practice. Our findings showed that while the proposed drivers had a positive impact on AI tools' acceptance, not all effects were direct. Trust and belief were found to fully mediate the effects of attitude on AI acceptance by clinicians. Discussion: The role of trust and beliefs as primary mediators of the acceptance of AI in medical practice suggest that these should be areas of focus in AI education, engagement and training. This has implications for how AI systems can gain greater clinician acceptance to engender greater trust and adoption amongst public health systems and professional networks which in turn would impact how populations interface with AI. Implications for policy and practice, as well as future research in this nascent field, are discussed.

Feature selection in clinical proteomics: with great power comes great reproducibility.

In clinical proteomics, reproducible feature selection is unattainable given the standard statistical hypothesis-testing framework. This leads to irreproducible signatures with no diagnostic power. Instability stems from high P-value variability (p_var), which is inevitable and insolvable. The impact of p_var can be reduced via power increment, for example increasing sample size and measurement accuracy. However, these are not realistic solutions in practice. Instead, workarounds using existing data such as signal boosting transformation techniques and network-based statistical testing is more practical. Furthermore, it is useful to consider other metrics alongside P-values including confidence intervals, effect sizes and cross-validation accuracies to make informed inferences.

Valproic acid mediates miR-124 to down-regulate a novel protein target, GNAI1.

Valproic acid (VPA) is an anti-convulsant drug that is recently shown to have neuroregenerative therapeutic actions. In this study, we investigate the underlying molecular mechanism of VPA and its effects on Bdnf transcription through microRNAs (miRNAs) and their corresponding target proteins. Using in silico algorithms, we predicted from our miRNA microarray and iTRAQ data that miR-124 is likely to target at guanine nucleotide binding protein alpha inhibitor 1 (GNAI1), an adenylate cyclase inhibitor. With the reduction of GNAI1 mediated by VPA, the cAMP is enhanced to increase Bdnf expression. The levels of GNAI1 protein and Bdnf mRNA can be manipulated with either miR-124 mimic or inhibitor. In summary, we have identified a novel molecular mechanism of VPA that induces miR-124 to repress GNAI1. The implication of miR-124→GNAI1→BDNF pathway with valproic acid treatment suggests that we could repurpose an old drug, valproic acid, as a clinical application to elevate neurotrophin levels in treating neurodegenerative diseases.

How advancement in biological network analysis methods empowers proteomics.

Proteomics provides important information--that may not be inferable from indirect sources such as RNA or DNA--on key players in biological systems or disease states. However, it suffers from coverage and consistency problems. The advent of network-based analysis methods can help in overcoming these problems but requires careful application and interpretation. This review considers briefly current trends in proteomics technologies and understanding the causes of critical issues that need to be addressed--i.e., incomplete data coverage and inter-sample inconsistency. On the coverage issue, we argue that holistic analysis based on biological networks provides a suitable background on which more robust models and interpretations can be built upon; and we introduce some recently developed approaches. On consistency, group-based approaches based on identified clusters, as well as on properly integrated pathway databases, are particularly useful. Despite that protein interactions and pathway networks are still largely incomplete, given proper quality checks, applications and reasonably sized data sets, they yield valuable insights that greatly complement data generated from quantitative proteomics.