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Background: Artificial intelligence (AI) is a rapidly evolving field which will have implications on both individual patient care and the health care system. There are many benefits to the integration of AI into health care, such as predicting acute conditions and enhancing diagnostic capabilities. Despite these benefits potential harms include algorithmic bias, inadequate consent processes, and implications on the patient-provider relationship. One tool to address patients' needs and prevent the negative implications of AI is through patient engagement. As it currently stands, patients have infrequently been involved in AI application development for patient care delivery. Furthermore, we are unaware of any frameworks or recommendations specifically addressing patient engagement within the field of AI in health care. Methods: We conducted four virtual focus groups with thirty patient participants to understand of how patients can and should be meaningfully engaged within the field of AI development in health care. Participants completed an educational module on the fundamentals of AI prior to participating in this study. Focus groups were analyzed using qualitative content analysis. Results: We found that participants in our study wanted to be engaged at the problem-identification stages using multiple methods such as surveys and interviews. Participants preferred that recruitment methodologies for patient engagement included both in-person and social media-based approaches with an emphasis on varying language modalities of recruitment to reflect diverse demographics. Patients prioritized the inclusion of underrepresented participant populations, longitudinal relationship building, accessibility, and interdisciplinary involvement of other stakeholders in AI development. We found that AI education is a critical step to enable meaningful patient engagement within this field. We have curated recommendations into a framework for the field to learn from and implement in future development. Conclusion: Given the novelty and speed at which AI innovation is progressing in health care, patient engagement should be the gold standard for application development. Our proposed recommendations seek to enable patient-centered AI application development in health care. Future research must be conducted to evaluate the effectiveness of patient engagement in AI application development to ensure that both AI application development and patient engagement are done rigorously, efficiently, and meaningfully. [ABSTRACT FROM AUTHOR]

Background: With large volumes of longitudinal data in electronic medical records from diverse patients, primary care is primed for disruption by artificial intelligence (AI) technology. With AI applications in primary care still at an early stage in Canada and most countries, there is a unique opportunity to engage key stakeholders in exploring how AI would be used and what implementation would look like. Objective: To identify the barriers that patients, providers, and health leaders perceive in relation to implementing AI in primary care and strategies to overcome them. Design: 12 virtual deliberative dialogues. Dialogue data were thematically analyzed using a combination of rapid ethnographic assessment and interpretive description techniques. Setting: Virtual sessions. Participants: Participants from eight provinces in Canada, including 22 primary care service users, 21 interprofessional providers, and 5 health system leaders Results: The barriers that emerged from the deliberative dialogue sessions were grouped into four themes: (1) system and data readiness, (2) the potential for bias and inequity, (3) the regulation of AI and big data, and (4) the importance of people as technology enablers. Strategies to overcome the barriers in each of these themes were highlighted, where participatory co-design and iterative implementation were voiced most strongly by participants. Limitations: Only five health system leaders were included in the study and no self-identifying Indigenous people. This is a limitation as both groups may have provided unique perspectives to the study objective. Conclusions: These findings provide insight into the barriers and facilitators associated with implementing AI in primary care settings from different perspectives. This will be vital as decisions regarding the future of AI in this space is shaped. [ABSTRACT FROM AUTHOR]

The assembly of proteins into dimers and oligomers is a necessary step for the proper function of transcription factors, muscle proteins, and proteases. In uncontrolled states, oligomerization can also contribute to illnesses such as Alzheimer's disease. The S100 protein family is a group of dimeric proteins that have important roles in enzyme regulation, cell membrane repair, and cell growth. Most S100 proteins have been examined in their homodimeric state, yet some of these important proteins are found in similar tissues implying that heterodimeric molecules can also be formed from the combination of two different S100 members. In this work, we have established co‐expression methods in order to identify and quantify the distribution of homo‐ and heterodimers for four specific pairs of S100 proteins in their calcium‐free states. The split GFP trap methodology was used in combination with other GFP variants to simultaneously quantify homo‐ and heterodimeric S100 proteins in vitro and in living cells. For the specific S100 proteins examined, NMR, mass spectrometry, and GFP trap experiments consistently show that S100A1:S100B, S100A1:S100P, and S100A11:S100B heterodimers are the predominant species formed compared to their corresponding homodimers. We expect the tools developed here will help establish the roles of S100 heterodimeric proteins and identify how heterodimerization might alter the specificity for S100 protein action in cells. [ABSTRACT FROM AUTHOR]

Summary Long noncoding RNAs (lncRNAs) exhibit diverse functions, including regulation of development. Here, we combine genome-wide mapping of SMAD3 occupancy with expression analysis to identify lncRNAs induced by activin signaling during endoderm differentiation of human embryonic stem cells (hESCs). We find that DIGIT is divergent to Goosecoid ( GSC ) and expressed during endoderm differentiation. Deletion of the SMAD3-occupied enhancer proximal to DIGIT inhibits DIGIT and GSC expression and definitive endoderm differentiation. Disruption of the gene encoding DIGIT and depletion of the DIGIT transcript reveal that DIGIT is required for definitive endoderm differentiation. In addition, we identify the mouse ortholog of DIGIT and show that it is expressed during development and promotes definitive endoderm differentiation of mouse ESCs. DIGIT regulates GSC in trans , and activation of endogenous GSC expression is sufficient to rescue definitive endoderm differentiation in DIGIT -deficient hESCs. Our study defines DIGIT as a conserved noncoding developmental regulator of definitive endoderm. [ABSTRACT FROM AUTHOR]