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PRIMPOL ready, set, reprime!

ABSTRACT: DNA replication forks are constantly challenged by DNA lesions induced by endogenous and exogenous sources. DNA damage tolerance mechanisms ensure that DNA replication continues with minimal effects on replication fork elongation either by using specialized DNA polymerases, which have the ability to replicate through the damaged template, or by skipping the damaged DNA, leaving it to be repaired after replication. These mechanisms are evolutionarily conserved in bacteria, yeast, and higher eukaryotes, and are paramount to ensure timely and faithful duplication of the genome. The Primase and DNA-directed Polymerase (PRIMPOL) is a recently discovered enzyme that possesses both primase and polymerase activities. PRIMPOL is emerging as a key player in DNA damage tolerance, particularly in vertebrate and human cells. Here, we review our current understanding of the function of PRIMPOL in DNA damage tolerance by focusing on the structural aspects that define its dual enzymatic activity, as well as on the mechanisms that control its chromatin recruitment and expression levels. We also focus on the latest findings on the mitochondrial and nuclear functions of PRIMPOL and on the impact of loss of these functions on genome stability and cell survival. Defining the function of PRIMPOL in DNA damage tolerance is becoming increasingly important in the context of human disease. In particular, we discuss recent evidence pointing at the PRIMPOL pathway as a novel molecular target to improve cancer cell response to DNA-damaging chemotherapy and as a predictive parameter to stratify patients in personalized cancer therapy.

SUBMITTER: Tirman S

PROVIDER: S-EPMC7906090 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Background More research is required to understand the effects of implementing structured goal-setting on trainee engagement in competency-based clinical learning environments. Objective To explore how residents experienced a rotation-specific goal-setting intervention on geriatric medicine rotations at 2 hospitals. Methods All rotating residents were expected to complete the intervention, consisting of a SMART-based (Specific, Measurable, Achievable, Relevant, and Time-Bound) goal-setting form and feedback sessions with teaching faculty. From November 2019 to June 2021, we recruited a convenience sample of rotating residents. Study participants completed pre- and postrotation 35-item Dutch Residency Educational Climate Test (D-RECT) questionnaires to compare scores from their rotation before the geriatric rotation and a postrotation semistructured interview, which we transcribed and analyzed using principles of constant comparison and reflexive thematic analysis. Results We interviewed 12 of 58 (20.7%) residents participating in the goal-setting intervention, 11 of whom completed both D-RECT questionnaires. Participants' D-RECT scores favored the geriatric medicine rotation versus the immediately preceding clinical rotation (M=4.29±0.37; M=3.84±0.44, P=.002). Analyses of interview transcripts yielded 3 themes on how participants perceived the intervention influenced their learning experience: (1) structured forms and processes mediate, inform, and constrain goal selection; (2) interactions with faculty, patients, and system factors influenced goal enactment; and (3) unstructured assessments led to uncertainty around goal achievement. Challenges included time restrictions and unpredictable clinical opportunities. Conclusions Goal-setting appeared to help many residents direct their learning efforts and engage in collaborative processes with teaching faculty. We identified challenges limiting residents' engagement with the goal-setting intervention, which may inform the practical implementation of goal-setting in other competency-based curricula.

Project description:ImportanceThe lack of data quality frameworks to guide the development of artificial intelligence (AI)-ready data sets limits their usefulness for machine learning (ML) research in health care and hinders the diagnostic excellence of developed clinical AI applications for patient care.ObjectiveTo discern what constitutes high-quality and useful data sets for health and biomedical ML research purposes according to subject matter experts.Design, setting, and participantsThis qualitative study interviewed data set experts, particularly those who are creators and ML researchers. Semistructured interviews were conducted in English and remotely through a secure video conferencing platform between August 23, 2022, and January 5, 2023. A total of 93 experts were invited to participate. Twenty experts were enrolled and interviewed. Using purposive sampling, experts were affiliated with a diverse representation of 16 health data sets/databases across organizational sectors. Content analysis was used to evaluate survey information and thematic analysis was used to analyze interview data.Main outcomes and measuresData set experts' perceptions on what makes data sets AI ready.ResultsParticipants included 20 data set experts (11 [55%] men; mean [SD] age, 42 [11] years), of whom all were health data set creators, and 18 of the 20 were also ML researchers. Themes (3 main and 11 subthemes) were identified and integrated into an AI-readiness framework to show their association within the health data ecosystem. Participants partially determined the AI readiness of data sets using priority appraisal elements of accuracy, completeness, consistency, and fitness. Ethical acquisition and societal impact emerged as appraisal considerations in that participant samples have not been described to date in prior data quality frameworks. Factors that drive creation of high-quality health data sets and mitigate risks associated with data reuse in ML research were also relevant to AI readiness. The state of data availability, data quality standards, documentation, team science, and incentivization were associated with elements of AI readiness and the overall perception of data set usefulness.Conclusions and relevanceIn this qualitative study of data set experts, participants contributed to the development of a grounded framework for AI data set quality. Data set AI readiness required the concerted appraisal of many elements and the balancing of transparency and ethical reflection against pragmatic constraints. The movement toward more reliable, relevant, and ethical AI and ML applications for patient care will inevitably require strategic updates to data set creation practices.

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PRIMPOL ready, set, reprime!

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