SummaryGene expression is dynamically regulated in a variety of mammalian physiologies. During mammalian aging, there are changes that occur in protein expression that are highly controlled by the regulatory steps in transcription, post‐transcription, and post‐translation. Although there are global profiles of human transcripts during the aging processes available, the mechanism(s) by which transcripts are differentially expressed between young and old cohorts remains unclear. Here, we report on N6‐methyladenosine (m6A) RNA modification profiles of human peripheral blood mononuclear cells (PBMCs) from young and old cohorts. An m6A RNA profile identified a decrease in overall RNA methylation during the aging process as well as the predominant modification on proteincoding mRNAs. The m6A‐modified transcripts tend to be more highly expressed than nonmodified ones. Among the many methylated mRNAs, those of DROSHA and AGO2 were heavily methylated in young PBMCs which coincided with a decreased steady‐state level of AGO2 mRNA in the old PBMC cohort. Similarly, downregulation of AGO2 in proliferating human diploid fibroblasts (HDFs) also correlated with a decrease in AGO2 mRNA modifications and steady‐state levels. In addition, the overexpression of RNA methyltransferases stabilized AGO2 mRNA but not DROSHA and DICER1 mRNA in HDFs. Moreover, the abundance of miRNAs also changed in the young and old PBMCs which are possibly due to a correlation with AGO2 expression as observed in AGO2‐depleted HDFs. Taken together, we uncovered the role of mRNA methylation on the abundance of AGO2 mRNA resulting in the repression of miRNA expression during the process of human aging.

PURPOSE: Despite evidence of clinical benefits, widespread implementation of remote symptom monitoring has been limited. We describe a process of adapting a remote symptom monitoring intervention developed in a research setting to a real-world clinical setting at two cancer centers. METHODS: This formative evaluation assessed core components and adaptations to improve acceptability and fit of remote symptom monitoring using Stirman's Framework for Modifications and Adaptations. Implementation outcomes were evaluated in pilot studies at the two cancer centers testing technology (phase I) and workflow (phase II and III) using electronic health data; qualitative evaluation with semistructured interviews of clinical team members; and capture of field notes from clinical teams and administrators regarding barriers and recommended adaptations for future implementation. RESULTS: Core components of remote symptom monitoring included electronic delivery of surveys with actionable symptoms, patient education on the intervention, a system to monitor survey compliance in real time, the capacity to generate alerts, training nurses to manage alerts, and identification of personnel responsible for managing symptoms. In the pilot studies, while most patients completed > 50% of expected surveys, adaptations were identified to address barriers related to workflow challenges, patient and clinician access to technology, digital health literacy, survey fatigue, alert fatigue, and data visibility. CONCLUSION: Using an implementation science approach, we facilitated adaptation of remote symptom monitoring interventions from the research setting to clinical practice and identified key areas to promote effective uptake and sustainability.

Eukaryotic mRNA metabolism regulates its stability, localization, and translation using complementarity with counter-part RNAs. To modulate their stability, small and long noncoding RNAs can establish complementarity with their target mRNAs. Although complementarity of small interfering RNAs and microRNAs with target mRNAs has been studied thoroughly, partial complementarity of long noncoding RNAs (lncRNAs) with their target mRNAs has not been investigated clearly. To address that research gap, our lab investigated whether the sequence complementarity of two lncRNAs, lincRNA-p21 and OIP5-AS1, influenced the quantity of target RNA expression. We predicted a positive correlation between lncRNA complementarity and target mRNA quantity. We confirmed this prediction using RNA affinity pull down, microarray, and RNA-sequencing analysis. In addition, we utilized the information from this analysis to compare the quantity of target mRNAs when two lncRNAs, lincRNA-p21 and OIP5-AS1, are depleted by siRNAs. We observed that human and mouse lincRNA-p21 regulated target mRNA abundance in complementarity-dependent and independent manners. In contrast, affinity pull down of OIP5-AS1 revealed that changes in OIP5-AS1 expression influenced the amount of some OIP5-AS1 target mRNAs and miRNAs, as we predicted from our sequence complementarity assay. Altogether, the current study demonstrates that partial complementarity of lncRNAs and mRNAs (even miRNAs) assist in determining target RNA expression and quantity.

PURPOSE Remote symptom monitoring (RSM) using electronic patient-reported outcomes enables patients with cancer to communicate symptoms between in-person visits. A better understanding of key RSM implementation outcomes is crucial to optimize efficiency and guide implementation efforts. This analysis evaluated the association between the severity of patient-reported symptom alerts and time to response by the health care team. METHODS This secondary analysis included women with stage I-IV breast cancer who received care at a large academic medical center in the Southeastern United States (October 2020-September 2022). Symptom surveys with at least one severe symptom alert were categorized as severe. Response time was categorized as optimal if the alert was closed by a health care team member within 48 hours. Odds ratios (ORs), predicted probabilities, and 95% CIs were estimated using a patient-nested logistic regression model. RESULTS Of 178 patients with breast cancer included in this analysis, 63% of patients identified as White and 85% of patients had a stage I-III or early-stage cancer. The median age at diagnosis was 55 years (IQR, 42-65). Of 1,087 surveys included, 36% reported at least one severe symptom alert and 77% had an optimal response time by the health care team. When compared with surveys that had no severe symptom alerts, surveys with at least one severe symptom alert had similar odds of having an optimal response time (OR, 0.97; 95% CI, 0.68 to 1.38). The results were similar when stratified by cancer stage. CONCLUSION Response times to symptom alerts were similar for alerts with at least one severe symptom compared with alerts with no severe symptoms. This suggests that alert management is being incorporated into routine workflows and not prioritized based on disease or symptom alert severity.

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