Remote ischemic conditioning (RIC) is a promising noninvasive intervention that may enhance endogenous cerebroprotective mechanisms following ischemic stroke. Although clinical studies suggest potential benefits, the physiological mechanisms underlying RIC remain incompletely understood, particularly whether RIC-associated hemodynamic changes are accompanied by changes in cerebral tissue oxygenation. No conceptual framework currently integrates these relationships to guide future research. The purpose of this paper is to present the Cerebral Oxygenation–Conditioning Response (CO-CoRe) Framework, a mechanistic conceptual framework describing the hypothesized relationships among RIC, compensatory cerebral blood flow, cerebroprotection, and cerebral regional oxygen saturation (CrSO₂) following ischemic stroke. The CO-CoRe Framework integrates evidence-supported relationships among RIC, compensatory cerebral blood flow, and cerebroprotection with a hypothesized relationship between RIC and CrSO 2 , providing a mechanistic foundation for examining cerebral tissue oxygenation following ischemic stroke. By synthesizing current evidence on ischemic conditioning, cerebrovascular physiology, and cerebral tissue oxygenation, the CO-CoRe Framework generates testable hypotheses and provides a foundation for future research evaluating CrSO₂, measured using near-infrared spectroscopy, as a physiological response to RIC. Ultimately, the framework may advance understanding of RIC-associated cerebroprotection and inform physiologic monitoring strategies in ischemic stroke.
Background: Glycemic variability (GV) is an important cardiometabolic risk marker in adults with type 1 diabetes (T1D). We estimated the effects of reallocating time among sleep, sedentary behavior, light physical activity (LPA), and moderate-to-vigorous physical activity (MVPA) on six GV outcomes in adults with T1D. Methods: This secondary analysis included 23 adults contributing 102 days with concurrent accelerometer-based activity monitoring and continuous glucose monitoring (CGM). Compositional rescaling was applied to ensure a 24-h in total. Isotemporal substitution models with linear mixed-effects regression were used to estimate associations of 30-min reallocations among behaviors (e.g., from sleep to LPA) with GV outcomes, including standard deviation of glucose (SD), coefficient of variation (CV), large amplitude of glycemic excursions (LAGE), mean amplitude of glycemic excursions (MAGE), continuous overlapping net glycemic action with a 60-min lag (CONGA-60), and time in range (TIR), while adjusting for covariates. Results: Participants had a mean age of 32.6 years, BMI of 28.2 kg/m 2 , and diabetes duration of 16.3 years. Reallocating 0.5 hours from sleep to sedentary time, LPA, or MVPA significantly reduced CV (estimate range: −14.4 to −15.2, all p < 0.05) and TIR (estimate range: −3.6 to −3.6, all p < 0.01). Replacing sedentary time with LPA significantly reduced LAGE (estimate = −3.4, p = 0.035). No significant associations were found for SD, MAGE, or CONGA-60. Conclusion: These findings highlight that optimizing glycemic stability in adults with T1D requires a nuanced consideration of how reallocations between sleep, sedentary time, LPA, and MVPA differentially affect GV, TIR, and LAGE.
Reproducibility and replicability are our scientific and ethical mandate, and the foundation of trust in nursing science and research. Both rest on transparent documentation of how data were actually analyzed. The rising use of generative artificial intelligence (GenAI) in research intersects with this mandate, with the potential to either support or undermine it, yet practical guidance for conducting GenAI-assisted quantitative analysis remains limited. This gap is particularly relevant for nurse scientists, who increasingly work with complex, biologically-informed datasets spanning genomics, symptoms, and patient outcomes. To address this gap, we present a practice-based guide for partnering with GenAI in quantitative data analysis in nursing research, grounded in a nursing-focused genomic research example. We cover GenAI literacy spanning three dimensions (technical, practical, and ethical) and a two-phase workflow for human-GenAI collaboration spanning planning and pre-specification through code development, formal analysis, translation, and human review and disclosure of GenAI use. The core principle is using GenAI as a tool, not a decision-maker, as scientific integrity requires that humans retain interpretive authority. We further propose a synthetic-data-first approach as a privacy safeguard. We illustrate these concepts through a genetics example, though the principles and workflow generalize to many quantitative nursing research contexts.
Cognitive symptoms are common in postmenopausal women treated for breast cancer (BC). In general and in BC, the Apolipoprotein E 4 allele ( APOE4) is linked to lower cognition and accelerated cognitive decline, but exercise generally benefits cognition. The purpose of this study was to examine whether the effect of aerobic exercise on cognitive function differs by APOE4 carriage in postmenopausal women with breast cancer. Longitudinal analysis of a 6-month randomized clinical trial of aerobic exercise in postmenopausal women with BC (2016–2022) was done. Subsample with APOE genotype and cognitive function data (before randomization and at intervention completion) were analyzed. Variables included group assignment (Control/Exercise), APOE4 carriage (No/Yes), and composite scores for 7 cognitive domains (Higher = better). Robust regression was used, adjusting for age, education, baseline cognitive score, and days on endocrine therapy, with group × APOE4 carrier interaction term. In this sample (n = 116, x̅ = 62 years old, 21% APOE4 carriage), APOE4 carriage moderated the effect of aerobic exercise on learning and memory (b = −0.544, p = 0.023). Non-carriers’ scores improved and APOE4 carriers’ scores declined. There were no other significant findings for attention, executive function, mental flexibility, processing speed, verbal memory, or working memory. In this hypothesis-generating study, aerobic exercise was associated with modest cognitive performance improvements in non-carriers, while APOE4 carriers did not demonstrate similar improvements. These exploratory findings suggest that a differing response for effects of exercise on cognition in post-menopausal women with BC may exist and be partially explained by APOE4 carriage.
At-home administration of oral anticancer medications (OAM) limits interactions with healthcare providers, therefore limiting opportunities for symptom management, posing threats to medication adherence. Individuals diagnosed with multiple myeloma (MM) experience symptoms from both disease- and treatment-related factors. Proteins Beta-2 Microglobulin (B2M) and Lactate Dehydrogenase (LDH) are routinely used in disease monitoring, yet their associations with symptoms and regulation via DNA methylation (DNAm) in individuals with MM have not been investigated. This study explored relationships among LDH and B2M proteins; DNAm of genes B2M, LDHA, LDHB ; and symptoms in patients prescribed OAMs for MM. This was a 6-month, longitudinal, exploratory analysis. Symptoms were assessed using self-reported measures. Protein levels were extracted from medical records. DNAm of B2M, LDHA, and LDHB promoter regions were quantified via pyrosequencing of peripheral blood samples. Linear mixed modeling explored cross-sectional and longitudinal associations. Significant cross-sectional associations were identified between fatigue, LDHA and B2M DNAm, and B2M protein ( p < .02); pain and LDHA and B2M DNAm ( p < .05); gastrointestinal disturbances and LDHA DNAm and LDH protein ( p < .05); shortness of breath and LDHA DNAm ( p < .04); and depression and LDH protein ( p < .04). Longitudinal interaction effects were significant for B2M DNAm and fatigue ( p < .05), LDHA and B2M DNAm and pain ( p < .04), and LDH protein and depression ( p < .04). Prognostic proteins B2M and LDH and their gene-level DNAm patterns were significantly associated with symptoms in individuals taking OAMs for MM. These preliminary findings support further exploration of multi-omics pathways underlying symptoms to inform symptom identification and management strategies.
