ICU survivors are at increased risk for persistent physical, cognitive, and psychological impairments, collectively known as Post-Intensive Care Syndrome (PICS). Critically ill patients, especially those on mechanical ventilation, experience increased oxidative stress burden, potentially contributing to telomere shortening. However, the relationship between oxidative stress, telomere attrition, and PICS-related outcomes remains unclear. Our objective was to examine associations among oxidative stress markers, telomere length, clinical characteristics, and PICS-related outcomes in mechanically ventilated ICU survivors. A cross-sectional study design was used. Blood samples were collected at two timepoints: study enrollment and within 48 hr of ICU discharge. PICS-related outcome measures were assessed within 48 hr of ICU discharge. Oxidative stress markers-including plasma protein carbonyls, vitamin C, reduced-to-oxidized glutathione ratio (GSH:GSSG), and total antioxidant capacity (TAC)-were quantified via ELISA. Three oxidative stress indices were calculated: vitamin C/GSH:GSSG, TAC/GSH:GSSG, and protein carbonyls/GSH:GSSG. Telomere length was determined using RT-qPCR. Clinical data included APACHE III and SOFA scores, ICU and hospital length of stay (LOS), and duration of mechanical ventilation. PICS-related outcomes included grip and foot strength, National Institutes of Health (NIH) Toolbox Cognition and Emotion assessments, and the Connor-Davidson Resilience Scale. Pearson's correlations were performed. Twenty-one participants were included in the final analysis. Plasma antioxidant status positively correlated with muscle strength and psychological resilience. Elevated plasma oxidant levels were associated with poorer cognitive outcomes. ICU LOS and duration of mechanical ventilation negatively correlated with muscle strength and cognitive performance. No significant correlations were observed between telomere length changes and PICS-related outcomes. Oxidative stress during acute critical illness may impede recovery and contribute to PICS. The lack of short-term associations with telomere length suggests that telomere-related effects on PICS may, possibly, become apparent over a longer post-ICU period.

BACKGROUND:Neuroinflammation is an important feature of traumatic brain injury (TBI) that remains poorly understood in the 3- to 12-month period post-TBI. OBJECTIVE:The purpose of our pilot study was to examine the relationships between biomarkers of neuroinflammation and functional outcomes in TBI patients 3 to 12 months postinjury. METHODS:TBI patients ( n =  36) 3 to 12 months post-TBI were recruited from a South Florida TBI clinic from May 2022 to June 2023. The Disability Rating Scale, Satisfaction with Life Scale, NIH Toolbox Sorting Working Memory, Neuro-Quality of Life Cognitive Function, Anxiety, Depression, and Sleep assessments were performed. Multiple plasma biomarkers were assayed. Analysis of variance was used to compare between-group results. Linear regression was performed to analyze relationships between biomarkers and outcomes. RESULTS:Brain-derived neurotrophic factor concentrations were higher as postinjury time interval increased and were associated with cognitive battery outcomes. S-100β and glial fibrillary acidic protein were associated with anxiety score and hospital length of stay; S-100β was also associated with depression. Interleukin 6 was associated with cognitive function score and time since injury. CONCLUSIONS:We found S-100β, glial fibrillary acidic protein, Interleukin 6, and brain-derived neurotrophic factor to play a larger role in the TBI recovery period than other biomarkers examined. Clinicians should continue to monitor for symptoms post-TBI, as the neuroinflammatory process continues to persist even into the later rehabilitation stage.

Warfarin, an anticoagulant medication, is formulated to prevent and address conditions associated with abnormal blood clotting, making it one of the most prescribed drugs globally. However, determining the suitable dosage remains challenging due to individual response variations, and prescribing an incorrect dosage may lead to severe consequences. Contextual bandit and reinforcement learning have shown promise in addressing this issue. Given the wide availability of observational data and safety concerns of decision-making in healthcare, we focused on using exclusively observational data from historical policies as demonstrations to derive new policies; we utilized offline policy learning and evaluation in a contextual bandit setting to establish the optimal personalized dosage strategy. Our learned policies surpassed these baseline approaches without genotype inputs, even when given a suboptimal demonstration, showcasing promising application potential.

California was the first state to implement statewide public health measures, including lockdown and curfews, to mitigate transmission of SARS-CoV-2. The implementation of these public health measures may have had unintended consequences related to mental health for persons in California. This study is a retrospective review of electronic health records of patients who sought care in the University of California Health System to examine changes in mental health status during the pandemic. Data were extracted prior to the pandemic (March-October 2019) and during the pandemic (March-October 2020). Weekly values of new mental health disorders were extracted and further classified based on age. Paired t-tests were performed to test for differences in the occurrence of each mental health disorder for each age group. A two-way ANOVA was performed to assess for between group differences. When compared with pre-pandemic diagnoses, persons aged 26-35 had the greatest increase in mental health diagnoses overall during the pandemic, specifically for anxiety, bipolar disorder, depression, mood disturbance, and psychosis. The mental health of persons age 25-35 were more affected than any other age group.

BACKGROUND: Patients with traumatic brain injury (TBI) experience a variety of physical, cognitive, and affective symptoms. However, the evolution of symptoms, especially during the 3- to 12-month convalescence period (when recovery of function is still possible), is understudied. OBJECTIVE: This study aims to identify symptoms and the relationships with functional outcomes that occur during the 3- to 12-month period after a TBI. METHODS: Participants who were 3 to 12 months post-TBI were recruited from a South Florida TBI clinic from May 2022 to June 2023. Clinical data were obtained from the electronic health record. Participants completed the Brain Injury Association of Virginia Symptom Checklist, Neuro-Quality of Life Cognitive Function, Anxiety, Depression, and Sleep Disturbance assessments to report symptoms, and the Disability Rating Scale and Satisfaction with Life Scale. Descriptive statistics were used to characterize demographics and symptoms. Linear regression was performed to analyze the relationships between symptoms and outcomes. RESULTS: A total of N = 39 patients participated in the study. Memory problems and difficulty concentrating were the most common symptoms. Hospital length of stay, intensive care unit length of stay, cognitive, and physical symptoms were significantly associated with the Disability Rating Scale score. Physical, cognitive, depressive, and anxiety symptoms had significant associations with the Satisfaction with Life Scale. CONCLUSION: Cognitive symptoms should be integrated into the clinical care of rehabilitating TBI patients. Nurses should monitor for physical, affective, and cognitive symptoms during the recovery phase of TBI.

Abstract Aims and Objectives To determine the frequency, timing, and duration of post‐acute sequelae of SARS‐CoV‐2 infection (PASC) and their impact on health and function. Background Post‐acute sequelae of SARS‐CoV‐2 infection is an emerging major public health problem that is poorly understood and has no current treatment or cure. PASC is a new syndrome that has yet to be fully clinically characterised. Design Descriptive cross‐sectional survey (n = 5163) was conducted from online COVID‐19 survivor support groups who reported symptoms for more than 21 days following SARS‐CoV‐2 infection. Methods Participants reported background demographics and the date and method of their covid diagnosis, as well as all symptoms experienced since onset of covid in terms of the symptom start date, duration, and Likert scales measuring three symptom‐specific health impacts: pain and discomfort, work impairment, and social impairment. Descriptive statistics and measures of central tendencies were computed for participant demographics and symptom data. Results Participants reported experiencing a mean of 21 symptoms (range 1–93); fatigue (79.0%), headache (55.3%), shortness of breath (55.3%) and difficulty concentrating (53.6%) were the most common. Symptoms often remitted and relapsed for extended periods of time (duration M = 112 days), longest lasting symptoms included the inability to exercise (M = 106.5 days), fatigue (M = 101.7 days) and difficulty concentrating, associated with memory impairment (M = 101.1 days). Participants reported extreme pressure at the base of the head, syncope, sharp or sudden chest pain, and “brain pressure” among the most distressing and impacting daily life. Conclusions Post‐acute sequelae of SARS‐CoV‐2 infection can be characterised by a wide range of symptoms, many of which cause moderate‐to‐severe distress and can hinder survivors' overall well‐being. Relevance to Clinical Practice This study advances our understanding of the symptoms of PASC and their health impacts.

Background: Pancreatic adenocarcinoma (PA) remains one of the leading causes of cancer related deaths worldwide. The pathogenesis of PA is unclear. However, studies show that the receptor for advanced glycation end-products (RAGE), a member of the immunoglobulin superfamily and pattern recognition receptor, likely plays a role in oncogenesis and cell proliferation. RAGE has multiple functions including amplification and perpetuation of the inflammatory response, protein transport and degradation, maintaining cell polarity, and promoting cell differentiation and division. RAGE may contribute to oncogenesis of PA through dysregulation of ubiquitin and tumor suppressor genes such as p53.

Background Telomeres are structures at the end of chromosomes that shorten with each cell division. The purpose of this pilot project is to report changes in telomere length (T/S ratio), indicators of oxidative stress (serum protein carbonyl, vitamin C, GSH:GSSG, and total antioxidant capacity) from Intensive Care Unit (ICU) admission to ICU discharge, and to explore their association with ICU-related morbidities among critically ill mechanically ventilated adults. Methods Blood was collected from mechanically ventilated patients ( n = 25) at enrollment and within 48 hours of ICU discharge. Telomere length from peripheral blood mononuclear cells (PBMCs) was determined using RTqPCR. ELISAs were used to measure indicators of oxidative stress. Descriptive analysis, paired t-tests, and Pearson’s correlations were performed. Results Mean age was 62.0 ± 12.3 years, 28.6% were male, and 76.2% were White with disease severity using APACHE III (74.6 ± 24.6) and SOFA (7.6 ± 3.2). Mean T/S ratios shortened (ICU: 0.712, post-ICU: 0.683, p < 0.001, n = 19) and serum protein carbonyl increased (ICU: 7437 nmol/mg ± 3328, post-ICU: 10,254 nmol/mg ± 3962, p < 0.005) as did the oxidative stress index (protein carbonyl/GSH:GSSG, ICU: 1049.972 ± 420.923, post-ICU: 1348.971 ± 417.175, p = 0.0104). T/S ratio was positively associated with APACHE III scores (ICU: r = 0.474, post-ICU: r = 0.628, p < 0.05). Conclusions Pilot findings suggest that critical illness significantly correlates with telomere attrition, perhaps due to increased oxidative stress. Future larger and longitudinal studies investigating mechanisms of telomere attrition and associations with clinical outcomes are needed to identify potential modifiable factors for subsequent intervention to improve outcomes for critically ill patients.

Traditional machine learning (ML) approaches learn to recognize patterns in the data but fail to go beyond observing associations. Such data-driven methods can lack generalizability when the data is outside the independent and identically distributed (i.i.d) setting. Using causal inference can aid data-driven techniques to go beyond learning spurious associations and frame the data-generating process in a causal lens. We can combine domain expertise and traditional ML techniques to answer causal questions on the data. In this paper, we estimate the causal effect of Pre-Exposure Prophylaxis (PrEP) on mortality in COVID-19 patients from an observational dataset of over 120,000 patients. With the help of medical experts, we hypothesize a causal graph that identifies the causal and non-causal associations, including the list of potential confounding variables. We use estimation techniques such as linear regression, matching, and machine learning (meta-learners) to estimate the causal effect. On average, our estimates show that taking PrEP can result in a 2.1% decrease in the death rate or a total of around 2,540 patients’ lives saved in the studied population.

Traumatic brain injury (TBI) is a significant problem in the United States and worldwide, with estimated direct and indirect expenditures totaling $60 billion annually in the United States alone (Coronado et al., 2015). Despite this problem, studying the brain is complex, and TBI remains poorly understood, owing in part to individual differences in the brain and variation of injury. Common causes of TBI include motor vehicle accidents, falls, assault, sports related, or war related (e.g., blast exposure). TBI occurs along a continuum and can range from mild to severe. The Glasgow Coma Scale (GCS) is often used to classify TBI into mild (GCS: 13–15), moderate (GCS: 9–12), and severe (GCS: 3–8), by evaluating an individual’s motor response, verbal response, and eyeopening response (Teasdale & Jennett, 1974). Although the GCS has some predictive ability, TBI can result in a range of symptoms and outcomes. For example, an individual who sustains a mild complicated TBI may have an initially high GCS score, but subsequent hemorrhaging can lead to later brain damage and reduced function and adverse outcomes. Physical symptoms of TBI may include headache, dizziness, vomiting, and pain (Dwahan et al., 2006), while behavioral symptoms may include depressed mood, anxiety, memory problems, and sleep problems (Belanger et al., 2005; Cole & Baile, 2016). TBI is an area of research that needs continued attention. Further understanding of what happens to the brain during injury evolution and resolution can help us connect the injury process with the alteration in brain structure and resultant symptoms and function. Disruption of the blood–brain barrier leads to a decrease in regulation of which solutes are able to enter the brain, and thus may result in more damaging inflammation (Wong et al., 2013). A better understanding of the effects of these inflammatory mediators, and resultant symptoms, may help us better understand the physiology of TBI and connect this to the patient experience. In addition, a better understanding of neuroinflammation can help us understand the complex healing and repair process (Mishra et al., 2017). Continuing to study the role of astrocytes, microglia, and related biomarkers can also help us understand TBI, as glial cells play an important role in the tissue repair process as well (Hernandez-Ontiveros et al., 2013; Perez et al., 2017). In addition, longitudinal studies that characterize the evolution and resolution of the injury, along with its sequelae are needed to better understand the temporal order of neuroinflammatory events and their relationship with cognitive, affective, and physical functioning. This may be particularly beneficial in understudied groups, such as persons who have sustained a moderate form of TBI. Interventional studies are needed. Our current understanding suggests that mild TBI results only in a brief loss of consciousness and is likely to manifest itself in less severe symptoms. Despite this, long-term complications from mild TBI may still result. The moderate and severe forms of TBI are more likely to result in severe symptoms that can permanently reduce function. However, the question remains whether nursing interventions could be used to salvage brain function or to prevent further damage during the evolution and resolution of the injury. This area needs careful exploration, especially with regards to type, timing, duration, dosage, and to whom an intervention should be given, among many other questions. Intervening early may salvage function, and the impact on human life would be significant. Another opportunity is the development of evidencebased best practices. For example, clinicians currently face challenges when advising rehabilitating TBI patients. Such as, is it safe to drive yet? Can I return to work? Similarly, sports coaches and trainers face the same challenges. When is it safe for a player who suffered a concussion to return to play? Future research that advances TBI science can help address these questions. Incorporating biomarkers in clinical guidelines and increasing our understanding of TBI prognosis, based on symptoms and imaging scans, through scientific inquiry, can begin to answer these questions and contribute to precision medicine—an initiative that aims to provide accurate guidelines to maximize functionality and maintain patient safety. In closing, the opportunities for 1157801 CNRXXX10.1177/10547738231157801Clinical Nursing ResearchGerber and Downs research-article2023

Traditional machine learning (ML) approaches learn to recognize patterns in the data but fail to go beyond observing associations. Such data-driven methods can lack generalizability when the data is outside the independent and identically distributed (i.i.d) setting. Using causal inference can aid data-driven techniques to go beyond learning spurious associations and frame the data-generating process in a causal lens. We can combine domain expertise and traditional ML techniques to answer causal questions on the data. Hypothetical questions on alternate realities can also be answered with such a framework. In this paper, we estimate the causal effect of Pre-Exposure Prophylaxis (PrEP) on mortality in COVID-19 patients from an observational dataset of over 120,000 patients. With the help of medical experts, we hypothesize a causal graph that identifies the causal and non-causal associations, including the list of potential confounding variables. We use estimation techniques such as linear regression, matching, and machine learning (meta-learners) to estimate the causal effect. On average, our estimates show that taking PrEP can result in a 2.1% decrease in the death rate or a total of around 2,540 patients’ lives saved in the studied population.

SARS-CoV-2 (COVID-19) has caused over 80 million infections 973,000 deaths in the United States, and mutations are linked to increased transmissibility. This study aimed to determine the effect of SARS-CoV-2 variants on respiratory features, mortality, and to determine the effect of vaccination status. A retrospective review of medical records (n = 55,406 unique patients) using the University of California Health COvid Research Data Set (UC CORDS) was performed to identify respiratory features, vaccination status, and mortality from 01/01/2020 to 04/26/2022. Variants were identified using the CDC data tracker. Increased odds of death were observed amongst unvaccinated individuals and fully vaccinated, partially vaccinated, or individuals who received any vaccination during multiple waves of the pandemic. Vaccination status was associated with survival and a decreased frequency of many respiratory features. More recent SARS-CoV-2 variants show a reduction in lower respiratory tract features with an increase in upper respiratory tract features. Being fully vaccinated results in fewer respiratory features and higher odds of survival, supporting vaccination in preventing morbidity and mortality from COVID-19.

Les progrès réalisés dans la lutte contre le cancer ont augmenté les taux de survie, entraînant un tel changement de paradigme que le cancer est maintenant considéré comme une maladie chronique; il nous faut donc évaluer notre connaissance de la survie au cancer (SC). C’est dans cette optique que les auteurs ont procédé à une recension exhaustive des écrits dans les référentiels CINAHL, MEDLINE et PUBMED de 2000 et 2021. En s’appuyant sur les concepts étudiés dans la littérature, ils ont répertorié les principaux facteurs qui influencent la survie au cancer dans l’ensemble des populations atteintes et ont proposé un modèle. Le présent article décrit ce modèle de survie au cancer (MSC). Le MSC prend en compte les facteurs prédisposant à la survie ainsi que les facteurs d’influence en jeu dans les trois phases de survie (aiguë, prolongée et permanente), à savoir le traitement et le traitement d’entretien (soins médicaux et psychosociaux), le bien-être, et d’autres éléments d’influence (expériences entraînant de profonds changements, incertitude, établissement de priorités, gestion du bien-être et conséquences indirectes) de même que les facteurs liés aux relations sociales qui jouent sur le fardeau des symptômes des survivants ainsi que l’expérience globale de la survie (état de santé et qualité de vie). Une étude de cas a d’ailleurs montré l’utilité du MSC. L’application du modèle est prometteuse pour l’avenir, tant pour améliorer la qualité de la survivance que pour guider la recherche et la pratique clinique en vue de favoriser et d’optimiser la bonne santé des survivants à long terme.

Background and Purpose: Cognitive, affective, and physical symptoms and alterations in their function are seen across chronic illnesses. Data suggest that environmental, psychological, and physiological factors contribute to symptom experience, potentially through loss of telomeres (telomere attrition), structures at the ends of chromosomes. Telomere length is affected by many factors including environmental (e.g., exercise, diet, smoking) and physiological (e.g., response to stress), as well as from oxidative damage and inflammation that occurs in many disease processes. Moreover, telomere attrition is associated with chronic disease (cancer, cardiovascular disease, Alzheimer's disease) and predicts higher morbidity and mortality rates. However, findings are inconsistent among telomere roles and relationships with health outcomes. This article aims to synthesize the current state-of-the-science of telomeres and their relationship with cognitive, affective, and physical function and symptoms. Method: A comprehensive literature search was performed in two databases: CINAHL and PUBMED. A total of 33 articles published between 2000 and 2022 were included in the final analysis. Results: Telomere attrition is associated with various changes in cognitive, affective, and physical function and symptoms. However, findings are inconsistent. Interventional studies (e.g., meditation and exercise) may affect telomere attrition, potentially impacting health outcomes. Conclusion: Nursing research and practice are at the forefront of furthering the understanding of telomeres and their relationships with cognitive, affective, and physical function and symptoms. Future interventions targeting modifiable risk factors may be developed to improve health outcomes across populations.

Advancements in cancer have increased survival rates leading to a paradigm shift such that cancer is considered a chronic disease, necessitating an evaluation of our understanding of cancer survivorship (CS). For this purpose, a comprehensive literature search was performed, using CINAHL, MEDLINE, and PUBMED from 2000-2021. Drawing from the concepts in the literature, salient factors that affect CS across cancer populations were identified and a proposed model was developed. This paper describes the Cancer Survivorship Model (CSM). The CSM represents predisposing factors for survivors and survivorship's acute, extended, and long-term phases, influencing factors: treatment and maintenance (medical/ psychosocial care), well-being, influencing aspects (life-changing experience, uncertainty, prioritizing life, wellness management, and collateral damage), and social relationship factors that impact survivors' symptom burdens and overall survivorship experience (health outcomes and quality of life). A case study demonstrates the CSM utility. Future application of the model holds promise for improving the quality of survivorship and informing research and clinical practice to promote and optimize survivors' outcomes throughout the evolving survivorship.

Postacute sequelae of SARS-CoV2 (PASC) infection is an emerging global health crisis, variably affecting millions worldwide. PASC has no established treatment. We describe 2 cases of PASC in response to opportune administration of over-the-counter antihistamines, with significant improvement in symptoms and ability to perform activities of daily living. Future studies are warranted to understand the potential role of histamine in the pathogenesis of PASC and explore the clinical benefits of antihistamines in the treatment of PASC.

Depressive symptoms, feelings of sadness, anger, and loss that interfere with a person's daily life, are prevalent health concerns across populations that significantly result in adverse health outcomes with direct and indirect economic burdens at a national and global level. This article aims to synthesize known mechanisms of depressive symptoms and the established and emerging methodologies used to understand depressive symptoms; implications and directions for future nursing research are discussed. A comprehensive search was performed by Cumulative Index to Nursing and Allied Health Literature, MEDLINE, and PUBMED databases between 2000-2021 to examine contributing factors of depressive symptoms. Many environmental, psychological, and physiological factors are associated with the development or increased severity of depressive symptoms (anhedonia, fatigue, sleep and appetite disturbances to depressed mood). This paper discusses biological and psychological theories that guide our understanding of depressive symptoms, as well as known biomarkers (gut microbiome, specific genes, multi-cytokine, and hormones) and established and emerging methods. Disruptions within the nervous system, hormonal and neurotransmitters levels, brain structure, gut-brain axis, leaky-gut syndrome, immune and inflammatory process, and genetic variations are significant mediating mechanisms in depressive symptomology. Nursing research and practice are at the forefront of furthering depressive symptoms' mechanisms and methods. Utilizing advanced technology and measurement tools (big data, machine learning/artificial intelligence, and multi-omic approaches) can provide insight into the psychological and biological mechanisms leading to effective intervention development. Thus, understanding depressive symptomology provides a pathway to improve patients' health outcomes, leading to reduced morbidity and mortality and the overall nation-wide economic burden.Supplemental data for this article is available online at https://doi.org/10.1080/01612840.2021.1998261 .

OBJECTIVES To examine colorectal cancer (CRC) survivors' symptom characteristics (occurrence, frequency, and severity) during acute cancer survivorship. PARTICIPANTS & SETTING A cross-sectional study of 117 CRC survivors was conducted at a National Cancer Institute-designated cancer center in South Florida. METHODS & VARIABLES Symptom characteristics were assessed by the Therapy-Related Symptom Checklist. Participants completed a 25-item demographic questionnaire. Mann-Whitney U and Kruskal-Wallis H tests assessed between-group differences based on sex, age, education, and months since diagnosis. Exploratory factor analysis was performed to identify preliminary symptom clusters. RESULTS 117 CRC survivors completed the study (age range = 21-88 years, 56% male, and 79% stage IV). Common symptoms included peripheral neuropathy, fatigue/feeling sluggish, and skin changes. Significance was found between months since diagnosis and number of symptoms (p = 0.03), suggesting that symptoms accumulate with time. Chemotherapy (85%) was the most common treatment type, and exploratory factor analysis identified two chemotherapy-related symptom clusters. IMPLICATIONS FOR NURSING Nurses are poised to identify, prevent, and promote self-management skills to reduce symptoms.

This cross-sectional study examined colorectal cancer (CRC) survivors' symptom and symptom cluster characteristics (occurrence, frequency, and severity), positive psychology (benefit-finding and post-traumatic growth), and quality of life (QoL), and determined whether positive psychology moderates symptoms and QoL relationship during acute cancer survivorship, time from diagnosis to treatment completion. A total of 117 CRC survivors completed demographics, symptoms, QoL, and positive psychology questionnaires. Descriptive statistics, multiple linear regression, and moderation analyses were performed. Participants reported high QoL (94%, M = 5.15) and moderate-high positive psychology (75%, M = 3.21). Nineteen symptoms and five symptom clusters were inversely related to QoL (p < .05). Positive psychology (M = ~≥3.24) moderated the relationship of QoL (p < .05) with symptoms occurrence (n = 10), symptom severity (n = 1), and with the generalized symptom cluster (weakness, fatigue, dizziness, drowsy, sleep disturbances, and pain). Positive psychology aids in symptom management and improves QoL. Nurses are poised to identify, prevent, promote, and advocate self-management skills to improve health-related outcomes.