Symptoms of complex illnesses such as cancer often present with a high degree of heterogeneity between patients. At the same time, there are often core symptoms that act as common drivers for other ...symptoms, such as fatigue leading to depression and cognitive dysfunction. These symptoms are termed bridge symptoms and when combined with heterogeneity in symptom presentation, are difficult to detect using traditional unsupervised clustering techniques. This article develops a method for identifying patient communities based on bridge symptoms termed concordance network clustering. An empirical study of breast cancer symptomatology is presented, and demonstrates the applicability of this method for identifying bridge symptoms.
Background
Research on quality of life (QoL) among women with breast cancer has often examined the impact of coping strategies on QoL. However, the transactional model of stress and coping would ...argue that QoL can impact coping. This reciprocal relationship between QoL and coping has been inadequately studied.
Purpose
This study examined reciprocal relationships over 18 months between QoL and coping (positive and negative coping) among women with breast cancer.
Methods
Three-wave cross-lagged structural equation modelling (SEM) analysis was used over three timepoints post-diagnosis (T1–T3;
N
= 637, 577, 553, respectively).
Results
SEM results revealed a significant reciprocal relationship between negative coping and QoL, indicating that negative coping predicted subsequent QoL, which in turn predicted later negative coping. Although QoL at cancer diagnosis predicted subsequent positive coping, we did not find a reciprocal relation between QoL and positive coping.
Conclusion
Findings expand our knowledge of the relation between QoL and coping by suggesting the reciprocal relationship between negative coping and QoL among women with breast cancer.
Purpose
The Shift and Persist model provides an informative framework to understand how adolescent and young adult (AYA) cancer patients and survivors (ages 15–39) may withstand stress and thrive ...despite adversity. The goal of the present study was to examine the psychometric properties of the Shift and Persist Questionnaire (SPQ) in this population and provide guidelines for interpretation.
Methods
AYA cancer patients and survivors were recruited via an online research panel. Participants reported demographics and health history and completed the SPQ and Patient-Reported Outcome Measurement Information System 29-item profile (PROMIS®-29). We evaluated the structural validity, internal consistency, and construct validity of the SPQ. Minimally important differences (MIDs) were estimated to inform SPQ score interpretation.
Results
572 eligible individuals completed the survey. On average, participants were aged 24 (SD = 7) at evaluation. Of the participants, 43.5% were female, 77.1% were white, and 17.5% were Hispanic (across races). The two-factor structure of the SPQ demonstrated very good structural validity (CFI > 0.95, SRMR < 0.08), and construct validity with PROMIS-29® domains (convergent
R
s = 0.17 to 0.43, divergent
R
s = − 0.11 to − 0.51). Internal consistency was adequate (
ω
= 0.76–0.83). Recommended MIDs were 1 point for the Shift subscale, 1–2 point(s) for the Persist subscale, and 2–3 points for the total SPQ score.
Conclusion
The SPQ is a psychometrically sound measure of skills that contribute to resilience in AYA cancer patients and survivors. MID recommendations enhance the interpretability of the SPQ in this population. Future studies examining shifting and persisting in this population may benefit from administering the SPQ.
Dietary changes associated with industrialization increase the prevalence of chronic diseases, such as obesity, type II diabetes, and cardiovascular disease. This relationship is often attributed to ...an 'evolutionary mismatch' between human physiology and modern nutritional environments. Western diets enriched with foods that were scarce throughout human evolutionary history (e.g. simple sugars and saturated fats) promote inflammation and disease relative to diets more akin to ancestral human hunter-gatherer diets, such as a Mediterranean diet. Peripheral blood monocytes, precursors to macrophages and important mediators of innate immunity and inflammation, are sensitive to the environment and may represent a critical intermediate in the pathway linking diet to disease. We evaluated the effects of 15 months of whole diet manipulations mimicking Western or Mediterranean diet patterns on monocyte polarization in a well-established model of human health, the cynomolgus macaque (
). Monocyte transcriptional profiles differed markedly between diets, with 40% of transcripts showing differential expression (FDR < 0.05). Monocytes from Western diet consumers were polarized toward a more proinflammatory phenotype. The Western diet shifted the co-expression of 445 gene pairs, including small RNAs and transcription factors associated with metabolism and adiposity in humans, and dramatically altered behavior. For example, Western-fed individuals were more anxious and less socially integrated. These behavioral changes were also associated with some of the effects of diet on gene expression, suggesting an interaction between diet, central nervous system activity, and monocyte gene expression. This study provides new molecular insights into an evolutionary mismatch and uncovers new pathways through which Western diets alter monocyte polarization toward a proinflammatory phenotype.
A bulk heterojunction of ordered titania nanopillars and PbS colloidal quantum dots is developed. By using a pre‐patterned template, an ordered titania nanopillar matrix with nearest neighbours 275 ...nm apart and height of 300 nm is fabricated and subsequently filled in with PbS colloidal quantum dots to form an ordered depleted bulk heterojunction exhibiting power conversion efficiency of 5.6%.
Cardiovascular disease (CVD) prevention is practiced concurrently by providers from several specialties. Our goal was to understand providers' preference of specialties in CVD prevention practice and ...the role of preventive cardiologists.
Between 11 October 2021 and 1 March 2022, we surveyed providers from internal medicine, family medicine, endocrinology, and cardiology specialties to examine their preference of specialties in managing various domains of CVD prevention. We examined categorical variables using Chi square test and continuous variables using t or analysis of variance test.
Of 956 invitees, 263 from 21 health systems and 9 states responded. Majority of respondents were women (54.5%), practicing physicians (72.5%), specializing in cardiology (43.6%), and working at academic centers (51.3%). Respondents favored all specialties to prescribe statins (43.2%), ezetimibe (37.8%), sodium-glucose cotransporter-2 (SGLT2) inhibitors (30.5%), and aspirin in primary prevention (36.3%). Only 7.9% and 9.5% selected cardiologists and preventive cardiologists, respectively, to prescribe SGLT2 inhibitors. Most preferred specialists (i.e. cardiology and endocrinology) to manage advanced lipid disorders, refractory hypertension, and premature coronary heart disease. The most common conditions selected for preventive cardiologists to manage were genetic lipid disorders (17%), cardiovascular risk assessment (15%), dyslipidemia (13%), and refractory/resistant hypertension (12%).
For CVD prevention practice, providers favored all specialties to manage common conditions, specialists to manage complex conditions, and preventive cardiologists to manage advanced lipid disorders. Cardiologists were least preferred to prescribe SGLT2 inhibitor. Future research should explore reasons for selected CVD prevention practice preferences to optimize care coordination and for effective use of limited expertise.
Purpose User-generated content on social media sites, such as health-related online forums, offers researchers a tantalizing amount of information, but concerns regarding scientific application of ...such data remain. This paper compares and contrasts symptom cluster patterns derived from messages on a breast cancer forum with those from a symptom checklist completed by breast cancer survivors participating in a research study. Methods Over 50,000 messages generated by 12,991 users of the breast cancer forum on MedHelp.org were transformed into a standard form and examined for the co-occurrence of 25 symptoms. The k-medoid clustering method was used to determine appropriate placement of symptoms within clusters. Findings were compared with a similar analysis of a symptom checklist administered to 653 breast cancer survivors participating in a research study. Results The following clusters were identified using forum data: menopausal/psychological, pain/fatigue, gastrointestinal, and miscellaneous. Study data generated the clusters: menopausal, pain, fatigue/sleep/gastrointestinal, psychological, and increased weight/appetite. Although the clusters are somewhat different, many symptoms that clustered together in the social media analysis remained together in the analysis of the study participants. Density of connections between symptoms, as reflected by rates of co-occurrence and similarity, was higher in the study data. Conclusions The copious amount of data generated by social media outlets can augment findings from traditional data sources. When different sources of information are combined, areas of overlap and discrepancy can be detected, perhaps giving researchers a more accurate picture of reality. However, data derived from social media must be used carefully and with understanding of its limitations.
Abstract
Background
Mobility limitation in older adults is common and associated with poor health outcomes and loss of independence. Identification of at-risk individuals remains challenging because ...of time-consuming clinical assessments and limitations of statistical models for dynamic outcomes over time. Therefore, we aimed to develop machine learning models for predicting future mobility limitation in older adults using repeated measures data.
Methods
We used annual assessments over 9 years of follow-up from the Health, Aging, and Body Composition study to model mobility limitation, defined as self-report of any difficulty walking a quarter mile or climbing 10 steps. We considered 46 predictors, including demographics, lifestyle, chronic conditions, and physical function. With a split sample approach, we developed mixed models (generalized linear and Binary Mixed Model forest) using (a) all 46 predictors, (b) a variable selection algorithm, and (c) the top 5 most important predictors. Age was included in all models. Performance was evaluated using area under the receiver operating curve in 2 internal validation data sets.
Results
Area under the receiver operating curve ranged from 0.80 to 0.84 for the models. The most important predictors of mobility limitation were ease of getting up from a chair, gait speed, self-reported health status, body mass index, and depression.
Conclusions
Machine learning models using repeated measures had good performance for identifying older adults at risk of developing mobility limitation. Future studies should evaluate the utility and efficiency of the prediction models as a tool in clinical settings for identifying at-risk older adults who may benefit from interventions aimed to prevent or delay mobility limitation.
Background
Abdominal adhesions are the most common surgical complication and without reliable prophylactics. This study presents a novel rat model for abdominal adhesions and reports pilot results of ...human placental stem cell (hPSC)‐based therapies.
Methods
Forty‐four (n = 44) male Sprague–Dawley rats (250‐350 g) were used in the experiment. Of these, thirty‐eight (n = 38) were included in a preliminary data set to determine a minimum treatment effect. Adhesions were created in a reproducible model to the abdominal wall and between organs. Experimental groups included the control group (Model No Treatment, MNT), Plasmalyte A (Media Alone, MA, 10 mL), hPSC (5 × 106 cells/10 mL Plasmalyte A), hPSC‐CM (hPSC secretome, conditioned media) in 10 mL Plasmalyte A, Seprafilm™ (Baxter, Deerfield, IL), and sham animals (laparotomy only). Treatments were inserted intraperitoneally (IP) and the study period was 14 days post‐operation. Results are reported as the difference between means of an index statistic (AIS, Animal Index Score) and compared by ANOVA with pairwise comparison.
Results
The overall mean AIS was 23 (SD 6.16) for the MNT group with an average of 75% of ischemic buttons involved in abdominal adhesions. Treatment groups MA (mean overall AIS 17.33 SD 6.4), hPSC (mean overall AIS 13.86 SD 5.01), hPSC‐CM (mean overall AIS 13.13 SD 6.15), and Seprafilm (mean overall AIS 13.43 SD 9.11) generated effect sizes of 5.67, 9.14, 9.87, and 9.57 decrease in mean overall AIS, respectively, versus the MNT.
Discussion
The presented rat model and scoring system represent the clinical adhesion disease process. hPSC‐based interventions significantly reduce abdominal adhesions in this pilot dataset.
At both the individual and societal levels, the health and economic burden of disability in older adults is enormous in developed countries, including the U.S. Recent studies have revealed that the ...disablement process in older adults often comprises episodic periods of impaired functioning and periods that are relatively free of disability, amid a secular and natural trend of decline in functioning. Rather than an irreversible, progressive event that is analogous to a chronic disease, disability is better conceptualized and mathematically modeled as states that do not necessarily follow a strict linear order of good to bad. Statistical tools, including Markov models, which allow bidirectional transition between states, and random effects models, which allow individual-specific rate of secular decline, are pertinent. In this article, we propose a mixed effects, multivariate, hidden Markov model to handle partially ordered disability states. The model generalizes the continuation ratio model for ordinal data in the generalized linear model literature and provides a formal framework for testing the effects of risk factors and/or an intervention on the transitions between different disability states. Under a generalization of the proportional odds ratio assumption, the proposed model circumvents the problem of a potentially large number of parameters when the number of states and the number of covariates are substantial. We describe a maximum likelihood method for estimating the partially ordered, mixed effects model and show how the model can be applied to a longitudinal dataset that consists of N = 2903 older adults followed for 10 years in the Health Aging and Body Composition Study. We further statistically test the effects of various risk factors upon the probabilities of transition into various severe disability states. The result can be used to inform geriatric and public health science researchers who study the disablement process. Supplementary materials for this article are available online.
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