Eden Medical Center

Abstract Rest-activity rhythms (RARs), 24-hour activity and sleep patterns, are cross-sectionally associated with fatigability, a prognostic marker of functional decline. Participants (N = 821, age=76.4±5.0 years) from the Study of Muscle, Mobility and Aging had perceived physical fatigability measured using the validated 10-item Pittsburgh Fatigability Scale (PFS; 0-50, higher score=greater fatigability) at baseline and up to three follow-up visits over 2.6±0.4 years. RARs were derived from 7-day wrist-worn accelerometry using the extended cosine model (parametric: amplitude, acrophase, alpha, beta) or non-parametric approaches (interdaily stability, intradaily variability, relative amplitude). Three RAR profiles (Synchronized, Concentrated High-Activity, Low-Fragmented) were identified using the k-means clustering algorithm. Compared to Synchronized, the Concentrated High-Activity group had the highest amplitude (5,176 vs. 2,258 counts/min) and earliest acrophase (11:54AM vs. 2:18PM). The Low-Fragmented group vs. Synchronized had the lowest amplitude (1,462 vs. 2,258 counts/min), interdaily stability (0.46 vs. 0.63), and highest intradaily variability (1.10 vs. 0.81). PFS score varied by RAR groups: Synchronized=15.2±8.5, Concentrated High-Activity=14.2±7.9, and Low-Fragmented=17.3±8.7, p = 0.004. Using linear mixed models, the Low-Fragmented group had higher perceived physical fatigability at baseline (β = 1.30, p = 0.046) and a steeper increase in PFS score over time (β = 0.46, p = 0.03) vs. Synchronized, adjusted for demographics, health conditions, lifestyle factors, and sleep efficiency. After further adjustment for cardiorespiratory fitness (VO2peak from treadmill testing) and daily step count, the Low-Fragmented group showed a significantly larger increase in PFS score over time (β = 0.46, p = 0.03; β = 0.49, p = 0.02), although the baseline association was attenuated. Our findings reveal that disrupted RARs are independently associated with persistently worsening perceived physical fatigability.

Abstract Objectives Sleep and pain are co-occurring issues in adulthood. Pain reactivity to worse sleep refers to person-specific changes in daily pain following nights of shorter-than-usual sleep duration or poorer-than-usual sleep quality. We examined the cross-sectional and longitudinal associations of sleep-related pain reactivity with psychological distress and the number of chronic conditions. Methods Data were obtained from the Work, Family, and Health Study, which collected data from healthcare and information technology workers (n = 311; Mage=41.38 years), who completed 8-days of daily diary. We controlled for sociodemographic and health covariates in a series of multilevel structural equation models and growth curve models in Mplus. Sensitivity analyses adjusted for sleep and pain medications and stratified analyses by age. Results Those who reported more pain locations and severity following poorer-than-usual sleep quality had more concurrent distress (higher pain location reactivity to worse sleep quality: B = 1.03, p<.001; higher pain severity reactivity to worse sleep quality: B = 0.56, p=.001). In the growth curve models, higher pain location reactivity to poorer sleep quality was associated with higher concurrent distress (Path Est.=1.67, p=.023) and decreases in distress over time (Path Est.=-0.74, p=.032). Higher pain severity reactivity to poorer sleep quality was associated with higher concurrent distress (Path Est.=0.62, p=.030) but not changes in distress over time. Pain reactivity to worse sleep was not associated with concurrent or change in the number of chronic conditions. Discussion Future directions are to examine the influence of pain reactivity to worse sleep in larger and more diverse samples across longer follow-up periods.

In Smyl et al. [Local and global trend Bayesian exponential smoothing models. International Journal of Forecasting, 2025.], a generalised exponential smoothing model was proposed that is able to capture strong trends and volatility in time series. This method achieved state-of-the-art performance in many forecasting tasks, but its fitting procedure, which is based on the NUTS sampler, is very computationally expensive. In this work, we propose several modifications to the original model, as well as a bespoke Gibbs sampler for posterior exploration; these changes improve sampling time by an order of magnitude, thus rendering the model much more practically relevant. The new model, and sampler, are evaluated on the M3 dataset and are shown to be competitive, or superior, in terms of accuracy to the original method, while being substantially faster to run.

Glioblastoma's (GBM) aggressive growth is driven by redundant activation of a myriad of signaling pathways and genomic alterations in tyrosine kinase receptors, such as epidermal growth factor receptor (EGFR), which is altered in over 50% of cases. Single agents targeting EGFR have not proven effective against GBM. In this study, we aimed to identify an effective anti-tumor regimen using pharmacogenomic testing of patient-derived GBM samples, in culture and in vivo. High-throughput pharmacological screens of ten EGFR-driven GBM samples identified the combination of erlotinib (EGFRi) and MLN0128 (a mammalian target of rapamycin inhibitor, or MTORi) as the most effective at inhibiting tumor cell viability. The anti-tumor activity of erlonitib+MLN0128 was synergistic and produced inhibition of the p-EGFR, mitogen-activated protein kinase (MAPK), and Phosphoinositide 3-kinase (PI3K) pathways in culture. Using an orthotopic murine model of GBM, we show that erlotinib+MLN0128 inhibited tumor growth in vivo and significantly prolonged the survival of tumor-bearing mice. Expression profiling of tumor tissues from treated mice revealed a unique gene signature induced by erlotinib+MLN0128, consisting of downregulation of immunosuppressive chemokines in the tumor microenvironment, including C-C motif chemokine ligand 2 (CCL2) and periostin. Lower periostin levels resulted in the inhibition of Iba1+ (tumor-promoting) macrophage infiltration of GBM xenografts. Taken together, our results demonstrate that pharmacological co-targeting of EGFR and MTOR using clinically available drugs represents an effective treatment paradigm for EGFR-driven GBMs, acting both by inhibiting tumor cell growth and modulating the immune tumor microenvironment.

Abstract Prospective studies have reported associations between individual dimensions of sleep and subsequent falls. Considering sleep as a multidimensional construct may provide a more comprehensive assessment. Using data from the MrOS Sleep study, we calculated a multidimensional index of sleep, operationalized as the number of disturbances in 5 dimensions of sleep (self-reported “poor”: satisfaction, duration, daytime sleepiness, latency and sleep midpoint) and categorized as: 0, 1-2, >=3. A total of 3111 men, mean age 76.4 years, were studied. Logistic regression generalized estimating equations were used to model repeated measures of annually self-reported recurrent falls (≥2 falls) over 4 years of follow-up. Compared to men with no poor sleep dimensions, the Odds Ratio (OR) of recurrent falls in men with scores of 1 or 2 was 1.13 (95% Confidence Intervals, 0.96, 1.34), and in men with scores >3, the OR was 1.46(1.15, 1.84). Worse multidimensional sleep health was associated with recurrent falls.

Objectives. This study tested the longitudinal relationship between back pain and mental health and examined the moderating role of subjective social status (SSS). Method. Community-dwelling older men from the MrOS Study provided four study visits of data collected between 2000-2016 (15,975 observations nested within 5,979 participants). Back pain frequency and severity were assessed at visits 1-4. General mental health was measured at each visit by the 12-item Short Form Survey Mental Component Score (SF-12 MCS; higher scores representing better mental health). National and community SSS were assessed at visits 1 and 3 with the MacArthur Scale. Growth curve models tested longitudinal within-person change associations after accounting for the repeated measures within each person. Age was used as the primary time variable. Results. At baseline, those with higher back pain-frequency/severity reported lower SF-12 MCS. After accounting for this between-person difference, there were bidirectional within-person associations between back pain frequency/severity and SF-12 MCS. On follow-up visits when back pain frequency/severity increased from baseline, participants reported lower SF-12 MCS (p<.001). On follow-up visits when SF-12 MCS decreased from baseline, participants also reported higher back pain frequency/severity (p<.001). Higher national and community SSS at baseline and having increases or consistently higher SSS over time attenuated the negative relationships between back pain frequency/severity and SF-12 MCS. Results were consistent after controlling for an extensive list of baseline health covariates and pain medications. Discussion. These findings highlight how self-perceived social status may buffer the relationship between greater back pain frequency/severity and lower mental health.

Abstract IntroductionPre-eclampsia (PE) is a leading cause of perinatal morbidity and mortality worldwide. Low-dose aspirin can prevent PE in high risk pregnancies if started early. However, despite intense research into the area, first-trimester screening for PE risk is still not a routine part of pregnancy care. Several studies have described the application of artificial intelligence (AI) and machine learning (ML) in risk prediction of PE and its subtypes. A systematic review of available literature is necessary to catalogue the current applications of AI/ML methods in early pregnancy screening for PE, in order to better inform the development of clinically relevant risk prediction algorithms which will enable timely intervention and the development of new treatment strategies. The aim of this systematic review is to identify and assess studies regarding the application of AI/ML methods in first-trimester screening for PE.MethodsA systematic review of peer-reviewed as well as the grey literature cohort or case-control studies will be conducted. Relevant information will be accessed from the following databases; PubMed, Google Scholar, Web of Science, Arxiv, BioRxiv, and MedRxiv. The studies will be evaluated by two reviewers in a parallel, blind assessment of the literature, a third reviewer will assess any studies in which the first two reviewers did not agree. The free online tool Rayyan, will be used in this literature assessment stage. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 checklist will be used to guide the review process and the methods of the studies will be assessed using the Newcastle-Ottawa scale. Narrative synthesis will be conducted for all included studies. Meta-analysis will also be conducted where data quality and availability allow.Ethics and disseminationThe review will not require ethical approval and the findings will be published in a peer-reviewed journal using the PRISMA guidelines.

Abstract Recurrent glioblastomas (GBM) are notoriously difficult to treat, and in spite of aggressive chemo- and radiation therapy they inevitably recur in almost all GBM patients within 14 months following initial diagnosis. To interrogate pathways driving therapeutic resistance, we compared matched primary and recurrent IDH wild type glioblastoma samples from a cohort of patients, using RNA-Seq. Our analyses showed that pathways involved with tumor immune and metabolic reprogramming were up-regulated in recurrent GBMs compared to untreated, primary samples. Based on these findings, we tested the anti-tumor efficacy of over 20 rationally selected targeted therapeutic agents alone and in combination in a high-throughput drug screen assay designed to measure long term cell viability of primary tumorspheres from patients who underwent surgery for recurrent GBM. Top performing drug combinations from these screens were validated in vivo using patient-derived intracranial mouse models of glioma. Our data demonstrated that the combination of the transcriptional/metabolic inhibitor TG02 and the dual PI3K/mTOR inhibitor GDC-0084 significantly prolonged in vivo survival of tumor bearing mice, performing better than either drug alone. Notably, both inhibitors are undergoing human clinical trials as single agents with positive initial safety profiles and superior blood brain barrier penetrance. RNA Seq and functional assays of tumor samples from treated mice showed that only the combination treatment (TG02 + GDC-0084) significantly inhibited expression of immunomodulatory cytokines driving tumor progression, including those identified by us to be overexpressed in recurrent human GBM samples. Overall, these data suggest that combining TG02 and GDC-0084 is an effective treatment for recurrent GBMs. Furthermore, our results support the use of a translational research platform consisting of a personalized pharmaco-genomics testing, using patient-derived tumor samples towards designing more effective treatment for recurrent GBM patients.

BackgroundPopulation screening with highly sensitive diagnostic tools, such as nucleic acid amplification testing (NAAT), can enable early identification and isolation of cases and reduce transmission of SARS-CoV-2.ObjectiveTo describe the results of a rigorous, large-scale COVID-19 testing and monitoring program with confirmatory processes and adjudication of positive results.DesignDescriptive Epidemiology StudySetting32 U.S. National Football League (NFL) Clubs during the 2020 seasonParticipantsNFL players and staffMethodsThe NFL/NFL Players Association instituted a COVID-19 Testing and Surveillance Program for the 2020 Season, which included daily testing for players and staff, full medical follow-up and adjudication of cases. Clinical adjudication was based on subsequent daily testing, symptoms, and clinical history; persons remained in isolation during adjudication.ResultsBetween August 1 and November 14, 2020, a total of 632,370 RT-PCR tests were administered to 11,668 individuals; 270 (2.4%) confirmed cases were observed. PPVs of the initial positive result ranged from 73–82% across RT-PCR platforms. Initial positive results were positive on re-processing 61–79% of the time. PPV increased when both results were positive to >95%; however, initial positives that were negative on confirmatory processing resulted in true cases a portion of the time, depending on machine and population prevalence. High Ct values (33 to 37) could indicate onset of infection. Infected individuals were identified and isolated early in infection, preventing spread.InterventionsDaily or frequent testing using three NAAT platforms, rapid point-of-care testing, and symptom monitoring.Main Outcome MeasurementsCOVID-19 infection.ConclusionRoutine RT-PCR testing allowed early detection of infection. Cycle threshold values provided a useful guidepost for understanding results. Confirmatory processing of initial positive values significantly improved PPV. Antigen POC testing was unable to reliably rule out COVID-19 early in infection. Adjudication processes were able to confirm or rule out SARS-CoV-2.