Brain morphometry in former American football players: Findings from the DIAGNOSE CTE research project.

Exposure to repetitive head impacts (RHIs) in contact sports is associated with neurodegenerative disorders including chronic traumatic encephalopathy (CTE) which currently can be diagnosed only at postmortem. American football players are at higher risk of developing CTE given their exposure to RHIs. One promising approach for diagnosing CTE in vivo is to explore known neuropathological abnormalities at postmortem in living individuals using structural magnetic resonance imaging (MRI). MRI brain morphometry was evaluated in 170 male former American football players ages 45-74 years (n = 114 professional; n = 56 college) and 54 same-age unexposed asymptomatic male controls (n = 58 age range 45-74). Cortical thickness and volume of regions of interest were selected based on established CTE pathology findings and were assessed using FreeSurfer. Group differences and interactions with age and exposure factors were evaluated using a generalized least squares model. A separate logistic regression and independent multinomial model were performed to predict each Traumatic Encephalopathy Syndrome (TES) diagnosis core clinical features and provisional level of certainty for CTE pathology using brain regions of interest. Former college and professional American football players (combined) showed significant cortical thickness and/or volume reductions compared to unexposed asymptomatic controls in the hippocampus amygdala entorhinal cortex parahippocampal gyrus insula temporal pole and superior frontal gyrus. Post-hoc analyses identified group-level differences between former professional players and unexposed asymptomatic controls in the hippocampus amygdala entorhinal cortex parahippocampal gyrus insula and superior frontal gyrus. Former college players showed significant volume reductions in the hippocampus amygdala and superior frontal gyrus compared to the unexposed asymptomatic controls. We did not observe age-by-group interactions for brain morphometric measures. Interactions between morphometry and exposure measures were limited to a single significant positive association between the age of first exposure to organized tackle football and right insular volume. We found no significant relationship between brain morphometric measures and the TES diagnosis core clinical features and provisional level of certainty for CTE pathology outcomes. These findings suggest that MRI morphometrics detects abnormalities in individuals with a history of RHI exposure that resemble the anatomic distribution of pathological findings from postmortem CTE studies. The lack of findings associating MRI measures with exposure metrics (except for one significant relationship) or TES diagnosis and core clinical features suggests that brain morphometry must be complemented by other types of measures to characterize individuals with RHIs.

Inflammatory biomarkers for neurobehavioral dysregulation in former American football players: findings from the DIAGNOSE CTE Research Project.

BACKGROUND: Traumatic encephalopathy syndrome (TES) is defined as the clinical manifestation of the neuropathological entity chronic traumatic encephalopathy (CTE). A core feature of TES is neurobehavioral dysregulation (NBD), a neuropsychiatric syndrome in repetitive head impact (RHI)-exposed individuals, characterized by a poor regulation of emotions/behavior. To discover biological correlates for NBD, we investigated the association between biomarkers of inflammation (interleukin (IL)-1ß, IL-6, IL-8, IL-10, C-reactive protein (CRP), tumor necrosis factor (TNF)-α) in cerebrospinal fluid (CSF) and NBD symptoms in former American football players and unexposed individuals. METHODS: Our cohort consisted of former American football players, with (n = 104) or without (n = 76) NBD diagnosis, as well as asymptomatic unexposed individuals (n = 55) from the DIAGNOSE CTE Research Project. Specific measures for NBD were derived (i.e., explosivity, emotional dyscontrol, impulsivity, affective lability, and a total NBD score) from a factor analysis of multiple self-report neuropsychiatric measures. Analyses of covariance tested differences in biomarker concentrations between the three groups. Within former football players, multivariable linear regression models assessed relationships among log-transformed inflammatory biomarkers, proxies for RHI exposure (total years of football, cumulative head impact index), and NBD factor scores, adjusted for relevant confounding variables. Sensitivity analyses tested (1) differences in age subgroups (< 60, ≥ 60 years); (2) whether associations could be identified with plasma inflammatory biomarkers; (3) associations between neurodegeneration and NBD, using plasma neurofilament light (NfL) chain protein; and (4) associations between biomarkers and cognitive performance to explore broader clinical symptoms related to TES. RESULTS: CSF IL-6 was higher in former American football players with NBD diagnosis compared to players without NBD. Furthermore, elevated levels of CSF IL-6 were significantly associated with higher emotional dyscontrol, affective lability, impulsivity, and total NBD scores. In older football players, plasma NfL was associated with higher emotional dyscontrol and impulsivity, but also with worse executive function and processing speed. Proxies for RHI exposure were not significantly associated with biomarker concentrations. CONCLUSION: Specific NBD symptoms in former American football players may result from multiple factors, including neuroinflammation and neurodegeneration. Future studies need to unravel the exact link between NBD and RHI exposure, including the role of other pathophysiological pathways.

Association of Vascular Risk Factors and CSF and Imaging Biomarkers With White Matter Hyperintensities in Former American Football Players.

BACKGROUND AND OBJECTIVES: Recent data link exposure to repetitive head impacts (RHIs) from American football with increased white matter hyperintensity (WMH) burden. WMH might have unique characteristics in the context of RHI beyond vascular risk and normal aging processes. We evaluated biological correlates of WMH in former American football players, including markers of amyloid, tau, inflammation, axonal injury, neurodegeneration, and vascular health. METHODS: Participants underwent clinical interviews, MRI, and lumbar puncture as part of the Diagnostics, Imaging, and Genetics Network for the Objective Study and Evaluation of Chronic Traumatic Encephalopathy Research Project. Structural equation modeling tested direct and indirect effects between log-transformed total fluid-attenuated inversion recovery (FLAIR) lesion volumes (TLV) and the revised Framingham stroke risk profile (rFSRP), MRI-derived global metrics of cortical thickness and fractional anisotropy (FA), and CSF levels of amyloid ß1-42, p-tau181, soluble triggering receptor expressed on myeloid cells 2 (sTREM2), and neurofilament light. Covariates included age, race, education, body mass index, APOE ε4 carrier status, and evaluation site. Models were performed separately for former football players and a control group of asymptomatic men unexposed to RHI. RESULTS: In 180 former football players (mean age = 57.2, 36% Black), higher log(TLV) had direct associations with the following: higher rFSRP score (B = 0.26, 95% CI 0.07-0.40), higher p-tau181 (B = 0.17, 95% CI 0.01-0.43), lower FA (B = -0.28, 95% CI -0.42 to -0.13), and reduced cortical thickness (B = -0.25, 95% CI -0.45 to -0.08). In 60 asymptomatic unexposed men (mean age = 59.3, 40% Black), there were no direct effects on log(TLV) (rFSRP: B = -0.03, 95% CI -0.48 to 0.57; p-tau181: B = -0.30, 95% CI -1.14 to 0.37; FA: B = -0.07, 95% CI -0.48 to 0.42; or cortical thickness: B = -0.28, 95% CI -0.64 to 0.10). The former football players showed stronger associations between log(TLV) and rFSRP (1,069% difference in estimates), p-tau181 (158%), and FA (287%) than the unexposed men. DISCUSSION: Risk factors and biological correlates of WMH differed between former American football players and asymptomatic unexposed men. In addition to vascular health, p-tau181 and diffusion tensor imaging indices of white matter integrity showed stronger associations with WMH in the former football players. FLAIR WMH may have specific risk factors and pathologic underpinnings in RHI-exposed individuals.

Vitreous Humor Biomarkers Reflect Pathological Changes in the Brain for Alzheimer's Disease and Chronic Traumatic Encephalopathy.

BACKGROUND: Patients with eye disease have an increased risk for developing neurodegenerative disease. Neurodegenerative proteins can be measured in the eye; however, correlations between biomarker levels in eye fluid and neuropathological diagnoses have not been established. OBJECTIVE: This exploratory, retrospective study examined vitreous humor from 41 postmortem eyes and brain tissue with neuropathological diagnoses of Alzheimer's disease (AD, n = 7), chronic traumatic encephalopathy (CTE, n = 15), both AD + CTE (n = 10), and without significant neuropathology (controls, n = 9). METHODS: Protein biomarkers i.e., amyloid-ß (Aß40,42), total tau (tTau), phosphorylated tau (pTau181,231), neurofilament light chain (NfL), and eotaxin-1 were quantitatively measured by immunoassay. Non-parametric tests were used to compare vitreous biomarker levels between groups. Spearman's rank correlation tests were used to correlate biomarker levels in vitreous and cortical tissue. The level of significance was set to α= 0.10. RESULTS: In pairwise comparisons, tTau levels were significantly increased in AD and CTE groups versus controls (p = 0.08 for both) as well as AD versus AD+CTE group and CTE versus AD+CTE group (p = 0.049 for both). Vitreous NfL levels were significantly increased in low CTE (Stage I/II) versus no CTE (p = 0.096) and in low CTE versus high CTE stage (p = 0.03). Vitreous and cortical tissue levels of pTau 231 (p = 0.02, r = 0.38) and t-Tau (p = 0.04, r = -0.34) were significantly correlated. CONCLUSION: The postmortem vitreous humor biomarker levels significantly correlate with AD and CTE pathology in corresponding brains, while vitreous NfL was correlated with the CTE staging. This exploratory study indicates that biomarkers in the vitreous humor may serve as a proxy for neuropathological disease.

CHAP-child: an open source method for estimating sit-to-stand transitions and sedentary bout patterns from hip accelerometers among children.

BACKGROUND: Hip-worn accelerometer cut-points have poor validity for assessing children's sedentary time, which may partly explain the equivocal health associations shown in prior research. Improved processing/classification methods for these monitors would enrich the evidence base and inform the development of more effective public health guidelines. The present study aimed to develop and evaluate a novel computational method (CHAP-child) for classifying sedentary time from hip-worn accelerometer data. METHODS: Participants were 278, 8-11-year-olds recruited from nine primary schools in Melbourne, Australia with differing socioeconomic status. Participants concurrently wore a thigh-worn activPAL (ground truth) and hip-worn ActiGraph (test measure) during up to 4 seasonal assessment periods, each lasting up to 8 days. activPAL data were used to train and evaluate the CHAP-child deep learning model to classify each 10-s epoch of raw ActiGraph acceleration data as sitting or non-sitting, creating comparable information from the two monitors. CHAP-child was evaluated alongside the current practice 100 counts per minute (cpm) method for hip-worn ActiGraph monitors. Performance was tested for each 10-s epoch and for participant-season level sedentary time and bout variables (e.g., mean bout duration). RESULTS: Across participant-seasons, CHAP-child correctly classified each epoch as sitting or non-sitting relative to activPAL, with mean balanced accuracy of 87.6% (SD = 5.3%). Sit-to-stand transitions were correctly classified with mean sensitivity of 76.3% (SD = 8.3). For most participant-season level variables, CHAP-child estimates were within ± 11% (mean absolute percent error [MAPE]) of activPAL, and correlations between CHAP-child and activPAL were generally very large (> 0.80). For the current practice 100 cpm method, most MAPEs were greater than ± 30% and most correlations were small or moderate (≤ 0.60) relative to activPAL. CONCLUSIONS: There was strong support for the concurrent validity of the CHAP-child classification method, which allows researchers to derive activPAL-equivalent measures of sedentary time, sit-to-stand transitions, and sedentary bout patterns from hip-worn triaxial ActiGraph data. Applying CHAP-child to existing datasets may provide greater insights into the potential impacts and influences of sedentary time in children.

Health effects and cost-effectiveness of a multilevel physical activity intervention in low-income older adults; results from the PEP4PA cluster randomized controlled trial.

BACKGROUND: Older adults are the least active population in the U.S. Low-income communities have fewer physical activity (PA) resources, contributing to less PA and increased chronic disease risk. This study assessed the effect of the multilevel, peer-led, Peer Empowerment Program 4 Physical Activity (PEP4PA) on moderate-to-vigorous PA (MVPA) and health outcomes, over 2 years of follow up. METHODS: In a cluster-randomized controlled trial, 12 senior or community centers serving low-income older adults were assigned to a PA intervention (n = 6) or usual programming (n = 6) condition. PEP4PA included self-monitoring, health coaching, group walks, social support, and community advocacy to improve walking conditions. The primary outcome was daily minutes of MVPA (7-day accelerometer). Secondary outcomes included Perceived Quality of Life (PQoL), 6-Minute Walk Test (6-MWT), blood pressure (BP), and depressive symptoms at baseline, 6, 12, 18 and 24 months. Mixed effects regression models estimated the effects on outcomes between groups over time and included random effects for repeated measures and center clustering. Effect modification by sex and income status was assessed. We calculated the incremental cost per daily minute of MVPA gained in the intervention group relative to the control group to assess cost effectiveness. RESULTS: We enrolled 476 older adults (50 + years). Participants were on average 71 years old, 76% female, 60% low income, and 38% identified as racial or ethnic minorities. Compared to the control group, intervention participants sustained roughly a 10 min/day increase in MVPA from baseline at all time points and increased mean PQoL scores from unsatisfied at baseline to satisfied at 12, 18 and 24 months. Males and higher-income groups had greater improvements in MVPA. No significant effects were observed for 6-MWT or depressive symptoms, and BP results were mixed. The incremental cost per minute MVPA gained per person was $0.25, $0.09, $0.06, and $0.05 at 6, 12, 18 and 24 months, respectively. CONCLUSIONS: PEP4PA achieved increases in MVPA and PQoL in low-income older adults, over 2 years of follow up. The peer-led, community-based intervention provides a sustainable and cost-effective model to improve health behaviors in underserved, aging populations. TRIAL REGISTRATION: ClinicalTrials.gov ( NCT02405325 ) March 20, 2015.

Agreement of Step-Based Metrics From ActiGraph and ActivPAL Accelerometers Worn Concurrently Among Older Adults.

Purpose: Our study evaluated the agreement of mean daily step counts, peak 1-min cadence, and peak 30-min cadence between the hip-worn ActiGraph GT3X+ accelerometer, using the normal filter (AGN) and the low frequency extension (AGLFE), and the thigh-worn activPAL3 micro (AP) accelerometer among older adults. Methods: Nine-hundred and fifty-three older adults (≥65 years) were recruited to wear the ActiGraph device concurrently with the AP for 4-7 days beginning in 2016. Using the AP as the reference measure, device agreement for each step-based metric was assessed using mean differences (AGN - AP and AGLFE - AP), mean absolute percentage error (MAPE), and Pearson and concordance correlation coefficients. Results: For AGN - AP, the mean differences and MAPE were: daily steps -1,851 steps/day and 27.2%, peak 1-min cadence -16.2 steps/min and 16.3%, and peak 30-min cadence -17.7 steps/min and 24.0%. Pearson coefficients were .94, .85, and .91 and concordance coefficients were .81, .65, and .73, respectively. For AGLFE - AP, the mean differences and MAPE were: daily steps 4,968 steps/day and 72.7%, peak 1-min cadence -1.4 steps/min and 4.7%, and peak 30-min cadence 1.4 steps/min and 7.0%. Pearson coefficients were .91, .91, and .95 and concordance coefficients were .49, .91, and .94, respectively. Conclusions: Compared with estimates from the AP, the AGN underestimated daily step counts by approximately 1,800 steps/day, while the AGLFE overestimated by approximately 5,000 steps/day. However, peak step cadence estimates generated from the AGLFE and AP had high agreement (MAPE ≤ 7.0%). Additional convergent validation studies of step-based metrics from concurrently worn accelerometers are needed for improved understanding of between-device agreement.

CHAP-Adult: A Reliable and Valid Algorithm to Classify Sitting and Measure Sitting Patterns Using Data From Hip-Worn Accelerometers in Adults Aged 35.

Background: Hip-worn accelerometers are commonly used, but data processed using the 100 counts per minute cut point do not accurately measure sitting patterns. We developed and validated a model to accurately classify sitting and sitting patterns using hip-worn accelerometer data from a wide age range of older adults. Methods: Deep learning models were trained with 30-Hz triaxial hip-worn accelerometer data as inputs and activPAL sitting/nonsitting events as ground truth. Data from 981 adults aged 35-99 years from cohorts in two continents were used to train the model, which we call CHAP-Adult (Convolutional Neural Network Hip Accelerometer Posture-Adult). Validation was conducted among 419 randomly selected adults not included in model training. Results: Mean errors (activPAL - CHAP-Adult) and 95% limits of agreement were: sedentary time -10.5 (-63.0, 42.0) min/day, breaks in sedentary time 1.9 (-9.2, 12.9) breaks/day, mean bout duration -0.6 (-4.0, 2.7) min, usual bout duration -1.4 (-8.3, 5.4) min, alpha .00 (-.04, .04), and time in ≥30-min bouts -15.1 (-84.3, 54.1) min/day. Respective mean (and absolute) percent errors were: -2.0% (4.0%), -4.7% (12.2%), 4.1% (11.6%), -4.4% (9.6%), 0.0% (1.4%), and 5.4% (9.6%). Pearson's correlations were: .96, .92, .86, .92, .78, and .96. Error was generally consistent across age, gender, and body mass index groups with the largest deviations observed for those with body mass index ≥30 kg/m2. Conclusions: Overall, these strong validation results indicate CHAP-Adult represents a significant advancement in the ambulatory measurement of sitting and sitting patterns using hip-worn accelerometers. Pending external validation, it could be widely applied to data from around the world to extend understanding of the epidemiology and health consequences of sitting.

Agreement of sedentary behaviour metrics derived from hip-worn and thigh-worn accelerometers among older adults: with implications for studying physical and cognitive health.

Little is known about how sedentary behaviour (SB) metrics derived from hip-worn and thigh-worn accelerometers agree for older adults. Thigh-worn activPAL micro monitors were concurrently worn with hip-worn ActiGraph GT3X+ accelerometers (with SB measured using the 100 count-per-minute (cpm) cut-point; ActiGraph100cpm) by 953 older adults (age 77±6.6, 54% women) for 4-to-7 days. Device agreement for sedentary time and 5 SB pattern metrics was assessed using mean error and correlations. Logistic regression tested associations with 4 health outcomes using standardized (i.e., z-scores) and unstandardized SB metrics. Mean errors (activPAL-ActiGraph100cpm) and 95% limits of agreement were: sedentary time -54.7(-223.4,113.9) min/d; time in 30+ minute bouts 77.6(-74.8,230.1) min/d; mean bout duration 5.9(0.5,11.4) min; usual bout duration 15.2(0.4,30) min; breaks in sedentary time -35.4(-63.1,-7.6) breaks/d; and alpha -0.5(-0.6,-0.4). Respective Pearson correlations were: 0.66, 0.78, 0.73, 0.79, 0.51, 0.40. Concordance correlations were: 0.57, 0.67, 0.40, 0.50, 0.14, 0.02. The statistical significance and direction of associations was identical for ActiGraph100cpm and activPAL metrics in 46 of 48 tests, though significant differences in the magnitude of odds ratios were observed among 9 of 24 tests for unstandardized and 2 of 24 for standardized SB metrics. Caution is needed when interpreting SB metrics and associations with health from ActiGraph100cpm due to the tendency for it to overestimate breaks in sedentary time relative to activPAL. However, high correlations between activPAL and ActiGraph100cpm measures and similar standardized associations with health outcomes suggest that studies using ActiGraph100cpm are useful, though not ideal, for studying SB in older adults.

Primary Intramedullary Spinal Melanoma: A Rare Disease of the Spinal Cord.

Primary malignant melanoma of the intramedullary region of the spinal cord has rarely been reported in the literature. These tumors can have variable appearance on magnetic resonance imaging (MRI) due to different extents of melanin and hemorrhage. Histopathologic confirmation and a comprehensive workup to rule out extra-spinal melanoma are required to make definitive diagnosis. We present a case of a patient diagnosed with primary intramedullary spinal melanoma in his lower thoracic spinal cord who was effectively treated with surgical resection, adjuvant radiation, and adjuvant immunotherapy. Gross total resection (GTR) is most vital in the management of this spinal tumor. Although several studies have established the efficacy of immunotherapy agents in advanced malignant melanoma, the use of these agents has not been studied in primary central nervous system melanomas. This case provides insight into the diagnostic approach and treatment options for this unique malignancy.

Application of Convolutional Neural Network Algorithms for Advancing Sedentary and Activity Bout Classification.

BACKGROUND: Machine learning has been used for classification of physical behavior bouts from hip-worn accelerometers; however, this research has been limited due to the challenges of directly observing and coding human behavior "in the wild." Deep learning algorithms, such as convolutional neural networks (CNNs), may offer better representation of data than other machine learning algorithms without the need for engineered features and may be better suited to dealing with free-living data. The purpose of this study was to develop a modeling pipeline for evaluation of a CNN model on a free-living data set and compare CNN inputs and results with the commonly used machine learning random forest and logistic regression algorithms. METHOD: Twenty-eight free-living women wore an ActiGraph GT3X+accelerometer on their right hip for 7 days. A concurrently worn thigh-mounted activPAL device captured ground truth activity labels. The authors evaluated logistic regression, random forest, and CNN models for classifying sitting, standing, and stepping bouts. The authors also assessed the benefit of performing feature engineering for this task. RESULTS: The CNN classifier performed best (average balanced accuracy for bout classification of sitting, standing, and stepping was 84%) compared with the other methods (56% for logistic regression and 76% for random forest), even without performing any feature engineering. CONCLUSION: Using the recent advancements in deep neural networks, the authors showed that a CNN model can outperform other methods even without feature engineering. This has important implications for both the model's ability to deal with the complexity of free-living data and its potential transferability to new populations.

Validity of Two Awake Wear-Time Classification Algorithms for activPAL in Youth, Adults, and Older Adults.

BACKGROUND: The authors assessed agreement between participant diaries and two automated algorithms applied to activPAL (PAL Technologies Ltd, Glasgow, United Kingdom) data for classifying awake wear time in three age groups. METHODS: Study 1 involved 20 youth and 23 adults who, by protocol, removed the activPAL occasionally to create nonwear periods. Study 2 involved 744 older adults who wore the activPAL continuously. Both studies involved multiple assessment days. In-bed, out-of-bed, and nonwear times were recorded in the participant diaries. The CREA (in PAL processing suite) and ProcessingPAL (secondary application) algorithms estimated out-of-bed wear time. Second- and day-level agreement between the algorithms and diary was investigated, as were associations of sedentary variables with self-rated health. RESULTS: The overall accuracy for classifying out-of-bed wear time as compared with the diary was 89.7% (Study 1) to 95% (Study 2) for CREA and 89.4% (Study 1) to 93% (Study 2) for ProcessingPAL. Over 90% of the nonwear time occurring in nonwear periods >165 min was detected by both algorithms, while <11% occurring in periods ≤165 min was detected. For the daily variables, the mean absolute errors for each algorithm were generally within 0-15% of the diary mean. Most Spearman correlations were very large (≥.81). The mean absolute errors and correlations were less favorable for days on which any nonwear time had occurred. The associations between sedentary variables and self-rated health were similar across processing methods. CONCLUSION: The automated awake wear-time classification algorithms performed similarly to the diary information on days without short (≤2.5-2.75 hr) nonwear periods. Because both diary and algorithm data can have inaccuracies, best practices likely involve integrating diary and algorithm output.

Endothelial-derived cardiovascular disease-related microRNAs elevated with prolonged sitting pattern among postmenopausal women.

Time spent sitting is positively correlated with endothelial dysfunction and cardiovascular disease risk. The underlying molecular mechanisms are unknown. MicroRNAs contained in extracellular vesicles (EVs) reflect cell/tissue status and mediate intercellular communication. We explored the association between sitting patterns and microRNAs isolated from endothelial cell (EC)-derived EVs. Using extant actigraphy based sitting behavior data on a cohort of 518 postmenopausal overweight/obese women, we grouped the woman as Interrupted Sitters (IS; N = 18) or Super Sitters (SS; N = 53) if they were in the shortest or longest sitting pattern quartile, respectively. The cargo microRNA in EC-EVs from the IS and SS women were compared. MicroRNA data were weighted by age, physical functioning, MVPA, device wear days, device wear time, waist circumference, and body mass index. Screening of CVD-related microRNAs demonstrated that miR-199a-5p, let-7d-5p, miR-140-5p, miR-142-3p, miR-133b level were significantly elevated in SS compared to IS groups. Group differences in let-7d-5p, miR-133b, and miR-142-3p were validated in expanded groups. Pathway enrichment analyses show that mucin-type O-glycan biosynthesis and cardiomyocyte adrenergic signaling (P < 0.001) are downstream of the three validated microRNAs. This proof-of-concept study supports the possibility that CVD-related microRNAs in EC-EVs may be molecular transducers of sitting pattern-associated CVD risk in overweight postmenopausal women.

The CNN Hip Accelerometer Posture (CHAP) Method for Classifying Sitting Patterns from Hip Accelerometers: A Validation Study.

INTRODUCTION: Sitting patterns predict several healthy aging outcomes. These patterns can potentially be measured using hip-worn accelerometers, but current methods are limited by an inability to detect postural transitions. To overcome these limitations, we developed the Convolutional Neural Network Hip Accelerometer Posture (CHAP) classification method. METHODS: CHAP was developed on 709 older adults who wore an ActiGraph GT3X+ accelerometer on the hip, with ground-truth sit/stand labels derived from concurrently worn thigh-worn activPAL inclinometers for up to 7 d. The CHAP method was compared with traditional cut-point methods of sitting pattern classification as well as a previous machine-learned algorithm (two-level behavior classification). RESULTS: For minute-level sitting versus nonsitting classification, CHAP performed better (93% agreement with activPAL) than did other methods (74%-83% agreement). CHAP also outperformed other methods in its sensitivity to detecting sit-to-stand transitions: cut-point (73%), TLBC (26%), and CHAP (83%). CHAP's positive predictive value of capturing sit-to-stand transitions was also superior to other methods: cut-point (30%), TLBC (71%), and CHAP (83%). Day-level sitting pattern metrics, such as mean sitting bout duration, derived from CHAP did not differ significantly from activPAL, whereas other methods did: activPAL (15.4 min of mean sitting bout duration), CHAP (15.7 min), cut-point (9.4 min), and TLBC (49.4 min). CONCLUSION: CHAP was the most accurate method for classifying sit-to-stand transitions and sitting patterns from free-living hip-worn accelerometer data in older adults. This promotes enhanced analysis of older adult movement data, resulting in more accurate measures of sitting patterns and opening the door for large-scale cohort studies into the effects of sitting patterns on healthy aging outcomes.

Hemorrhagic Cystitis Secondary to Adenovirus and BK Virus Infection in a Diffuse Large B-Cell Lymphoma Patient with Recent CAR T-Cell Therapy.

Patients who undergo chimeric antigen receptor T-cell therapy (CAR T-cell therapy) are immunosuppressed due to multiple factors. While adenovirus and BK virus are well-known pathogens in the context of hematopoietic stem cell transplant, there are no detailed reports of these infections in the setting of CAR T-cell therapy. We describe a 70-year-old male who recently underwent CAR T-cell therapy for diffuse large B-cell lymphoma. He presented with intractable gross hematuria and dysuria. Workup revealed adenovirus viremia and viruria and BK virus viruria. He was treated for adenovirus hemorrhagic cystitis with intravenous cidofovir 1 mg/kg/day, every three days for three weeks, with good clinical response. We also discuss the mechanisms of immunosuppression in CAR T-cell therapy as well as the principles of treatment of adenovirus and BK virus infections in the immunosuppressed patient.

Zika Virus: Immune Evasion Mechanisms, Currently Available Therapeutic Regimens, and Vaccines.

The sudden emergence of infectious pathogens such as Zika virus (ZIKV) holds global health concerns. Recent dissemination of ZIKV from Pacific to Americas with an upsurge of congenital anomalies and Guillain Barre Syndrome (GBS) in adults has created an alarming situation. High-throughput studies are in progress to understand ZIKV's mode of pathogenesis and mechanism of immune escape, yet the pathogenesis remains obscure. Mainly ZIKV's envelope (E) protein and nonstructural proteins (mainly NS1 and NS5) manipulate host cell to support viral immune escape by modulation of the interferon pathway and complement antagonism. The development of direct therapeutics for ZIKV infection is required to overcome the rapidly evolving viral threat. Currently, the existing strategies for ZIKV treatment are only supportive. Although, there is no prophylactic or therapeutic vaccine presently available, however, recent efforts have brought up ZIKV vaccines into clinical trial phase 1. This review presents the highlights of recent advances in understanding immune evasion strategies adapted by ZIKV and existing therapies against the virus.

Arsenic-Redox Transformation and Plant Growth Promotion by Purple Nonsulfur Bacteria Rhodopseudomonas palustris CS2 and Rhodopseudomonas faecalis SS5.

Arsenic (As) is a well-known toxic metalloid found naturally and released by different industries, especially in developing countries. Purple nonsulfur bacteria (PNSB) are known for wastewater treatment and plant growth promoting abilities. As-resistant PNSB were isolated from a fish pond. Based on As-resistance and plant growth promoting attributes, 2 isolates CS2 and SS5 were selected and identified as Rhodopseudomonas palustris and Rhodopseudomonas faecalis, respectively, through 16S rRNA gene sequencing. Maximum As(V) resistance shown by R. faecalis SS5 and R. palustris CS2 was up to 150 and 100 mM, respectively. R. palustris CS2 showed highest As(V) reduction up to 62.9% (6.29 ± 0.24 mM), while R. faecalis SS5 showed maximum As(III) oxidation up to 96% (4.8 ± 0.32 mM), respectively. Highest auxin production was observed by R. palustris CS2 and R. faecalis SS, up to 77.18 ± 3.7 and 76.67 ± 2.8 µg mL-1, respectively. Effects of these PNSB were tested on the growth of Vigna mungo plants. A statistically significant increase in growth was observed in plants inoculated with isolates compared to uninoculated plants, both in presence and in absence of As. R. palustris CS2 treated plants showed 17% (28.1 ± 0.87 cm) increase in shoot length and 21.7% (7.07 ± 0.42 cm) increase in root length, whereas R. faecalis SS5 treated plants showed 12.8% (27.09 ± 0.81 cm) increase in shoot length and 18.8% (6.9 ± 0.34 cm) increase in root length as compared to the control plants. In presence of As, R. palustris CS2 increased shoot length up to 26.3% (21.0 ± 1.1 cm), while root length increased up to 31.3% (5.3 ± 0.4 cm), whereas R. faecalis SS5 inoculated plants showed 25% (20.7 ± 1.4 cm) increase in shoot length and 33.3% (5.4 ± 0.65 cm) increase in root length as compared to the control plants. Bacteria with such diverse abilities could be ideal for plant growth promotion in As-contaminated sites.