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In the medical diagnostics domain, pathology and histology are pivotal for the precise identification of diseases. Digital histopathology, enhanced by automation, facilitates the efficient analysis of massive amount of biopsy images produced on a daily basis, streamlining the evaluation process. This study focuses in Stain Color Normalization (SCN) within a Whole-Slide Image (WSI) cohort, aiming to reduce batch biases. Building on published graphical method, this research demonstrates a mathematical population or data-driven method that optimizes the dependency on the number of reference WSIs and corresponding aggregate sums, thereby increasing SCN process efficiency. This method expedites the analysis of color convergence 50-fold by using stain vector Euclidean distance analysis, slashing the requirement for reference WSIs by more than half. The approach is validated through a tripartite methodology: 1) Stain vector euclidean distances analysis, 2) Distance computation timing, and 3) Qualitative and quantitative assessments of SCN across cancer tumors regions of interest. The results validate the performance of data-driven SCN method, thus potential to enhance the precision and reliability of computational pathology analyses. This advancement is poised to enhance diagnostic processes, therapeutic strategies, and patient prognosis.

Background: The prevalence of mental health disorders calls for valid and reliable instruments that are easy to administer and assess for clinicians and researchers. The Depression Anxiety Stress Scale-21 (DASS-21) is a commonly used instrument to assess psychological distress; however, model fit and internal reliability issues have been reported. Our objective was to assess the factorial and structural validity of the DASS-21. Methods: A confirmatory factor analysis (CFA) was conducted on the full sample (n = 1036) to assess the proposed three-factor DASS-21 using a priori cut-off values. Because model fit indices were not met, an exploratory factor analysis (EFA) was conducted to identify a parsimonious model. The resulting three-factor structure (i.e., DASS-9) was then assessed using CFA and multigroup invariance testing procedures. Results: The proposed three-factor DASS-21 did not meet model fit criteria. The DASS-9 did meet recommended model fit criteria and was invariant between sex, injury status, mental health diagnosis, and activity level groups. Statistically different group means were found between mental health diagnosis and activity level groups, while no differences between sex or injury status groups were found. Conclusions: The current study provides support to use a condensed DASS-21 instrument, such as the DASS-9. Future research is necessary to establish the DASS-9 prior to its adoption in research and clinical practice. Additionally, there is a need to identify and review all condensed versions of the DASS-21, so individuals know which instrument can be used for clinical or research purposes.

Background: Gestational exposures to single toxic chemicals have been associated with cognitive deficits in children, but few studies have explored chemical mixtures. Objectives: To evaluate the associations between gestational chemical biomarker mixtures and cognitive abilities in children from two prospective cohorts. Methods: This study includes 617 birthing parent–child pairs from the Health Outcomes and Measures of the Environment (HOME) and Maternal-Infant Research on Environmental Chemicals (MIREC) Studies. We measured 29 chemical biomarkers (metals, persistent organic pollutants, perfluoroalkyl substances, organophosphate esters, phenols, phthalates, organophosphate pesticides, and parabens) in pregnant individuals during early pregnancy and their children’s cognitive abilities at ages 3 to 5 years using Wechsler Intelligence Scales. We assessed linear associations using quantile g-computation and non-linear associations using Bayesian Kernel Machine Regression (BKMR) methods, adjusted for covariates. Results: Using quantile g-computation, we observed overall null associations between the chemical biomarker mixture and cognitive outcomes among preschool-age children. Although statistical significance was not attained for child sex as an effect modifier, our stratified analysis unveiled a moderate divergence in association trends. We noted a marginal inverse trend between the chemical biomarker mixture and cognitive scores [Full-Scale Intelligence Quotient (FSIQ) & Performance Intelligence Quotient (PIQ)] among males. Using quantile g-computation and BKMR methods, we observed that PBDE47, PFHxS, and di-ethyl organophosphates commonly contributed towards a decline in FSIQ scores in males. Among males, a quartile increase in the chemical biomarker mixture was associated with a 0.64-point decrease (95% CI: −2.59, 1.31) in the FSIQ score and a 1.59-point decrease (95% CI: −3.72, 0.54) in the PIQ score. Conclusion: In this study, we observed a weak negative trend between the gestational chemical biomarker mixture and cognitive scores (FSIQ/PIQ) among males. Further studies are needed to confirm the findings between the longitudinal chemical biomarkers and child cognitive scores at school ages.

IntroductionHippocampal glutamate (Glu) dysfunction is a pertinent indicator of neurodegeneration, yet mapping typical age-related changes in Glu has been challenging. Here, we use a 7T MRI approach, Glutamate Chemical Exchange Saturation Transfer (GluCEST), to measure bilateral hippocampal Glu in healthy old (HOA) and young (HYA) adults.MethodsBilateral hippocampal GluCEST data was acquired from 27 HOA and 22 HYA using 7T MRI. GluCEST differences by age and hemisphere were tested with a linear mixed model. GluCEST asymmetry index was also evaluated by age. Exploratory analyses examined associations between hippocampal GluCEST, age group, and scores on the Montreal Cognitive Assessment (MoCA) and Cognitive Complaints Index (CCI).ResultsGluCEST levels showed an age group and hemisphere interaction. In HOA, GluCEST was higher in left than right hippocampus, but in HYA, GluCEST level was equivalent across hemispheres. HOA had lower GluCEST than HYA in the right hippocampus. GluCEST asymmetry index confirmed significant left asymmetry in HOA. Lower GluCEST levels in HOA were associated with subjective cognitive complaints as measured by the CCI.DiscussionHippocampal GluCEST provides insight into age-related neural changes, with lower GluCEST in the right hippocampus in older adults. These findings offer a step toward elucidating the asymmetrical trajectory of hippocampal glutamatergic alterations and their relationship to cognitive phenotypes.

Background and Aims: Inadequate bowel preparation which occurs in 25% of colonoscopies is a major barrier to the effectiveness of screening for colorectal cancer. We aim to develop an artificial intelligence (machine learning) algorithm to assess photos of stool output after bowel preparation to predict inadequate bowel preparation before colonoscopy. Methods: Patients were asked to text a photo of their stool in the commode when they believed that they neared completion of their colonoscopy bowel preparation. Boston Bowel Preparation Scores of 7 and below were labeled as inadequate or fair. Boston Bowel Preparation Scores of 8 and 9 were considered good. A binary classification image-based machine learning algorithm was designed. Results: In a test set of 61 images, the binary classification machine learning algorithm was able to distinguish inadequate/fair preparation from good preparation with a positive predictive value of 78.6% and a negative predictive value of 60.8%. In a test set of 56 images, the algorithm was able to distinguish normal colonoscopy duration (25 minutes) with a positive predictive value of 78.6% and a negative predictive value of 65.5%. Conclusion: Patients are willing to submit photos of their stool output during bowel preparation through text messages before colonoscopy. This machine learning algorithm demonstrates the ability to predict inadequate/fair preparation from good preparation based on image classification of stool output. It was less accurate to predict long duration of colonoscopy.

Abstract While precision medicine applications of radiomics analysis are promising, differences in image acquisition can cause “batch effects” that reduce reproducibility and affect downstream predictive analyses. Harmonization methods such as ComBat have been developed to correct these effects, but evaluation methods for quantifying batch effects are inconsistent. In this study, we propose the use of the multivariate statistical test PERMANOVA and the Robust Effect Size Index (RESI) to better quantify and characterize batch effects in radiomics data. We evaluate these methods in both simulated and real radiomics features extracted from full-field digital mammography (FFDM) data. PERMANOVA demonstrated higher power than standard univariate statistical testing, and RESI was able to interpretably quantify the effect size of site at extremely large sample sizes. These methods show promise as more powerful and interpretable methods for the detection and quantification of batch effects in radiomics studies.

Abstract Ethanol consumption is associated with positive, negative, and neutral effects on the skeletal system. Our previous work using a nonhuman primate model of voluntary ethanol consumption showed that chronic ethanol use has an impact on skeletal attributes, most notably on biochemical markers of bone turnover. However, these studies were limited by small sample sizes and resulting lack of statistical power. Here, we applied a machine learning framework to integrate data from 155 monkeys (100 ethanol and 55 controls) to identify the bone features associated with chronic ethanol use. Specifically, we analyzed the influence of ethanol consumption on biomarkers of bone turnover and cancellous and cortical bone architecture in tibia. We hypothesized that chronic ethanol use for 6 months to 2.5 years would result in measurable changes to cancellous features and the biochemical markers compared to control animals. We observed a decrease in bone turnover in monkeys exposed to ethanol; however, we did not find that ethanol consumption resulted in measurable changes in bone architecture.

Abstract Background Minor physical anomalies (MPAs) are congenital morphological abnormalities linked to disruptions of fetal development. MPAs are common in 22q11.2 deletion syndrome (22q11DS) and psychosis spectrum disorders (PS) and likely represent a disruption of early embryologic development that may help identify overlapping mechanisms linked to psychosis in these disorders. Methods Here, 2D digital photographs were collected from 22q11DS (n = 150), PS (n = 55), and typically developing (TD; n = 93) individuals. Photographs were analyzed using two computer-vision techniques: (1) DeepGestalt algorithm (Face2Gene (F2G)) technology to identify the presence of genetically mediated facial disorders, and (2) Emotrics—a semi-automated machine learning technique that localizes and measures facial features. Results F2G reliably identified patients with 22q11DS; faces of PS patients were matched to several genetic conditions including FragileX and 22q11DS. PCA-derived factor loadings of all F2G scores indicated unique and overlapping facial patterns that were related to both 22q11DS and PS. Regional facial measurements of the eyes and nose were smaller in 22q11DS as compared to TD, while PS showed intermediate measurements. Conclusions The extent to which craniofacial dysmorphology 22q11DS and PS overlapping and evident before the impairment or distress of sub-psychotic symptoms may allow us to identify at-risk youths more reliably and at an earlier stage of development.

Abstract Background Accumulating evidence has demonstrated that chronic tobacco smoking directly contributes to skeletal muscle dysfunction independent of its pathological impact to the cardiorespiratory systems. The mechanisms underlying tobacco smoke toxicity in skeletal muscle are not fully resolved. In this study, the role of the aryl hydrocarbon receptor (AHR), a transcription factor known to be activated with tobacco smoke, was investigated. Methods AHR related gene (mRNA) expression was quantified in skeletal muscle from adult controls and patients with chronic obstructive pulmonary disease (COPD), as well as mice with and without cigarette smoke exposure. Utilizing both skeletal muscle‐specific AHR knockout mice exposed to chronic repeated (5 days per week for 16 weeks) cigarette smoke and skeletal muscle‐specific expression of a constitutively active mutant AHR in healthy mice, a battery of assessments interrogating muscle size, contractile function, mitochondrial energetics, and RNA sequencing were employed. Results Skeletal muscle from COPD patients (N = 79, age = 67.0 ± 8.4 years) had higher levels of AHR (P = 0.0451) and CYP1B1 (P

Abstract Human cortical maturation has been posited to be organized along the sensorimotor-association axis, a hierarchical axis of brain organization that spans from unimodal sensorimotor cortices to transmodal association cortices. Here, we investigate the hypothesis that the development of functional connectivity during childhood through adolescence conforms to the cortical hierarchy defined by the sensorimotor-association axis. We tested this pre-registered hypothesis in four large-scale, independent datasets (total n = 3355; ages 5–23 years): the Philadelphia Neurodevelopmental Cohort (n = 1207), Nathan Kline Institute-Rockland Sample (n = 397), Human Connectome Project: Development (n = 625), and Healthy Brain Network (n = 1126). Across datasets, the development of functional connectivity systematically varied along the sensorimotor-association axis. Connectivity in sensorimotor regions increased, whereas connectivity in association cortices declined, refining and reinforcing the cortical hierarchy. These consistent and generalizable results establish that the sensorimotor-association axis of cortical organization encodes the dominant pattern of functional connectivity development.

Instrument-assisted soft tissue mobilization (IASTM) techniques use specialized hand-held instruments for applying controlled mechanical forces to the body with the goal of facilitating healing, improving range of motion, and reducing pain. Nevertheless, an optimal range of forces for achieving clinical outcomes has yet to be established. A barrier to advancing research on IASTM force optimization is the lack of commercially available instruments that quantify treatment forces. The aim of the current study was to assess the feasibility of attaching a flexible force sensor to a commercially available IASTM instrument to obtain valid force measurements. The validity of this novel approach was assessed by comparing data between the flexible force sensor and a force plate during a simulated treatment. Intraclass correlation coefficients, linear regression models, and Bland Altman plots all indicated excellent agreement between the force plate and flexible sensor when the instrument was used at 45°, 65°, and 90° treatment angles. Agreement between measures decreased when the instrument was held at 30°. Thus, commercially available instruments with attached sensors could make force measurement more accessible and feasible for a wider range of research settings, facilitating the advancement of IASTM research and ultimately informing clinical decision-making to improve patient care.

Abstract Background A shortage of healthcare providers, particularly in primary care and mental health, exists in the predominately rural state of Idaho. There are also barriers to retaining healthcare providers to work in rural and remote communities. Limited research using U.S. samples has explored factors that may affect the retention of healthcare providers in rural areas. Additionally, due to differences between communities, it is important to conduct community-level investigations to better understand how these factors may affect retention in rural areas. Therefore, the purpose of this study was to explore factors affecting healthcare provider retention in a rural community in Northern Idaho. Methods A modified version of the Nursing Community Apgar Questionnaire (NCAQ) was completed by 30 healthcare providers in a rural and frontier community in Northern Idaho to assess factors influencing healthcare provider retention. Factors were classified into classes including geographic, economic, scope of practice, medical support, and facility and community support classes. Retention factors were assessed on their perceived importance to retention as well as whether they were perceived as an advantage or challenge to retention based on Likert scales. A “Community Apgar” score was also created by combining the importance and advantage/challenge factors. Results Overall, items in the medical support group had the highest importance of any other class and included factors such as nursing workforce. Additionally, the facility and community support class, which included factors such as televideo support, was rated the highest advantage class and had the highest Apgar score, indicating it contained the factor that healthcare providers identified as the most important advantage (i.e., medical reference resources). Conclusion Our study identified multiple factors that healthcare providers deemed as important advantages or disadvantages to retaining healthcare providers in rural areas. Overall, facility and community support factors were found to have the highest advantage in the retention of rural providers. Rural healthcare organizations looking to increase healthcare provider retention should target retention efforts towards these factors. Additional research should also be conducted on other rural samples across the U.S. to make comparisons of findings.

Dimension reduction tools preserving similarity and graph structure such as t-SNE and UMAP can capture complex biological patterns in high-dimensional data. However, these tools typically are not designed to separate effects of interest from unwanted effects due to confounders. We introduce the partial embedding (PARE) framework, which enables removal of confounders from any distance-based dimension reduction method. We then develop partial t-SNE and partial UMAP and apply these methods to genomic and neuroimaging data. For lower-dimensional visualization, our results show that the PARE framework can remove batch effects in single-cell sequencing data as well as separate clinical and technical variability in neuroimaging measures. We demonstrate that the PARE framework extends dimension reduction methods to highlight biological patterns of interest while effectively removing confounding effects.

The hypothalamus and dorsal vagal complex (DVC) are both important for integration of signals that regulate energy balance. Increased leptin transgene expression in either the hypothalamus or DVC of female rats was shown to decrease white adipose tissue and circulating levels of leptin and adiponectin. However, in contrast to hypothalamus, leptin transgene expression in the DVC had no effect on food intake, circulating insulin, ghrelin and glucose, nor on thermogenic energy expenditure. These findings imply different roles for hypothalamus and DVC in leptin signaling. Leptin signaling is required for normal bone accrual and turnover. Leptin transgene expression in the hypothalamus normalized the skeletal phenotype of leptin-deficient ob/ob mice but had no long-duration (≥10 weeks) effects on the skeleton of leptin-replete rats. The goal of this investigation was to determine the long-duration effects of leptin transgene expression in the DVC on the skeleton of leptin-replete rats. To accomplish this goal, we analyzed bone from three-month-old female rats that were microinjected with recombinant adeno-associated virus encoding either rat leptin (rAAV-Leptin, n = 6) or green fluorescent protein (rAAV-GFP, control, n = 5) gene. Representative bones from the appendicular (femur) and axial (3rd lumbar vertebra) skeleton were evaluated following 10 weeks of treatment. Selectively increasing leptin transgene expression in the DVC had no effect on femur cortical or cancellous bone microarchitecture. Additionally, increasing leptin transgene expression had no effect on vertebral osteoblast-lined or osteoclast-lined bone perimeter or marrow adiposity. Taken together, the findings suggest that activation of leptin receptors in the DVC has minimal specific effects on the skeleton of leptin-replete female rats.

Abstract Glioblastoma is a highly heterogeneous disease, with variations observed at both phenotypical and molecular levels. Personalized therapies would be facilitated by non-invasive in vivo approaches for characterizing this heterogeneity. In this study, we developed unsupervised joint machine learning between radiomic and genomic data, thereby identifying distinct glioblastoma subtypes. A retrospective cohort of 571 IDH-wildtype glioblastoma patients were included in the study, and pre-operative multi-parametric MRI scans and targeted next-generation sequencing (NGS) data were collected. L21-norm minimization was used to select a subset of 12 radiomic features from the MRI scans, and 13 key driver genes from the five main signal pathways most affected in glioblastoma were selected from the genomic data. Subtypes were identified using a joint learning approach called Anchor-based Partial Multi-modal Clustering on both radiomic and genomic modalities. Kaplan–Meier analysis identified three distinct glioblastoma subtypes: high-risk, medium-risk, and low-risk, based on overall survival outcome (p

Abstract Background The field of point-of-care ultrasound (POCUS) has advanced in recent decades due to the benefits it holds for medical providers. However, aspiring POCUS practitioners require adequate training. Unfortunately, there remains a paucity of resources to deliver this training, particularly in rural and underserved areas. Despite these barriers, calls for POCUS training in undergraduate medical education are growing, and many medical schools now deliver some form of POCUS education. Our program lacked POCUS training; therefore, we developed and implemented a POCUS curriculum for our first-year medical students. Methods We developed a POCUS curriculum for first year medical students in a rural medically underserved region of the United States. To evaluate our course, we measured learning outcomes, self-reported confidence in a variety of POCUS domains, and gathered feedback on the course with a multi-modal approach: an original written pre- and post-test, survey tool, and semi-structured interview protocol, respectively. Results Student (n=24) knowledge of POCUS significantly increased (pre-test average score = 55%, post-test average score = 79%, P

Abstract Individual differences in cognition during childhood are associated with important social, physical, and mental health outcomes in adolescence and adulthood. Given that cortical surface arealization during development reflects the brain’s functional prioritization, quantifying variation in the topography of functional brain networks across the developing cortex may provide insight regarding individual differences in cognition. We test this idea by defining personalized functional networks (PFNs) that account for interindividual heterogeneity in functional brain network topography in 9–10 year olds from the Adolescent Brain Cognitive Development℠ Study. Across matched discovery (n = 3525) and replication (n = 3447) samples, the total cortical representation of fronto-parietal PFNs positively correlates with general cognition. Cross-validated ridge regressions trained on PFN topography predict cognition in unseen data across domains, with prediction accuracy increasing along the cortex’s sensorimotor-association organizational axis. These results establish that functional network topography heterogeneity is associated with individual differences in cognition before the critical transition into adolescence.

Abstract Recent work has demonstrated that the relationship between structural and functional connectivity varies regionally across the human brain, with reduced coupling emerging along the sensory-association cortical hierarchy. The biological underpinnings driving this expression, however, remain largely unknown. Here, we postulate that intracortical myelination and excitation-inhibition (EI) balance mediate the heterogeneous expression of structure-function coupling (SFC) and its temporal variance across the cortical hierarchy. We employ atlas- and voxel-based connectivity approaches to analyze neuroimaging data acquired from two groups of healthy participants. Our findings are consistent across six complementary processing pipelines: 1) SFC and its temporal variance respectively decrease and increase across the unimodal-transmodal and granular-agranular gradients; 2) increased myelination and lower EI-ratio are associated with more rigid SFC and restricted moment-to-moment SFC fluctuations; 3) a gradual shift from EI-ratio to myelination as the principal predictor of SFC occurs when traversing from granular to agranular cortical regions. Collectively, our work delivers a framework to conceptualize structure-function relationships in the human brain, paving the way for an improved understanding of how demyelination and/or EI-imbalances induce reorganization in brain disorders.

Childhood environments are critical in shaping cognitive neurodevelopment. With the increasing availability of large-scale neuroimaging datasets with deep phenotyping of childhood environments, we can now build upon prior studies that have considered relationships between one or a handful of environmental and neuroimaging features at a time. Here, we characterize the combined effects of hundreds of inter-connected and co-occurring features of a child’s environment (“exposome”) and investigate associations with each child’s unique, multidimensional pattern of functional brain network organization (“functional topography”) and cognition. We apply data-driven computational models to measure the exposome and define personalized functional brain networks in pre-registered analyses. Across matched discovery (n=5139, 48.5% female) and replication (n=5137, 47.1% female) samples from the Adolescent Brain Cognitive Development study, the exposome was associated with current (ages 9–10) and future (ages 11–12) cognition. Changes in the exposome were also associated with changes in cognition after accounting for baseline scores. Cross-validated ridge regressions revealed that the exposome is reflected in functional topography and can predict performance across cognitive domains. Importantly, a single measure capturing a child’s exposome could more accurately and parsimoniously predict cognition than a wealth of personalized neuroimaging data, highlighting the importance of children’s complex, multidimensional environments in cognitive neurodevelopment.

Objectives The primary purpose of the study was to assess the one-factor and two-factor structure of the Injury Psychological Readiness to Return to Sport Scale (IPRRS) in an injured physically active population using confirmatory factor analysis (CFA) procedures and assess group (ie, sex, age, injury type, athlete status) and longitudinal differences using structural equation modelling (eg, invariance testing).Methods The non-experimental study included a sample of 629 physically active individuals who suffered a musculoskeletal injury who sought treatment at an outpatient integrated sport medicine and rehabilitation therapy clinic. Participants filled out a questionnaire packet at three time points. Data analysis included a CFA and multigroup and longitudinal invariance.Results Sample mean age was 26.3 years, with females comprising 49.5%. Chronic injuries represented 29.6% of the sample and 35.0% were classified as competitive athletes. A six-item, one-factor model was confirmed in the sample with factor loadings ranging from 0.67 to 0.86. Multigroup and longitudinal invariance were established. Multigroup invariance demonstrated null differences between sex and injury type, and statistical differences between age and athlete status subgroups. Longitudinal invariance demonstrated a statistically significant increase in psychological readiness over time.Conclusions The findings support the use of the IPRRS as a tool to measure aspects of psychological readiness. Clinicians and researchers can use the IPRRS to assess interventions in future research.

Objective: Centers for Medicare and Medicaid Services (CMS) has requested hospitals collect and report patient-reported outcomes (PROs) beginning in 2024 including the Hip Disability and Osteoarthritis Outcome Score Joint Replacement (HOOS-JR). However, scale structural validity of the HOOS-JR has minimally been assessed. The purpose of this study was to assess internal consistency, structural validity, and multi-group invariance properties of the HOOS-JR in a large sample of patients who underwent a total hip arthroplasty (THA). Methods: A cross-sectional study using the Surgical Outcomes System was retrospectively queried for patients who underwent a THA. Internal consistency was assessed using Cronbach's alpha and McDonald's Omega. A confirmatory factor analysis (CFA) was performed on the HOOS-JR using a priori cut-off values. Multi-group invariance testing was also performed on the sample of patients across sex and age groups. Results: Internal consistency was acceptable for 6-item (alpha ​= ​0.88; omega ​= ​0.88) and 5-item (alpha ​= ​0.86; omega ​= ​0.86) HOOS-JR. The one-factor, 6-item CFA did not meet the recommended fit indices. The one-factor, 5-item CFA had acceptable fit for the sample data. Invariance testing criteria were met between the age groups; however, scalar invariance was not met for sex. Conclusion: The 6-item HOOS-JR did not meet contemporary model fit indices indicating that scale refinement is warranted. The 5-item met most goodness-of-fit indices and invariance criteria. However, further scale refinement may be warranted as localized fit issues were identified.

This study investigated the association between self-compassion and psychological pain across various demographic variables. Using canonical correlation analysis, we observed an inverse relationship between the combined factors of the Self-Compassion Scale (SCS) and the Orbach and Mikulincer Mental Pain Scale (OMMP-8). Subgroup analyses revealed differences in SCS subscales among demographic groups with females, individuals with mental health diagnoses, and non-athletes displaying higher scores on negative SCS subscales and PsyPn. Injury status did not significantly affect self-compassion levels, although injured individuals scored higher on the irreversibility subscale of PsyPn. Negative SCS factors exhibited larger group differences and stronger correlations with PsyPn, indicating the potency of negative thinking in influencing psychological pain. These findings underscore the importance of self-compassion in mental health and suggest potential implications for intervention strategies.

The cerebral cortex underlies many of our unique strengths and vulnerabilities, but efforts to understand human cortical organization are challenged by reliance on incompatible measurement methods at different spatial scales. Macroscale features such as cortical folding and functional activation are accessed through spatially dense neuroimaging maps, whereas microscale cellular and molecular features are typically measured with sparse postmortem sampling. Here, we integrate these distinct windows on brain organization by building upon existing postmortem data to impute, validate, and analyze a library of spatially dense neuroimaging-like maps of human cortical gene expression. These maps allow spatially unbiased discovery of cortical zones with extreme transcriptional profiles or unusually rapid transcriptional change which index distinct microstructure and predict neuroimaging measures of cortical folding and functional activation. Modules of spatially coexpressed genes define a family of canonical expression maps that integrate diverse spatial scales and temporal epochs of human brain organization – ranging from protein–protein interactions to large-scale systems for cognitive processing. These module maps also parse neuropsychiatric risk genes into subsets which tag distinct cyto-laminar features and differentially predict the location of altered cortical anatomy and gene expression in patients. Taken together, the methods, resources, and findings described here advance our understanding of human cortical organization and offer flexible bridges to connect scientific fields operating at different spatial scales of human brain research.

Stereotype threat is a well-known construct in psychology wherein individuals who belong to a negatively stereotyped demographic group underperform on cognitive or academic tasks due to the detrimental effects of a stereotype. Many psychologists have suggested that stereotype threat may be one of the reasons that some demographic groups are underrepresented in advanced academic programs and STEM fields. However, others have raised concerns about the quality of the stereotype threat research, suggesting that its apparent effects are inflated and that the phenomenon may be an illusion of questionable research practices and publication bias. The purpose of this proposed meta-analysis is to evaluate the existence of stereotype threat by (1) identifying the average effect size of stereotype threat studies in different types of studies, (2), investigating whether publication bias and p-hacking are present in the empirical research on stereotype threat, (3) testing for the influence of theoretical and methodological moderators, (4) assessing the overall quality of the research on stereotype threat, (5) and identifying the average effect in the methodologically strongest studies. This meta-analysis will be limited to studies that report data from African Americans because this population is a theoretically important group in stereotype threat research, and the size of score gaps between the African American and non-stereotyped populations in the United States should make the stereotype threat effect easiest to detect.

# BACKGROUND Improving single leg squat (SLS) movement symmetry may benefit rehabilitation protocols. The Total Motion Release® (TMR®) protocol has been theorized to evaluate and improve patient-perceived movement asymmetries. # HYPOTHESIS/PURPOSE The purpose of this study was to evaluate whether perceived asymmetries identified by a TMR® scoring protocol were related to biomechanical asymmetries and whether improving perceived asymmetries influenced movement mechanics. It was hypothesized that participants with perceived asymmetries would also present with biomechanical asymmetries. A secondary hypothesis was that participants would reduce their perceived asymmetries after performing the TMR® protocol and subsequently have greater biomechanical symmetry. # STUDY DESIGN Descriptive Cohort (Laboratory Study). # METHODS Twenty participants (10 female, 10 male) with self-identified bilateral differences of 10 points or greater on the TMR® scoring scale were recruited for the study. The non-preferred side was defined as the side that scored higher. 3Dimensional motion capture was used to bilaterally assess baseline SLS depth as well as hip, knee, and ankle kinematics and kinetics. For the TMR® protocol, sets of 10 SLSs were performed on the preferred leg until their perceived asymmetries were resolved (i.e., both sides scored equally), or four sets had been completed. Kinematics and kinetics were collected immediately after the intervention and after a 10-minute rest period. # RESULTS Participants had biomechanical asymmetries at baseline for knee flexion, ankle flexion, and knee moments. Following the intervention, participants had reduced TMR® scores on the non-preferred leg, and this coincided with increased knee joint moments on that side. Although perceived asymmetries were resolved after the intervention, kinematic and kinetic asymmetries at the knee and ankle were still present. # CONCLUSIONS A TMR® intervention could benefit rehabilitation protocols by reducing factors of dysfunction and increasing the ability of patients to load the non-preferred knee. Further investigations are necessary to elucidate the importance of asymmetrical movement patterns. # LEVEL OF EVIDENCE 3b

Background The modified Blalock‐Taussig‐Thomas shunt is the gold standard palliation for securing pulmonary blood flow in infants with ductal‐dependent pulmonary blood flow. Recently, the ductus arteriosus stent (DAS) has become a viable alternative. Methods and Results This was a retrospective multicenter study of neonates ≤30 days undergoing DAS or Blalock‐Taussig‐Thomas shunt placement between January 1, 2017 and December 31, 2020 at hospitals reporting to the Pediatric Health Information Systems database. We performed generalized linear mixed‐effects modeling to evaluate trends in intervention and intercenter variation, propensity score adjustment and inverse probability weighting with linear mixed‐effects modeling to analyze length of stay and cost of hospitalization, and generalized linear mixed modeling to analyze differences in 30‐day outcomes. There were 1874 subjects (58% male, 61% White) from 45 centers (29% DAS). Odds of DAS increased with time (odds ratio [OR] 1.23, annually, P

Mice are typically housed at room temperature (∼22 °C), which is well below their thermoneutral zone and results in cold stress. Chronic cold stress leads to increased adaptive thermogenesis and reductions in cancellous bone volume and bone marrow adipose tissue mass in long bones of growing mice. There is strong evidence that increased neuronal activity initiates the metabolic response of intrascapular brown adipose tissue (BAT) to cold stress, but it is less clear whether bone is regulated through a similar mechanism. Therefore, we compared the short-term response of BAT and whole tibia to a reduction in environmental temperature. To accomplish this, we transferred a group of 6-week-old male mice from 32 °C to 22 °C housing and sacrificed the mice 24 h later. Age-matched controls were maintained at 32 °C. We then evaluated expression levels of a panel of genes related to adipocyte differentiation and fat metabolism in BAT and tibia, and a panel of genes related to bone metabolism in tibia. The decrease in housing temperature resulted in changes in expression levels for 47/86 genes related to adipocyte differentiation and fat metabolism in BAT, including 9-fold and 17-fold increases in Ucp1 and Dio2, respectively. In contrast, only 1/86 genes related to adipocyte differentiation and fat metabolism and 4/84 genes related to bone metabolism were differentially expressed in tibia. These findings suggest that bone, although innervated with sensory and sympathetic neurons, does not respond as rapidly as BAT to changes in environmental temperature.

Abstract Introduction Idaho is a medically underserved and rural state comprised mostly health professional shortage areas for primary and specialty care. Given the state shortages and barriers to access care, the testing and treatment of COVID‐19 may be improved through the implementation of telementoring education programs such as Project Extension for Community Healthcare Outcomes (ECHO). The purpose of this article was to assess session attendance and self‐reported healthcare clinician perceptions of the ECHO Idaho COVID‐19 series (2020 and 2021), a modified Project ECHO model provided during the COVID‐19 pandemic in Idaho. Methods ECHO Idaho developed a COVID‐19 ECHO series for healthcare providers to attend in 2020 and 2021. The sessions included COVID‐19 updates specific to Idaho, didactic presentations on prevention, screening, diagnosis, and evidence‐based treatments for COVID‐19, and case‐based presentations. Results A total of 664 individuals attended the 2020 series and 260 individuals attended the 2021 series, with 1752.5 continuing medical education hours being claimed. Participants reported series participation increased overall knowledge of COVID‐19, knowledge of best practices for treatment, and awareness of resources available in Idaho. Further, series participation was perceived to reduce feelings of isolation, enhance access to information, and support healthcare professionals (e.g., provided resources) during the pandemic. Conclusion Results of this evaluation indicate that the ECHO Idaho COVID‐19 series was a useful and valuable program to implement during a pandemic in a rural and frontier state to improve physician knowledge and specialty training.

Background: Idaho, a predominately rural state, has a high prevalence of mental illness with minimal access to care. Barriers in diagnosis and treatment of pediatric behavioral health disorders could be mitigated with an accessible and effective specialty training program. Methods: A 10-session Project Extension for Community Health Outcomes (ECHO) series was designed to expand provider knowledge about pediatric behavioral health conditions and improve perceived clinical practice skills. Pre- and postseries evaluation surveys and individual session evaluations were used to assess the program. Results: A total of 148 individuals attended at least 1 of the 10 sessions. Participants reported high satisfaction with individual sessions and indicated that attendance positively impacted their knowledge and competency. Participants also reported that the knowledge and skills gained from the series would benefit more than half of their patients or clients. Conclusion: The short ECHO series appears to be a viable and valuable option to provide Idaho providers with effective specialty training that is well attended and well received.

Abstract Our study investigates the effects of heterogeneity in image parameters on the reproducibility of prognostic performance of models built using radiomic biomarkers. We compare the prognostic performance of models derived from the heterogeneity-mitigated features with that of models obtained from raw features, to assess whether reproducibility of prognostic scores improves upon application of our methods. We used two datasets: The Breast I-SPY1 dataset—Baseline DCE-MRI scans of 156 women with locally advanced breast cancer, treated with neoadjuvant chemotherapy, publicly available via The Cancer Imaging Archive (TCIA); The NSCLC IO dataset—Baseline CT scans of 107 patients with stage 4 non-small cell lung cancer (NSCLC), treated with pembrolizumab immunotherapy at our institution. Radiomic features (n = 102) are extracted from the tumor ROIs. We use a variety of resampling and harmonization scenarios to mitigate the heterogeneity in image parameters. The patients were divided into groups based on batch variables. For each group, the radiomic phenotypes are combined with the clinical covariates into a prognostic model. The performance of the groups is assessed using the c-statistic, derived from a Cox proportional hazards model fitted on all patients within a group. The heterogeneity-mitigation scenario (radiomic features, derived from images that have been resampled to minimum voxel spacing, are harmonized using the image acquisition parameters as batch variables) gave models with highest prognostic scores (for e.g., IO dataset; batch variable: high kernel resolution—c-score: 0.66). The prognostic performance of patient groups is not comparable in case of models built using non-heterogeneity mitigated features (for e.g., I-SPY1 dataset; batch variable: small pixel spacing—c-score: 0.54, large pixel spacing—c-score: 0.65). The prognostic performance of patient groups is closer in case of heterogeneity-mitigated scenarios (for e.g., scenario—harmonize by voxel spacing parameters: IO dataset; thin slice—c-score: 0.62, thick slice—c-score: 0.60). Our results indicate that accounting for heterogeneity in image parameters is important to obtain more reproducible prognostic scores, irrespective of image site or modality. For non-heterogeneity mitigated models, the prognostic scores are not comparable across patient groups divided based on batch variables. This study can be a step in the direction of constructing reproducible radiomic biomarkers, thus increasing their application in clinical decision making.

# Background The Knee Osteoarthritis Outcome Score for Joint Replacement (KOOS-JR) is a seven-item patient reported outcome measure used to assess perceived knee health. Though commonly used, the longitudinal psychometric properties of the KOOS-JR have not been established and further characterization of its structural validity and multi-group invariance properties is warranted. # Purpose The purpose of this study was to evaluate psychometric properties of the KOOS-JR in a large sample of patients who received care for knee pathology. # Study Design Original research. # Methods Longitudinal data extracted from the Surgical Outcome System (SOS) database of 13,470 knee pathology patients who completed the KOOS-JR at baseline, three-months, six- months, and one-year. Scale structure was assessed with confirmatory factor analysis (CFA), while multi-group and longitudinal invariance properties were assessed with CFA-based procedures. Latent group means were compared with statistical significance set at α ≤ .05 and Cohen’s d effect size as d = 0.2 (small), d = 0.5 (medium), and d = 0.8 (large). # Results CFA results exceeded goodness-of-fit indices at all timepoints. Multi-group invariance properties passed test requirements. Longitudinal analysis identified a biased item resulting in removal of item #1; the retained six-item model (KOOS-JR-6) passed longitudinal invariance requirements. KOOS-JR-6 scores significantly changed over time (p ≤ .001, Mdiff = 1.08, Cohen’s d = 0.57): the highest scores were at baseline examination and the lowest at 12-month assessment. # Conclusions The KOOS-JR can be used to assess baseline differences between males and females, middle and older aged adults, and patients receiving total knee arthroplasty or non-operative care. Caution is warranted if the KOOS-JR is used longitudinally due to potential measurement error associated with item #1. The KOOS-JR-6 may be a more viable option to assess change over time; however, more research is warranted. # Level of Evidence 3 © The Author(s)

Abstract Radiomic approaches in precision medicine are promising, but variation associated with image acquisition factors can result in severe biases and low generalizability. Multicenter datasets used in these studies are often heterogeneous in multiple imaging parameters and/or have missing information, resulting in multimodal radiomic feature distributions. ComBat is a promising harmonization tool, but it only harmonizes by single/known variables and assumes standardized input data are normally distributed. We propose a procedure that sequentially harmonizes for multiple batch effects in an optimized order, called OPNested ComBat. Furthermore, we propose to address bimodality by employing a Gaussian Mixture Model (GMM) grouping considered as either a batch variable (OPNested + GMM) or as a protected clinical covariate (OPNested − GMM). Methods were evaluated on features extracted with CapTK and PyRadiomics from two public lung computed tomography (CT) datasets. We found that OPNested ComBat improved harmonization performance over standard ComBat. OPNested + GMM ComBat exhibited the best harmonization performance but the lowest predictive performance, while OPNested − GMM ComBat showed poorer harmonization performance, but the highest predictive performance. Our findings emphasize that improved harmonization performance is no guarantee of improved predictive performance, and that these methods show promise for superior standardization of datasets heterogeneous in multiple or unknown imaging parameters and greater generalizability.

The single leg squat (SLS), forward step down (FSD), and lateral step down (LSD) are clinically reliable movement screens for identifying motion imbalances. The current understanding for the kinematic profiles of each task is limited to discrete time points such as peak knee flexion. However, analyses of the entire movement would better aid clinicians when selecting the appropriate task for rehabilitation or movement screen purposes. The current study used Statistical Parametric Mapping to ascertain differences in the kinematic waveforms for the entire duration of each task. The trunk, pelvis, hip, and knee were analyzed in the sagittal and frontal planes. Data for each variable and task were analyzed from 0–100% of the movement. Primary findings indicated that the FSD provoked a greater magnitude of knee abduction than the SLS and LSD from 26–66% of the movement. The SLS generated the greatest amounts of trunk, pelvic, and hip flexion for the entirety of the movement. The LSD elicited the least amount of ipsilateral trunk lean (90–100%). Thus, the FSD may be optimal for assessing frontal plane knee motion as a screen for injury risk, while the SLS has potential to place increased sagittal plane demand on the muscles of the hip.

Abstract Spatial structure in biology, spanning molecular, organellular, cellular, tissue, and organismal scales, is encoded through a combination of genetic and epigenetic factors in individual cells. Microscopy remains the most direct approach to exploring the intricate spatial complexity defining biological systems and the structured dynamic responses of these systems to perturbations. Genetic screens with deep single‐cell profiling via image features or gene expression programs have the capacity to show how biological systems work in detail by cataloging many cellular phenotypes with one experimental assay. Microscopy‐based cellular profiling provides information complementary to next‐generation sequencing (NGS) profiling and has only recently become compatible with large‐scale genetic screens. Optical screening now offers the scale needed for systematic characterization and is poised for further scale‐up. We discuss how these methodologies, together with emerging technologies for genetic perturbation and microscopy‐based multiplexed molecular phenotyping, are powering new approaches to reveal genotype–phenotype relationships.

Kaitlyn Blacha,1 Andrew Cade,1 Tyler Russell,1 Daniel Skinner2 1Ohio University, Heritage College of Osteopathic Medicine, Dublin, OH, USA; 2Department of Social Medicine, Ohio University, Heritage College of Osteopathic Medicine, Dublin, OH, USACorrespondence: Daniel Skinner, Email skinnerd@ohio.eduObjective: This interview-based thematic analysis examines the evolving professional identities and practices of osteopathic physicians (DOs) in North American professional sports (baseball, basketball, football, hockey, and soccer) to understand the extent to which DOs employ osteopathic-specific skill sets in their work with player-patients. In addition, the study sought to understand the prevalence of professional bias, interprofessional dynamics between DOs and other health professionals, and other factors such as management.Methods: This qualitative, interview-based study is grounded in twelve semi-structured interviews with 41% of DOs (all nonsurgical, primary care sports medicine trained) working with North American professional sports teams. Interviews were transcribed and coded to identify key themes and representative quotes.Results: Findings suggest that DOs believe their training and philosophical orientation positions them well to make important contributions to serving the medical needs of professional sports teams. At the same time, DOs are careful about championing an osteopathically-specific approach to patient care, such as evoking osteopathic principles of care or techniques such as Osteopathic Manipulative Medicine. This is the case for several reasons, the most important of which is a strong drive toward collaboration and collegiality within professional sports medicine teams.Conclusion: This study finds that in navigating the complexities of osteopathic identity, DOs working in professional sports are proud of their osteopathic orientation and report experiencing little discrimination, but also downplay what has historically distinguished their practice from that of other healthcare professionals. These findings have consequences for how scholars think about emerging professional identities in sports medicine, generally, as well as the fast-growing profession of osteopathic medicine specifically.Keywords: osteopathic medicine, sports medicine, professional identity, chiropractic

# Background The International Knee Document Committee Subjective Knee Form (IKDC-SKF) is a patient-reported outcome measure used in orthopedics and sports medicine. Further psychometric assessment is necessary to confirm measurement properties in a large, heterogenous sample. # Purpose The purpose of the study was to assess the psychometric properties of the IKDC-SKF in a large, heterogenous sample. # Study Design Cross-Sectional Study # Methods An exploratory factor analysis (EFA) was conducted to identify a sound latent structure and to assess internal consistency in a large sample of patients who underwent knee arthroscopy. A confirmatory factor analysis (CFA) was conducted to confirm structural validity. Multi-group invariance was conducted to assess factorial stability across sex and age groups, while longitudinal invariance procedures were performed to assess stability over time. # Results A 3-factor, 9-item IKDC-SKF short form was identified with EFA procedures. The model was confirmed with CFA (CFI = 0.983; TLI = 0.975; IFI = 0.983; RMSEA = 0.057), while a sound 2-factor, 6-item model was also identified (CFI = 1.0; TLI = 0.999; IFI = 1.0; RMSEA = 0.11). The 9-item IKDC-SKF short form was invariant across groups but not time; removal of a single item (i.e., 8-item IKDC-SKF short form) resulted in longitudinal invariance. The 6-item IKDC-SKF short form was invariant across groups and time. # Conclusion The 6-item, 8-item, and 9-item short form versions of the IKDC-SKF exceed contemporary fit recommendations and present as plausible alternatives to the IKDC-SKF with improved measurement properties, reduced scale response burden, and evidence of multi-group and longitudinal invariance. Further, the 6- and 8-item IKDC-SKF short forms may be used to assess group differences or change across time. # Level of evidence here Level 3

Magnetic resonance imaging and computed tomography from multiple batches (e.g. sites, scanners, datasets, etc.) are increasingly used alongside complex downstream analyses to obtain new insights into the human brain. However, significant confounding due to batch-related technical variation, called batch effects, is present in this data; direct application of downstream analyses to the data may lead to biased results. Image harmonization methods seek to remove these batch effects and enable increased generalizability and reproducibility of downstream results. In this review, we describe and categorize current approaches in statistical and deep learning harmonization methods. We also describe current evaluation metrics used to assess harmonization methods and provide a standardized framework to evaluate newly-proposed methods for effective harmonization and preservation of biological information. Finally, we provide recommendations to end-users to advocate for more effective use of current methods and to methodologists to direct future efforts and accelerate development of the field.

Mice are typically housed at temperatures well below their thermoneutral zone. When individually housed at room temperature (~22 °C) mice experience cold stress which results in cancellous bone loss and has the potential to alter the skeletal response to treatment. It is not clear if there is a threshold temperature for cold stress-induced bone loss. It is also not clear if alternative strategies for attenuating cold stress, such as group housing, influence bone accrual and turnover. This study aimed to determine how small differences in temperature (4 °C) or heat loss (individual versus group housing with nestlets) influence bone in growing female C57BL/6 J mice. Five-week-old mice were randomized by weight to 1 of 4 treatment groups (N = 10/group): 1) baseline, 2) single housed at 22 °C, 3) single housed at 26 °C, or 4) group housed (n = 5/cage) with nestlets at 22 °C. Mice in the baseline group were sacrificed 1 week later, at 6 weeks of age. The other 3 groups of mice were maintained at their respective temperatures and housing conditions for 13 weeks until 18 weeks of age. Compared to baseline, mice single housed at room temperature had increased body weight and femur size, but dramatically decreased cancellous bone volume fraction in distal femur metaphysis. The cancellous bone loss was attenuated but not prevented in mice individually housed at 26 °C or group housed at 22 °C. In conclusion, by impacting thermogenesis or heat loss, modest differences in housing conditions could influence experimental results.

Abstract We aim to determine the feasibility of a novel radiomic biomarker that can integrate with other established clinical prognostic factors to predict progression-free survival (PFS) in patients with non-small cell lung cancer (NSCLC) undergoing first-line immunotherapy. Our study includes 107 patients with stage 4 NSCLC treated with pembrolizumab-based therapy (monotherapy: 30%, combination chemotherapy: 70%). The ITK-SNAP software was used for 3D tumor volume segmentation from pre-therapy CT scans. Radiomic features (n = 102) were extracted using the CaPTk software. Impact of heterogeneity introduced by image physical dimensions (voxel spacing parameters) and acquisition parameters (contrast enhancement and CT reconstruction kernel) was mitigated by resampling the images to the minimum voxel spacing parameters and harmonization by a nested ComBat technique. This technique was initialized with radiomic features, clinical factors of age, sex, race, PD-L1 expression, ECOG status, body mass index (BMI), smoking status, recurrence event and months of progression-free survival, and image acquisition parameters as batch variables. Two phenotypes were identified using unsupervised hierarchical clustering of harmonized features. Prognostic factors, including PDL1 expression, ECOG status, BMI and smoking status, were combined with radiomic phenotypes in Cox regression models of PFS and Kaplan Meier (KM) curve-fitting. Cox model based on clinical factors had a c-statistic of 0.57, which increased to 0.63 upon addition of phenotypes derived from harmonized features. There were statistically significant differences in survival outcomes stratified by clinical covariates, as measured by the log-rank test (p = 0.034), which improved upon addition of phenotypes (p = 0.00022). We found that mitigation of heterogeneity by image resampling and nested ComBat harmonization improves prognostic value of phenotypes, resulting in better prediction of PFS when added to other prognostic variables.

Abstract Multi-omic data, i.e., clinical measures, radiomic, and genetic data, capture multi-faceted tumor characteristics, contributing to a comprehensive patient risk assessment. Here, we investigate the additive value and independent reproducibility of integrated diagnostics in prediction of overall survival (OS) in isocitrate dehydrogenase (IDH)-wildtype GBM patients, by combining conventional and deep learning methods. Conventional radiomics and deep learning features were extracted from pre-operative multi-parametric MRI of 516 GBM patients. Support vector machine (SVM) classifiers were trained on the radiomic features in the discovery cohort (n = 404) to categorize patient groups of high-risk (OS

Studies of brain network development typically focus on a single scale. Here, the authors derived personalized functional networks across scales, and find that network development systematically adheres to and strengthens hierarchical cortical organization.

Abstract Background Musculoskeletal modeling is currently a preferred method for estimating the muscle forces that underlie observed movements. However, these estimates are sensitive to a variety of assumptions and uncertainties, which creates difficulty when trying to interpret the muscle forces from musculoskeletal simulations. Here, we describe an approach that uses Bayesian inference to identify plausible ranges of muscle forces for a simple motion while representing uncertainty in the measurement of the motion and the objective function used to solve the muscle redundancy problem. Methods We generated a reference elbow flexion–extension motion and computed a set of reference forces that would produce the motion while minimizing muscle excitations cubed via OpenSim Moco. We then used a Markov Chain Monte Carlo (MCMC) algorithm to sample from a posterior probability distribution of muscle excitations that would result in the reference elbow motion. We constructed a prior over the excitation parameters which down-weighted regions of the parameter space with greater muscle excitations. We used muscle excitations to find the corresponding kinematics using OpenSim, where the error in position and velocity trajectories (likelihood function) was combined with the sum of the cubed muscle excitations integrated over time (prior function) to compute the posterior probability density. Results We evaluated the muscle forces that resulted from the set of excitations that were visited in the MCMC chain (seven parallel chains, 500,000 iterations per chain). The estimated muscle forces compared favorably with the reference forces generated with OpenSim Moco, while the elbow angle and velocity from MCMC matched closely with the reference (average RMSE for elbow angle = 2°; and angular velocity = 32°/s). However, our rank plot analyses and potential scale reduction statistics, which we used to evaluate convergence of the algorithm, indicated that the chains did not fully mix. Conclusions While the results from this process are a promising step towards characterizing uncertainty in muscle force estimation, the computational time required to search the solution space with, and the lack of MCMC convergence indicates that further developments in MCMC algorithms are necessary for this process to become feasible for larger-scale models.

Abstract Radiomic features have a wide range of clinical applications, but variability due to image acquisition factors can affect their performance. The harmonization tool ComBat is a promising solution but is limited by inability to harmonize multimodal distributions, unknown imaging parameters, and multiple imaging parameters. In this study, we propose two methods for addressing these limitations. We propose a sequential method that allows for harmonization of radiomic features by multiple imaging parameters (Nested ComBat). We also employ a Gaussian Mixture Model (GMM)-based method (GMM ComBat) where scans are split into groupings based on the shape of the distribution used for harmonization as a batch effect and subsequent harmonization by a known imaging parameter. These two methods were evaluated on features extracted with CapTK and PyRadiomics from two public lung computed tomography datasets. We found that Nested ComBat exhibited similar performance to standard ComBat in reducing the percentage of features with statistically significant differences in distribution attributable to imaging parameters. GMM ComBat improved harmonization performance over standard ComBat (− 11%, − 10% for Lung3/CAPTK, Lung3/PyRadiomics harmonizing by kernel resolution). Features harmonized with a variant of the Nested method and the GMM split method demonstrated similar c-statistics and Kaplan–Meier curves when used in survival analyses.

ABSTRACT: Diffusion MRI is the dominant non-invasive imaging method used to characterize white matter organization in health and disease. Increasingly, fiber-specific properties within a voxel are analyzed using fixels. While tools for conducting statistical analyses of fixel-wise data exist, currently available tools support only a limited number of statistical models. Here we introduce ModelArray, an R package for mass-univariate statistical analysis of fixel-wise data. At present, ModelArray supports linear models as well as generalized additive models (GAMs), which are particularly useful for studying nonlinear effects in lifespan data. In addition, ModelArray also aims for scalable analysis. With only several lines of code, even large fixel-wise datasets can be analyzed using a standard personal computer. Detailed memory profiling revealed that ModelArray required only limited memory even for large datasets. As an example, we applied ModelArray to fixel-wise data derived from diffusion images acquired as part of the Philadelphia Neurodevelopmental Cohort (n = 938). ModelArray revealed anticipated nonlinear developmental effects in white matter. Moving forward, ModelArray is supported by an open-source software development model that can incorporate additional statistical models and other imaging data types. Taken together, ModelArray provides a flexible and efficient platform for statistical analysis of fixel-wise data.

Objective The purpose of this study was to examine whether the forces used by trained clinicians during a simulated instrument-assisted soft tissue mobilisation (IASTM) treatment varied across five different instruments during one-handed and two-handed IASTM grips.Methods Nine athletic trainers who previously completed IASTM training and used the technique in professional practice were included in the study. A skin simulant was attached to a force plate and used to evaluate force production during a simulated IASTM treatment scenario. Peak (Fpeak) and mean (Fmean) forces were recorded for both one-handed and two-handed grips for each participant across the five instruments. Data were analysed using separate 2 (grip type) × 5 (IASTM instrument) repeated measures analysis of variance for both Fpeak and Fmean.Results Data for Fpeak demonstrated a significant main effect for grip type (F(1, 8)=46.39, p

ObjectivesPerivascular spaces have been involved in neuroinflammatory and neurodegenerative diseases. Upon a certain size, these spaces can become visible on magnetic resonance imaging (MRI), referred to as enlarged perivascular spaces (EPVS) or MRI-visible perivascular spaces (MVPVS). However, the lack of systematic evidence on etiology and temporal dynamics of MVPVS hampers their diagnostic utility as MRI biomarker. Thus, the goal of this systematic review was to summarize potential etiologies and evolution of MVPVS.MethodsIn a comprehensive literature search, out of 1,488 unique publications, 140 records assessing etiopathogenesis and dynamics of MVPVS were eligible for a qualitative summary. 6 records were included in a meta-analysis to assess the association between MVPVS and brain atrophy.ResultsFour overarching and partly overlapping etiologies of MVPVS have been proposed: (1) Impairment of interstitial fluid circulation, (2) Spiral elongation of arteries, (3) Brain atrophy and/or perivascular myelin loss, and (4) Immune cell accumulation in the perivascular space. The meta-analysis in patients with neuroinflammatory diseases did not support an association between MVPVS and brain volume measures [R: −0.15 (95%-CI −0.40–0.11)]. Based on few and mostly small studies in tumefactive MVPVS and in vascular and neuroinflammatory diseases, temporal evolution of MVPVS is slow.ConclusionCollectively, this study provides high-grade evidence for MVPVS etiopathogenesis and temporal dynamics. Although several potential etiologies for MVPVS emergence have been proposed, they are only partially supported by data. Advanced MRI methods should be employed to further dissect etiopathogenesis and evolution of MVPVS. This can benefit their implementation as an imaging biomarker.Systematic review registrationhttps://www.crd.york.ac.uk/prospero/display_record.php?RecordID=346564, identifier CRD42022346564.

Abstract Background Chronic obstructive pulmonary disease (COPD) patients exhibit skeletal muscle atrophy, denervation, and reduced mitochondrial oxidative capacity. Whilst chronic tobacco smoke exposure is implicated in COPD muscle impairment, the mechanisms involved are ambiguous. The aryl hydrocarbon receptor (AHR) is a ligand‐activated transcription factor that activates detoxifying pathways with numerous exogenous ligands, including tobacco smoke. Whereas transient AHR activation is adaptive, chronic activation can be toxic. On this basis, we tested the hypothesis that chronic smoke‐induced AHR activation causes adverse muscle impact. Methods We used clinical patient muscle samples, and in vitro (C2C12 myotubes) and in vivo models (mouse), to perform gene expression, mitochondrial function, muscle and neuromuscular junction morphology, and genetic manipulations (adeno‐associated virus‐mediated gene transfer). Results Sixteen weeks of tobacco smoke exposure in mice caused muscle atrophy, neuromuscular junction degeneration, and reduced oxidative capacity. Similarly, smoke exposure reprogrammed the muscle transcriptome, with down‐regulation of mitochondrial and neuromuscular junction genes. In mouse and human patient specimens, smoke exposure increased muscle AHR signalling. Mechanistically, experiments in cultured myotubes demonstrated that smoke condensate activated the AHR, caused mitochondrial impairments, and induced an AHR‐dependent myotube atrophy. Finally, to isolate the role of AHR activity, expression of a constitutively active AHR mutant without smoke exposure caused atrophy and mitochondrial impairments in cultured myotubes, and muscle atrophy and neuromuscular junction degeneration in mice. Conclusions These results establish that chronic AHR activity, as occurs in smokers, phenocopies the atrophy, mitochondrial impairment, and neuromuscular junction degeneration caused by chronic tobacco smoke exposure.

Histopathologic evaluation of Hematoxylin & Eosin (H&E) stained slides is essential for disease diagnosis, revealing tissue morphology, structure, and cellular composition. Variations in staining protocols and equipment result in images with color nonconformity. Although pathologists compensate for color variations, these disparities introduce inaccuracies in computational whole slide image (WSI) analysis, accentuating data domain shift and degrading generalization. Current state-of-the-art normalization methods employ a single WSI as reference, but selecting a single WSI representative of a complete WSI-cohort is infeasible, inadvertently introducing normalization bias. We seek the optimal number of slides to construct a more representative reference based on composite/aggregate of multiple H&E density histograms and stain-vectors, obtained from a randomly selected WSI population (WSI-Cohort-Subset). We utilized 1,864 IvyGAP WSIs as a WSI-cohort, and built 200 WSI-Cohort-Subsets varying in size (from 1 to 200 WSI-pairs) using randomly selected WSIs. The WSI-pairs' mean Wasserstein Distances and WSI-Cohort-Subsets' standard deviations were calculated. The Pareto Principle defined the optimal WSI-Cohort-Subset size. The WSI-cohort underwent structure-preserving color normalization using the optimal WSI-Cohort-Subset histogram and stain-vector aggregates. Numerous normalization permutations support WSI-Cohort-Subset aggregates as representative of a WSI-cohort through WSI-cohort CIELAB color space swift convergence, as a result of the law of large numbers and shown as a power law distribution. We show normalization at the optimal (Pareto Principle) WSI-Cohort-Subset size and corresponding CIELAB convergence: a) Quantitatively, using 500 WSI-cohorts; b) Quantitatively, using 8,100 WSI-regions; c) Qualitatively, using 30 cellular tumor normalization permutations. Aggregate-based stain normalization may contribute in increasing computational pathology robustness, reproducibility, and integrity.

Abstract Background Patient-centered care and evidence-based practice (EBP) are core competencies for health care professionals. The importance of EBP has led to an increase in research involving clinical outcomes; current recommendations emphasize collecting patient focused measures, thus increasing the need for psychometrically sound patient reported outcome measures (PROMs) of health. Disablement has been identified as a valuable multi-dimensional construct for patient care. The Disablement in the Physically Active Scale Short Form-8 (DPA SF-8) has been proposed as a tool to be used in the physically active population that assesses a physical summary component of health and a quality of life component however, further analysis is necessary to ensure the instrument is psychometrically sound. Methods Confirmatory factor analyses (CFAs) were conducted on the DPA SF-8 at each time point to ensure factor structure. Reliability of the scale and internal consistency of the subscales were assessed, and a minimal detectable change (MDC) calculated. Additionally, a minimal clinically important difference (MCID) was also established, and invariance testing across three time points and groups was conducted. Results The CFAs at all three visits exceeded recommended model fit indices. The interclass correlation coefficient value (.924) calculated indicated excellent scale reliability and Cronbach’s alpha for subscales PHY and QOL were within recommend values. The MDC value calculated was 5.83 and the MCID for persistent injuries were 2 points and for acute injuries, 3 points. The DPA SF-8 was invariant across time and across subgroups. Conclusions The DPA SF-8 met CFA recommendations and criteria for multi-group and longitudinal invariance testing, which indicates the scale may be used to assess for differences between the groups or across time. Our overall analysis indicates the DPA SF-8 is a valid, reliable, and responsive instrument to assess patient improvement in the physically active population.