TLR7 activation of age-associated B cells mediates disease in a mouse model of primary Sjögren's disease.

Primary Sjögren's disease (pSD) (also referred to as Sjögren's syndrome) is an autoimmune disease that primarily occurs in women. In addition to exocrine gland dysfunction, pSD patients exhibit B cell hyperactivity. B cell-intrinsic TLR7 activation is integral to the pathogenesis of systemic lupus erythematosus, a disease that shares similarities with pSD. The role of TLR7-mediated B cell activation in pSD, however, remains poorly understood. We hypothesized that age-associated B cells (ABCs) were expanded in pSD and that TLR7-stimulated ABCs exhibited pathogenic features characteristic of disease. Our data revealed that ABC expansion and TLR7 expression were enhanced in a pSD mouse model in a Myd88-dependent manner. Splenocytes from pSD mice showed enhanced sensitivity to TLR7 agonism as compared with those derived from control animals. Sort-purified marginal zone B cells and ABCs from pSD mice showed enhanced inflammatory cytokine secretion and were enriched for antinuclear autoantibodies following TLR7 agonism. Finally, IgG from pSD patient sera showed elevated antinuclear autoantibodies, many of which were secreted preferentially by TLR7-stimulated murine marginal zone B cells and ABCs. These data indicate that pSD B cells are hyperresponsive to TLR7 agonism and that TLR7-activated B cells contribute to pSD through cytokine and autoantibody production. Thus, therapeutics that target TLR7 signaling cascades in B cells may have utility in pSD patients.

Modeling longitudinal oral health status and pneumonia risk: secondary data analyses of an integrated dental-medical cohort.

BACKGROUND: Mounting evidence indicates potential associations between poor oral health status (OHS) and increased pneumonia risk. Relative pneumonia risk was assessed in the context of longitudinally documented OHS. METHODS: Electronic medical/dental patient data captured from 2007 through 2019 were retrieved from the integrated health records of Marshfield Clinic Health Systems. Participant eligibility initiated with an assessment of OHS, stratified into the best, moderate, or worst OHS groups, with the additional criterion of 'no pneumonia diagnosis in the past 90 days'. Pneumonia incidence was longitudinally monitored for up to 1 year from each qualifying dental visit. Models were assessed, with and without adjustment for prior pneumonia incidence, adjusted for smoking and subjected to confounding mitigation attributable to known pneumonia risk factors by applying propensity score analysis. Time-to-event analysis and proportional hazard modeling were applied to investigate relative pneumonia risk over time among the OHS groups. RESULTS: Modeling identified associations between any incident pneumonia subtype and 'number of missing teeth' (p < 0.001) and 'clinically assessed periodontal status' (p < 0.01), which remained significant following adjustment for prior pneumonia incidence and smoking. The hazard ratio (HR) for 'any incident pneumonia' in the best OHS group for 'number of missing teeth' was 0.65, 95% confidence interval (CI) [0.54 - 0.79] (unadjusted) and 0.744, 95% CI [0.61 - 0.91] (adjusted). The HR for 'any incident pneumonia' in the best 'clinically assessed periodontal status' group was 0.72, 95% CI [0.58 - 0.90] (unadjusted) and 0.78, 95% CI [0.62 - 0.97] (adjusted). CONCLUSION/CLINICAL RELEVANCE: Poor OHS increased pneumonia risk. Proactive attention of medical providers to patient OHS and health literacy surrounding oral-systemic disease association is vital, especially in high-risk populations.

Sensitivity and Specificity of Exercise Intolerance on Graded Exertion Testing for Diagnosing Sport-Related Concussion: A Systematic Review and Exploratory Meta-Analysis.

Abstract There is no single gold standard test to diagnose sport-related concussion (SRC). Concussion-related exercise intolerance, that is, inability to exercise to the individual's appropriate level due to exacerbation of concussion-like symptoms, is a frequent finding in athletes early after SRC that has not been systematically evaluated as a diagnostic test of SRC. We performed a systematic review and proportional meta-analysis of studies that evaluated graded exertion testing in athletes after SRC. We also included studies of exertion testing in healthy athletic participants without SRC to assess specificity. Pubmed and Embase were searched in January 2022 for articles published since 2000. Eligible studies included those that performed graded exercise tolerance tests in symptomatic concussed participants (> 90% of subjects had an SRC, seen within 14 days of injury), at the time of clinical recovery from SRC, in healthy athletes, or both. Study quality was assessed using the Newcastle-Ottawa Scale. Twelve articles met inclusion criteria, most of which were of poor methodological quality. The pooled estimate of incidence of exercise intolerance in participants with SRC equated to an estimated sensitivity of 94.4% (95% confidence interval [CI]: 90.8, 97.2). The pooled estimate of incidence of exercise intolerance in participants without SRC equated to an estimated specificity of 94.6% (95% CI: 91.1, 97.3). The results suggest that exercise intolerance measured on systematic testing within 2 weeks of SRC may have excellent sensitivity for helping to rule in the diagnosis of SRC and excellent specificity for helping to rule out SRC. A prospective validation study to determine the sensitivity and specificity of exercise intolerance on graded exertion testing for diagnosing SRC after head injury as the source of symptoms is warranted.

MOSCATO: a supervised approach for analyzing multi-Omic single-Cell data.

BACKGROUND: Advancements in genomic sequencing continually improve personalized medicine, and recent breakthroughs generate multimodal data on a cellular level. We introduce MOSCATO, a technique for selecting features across multimodal single-cell datasets that relate to clinical outcomes. We summarize the single-cell data using tensors and perform regularized tensor regression to return clinically-associated variable sets for each 'omic' type. RESULTS: Robustness was assessed over simulations based on available single-cell simulation methods, and applicability was assessed through an example using CITE-seq data to detect genes associated with leukemia. We find that MOSCATO performs favorably in selecting network features while also shown to be applicable to real multimodal single-cell data. CONCLUSIONS: MOSCATO is a useful analytical technique for supervised feature selection in multimodal single-cell data. The flexibility of our approach enables future extensions on distributional assumptions and covariate adjustments.

High precision implementation of Steck's recursion method for use in goodness-of-fit tests.

Classical continuous goodness-of-fit (GOF) testing is employed for examining whether the data come from an assumed parametric model. In many cases, GOF tests assume a uniform null distribution and examine extreme values of the order statistics of the samples. Many of these statistics can be expressed by a function of the order statistics and the p-values amount to a joint probability statement based on the uniform order statistics. In this paper, we utilize Steck's recursion method and propose two high precision computing algorithms to compute the p-values for these GOF statistics. The numerical difficulties in implementing Steck's method are discussed and compared with solutions provided in high precision libraries.

A Methodological Approach to Validate Pneumonia Encounters from Radiology Reports Using Natural Language Processing.

INTRODUCTION: Pneumonia is caused by microbes that establish an infectious process in the lungs. The gold standard for pneumonia diagnosis is radiologist-documented pneumonia-related features in radiology notes that are captured in electronic health records in an unstructured format. OBJECTIVE: The study objective was to develop a methodological approach for assessing validity of a pneumonia diagnosis based on identifying presence or absence of key radiographic features in radiology reports with subsequent rendering of diagnostic decisions into a structured format. METHODS: A pneumonia-specific natural language processing (NLP) pipeline was strategically developed applying Clinical Text Analysis and Knowledge Extraction System (cTAKES) to validate pneumonia diagnoses following development of a pneumonia feature-specific lexicon. Radiographic reports of study-eligible subjects identified by International Classification of Diseases (ICD) codes were parsed through the NLP pipeline. Classification rules were developed to assign each pneumonia episode into one of three categories: "positive," "negative," or "not classified: requires manual review" based on tagged concepts that support or refute diagnostic codes. RESULTS: A total of 91,998 pneumonia episodes diagnosed in 65,904 patients were retrieved retrospectively. Approximately 89% (81,707/91,998) of the total pneumonia episodes were documented by 225,893 chest X-ray reports. NLP classified and validated 33% (26,800/81,707) of pneumonia episodes classified as "Pneumonia-positive," 19% as (15401/81,707) as "Pneumonia-negative," and 48% (39,209/81,707) as "episode classification pending further manual review." NLP pipeline performance metrics included accuracy (76.3%), sensitivity (88%), and specificity (75%). CONCLUSION: The pneumonia-specific NLP pipeline exhibited good performance comparable to other pneumonia-specific NLP systems developed to date.

Influence of Tyrosine Kinase Inhibition on Organic Anion Transporting Polypeptide 1B3-Mediated Uptake.

The organic anion transporting polypeptide family member (OATP) 1B3 is a hepatic uptake transporter that has a broad substrate recognition and plays a significant role in regulating elimination of endogenous biomolecules or xenobiotics. OATP1B3 works in tandem with OATP1B1, with which it shares approximately 80% sequence homology and a high degree of substrate overlap. Despite some substrates being recognized solely by OATP1B3, its ability to compensate for loss of OATP1B1-mediated elimination and recognition by regulatory agencies, little is known about OATP1B3 regulatory factors and how they are involved with drug-drug interaction. It was recently discovered that OATP1B1 function is mediated by the activity of a particular tyrosine kinase that is sensitive to a variety of tyrosine kinase inhibitors (TKIs). This study reports that OATP1B3 is similarly regulated, as at least 50% of its activity is reduced by 20 US Food and Drug Administration -approved TKIs. Nilotinib was assessed as the most potent OATP1B3 inhibitor among the investigated TKIs, which can occur at clinically relevant concentrations and acted predominantly through noncompetitive inhibition without impacting membrane expression. Finally, OATP1B3 function was determined to be sensitive to the knockdown of the Lck/Yes novel tyrosine kinase that is sensitive to nilotinib and has been previously implicated in mediating OATP1B1 activity. Collectively, our findings identify tyrosine kinase activity as a major regulator of OATP1B3 function which is sensitive to kinase inhibition. Given that OATP1B1 is similarly regulated, simultaneous disruption of these transporters can have drastic effects on systemic drug concentrations, which would promote adverse events. SIGNIFICANCE STATEMENT: The organic anion transporting polypeptide family member (OATP) 1B3 is a facilitator of hepatic drug elimination, although much is unknown of how OATP1B3 activity is mediated, or how such regulators contribute to drug-drug interactions. This study reports that OATP1B3 activity is dependent on the Lck/Yes novel tyrosine kinase, which is sensitive to numerous tyrosine kinase inhibitors. These findings provide insight into the occurrence of many clinical drug-drug interactions, and a rationale for future study of tyrosine kinases regulating drug disposition.

Identifying Pneumonia Subtypes from Electronic Health Records Using Rule-Based Algorithms.

BACKGROUND: The International Classification of Disease (ICD) coding for pneumonia classification is based on causal organism or use of general pneumonia codes, creating challenges for epidemiological evaluations where pneumonia is standardly subtyped by settings, exposures, and time of emergence. Pneumonia subtype classification requires data available in electronic health records (EHRs), frequently in nonstructured formats including radiological interpretation or clinical notes that complicate electronic classification. OBJECTIVE: The current study undertook development of a rule-based pneumonia subtyping algorithm for stratifying pneumonia by the setting in which it emerged using information documented in the EHR. METHODS: Pneumonia subtype classification was developed by interrogating patient information within the EHR of a large private Health System. ICD coding was mined in the EHR applying requirements for "rule of two" pneumonia-related codes or one ICD code and radiologically confirmed pneumonia validated by natural language processing and/or documented antibiotic prescriptions. A rule-based algorithm flow chart was created to support subclassification based on features including symptomatic patient point of entry into the health care system timing of pneumonia emergence and identification of clinical, laboratory, or medication orders that informed definition of the pneumonia subclassification algorithm. RESULTS: Data from 65,904 study-eligible patients with 91,998 episodes of pneumonia diagnoses documented by 380,509 encounters were analyzed, while 8,611 episodes were excluded following Natural Language Processing classification of pneumonia status as "negative" or "unknown." Subtyping of 83,387 episodes identified: community-acquired (54.5%), hospital-acquired (20%), aspiration-related (10.7%), health care-acquired (5%), and ventilator-associated (0.4%) cases, and 9.4% cases were not classifiable by the algorithm. CONCLUSION: Study outcome indicated capacity to achieve electronic pneumonia subtype classification based on interrogation of big data available in the EHR. Examination of portability of the algorithm to achieve rule-based pneumonia classification in other health systems remains to be explored.

TLR7 agonism accelerates disease in a mouse model of primary Sjögren's syndrome and drives expansion of T-bet+ B cells.

Primary Sjögren's syndrome (pSS) is a systemic autoimmune disease characterized by chronic inflammation of exocrine tissue, resulting in loss of tears and saliva. Patients also experience many extra-glandular disease manifestations. Treatment for pSS is palliative, and there are currently no treatments available that target disease etiology. Previous studies in our lab demonstrated that MyD88 is crucial for pSS pathogenesis in the NOD.B10Sn-H2b (NOD.B10) pSS mouse model, although the way in which MyD88-dependent pathways become activated in disease remains unknown. Based on its importance in other autoimmune diseases, we hypothesized that TLR7 activation accelerates pSS pathogenesis. We administered the TLR7 agonist Imiquimod (Imq) or sham treatment to pre-disease NOD.B10 females for 6 weeks. Parallel experiments were performed in age and sex-matched C57BL/10 controls. Imq-treated pSS animals exhibited cervical lymphadenopathy, splenomegaly, and expansion of TLR7-expressing B cells. Robust lymphocytic infiltration of exocrine tissues, kidney and lung was observed in pSS mice following treatment with Imq. TLR7 agonism also induced salivary hypofunction in pSS mice, which is a hallmark of disease. Anti-nuclear autoantibodies, including Ro (SSA) and La (SSB) were increased in pSS mice following Imq administration. Cervical lymph nodes from Imq-treated NOD.B10 animals demonstrated an increase in the percentage of activated/memory CD4+ T cells. Finally, T-bet+ B cells were expanded in the spleens of Imq-treated pSS mice. Thus, activation of TLR7 accelerates local and systemic disease and promotes expansion of T-bet-expressing B cells in pSS.

PodoSighter: A Cloud-Based Tool for Label-Free Podocyte Detection in Kidney Whole-Slide Images.

BACKGROUND: Podocyte depletion precedes progressive glomerular damage in several kidney diseases. However, the current standard of visual detection and quantification of podocyte nuclei from brightfield microscopy images is laborious and imprecise. METHODS: We have developed PodoSighter, an online cloud-based tool, to automatically identify and quantify podocyte nuclei from giga-pixel brightfield whole-slide images (WSIs) using deep learning. Ground-truth to train the tool used immunohistochemically or immunofluorescence-labeled images from a multi-institutional cohort of 122 histologic sections from mouse, rat, and human kidneys. To demonstrate the generalizability of our tool in investigating podocyte loss in clinically relevant samples, we tested it in rodent models of glomerular diseases, including diabetic kidney disease, crescentic GN, and dose-dependent direct podocyte toxicity and depletion, and in human biopsies from steroid-resistant nephrotic syndrome and from human autopsy tissues. RESULTS: The optimal model yielded high sensitivity/specificity of 0.80/0.80, 0.81/0.86, and 0.80/0.91, in mouse, rat, and human images, respectively, from periodic acid-Schiff-stained WSIs. Furthermore, the podocyte nuclear morphometrics extracted using PodoSighter were informative in identifying diseased glomeruli. We have made PodoSighter freely available to the general public as turnkey plugins in a cloud-based web application for end users. CONCLUSIONS: Our study demonstrates an automated computational approach to detect and quantify podocyte nuclei in standard histologically stained WSIs, facilitating podocyte research, and enabling possible future clinical applications.

SuMO-Fil: Supervised multi-omic filtering prior to performing network analysis.

Multi-omic analyses that integrate many high-dimensional datasets often present significant deficiencies in statistical power and require time consuming computations to execute the analytical methods. We present SuMO-Fil to remedy against these issues which is a pre-processing method for Supervised Multi-Omic Filtering that removes variables or features considered to be irrelevant noise. SuMO-Fil is intended to be performed prior to downstream analyses that detect supervised gene networks in sparse settings. We accomplish this by implementing variable filters based on low similarity across the datasets in conjunction with low similarity with the outcome. This approach can improve accuracy, as well as reduce run times for a variety of computationally expensive downstream analyses. This method has applications in a setting where the downstream analysis may include sparse canonical correlation analysis. Filtering methods specifically for cluster and network analysis are introduced and compared by simulating modular networks with known statistical properties. The SuMO-Fil method performs favorably by eliminating non-network features while maintaining important biological signal under a variety of different signal settings as compared to popular filtering techniques based on low means or low variances. We show that the speed and accuracy of methods such as supervised sparse canonical correlation are increased after using SuMO-Fil, thus greatly improving the scalability of these approaches.

Gene-set integrative analysis of multi-omics data using tensor-based association test.

MOTIVATION: Facilitated by technological advances and the decrease in costs, it is feasible to gather subject data from several omics platforms. Each platform assesses different molecular events, and the challenge lies in efficiently analyzing these data to discover novel disease genes or mechanisms. A common strategy is to regress the outcomes on all omics variables in a gene set. However, this approach suffers from problems associated with high-dimensional inference. RESULTS: We introduce a tensor-based framework for variable-wise inference in multi-omics analysis. By accounting for the matrix structure of an individual's multi-omics data, the proposed tensor methods incorporate the relationship among omics effects, reduce the number of parameters, and boost the modeling efficiency. We derive the variable-specific tensor test and enhance computational efficiency of tensor modeling. Using simulations and data applications on the Cancer Cell Line Encyclopedia (CCLE), we demonstrate our method performs favorably over baseline methods and will be useful for gaining biological insights in multi-omics analysis. AVAILABILITY AND IMPLEMENTATION: R function and instruction are available from the authors' website: https://www4.stat.ncsu.edu/~jytzeng/Software/TR.omics/TRinstruction.pdf. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Automated Computational Detection of Interstitial Fibrosis, Tubular Atrophy, and Glomerulosclerosis.

BACKGROUND: Interstitial fibrosis, tubular atrophy (IFTA), and glomerulosclerosis are indicators of irrecoverable kidney injury. Modern machine learning (ML) tools have enabled robust, automated identification of image structures that can be comparable with analysis by human experts. ML algorithms were developed and tested for the ability to replicate the detection and quantification of IFTA and glomerulosclerosis that renal pathologists perform. METHODS: A renal pathologist annotated renal biopsy specimens from 116 whole-slide images (WSIs) for IFTA and glomerulosclerosis. A total of 79 WSIs were used for training different configurations of a convolutional neural network (CNN), and 17 and 20 WSIs were used as internal and external testing cases, respectively. The best model was compared against the input of four renal pathologists on 20 new testing slides. Further, for 87 testing biopsy specimens, IFTA and glomerulosclerosis measurements made by pathologists and the CNN were correlated to patient outcome using classic statistical tools. RESULTS: The best average performance across all image classes came from a DeepLab version 2 network trained at 40× magnification. IFTA and glomerulosclerosis percentages derived from this CNN achieved high levels of agreement with four renal pathologists. The pathologist- and CNN-based analyses of IFTA and glomerulosclerosis showed statistically significant and equivalent correlation with all patient-outcome variables. CONCLUSIONS: ML algorithms can be trained to replicate the IFTA and glomerulosclerosis assessment performed by renal pathologists. This suggests computational methods may be able to provide a standardized approach to evaluate the extent of chronic kidney injury in situations in which renal-pathologist time is restricted or unavailable.

Symptoms upon postural change and orthostatic hypotension in adolescents with concussion.

Objective: Concussion is associated with dysautonomia, altered blood pressure (BP) control, and may cause Orthostatic Hypotension (OH). We measured prevalence of OH using the 1-minute supine-to-standing OH Test in adolescents with concussion and controls.Participants: Adolescents within 10 days of injury (Concussion Group, n = 297, 15.0 ± 1.7 years, 59% male) were compared with controls (Control Group, n = 214, 15.0 ± 1.5 years, 58% male).Methods: BP, heart rate (HR), and complaints of lightheadedness/dizziness were measured after 2-minute supine and 1-minute standing. Control Group was assessed once. Concussion Group was assessed twice; (1) initial visit (mean 6.0 ± 3 days-since-injury) and (2) after clinical recovery (mean 46.3 ± 42 days-since-injury).Results: Initial visit; Concussion Group reported feeling lightheaded/dizzy on postural change more often than the Control Group (37% vs 4%, p < .001) but did not differ in meeting standard OH criteria (3% vs 5%, p = .32). Experiencing symptoms did not correlate with meeting OH criteria, but correlated with abnormal vestibulo-ocular reflex. After clinical recovery; Concussion Group did not differ in experiencing lightheaded/dizziness on postural change than controls (4%, p = .65).Conclusion: Adolescents commonly experience orthostatic intolerance after concussion without meeting the standard criteria for OH.

Balanced Functional Module Detection in genomic data.

Motivation: High-dimensional genomic data can be analyzed to understand the effects of variables on a target variable such as a clinical outcome. For understanding the underlying biological mechanism affecting the target, it is important to discover the complete set of relevant variables. Thus variable selection is a primary goal, which differs from a prediction criterion. Of special interest are functional modules, cooperating sets of variables affecting the target which can be characterized by a graph. In applications such as social networks, the concept of balance in undirected signed graphs characterizes the consistency of associations within the network. This property requires that the module variables have a joint effect on the target outcome with no internal conflict, an efficiency that may be applied to biological networks. Results: In this paper, we model genomic variables in signed undirected graphs for applications where the set of predictor variables influences an outcome. Consequences of the balance property are exploited to implement a new module discovery algorithm, balanced Functional Module Detection (bFMD), which selects a subset of variables from high-dimensional data that compose a balanced functional module. Our bFMD algorithm performed favorably in simulations as compared to other module detection methods. Additionally, bFMD detected interpretable results in an application using RNA-seq data obtained from subjects with Uterine Corpus Endometrial Carcinoma using the percentage of tumor invasion as the outcome of interest. The variables selected by bFMD have improved interpretability due to the logical consistency afforded by the balance property. Supplementary information: Supplementary data are available at Bioinformatics Advances online.

Derivation of a Focused, Brief Concussion Physical Examination for Adolescents With Sport-Related Concussion.

OBJECTIVE: To describe the derivation of a brief but pertinent physical examination (PE) for adolescents who have sustained sport-related concussion (SRC). DESIGN: Prospective cohort. SETTING: University concussion management clinic. PARTICIPANTS: Acutely concussed (AC, n = 52, 15.5 ± 1.4 years, 4.4 ± 2 days since injury, 26.2 ± 38 days to recovery, 71% males) and healthy control (HC) adolescents (n = 30, 15.8 ± 1.4 years, 73% males). INTERVENTION: Acutely concussed had a PE on visit 1 and were retested at visit 2 (13.6 ± 1 day after visit 1). Acutely concussed were further characterized as normal recovery (NR, n = 41, 15.5 ± 1.5 years, recovery time 13.0 ± 7 days) and delayed recovery (DR, n = 11, 15.5 ± 1.2 years, recovery time 75.4 ± 63 days). MAIN OUTCOME MEASURE: Physical examination findings, including cervical, vestibular, and oculomotor systems. RESULTS: Visit 1 abnormal PE signs were significantly greater in AC versus HC (2.79 ± 2.13 vs 0.07 ± 0.37, P < 0.0001) but not in NR versus DR (2.61 ± 2.2 vs 3.45 ± 1.8, P = 0.246). Visit 2 abnormal PE signs differentiated NR versus DR (0.17 ± 0.7 vs 2.45 ± 2.1, P < 0.0001). CONCLUSIONS: A brief focused PE can help to diagnose SRC, establish recovery, and may have prognostic value.

Identification of supervised and sparse functional genomic pathways.

Functional pathways involve a series of biological alterations that may result in the occurrence of many diseases including cancer. With the availability of various "omics" technologies it becomes feasible to integrate information from a hierarchy of biological layers to provide a more comprehensive understanding to the disease. In many diseases, it is believed that only a small number of networks, each relatively small in size, drive the disease. Our goal in this study is to develop methods to discover these functional networks across biological layers correlated with the phenotype. We derive a novel Network Summary Matrix (NSM) that highlights potential pathways conforming to least squares regression relationships. An algorithm called Decomposition of Network Summary Matrix via Instability (DNSMI) involving decomposition of NSM using instability regularization is proposed. Simulations and real data analysis from The Cancer Genome Atlas (TCGA) program will be shown to demonstrate the performance of the algorithm.

The Predictive Capacity of the Buffalo Concussion Treadmill Test After Sport-Related Concussion in Adolescents.

The Buffalo Concussion Treadmill Test (BCTT) identifies the heart rate threshold (HRt) of exercise tolerance in concussed patients. A previous study found that an absolute HRt of < 135 bpm was associated with prolonged recovery (>30 days) from sport-related concussion (SRC). In this study, we assessed the relationship of ΔHR (difference between resting HR and HRt) and recovery from SRC. Using a retrospective cohort design, we compared acutely (<10 days since injury) concussed adolescents who were prescribed either (1) relative rest (RG, n = 27, 15.2 ± 1 years, 33% female, median 17 days to recovery, ΔHR = 69.6 ± 28 bpm), (2) a placebo-stretching program (PG, n = 51, 15.4 ± 2 years, 49% female, median 17 days to recovery, ΔHR = 60.9 ± 22 bpm), or (3) sub-threshold aerobic exercise (AG, n = 52, 15.3 ± 2 years, 46% female, median 13 days to recovery, ΔHR = 62.4 ± 26 bpm). Linear regression showed that ΔHR significantly correlated with duration of clinical recovery for RG (p = 0.012, R 2 = 0.228) and PG (p = 0.011, R 2 = 0.126) but not for AG (p = 0.084, R 2 = 0.059). ΔHR values were significantly lower in participants with prolonged recovery (>30 days) in RG (p = 0.01) and PG (p = 0.04). A ΔHR of ≤50 bpm on the BCTT is 73% sensitive and 78% specific for predicting prolonged recovery in concussed adolescents who were prescribed the current standard of care (i.e., cognitive and physical rest).

The salivary microbiome of diabetic and non-diabetic adults with periodontal disease.

BACKGROUND: A comparison of the salivary microbiome of non-diabetic and diabetic cohorts having periodontal health, gingivitis and periodontitis could reveal microbial signatures unique to each group that will increase understanding of the role of oral microbiota in the pathogenesis of disease, and assist with diagnosis and risk assessment for both periodontal disease and diabetes. METHODS: A group of individuals diagnosed with type 2 diabetes (T2D) was compared with a group without T2D. For both the diabetic and non-diabetic cohorts, three subgroups were established: periodontal health, gingivitis, and periodontitis. Salivary DNA was extracted (n = 146), polymerase chain reaction was performed to amplify 16S rRNA hypervariable region V3-V4, and constructed libraries were sequenced and subjected to bioinformatic and statistical analyses. RESULTS: Microbiome analysis resulted in 88 different genus level operational taxonomic units (OTUs) for differential abundance testing. Results were largely described by two trends. Trend 1 showed OTUs that increased in abundance with increasing periodontal disease, and in diabetics relative to non-diabetics. Trend 1 OTUs comprised a mix of primarily anaerobic commensals and potential periodontopathogens. Trend 2 was driven primarily by genera that decreased in abundance in those with diabetes relative to those without diabetes, which included other anaerobes associated with periodontal disease. Overall, oral microbial diversity decreased in diabetics and increased with progression of periodontal disease compared with periodontally healthy controls. CONCLUSION: Although select microbiota increased in both diabetes and periodontal disease progression, these genera decreased in co-existing diabetes and periodontal disease. These findings suggest that the genera abundance continues to change with additional stress imposed by co-existing conditions.

A Preliminary Study of Early-Onset Dementia of Former Professional Football and Hockey Players.

OBJECTIVE: To provide an overview of 3 studies of the same population of retired professional contact sport athletes compared with age-matched noncontact sport athlete controls on cognition, executive function, behavior, and advanced brain imaging. SETTING: University Concussion Management Clinic. PARTICIPANTS: Twenty-two retired professional hockey and football athletes (average age 56 years) and 21 age-matched noncontact sport athlete controls. DESIGN: Case control. MAIN MEASURES: Participants were assessed on a broad range of neuropsychological measures that are associated with identification of mild cognitive impairment and executive function. Athletes were also assessed using self-report measures of executive function and personality. Advanced structural and functional imaging techniques were utilized as well. RESULTS: The former National Football League and National Hockey League athletes perceived themselves to have impaired executive function, but this was not confirmed by objective neurocognitive assessment. No significant differences were found when comparing contact-sport athletes with controls on the presence of mild cognitive impairment or brain structural and functional tissue injury. Contact sport athletes were more anxious and more likely to report unusual beliefs and experiences. CONCLUSION: None of the retired contact sport athletes qualified as having early-onset dementia consistent with chronic traumatic encephalopathy. There were no remarkable differences in imaging, cognition, behavior, or executive function from noncontact sport athletes. The results underscore an apparent disconnect between public perceptions and evidence-based conclusions about the inevitability of chronic traumatic encephalopathy and the potential neurodegenerative effect on former athletes from contact sports.