Impact of an internet-based insomnia intervention on suicidal ideation and associated correlates in veterans at elevated suicide risk.

Improving public health approaches to suicide prevention requires scalable evidence-based interventions that can be easily disseminated. Given empirical data supporting the association between insomnia and suicide risk, internet-delivered insomnia interventions are promising candidates to meet this need. The purpose of this study was to examine whether an unguided internet-delivered cognitive-behavioral therapy for insomnia (iCBT-I) improved insomnia severity, suicidal ideation (SI), and suicide risk correlates (depression, post-traumatic stress disorder, anxiety, hostility, belongingness, hopelessness, agitation, irritability, concentration) in a sample of veterans. Secondary data analysis of Operation Enduring Freedom, Operation Iraqi Freedom, and Operation New Dawn veterans (n = 50) with clinically significant insomnia and elevated SI drawn from a larger randomized controlled trial (RCT) of an iCBT-I, Sleep Healthy Using the Internet (SHUTi). Two-sample t-tests or Wilcoxon rank sum tests were used to evaluate between-group differences (SHUTi vs. Insomnia Education Website control) in symptom improvement from baseline to post-intervention. SHUTi participants experienced a significant improvement in insomnia severity (P < .001; d = -1.08) and a non-significant with small (subthreshold medium) effect size reduction of SI (P = .17, d = 0.40), compared to control participants. Significant improvement in hopelessness was observed (medium effect size), with non-significant small to medium effect size reductions in most remaining suicide risk correlates. Self-administered iCBT-I was associated with improvements in insomnia severity in veterans at elevated risk for suicide. These preliminary findings suggest that SI and suicide risk correlates may improve following an iCBT-I intervention, demonstrating the need for future well-powered iCBT-I RCTs targeted for populations at elevated suicide risk.

In this secondary data analysis, we examined improvements in insomnia severity, suicidal ideation (SI), and suicide risk correlates in veterans with clinically significant insomnia and elevated SI drawn from a larger randomized controlled trial (RCT) examining an unguided internet-delivered cognitive-behavioral therapy for insomnia (iCBT-I). Veterans in the iCBT-I group experienced greater improvements in insomnia severity and hopelessness than veterans in the Insomnia Education Website control. Although between-group differences in SI and other suicide risk correlates were not statistically significant, effect sizes suggest that SI and symptoms of depression, irritability, concentration, post-traumatic stress disorder, and hostility may improve following iCBT-I intervention. These results suggest that digital and iCBT-I interventions may be especially powerful tools for use in suicide prevention among veterans but highlight the critical need for additional large-scale studies to examine suicide-specific mechanisms and outcomes to guide implementation efforts.

Self-supervised learning for medical image analysis: Discriminative, restorative, or adversarial?

Discriminative, restorative, and adversarial learning have proven beneficial for self-supervised learning schemes in computer vision and medical imaging. Existing efforts, however, fail to capitalize on the potentially synergistic effects these methods may offer in a ternary setup, which, we envision can significantly benefit deep semantic representation learning. Towards this end, we developed DiRA, the first framework that unites discriminative, restorative, and adversarial learning in a unified manner to collaboratively glean complementary visual information from unlabeled medical images for fine-grained semantic representation learning. Our extensive experiments demonstrate that DiRA: (1) encourages collaborative learning among three learning ingredients, resulting in more generalizable representation across organs, diseases, and modalities; (2) outperforms fully supervised ImageNet models and increases robustness in small data regimes, reducing annotation cost across multiple medical imaging applications; (3) learns fine-grained semantic representation, facilitating accurate lesion localization with only image-level annotation; (4) improves reusability of low/mid-level features; and (5) enhances restorative self-supervised approaches, revealing that DiRA is a general framework for united representation learning. Code and pretrained models are available at https://github.com/JLiangLab/DiRA.

Association of Blast Exposure in Military Breaching with Intestinal Permeability Blood Biomarkers Associated with Leaky Gut.

Injuries and subclinical effects from exposure to blasts are of significant concern in military operational settings, including tactical training, and are associated with self-reported concussion-like symptomology and physiological changes such as increased intestinal permeability (IP), which was investigated in this study. Time-series gene expression and IP biomarker data were generated from "breachers" exposed to controlled, low-level explosive blast during training. Samples from 30 male participants at pre-, post-, and follow-up blast exposure the next day were assayed via RNA-seq and ELISA. A battery of symptom data was also collected at each of these time points that acutely showed elevated symptom reporting related to headache, concentration, dizziness, and taking longer to think, dissipating ~16 h following blast exposure. Evidence for bacterial translocation into circulation following blast exposure was detected by significant stepwise increase in microbial diversity (measured via alpha-diversity p = 0.049). Alterations in levels of IP protein biomarkers (i.e., Zonulin, LBP, Claudin-3, I-FABP) assessed in a subset of these participants (n = 23) further evidenced blast exposure associates with IP. The observed symptom profile was consistent with mild traumatic brain injury and was further associated with changes in bacterial translocation and intestinal permeability, suggesting that IP may be linked to a decrease in cognitive functioning. These preliminary findings show for the first time within real-world military operational settings that exposures to blast can contribute to IP.

Brain and blood transcriptome profiles delineate common genetic pathways across suicidal ideation and suicide.

Human genetic studies indicate that suicidal ideation and behavior are both heritable. Most studies have examined associations between aberrant gene expression and suicide behavior, but behavior risk is linked to the severity of suicidal ideation. Through a gene network approach, this study investigates how gene co-expression patterns are associated with suicidal ideation and severity using RNA-seq data in peripheral blood from 46 live participants with elevated suicidal ideation and 46 with no ideation. Associations with the presence of suicidal ideation were found within 18 co-expressed modules (p < 0.05), as well as in 3 co-expressed modules associated with suicidal ideation severity (p < 0.05, not explained by severity of depression). Suicidal ideation presence and severity-related gene modules with enrichment of genes involved in defense against microbial infection, inflammation, and adaptive immune response were identified and investigated using RNA-seq data from postmortem brain that revealed gene expression differences with moderate effect sizes in suicide decedents vs. non-suicides in white matter, but not gray matter. Findings support a role of brain and peripheral blood inflammation in suicide risk, showing that suicidal ideation presence and severity are associated with an inflammatory signature detectable in blood and brain, indicating a biological continuity between ideation and suicidal behavior that may underlie a common heritability.

A cross-sectional study of correlations among blood-based biomarkers for intestinal permeability: A pilot study of United States veterans with posttraumatic stress disorder symptoms.

While many studies of intestinal permeability (IP) are focused on those with gastrointestinal (GI) disorders, there is a rising trend to analyze IP among individuals with mental health conditions including posttraumatic stress disorder (PTSD) with and without diagnosed GI conditions. This interest stems from the association between gut dysbiosis and chronic inflammation, which are mechanisms linked to stress-related somatic and mental health conditions. Efforts to date have resulted in the exploration of non-invasive and feasible measures to identify an IP biomarker that could also serve as a treatment target. Additionally, those conducting studies regarding IP often recruit individuals without health problems and compare levels of biomarkers of IP to those obtained from participants with conditions of interest. This study aimed to assess correlations between blood-based biomarkers of IP, as well as examine the association between blood-based biomarkers of IP and PTSD symptoms. Blood was sampled from seventeen United States military Veterans with variable severity of PTSD symptoms per the posttraumatic checklist for DSM-5 (PCL-5) (n = 6 with scores over 31 indicating clinically meaningful symptoms of PTSD; overall range 0-49, mean 20.8, standard deviation 15.7) and analyzed blood biomarkers of IP including citrulline, diamine oxidase, glucagon-like peptide-2, intestinal fatty-acid binding protein, lipopolysaccharide binding protein, lipopolysaccharide, and zonulin. Correlations between the IP blood-based biomarkers ranged from ρ of -0.31 to 0.35. None of the measured biomarkers were significantly correlated to PTSD symptom severity scores (ρ of -0.34 to 0.05). Although based on limited sample size, our results call into question the specificity of blood-based biomarkers of IP when: (1) studying persons with and without PTSD symptoms in whom clinical GI disorders are not necessarily the focus of the study; and (2) comparing IP results among individuals with well-defined disease states to those without the disease (e.g., controls). Further studies are needed to explore the role of external factors (e.g., nutrition, obesity, alcohol use) on IP and to determine if the biomarkers studied are appropriate for measuring IP in people with a range of symptoms related to PTSD.

Association of interleukin-6 with suicidal ideation in veterans: a longitudinal perspective.

Introduction: Studies showing associations between inflammation in suicide are typically cross-sectional. Present study investigated how cytokine levels track with suicidal ideation and severity longitudinally. Methods: Veterans with a diagnosis of major depressive disorder (MDD) with or without suicide attempt history (MDD/SA n = 38, MDD/NS n = 41) and non-psychiatric non-attempter controls (HC n = 33) were recruited, MDD/SA and HC groups were followed longitudinally at 3 months and 6 months. Blood plasma was collected and processed using Luminex Immunology Multiplex technology. Results: Significant differences in depression severity (BDI) and suicidal ideation severity (SSI) were observed across all groups at study entry, wherein MDD/SA group had the highest scores followed by MDD/NS and HC, respectively. Cytokines IL-1ß, IL-4, TNF-α, IFN-γ, and IL-6 were examined at study entry and longitudinally, with IL6 levels differing significantly across the groups (p = 0.0123) at study entry. Significant differences in changes in cytokine levels between depressed attempters and the control group were detected for IL-6 (interaction F1,91.77 = 5.58, p = 0.0203) and TNF-α (F1,101.73 = 4.69, p = 0.0327). However, only depressed attempters showed a significant change, in IL-6 and TNF-α levels, decreasing over time [IL-6: b = -0.04, 95% CI = (-0.08, -0.01), p = 0.0245 and TNF-α: b = -0.02, 95% CI = (-0.04, -0.01), p = 0.0196]. Although IL-6 levels were not predictive of suicidal ideation presence [OR = 1.34, 95% CI = (0.77, 2.33), p = 0.3067], IL-6 levels were significantly associated with suicidal ideation severity (b = 0.19, p = 0.0422). Discussion: IL-6 was not associated with presence of suicidal ideation. IL-6 however, was associated with severity of ideation, suggesting that IL-6 may be useful in clinical practice, as an objective marker of heightened suicide risk.

The relationship of myocardial and liver T2* values with cardiac function and laboratory findings in transfusion-dependent thalassemia major patients: A retrospective cardiac MRI study.

Introduction: Cardiac complications are the leading cause of death in thalassemia patients. It is assumed that progressive iron accumulation results in myocyte damage. Myocardial T2* measurement by cardiac MRI quantifies iron overload. We aimed to study the association between left and right ventricular (LV and RV) function and iron deposition estimation by cardiac MRI T2* in a sample of Iranian patients. Methods: Cardiac MRI exams of 118 transfusion-dependent thalassemia major patients were evaluated retrospectively. Biventricular function and volume and myocardial and liver T2* values were measured. The demographic and lab data were registered. Poisson and chi-square regression analyses investigated the correlation between the T2* value and ventricular dysfunction. Results: The study participants' mean (SD) age was 32.7y (9.02), and 47.46% were female. Forty-nine cases (41.52%) revealed at least uni-ventricular dysfunction. LV dysfunction was noted in 20 cases, whereas 47 patients revealed RV dysfunction. The risk of LV dysfunction was 5.3-fold higher in patients with cardiac T2* value less than 10msec (RR=5.3, 95% CI=1.6, 17.1, P<0.05). No association was found between age, liver T2* value, serum ferritin level, and chelation therapy with the risk of LV and RV dysfunction. Conclusion: Cardiac MRI T2* measure is a good indicator of LV dysfunction. Moreover, MRI parameters, especially RV functional measures, may have a substantial role in patient management. Therefore, cardiac MRI should be included in beta-thalassemia patients' management strategies.

Functional Architecture of Brain and Blood Transcriptome Delineate Biological Continuity Between Suicidal Ideation and Suicide.

Human genetic studies indicate that suicidal ideation and behavior are both heritable. Most studies have examined associations between aberrant gene expression and suicide behavior, but behavior risk is linked to severity of suicidal ideation. Through a gene network approach, this study investigates how gene co-expression patterns are associated with suicidal ideation and severity using RNA-seq data in peripheral blood from 46 live participants with elevated suicidal ideation and 46 with no ideation. Associations with presence and severity of suicidal ideation were found within 18 and 3 co-expressed modules respectively (p < 0.05), not explained by severity of depression. Suicidal ideation presence and severity-related gene modules with enrichment of genes involved in defense against microbial infection, inflammation, and adaptive immune response were identified, and tested using RNA-seq data from postmortem brain that revealed gene expression differences in suicide decedents vs. non-suicides in white matter, but not gray matter. Findings support a role of brain and peripheral blood inflammation in suicide risk, showing that suicidal ideation presence and severity is associated with an inflammatory signature detectable in blood and brain, indicating a biological continuity between ideation and suicidal behavior that may underlie a common heritability.

Assessing sleep health dimensions in frontline registered nurses during the COVID-19 pandemic: implications for psychological health and wellbeing.

The COVID-19 pandemic altered work environments of nurses, yielding high rates of stress and burnout. Potential protective factors, including effective sleep, may influence psychological health and wellbeing. Evidence about sleep in nurses may help develop interventions that mitigate burnout and poor psychological outcomes. A cross sectional survey was distributed across three hospitals to nurses in New York City (NYC). During the first wave of the pandemic (March-April 2020), NYC had the highest incidence of laboratory-confirmed COVID-19 cases (915/100 000) and half of all COVID-related deaths nationwide. Multivariable logistic regression was used to determine associations between Pittsburgh Sleep Quality Index (PSQI) global sleep score, PSQI sleep dimensions, and psychological health (burnout, depression, anxiety, and compassion fatigue), unadjusted and then controlling for individual and professional characteristics. More than half of the participants reported burnout (64%), depression, (67%), and anxiety (77%). Eighty percent of participants had PSQI global scores >5 (poor sleep) (mean 9.27, SD 4.14). Respondents reporting good sleep (PSQI ≤ 5) had over five times the odds of no burnout (OR: 5.65, 95% CI: 2.60, 12.27); increased odds of screening negative for depression (OR: 6.91, 95% CI: 3.24, 14.72), anxiety (OR: 10.75, 95% CI: 4.22, 27.42), and compassion fatigue (OR: 7.88, 95% CI: 1.97, 31.51). Poor subjective sleep quality PSQI subcomponent was associated with burnout (OR: 2.21, 95% CI: 1.41, 3.48) but sleep duration subcomponent was not (OR: 0.84, 95% CI: 0.59, 1.19). Daytime dysfunction was significantly associated with all psychological outcomes. Sleep disturbances and medications yielded higher anxiety odds. Overall, sleep quality appears more strongly related to burnout than sleep duration in nurses working during the COVID-19 pandemic. Sleep interventions should target individual sleep dimensions in nurses.

Smaller rostral cingulate volume and psychosocial correlates in veterans at risk for suicide.

Suicide research/clinical work remain in dire need of effective tools that can better predict suicidal behavior. A growing body of literature has started to focus on the role that neuroimaging may play in helping explain the path towards suicide. Specifically, structural alterations of rostral anterior cingulate cortex (rost-ACC) may represent a biological marker and/or indicator of suicide risk in Major Depressive Disorder (MDD). Furthermore, the construct of "grit," defined as perseverance for goal-attainment and shown to be associated with suicidality, is modulated by rost-ACC. The aim was to examine relationships among rost-ACC gray matter volume, grit, and suicidality in U.S. Military Veterans. Participants were age-and-sex-matched Veterans with MDD: with suicide attempt (MDD+SA:n = 23) and without (MDD-SA:n = 37). Groups did not differ in depression symptomatology. Participants underwent diagnostic interview, clinical symptom assessment, and 3T-MRI-scan. A Group (SA-vs.-No-SA) x Cingulate-region (rostral-caudal-posterior) x Hemisphere (left-right) mixed-model-multivariate-ANOVA was conducted. Left-rost-ACC was significantly smaller in MDD+SA, Group x Cingulate-region x Hemisphere-interaction. Lower grit and less left-rost-ACC gray matter each predicted suicide attempt history, but grit level was a more robust predictor of SA. Both structural alterations of rost-ACC and grit level represent potentially valuable tools for suicide risk assessment.

Delivery of Telehealth Complementary and Integrated Health Interventions Improves Mental Health and Overall Wellness to Broaden Veterans' Access to Care.

Background: Complementary and integrative health (CIH) interventions show promise in improving overall wellness and engaging Veterans at risk of suicide. Methods: An intensive 4-week telehealth CIH intervention programming was delivered motivated by the COVID-19 pandemic, and outcomes were measured pre-post program completion. Results: With 93% program completion (121 Veterans), significant reduction in depression and post-traumatic stress disorder symptoms were observed pre-post telehealth CIH programing, but not in sleep quality. Improvements in pain symptoms, and stress management skills were observed in Veterans at risk of suicide. Discussion: Telehealth CIH interventions show promise in improving mental health symptoms among at-risk Veterans, with great potential to broaden access to care toward suicide prevention.

CAiD: Context-Aware Instance Discrimination for Self-supervised Learning in Medical Imaging.

Recently, self-supervised instance discrimination methods have achieved significant success in learning visual representations from unlabeled photographic images. However, given the marked differences between photographic and medical images, the efficacy of instance-based objectives, focusing on learning the most discriminative global features in the image (i.e., wheels in bicycle), remains unknown in medical imaging. Our preliminary analysis showed that high global similarity of medical images in terms of anatomy hampers instance discrimination methods for capturing a set of distinct features, negatively impacting their performance on medical downstream tasks. To alleviate this limitation, we have developed a simple yet effective self-supervised framework, called Context-Aware instance Discrimination (CAiD). CAiD aims to improve instance discrimination learning by providing finer and more discriminative information encoded from a diverse local context of unlabeled medical images. We conduct a systematic analysis to investigate the utility of the learned features from a three-pronged perspective: (i) generalizability and transferability, (ii) separability in the embedding space, and (iii) reusability. Our extensive experiments demonstrate that CAiD (1) enriches representations learned from existing instance discrimination methods; (2) delivers more discriminative features by adequately capturing finer contextual information from individual medial images; and (3) improves reusability of low/mid-level features compared to standard instance discriminative methods. As open science, all codes and pre-trained models are available on our GitHub page: https://github.com/JLiangLab/CAiD.

POPAR: Patch Order Prediction and Appearance Recovery for Self-supervised Medical Image Analysis.

Vision transformer-based self-supervised learning (SSL) approaches have recently shown substantial success in learning visual representations from unannotated photographic images. However, their acceptance in medical imaging is still lukewarm, due to the significant discrepancy between medical and photographic images. Consequently, we propose POPAR (patch order prediction and appearance recovery), a novel vision transformer-based self-supervised learning framework for chest X-ray images. POPAR leverages the benefits of vision transformers and unique properties of medical imaging, aiming to simultaneously learn patch-wise high-level contextual features by correcting shuffled patch orders and fine-grained features by recovering patch appearance. We transfer POPAR pretrained models to diverse downstream tasks. The experiment results suggest that (1) POPAR outperforms state-of-the-art (SoTA) self-supervised models with vision transformer backbone; (2) POPAR achieves significantly better performance over all three SoTA contrastive learning methods; and (3) POPAR also outperforms fully-supervised pretrained models across architectures. In addition, our ablation study suggests that to achieve better performance on medical imaging tasks, both fine-grained and global contextual features are preferred. All code and models are available at GitHub.com/JLiangLab/POPAR.

Benchmarking and Boosting Transformers for Medical Image Classification.

Visual transformers have recently gained popularity in the computer vision community as they began to outrank convolutional neural networks (CNNs) in one representative visual benchmark after another. However, the competition between visual transformers and CNNs in medical imaging is rarely studied, leaving many important questions unanswered. As the first step, we benchmark how well existing transformer variants that use various (supervised and self-supervised) pre-training methods perform against CNNs on a variety of medical classification tasks. Furthermore, given the data-hungry nature of transformers and the annotation-deficiency challenge of medical imaging, we present a practical approach for bridging the domain gap between photographic and medical images by utilizing unlabeled large-scale in-domain data. Our extensive empirical evaluations reveal the following insights in medical imaging: (1) good initialization is more crucial for transformer-based models than for CNNs, (2) self-supervised learning based on masked image modeling captures more generalizable representations than supervised models, and (3) assembling a larger-scale domain-specific dataset can better bridge the domain gap between photographic and medical images via self-supervised continuous pre-training. We hope this benchmark study can direct future research on applying transformers to medical imaging analysis. All codes and pre-trained models are available on our GitHub page https://github.com/JLiangLab/BenchmarkTransformers.

DiRA: Discriminative, Restorative, and Adversarial Learning for Self-supervised Medical Image Analysis.

Discriminative learning, restorative learning, and adversarial learning have proven beneficial for self-supervised learning schemes in computer vision and medical imaging. Existing efforts, however, omit their synergistic effects on each other in a ternary setup, which, we envision, can significantly benefit deep semantic representation learning. To realize this vision, we have developed DiRA, the first framework that unites discriminative, restorative, and adversarial learning in a unified manner to collaboratively glean complementary visual information from unlabeled medical images for fine-grained semantic representation learning. Our extensive experiments demonstrate that DiRA (1) encourages collaborative learning among three learning ingredients, resulting in more generalizable representation across organs, diseases, and modalities; (2) outperforms fully supervised ImageNet models and increases robustness in small data regimes, reducing annotation cost across multiple medical imaging applications; (3) learns fine-grained semantic representation, facilitating accurate lesion localization with only image-level annotation; and (4) enhances state-of-the-art restorative approaches, revealing that DiRA is a general mechanism for united representation learning. All code and pretrained models are available at https://github.com/JLiangLab/DiRA.

Correction to "Does the repeat dose of gonadotropin-releasing hormone agonist trigger in polycystic ovarian syndrome improve in vitro fertilization cycles outcome? A clinical trial study" [Int J Reprod BioMed 2020; 18: 485-490].

[This corrects the article on p. 485 in vol. 18 PMCPMC7385917.].

Diagnostic Value of Immunoglobulin G Anti-Deamidated Gliadin Peptide Antibody for Diagnosis of Pediatric Celiac Disease: A Study from Shiraz, Iran.

Purpose: Screening serologic tests are important tools for the diagnosis of celiac disease (CD). Immunoglobulin (Ig)G anti-deamidated gliadin peptide (anti-DGP) is a relatively new autoantibody thought to have good diagnostic accuracy, comparable to that of anti-tissue transglutaminase (anti-tTG) antibody. Methods: Pediatric patients (n=86) with a clinical suspicion of CD were included. Duodenal biopsy, anti-tTG, and IgG anti-DGP antibody tests were performed. The patients were divided into CD and control groups based on the pathological evaluation of duodenal biopsies. The diagnostic accuracy of serological tests was determined. Results: IgA anti-tTG and IgG anti-DGP antibodies were positive in 86.3% and 95.4% of patients, respectively. The sensitivity, specificity, and diagnostic accuracy of the IgA anti-tTG test were 86.3%, 50.0%, and 68.6%, respectively, and those of the IgG anti-DGP test were 95.4%, 85.7%, and 90.7%, respectively. The area under the receiver operating characteristic (ROC) curve was 0.84 (95% confidence interval [CI], 0.74-0.91) for IgA anti-tTG test and 0.93 (95% CI, 0.86-0.97) for IgG anti-DGP test. The comparison of IgA anti-tTG and IgG anti-DGP ROC curves showed a higher sensitivity and specificity of the IgG anti-DGP test. Conclusion: IgG anti-DGP is a reliable serological test for CD diagnosis in children. High tTG and DGP titers in the serum are suggestive of severe duodenal atrophy. The combined use of IgA anti-tTG and IgG anti-DGP tests for the initial screening of CD can improve diagnostic sensitivity.

A review on targeting tumor microenvironment: The main paradigm shift in the mAb-based immunotherapy of solid tumors.

Monoclonal antibodies (mAbs) as biological macromolecules have been remarked the large and growing pipline of the pharmaceutical market and also the most promising tool in modern medicine for cancer therapy. These therapeutic entities, which consist of whole mAbs, armed mAbs (i.e., antibody-toxin conjugates, antibody-drug conjugates, and antibody-radionuclide conjugates), and antibody fragments, mostly target tumor cells. However, due to intrinsic heterogeneity of cancer diseases, tumor cells targeting mAb have been encountered with difficulties in their unpredictable efficacy as well as variability in remission and durable clinical benefits among cancer patients. To address these pitfalls, the area has undergone two major evolutions with the intent of minimizing anti-drug responses and addressing limitations experienced with tumor cell-targeted therapies. As a novel hallmark of cancer, the tumor microenvironment (TME) is becoming the great importance of attention to develop innovative strategies based on therapeutic mAbs. Here, we underscore innovative strategies targeting TME by mAbs which destroy tumor cells indirectly through targeting vasculature system (e.g., anti-angiogenesis), immune system modulation (i.e., stimulation, suppression, and depletion), the targeting and blocking of stroma-based growth signals (e.g., cancer-associated fibroblasts), and targeting cancer stem cells, as well as, their effector mechanisms, clinical uses, and relevant mechanisms of resistance.

Contribution of Age, Brain Region, Mood Disorder Pathology, and Interindividual Factors on the Methylome of Human Microglia.

BACKGROUND: Transcriptome studies have revealed age-, disease-, and region-associated microglial phenotypes reflecting changes in microglial function during development, aging, central nervous system homeostasis, and pathology. The molecular mechanisms that contribute to these transcriptomic changes are largely unknown. The aim of this study was to characterize the DNA methylation landscape of human microglia and the factors that contribute to variations in the microglia methylome. We hypothesized that both age and brain region would have a large impact on DNA methylation in microglia. METHODS: Microglia from postmortem brain tissue of four different brain regions of 22 donors, encompassing 1 patient with schizophrenia, 13 patients with mood disorder pathology, and 8 control subjects, were isolated and assayed using a genome-wide methylation array. RESULTS: We found that human microglial cells have a methylation profile distinct from bulk brain tissue and neurons, and age explained a considerable part of the variation. Additionally, we showed that interindividual factors had a much larger effect on the methylation landscape of microglia than brain region, which was also seen at the transcriptome level. In our exploratory analysis, we found various differentially methylated regions that were related to disease status (mood disorder vs. control). This included differentially methylated regions that are linked to gene expression in microglia, as well as to myeloid cell function or neuropsychiatric disorders. CONCLUSIONS: Although based on relatively small samples, these findings suggest that the methylation profile of microglia is responsive to interindividual variations and thereby plays an important role in the heterogeneity of microglia observed at the transcriptome level.

Penetrating Ballistic Brain Injury Produces Acute Alterations in Sleep and Circadian-Related Genes in the Rodent Cortex: A Preliminary Study.

Traumatic brain injury (TBI) affects millions of Americans each year, with extremely high prevalence in the Veteran community, and sleep disturbance is one of the most commonly reported symptoms. Reduction in the quality and amount of sleep can negatively impact recovery and result in a wide range of behavioral and physiological symptoms, such as impaired cognition, mood and anxiety disorders, and cardiovascular effects. Thus, to improve long-term patient outcomes and develop novel treatments, it is essential to understand the molecular mechanisms involved in sleep disturbance following TBI. In this effort, we performed transcriptional profiling in an established rodent model of penetrating ballistic brain injury (PBBI) in conjunction with continuous sleep/wake EEG/EMG recording of the first 24 h after injury. Rats subjected to PBBI showed profound differences in sleep architecture. Injured animals spent significantly more time in slow wave sleep and less time in REM sleep compared to sham control animals. To identify PBBI-related transcriptional differences, we then performed transcriptome-wide gene expression profiling at 24 h post-injury, which identified a vast array of immune- related genes differentially expressed in the injured cortex as well as sleep-related genes. Further, transcriptional changes associated with total time spent in various sleep stages were identified. Such molecular changes may underlie the pathology and symptoms that emerge following TBI, including neurodegeneration, sleep disturbance, and mood disorders.