Traumatic Cerebral Microbleeds in the Subacute Phase Are Practical and Early Predictors of Abnormality of the Normal-Appearing White Matter in the Chronic Phase

BACKGROUND AND PURPOSE:In the chronic phase after traumatic brain injury, DTI findings reflect WM integrity. DTI interpretation in the subacute phase is less straightforward. Microbleed evaluation with SWI is straightforward in both phases. We evaluated whether the microbleed concentration in the subacute phase is associated with the integrity of normal-appearing WM in the chronic phase.MATERIALS AND METHODS:Sixty of 211 consecutive patients 18 years of age or older admitted to our emergency department ≤24 hours after moderate to severe traumatic brain injury matched the selection criteria. Standardized 3T SWI, DTI, and T1WI were obtained 3 and 26 weeks after traumatic brain injury in 31 patients and 24 healthy volunteers. At baseline, microbleed concentrations were calculated. At follow-up, mean diffusivity (MD) was calculated in the normal-appearing WM in reference to the healthy volunteers (MD_z). Through linear regression, we evaluated the relation between microbleed concentration and MD_z in predefined structures.RESULTS:In the cerebral hemispheres, MD_z at follow-up was independently associated with the microbleed concentration at baseline (left: B = 38.4 [95% CI 7.5–69.3], P = .017; right: B = 26.3 [95% CI 5.7–47.0], P = .014). No such relation was demonstrated in the central brain. MD_z in the corpus callosum was independently associated with the microbleed concentration in the structures connected by WM tracts running through the corpus callosum (B = 20.0 [95% CI 24.8–75.2], P < .000). MD_z in the central brain was independently associated with the microbleed concentration in the cerebral hemispheres (B = 25.7 [95% CI 3.9–47.5], P = .023).CONCLUSIONS:SWI-assessed microbleeds in the subacute phase are associated with DTI-based WM integrity in the chronic phase. These associations are found both within regions and between functionally connected regions.

The yearly incidence of traumatic brain injury (TBI) is around 300 per 100,000 persons.^1,2 Almost three-quarters of patients with moderate to severe TBI have traumatic axonal injury (TAI).³ TAI is a major predictor of functional outcome,^4,5 but it is mostly invisible on CT and conventional MR imaging.^6,7DTI provides direct information on WM integrity and axonal injury.^5,8 However, DTI abnormalities are neither specific for TAI nor stable over time. Possibly because of the release of mass effect and edema and resorption of blood products, the effects of concomitant (non-TAI) injury on DTI are larger in the subacute than in the chronic phase (>3 months).^4,9,10 Therefore, DTI findings are expected to reflect TAI more specifically in the chronic than in the subacute phase (1 week–3 months).⁴ Even in regions without concomitant injury, the effects of TAI on DTI are dynamic, possibly caused by degeneration and neuroplastic changes.^6,11,12 These ongoing pathophysiological processes possibly contribute to the emerging evidence that DTI findings in the chronic phase are most closely associated with the eventual functional outcome.^12,13Although DTI provides valuable information, its acquisition, postprocessing, and interpretation in individual patients are demanding. SWI, with which microbleeds can be assessed with high sensitivity, is easier to interpret and implement in clinical practice. In contrast to DTI, SWI-detected traumatic microbleeds are more stable¹ except in the hyperacute^14,15 and the late chronic phases.¹⁶ Traumatic cerebral microbleeds are commonly interpreted as signs of TAI. However, the relation is not straightforward. On the one hand, nontraumatic microbleeds may be pre-existing. On the other hand, even if traumatic in origin, microbleeds represent traumatic vascular rather than axonal injury.¹⁷ Indeed, TAI is not invariably hemorrhagic.¹⁸ Additionally, microbleeds may secondarily develop after trauma through mechanisms unrelated to axonal injury, such as secondary ischemia.¹⁸DTI is not only affected by pathophysiological changes but also by susceptibility.¹⁹ The important susceptibility-effect generated by microbleeds renders the interpretation of DTI findings at the location of microbleeds complex. In the chronic phase, mean diffusivity (MD) is the most robust marker of WM integrity.^4,6 For these reasons, we evaluated MD in the normal-appearing WM.Much TAI research focuses on the corpus callosum because it is commonly involved in TAI^5,18,20 and it can reliably be evaluated with DTI,^5,21 and TAI in the corpus callosum is related to clinical prognosis.^6,20 The corpus callosum consists of densely packed WM tracts that structurally and functionally connect left- and right-sided brain structures.²² The integrity of the corpus callosum is associated with the integrity of the brain structures it connects.²³ Therefore, microbleeds in brain structures that are connected through the corpus callosum may affect callosal DTI findings. Analogous to this, microbleeds in the cerebral hemispheres, which exert their function through WM tracts traveling through the deep brain structures and brain stem,^24,25 may affect DTI findings in the WM of the latter.Our purpose was to evaluate whether the microbleed concentration in the subacute phase is associated with the integrity of normal-appearing WM in the chronic phase. We investigated this relation within the cerebral hemispheres and the central brain and between regions that are functionally connected by WM tracts.     

Pump Speed Optimization in Patients Implanted With the HeartMate 3 Device

Background

Pump speed optimization in patients implanted with a ventricular assist device represents a major challenge during the follow-up period. We present our findings on whether combined invasive hemodynamic ramp tests and cardiopulmonary exercise testing (CPX) can help optimize patient management.

Epithelial ovarian cancer risk: A review of the current genetic landscape

Ovarian cancer is the fourth most common cause of cancer-related death in women in the developed world, and one of the most heritable cancers. One of the most significant risk factors for epithelial ovarian cancer (EOC) is a family history of breast and/or ovarian cancer. Combined risk factors can be used in models to stratify risk of EOC, and aid in decisions regarding risk-reduction strategies. Germline pathogenic variants in EOC susceptibility genes including those involved in homologous recombination and mismatch repair pathways are present in approximately 22% to 25% of EOC. These genes are associated with an estimated lifetime risk of EOC of 13% to 60% for BRCA1 variants and 10% to 25% for BRCA2 variants, with lower risks associated with remaining genes. Genome-wide association studies have identified single nucleotide polymorphisms (SNPs) thought to explain an additional 6.4% of the familial risk of ovarian cancer, with 34 susceptibility loci identified to date. However, an unknown proportion of the genetic component of EOC risk remains unexplained. This review comprises an overview of individual genes and SNPs suspected to contribute to risk of EOC, and discusses use of a polygenic risk score to predict individual cancer risk more accurately.     

Trade-offs between acquired and innate immune defenses in humans

Immune defenses provide resistance against infectious disease that is critical to survival. But immune defenses are costly, and limited resources allocated to immunity are not available for other physiological or developmental processes. We propose a framework for explaining variation in patterns of investment in two important subsystems of anti-pathogen defense: innate (non-specific) and acquired (specific) immunity. The developmental costs of acquired immunity are high, but the costs of maintenance and activation are relatively low. Innate immunity imposes lower upfront developmental costs, but higher operating costs. Innate defenses are mobilized quickly and are effective against novel pathogens. Acquired responses are less effective against novel exposures, but more effective against secondary exposures due to immunological memory. Based on their distinct profiles of costs and effectiveness, we propose that the balance of investment in innate versus acquired immunity is variable, and that this balance is optimized in response to local ecological conditions early in development. Nutritional abundance, high pathogen exposure and low signals of extrinsic mortality risk during sensitive periods of immune development should all favor relatively higher levels of investment in acquired immunity. Undernutrition, low pathogen exposure, and high mortality risk should favor innate immune defenses. The hypothesis provides a framework for organizing prior empirical research on the impact of developmental environments on innate and acquired immunity, and suggests promising directions for future research in human ecological immunology.     

Growth of the Social #RadOnc Network on Twitter

PurposeTwitter is an increasingly popular social media platform within the health care community. The objective of this analysis is to characterize the profile of radiation oncology–related tweets and Twitter users over the past 6 years.Methods and MaterialsUsing the web-based social media analytics platform Symplur Signals, we filtered tweets containing at least 1 of the following hashtags or key words: #radonc, #radiationoncology, "rad onc," or "radiation oncology." We evaluated radiation oncology–related Twitter activity between October 2014 and March 2020 for tweet frequency, tweet content, and individuals or groups posting tweets. We identified the most influential Twitter users contributing to radiation oncology–related tweets.ResultsFrom 2014 to 2020, the quarterly volume of radiation oncology–related tweets increased from 5027 to 29,763. Physicians contributed the largest growth in tweet volume. Academic radiation oncologists comprise 60% of the most influential Twitter accounts responsible for radiation oncology–related content. The number of radiation-oncology resident physicians on Twitter increased from 25 to 328 over the past 6 years, and 20% of radiation-oncology residency programs have a Twitter account. Seventy-one percent of radiation oncology–related tweets generated direct communication via mentions, and 59% of tweets contain links to external sources, including scientific articles.ConclusionsThe number of physicians contributing radiation oncology–related Twitter content has increased significantly in recent years. Academic radiation oncologists are the primary influencers of radiation oncology–related Twitter activity. Twitter is used by radiation oncologists to both professionally network and discuss findings related to the field. There remains the opportunity for radiation oncologists to broaden their audience on Twitter to encompass a more diverse community, including patients.