Citations

Abstract Activation of innate immunity in the brain is a prominent feature of Alzheimer's disease (AD). The present study investigated the regulation of innate immunity by young serum in a transgenic AD mouse model. We found that young serum significantly reduced the number of neutrophils and microglial reactivity in the brains of APP/PS1 mice. Neutrophil depletion via Ly6G neutralizing antibodies mimicked the benefits of young serum on AD brain functions. Serum proteomic analysis of young serum revealed significant enrichment of the factors VEGF-A and CXCL1, which are crucial for neutrophil migration and chemotaxis, leukocyte migration, and cell chemotaxis. Intravenously injected VEGF-A reversed Aβ-induced decreases in Cdk5 and increases in CXCL1 in vitro and blocked neutrophil infiltration into the AD brain. Endothelial Cdk5 overexpression conferred an inhibitory effect on CXCL1 and neutrophil infiltration and thereby restored memory in APP/PS1 mice. Our data uncover a previously unknown link between blood-derived VEGF signaling and neutrophil infiltration and provide a rationale for targeting endothelial Cdk5 signaling as a potential therapeutic strategy for AD.

Abstract Accurate estimation of mammographic breast density could aid in augmenting breast cancer risk assessment for women undergoing breast screening with full-field digital mammography (FFDM). Breast density can be estimated from FFDM and is most commonly assessed in the clinic by visual grading into one of the four categories defined by the American College of Radiology BI-RADS. However, BI-RADS density assessment is highly subjective and does not provide a quantitative, continuous measure of percent density (PD), which would allow for more refined risk stratification and assessment of density changes. Here, we introduce Deep-LIBRA, an artificial intelligence (AI) tool for fully-automated assessment of breast PD from FFDM images. Two key modules form the core of Deep-LIBRA: 1) an implementation of a modified U-Net architecture for breast segmentation and 2) a radiomic machine learning module that performs PD estimation within the segmented breast region. To develop and validate Deep-LIBRA, raw (i.e., "For Processing") FFDM images (Selenia Dimensions, Hologic Inc.) acquired at two breast cancer screening practices were retrospectively analyzed. For the breast segmentation module, we used a total of 12,100 FFDM studies from 2,200 individual women and a 90%-10% split-sample training-validation approach, using the Dice coefficient to evaluate the accuracy of Deep-LIBRA versus ground-truth manual breast segmentation. For the PD estimation module we used a total of 3,304 FFDM images from 1,652 individual women; manual PD scores obtained with the widely used Cumulus software were used as the "gold standard" in a three-fold cross-validation setting to assess the accuracy of Deep-LIBRA in PD estimation. PD estimates from Deep-LIBRA were also compared with breast density estimates from the commercially available Volpara software. Breast segmentation had a Dice coefficient of 95.31% when compared to ground-truth manual breast segmentation in the validation set. Deep-LIBRA average differences from ground-truth PD scores in the three cross-validation folds were 4.91%, 4.65%, and 4.22%, while Volpara had corresponding average differences of 6.20%, 6.01%, and 5.94%. Deep-LIBRA PD scores were also significantly different from Volpara PD (t-test p-value < 0.001 in all three folds). Preliminary evaluation results show that Deep-LIBRA is a promising AI approach for accurately assessing PD from FFDM images. Citation Format: Omid Haji Maghsoudi, Scott Christopher, Aimilia Gastounioti, Lauren Pantalone, Fang-Fang Wu, Eric A. Cohen, Winham Stacey, Emily F. Conant, Celine Vachon, Despina Kontos. Deep-LIBRA: An artificial intelligence approach for fully-automated assessment of breast density in digital mammography [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 2600.

Abstract Most patients with coronavirus disease 2019 (COVID-19) have mild to moderate illness not requiring hospitalization. However, no study has detailed the evolution of symptoms in the first month of illness. At our institution, we conducted remote (telephone and video) visits for all adult outpatients diagnosed with COVID-19 within 24 h of a positive nasopharyngeal polymerase chain test for SARS-CoV-2. We repeated regular video visits at 7, 14, and 28 days after the positive test, retrospectively reviewed the prospective data collected in the remote visits, and constructed a week by week profile of clinical illness, through week 4 of illness. We reviewed the courses of 458 symptomatic patients diagnosed between March 12, 2020, and June 22, 2020, and characterized their weekly courses. Common initial symptoms included fever, headache, cough, and chest pain, which frequently persisted through week 3 or longer. Upper respiratory or gastrointestinal symptoms were much shorter lived, present primarily in week 1. Anosmia/ageusia peaked in weeks 2 to 3. Emergency department visits were frequent, with 128 visits in the 423 patients who were not hospitalized and 48 visits among the 35 outpatients (7.6%) who were eventually hospitalized (2 subsequently died). By the fourth week, 28.9% said their illness had completely resolved. After the 4-week follow up, 20 (4.7%) of the 423 nonhospitalized patients had further medical evaluation and management for subacute or chronic COVID-19 symptoms. Mild to moderate outpatient COVID-19 is a prolonged illness, with evolving symptoms commonly lasting into the fourth week of illness.

Abstract Importance: The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections outbreak in China is now a global issue. There is only a limited understanding of the clinical characteristics of patients with SARS-CoV-2 infections is available.Objective:To describe the characteristics, management strategies, and outcomes of critically ill patients with SARS-CoV-2 infection.Design, Setting, and Patients: This is aretrospective, multi-center case series of 50 critically ill patients with confirmed SARS-CoV-2 infection who were admitted at Zhongnan Hospital of Wuhan University and Wuhan Pulmonary Hospital in Wuhan, China, from January 8 to February 9, 2020.Exposures:Documented Corona Virus Disease, 2019 (COVID-19).Main Outcome Measures: Demographic, clinical, laboratory, imaging data were collected along with management strategies, complications and outcomes of enrolled individuals. Results Fifty critically ill patients with SARS-CoV-2 infections were enrolled. Their median age was 62 (range, 29-92) [IQR,49.5-69.0] years, 68% were male, and 28 (56%) patients had comorbidities, the most common being hypertension. In this cohort, 20(40%) patients survived ,16(32%) patients died, and the rest remained hospitalized. The invasive mechanical ventilator was used in 36(72%) patients with 15(30%) of them requiring prone positioning, and 17(34%) switched to ECMO. The compliance scores of lungs (Cstat)on the day of ICU admission among survivors were higher than those in non-survivors [42.0(18.0-47.0), vs. 19.5(14.0-24.2), p=0.038].The blood IL-6 levels and neutrophils counts at the first day of ICU admission were significantly higher in non-survivors compared to survivors [123.7(85.3-228.8), vs. 20.2(6.8-67.2) ng/ml, p=0.025 for IL-6, and 20.2(6.8-67.2) vs. 4.01(1.99-7.05) × 10⁹/L, p=0.02 for neutrophils counts].The heart rates, PaCO2, lung injury scale (LIS), and positive end-expiratory pressure levels were constantly higher for 10 days in non-survivors than those who survived (p<0.05). The frequency of vasopressor uses and neuromuscular blockers was higher in non-survivors from day 1 to day 10 compared to survivors (p<0.05). In the whole cohort, the most common complications were ARDS (97%), shock (44%), arrhythmia (38%), acute cardiac injury (26%), and acute kidney injury (22%). A secondary bacterial infection was noted in 17(34%) patients. Univariate analysis indicated that lower lung complianceand higher neutrophil counts at the day of ICU admission were related to higher mortality (p-0.03, and 0.04, respectively)ConclusionWe demonstrated that SARS-CoV-2 infection-related critical illness predominantly affected old individuals with comorbidities and characterized by severe hypoxemic respiratory failure, often requiring prolonged mechanical ventilation and rescue therapies. Low lung compliance and persistently elevated PaCO2 indicated poor outcomes.

Abstract Gene expression profiling has identified molecular subtypes that classify invasive breast cancers into distinct categories that vary in their clinical behavior and response to treatment. These subtypes highlight the many possible biologically and clinically distinct types of breast cancer. Immunohistochemical (IHC) staining of tumor sections using antibody panels has been used as a surrogate for the molecular subtypes identified through gene expression profiling, resulting in a set of intrinsic molecular subtypes. Given the heterogeneity within breast cancer, one might expect that different risk factors influence specific subtypes of breast cancer through various etiologic pathways. We evaluated whether percent mammographic density (MD), the proportion of fibroglandular or white tissue on a mammogram and one of the strongest and most consistent risk factors for breast cancer, is associated equally with all intrinsic molecular subtypes. Data were pooled from six cohort or case-control studies including 3389 women with invasive breast cancer and 9860 without, who underwent screening mammography. Percent MD was assessed from digitized film-screen mammograms (for cases, at a median 4 years prior to diagnosis) using a computer-assisted threshold technique, and categorized as 0-10%, 11-25%, 26-50% and 51%+. Receptor status was abstracted from clinical pathology records and supplemented by IHC staining of tumor sections or microarrays. With estrogen receptor (ER), progesterone receptor (PR) and human epidermal growth factor receptor (HER2) information, we classified tumors into the intrinsic molecular subtypes: Luminal A ( ER+ and/or PR+ and HER2- and grade 1 or 2), Luminal B (ER+ and/or PR+ and HER2+ or Luminal A and grade 3), HER2 expressing (HER2+/ER-/PR-) and triple negative (ER-/PR-/HER2-). For cancers found to be triple negative (TN), we also stained for epidermal growth factor receptor (EGFR) and cytokeratins (CK) 5/6 to differentiate basal-like tumors (positive for EGFR and/or CK 5/6) from the unclassifieds (negative on both EGFR and CK 5/6). We used polytomous logistic regression to calculate the odds (OR) of each of the intrinsic subtypes of breast cancer by categories of percent MD, adjusting for age, BMI and study. Contrasts were constructed and tested within the polytomous model framework to investigate differences in the strength of association of percent MD across subtypes. On average, cases were slightly older (56.4 years [SD=11.3]) than controls (55.8 [10.9]). Of the 3389 invasive breast cancer cases, 2146 (63%) were classified as Luminal A, 709 (21%) as Luminal B, 169 (5%) as HER-2 expressing, and 365(11%) as triple negative (TN). Of the TN tumors, 203 had tumor tissue available and were evaluated for CK 5/6 and EGFR, with 167 (82%) classified as basal-like and 36 (18%) as unclassified. Percent (%) MD was associated with breast cancer risk across all intrinsic subtypes. For Luminal A, compared to women with 11-25% MD (reference), women with 0-10% MD had a reduced risk of breast cancer (OR=0.64 [95% confidence interval (CI): 0.55, 0.74]) while women with 26-50% MD had an OR=1.5 (1.3,1.7) and women with 51%+ MD had the highest risk, OR=2.4 (2.0, 2.8). Similar associations were seen for Luminal B, HER2 expressing, and TN across percent MD categories. The small differences among the percent MD-breast cancer associations observed for the different subtypes were not statistically significant, either when comparing the four (Luminal A, Luminal B, HER-2 expressing and TN; p=0.61) or five categories of subtypes (Luminal A, Luminal B, HER-2 expressing, basal-like, unclassified; p=0.66). Our results suggest percent MD is a risk factor for all intrinsic molecular subtypes. Additional study is needed to examine whether these results hold for younger and older aged women. Understanding the importance of mammographic density measures for subtypes of breast cancer has significance for development of subtype-specific risk models. Citation Format: Celine Vachon, Rulla Tamimi, Yunn-Yi Chen, Christopher Scott, Kimberly Bertrand, Matthew Jensen, Daniel Visscher, Aaron Norman, Fergus Couch, John Shepherd, Bo Fan, Fang-Fang Wu, Lin Ma, Andrew Beck, Steve Cummings, V. Shane Pankratz, Karla Kerlikowske. Mammographic density: A risk factor for all breast cancers or only specific subtypes? [abstract]. In: Proceedings of the Eighth AACR Conference on The Science of Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; Nov 13-16, 2015; Atlanta, GA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2016;25(3 Suppl):Abstract nr IA22.