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Background Cognitive deficits are common in patients with sepsis. Previous studies in sepsis-associated encephalopathy (SAE) implicated the C-X-C chemokine receptor type (CXCR) 5. The present study used a mouse model of SAE to examine whether CXCR5 down-regulation could attenuate cognitive deficits. Methods Sepsis was induced in adult male C57BL/6 J and CXCR5−/− mice by cecal ligation and puncture (CLP). At 14–18 days after surgery, animals were tested in a Morris water maze, followed by a fear conditioning test. Transmission electron microscopy of hippocampal sections was used to assess levels of autophagy. Primary microglial cultures challenged with lipopolysaccharide (LPS) were used to examine the effects of short interfering RNA targeting CXCR5, and to investigate the possible involvement of the p38MAPK/NF-κB/STAT3 signaling pathway. Results CLP impaired learning and memory and up-regulated CXCR5 in hippocampal microglia. CLP activated hippocampal autophagy, as reflected by increases in numbers of autophagic vacuoles, conversion of microtubule-associated protein 1 light chain 3 (LC3) from form I to form II, accumulation of beclin-1 and autophagy-related gene-5, and a decrease in p62 expression. CLP also shifted microglial polarization to the M1 phenotype, and increased levels of IL-1β, IL-6 and phosphorylated p38MAPK. CXCR5 knockout further enhanced autophagy but partially reversed all the other CLP-induced effects, including cognitive deficits. Similar effects on autophagy and cytokine expression were observed after knocking down CXCR5 in LPS-challenged primary microglial cultures; this knockdown also partially reversed LPS-induced up-regulation of phosphorylated NF-κB and STAT3. The p38MAPK agonist P79350 partially reversed the effects of CXCR5 knockdown in microglial cultures. Conclusions CXCR5 may act via p38MAPK/NF-κB/STAT3 signaling to inhibit hippocampal autophagy during sepsis and thereby contribute to cognitive dysfunction. Down-regulating CXCR5 can restore autophagy and mitigate the proinflammatory microenvironment in the hippocampus.

Background Minimal clinically important difference (MCID) estimates are useful for gauging clinical relevance when interpreting changes or differences in patient-reported outcomes scores. These values are lacking in the setting of elbow trauma. Our primary purpose was to estimate the MCID of the Patient-Reported Outcomes Measurement Information System (PROMIS) physical function (PF) computer adaptive test (CAT), the PROMIS upper extremity (UE) CAT, and the QuickDASH using an anchor-based approach for patients recovering from elbow trauma and related surgeries. Secondarily, we aimed to estimate the MCID using the 1/2 standard deviation method. Materials & methods Adult patients undergoing treatment for isolated elbow injuries between July 2014 and April 2020 were identified at a single tertiary academic medical center. Outcomes, including the PROMIS PF CAT v1.2/2.0, PROMIS UE CAT v1.2, and QuickDASH, were collected via a tablet computer. For inclusion, baseline (6 months before injury up to 11 days postoperatively or after injury) and follow-up (11 to 150 days postoperative or after injury) PF or UE CAT scores were required, as well as a response to an anchor question querying improvement in physical function. The MCID was calculated using (1) an anchor-based approach using the difference in mean score change between anchor groups reporting “No change” and “Slightly Improved/Improved” and (2) the 1/2 standard deviation method. Results Of the 146 included patients, the mean age was 46 ± 18 years and 67 (46%) were women. Most patients (129 of 146 or 88%) were recovering from surgery, and the remaining 12% were recovering from nonoperatively managed fractures and/or dislocations. The mean follow-up was 157 ± 192 days. Scores for each instrument improved significantly between baseline and follow-up. Anchor-based MCID values were calculated as follows: 5.7, 4.6, and 5.3 for the PROMIS PF CAT, PROMIS UE CAT, and QuickDASH, respectively. MCID values estimated using the 1/2 standard deviation method were 4.3, 4.8, and 11.7 for the PROMIS PF CAT, PROMIS UE CAT, and QuickDASH, respectively. Conclusions In the setting of elbow trauma, we propose MCID ranges of 4.3 to 5.7 for the PROMIS PF CAT, 4.6 to 4.8 for the PROMIS UE CAT, and 5.3 to 11.7 for the QuickDASH. These values will provide a framework for clinical relevance when interpreting clinical outcomes studies, or powering clinical trials, for populations recovering from trauma.

Background: Breast cancer is the most common cancer worldwide. There has been emerging interest in studying differences in breast tumor phenotypes between South Asian and South American women given that racial/ethnic disparities in incidence and mortality have been demonstrated in multiple studies. South Asian women are more likely to be diagnosed with an advanced stage breast tumor despite lower incidence than in North American women. There is evidence that computer-extracted nuclear morphological features on H&E slide images may be associated with breast cancer aggressiveness, specifically recurrence and disease free survival. However, studies have mostly focused on North American women. In this work, we evaluated whether there is a difference in computer extracted features of nuclear morphology from H&E tissue slide images between South Asian (SA) and North American (NA) women and we also investigated how these differences could impact the development of population specific breast cancer prognostic models. Methods: H&E slides of breast tumors from patients who were diagnosed with ER+ early stage invasive breast cancer from Tata Memorial Centre, India (SA: 69 (20 recurrence)) and from University Hospitals Cleveland Medical Center (NA: 121 (20 recurrence)), along with outcome information were collected. All slides were digitized on either a Ventana DP 200 or a Roche Ventana iScan HT slide scanner. For each image, a conditional Generative Adversarial Network model was employed to segment the individual nuclei, which were used to generate 241 nuclear features including nuclear architecture, shape, orientation disorder, and texture features. Half of the patients (95) were randomly selected as the training set (Stra) with the remaining patients as the hold-out validation set (Stest). Three elastic net regularized Cox regression models (MSA, MNA, MSA+NA) were trained fitting between the nuclear features and disease free survival (DFS) respectively for SA subset, NA subset, and SA & NA set in Stra. The top five prognostic features were identified respectively from each of the three models (MSA, MNA, MSA+NA) which were further validated on Stest to evaluate their prognostic value in prediction of DFS. Results: We found that the prognostic features identified by MNA and MSA+NA were mostly shape features (three out of five for MNA, four out of five for MSA+NA), while the prognostic features identified by the model specifically trained with the SA population (MSA) were mostly texture features (three out of five), possibly reflecting chromatin patterns in the cell. MSA yielded a better performance (Hazard Ratio=4.99 (p=0.00928, CI=1.32-18.9) from log-rank test between model derived high and low risk categories) on SA population in Stest compared to MNA and MSA+NA. Conclusion: We found that nuclear histomorphometry features were different between breast cancer patients from South Asia and those from North America. The prognostic capability of the computational pathology-based models for South Asian women could be significantly improved by taking into account population-specific information. An additional independent validation set is needed to confirm the preliminary findings presented here. Citation Format: Haojia Li, Kaustav Bera, Paula Toro, Pingfu Fu, Vidya Rao, Shabina Siddique, Aparna Harbhajanka, Haley Sechrist, Zelin Zhang, Sangeeta Desai, Vani Parmar, Anant Madabhushi. Computerized image analysis of nuclear morphological features reveals differences in phenotype and prognosis of disease free survival of early stage ER+ breast cancers for South Asian and North American women [abstract]. In: Proceedings of the 2020 San Antonio Breast Cancer Virtual Symposium; 2020 Dec 8-11; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2021;81(4 Suppl):Abstract nr PS4-45.

IntroductionAlthough the association between blood pressure and vitamin D has been well studied, the effects of dietary magnesium intake on this relationship are still unclear. Thus, this study aimed to determine the effects of dietary magnesium intake on the association between vitamin D and blood pressure.MethodsThe present study analyzed data from the continuous the National Health and Nutrition Examination Survey (NHANES) 2007–2014. We included 8,799 participants aged 20 years or older. Multivariable linear regression was performed to assess the association between vitamin D and systolic blood pressure (SBP) and diastolic blood pressure (DBP). Dietary magnesium intake was stratified by low magnesium intake (ResultsIn this cross-sectional study, we found that vitamin D was negatively related to SBP, but not to DBP. The relationship between vitamin D and SBP was different in the low and high magnesium intake group (β: −0.25 95%Cl: −0.4~0.07 vs β: −0.32 95%Cl: −0.52~-0.12). Furthermore, magnesium intake significantly modified the negative relationship between vitamin D and SBP in most of the models.ConclusionOur research showed that magnesium and vitamin D have an interactive effect in reducing SBP, which may have great importance for clinical medication.

Introduction: Circulating vitamin D concentrations have been associated with the risk of type 2 diabetes (T2D). Magnesium has also been reported to be associated with lower T2D risk. Besides, magnesium is an essential cofactor for vitamin D activation. However, the effect of dietary magnesium intake on the association between vitamin D and the risk of T2D has not been studied comprehensively. Therefore, we designed this cross-sectional study to assess the effect modification of magnesium intake on the association between vitamin D and risk of T2D.Research Design and Methods: The present study analyzed data from the National Health and Nutrition Examination Survey (NHANES) continuously from 2007 to 2014, involving 10,249 participants. By having stratified participants based on magnesium intake category (low magnesium intake Results: In this cross-sectional study, the association of serum vitamin D with the incidence of T2D appeared to differ between the low magnesium intake group and the high magnesium intake group (OR: 0.968, 95%Cl: 0.919–1.02 vs. OR: 0.925, 95%Cl: 0.883–0.97). Furthermore, there was evidence of interaction between vitamin D levels and magnesium intake on decreasing the incidence of T2D (p-value for interaction = 0.001).Conclusions: The results of our study indicated that magnesium intake might affect the association of serum vitamin D with the risk of T2D. Such a finding requires further randomized controlled trials to provide more evidence.

Collagen fiber organization has been found to be implicated in breast cancer prognosis. In this study, we evaluated whether computerized features of Collagen Fiber Orientation Disorder in Tumor-associated Stroma (CFOD-TS) on Hematoxylin & Eosin (H&E) slide images were prognostic of Disease Free Survival (DFS) in early stage Estrogen Receptor Positive (ER+) Invasive Breast Cancers (IBC). A Cox regression model named MCFOD-TS, was constructed using cohort St (N = 78) to predict DFS based on CFOD-TS features. The prognostic performance of MCFOD-TS was validated on cohort Sv (N = 219), a prospective clinical trial dataset (ECOG 2197). MCFOD-TS was prognostic of DFS in both St and Sv, independent of clinicopathological variables. Additionally, the molecular pathways regarding cell cycle regulation were identified as being significantly associated with MCFOD-TS derived risk scores. Our results also found that collagen fiber organization was more ordered in patients with short DFS. Our study provided a H&E image-based pipeline to derive a potential prognostic biomarker for early stage ER+ IBC without the need of special collagen staining or advanced microscopy techniques.

Allogenic hematopoietic stem cell transplant (HCT) is a curative therapy for acute myeloid leukemia (AML). Relapse after HCT is the most common cause of treatment failure and is associated with poor prognosis. Early identification of which patients are at elevated risk of relapse may justify use of aggressive post-HCT treatment options, potentially preventing relapse and treatment failure. In this study, our goal was to predict relapse after HCT in AML patients using quantitative features extracted from digitized Wright-Giemsa stained posttransplant aspirate smears. We collected 39 aspirate specimens from a cohort of 39 AML patients after HCT, of which 25 experienced relapse, while 14 did not. Our approach comprised the following main steps. First, a deep learning model was developed to segment myeloblasts, a cell type in bone marrow that accumulates and characterizes AML. A total of 161 texture and shape descriptors were then extracted from these segmented myeloblasts. The top eight predictive features were identified using a Wilcoxon rank sum test over 100 iterations of 3-fold cross validation. A model was subsequently built employing these features and yielded an average area under the receiver operating characteristic curve of 0.80±0.05 in cross validation. The top eight features include four Haralick texture features and four fractal dimension features. The texture features appear to characterize chromatin patterns in myeloblasts while the fractal features quantify morphological irregularity and complexity of myeloblasts, in alignment with findings previously reported for AML patients post-treatment.

Background: Ductal Carcinoma in Situ (DCIS) is a pre-invasive stage of breast cancer, where malignant cells line the duct but have not spread into other parts of the breast. Oncotype DX (ODX) is a genomic test, which divide patients into three risk of recurrence categories (Low, Intermediate, and High) to help physicians determine if patients require adjuvant therapy. However, ODX is expensive, tissue destructive, and has a turnaround time of 7-10 days. There has been an interest in the use of image analysis of routine H&E histopathology slides to predict the course of the disease; the rationale being that the analytics are able to unearth subtle sub-visual cues regarding disease morphology that may escape visual examination. In this work, we evaluate the role of computer extracted features of nuclear morphology and the necrotic regions from surgically resected specimens to predict ODX categories in patients with DCIS. Methods: H&E slides from breast tissue of 37 patients who were diagnosed with DCIS and underwent a lumpectomy were acquired. Nine of the 37 had high ODX score (higher than 54), while the rest had a low score (lower than 39). All the slides were digitized on a Philips slide scanner. For each image, a watershed algorithm segmented the individual nuclei, which were used to generate 230 nuclei features including nuclear architecture, nuclear shape and nuclear texture features within each candidate breast duct. In addition, we captured the area of necrosis and empty lumen region inside breast ducts to generate features pertaining to tubule packing. The average feature values for each patient were calculated across all the breast ducts in each slide. A 3-fold cross validation scheme with 50 repetitions was used with the Support Vector Machine (SVM) classifier to predict the ODX risk category for each patient. We used a covariance algorithm to select the top 4 features that were independent of each other but relevant to the ODX class label. Results: The top ranked features included features from three categories: nuclei architectural features (standard deviation of triangle area in Delaunay graph, skewness of edge length in Cell Cluster Graph), nuclear texture (standard deviation of Haralick matrix intensity) possibly reflecting chromatin patterns in the cell, and the Tubule Packing Ratio, a measure of the ratio of necrosis area and empty lumen area inside the breast ducts compared to the whole breast duct area. The SVM in conjunction with these 4 features yielded a mean area under receive operator characteristic curve (AUC) of 0.95 in correctly predicting high and low ODX risk categories. Conclusion: We found that our histomorphometry features pertaining to nuclear arrangement, nuclear texture and necrosis could differentiate between DCIS patients with high and low ODX risk categories. Additional independent validation of the approach is needed to confirm the preliminary findings presented here. Citation Format: Li H, Whitney J, Thawani R, Gilmore H, Badve S, Madabhushi A. Quantitative image features of nuclear and tubule architecture distinguish high and low oncotype DX risk categories of ductal carcinoma in situ from H&E tissue images [abstract]. In: Proceedings of the 2017 San Antonio Breast Cancer Symposium; 2017 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2018;78(4 Suppl):Abstract nr P4-09-12.

Background: Recent research has shown that collagen fiber arrangement in the tumor microenvironment plays a key role determining breast cancer prognosis. In this work, we present a new quantitative histomorphometric approach to identify and evaluate whether Collagen Fiber Orientation Disorder from Tumor-associated Stroma (CFOD-TS) on digitized H&E slides are associated with disease free survival (DFS) in estrogen receptor positive (ER+) breast cancer patients. Method: 234 and 171 ER+ invasive breast cancer patient’s slides were acquired and digitized from E2197 (training set) and TCGA-BRCA (test set) respectively. Collagen fibers were first segmented out on the digitized H&E slides by a combination of Derivative of Gaussian filters. The CFOD-TS features were then extracted from tumor patches in the whole slide image. A multiple linear regression model (MLR) was then trained in conjunction with the CFOD-TS features distinguishing patients likely to recur from not (>15 year follow-up period) to predict DFS for the non-censored patients. Result: The CFOD-TS discriminated patients (p=0.01) with and without recurrence and was significantly associated with DFS on ECOG 2197. The univariate and multivariate analysis on the training set were summarized in table 1 and table 2. CFOD-TS was also independently prognostic on patients in the TCGA for stratifying patients into low- and high-risk groups with p=0.009 via a log rank test and a hazard ratio (HR) of 3.09 (95% CI =1.33~7.18, p=0.0088). HR between the two risk groups was 7.11 (n=104, 95% CI =1.53~32.94, p=0.01) for stage I / II tumors and 3.03 (n=39, 95% CI =0.79-8.67, p=0.11) for stage III / IV cancers. In a multivariate setting, CFOD-TS (HR -3.7) was more significant than T-stage (HR-2.88) in stratifying risk. (Table2) Table 1: Univariate DFS analysis on the training setP-valueHR(95% CI)Case numberCFDO-TS derived risk groups0.00272.04 (1.28 – 3.26)234low / intermediate Oncotype DX (ODx) vs High ODx0.940.96(0.36 – 2.56)68low ODx vs intermediate / High ODx0.00323.49 (1.52 – 8.01)68grade0.300.82 (0.56 – 1.19)209 Table 2: Multivariate DFS analysis on the training set (n=68) and test set (n = 171)VariableP-valueHR(95% CI)ECOG 2197low ODx vs intermediate / High ODx0.00144.00 (1.71 – 9.34)CFDO-TS derived risk groups: Low vs high-risk0.0192.53 (1.17 – 5.47)TCGAT-stage: T1/T2 vs T3/T40.032.88 (1.10 -7.50)N-stage: N0 vs N1/N2/N30.411.47 (0.59 - 3.70)CFOD-TS derived risk groups: Low vs high-risk0.0033.70 (1.57 - 8.67) Conclusion: Over-expression of CFOD-TS was found to be independently associated with lower likelihood of recurrence and could potentially serve as a prognostic marker of outcome for ER+ breast cancer. Citation Format: Haojia Li, Kaustav Bera, Hannah Gilmore, Nancy E Davidson, Lori J Goldstein, Anant Madabhushi. Histomorphometric measure of disorder of collagen fiber orientation is associated with risk of recurrence in ER+ breast cancers in ECOG-ACRIN E2197 and TCGA-BRCA [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr P5-06-16.

Background: Ductal Carcinoma in Situ (DCIS) of the breast comprises a diverse group of cancerous lesions confined to the breast ducts. About 25% of all breast cancers in the US are DCIS and women with DCIS have a 10 times higher lifetime risk of progressing to invasive breast cancer (IBC). While DCIS is treated routinely with breast conserving therapy which includes a lumpectomy followed by adjacent radiation therapy (RT). However, studies have shown that RT can be omitted in women with a lower risk of recurrence/progression. Gene expression methods like Oncotype Dx can be used to risk stratify patients but are expensive, tissue-destructive and time consuming. Recently, there has been substantial interest in developing computerized approaches to quantitatively extract tumor histomorphometric (QH) features from digitized images of tissue slides to predict cancer outcome. In this work, we evaluate the role of QH features related to the architecture, shape, orientation disorder and texture of cancer nuclei from lumpectomy specimens of DCIS to predict disease-free survival (DFS). Methods: H&E whole slide images (WSI) and corresponding outcome information for 121 DCIS patients (61 with progression/recurrence and 60 without) from the no adjuvant treatment arm of the UK/ANZ DCIS trial were retrieved and collected from Queen Mary university of London. WSIs were scanned by 3DHistech scanner at 43x magnification. For each image, a deep learning based generative adversarial network model was employed to segment the cancer nuclei, following which 379 nuclear features from four feature families including architecture, shape, orientation disorder and Haralick texture features were extracted within each candidate breast duct in the WSI and averaged across the multiple cancerous breast ducts for each patient. The dataset was randomly split into training (D1, n=60) and a hold-out validation set (D2,n=61) with events split equally in the two sets. For each of the four feature families, a set of 6 top features was identified by an elastic Cox Model to predict DFS (time from diagnosis to recurrence/invasive breast cancer or death whichever is sooner) onD1. The feature sets giving significant prognostic value (assessed by log rank test between the feature derived risk groups) were combined, from which 6 top features were further identified by a Cox Model to predict DFS on D1. Results: The top ranked QH features included features from three feature families: 2 shape features, 1 orientation disorder feature and 3 texture features. The Elastic Cox model predicted DFS on D1 with hazard ratio (HR) of 7.29 (95% CI = 2.94 ~18.05, p=1.75e-05) and on D2 with HR of 2.07 (95% CI = 1.02 ~2.94, p=0.045). Conclusion: Computer extracted nuclear histomorphometric features pertaining to shape, orientation disorder and texture were associated with disease free survival for DCIS. Additional independent multi-site validation of the approach is planned to further confirm the preliminary findings presented here. Citation Format: Haojia Li, Kaustav Bera, Hannah Gilmore, Zelin Zhang, Jack Cuzick, Mangesh A Thorat, Anant Madabhushi. Computer extracted features of nuclear shape, orientation disorder and texture from H&E Whole slide images are associated with disease-free survival in ductal carcinoma in situ (DCIS) [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr P5-06-15.

Pedestrian detection has become a significant research topic in the field of computer vision. The performance of existing methods based on deep learning is not so good in pedestrian detection for complex background. Considering the problem of pedestrian detection in complex scenes with small and crowded objects, we propose a SSD-based crowded pedestrian detection method in this paper. Firstly, we increase density of default boxes on the horizontal direction by setting an offset, which can effectively eliminate the influence of missing matching default boxes and separate a person from the crowd much easier. So our detector is more suitable for complex scenes. Secondly, SSD is designed for general object detection, thus it is unfit for pedestrian detection because of the large aspect ratio of pedestrians. Therefore, we adopt abnormal 5*1 convolutional kernels instead of the standard 3*3 ones in order to adapt to pedestrian detection. Finally, we present experimental results on public benchmark datasets including Caltech dataset and INRIA dataset, which indicate that our method has better performance for pedestrian detection.