GLORIA

GEOMAR Library Ocean Research Information Access

Your email was sent successfully. Check your inbox.

An error occurred while sending the email. Please try again.

Proceed reservation?

Export
  • 1
    In: Cancer Research, American Association for Cancer Research (AACR), Vol. 80, No. 16_Supplement ( 2020-08-15), p. 3855-3855
    Abstract: Lung cancer remains the leading cause of cancer-related deaths worldwide, with an estimated 1.6 million deaths each year. Non-small cell lung cancer (NSCLC) is with 85% by far the most common subtype of lung cancer, comprising adenocarcinomas and lung squamous cell carcinoma. Mutations in Kirsten rat sarcoma viral oncogene homolog (KRAS), epidermal growth factor receptor (EGFR) and anaplastic lymphoma receptor tyrosine kinase (ALK) genes are common with the worst overall survival for KRAS mutant adenocarcinoma patients. We have established a murine model of lung cancer, wherein expression of oncogenic Kras can be controlled genetically, allowing activation of oncogenic KrasG12D (Kras*) to initiate tumor growth, tumor eradication upon Kras* depletion and re-activation as a means to model relapse. Oncogenic Kras depletion (deactivation) has previously been reported to result in tumor cell apoptosis even in the presence of tumor suppressor loss. However, the mechanisms of apoptosis, the role of the immune system on these changes, and the mechanisms allowing some tumor cells to escape apoptosis, which typically results in tumor relapse, are unknown. Here, we interrogated the immune response in mediating tumor regression and relapse using this genetically engineered models. Multiplex immunohistochemistry as well as CyTOF provided insight into the changes in immune contexture upon Kras* depletion in mice haploinsufficient for tumor suppressor p53 or mutant for p53 (R172H). Interestingly, total number of T cells including cytotoxic T cells (CTLs) was elevated in lung tumors from p53 mutant mice supporting findings of heightened immune activation and overall response to immune therapy with an increased mutational burden. Kras* inactivation and thus inhibition of MAPK signaling resulted in an overall decrease in abundance of CTLs and antigen presenting cells (APC) as well as engagement of CTL with tumor cells and APCs indicating a decrease in immune presence likely due to proceeding tumor cell kill and immune recruitment. However, intracellular distance of CTL with tumor cells indicated active tumor cell kill of the CTLs to eradicate remaining tumor cells. In summary, these findings support recent observation of increased immune activation in tumors with higher mutational load as well as changes mediated by inhibition of MAPK signaling which both maybe harnessed for enhancing future immunotherapies. Citation Format: Nina Steele, Kristena Y. Abdelmalak, Sarah F. Ferris, Jennifer M. Lee, Carlos Espinoza, Yaqing Zhang, Sundaresh Ram, Craig Galban, Nithya Ramnath, Timothy L. Frankel, Marina Pasca di Magliano, Stefanie Galbán. Oncogenic Kras-mediated regulation of the tumor microenvironment in lung cancer [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 3855.
    Type of Medium: Online Resource
    ISSN: 0008-5472 , 1538-7445
    RVK:
    RVK:
    Language: English
    Publisher: American Association for Cancer Research (AACR)
    Publication Date: 2020
    detail.hit.zdb_id: 2036785-5
    detail.hit.zdb_id: 1432-1
    detail.hit.zdb_id: 410466-3
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
  • 2
    Online Resource
    Online Resource
    Institute of Electrical and Electronics Engineers (IEEE) ; 2019
    In:  IEEE Transactions on Image Processing Vol. 28, No. 4 ( 2019-4), p. 1705-1719
    In: IEEE Transactions on Image Processing, Institute of Electrical and Electronics Engineers (IEEE), Vol. 28, No. 4 ( 2019-4), p. 1705-1719
    Type of Medium: Online Resource
    ISSN: 1057-7149 , 1941-0042
    Language: Unknown
    Publisher: Institute of Electrical and Electronics Engineers (IEEE)
    Publication Date: 2019
    detail.hit.zdb_id: 2034319-X
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
  • 3
    Online Resource
    Online Resource
    Inderscience Publishers ; 2023
    In:  International Journal of Image Mining Vol. 4, No. 2 ( 2023), p. 177-192
    In: International Journal of Image Mining, Inderscience Publishers, Vol. 4, No. 2 ( 2023), p. 177-192
    Type of Medium: Online Resource
    ISSN: 2055-6039 , 2055-6047
    Language: English
    Publisher: Inderscience Publishers
    Publication Date: 2023
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
  • 4
    In: Gastroenterology, Elsevier BV, Vol. 162, No. 7 ( 2022-05), p. S-1240-S-1241
    Type of Medium: Online Resource
    ISSN: 0016-5085
    RVK:
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2022
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
  • 5
    In: Frontiers in Physiology, Frontiers Media SA, Vol. 14 ( 2023-4-21)
    Abstract: Purpose: The purpose of this study was to train and validate machine learning models for predicting rapid decline of forced expiratory volume in 1 s (FEV 1 ) in individuals with a smoking history at-risk-for chronic obstructive pulmonary disease (COPD), Global Initiative for Chronic Obstructive Lung Disease (GOLD 0), or with mild-to-moderate (GOLD 1–2) COPD. We trained multiple models to predict rapid FEV 1 decline using demographic, clinical and radiologic biomarker data. Training and internal validation data were obtained from the COPDGene study and prediction models were validated against the SPIROMICS cohort. Methods: We used GOLD 0–2 participants ( n = 3,821) from COPDGene (60.0 ± 8.8 years, 49.9% male) for variable selection and model training. Accelerated lung function decline was defined as a mean drop in FEV 1 % predicted of & gt; 1.5%/year at 5-year follow-up. We built logistic regression models predicting accelerated decline based on 22 chest CT imaging biomarker, pulmonary function, symptom, and demographic features. Models were validated using n = 885 SPIROMICS subjects (63.6 ± 8.6 years, 47.8% male). Results: The most important variables for predicting FEV 1 decline in GOLD 0 participants were bronchodilator responsiveness (BDR), post bronchodilator FEV 1 % predicted (FEV 1 .pp.post), and CT-derived expiratory lung volume; among GOLD 1 and 2 subjects, they were BDR, age, and PRM lower lobes fSAD . In the validation cohort, GOLD 0 and GOLD 1–2 full variable models had significant predictive performance with AUCs of 0.620 ± 0.081 ( p = 0.041) and 0.640 ± 0.059 ( p & lt; 0.001). Subjects with higher model-derived risk scores had significantly greater odds of FEV 1 decline than those with lower scores. Conclusion: Predicting FEV 1 decline in at-risk patients remains challenging but a combination of clinical, physiologic and imaging variables provided the best performance across two COPD cohorts.
    Type of Medium: Online Resource
    ISSN: 1664-042X
    Language: Unknown
    Publisher: Frontiers Media SA
    Publication Date: 2023
    detail.hit.zdb_id: 2564217-0
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
  • 6
    In: Cells, MDPI AG, Vol. 11, No. 4 ( 2022-02-16), p. 699-
    Abstract: Chronic rejection of lung allografts has two major subtypes, bronchiolitis obliterans syndrome (BOS) and restrictive allograft syndrome (RAS), which present radiologically either as air trapping with small airways disease or with persistent pleuroparenchymal opacities. Parametric response mapping (PRM), a computed tomography (CT) methodology, has been demonstrated as an objective readout of BOS and RAS and bears prognostic importance, but has yet to be correlated to biological measures. Using a topological technique, we evaluate the distribution and arrangement of PRM-derived classifications of pulmonary abnormalities from lung transplant recipients undergoing redo-transplantation for end-stage BOS (N = 6) or RAS (N = 6). Topological metrics were determined from each PRM classification and compared to structural and biological markers determined from microCT and histopathology of lung core samples. Whole-lung measurements of PRM-defined functional small airways disease (fSAD), which serves as a readout of BOS, were significantly elevated in BOS versus RAS patients (p = 0.01). At the core-level, PRM-defined parenchymal disease, a potential readout of RAS, was found to correlate to neutrophil and collagen I levels (p 〈 0.05). We demonstrate the relationship of structural and biological markers to the CT-based distribution and arrangement of PRM-derived readouts of BOS and RAS.
    Type of Medium: Online Resource
    ISSN: 2073-4409
    Language: English
    Publisher: MDPI AG
    Publication Date: 2022
    detail.hit.zdb_id: 2661518-6
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
  • 7
    In: Remote Sensing, MDPI AG, Vol. 14, No. 2 ( 2022-01-06), p. 255-
    Abstract: In low-resolution wide-area aerial imagery, object detection algorithms are categorized as feature extraction and machine learning approaches, where the former often requires a post-processing scheme to reduce false detections and the latter demands multi-stage learning followed by post-processing. In this paper, we present an approach on how to select post-processing schemes for aerial object detection. We evaluated combinations of each of ten vehicle detection algorithms with any of seven post-processing schemes, where the best three schemes for each algorithm were determined using average F-score metric. The performance improvement is quantified using basic information retrieval metrics as well as the classification of events, activities and relationships (CLEAR) metrics. We also implemented a two-stage learning algorithm using a hundred-layer densely connected convolutional neural network for small object detection and evaluated its degree of improvement when combined with the various post-processing schemes. The highest average F-scores after post-processing are 0.902, 0.704 and 0.891 for the Tucson, Phoenix and online VEDAI datasets, respectively. The combined results prove that our enhanced three-stage post-processing scheme achieves a mean average precision (mAP) of 63.9% for feature extraction methods and 82.8% for the machine learning approach.
    Type of Medium: Online Resource
    ISSN: 2072-4292
    Language: English
    Publisher: MDPI AG
    Publication Date: 2022
    detail.hit.zdb_id: 2513863-7
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
  • 8
    Online Resource
    Online Resource
    Institute of Electrical and Electronics Engineers (IEEE) ; 2020
    In:  IEEE Access Vol. 8 ( 2020), p. 199562-199572
    In: IEEE Access, Institute of Electrical and Electronics Engineers (IEEE), Vol. 8 ( 2020), p. 199562-199572
    Type of Medium: Online Resource
    ISSN: 2169-3536
    Language: Unknown
    Publisher: Institute of Electrical and Electronics Engineers (IEEE)
    Publication Date: 2020
    detail.hit.zdb_id: 2687964-5
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
  • 9
    Online Resource
    Online Resource
    American Association for Cancer Research (AACR) ; 2021
    In:  Clinical Cancer Research Vol. 27, No. 5_Supplement ( 2021-03-01), p. PO-086-PO-086
    In: Clinical Cancer Research, American Association for Cancer Research (AACR), Vol. 27, No. 5_Supplement ( 2021-03-01), p. PO-086-PO-086
    Abstract: Purpose: Lung cancer has been the leading cause of cancer-related deaths worldwide. To address the clinical need for efficacious treatments, genetically engineered mouse models (GEMMs) have become integral in identifying and evaluating unique pathways that may be exploited as therapeutic targets. Assessment of GEMM tumor burden on histopathological sections performed by manual inspection is both time consuming and prone to subjective bias. Therefore, an interplay of needs and challenges exists for computer aided detection tools, for the accurate and efficient analysis of these histopathology images. Our work demonstrates a simple machine learning approach called sparse principal component analysis (PCA) network, for automated detection of cancerous lesions on histological lung slides stained by hematoxylin and eosin (H & E). Methods: Our method comprises four steps: 1) cascaded sparse PCA; 2) graph-based PCA hashing; 3) block-wise histograms; and 4) support vector machine (SVM) classification. In our proposed architecture, sparse PCA is employed to learn the filter banks of the multiple stages. This is followed by a graph-based PCA hashing and block histograms for indexing and pooling. The meaningful features extracted from this sparse PCA are then fed to an SVM classifier. We tested the proposed sparse PCA network on H & E slides obtained from an inducible KrasG12D lung cancer mouse model. Our dataset consists of N = 21 whole slide histopathology lung images with 9 non-tumor bearing control mice and 12 mice with visible lung tumors. Tumor lesions from 12 lung images with visible tumors were visually identified by three trained individuals, which served as ground truth. The size of each image in our dataset is 2048 × 2048 pixels. Each image was divided into non-overlapping image patches of size 20 × 20 pixels consisting of a total of 12,361 cancer lesion patches and 207,839 non-cancer patches. We used 50% of the data for training and 50% of the data for testing our proposed sparse PCA network. We evaluated our algorithm using conventional metrics that have been used for evaluation of classification algorithms, namely precision (P), recall (R), and coverage measure (F-score). Results: The automatic cancer lesion detection results were compared with manually annotated ground truth. The proposed method achieves a cancer lesion detection accuracy of 97.98% with P = 0.8624, R = 0.9062 and F-score = 0.8790. The proposed method was found to take on average 17 minutes to train and learn a good representation for accurate and efficient classification of cancerous lesions within the images. Conclusion: We demonstrated a simple machine learning methodology for detection of cancerous lesions within histopathological lung images. Experimental results show that the proposed method is able to classify the regions of interest both efficiently and accurately. Future work will focus on feature extraction of individual tumors and tumor location within lungs. Citation Format: Sundaresh Ram, Wenfei Tang, Alexander J. Bell, Cara Spencer, Alexander Buschhuas, Charles R. Hatt, Marina P. di Magliano, Stefanie Galban, Craig J. Galban. Detection of cancer lesions in histopathological lung images using a sparse PCA network [abstract]. In: Proceedings of the AACR Virtual Special Conference on Artificial Intelligence, Diagnosis, and Imaging; 2021 Jan 13-14. Philadelphia (PA): AACR; Clin Cancer Res 2021;27(5_Suppl):Abstract nr PO-086.
    Type of Medium: Online Resource
    ISSN: 1078-0432 , 1557-3265
    RVK:
    Language: English
    Publisher: American Association for Cancer Research (AACR)
    Publication Date: 2021
    detail.hit.zdb_id: 1225457-5
    detail.hit.zdb_id: 2036787-9
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
  • 10
    Online Resource
    Online Resource
    Elsevier BV ; 2018
    In:  Pattern Recognition Vol. 83 ( 2018-11), p. 174-184
    In: Pattern Recognition, Elsevier BV, Vol. 83 ( 2018-11), p. 174-184
    Type of Medium: Online Resource
    ISSN: 0031-3203
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2018
    detail.hit.zdb_id: 1466343-0
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
Close ⊗
This website uses cookies and the analysis tool Matomo. More information can be found here...