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  1. Article ; Online: Ultrasound-visible engineered bacteria for tumor chemo-immunotherapy.

    Yang, Yaozhang / Wang, Yuanyuan / Zeng, Fengyi / Chen, Yuhao / Chen, Zhiyi / Yan, Fei

    Cell reports. Medicine

    2024  , Page(s) 101512

    Abstract: Our previous work developed acoustic response bacteria, which enable the precise tuning of transgene expression through ultrasound. However, it is still difficult to visualize these bacteria in order to guide the sound wave to precisely irradiate them. ... ...

    Abstract Our previous work developed acoustic response bacteria, which enable the precise tuning of transgene expression through ultrasound. However, it is still difficult to visualize these bacteria in order to guide the sound wave to precisely irradiate them. Here, we develop ultrasound-visible engineered bacteria and chemically modify them with doxorubicin (DOX) on their surfaces. These engineered bacteria (Ec@DIG-GVs) can produce gas vesicles (GVs), providing a real-time imaging guide for remote hyperthermia high-intensity focused ultrasound (hHIFU) to induce the expression of the interferon (IFN)-γ gene. The production of IFN-γ can kill tumor cells, induce macrophage polarization from the M2 to the M1 phenotype, and promote the maturation of dendritic cells. DOX can be released in the acidic tumor microenvironment, resulting in immunogenic cell death of tumor cells. The concurrent effects of IFN-γ and DOX activate a tumor-specific T cell response, producing the synergistic anti-tumor efficacy. Our study provides a promising strategy for bacteria-mediated tumor chemo-immunotherapy.
    Language English
    Publishing date 2024-04-12
    Publishing country United States
    Document type Journal Article
    ISSN 2666-3791
    ISSN (online) 2666-3791
    DOI 10.1016/j.xcrm.2024.101512
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article: Nanosized Contrast Agents in Ultrasound Molecular Imaging.

    Zeng, Fengyi / Du, Meng / Chen, Zhiyi

    Frontiers in bioengineering and biotechnology

    2021  Volume 9, Page(s) 758084

    Abstract: Applying nanosized ultrasound contrast agents (nUCAs) in molecular imaging has received considerable attention. nUCAs have been instrumental in ultrasound molecular imaging to enhance sensitivity, identification, and quantification. nUCAs can achieve ... ...

    Abstract Applying nanosized ultrasound contrast agents (nUCAs) in molecular imaging has received considerable attention. nUCAs have been instrumental in ultrasound molecular imaging to enhance sensitivity, identification, and quantification. nUCAs can achieve high performance in molecular imaging, which was influenced by synthetic formulations and size. This review presents an overview of nUCAs from different synthetic formulations with a discussion on imaging and detection technology. Then we also review the progress of nUCAs in preclinical application and highlight the recent challenges of nUCAs.
    Language English
    Publishing date 2021-11-29
    Publishing country Switzerland
    Document type Journal Article ; Review
    ZDB-ID 2719493-0
    ISSN 2296-4185
    ISSN 2296-4185
    DOI 10.3389/fbioe.2021.758084
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Article ; Online: Genetic Modulation of Biosynthetic Gas Vesicles for Ultrasound Imaging.

    Fu, Meijun / Wang, Yuanyuan / Wang, Jieqiong / Hao, Yongsheng / Zeng, Fengyi / Zhang, Zhaomeng / Du, Jianxiong / Long, Huan / Yan, Fei

    Small (Weinheim an der Bergstrasse, Germany)

    2024  , Page(s) e2310008

    Abstract: Gas vesicles (GVs) from microorganisms are genetically air-filled protein nanostructures, and serve as a new class of nanoscale contrast agents for ultrasound imaging. Recently, the genetically encoded GV gene clusters have been heterologously expressed ... ...

    Abstract Gas vesicles (GVs) from microorganisms are genetically air-filled protein nanostructures, and serve as a new class of nanoscale contrast agents for ultrasound imaging. Recently, the genetically encoded GV gene clusters have been heterologously expressed in Escherichia coli, allowing these genetically engineered bacteria to be visualized in vivo in a real-time manner by ultrasound. However, most of the GV genes remained functionally uncharacterized, which makes it difficult to regulate and modify GVs for broad medical applications. Here, the impact of GV proteins on GV formation is systematically investigated. The results first uncovered that the deletions of GvpR or GvpU resulted in the formation of a larger proportion of small, biconical GVs compared to the full-length construct, and the deletion of GvpT resulted in a larger portion of large GVs. Meanwhile, the combination of gene deletions has resulted in several genotypes of ultrasmall GVs that span from 50 to 20 nm. Furthermore, the results showed that E. coli carrying the ΔGvpCRTU mutant can produce strong ultrasound contrast signals in mouse liver. In conclusion, the study provides new insights into the roles of GV proteins in GV formation and produce ultrasmall GVs with a wide range of in vivo research.
    Language English
    Publishing date 2024-03-27
    Publishing country Germany
    Document type Journal Article
    ZDB-ID 2168935-0
    ISSN 1613-6829 ; 1613-6810
    ISSN (online) 1613-6829
    ISSN 1613-6810
    DOI 10.1002/smll.202310008
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  4. Article ; Online: Application of Genetically Encoded Molecular Imaging Probes in Tumor Imaging.

    Du, Meng / Wang, Ting / Yang, Yaozhang / Zeng, Fengyi / Li, Yue / Chen, Zhiyi

    Contrast media & molecular imaging

    2022  Volume 2022, Page(s) 5473244

    Abstract: In recent years, imaging technology has made rapid progress to improve the sensitivity of tumor diagnostic. With the development of genetic engineering and synthetic biology, various genetically encoded molecular imaging probes have also been extensively ...

    Abstract In recent years, imaging technology has made rapid progress to improve the sensitivity of tumor diagnostic. With the development of genetic engineering and synthetic biology, various genetically encoded molecular imaging probes have also been extensively developed. As a biomedical imaging method with excellent detectable sensitivity and spatial resolution, genetically encoded molecular imaging has great application potential in the visualization of cellular and molecular functions during tumor development. Compared to chemosynthetic dyes and nanoparticles with an imaging function, genetically encoded molecular imaging probes can more easily label specific cells or proteins of interest in tumor tissues and have higher stability and tissue contrast
    MeSH term(s) Humans ; Molecular Imaging/methods ; Molecular Probes ; Nanoparticles ; Neoplasms/diagnostic imaging ; Neoplasms/genetics
    Chemical Substances Molecular Probes
    Language English
    Publishing date 2022-08-27
    Publishing country England
    Document type Journal Article ; Review ; Research Support, Non-U.S. Gov't
    ZDB-ID 2232678-9
    ISSN 1555-4317 ; 1555-4309
    ISSN (online) 1555-4317
    ISSN 1555-4309
    DOI 10.1155/2022/5473244
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

    Zs.A 6273: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 2021: Bestellungen von Artikeln über das Online-Bestellformular
    ab Jg. 2022: Lesesaal (EG)

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  5. Article ; Online: Evaluation of oocyte maturity using artificial intelligence quantification of follicle volume biomarker by three-dimensional ultrasound.

    Liang, Xiaowen / Liang, Jiamin / Zeng, Fengyi / Lin, Yan / Li, Yuewei / Cai, Kuan / Ni, Dong / Chen, Zhiyi

    Reproductive biomedicine online

    2022  Volume 45, Issue 6, Page(s) 1197–1206

    Abstract: Research question: Can a novel deep learning-based follicle volume biomarker using three-dimensional ultrasound (3D-US) be established to aid in the assessment of oocyte maturity, timing of HCG administration and the individual prediction of ovarian ... ...

    Abstract Research question: Can a novel deep learning-based follicle volume biomarker using three-dimensional ultrasound (3D-US) be established to aid in the assessment of oocyte maturity, timing of HCG administration and the individual prediction of ovarian hyper-response?
    Design: A total of 515 IVF cases were enrolled, and 3D-US scanning was carried out on HCG administration day. A follicle volume biomarker established by means of a deep learning-based segmentation algorithm was used to calculate optimal leading follicle volume for predicting number of mature oocytes retrieved and optimizing HCG trigger timing. Performance of the novel biomarker cut-off value was compared with conventional two-dimensional ultrasound (2D-US) follicular diameter measurements in assessing oocyte retrieval outcome. Moreover, demographics, infertility work-up and ultrasound biomarkers were used to build models for predicting ovarian hyper-response.
    Results: On the basis of the deep learning method, the optimal cut-off value of the follicle volume biomarker was determined to be 0.5 cm
    Conclusions: Deep learning segmentation methods and multivariate classifiers based on 3D-US were found to be potentially effective approaches for assessing mature oocyte retrieval outcome and individual prediction of ovarian hyper-response.
    MeSH term(s) Female ; Animals ; Ovulation Induction/methods ; Artificial Intelligence ; Oocytes/physiology ; Prospective Studies ; Oocyte Retrieval/methods ; Biomarkers ; Fertilization in Vitro/methods
    Chemical Substances Biomarkers
    Language English
    Publishing date 2022-07-28
    Publishing country Netherlands
    Document type Journal Article
    ZDB-ID 2113823-0
    ISSN 1472-6491 ; 1472-6483
    ISSN (online) 1472-6491
    ISSN 1472-6483
    DOI 10.1016/j.rbmo.2022.07.012
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

    Zs.A 5792: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
    Jg. 1995 - 2021: Lesesall (2.OG)
    ab Jg. 2022: Lesesaal (EG)

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  6. Article ; Online: CR-Unet-Based Ultrasonic Follicle Monitoring to Reduce Diameter Variability and Generate Area Automatically as a Novel Biomarker for Follicular Maturity.

    Liang, Xiaowen / Fang, Jinghui / Li, Haoming / Yang, Xin / Ni, Dong / Zeng, Fengyi / Chen, Zhiyi

    Ultrasound in medicine & biology

    2020  Volume 46, Issue 11, Page(s) 3125–3134

    Abstract: Follicle size is closely related to ovarian function and is an important biomarker in transvaginal ultrasound examinations for assessing follicular maturity during an assisted reproduction cycle. However, manual measurement is time consuming and subject ... ...

    Abstract Follicle size is closely related to ovarian function and is an important biomarker in transvaginal ultrasound examinations for assessing follicular maturity during an assisted reproduction cycle. However, manual measurement is time consuming and subject to high inter- and intra- observer variability. Based on the deep learning model CR-Unet described in our previous study, the aim of our present study was to investigate further the feasibility of using this model in clinical practice by validating its performance in reducing the inter- and intra-observer variability of follicle diameter measurement. This study also investigated whether follicular area is a better biomarker than diameter in assessing follicular maturity. Data on 106 ovaries and 230 follicles collected from 80 cases of single follicular cycles and 26 cases of multiple follicular cycles constituted the validation set. Intra-observer variability was 0.973 and 0.982 for the senior sonographer and junior sonographer in single follicular cycles and 0.979 (0.971, 0.985) and 0.920 (0.892, 0.943) in multiple follicular cycles, respectively, while CR-Unet had no intra-group variation. Bland-Altman plot analysis indicated that the 95% limits of agreement between senior sonographer and CR-Unet (-2.1 to 1.1 mm, -2.02 to 0.75 mm) were smaller than those between senior sonographer and junior sonographer (-1.51 to 1.15 mm, -2.1 to 1.56 mm) in single and multiple follicular cycles. The average operating times of diameter measurement taken by the junior sonographer, senior sonographer and CR-Unet were 7.54 ± 1.8, 4.87 ± 0.84 and 1.66 ± 0.76 s, respectively (p < 0.001). Correlation analysis indicated that both manual and automated follicular area correlated better with follicular volume than diameter. The deep learning algorithm and the new biomarker of follicular area hold potential for clinical application of ultrasonic follicular monitoring.
    MeSH term(s) Adult ; Female ; Humans ; Observer Variation ; Organ Size ; Ovarian Follicle/anatomy & histology ; Ovarian Follicle/diagnostic imaging ; Prospective Studies ; Ultrasonography/methods
    Language English
    Publishing date 2020-08-22
    Publishing country England
    Document type Journal Article ; Research Support, Non-U.S. Gov't ; Validation Study
    ZDB-ID 186150-5
    ISSN 1879-291X ; 0301-5629
    ISSN (online) 1879-291X
    ISSN 0301-5629
    DOI 10.1016/j.ultrasmedbio.2020.07.020
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

    Zs.A 963: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
    Jg. 1995 - 2021: Lesesall (1.OG)
    ab Jg. 2022: Lesesaal (EG)

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  7. Article ; Online: Contrastive rendering with semi-supervised learning for ovary and follicle segmentation from 3D ultrasound.

    Yang, Xin / Li, Haoming / Wang, Yi / Liang, Xiaowen / Chen, Chaoyu / Zhou, Xu / Zeng, Fengyi / Fang, Jinghui / Frangi, Alejandro / Chen, Zhiyi / Ni, Dong

    Medical image analysis

    2021  Volume 73, Page(s) 102134

    Abstract: Segmentation of ovary and follicles from 3D ultrasound (US) is the crucial technique of measurement tools for female infertility diagnosis. Since manual segmentation is time-consuming and operator-dependent, an accurate and fast segmentation method is ... ...

    Abstract Segmentation of ovary and follicles from 3D ultrasound (US) is the crucial technique of measurement tools for female infertility diagnosis. Since manual segmentation is time-consuming and operator-dependent, an accurate and fast segmentation method is highly demanded. However, it is challenging for current deep-learning based methods to segment ovary and follicles precisely due to ambiguous boundaries and insufficient annotations. In this paper, we propose a contrastive rendering (C-Rend) framework to segment ovary and follicles with detail-refined boundaries. Furthermore, we incorporate the proposed C-Rend with a semi-supervised learning (SSL) framework, leveraging unlabeled data for better performance. Highlights of this paper include: (1) A rendering task is performed to estimate boundary accurately via enriched feature representation learning. (2) Point-wise contrastive learning is proposed to enhance the similarity of intra-class points and contrastively decrease the similarity of inter-class points. (3) The C-Rend plays a complementary role for the SSL framework in uncertainty-aware learning, which could provide reliable supervision information and achieve superior segmentation performance. Through extensive validation on large in-house datasets with partial annotations, our method outperforms state-of-the-art methods in various evaluation metrics for both the ovary and follicles.
    MeSH term(s) Benchmarking ; Female ; Humans ; Ovary/diagnostic imaging ; Supervised Machine Learning ; Ultrasonography ; Uncertainty
    Language English
    Publishing date 2021-06-22
    Publishing country Netherlands
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 1356436-5
    ISSN 1361-8423 ; 1361-8431 ; 1361-8415
    ISSN (online) 1361-8423 ; 1361-8431
    ISSN 1361-8415
    DOI 10.1016/j.media.2021.102134
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

    Zs.A 5206: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
    Jg. 1995 - 2021: Lesesall (2.OG)
    ab Jg. 2022: Lesesaal (EG)

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