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  1. Article ; Online: An FeCl

    Fang, Ruilin / Zheng, Lei / Chen, Xuyang / Wang, Can / Chen, Yunfeng

    Organic & biomolecular chemistry

    2024  

    Abstract: ... An ... ...

    Abstract An FeCl
    Language English
    Publishing date 2024-04-22
    Publishing country England
    Document type Journal Article
    ZDB-ID 2097583-1
    ISSN 1477-0539 ; 1477-0520
    ISSN (online) 1477-0539
    ISSN 1477-0520
    DOI 10.1039/d4ob00207e
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article: Editorial: Microbial interaction with banana: mechanisms, symbiosis, and integrated diseases control.

    Zheng, Si-Jun / Hu, Huigang / Li, Yunfeng / Chen, Jian / Li, Xundong / Bai, Tingting

    Frontiers in microbiology

    2024  Volume 15, Page(s) 1390969

    Language English
    Publishing date 2024-04-05
    Publishing country Switzerland
    Document type Editorial
    ZDB-ID 2587354-4
    ISSN 1664-302X
    ISSN 1664-302X
    DOI 10.3389/fmicb.2024.1390969
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Article ; Online: Applying ChatGPT to tackle the side effects of personal learning environments from learner and learning perspective

    XiaoShu Xu / XiBing Wang / YunFeng Zhang / Rong Zheng

    PLoS ONE, Vol 19, Iss

    An interview of experts in higher education

    2024  Volume 1

    Keywords Medicine ; R ; Science ; Q
    Language English
    Publishing date 2024-01-01T00:00:00Z
    Publisher Public Library of Science (PLoS)
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  4. Article: Biliary reconstruction with localized creation: One case report of repairing bile duct injury and defect with autografts.

    Zheng, Feibo / Zhang, Yuqing / Ha, Liang / Xia, Jipeng / Cui, Yunfeng

    International journal of surgery case reports

    2024  Volume 118, Page(s) 109597

    Abstract: Introduction: Bile duct injuries caused by any reason are a disaster for patients and pose a significant psychological and technical challenge for surgeons. The use of Ligamentum teres hepatis and gallbladder flap as autografts is showing promising ... ...

    Abstract Introduction: Bile duct injuries caused by any reason are a disaster for patients and pose a significant psychological and technical challenge for surgeons. The use of Ligamentum teres hepatis and gallbladder flap as autografts is showing promising results in the repair of bile duct injury.
    Case presentation: This article presents a challenging case of a patient with Mirizzi syndrome who experienced a complex bile duct defect and injury during cholecystectomy. We describe the successful reconstruction of the bile duct using ligamentum teres hepatis and remnant gallbladder flap simultaneously.
    Discussion: Ligamentum teres hepatis and remnant gallbladder flap are ideal repair materials for repairing and reconstructing bile duct injuries due to their easy availability, good tissue compatibility, and low incidence of postoperative complications. It is essential to seek the assistance of an experienced biliary surgeon when bile duct injury occurs during operation.
    Conclusion: Ligamentum teres hepatis and gallbladder flap, as suitable autologous tissues, are viable options for repairing bile duct injuries and defects.
    Language English
    Publishing date 2024-04-02
    Publishing country Netherlands
    Document type Case Reports
    ISSN 2210-2612
    ISSN 2210-2612
    DOI 10.1016/j.ijscr.2024.109597
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  5. Article ; Online: Topical Delivery of microRNA-125b by Framework Nucleic Acids for Psoriasis Treatment.

    Han, Yunfeng / Xi, Long / Leng, Fang / Xu, Chenjie / Zheng, Ying

    International journal of nanomedicine

    2024  Volume 19, Page(s) 2625–2638

    Abstract: Purpose: Psoriasis is a chronic and recurrent inflammatory dermatitis characterized by T cell imbalance and abnormal keratinocyte proliferation. MicroRNAs (miRNAs) hold promise as therapeutic agents for this disease; however, their clinical application ... ...

    Abstract Purpose: Psoriasis is a chronic and recurrent inflammatory dermatitis characterized by T cell imbalance and abnormal keratinocyte proliferation. MicroRNAs (miRNAs) hold promise as therapeutic agents for this disease; however, their clinical application is hindered by poor stability and limited skin penetration. This study demonstrates the utilization of Framework Nucleic Acid (FNA) for the topical delivery of miRNAs in psoriasis treatment.
    Methods: By utilizing miRNA-125b as the model drug, FNA-miR-125b was synthesized via self-assembly. The successful synthesis and stability of FNA-miR-125b in bovine fetal serum (FBS) were verified through gel electrophoresis. Subsequently, flow cytometry was employed to investigate the cell internalization on HaCaT cells, while qPCR determined the effects of FNA-miR-125b on cellular functions. Additionally, the skin penetration ability of FNA-miR-125b was assessed. Finally, a topical administration study involving FNA-miR-125b cream on imiquimod (IMQ)-induced psoriasis mice was conducted to evaluate its therapeutic efficacy.
    Results: The FNA-miR-125b exhibited excellent stability, efficient cellular internalization, and potent inhibition of keratinocyte proliferation. In the psoriasis mouse model, FNA-miR-125b effectively penetrated the skin tissue, resulting in reduced epidermal thickness and PASI score, as well as decreased levels of inflammatory cytokines.
    MeSH term(s) Animals ; Cattle ; Mice ; MicroRNAs/genetics ; Keratinocytes ; Skin ; Psoriasis/drug therapy ; Psoriasis/chemically induced ; Imiquimod/therapeutic use ; Disease Models, Animal ; Mice, Inbred BALB C
    Chemical Substances MicroRNAs ; Imiquimod (P1QW714R7M)
    Language English
    Publishing date 2024-03-15
    Publishing country New Zealand
    Document type Journal Article
    ZDB-ID 2364941-0
    ISSN 1178-2013 ; 1176-9114
    ISSN (online) 1178-2013
    ISSN 1176-9114
    DOI 10.2147/IJN.S441353
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  6. Article ; Online: Applying ChatGPT to tackle the side effects of personal learning environments from learner and learning perspective: An interview of experts in higher education.

    Xu, XiaoShu / Wang, XiBing / Zhang, YunFeng / Zheng, Rong

    PloS one

    2024  Volume 19, Issue 1, Page(s) e0295646

    Abstract: This paper investigates the capacity of ChatGPT, an advanced language model created by OpenAI, to mitigate the side effects encountered by learners in Personal Learning Environments (PLEs) within higher education. A series of semi-structured interviews ... ...

    Abstract This paper investigates the capacity of ChatGPT, an advanced language model created by OpenAI, to mitigate the side effects encountered by learners in Personal Learning Environments (PLEs) within higher education. A series of semi-structured interviews were conducted with six professors and three Information and Communication Technology (ICT) experts. Employing thematic analysis, the interview data were assessed, revealing that the side effects stemming from the learner and learning perspectives could be primarily categorized into cognitive, non-cognitive, and metacognitive challenges. The findings of the thematic analysis indicate that, from a cognitive standpoint, ChatGPT can generate relevant and trustworthy information, furnish personalized learning resources, and facilitate interdisciplinary learning to fully actualize learners' potential. Moreover, ChatGPT can aid learners in cultivating non-cognitive skills, including motivation, perseverance, self-regulation, and self-efficacy, as well as metacognitive abilities such as self-determination, self-efficacy, and self-regulation, by providing tailored feedback, fostering creativity, and stimulating critical thinking activities. This study offers valuable insights for integrating artificial intelligence technologies to unleash the full potential of PLEs in higher education.
    MeSH term(s) Artificial Intelligence ; Creativity ; Learning ; Metacognition ; Thinking
    Language English
    Publishing date 2024-01-03
    Publishing country United States
    Document type Interview
    ZDB-ID 2267670-3
    ISSN 1932-6203 ; 1932-6203
    ISSN (online) 1932-6203
    ISSN 1932-6203
    DOI 10.1371/journal.pone.0295646
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  7. Article ; Online: Ancient Chinese Character Recognition with Improved Swin-Transformer and Flexible Data Enhancement Strategies.

    Zheng, Yi / Chen, Yi / Wang, Xianbo / Qi, Donglian / Yan, Yunfeng

    Sensors (Basel, Switzerland)

    2024  Volume 24, Issue 7

    Abstract: The decipherment of ancient Chinese scripts, such as oracle bone and bronze inscriptions, holds immense significance for understanding ancient Chinese history, culture, and civilization. Despite substantial progress in recognizing oracle bone script, ... ...

    Abstract The decipherment of ancient Chinese scripts, such as oracle bone and bronze inscriptions, holds immense significance for understanding ancient Chinese history, culture, and civilization. Despite substantial progress in recognizing oracle bone script, research on the overall recognition of ancient Chinese characters remains somewhat lacking. To tackle this issue, we pioneered the construction of a large-scale image dataset comprising 9233 distinct ancient Chinese characters sourced from images obtained through archaeological excavations. We propose the first model for recognizing the common ancient Chinese characters. This model consists of four stages with Linear Embedding and Swin-Transformer blocks, each supplemented by a CoT Block to enhance local feature extraction. We also advocate for an enhancement strategy, which involves two steps: firstly, conducting adaptive data enhancement on the original data, and secondly, randomly resampling the data. The experimental results, with a top-one accuracy of 87.25% and a top-five accuracy of 95.81%, demonstrate that our proposed method achieves remarkable performance. Furthermore, through the visualizing of model attention, it can be observed that the proposed model, trained on a large number of images, is able to capture the morphological characteristics of ancient Chinese characters to a certain extent.
    Language English
    Publishing date 2024-03-28
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2052857-7
    ISSN 1424-8220 ; 1424-8220
    ISSN (online) 1424-8220
    ISSN 1424-8220
    DOI 10.3390/s24072182
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  8. Article ; Online: A Soft Label Method for Medical Image Segmentation with Multirater Annotations.

    Zhang, Jichang / Zheng, Yuanjie / Shi, Yunfeng

    Computational intelligence and neuroscience

    2023  Volume 2023, Page(s) 1883597

    Abstract: In medical image analysis, collecting multiple annotations from different clinical raters is a typical practice to mitigate possible diagnostic errors. For such multirater labels' learning problems, in addition to majority voting, it is a common practice ...

    Abstract In medical image analysis, collecting multiple annotations from different clinical raters is a typical practice to mitigate possible diagnostic errors. For such multirater labels' learning problems, in addition to majority voting, it is a common practice to use soft labels in the form of full-probability distributions obtained by averaging raters as ground truth to train the model, which benefits from uncertainty contained in soft labels. However, the potential information contained in soft labels is rarely studied, which may be the key to improving the performance of medical image segmentation with multirater annotations. In this work, we aim to improve soft label methods by leveraging interpretable information from multiraters. Considering that mis-segmentation occurs in areas with weak supervision of annotations and high difficulty of images, we propose to reduce the reliance on local uncertain soft labels and increase the focus on image features. Therefore, we introduce local self-ensembling learning with consistency regularization, forcing the model to concentrate more on features rather than annotations, especially in regions with high uncertainty measured by the pixelwise interclass variance. Furthermore, we utilize a label smoothing technique to flatten each rater's annotation, alleviating overconfidence of structural edges in annotations. Without introducing additional parameters, our method improves the accuracy of the soft label baseline by 4.2% and 2.7% on a synthetic dataset and a fundus dataset, respectively. In addition, quantitative comparisons show that our method consistently outperforms existing multirater strategies as well as state-of-the-art methods. This work provides a simple yet effective solution for the widespread multirater label segmentation problems in clinical diagnosis.
    MeSH term(s) Humans ; Diagnostic Errors ; Learning ; Probability ; Uncertainty
    Language English
    Publishing date 2023-02-18
    Publishing country United States
    Document type Journal Article
    ZDB-ID 2388208-6
    ISSN 1687-5273 ; 1687-5273
    ISSN (online) 1687-5273
    ISSN 1687-5273
    DOI 10.1155/2023/1883597
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  9. Article ; Online: IoT-Enabled Few-Shot Image Generation for Power Scene Defect Detection Based on Self-Attention and Global-Local Fusion.

    Chen, Yi / Yan, Yunfeng / Wang, Xianbo / Zheng, Yi

    Sensors (Basel, Switzerland)

    2023  Volume 23, Issue 14

    Abstract: Defect detection in power scenarios is a critical task that plays a significant role in ensuring the safety, reliability, and efficiency of power systems. The existing technology requires enhancement in its learning ability from large volumes of data to ... ...

    Abstract Defect detection in power scenarios is a critical task that plays a significant role in ensuring the safety, reliability, and efficiency of power systems. The existing technology requires enhancement in its learning ability from large volumes of data to achieve ideal detection effect results. Power scene data involve privacy and security issues, and there is an imbalance in the number of samples across different defect categories, all of which will affect the performance of defect detection models. With the emergence of the Internet of Things (IoT), the integration of IoT with machine learning offers a new direction for defect detection in power equipment. Meanwhile, a generative adversarial network based on multi-view fusion and self-attention is proposed for few-shot image generation, named MVSA-GAN. The IoT devices capture real-time data from the power scene, which are then used to train the MVSA-GAN model, enabling it to generate realistic and diverse defect data. The designed self-attention encoder focuses on the relevant features of different parts of the image to capture the contextual information of the input image and improve the authenticity and coherence of the image. A multi-view feature fusion module is proposed to capture the complex structure and texture of the power scene through the selective fusion of global and local features, and improve the authenticity and diversity of generated images. Experiments show that the few-shot image generation method proposed in this paper can generate real and diverse defect data for power scene defects. The proposed method achieved FID and LPIPS scores of 67.87 and 0.179, surpassing SOTA methods, such as FIGR and DAWSON.
    Language English
    Publishing date 2023-07-19
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2052857-7
    ISSN 1424-8220 ; 1424-8220
    ISSN (online) 1424-8220
    ISSN 1424-8220
    DOI 10.3390/s23146531
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  10. Article ; Online: Analysis of Clinical Efficacy and Influencing Factors of Nerve Growth Factor (NGF) Treatment for Sudden Sensorineural Hearing Loss.

    Liang, Zhengrong / Gao, Minqian / Jia, Haiying / Han, Wenjing / Zheng, Yiqing / Zhao, Yunfeng / Yang, Haidi

    Ear, nose, & throat journal

    2023  , Page(s) 1455613231181711

    Abstract: Objective: ...

    Abstract Objective:
    Language English
    Publishing date 2023-06-28
    Publishing country United States
    Document type Journal Article
    ZDB-ID 750153-5
    ISSN 1942-7522 ; 0145-5613
    ISSN (online) 1942-7522
    ISSN 0145-5613
    DOI 10.1177/01455613231181711
    Database MEDical Literature Analysis and Retrieval System OnLINE

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