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Fei You

Bio: Fei You is an academic researcher. The author has contributed to research in topics: Medicine & Cardiac function curve. The author has an hindex of 1, co-authored 1 publications receiving 5 citations.

Papers
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Journal ArticleDOI
TL;DR: Parkin deficiency unmasked HFD-induced decline in voltage-dependent anion channel (VDAC) type 1 degradation through the ubiquitin-proteasome system but not other VDAC isoforms or mitochondrial Ca2+ uniporter complex as discussed by the authors .

8 citations

Journal ArticleDOI
TL;DR: This work proposes a simple convolutional neural network model trained from scratch for discriminating benign and malignant breast cancer tumors in histopathological images and explores how optimizers aid in finding good sets of parameters that help minimize loss and increase overall classification accuracy.
Abstract: Conventional approaches to breast cancer diagnosis are associated with drawbacks that ultimately affect the quality of diagnosis and subsequent treatment, pushing for the need for automatic and precise classification of breast cancer tumors. The advent of deep learning methods has witnessed an increasing interest in their applications in many tasks. The specific case of using convolutional neural networks with transfer learning has witnessed tremendous successes in many classification tasks. Nonetheless, with transfer learning, the sheer number of parameters associated with deep networks coupled with the distance disparity between source data and target data leave networks prone to overfitting, particularly in the case of limited data. Also, negative transfer may occur in the situation where the source and target domains are not related. This work proposes a simple convolutional neural network model trained from scratch for discriminating benign and malignant breast cancer tumors in histopathological images. Four deep learning optimization algorithms are leveraged and explored to ascertain how optimizers aid in finding good sets of parameters that help minimize loss and increase overall classification accuracy. By adopting a polynomial learning rate decay scheduling and implementing several

7 citations

Journal ArticleDOI
01 Sep 2022
TL;DR: In this article , the authors examined the involvement of Beclin1 haploinsufficiency in acute ethanol-induced cardiac contractile dysfunction, in any, and the impact of BEclin 1 haplosufficiency on ethanol cardiotoxicity with a focus on autophagy-related ferroptosis.
Abstract: Binge drinking leads to compromised mitochondrial integrity and contractile function in the heart although little effective remedy is readily available. Given the possible derangement of autophagy in ethanol-induced cardiac anomalies, this study was designed to examine involvement of Beclin1 in acute ethanol-induced cardiac contractile dysfunction, in any, and the impact of Beclin1 haploinsufficiency on ethanol cardiotoxicity with a focus on autophagy-related ferroptosis.WT and Beclin1 haploinsufficiency (BECN+/-) mice were challenged with ethanol for one week (2 g/kg, i.p. on day 1, 3 and 7) prior to assessment of cardiac injury markers (LDH, CK-MB), cardiac geometry, contractile and mitochondrial integrity, oxidative stress, lipid peroxidation, apoptosis and ferroptosis.Ethanol exposure compromised cardiac geometry and contractile function accompanied with upregulated Beclin1 and autophagy, mitochondrial injury, oxidative stress, lipid peroxidation and apoptosis, and ferroptosis (GPx4, SLC7A11, NCOA4). Although Beclin1 deficiency did not affect cardiac function in the absence of ethanol challenge, it alleviated ethanol-induced changes in cardiac injury biomarkers, cardiomyocyte area, interstitial fibrosis, echocardiographic and cardiomyocyte mechanical properties along with mitochondrial integrity, oxidative stress, lipid peroxidation, apoptosis and ferroptosis. Ethanol challenge evoked pronounced ferroptosis (downregulated GPx4, SLC7A11 and elevated NCOA4, lipid peroxidation), the effect was alleviated by Beclin1 haploinsufficiency. Inhibition of ferroptosis using LIP-1 rescued ethanol-induced cardiac mechanical anomalies. In vitro study noted that ferroptosis induction using erastin abrogated Beclin1 haploinsufficiency-induced response against ethanol.In sum, our data suggest that Beclin1 haploinsufficiency benefits acute ethanol challenge-induced myocardial remodeling and contractile dysfunction through ferroptosis-mediated manner.

1 citations


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Journal ArticleDOI
TL;DR: The public is urged to take action to protect themselves and their loved ones from disease-causing substances.

781 citations

Journal ArticleDOI
TL;DR: The results show that the skin disease image recognition method based on deep learning is better than those of dermatologists and other computer-aided treatment methods in skin disease diagnosis, especially the multi deep learning model fusion method has the best recognition effect.
Abstract: The application of deep learning methods to diagnose diseases has become a new research topic in the medical field. In the field of medicine, skin disease is one of the most common diseases, and its visual representation is more prominent compared with the other types of diseases. Accordingly, the use of deep learning methods for skin disease image recognition is of great significance and has attracted the attention of researchers. In this study, we review 45 research efforts on the identification of skin disease by using deep learning technology since 2016. We analyze these studies from the aspects of disease type, data set, data processing technology, data augmentation technology, model for skin disease image recognition, deep learning framework, evaluation indicators, and model performance. Moreover, we summarize the traditional and machine learning-based skin disease diagnosis and treatment methods. We also analyze the current progress in this field and predict four directions that may become the research topic in the future. Our results show that the skin disease image recognition method based on deep learning is better than those of dermatologists and other computer-aided treatment methods in skin disease diagnosis, especially the multi deep learning model fusion method has the best recognition effect.

37 citations

Journal ArticleDOI
TL;DR: In this article , the role of transmembrane BAX inhibitor motif containing 6 (TMBIM6) in septic cardiomyopathy (SCM) was evaluated using mice.
Abstract: The regulatory mechanisms involved in mitochondrial quality control (MQC) dysfunction during septic cardiomyopathy (SCM) remain incompletely characterized. Transmembrane BAX inhibitor motif containing 6 (TMBIM6) is an endoplasmic reticulum protein with Ca2+ leak activity that modulates cellular responses to various cellular stressors.In this study, we evaluated the role of TMBIM6 in SCM using cardiomyocyte-specific TMBIM6 knockout (TMBIM6CKO) and TMBIM6 transgenic (TMBIM6TG) mice.Myocardial TMBIM6 transcription and expression were significantly downregulated in wild-type mice upon LPS exposure, along with characteristic alterations in myocardial systolic/diastolic function, cardiac inflammation, and cardiomyocyte death. Notably, these alterations were further exacerbated in LPS-treated TMBIM6CKO mice, and largely absent in TMBIM6TG mice. In LPS-treated primary cardiomyocytes, TMBIM6 deficiency further impaired mitochondrial respiration and ATP production, while defective MQC was suggested by enhanced mitochondrial fission, impaired mitophagy, and disrupted mitochondrial biogenesis. Structural protein analysis, Co-IP, mutant TMBIM6 plasmid transfection, and molecular docking assays subsequently indicated that TMBIM6 exerts cardioprotection against LPS-induced sepsis by interacting with and preventing the oligomerization of voltage-dependent anion channel-1 (VDAC1), the major route of mitochondrial Ca2+ uptake.We conclude that the TMBIM6-VDAC1 interaction prevents VDAC1 oligomerization and thus sustains mitochondrial Ca2+ homeostasis as well as MQC, contributing to improved myocardial function in SCM.

9 citations

Journal ArticleDOI
TL;DR: This work proposes a semisupervised learning method that integrates self-training and self-paced learning to generate and select pseudolabeled samples for classifying breast cancer histopathological images and proposes a class balancing framework that normalizes the class-wise confidence scores, hence effectively handling the issue of data imbalance.
Abstract: The unavailability of large amounts of well-labeled data poses a significant challenge in many medical imaging tasks. Even in the likelihood of having access to sufficient data, the process of accurately labeling the data is an arduous and time-consuming one, requiring expertise skills. Again, the issue of unbalanced data further compounds the abovementioned problems and presents a considerable challenge for many machine learning algorithms. In lieu of this, the ability to develop algorithms that can exploit large amounts of unlabeled data together with a small amount of labeled data, while demonstrating robustness to data imbalance, can offer promising prospects in building highly efficient classifiers. This work proposes a semisupervised learning method that integrates self-training and self-paced learning to generate and select pseudolabeled samples for classifying breast cancer histopathological images. A novel pseudolabel generation and selection algorithm is introduced in the learning scheme to generate and select highly confident pseudolabeled samples from both well-represented classes to less-represented classes. Such a learning approach improves the performance by jointly learning a model and optimizing the generation of pseudolabels on unlabeled-target data to augment the training data and retraining the model with the generated labels. A class balancing framework that normalizes the class-wise confidence scores is also proposed to prevent the model from ignoring samples from less represented classes (hard-to-learn samples), hence effectively handling the issue of data imbalance. Extensive experimental evaluation of the proposed method on the BreakHis dataset demonstrates the effectiveness of the proposed method.

8 citations