Publications by authors named "Yanwu Yang"

The multi-modal neuroimage study has provided insights into understanding the heteromodal relationships between brain network organization and behavioral phenotypes. Integrating data from various modalities facilitates the characterization of the interplay among anatomical, functional, and physiological brain alterations or developments. Graph Neural Networks (GNNs) have recently become popular in analyzing and fusing multi-modal, graph-structured brain networks.

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Objective: The aim was to predict the effectiveness of using frailty, defined by the frailty index (FI), for predicting recurrent pneumonia and death in patients 50 years and older with vascular cognitive impairment (VCI) during long-term hospitalization.

Measurements: This retrospective cohort study was conducted at a teaching hospital in western China and included VCI patients aged ≥50 years undergoing long-term hospitalization. The relevant data were collected from the electronic medical record system.

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Medical image segmentation demands precise accuracy and the capability to assess segmentation uncertainty for informed clinical decision-making. Denoising Diffusion Probability Models (DDPMs), with their advancements in image generation, can treat segmentation as a conditional generation task, providing accurate segmentation and uncertainty estimation. However, current DDPMs used in medical image segmentation suffer from low inference efficiency and prediction errors caused by excessive noise at the end of the forward process.

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Foundation models pretrained on large-scale datasets via self-supervised learning demonstrate exceptional versatility across various tasks. Due to the heterogeneity and hard-to-collect medical data, this approach is especially beneficial for medical image analysis and neuroscience research, as it streamlines broad downstream tasks without the need for numerous costly annotations. However, there has been limited investigation into brain network foundation models, limiting their adaptability and generalizability for broad neuroscience studies.

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Unlabelled: The risk of hypoglycemia and its serious medical sequelae restrict insulin replacement therapy for diabetes mellitus. Such adverse clinical impact has motivated development of diverse glucose-responsive technologies, including algorithm-controlled insulin pumps linked to continuous glucose monitors ("closed-loop systems") and glucose-sensing ("smart") insulins. These technologies seek to optimize glycemic control while minimizing hypoglycemic risk.

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Source-free domain adaptation (SFDA) aims to adapt models trained on a labeled source domain to an unlabeled target domain without access to source data. In medical imaging scenarios, the practical significance of SFDA methods has been emphasized due to data heterogeneity and privacy concerns. Recent state-of-the-art SFDA methods primarily rely on self-training based on pseudo-labels (PLs).

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Article Synopsis
  • The study examines the "resource curse hypothesis," suggesting that countries rich in natural resources may experience economic challenges, and confirms its presence in selected emerging nations between 1990 and 2020.
  • It highlights the importance of human development and gross domestic product (GDP) as key factors promoting long-term financial development, while political stability is also identified as a positive influence.
  • Findings indicate that while the relationships hold in both the short and long term, the impacts are generally more significant over time, leading to policy recommendations for improving economic conditions in the sample countries.
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One of the core challenges of deep learning in medical image analysis is data insufficiency, especially for 3D brain imaging, which may lead to model over-fitting and poor generalization. Regularization strategies such as knowledge distillation are powerful tools to mitigate the issue by penalizing predictive distributions and introducing additional knowledge to reinforce the training process. In this paper, we revisit knowledge distillation as a regularization paradigm by penalizing attentive output distributions and intermediate representations.

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Aim: This study aimed to investigate the clinical efficacy of externally applied Traditional Chinese Medicine (TCM) on diabetic foot.

Methods: We searched the China Knowledge Network (CNKI), Wanfang Database, PubMed and Web of Science from inception to July 31, 2022, to find all randomized control trials (RCTs) related to externally applied TCMs in diabetic foot treatment. Information about the total effective rate, healing rate, and healing time were extracted.

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Objective: We conducted this systematic review and meta-analysis to summarize the prevalence of sarcopenia and its impact on mortality in patients undergoing TAVI.

Method: Medline, EMBASE, and PubMed were searched from inception to October 14, 2022 to retrieve eligible studies that assessed sarcopenia in patients undergoing TAVI. Pooled sarcopenia prevalence was calculated with 95% confidence interval (CI), and heterogeneity was estimated using the I test.

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Recently, the study of multi-modal brain connectome has recorded a tremendous increase and facilitated the diagnosis of brain disorders. In this paradigm, functional and structural networks, e.g.

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Article Synopsis
  • Sarcopenia, which is the loss of muscle, may affect how well people with certain cancers do, and scientists are studying a specific muscle thickness called temporalis muscle thickness (TMT) in brain tumor patients.*
  • A review of 19 studies showed that people with thinner TMT tend to live less long and have worse outcomes after treatment for brain tumors.*
  • The findings suggest that checking TMT could help doctors make better decisions about the care of patients with brain tumors.*
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Multivariate analysis approaches provide insights into the identification of phenotype associations in brain connectome data. In recent years, deep learning methods including convolutional neural network (CNN) and graph neural network (GNN), have shifted the development of connectome-wide association studies (CWAS) and made breakthroughs for connectome representation learning by leveraging deep embedded features. However, most existing studies remain limited by potentially ignoring the exploration of region-specific features, which play a key role in distinguishing brain disorders with high intra-class variations, such as autism spectrum disorder (ASD), and attention deficit hyperactivity disorder (ADHD).

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Y-encoded transcription factor SRY initiates male differentiation in therian mammals. This factor contains a high-mobility-group (HMG) box, which mediates sequence-specific DNA binding with sharp DNA bending. A companion article in this issue described sex-reversal mutations at box position 72 (residue 127 in human SRY), invariant as Tyr among mammalian orthologs.

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Since the ambiguous boundary of the lesion and inter-observer variability, white matter hyperintensity segmentation annotations are inherently noisy and uncertain. On the other hand, the high capacity of deep neural networks (DNN) enables them to overfit labels with noise and uncertainty, which may lead to biased models with weak generalization ability. This challenge has been addressed by leveraging multiple annotations per image.

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Whole-brain segmentation from T1-weighted magnetic resonance imaging (MRI) is an essential prerequisite for brain structural analysis, e.g., locating morphometric changes for brain aging analysis.

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Neuroimaging-driven brain age estimation has become popular in measuring brain aging and identifying neurodegenerations. However, the single estimated brain age (gap) compromises regional variations of brain aging, losing spatial specificity across diseases which is valuable for early screening. In this study, we combined brain age modeling with Shapley Additive Explanations to measure brain aging as a feature contribution vector underlying spatial pathological aging mechanism.

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The mutant proinsulin syndrome is a monogenic cause of diabetes mellitus due to toxic misfolding of insulin's biosynthetic precursor. Also designated (MIDY), this syndrome defines molecular determinants of foldability in the endoplasmic reticulum (ER) of β-cells. Here, we describe a peptide model of a key proinsulin folding intermediate and variants containing representative clinical mutations; the latter perturb invariant core sites in native proinsulin (Leu→Pro, Leu→Pro, and Phe→Ser).

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Toxic misfolding of proinsulin variants in β-cells defines a monogenic diabetes syndrome, designated (MIDY). In our first study (previous article in this issue), we described a one-disulfide peptide model of a proinsulin folding intermediate and its use to study such variants. The mutations (Leu→Pro, Leu→Pro, and Phe→Ser) probe residues conserved among vertebrate insulins.

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Insulin-signaling requires conformational change: whereas the free hormone and its receptor each adopt autoinhibited conformations, their binding leads to structural reorganization. To test the functional coupling between insulin's "hinge opening" and receptor activation, we inserted an artificial ligand-dependent switch into the insulin molecule. Ligand-binding disrupts an internal tether designed to stabilize the hormone's native closed and inactive conformation, thereby enabling productive receptor engagement.

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Article Synopsis
  • Researchers are studying brain connectivity changes in idiopathic rapid eye movement sleep behavior disorder (iRBD) to understand its progression to Parkinson's disease (PD).
  • The study involved 151 participants (iRBD, PD, and healthy controls) who underwent diffusion MRI scans to analyze brain structural networks.
  • Findings revealed significant changes in brain connectivity in certain regions as PD progresses, and machine learning improved the classification of these patterns, potentially aiding in early prediction of PD.
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Benzoquinone has the ability to serve as an electron acceptor, and tetrathiafulvalene has the ability to serve as an electron donor. Based on the facts above, this work creatively cycles the benzoquinone unit and the tetrathiafulvalene unit alternately into macrocyclic molecules, the cyclopolymers of benzoquinone-tetrafluorene (C[n]QTTF, n = 3~6). To explore their structure and properties, the M06-2X functional of density functional theory (DFT) with 6-311g(d) basis set was used to optimize the ground-state structures of C[n]QTTF.

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Proteins have evolved to be foldable, and yet determinants of foldability may be inapparent once the native state is reached. Insight has emerged from studies of diseases of protein misfolding, exemplified by monogenic diabetes mellitus due to mutations in proinsulin leading to endoplasmic reticulum stress and β-cell death. Cellular foldability of human proinsulin requires an invariant Phe within a conserved crevice at the receptor-binding surface (position B24).

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Background/aims: Mesenchymal stem cells (MSCs) transplantation has been considered a possible therapeutic method for Multiple Sclerosis (MS). However, no quantitative data synthesis of MSCs therapy for MS exists. We conducted a systematic review and meta-analysis to evaluate the effects of MSCs in experimental autoimmune encephalomyelitis (EAE) animal model of MS.

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Globular protein sequences encode not only functional structures (the native state) but also protein foldability, a conformational search that is both efficient and robustly minimizes misfolding. Studies of mutations associated with toxic misfolding have yielded insights into molecular determinants of protein foldability. Of particular interest are residues that are conserved yet dispensable in the native state.

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