249 research outputs found
Modification Of 4,5- Aminoglycosides To Overcome Drug Resistance Bacteria And Toxic Side Effect
ABSTRACT
MODIFICATION OF 4,5- AMINOGLYCOSIDES TO OVERCOME DRUG RESISTANCE BACTERIA AND TOXIC SIDE EFFECT
by
Guanyu Yang
September 2018
Advisor: Professor David Crich
Major: Chemistry
Degree: Doctor of Philosophy
Infectious diseases causing by antibiotic resistant pathogen are one of the major threat to human health and society today. Many researchers tried to develop next generation of antibiotics by reinvesting the existing antibacterial drugs. Aminoglycosides have long been used as highly potent and broad-spectrum antibiotics for treating bacterial infections. But their side effect, especially the irreversible ototoxicity, and the fast-growing resistant problem limit their application. The goal of this research was to develop next generation of AGAs that are less toxic and resistance-proof by modifying known aminoglycosides.
Chapter one briefly explains the MDR bacterial infection problem and its influence. Aminoglycosides are also well discussed in this chapter, including their history, classifications, mechanism of action, toxicity and resistance problems, as well as the recent research advances.
Chapter two discusses the synthesis and biological evaluation of 6β-deshydroxymethyl paromomycin The loss of activity shows in the biological test suggested that the 6\u27-deshydroxymethyl modification was not an effective modification.
Chapter three discusses the 3β-deoxy modification on different 4,5-AGAs. A novel synthetic method utilizing samarium iodide reduction to achieve 3β-deoxygenation modification is introduced. This new method shows good substrate compatibility and avoids the tedious scheme in the traditional method. The 3β-deoxy 4,5-AGAs retain their antibacterial activity and exhibit activity against some AGA resistance strains. But they still suffers from APH(3β,5ββ) resistance mechanism.
Chapter four describes the synthesis and biological test results of the 3β,5ββ-dideoxy-5ββ-formamido paromomycin. The synthesis of this doubly modified compound demonstrates the wide application potency of the samarium iodide reduction for 3β-deoxy modification. The biological experiment results show that the doubly modified compound has good antibacterial activity even in the presence of some common AMEs.
Chapter five discussed the synthesis and biological evaluation of a triply modified paromomycin derivative. The combination of 3β-deoxy, 4β-deoxy-4β-C-propyl and 5ββ-deoxy-5ββ-formamido modification into paromomycin leads to unexpected loss of antiribosomal and antibacterial activity.
Finally, chapter six documents the experiment procedure and characterization data for the synthesized compounds and chapter seven presents the overall conclusion
Partial Vessels Annotation-based Coronary Artery Segmentation with Self-training and Prototype Learning
Coronary artery segmentation on coronary-computed tomography angiography
(CCTA) images is crucial for clinical use. Due to the expertise-required and
labor-intensive annotation process, there is a growing demand for the relevant
label-efficient learning algorithms. To this end, we propose partial vessels
annotation (PVA) based on the challenges of coronary artery segmentation and
clinical diagnostic characteristics. Further, we propose a progressive weakly
supervised learning framework to achieve accurate segmentation under PVA.
First, our proposed framework learns the local features of vessels to propagate
the knowledge to unlabeled regions. Subsequently, it learns the global
structure by utilizing the propagated knowledge, and corrects the errors
introduced in the propagation process. Finally, it leverages the similarity
between feature embeddings and the feature prototype to enhance testing
outputs. Experiments on clinical data reveals that our proposed framework
outperforms the competing methods under PVA (24.29% vessels), and achieves
comparable performance in trunk continuity with the baseline model using full
annotation (100% vessels).Comment: Accepted at MICCAI 202
Visible and Near Infrared Image Fusion Based on Texture Information
Multi-sensor fusion is widely used in the environment perception system of
the autonomous vehicle. It solves the interference caused by environmental
changes and makes the whole driving system safer and more reliable. In this
paper, a novel visible and near-infrared fusion method based on texture
information is proposed to enhance unstructured environmental images. It aims
at the problems of artifact, information loss and noise in traditional visible
and near infrared image fusion methods. Firstly, the structure information of
the visible image (RGB) and the near infrared image (NIR) after texture removal
is obtained by relative total variation (RTV) calculation as the base layer of
the fused image; secondly, a Bayesian classification model is established to
calculate the noise weight and the noise information and the noise information
in the visible image is adaptively filtered by joint bilateral filter; finally,
the fused image is acquired by color space conversion. The experimental results
demonstrate that the proposed algorithm can preserve the spectral
characteristics and the unique information of visible and near-infrared images
without artifacts and color distortion, and has good robustness as well as
preserving the unique texture.Comment: 10 pages,11 figure
Dynamic Snake Convolution based on Topological Geometric Constraints for Tubular Structure Segmentation
Accurate segmentation of topological tubular structures, such as blood
vessels and roads, is crucial in various fields, ensuring accuracy and
efficiency in downstream tasks. However, many factors complicate the task,
including thin local structures and variable global morphologies. In this work,
we note the specificity of tubular structures and use this knowledge to guide
our DSCNet to simultaneously enhance perception in three stages: feature
extraction, feature fusion, and loss constraint. First, we propose a dynamic
snake convolution to accurately capture the features of tubular structures by
adaptively focusing on slender and tortuous local structures. Subsequently, we
propose a multi-view feature fusion strategy to complement the attention to
features from multiple perspectives during feature fusion, ensuring the
retention of important information from different global morphologies. Finally,
a continuity constraint loss function, based on persistent homology, is
proposed to constrain the topological continuity of the segmentation better.
Experiments on 2D and 3D datasets show that our DSCNet provides better accuracy
and continuity on the tubular structure segmentation task compared with several
methods. Our codes will be publicly available.Comment: Accepted by ICCV 202
NCAGC: A Neighborhood Contrast Framework for Attributed Graph Clustering
Attributed graph clustering is one of the most fundamental tasks among graph
learning field, the goal of which is to group nodes with similar
representations into the same cluster without human annotations. Recent studies
based on graph contrastive learning method have achieved remarkable results
when exploit graph-structured data. However, most existing methods 1) do not
directly address the clustering task, since the representation learning and
clustering process are separated; 2) depend too much on data augmentation,
which greatly limits the capability of contrastive learning; 3) ignore the
contrastive message for clustering tasks, which adversely degenerate the
clustering results. In this paper, we propose a Neighborhood Contrast Framework
for Attributed Graph Clustering, namely NCAGC, seeking for conquering the
aforementioned limitations. Specifically, by leveraging the Neighborhood
Contrast Module, the representation of neighbor nodes will be 'push closer' and
become clustering-oriented with the neighborhood contrast loss. Moreover, a
Contrastive Self-Expression Module is built by minimizing the node
representation before and after the self-expression layer to constraint the
learning of self-expression matrix. All the modules of NCAGC are optimized in a
unified framework, so the learned node representation contains
clustering-oriented messages. Extensive experimental results on four attributed
graph datasets demonstrate the promising performance of NCAGC compared with 16
state-of-the-art clustering methods. The code is available at
https://github.com/wangtong627/NCAGC
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