Biological Tissue Damage Monitoring Method Based on IMWPE and PNN during HIFU Treatment

Liu, Bei; Zhang, Xian; Zou, Xiao; Cao, Jiling; Peng, Ziqi

doi:10.3390/info12100404

Cited by 3 publications

(2 citation statements)

References 29 publications

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“…In addition, although the increased cytotoxicity of the MSNs was not accompanied by any toxicity after three weeks of treatment, ultrasound destruction is well-known to induce damage to healthy tissues, limiting the applicability in clinical trials. One possibility to circumvent this caveat would be to monitor biological tissue damage using, for example, the IMWPE-PNN method [as described in Bei Liu et al [85]]. The combination of cisplatin and gemcitabine is associated with high toxicity, yet recent clinical trials imply an added benefit of including cisplatin in existing PDAC treatment regimens [86,87].…”

Section: Cytotoxicity Of Classical Msns In Pdacmentioning

confidence: 99%

Mesoporous Silica Nanoparticle-Based Drug Delivery Systems for the Treatment of Pancreatic Cancer: A Systematic Literature Overview

et al. 2022

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Pancreatic cancer is a devastating disease with the worst outcome of any human cancer. Despite significant improvements in cancer treatment in general, little progress has been made in pancreatic cancer (PDAC), resulting in an overall 5-year survival rate of less than 10%. This dismal prognosis can be attributed to the limited clinical efficacy of systemic chemotherapy due to its high toxicity and consequent dose reductions. Targeted delivery of chemotherapeutic drugs to PDAC cells without affecting healthy non-tumor cells will largely reduce collateral toxicity leading to reduced morbidity and an increased number of PDAC patients eligible for chemotherapy treatment. To achieve targeted delivery in PDAC, several strategies have been explored over the last years, and especially the use of mesoporous silica nanoparticles (MSNs) seem an attractive approach. MSNs show high biocompatibility, are relatively easy to surface modify, and the porous structure of MSNs enables high drug-loading capacity. In the current systematic review, we explore the suitability of MSN-based targeted therapies in the setting of PDAC. We provide an extensive overview of MSN-formulations employed in preclinical PDAC models and conclude that MSN-based tumor-targeting strategies may indeed hold therapeutic potential for PDAC, although true clinical translation has lagged behind.

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Section: Cytotoxicity Of Classical Msns In Pdacmentioning

confidence: 99%

Mesoporous Silica Nanoparticle-Based Drug Delivery Systems for the Treatment of Pancreatic Cancer: A Systematic Literature Overview

et al. 2022

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“…It can also meet the requirements of modification at any time during sample training. At present, PNNs are widely used in the field of fault diagnosis [32,33]. There are difficulties in welding defect damage identification research, metal materials are industrialized products and the degree of mechanization, automation and intelligence of infrastructure is low.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Metal Welding Defect Detection Model on Improved FAST-PNN

Liu

2022

Coatings

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In order to solve the problem of accurate and efficient detection of welding defects in the process of batch welding of metal parts, an improved Probabilistic Neural Network (PNN) algorithm was proposed to build an automatic identification model of welding defects. Combined with the characteristics of the PNN model, the structure and algorithm flow of the FAST-PNN algorithm model are proposed. Extraction of welding defect image texture features of metal welded parts by a Gray Level Co-occurrence Matrix (GLCM) screens out the characteristic indicators that can effectively characterize welding defects. Weld defect texture features are used as input to build a defect classification model with FAST-PNN, for accurate and efficient classification of welding defects. The results show that the improved FAST-PNN model can effectively identify the types of welding defects such as burn-through, pores and cracks, etc. The classification recognition accuracy and recognition efficiency have been significantly improved. The proposed defect welding identification method can accurately and effectively identify the damage types of welding defects based on a small number of defect sample images. Welding defects can be quickly identified and classified by simply collecting weld images, which helps to solve the problem of intelligent, high-precision, fast real-time online detection of welding defects in modern metal structures; it provides corresponding evidence for formulating response strategies, with a certain theoretical basis and numerical reference.

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Proposal of dental demineralization diagnosis with OCT echo based on multiscale entropy analysis

Peng,

Okaneya,

Bai

et al. 2024

MBE

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<abstract> <p>Optical coherence tomography (OCT) has been widely used for the diagnosis of dental demineralization. Most methods rely on extracting optical features from OCT echoes for evaluation or diagnosis. However, due to the diversity of biological samples and the complexity of tissues, the separability and robustness of extracted optical features are inadequate, resulting in a low diagnostic efficiency. Given the widespread utilization of entropy analysis in examining signals from biological tissues, we introduce a dental demineralization diagnosis method using OCT echoes, employing multiscale entropy analysis. Three multiscale entropy analysis methods were used to extract features from the OCT one-dimensional echo signal of normal and demineralized teeth, and a probabilistic neural network (PNN) was used for dental demineralization diagnosis. By comparing diagnostic efficiency, diagnostic speed, and parameter optimization dependency, the multiscale dispersion entropy-PNN (MDE-PNN) method was found to have comprehensive advantages in dental demineralization diagnosis with a diagnostic efficiency of 0.9397. Compared with optical feature-based dental demineralization diagnosis methods, the entropy features-based analysis had better feature separability and higher diagnostic efficiency, and showed its potential in dental demineralization diagnosis with OCT.</p> </abstract>

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Biological Tissue Damage Monitoring Method Based on IMWPE and PNN during HIFU Treatment

Cited by 3 publications

References 29 publications

Mesoporous Silica Nanoparticle-Based Drug Delivery Systems for the Treatment of Pancreatic Cancer: A Systematic Literature Overview

Mesoporous Silica Nanoparticle-Based Drug Delivery Systems for the Treatment of Pancreatic Cancer: A Systematic Literature Overview

Intelligent Metal Welding Defect Detection Model on Improved FAST-PNN

Proposal of dental demineralization diagnosis with OCT echo based on multiscale entropy analysis

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