Laser facilitates vaccination

Wang, Ji; Li, Peiyu; Chen, Xinyuan; Wu, Minxian

doi:10.1142/s1793545816300032

Cited by 3 publications

(2 citation statements)

References 46 publications

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“…Osteosarcoma is a malignant tumour that arises from bone and rapidly develops to form bone cancer [1]. It is the most common type of orthopaedic disease.…”

Section: Introductionmentioning

confidence: 99%

Group Teaching Optimization With Deep Learning-Driven Osteosarcoma Detection Using Histopathological Images

Alsubai,

Dutta,

Alghayadh

et al. 2024

IEEE Access

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Osteosarcoma is the most normal kind of cancer that arises in bones, which appears on the surface to resemble earlier types of bone cells that assist in forging new bone tissues, but the tissue in osteosarcoma is weaker and softer than normal bone tissue. The usage of automated techniques for the detection of osteosarcoma has the potential to mitigate the obligations and burdens confronted by pathologists owing to its abundant quantity of cases. Artificial intelligence (AI) has an emerging progress in diagnostic pathology. In recent years, numerous studies using deep learning (DL) techniques to histopathological images (HI) have been published. While several studies claim higher accuracy, they might lack generalization and fall into the pitfall of overfitting owing to the wide range of HI. The study objective is to enhance the diagnosis and detection of osteosarcoma by employing computer-assisted detection (CAD) and diagnoses (CADx). Technique like convolutional neural networks (CNN) make better prognoses for patient conditions and considerably reduce the surgeon's workload. CNN needs to be trained on the massive quantity of data to accomplish a remarkable performance. Therefore, the study presents a novel Group Teaching Optimization Algorithm with Deep Learning-Driven Osteosarcoma Detection on Histopathological Images (GTOADL-ODHI) technique. The purpose of the GTOADL-ODHI technique is to examine the HIs for the detection and classification of osteosarcoma. To accomplish this, the GTOADL-ODHI algorithm applies the Gaussian filtering (GF) method for image pre-processed to become rid of the noise. Besides, the capsule network (CapsNet) model is utilized for the extractor of the feature vector. Furthermore, the hyperparameter selection of the CapsNet approach takes place using the GTOA. Finally, the self-attention bidirectional long short-term memory (SA-BiLSTM) model can be employed for osteosarcoma recognition and classification. The widespread experimental analysis of the GTOADL-ODHI method is tested on the benchmark datasets. The simulation validation reported the optimum solution of the GTOADL-ODHI algorithm related to existing systems concerning distinct aspects.

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“…Osteosarcoma is a malignant tumour that arises from bone and rapidly develops to form bone cancer [1]. It is the most common type of orthopaedic disease.…”

Section: Introductionmentioning

confidence: 99%

Group Teaching Optimization With Deep Learning-Driven Osteosarcoma Detection Using Histopathological Images

Alsubai,

Dutta,

Alghayadh

et al. 2024

IEEE Access

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“…The laser has been recently developed as a new category of vaccine adjuvant that activates the immune system without introducing non-self molecules into human body ( Chen et al, 2010 , 2012 , 2013 ; Wang et al, 2014 , 2015 , 2016a , b ; Kashiwagi et al, 2016 ; Kim et al, 2016 ; Lopes et al, 2018 ). Of these vaccine adjuvants, the non-ablative fractional laser (NAFL) is very promising.…”

Section: Introductionmentioning

confidence: 99%

Topical Application of a Vitamin A Derivative and Its Combination With Non-ablative Fractional Laser Potentiates Cutaneous Influenza Vaccination

Wang

Chen

et al. 2018

Front. Microbiol.

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Skin contains a large number of antigen presenting cells, making intradermal (ID) injection one of the most effective ways for vaccine administration. However, although current adjuvants may cause severe local reactions and inflammations in the skin, no adjuvant has been approved for ID vaccination so far. Here, we report that topical application of all-trans retinoic acid (ATRA), a vitamin A derivative produced in the human body, augmented cutaneous influenza vaccination. The adjuvant effects were evaluated in a murine vaccination/challenge model by using A/California/07/2009 pandemic vaccine (09V) or a seasonal influenza vaccine (SIV). ATRA drove a Th2-biased immune response, as demonstrated by profoundly elevated IgG1 titer rather than IgG2 titer. Combining ATRA with a non-ablative fractional laser (NAFL), which represents a new category of vaccine adjuvant utilizing physical stimuli to induce self-immune stimulators, further enhanced the efficacy of influenza vaccines with a more balanced Th1/Th2 immune response. The dual adjuvant strengthened cross-reactive immune responses against both homogenous and heterogeneous influenza viral strains. Analysis of gene expression profile showed that ATRA/NAFL stimulated upregulation of cytosolic nucleic acid sensors and their downstream factors, leading to a synergistic elevation of type I interferon expression. Consistent with this finding, knocking out IRF3 or IRF7, two key downstream regulatory factors in most nucleic acid sensing pathways, resulted in a significant decrease in the adjuvant effect of ATRA/NAFL. Thus, our study demonstrates that the self molecule ATRA could boost cutaneous influenza vaccination either alone or ideally in combination with NAFL.

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CT/FMT dual-model imaging of breast cancer based on peptide-lipid nanoparticles

Lin

Lian

et al. 2016

SPIE Proceedings

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Laser facilitates vaccination

Cited by 3 publications

References 46 publications

Group Teaching Optimization With Deep Learning-Driven Osteosarcoma Detection Using Histopathological Images

Group Teaching Optimization With Deep Learning-Driven Osteosarcoma Detection Using Histopathological Images

Topical Application of a Vitamin A Derivative and Its Combination With Non-ablative Fractional Laser Potentiates Cutaneous Influenza Vaccination

CT/FMT dual-model imaging of breast cancer based on peptide-lipid nanoparticles

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