Review Article
Cancer Mortality Surges Post COVID ModRNA Vaccination Ronald Palacios Castrillo
Ronald Palacios Castrillo*
Issue:
Volume 10, Issue 2, April 2024
Pages:
23-27
Received:
3 June 2024
Accepted:
20 June 2024
Published:
29 June 2024
Abstract: Recent investigations have revealed a concerning association between the administration of the third dose of the ModRNA COVID-19 vaccine and statistically significant increases in cancer mortality rates. A study conducted in Japan highlighted this correlation, noting a marked rise in cancer-related deaths post-vaccination. This phenomenon is not isolated to Japan; similar trends have been observed in Europe, Australia, and the USA, with an excess of deaths reported from 2020 to 2023 compared to 2019. In this review, we explore seven potential mechanisms through which ModRNA COVID-19 vaccines may contribute to the initiation and progression of cancer. Each mechanism is discussed in detail, with a focus on the underlying molecular and cellular pathways. The potential for varied combinations of these mechanisms to influence different cancer types is also considered, providing a comprehensive overview of how ModRNA vaccines might impact cancer biology. Our analysis underscores the necessity for further research to elucidate the precise relationship between ModRNA COVID-19 vaccination and cancer progression. Understanding these mechanisms is critical for developing strategies to mitigate potential adverse effects while harnessing the benefits of vaccination.
Abstract: Recent investigations have revealed a concerning association between the administration of the third dose of the ModRNA COVID-19 vaccine and statistically significant increases in cancer mortality rates. A study conducted in Japan highlighted this correlation, noting a marked rise in cancer-related deaths post-vaccination. This phenomenon is not is...
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Research Article
Artificial Intelligence (Neural Network) in the Diagnosis of Benign Skin Tumors in Pediatric Patients
Issue:
Volume 10, Issue 2, April 2024
Pages:
28-37
Received:
17 July 2024
Accepted:
15 August 2024
Published:
6 September 2024
DOI:
10.11648/j.ejcbs.20241002.12
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Abstract: Relevance: ten years ago, artificial intelligence (AI), particularly neural networks (NN), as a diagnostic option in practice seemed a distant prospect. Today, the use of AI is becoming an increasingly popular and daily improving approach in all aspects of clinical and fundamental medicine. Purpose: design and learning of a NN to recognize four types of benign melanocytic skin tumors, integration into a mobile app to apply in practice. Material and methods: сlinical and dermatoscopic analysis of skin tumors was carried out in 600 children. In 65 cases the tumors were removed. Histological types were dermal nevus – 43% (n=28), complex nevus - 33.8% (n=22), pyogenic granuloma - 10.8% (n=7), Spitz-nevus - 6.2% (n=4), blue nevus - 3.1% (n=2), melanoma - 3.1% (n=2). Seven patients with pyogenic granulomas and two patients with melanoma were excluded. The test set included 56 dermatoscopic images. Due to the small number of images augmentation was performed. The database has been increased from 600 images to 1800. NN is written in the machine language Python. The machine learning framework was TensorFlow 2.0. The network architecture is based on the pre-trained model “EfficientNet B7”. This model uses the “supervised learning” paradigm. Each element of the sample had a class affiliation. Results: an accuracy of 83% was achieved after a period of learning on the test set. Mathematical metrics calculated in the Scikit-learn library. Sensitivity was 100% (blue nevus), 73% (complex nevus), 93% (dermal nevus), 75% (Spitz-nevus), and specificity were 98%; 94%; 82%; 98%, respectively. AI was integrated into the mobile app “KIDS NEVI”. Conclusion: AI as an auxiliary method for the skin tumors diagnosis in children and adolescents has demonstrated high potential and great opportunities. Dermatoscopic analysis of a skin tumor and a mobile app are able to provide “double control”, quick and correct clinical diagnosis and determination treatment tactics.
Abstract: Relevance: ten years ago, artificial intelligence (AI), particularly neural networks (NN), as a diagnostic option in practice seemed a distant prospect. Today, the use of AI is becoming an increasingly popular and daily improving approach in all aspects of clinical and fundamental medicine. Purpose: design and learning of a NN to recognize four typ...
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