Cellular Metabolism in Malignant Tumor

A special issue of Biomedicines (ISSN 2227-9059). This special issue belongs to the section "Cancer Biology and Oncology".

Deadline for manuscript submissions: closed (15 July 2023) | Viewed by 3347

Special Issue Editors


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Guest Editor
Department of Research, Taipei Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, New Taipei City 231, Taiwan
Interests: oxidant stress; DNA damage and repair; microenvironment and tumorigenesis; nature products; inflammation and cancer
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Guest Editor
Department of Clinical Nutrition, the Second Affiliated Hospital of Fujian Medical University, No. 34, Zhongshanbei Rd, Licheng District, Quanzhou 362000, China
Interests: nutrition; metabolites; immunity; microbiome; microenvironment; cancer therapy; inflammation

Special Issue Information

Dear Colleagues,

Regardless of diet, environment, intestinal flora imbalance, and abnormal growth of tumor cells caused by innate genes, changes in the cellular microenvironment can be caused. In particular, different types of tumor cells have their specific metabolites or their regulatory pathways. This special issue welcomes research on the mechanisms underlying the growth and metabolism of tumor cells, as well as studies on the regulation of metabolism by tumor therapy.

Dr. Chan-Yen Kuo
Dr. Chih-Yuan Ko
Guest Editors

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Keywords

  • metabolites
  • immunity
  • microbiome
  • microenvironment
  • inflammation

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Published Papers (1 paper)

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14 pages, 2391 KiB  
Technical Note
Open Source Repository and Online Calculator of Prediction Models for Diagnosis and Prognosis in Oncology
by Iva Halilaj, Cary Oberije, Avishek Chatterjee, Yvonka van Wijk, Nastaran Mohammadian Rad, Prabash Galganebanduge, Elizaveta Lavrova, Sergey Primakov, Yousif Widaatalla, Anke Wind and Philippe Lambin
Biomedicines 2022, 10(11), 2679; https://doi.org/10.3390/biomedicines10112679 - 23 Oct 2022
Cited by 2 | Viewed by 2282
Abstract
(1) Background: The main aim was to develop a prototype application that would serve as an open-source repository for a curated subset of predictive and prognostic models regarding oncology, and provide a user-friendly interface for the included models to allow online calculation. The [...] Read more.
(1) Background: The main aim was to develop a prototype application that would serve as an open-source repository for a curated subset of predictive and prognostic models regarding oncology, and provide a user-friendly interface for the included models to allow online calculation. The focus of the application is on providing physicians and health professionals with patient-specific information regarding treatment plans, survival rates, and side effects for different expected treatments. (2) Methods: The primarily used models were the ones developed by our research group in the past. This selection was completed by a number of models, addressing the same cancer types but focusing on other outcomes that were selected based on a literature search in PubMed and Medline databases. All selected models were publicly available and had been validated TRIPOD (Transparent Reporting of studies on prediction models for Individual Prognosis Or Diagnosis) type 3 or 2b. (3) Results: The open source repository currently incorporates 18 models from different research groups, evaluated on datasets from different countries. Model types included logistic regression, Cox regression, and recursive partition analysis (decision trees). (4) Conclusions: An application was developed to enable physicians to complement their clinical judgment with user-friendly patient-specific predictions using models that have received internal/external validation. Additionally, this platform enables researchers to display their work, enhancing the use and exposure of their models. Full article
(This article belongs to the Special Issue Cellular Metabolism in Malignant Tumor)
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