Investigating Radiosensitization Mechanisms of Hematoporphyrin Derivatives in Lung Adenocarcinoma Cells
Hongtao Yin,
Hongxu Zhang,
Chunbo Wang,
Wencheng Shao
Issue:
Volume 8, Issue 4, December 2022
Pages:
51-57
Received:
29 September 2022
Accepted:
19 October 2022
Published:
28 October 2022
DOI:
10.11648/j.rst.20220804.11
Downloads:
Views:
Abstract: Background: Radiotherapy effectiveness is drastically reduced in malignant tumors with low radiosensitivity. Combining hematoporphyrin derivatives (HPDs) with radiotherapy may have radiosensitizing effects, but radiosensitization mechanisms of HPDs in lung adenocarcinoma remains unclear. This study used in vitro experiments to verify whether HPDs could increase the sensitivity of lung adenocarcinoma cells to radiotherapy, and to determine how HPDs induce radiosensitization. Methods: Cloning experiments were performed on lung adenocarcinoma (A549) cells under three conditions: untreated, X-ray radiation alone, and HPDs combined with X-ray radiation. We analyzed results from previous research screening target gene HSP90AA1, then used western blotting to detect autophagosome formation. Differences in AKT, mTOR, and LC3 expression before and after X-ray/HPD treatment in A549 cells were analyzed. Results: Optimal HPD concentration and X-ray dose were 10 and 10 Gy, respectively. The combination of HPDs and X-ray inhibited proliferation and promoted apoptosis of A549 cells in a dose-dependent manner. Western blotting revealed few autophagosomes in the control group, whereas autophagosomes increased significantly in A549 cells after X-ray irradiation. Combining HPDs and X-ray decreased autophagosome numbers. Compared to X-ray only, HPDs + X-ray resulted in decreased LC3II expression and LC3II/LC3I ratio. Additionally, the LC3II/LC3I ratio was higher in the X-ray group than in the control group. Conclusions: The combination of X-ray irradiation and HPDs inhibited cell proliferation and induced radiosensitization in A549 cells. The radiosensitizing effect of HSP90AA1 may be related to autophagy. Thus, HSP90AA1 is a potential biomarker for enhanced radiosensitivity after HPD treatment.
Abstract: Background: Radiotherapy effectiveness is drastically reduced in malignant tumors with low radiosensitivity. Combining hematoporphyrin derivatives (HPDs) with radiotherapy may have radiosensitizing effects, but radiosensitization mechanisms of HPDs in lung adenocarcinoma remains unclear. This study used in vitro experiments to verify whether HPDs c...
Show More
Exploring the Knowledge, Attitudes, and Practices of Radiographers Regarding the Use of Artificial Intelligence in CT in Selected Private Hospitals in KZN
Nondumiso Praise Zuma,
Timika Mewalall,
Thandokuhle Emmanuel Khoza
Issue:
Volume 8, Issue 4, December 2022
Pages:
58-63
Received:
17 August 2022
Accepted:
15 September 2022
Published:
30 November 2022
Abstract: Artificial Intelligence (AI) has become increasingly important to daily lives. AI has introduced several algorithms in Computed Tomography (CT) which allow for improved image quality at a low dose. These systems execute tasks that are normally done by a human (Radiographers). Hence Radiographers need to have adequate knowledge of these AI applications. Previous studies reveal that Radiographers lack knowledge of the AI and its algorithms that are used in CT, which has been identified as a problem because limited information is passed on to students and trainees. The aim of this study was to explore Radiographers’ knowledge, attitudes, and practices toward the use of AI in CT. The research was conducted in selected private hospitals in Kwa-Zulu Natal in which semi-structured and in-depth face to face interviews using open-ended questions were used to collect data from 10 participants. Three main themes generated from the study’s theoretical framework were used for data analysis, namely knowledge, attitudes, and practices. Findings in this study indicate that Radiographers lack knowledge of AI and its algorithms that are used in CT. Their lack of knowledge is a result of a lack of training and education. Findings also suggest that a lack of knowledge contributes to uncertainty about the potential impact of AI implementation. However, Radiographers demonstrated interest in wanting to gain more information. Radiographers that participated in this study demonstrated a lack of knowledge, but also an interest in learning more about AI. This, therefore, necessitates collaboration between educational institutes and professional organizations to develop structured training programs for Radiographers.
Abstract: Artificial Intelligence (AI) has become increasingly important to daily lives. AI has introduced several algorithms in Computed Tomography (CT) which allow for improved image quality at a low dose. These systems execute tasks that are normally done by a human (Radiographers). Hence Radiographers need to have adequate knowledge of these AI applicati...
Show More
