Current methods of colon cancer treatment, especially chemotherapy, require new treatment methods due to adverse side effects. One important area of interest in recent years is the use of nanoparticles as drug delivery vehicles since several studies have revealed that they can improve the target specificity of the treatment thus lowering the dosage of the drugs while preserving the effectiveness of the treatment thus reducing the side effects. The use of traditional medicine has also been a favorite topic of interest in recent years in medical research, especially cancer research. In this research work, the green synthesis of Fe nanoparticles was carried out using Mentha spicata extract and the synthesized nanoparticles were identified using FT-IR, XRD, FE-SEM and EDS techniques. Then the effect of Mentha spicata, Fe nanoparticles, and Mentha spicata -loaded Fe nanoparticles on LS174t colon cancer cells, and our result concluded that all three, especially Mentha spicata -loaded Fe nanoparticles, have great cytotoxic effects against LS174t cells, and exposure to radiotherapy just further intensified these results. The in vitro condition revealed alterations in the expression of pro-apoptotic BAX and anti-apoptotic Bcl2, suggesting a pro-apoptotic effect from all three components, particularly the Mentha spicata-loaded Fe nanoparticles. After further clinical trials, these nanoparticles can be used to treat colon cancer.

Colon carcinoma poses a significant health challenge globally, particularly in developed nations where sedentary lifestyles, poor dietary choices, and genetic factors play a crucial role in its prevalence. Chemotherapy, the primary treatment method, carries severe side effects that can jeopardize patients' lives. Herbal extracts such as Ocimum Basillicum extract have shown effectiveness against cancer cells. Additionally, nanoparticles can significantly enhance drug delivery efficacy in these scenarios. This article aims to investigate the impact of copper nanoparticles coated with Ocimum Bassilicum at chemoradiotherapy of Colon Carcinoma to hopefully create new treatment options with fewer side effects for patients. CuO bio-NPs were produced by the addition of 15 mL of extract dropwise to 80 mL of a 5 mM Cu (OAc)₂ aqueous solution, which was then refluxed for 2 h at 100 °C. The mixture gradually became darker brown in color as a result of the heating procedure. The production of CuO NPs and the hydrogen-donating activity of antioxidant phenols within the plant are signaled by surface plasmon resonance excitation, which is the cause of this. In the cell culture, LS174t colon cancer cells were treated with OB extract, CuNPs, and OB-coated CuNPs with and without different radiation levels in order to assess cell viability, through the MTT assay, and the pro-apoptotic BAX and anti-apoptotic BCL2 expressions, through qPCR assay. The results demonstrate a decrease in cell viability and the expression of BCL2 and an increase in the expression of BAX especially when treated with OB-coated CuNPs and even furthermore when paired with radiation therapy. After doing the clinical trial studies, the recent nanoparticles can be used for the treatment of Colorectal carcinoma.

Radiation-induced esophagitis remains a significant challenge in thoracic and neck cancer treatment, impacting patient quality of life and potentially limiting therapeutic efficacy. This study aimed to develop and validate a decision tree-based model for predicting acute esophagitis grades in patients undergoing chemoradiotherapy. Data from 100 patients receiving thoracic and neck radiotherapy were analyzed. The dataset comprised 33 features, including demographic, clinical, and dosimetric parameters. A decision tree classifier was implemented for both binary (Grade ≥2 vs. <2) and multi-class (Grades 1, 2, and 3) classification. Model performance was evaluated using standard metrics including accuracy, precision, recall, and F1-score. The binary classification model achieved 97 % accuracy in distinguishing acute esophagitis. The multi-class model demonstrated 98 % accuracy in predicting specific grades. Key predictive features included V40 (volume receiving 40 Gy), V60, and average esophageal dose. The model generated interpretable decision rules, with V60 ≥ 2.3 strongly indicating Grade 3 esophagitis. The decision tree model demonstrates high accuracy in predicting radiation-induced esophagitis grades while maintaining clinical interpretability. This approach offers potential for treatment optimization and personalized risk assessment in radiotherapy planning. The model's transparency and reliability make it a promising tool for clinical decision support in radiation oncology.