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Public health indicator analysis plays a crucial part in colorectal cancer (CRC) recognition by leveraging epidemiological data and health metrics to recognize trends, disparities, and risk factors in diagnosis and screening. Public health officials use screening, incidence, and mortality rates to analyze current screening programs, target interventions for underserved populations, and improve early recognition and treatment results. With growing case numbers, CRC ranks second in women and third in men. The growing diagnostic workload and variability in biomarker assessment have prompted the exploration of robust machine-based models for pathology labs. Diagnosing CRC from histopathological images involves analyzing tissue samples for cancerous cellular changes. These images offer elaborate insights into tissue microstructure, aiding pathologists in examining cellular characteristics such as size, shape, and organization. Artificial Intelligence (AI) and fractals algorithms have proven advanced in the healthcare field, having the inherent ability for medical applications. The incorporation of AI in CRC diagnosis aims to improve the efficiency of pathologists, reduce diagnostic errors, and ultimately contribute to better patient outcomes through early and accurate detection of cancerous lesions. The study proposes a Golden Jackal Fractal Optimization with Deep Learning Assisted CRC Detection (GJODL-CRCD) technique on HIs. The GJODL-CRCD technique mainly examines the HIs to recognize and identify the CRC. The GJODL-CRCD method applies the Gabor filtering (GF) approach as a noise removal procedure to achieve this. Furthermore, the GJODL-CRCD technique employs the DenseNet model for learning and capturing intrinsic feature patterns from the preprocessed imageries. For hyperparameter selection of the DenseNet model, the GJODL-CRCD approach uses the GJO fractal algorithm. Finally, the GJODL-CRCD method uses the Elman Neural Network (ENN) model to identify and classify CRC. The performance evaluation of the GJODL-CRCD technique takes place on benchmark medical image data. The experimental results pointed out the betterment of the GJODL-CRCD model over other methods.

Colorectal cancer is the third leading cause of cancer-related death worldwide. Hence, there is a need to identify new therapeutic agents to improve the current repertoire of therapeutic drugs. Wogonin, a flavonoid from the herbal medicine Scutellaria baicalensis, has unique antitumor activity. Our study aimed to further explore the inhibitory effects of wogonin on colorectal cancer and its specific mechanism. The results showed that wogonin significantly inhibited the proliferation of colorectal cancer cells as well as their ability to invade and metastasize. We detected phosphorylation of tumor-associated signaling pathways using a phosphorylated protein microarray and found that wogonin intervention significantly inhibited the phosphorylation level of the AKT protein in colorectal cancer cells. Through in vitro and in vivo experiments, it was confirmed that wogonin exerted its antitumor effects against colorectal cancer by inhibiting phosphorylation in the AKT pathway. Our discovery of wogonin as an inhibitor of AKT phosphorylation provides new opportunities for the pharmacological treatment of colorectal cancer.

Cancer is a complex disease that cannot be diagnosed reliably using only single gene expression analysis. Using gene-set analysis on high throughput gene expression profiling controlled by various environmental factors is a commonly adopted technique used by the cancer research community. This work develops a comprehensive gene expression analysis tool (gene-set activity toolbox: (GAT)) that is implemented with data retriever, traditional data pre-processing, several gene-set analysis methods, network visualization and data mining tools. The gene-set analysis methods are used to identify subsets of phenotype-relevant genes that will be used to build a classification model. To evaluate GAT performance, we performed a cross-dataset validation study on three common cancers namely colorectal, breast and lung cancers. The results show that GAT can be used to build a reasonable disease diagnostic model and the predicted markers have biological relevance. GAT can be accessed from http://gat.sit.kmutt.ac.th where GAT’s java library for gene-set analysis, simple classification and a database with three cancer benchmark datasets can be downloaded.

In addition to tumor cells, a large number of immune cells are found in the tumor microenvironment (TME) of cancer patients. Tumor-infiltrating immune cells play an important role in tumor progression and patient outcome. We improved the relative proportion estimation algorithm of immune cells based on RNA-seq gene expression profiling and solved the multiple linear regression model by support vector regression ($v$-SVR). These steps resulted in increased robustness of the algorithm and more accurate calculation of the relative proportion of different immune cells in cancer tissues. This method was applied to the analysis of infiltrating immune cells based on 41 pairs of colorectal cancer tissues and normal solid tissues. Specifically, we compared the relative fractions of six types of immune cells in colorectal cancer tissues to those found in normal solid tissue samples. We found that tumor tissues contained a higher proportion of CD8 T cells and neutrophils, while B cells and monocytes were relatively low. Our pipeline for calculating immune cell proportion using gene expression profile data can be freely accessed from GitHub at https://github.com/gutmicrobes/EICS.git.

En-bloc tumor resection is the standard treatment for locally advanced colorectal cancer (CRC). An extensive histopathological assessment is necessary to evaluate the metastatic spread and adjuvant therapy. Sentinel lymph node biopsy decreases the histopathological burden when only sentinel lymph nodes (SLNs) are examined. This study aims to evaluate the spread of a magnetic tracer throughout the lymphatic system after ex vivo injection in en-bloc resected specimens of patients with CRC. To achieve this, lymph nodes (LNs) were quantified using a new magnetic detection method. Fifteen patients with CRC diagnosed with clinically negative LNs were included in this study and received 2–4 ex vivo magnetic tracer injections (total volume of 2mL). Magnetic sample series were acquired to create a look-up table for magnetic tracer quantification. In 80% of the patients, at least one magnetic LN was detected. A total of 33 LNs were marked as magnetic, containing an average of 8.1$\mu$g iron. In 71% of the patients, metastases were found in nonmagnetic LNs. Ex vivo injection leads to sub-optimal tracer spread and therefore inaccurate diagnosis. This study presents a novel magnetic detection method to quantify magnetic tracer in lymph nodes. Detecting the SLNs in en-bloc resected specimens and involving only these LNs in histopathological investigation enable a decrease in healthcare costs or an increased diagnostic potential.