The early detection of esophageal cancer presents a substantial difficulty, which contributes to its status as a primary cause of cancer-related fatalities. This study used You Only Look Once (YOLO) frameworks, specifically YOLOv5 and YOLOv8, to predict and detect early-stage EC by using a dataset sourced from the Division of Gastroenterology and Hepatology, Ditmanson Medical Foundation, Chia-Yi Christian Hospital. The dataset comprised 2741 white-light images (WLI) and 2741 hyperspectral narrowband images (HSI-NBI). They were divided into 60% training, 20% validation, and 20% test sets to facilitate robust detection. The images were produced using a conversion method called the spectrum-aided vision enhancer (SAVE). This algorithm can transform a WLI into an NBI without requiring a spectrometer or spectral head. The main goal was to identify dysplasia and squamous cell carcinoma (SCC). The model’s performance was evaluated using five essential metrics: precision, recall, F1-score, mAP, and the confusion matrix. The experimental results demonstrated that the HSI model exhibited improved learning capabilities for SCC characteristics compared with the original RGB images. Within the YOLO framework, YOLOv5 outperformed YOLOv8, indicating that YOLOv5’s design possessed superior feature-learning skills. The YOLOv5 model, when used in conjunction with HSI-NBI, demonstrated the best performance. It achieved a precision rate of 85.1% (CI95: 83.2–87.0%, p < 0.01) in diagnosing SCC and an F1-score of 52.5% (CI95: 50.1–54.9%, p < 0.01) in detecting dysplasia. The results of these figures were much better than those of YOLOv8. YOLOv8 achieved a precision rate of 81.7% (CI95: 79.6–83.8%, p < 0.01) and an F1-score of 49.4% (CI95: 47.0–51.8%, p < 0.05). The YOLOv5 model with HSI demonstrated greater performance than other models in multiple scenarios. This difference was statistically significant, suggesting that the YOLOv5 model with HSI significantly improved detection capabilities.

Liver cancer remains a leading cause of death, despite advances in anti-cancer therapies. To develop novel drugs, natural products are being considered as a good source for exploration. In this study, a natural product isolated from a soft coral was applied to evaluate its anti-cancer activities in hepatocellular carcinoma SK-HEP-1 cells. Sinularin was determined to have half-maximal inhibitory concentration (IC₅₀) values of ~10 μM after 24, 48, and 72 h. The TUNEL assay and annexin V/PI staining results showed that sinularin induced DNA fragmentation and apoptosis, respectively. An investigation at the molecular level demonstrated that the expression levels of cleaved caspases 3/9 were significantly elevated at 10 μM sinularin. Mitochondrial and intracellular reactive oxygen species (ROS) levels were significantly increased following sinularin treatment, which also affected the mitochondrial membrane potential. In addition, it significantly lowered the mitochondrial respiration parameters and extracellular acidification rates at 10 μM. Further investigation showed that sinularin significantly attenuated wound healing, cell migration, and potential colony formation at 10 μM. Fluorescence microscopic observations showed that the distribution of F-actin filaments was significantly altered at 10 μM sinularin. Supported by Western blot analyses, the expression levels of AKT, p-ERK (extracellular-signal-related kinase), vimentin and VEGF were significantly down-regulated, whereas p-p38, pJNK and E-cadherin were significantly increased. Overall, at the IC₅₀ concentration, sinularin was able to significantly affect SK-HEP-1 cells.

Sialylation of glycoproteins is modified by distinct sialyltransferases such as ST3Gal, ST6Gal, ST6GalNAc, or ST8SIA with α2,3-, α2,6-, or α2,8-linkages. Alteration of these sialyltransferases causing aberrant sialylation is associated with the progression of colon cancer. However, among the ST8- sialyltransferases, the role of ST8SIA6 in colon cancer remains poorly understood. In this study, we explored the involvement of ST8SIA6 in colon cancer using multiple gene databases. The relationship between ST8SIA6 expression and tumor stages/grades was investigated by UALCAN analysis, and Kaplan–Meier Plotter analysis was used to analyze the expression of ST8SIA6 on the survival outcome of colon cancer patients. Moreover, the biological functions of ST8SIA6 in colon cancer were explored using LinkedOmics and cancer cell metabolism gene DB. Finally, TIMER and TISMO analyses were used to delineate ST8SIA6 levels in tumor immunity and immunotherapy responses, respectively. ST8SIA6 downregulation was associated with an advanced stage and poorly differentiated grade; however, ST8SIA6 expression did not affect the survival outcomes in patients with colon cancer. Gene ontology analysis suggested that ST8SIA6 participates in cell surface adhesion, angiogenesis, and membrane vesicle trafficking. In addition, ST8SIA6 levels affected immunocyte infiltration and immunotherapy responses in colon cancer. Collectively, these results suggest that ST8SIA6 may serve as a novel therapeutic target towards personalized medicine for colon cancer.

Objective: Esophageal carcinoma (EC) is the eighth most prevalent cancer and the sixth leading cause of cancer-related mortality worldwide. Early detection is vital for improving prognosis, particularly for dysplasia and squamous cell carcinoma (SCC). Methods: This study evaluates a hyperspectral imaging conversion method, the Spectrum-Aided Vision Enhancer (SAVE), for its efficacy in enhancing esophageal cancer detection compared to conventional white-light imaging (WLI). Five deep learning models (YOLOv9, YOLOv10, YOLO-NAS, RT-DETR, and Roboflow 3.0) were trained and evaluated on a dataset comprising labeled endoscopic images, including normal, dysplasia, and SCC classes. Results: Across all five evaluated deep learning models, the SAVE consistently outperformed conventional WLI in detecting esophageal cancer lesions. For SCC, the F1 score improved from 84.3% to 90.4% in regard to the YOLOv9 model and from 87.3% to 90.3% in regard to the Roboflow 3.0 model when using the SAVE. Dysplasia detection also improved, with the precision increasing from 72.4% (WLI) to 76.5% (SAVE) in regard to the YOLOv9 model. Roboflow 3.0 achieved the highest F1 score for dysplasia of 64.7%. YOLO-NAS exhibited balanced performance across all lesion types, with the dysplasia precision rising from 75.1% to 79.8%. Roboflow 3.0 also recorded the highest SCC sensitivity of 85.7%. In regard to SCC detection with YOLOv9, the WLI F1 score was 84.3% (95% CI: 71.7–96.9%) compared to 90.4% (95% CI: 80.2–100%) with the SAVE (p = 0.03). For dysplasia detection, the F1 score increased from 60.3% (95% CI: 51.5–69.1%) using WLI to 65.5% (95% CI: 57.0–73.8%) with SAVE (p = 0.04). These findings demonstrate that the SAVE enhances lesion detectability and diagnostic performance across different deep learning models. Conclusions: The amalgamation of the SAVE with deep learning algorithms markedly enhances the detection of esophageal cancer lesions, especially squamous cell carcinoma and dysplasia, in contrast to traditional white-light imaging. This underscores the SAVE’s potential as an essential clinical instrument for the early detection and diagnosis of cancer.

Diagnosing gastrointestinal disorders (GIDs) remains a significant challenge, particularly when relying on wireless capsule endoscopy (WCE), which lacks advanced imaging enhancements like Narrow Band Imaging (NBI). To address this, we propose a novel framework, the Spectrum-Aided Vision Enhancer (SAVE), especially designed to transform standard white light (WLI) endoscopic images into spectrally enriched representations that emulate both hyperspectral imaging (HSI) and NBI formats. By leveraging color calibration through the Macbeth Color Checker, gamma correction, CIE 1931 XYZ transformation, and principal component analysis (PCA), SAVE reconstructs detailed spectral information from conventional RGB inputs. Performance was evaluated using the Kvasir-v2 dataset, which includes 6490 annotated images spanning eight GI-related categories. Deep learning models like Inception-Net V3, MobileNetV2, MobileNetV3, and AlexNet were trained on both original WLI- and SAVE-enhanced images. Among these, MobileNetV2 achieved an F1-score of 96% for polyp classification using SAVE, and AlexNet saw a notable increase in average accuracy to 84% when applied to enhanced images. Image quality assessment showed high structural similarity (SSIM scores of 93.99% for Olympus endoscopy and 90.68% for WCE), confirming the fidelity of the spectral transformations. Overall, the SAVE framework offers a practical, software-based enhancement strategy that significantly improves diagnostic accuracy in GI imaging, with strong implications for low-cost, non-invasive diagnostics using capsule endoscopy systems.

Briareum stechei is proven to be a rich source of 3,8-cyclized cembranoids (briarane) with a bicyclo[8.4.0] carbon core. In the present study, four previously unreported briaranes, briarenols W–Z (1–4), along with solenolide A (5), briarenolide M (6), briaexcavatolide F (7), and brianolide (8), were isolated and characterized through spectroscopic analysis, and the absolute configuration of 8 was corroborated by a single-crystal x-ray diffraction analysis. Briaranes 2 and 5 were found to induce significant inflammatory activity in lipopolysaccharide (LPS)-induced RAW 264.7 mouse macrophage cells by enhancing the expression of the inducible nitric oxide synthase (iNOS) and cyclooxygenase-2 (COX-2) proteins.