Background: The accurate segmentation of the appendix with well-defined boundaries is critical for diagnosing conditions such as acute appendicitis. The manual identification of the appendix is time-consuming and highly dependent on the expertise of the radiologist. Method: In this study, we propose a fully automated approach to the detection of the appendix using deep learning architecture based on the U-Net with specific training parameters in CT scans. The proposed U-Net architecture is trained on an annotated original dataset of abdominal CT scans to segment the appendix efficiently and with high performance. In addition, to extend the training set, data augmentation techniques are applied for the created dataset. Results: In experimental studies, the proposed U-Net model is implemented using hyperparameter optimization and the performance of the model is evaluated using key metrics to measure diagnostic reliability. The trained U-Net model achieved the segmentation performance for the detection of the appendix in CT slices with a Dice Similarity Coefficient (DSC), Volumetric Overlap Error (VOE), Average Symmetric Surface Distance (ASSD), Hausdorff Distance 95 (HD95), Precision (PRE) and Recall (REC) of 85.94%, 23.29%, 1.24 mm, 5.43 mm, 86.83% and 86.62%, respectively. Moreover, our model outperforms other methods by leveraging the U-Net’s ability to capture spatial context through encoder–decoder structures and skip connections, providing a correct segmentation output. Conclusions: The proposed U-Net model showed reliable performance in segmenting the appendix region, with some limitations in cases where the appendix was close to other structures. These improvements highlight the potential of deep learning to significantly improve clinical outcomes in appendix detection.

Background: A leading cause of emergency abdominal surgery, appendicitis is a common condition affecting millions of people worldwide. Automatic and accurate segmentation of the appendix from medical imaging is a challenging task, due to its small size, variability in shape, and proximity to other anatomical structures. Methods: In this study, we propose a backbone-enriched Mask R-CNN architecture (MaskAppendix) on the Detectron platform, enhanced with Gradient-weighted Class Activation Mapping (Grad-CAM), for precise appendix segmentation on computed tomography (CT) scans. In the proposed MaskAppendix deep learning model, ResNet101 network is used as the backbone. By integrating Grad-CAM into the MaskAppendix network, our model improves feature localization, allowing it to better capture subtle variations in appendix morphology. Results: We conduct extensive experiments on a dataset of abdominal CT scans, demonstrating that our method achieves state-of-the-art performance in appendix segmentation, outperforming traditional segmentation techniques in terms of both accuracy and robustness. In the automatic segmentation of the appendix region in CT slices, a DSC score of 87.17% was achieved with the proposed approach, and the results obtained have the potential to improve clinical diagnostic accuracy. Conclusions: This framework provides an effective tool for aiding clinicians in the diagnosis of appendicitis and other related conditions, reducing the potential for diagnostic errors and enhancing clinical workflow efficiency.

Background/Objectives: The healing rate of wounds resulting from postoperative abdominal surgery interventions increases as the inflammation and infections in the wound are reduced. However, antibiotic resistance among microorganisms increases the incidence of surgical site infections (SSIs). Therefore, the need for new products that exhibit antimicrobial and anti-inflammatory activities, in addition to antibiotics, is increasing. Methods: Silver nanoparticles (CO-AgNPs) were obtained using the green synthesis technique with Cydonia oblonga L. leaves, which constitute a significant amount of waste, and the effects of the obtained nanoparticles on in vitro wound healing were determined. Results: It was observed that CO-AgNPs inhibited myeloperoxidase and collagenase, enzymes that negatively affect wound healing. Furthermore, they exhibited good antimicrobial activity against Pseudomonas aeruginosa, Escherichia coli, Bacillus subtilis, and Staphylococcus aureus, which are common hospital pathogens. The CO-AgNPs could exhibit enhanced wound-healing properties by inhibiting microorganisms. Conclusions: It was clear that the development of new, environmentally friendly, and biocompatible products containing CO-AgNPs could be feasible, particularly for wound healing following infected abdominal surgery.

Background/Objectives: Acute pancreatitis (AP) is a significant inflammatory pancreatic disease with high morbidity and mortality rates that requires early and accurate diagnosis. In this study, a deep learning-based classification system is developed and evaluated for the automatic classification of AP and normal pancreas from contrast-enhanced CT images, with a focus on patient-level assessment to enhance clinical applicability. Methods: A study-specific dataset is created for the study in CT images from 183 patients (103 normal and 80 with AP). To prevent data leakage and objectively evaluate model performance, the dataset is divided into training and test sets based on patient-level data. Convolutional neural network (CNN)-based architectures, such as ResNet50, EfficientNet, and ConvNeXtV2, are compared with Transformer-based architectures, such as Swin Transformer (Swin) and Vision Transformer (ViT). Results: In slice-level analysis, all models achieve high performance. Swin shows the highest accuracy (84.06%), and ViT revealed the most balanced performance with an F1-score of 82.90%. In the more clinically significant patient-level evaluation, the ViT model outperforms all others with an accuracy of 89.19%, an F1-score of 86.67%, and an area under the curve (AUC) of 0.946. The ViT model’s high AUC and recall values demonstrate its ability to reliably distinguish between AP and normal pancreas classes, even under different threshold values. Conclusions: These results suggest that transformer-based architectures can extract stronger and more reliable feature representations from pancreatic CT images due to their capacity to model global contextual features. Furthermore, the patient-level evaluation approach enables the model to generate results that are more compatible with clinical decision-making processes, thereby enhancing its usability in real clinical settings. In conclusion, the proposed ViT-based approach is a promising method for diagnosing acute pancreatitis.

Colorectal cancer poses a significant global health challenge, with a considerable proportion arising from colon adenomas. Understanding the molecules involved in the carcinogenesis process is crucial for improving colon cancer diagnosis and prognosis. While research on the role of epiplakin in cancer remains limited compared to other plakin group proteins, comprehending its expression patterns and correlations can offer valuable insights into colon carcinogenesis. In this study, we analyzed 60 tissue samples, including colon adenocarcinomas, tubular adenomas (low malignancy risk group), tubulovillous adenomas (high malignancy risk group), and adjacent normal colon tissues. Classification and grading were reevaluated by histological examination. Immunohistochemistry was performed to assess epiplakin and Ki67 expression. Epiplakin optical density and the Ki67 proliferation index were calculated using ImageJ. Statistical analyses were conducted to evaluate correlations and significance. Epiplakin expression was significantly decreased in colon adenocarcinomas [optical density median 4.04 (95% CI, 3.98 to 4.24)] and tubulovillous adenomas [4.32 (95% CI, 4.08 to 4.32)] compared to normal colon tissues [4.61 (95% CI, 4.50 to 4.67)] and tubular adenomas [4.87 (95% CI, 4.67 to 4.88)] (P < 0.05). Moreover, adenoma groups exhibited higher proliferation indices (P < 0.05), and a positive correlation was found between epiplakin expression and the Ki67 proliferation index (r = 0.317, P < 0.05). Our study highlights the potential significance of epiplakin in colorectal cancer. Decreased epiplakin expression is associated with colon malignancy progression, suggesting its role as a potential marker.

Aim: Acute appendicitis is one of the most common causes of acute abdominal pain and has traditionally been treated with appendectomy. However, increasing evidence suggests that antibiotic therapy may be a safe alternative in selected patients with uncomplicated acute appendicitis. This study aimed to evaluate the effectiveness and safety of primary medical treatment in patients with uncomplicated acute appendicitis using real-world clinical and radiological data.Materials and Methods: A single-center retrospective cohort study was conducted including 289 patients diagnosed with acute appendicitis. Patients were classified as having uncomplicated appendicitis based on clinical and computed tomography findings. Twenty-three patients (7.9%) received primary medical treatment, while 266 patients (92.1%) underwent primary surgery. Demographic characteristics, appendix diameter, presence of appendicolith, treatment outcomes, and need for delayed surgery were analyzed.Results: The mean age was 40.6 years in the medical treatment group and 44.8 years in the surgical group. The median appendix diameter was significantly smaller in patients treated medically (8.0 mm [IQR: 7.5–8.25]) compared to those treated surgically (11.5 mm [IQR: 9.5–13.0], p < 0.001). No appendicolith was detected in the medical treatment group, whereas it was present in 12.8% of surgically treated patients.Clinical recovery without surgery was achieved in 19 of 23 patients (82.6%) receiving medical treatment. Four patients (17.4%) required delayed appendectomy due to clinical deterioration. No cases of perforation, generalized peritonitis, or major postoperative complications were observed.Conclusion; Primary medical treatment is a safe and effective alternative to surgery in carefully selected patients with uncomplicated acute appendicitis. Appropriate patient selection, particularly exclusion of appendicolith and close clinical follow-up, allows a substantial proportion of patients to avoid surgical intervention without increasing complication rates.