Implant in the Area of Cemento-osseous Dysplasia: Secondary Infection After Osseointegration and Loading.

This case report presents the clinical details of a 60-year-old woman who experienced a secondary infection 5 years postimplant placement and loading in a region affected by cemento-osseous dysplasia (COD). We conducted the simultaneous removal of the implant and the COD mass, which had become osseointegrated with the implant. Healing of the affected area was successful. Caution is paramount during implant placement in COD-affected areas, particularly during the intermediate and osteosclerotic stages, due to compromised vascularity.

Deep learning-based prediction of indication for cracked tooth extraction using panoramic radiography.

BACKGROUND: We aimed to determine the feasibility of utilizing deep learning-based predictions of the indications for cracked tooth extraction using panoramic radiography. METHODS: Panoramic radiographs of 418 teeth (group 1: 209 normal teeth; group 2: 209 cracked teeth) were evaluated for the training and testing of a deep learning model. We evaluated the performance of the cracked diagnosis model for individual teeth using InceptionV3, ResNet50, and EfficientNetB0. The cracked tooth diagnosis model underwent fivefold cross-validation with 418 data instances divided into training, validation, and test sets at a ratio of 3:1:1. RESULTS: To evaluate the feasibility, the sensitivity, specificity, accuracy, and F1 score of the deep learning models were calculated, with values of 90.43-94.26%, 52.63-60.77%, 72.01-75.84%, and 76.36-79.00%, respectively. CONCLUSION: We found that the indications for cracked tooth extraction can be predicted to a certain extent through a deep learning model using panoramic radiography.

Deep learning-based prediction of osseointegration for dental implant using plain radiography.

BACKGROUND: In this study, we investigated whether deep learning-based prediction of osseointegration of dental implants using plain radiography is possible. METHODS: Panoramic and periapical radiographs of 580 patients (1,206 dental implants) were used to train and test a deep learning model. Group 1 (338 patients, 591 dental implants) included implants that were radiographed immediately after implant placement, that is, when osseointegration had not yet occurred. Group 2 (242 patients, 615 dental implants) included implants radiographed after confirming successful osseointegration. A dataset was extracted using random sampling and was composed of training, validation, and test sets. For osseointegration prediction, we employed seven different deep learning models. Each deep-learning model was built by performing the experiment 10 times. For each experiment, the dataset was randomly separated in a 60:20:20 ratio. For model evaluation, the specificity, sensitivity, accuracy, and AUROC (Area under the receiver operating characteristic curve) of the models was calculated. RESULTS: The mean specificity, sensitivity, and accuracy of the deep learning models were 0.780-0.857, 0.811-0.833, and 0.799-0.836, respectively. Furthermore, the mean AUROC values ranged from to 0.890-0.922. The best model yields an accuracy of 0.896, and the worst model yields an accuracy of 0.702. CONCLUSION: This study found that osseointegration of dental implants can be predicted to some extent through deep learning using plain radiography. This is expected to complement the evaluation methods of dental implant osseointegration that are currently widely used.

Clinical outcomes of colorectal neoplasm with positive resection margin after endoscopic submucosal dissection.

A positive resection margin after colorectal endoscopic submucosal dissection (ESD) is associated with an increased risk of recurrence. We aimed to identify the clinical significance of positive resection margins in colorectal neoplasms after ESD. We reviewed 632 patients who had en bloc colorectal ESD at two hospitals between 2015 and 2020. The recurrence rates and presence of residual tumor after surgery were evaluated. The rate of additional surgery after ESD and recurrence rate were significantly higher in patients with incomplete resection (n = 75) compared to patients with complete resection (n = 557). When focusing solely on non-invasive lesions, no significant differences in recurrence rates were observed between the groups with complete and incomplete resection (0.2% vs. 1.9%, p = 0.057). Among 84 patients with submucosal invasive carcinoma, 39 patients underwent additional surgery due to non-curative resection. Positive vertical margin and lymphovascular invasion were associated with residual tumor. Lymphovascular invasion was associated with lymph node metastasis. However, no residual tumor nor lymph node metastases were found in patients with only one unfavorable histological factor. In conclusion, a positive resection margin in non-invasive colorectal lesions, did not significantly impact the recurrence rate. Also, in T1 colorectal cancer with a positive vertical resection margin, salvage surgery can be considered in selected patients with additional risk factors.

Cytochrome P450 Family 46 Subfamily A Member 1 Promotes the Progression of Colorectal Cancer by Inducing Tumor Cell Proliferation and Angiogenesis.

BACKGROUND/AIM: Cytochrome P450 family 46 subfamily A member 1 (CYP46A1) has been implicated in the development and progression of various cancers. This study aimed to analyze the expression of CYP46A1, examining its relationship with oncogenic behaviors, and determining its prognostic implications in colorectal cancer (CRC). MATERIALS AND METHODS: A total of 225 patients with CRC who underwent curative surgical resection were examined using paraffin-embedded tissue blocks and subjected to tumor-specific survival analysis. The expression of CYP46A1 was assessed in CRC tissues through reverse transcription-polymerase chain reaction, western blotting, and immunohistochemistry. The CRC cells' apoptosis, proliferation, angiogenesis, and lymphangiogenesis were analyzed using terminal deoxynucleotidyl transferase-mediated dUTP nick end labeling assays, alongside immunohistochemical staining for Ki-67, CD34, and D2-40 antibodies. RESULTS: CYP46A1 expression was found to be up-regulated in CRC tissues compared to normal colorectal mucosa. Such expression was significantly associated with advanced stage, deeper tumor invasion, lymph node metastasis, distant metastasis, and decreased survival. Furthermore, the mean Ki-67 labeling index and microvessel density values in CYP46A1-positive tumors were significantly elevated compared to CYP46A1-negative tumors. However, there was no discernible correlation between CYP46A1 expression and either the apoptotic index or lymphatic vessel density value. CONCLUSION: CYP46A1 promotes CRC progression, specifically through the induction of tumor cell proliferation and angiogenesis. The insights provided may hold potential implications for future therapeutic interventions targeting CYP46A1.

Myasthenia gravis and myopathy after nivolumab treatment for non-small cell lung carcinoma: A case report.

Here, we report a case of myasthenia gravis and myopathy in a patient treated with nivolumab. A 76-year-old man who had been treated with four doses of nivolumab because of non-small cell lung cancer (NSCLC) presented with proximal-dominant muscle weakness and fluctuating ptosis and diplopia. Serologic studies revealed increased levels of muscle enzymes including creatine phosphokinase (2934 U/L), and acetylcholine receptor antibody was positive (1.31 nmol/L). Following electrodiagnostic study, he was diagnosed with myasthenia gravis and active stage of myopathy. After discontinuation of nivolumab, he was treated with corticosteroids, intravenous immunoglobulin G, and pyridostigmine. The neuromuscular symptoms and serologic abnormalities of the patient markedly improved. Currently, he is taking oral steroids and pyridostigmine without further immunotherapy.