Structural insights into the allosteric inhibition of P2X4 receptors.

P2X receptors are ATP-activated cation channels, and the P2X4 subtype plays important roles in the immune system and the central nervous system, particularly in neuropathic pain. Therefore, P2X4 receptors are of increasing interest as potential drug targets. Here, we report the cryo-EM structures of the zebrafish P2X4 receptor in complex with two P2X4 subtype-specific antagonists, BX430 and BAY-1797. Both antagonists bind to the same allosteric site located at the subunit interface at the top of the extracellular domain. Structure-based mutational analysis by electrophysiology identified the important residues for the allosteric inhibition of both zebrafish and human P2X4 receptors. Structural comparison revealed the ligand-dependent structural rearrangement of the binding pocket to stabilize the binding of allosteric modulators, which in turn would prevent the structural changes of the extracellular domain associated with channel activation. Furthermore, comparison with the previously reported P2X structures of other subtypes provided mechanistic insights into subtype-specific allosteric inhibition.

Efficient 18F-Fluorodeoxyglucose Positron Emission Tomography/Computed Tomography-based machine learning model for predicting epidermal growth factor receptor mutations in non-small cell lung cancer.

BACKGROUND: Beyond the human eye's limitations, radiomics provides more information that can be used for diagnosis. We develop a personalized and efficient model based on 18F-Fluorodeoxyglucose (18F-FDG) Positron Emission Tomography/Computed Tomography (PET/CT) to predict epidermal growth factor receptor (EGFR) mutations to help identify which non-small cell cancer (NSCLC) patients are candidates for EGFR-tyrosine kinase inhibitors (TKIs) therapy. METHODS: We retrospectively included 100 patients with NSCLC and randomized them according to 70 patients in the training group and 30 patients in the validation group. The least absolute shrinkage and selection operator logistic regression (LLR) algorithm and Support Vector Machine (SVM) classifier were used to build the models and predict whether EGFR is mutated or not. The predictive efficacy of the LLR algorithm-based model and the SVM classifier-based model was evaluated by plotting the receiver operating characteristic (ROC) curves and calculating the area under the curve (AUC). RESULTS: The AUC, sensitivity and specificity of our radiomics model by LLR algorithm were 0.792, 0.967, and 0.600 for the training group and 0.643, 1.00, and 0.378 for the validation group, respectively, in predicting EGFR mutations. The AUC was 0.838 for the training group and 0.696 for the validation group after combining radiomics features with clinical features. The prediction results based on the SVM classifier showed that the validation group had the best performance when based on radial kernel function with AUC, sensitivity, and specificity of 0.741, 0.667, and 0.825, respectively. CONCLUSIONS: Radiomics models based on 18F-FDG PET/CT modeled with different machine learning algorithms can improve the predictive efficacy of the models. Models that combine clinical features are more clinically valuable.

FDG-avid antrum-pylorus ulcer, adjacent lymph node, and abdominal wall nodule mimicking gastric cancer with metastases.

Gastric cancer presents with similar clinical symptoms as gastric ulcer, and the morphologic features of gastroscopy overlap considerably. We report a 58-year-old man with the clinical presentation of recurrent gastric discomfort and black stools. A suspected malignant tumor of the gastric antrum-pylorus was observed on gastroscopy. Contrast-enhanced CT showed enhancement of the lesion. PET/CT revealed an FDG-avid lesion at the gastric antrum-pylorus, an intense FDG-uptake perigastric lymph node, and an enlarged nodule with high FDG uptake in the right abdominal wall. Subsequent surgical pathology revealed an inflammatory ulcer of the gastric antrum-pylorus with reactive hyperplastic lymph node, while the lesion in the right abdominal wall was a scar nodule. This case suggests that when multiple FDG-avid lesions accompany an atypical gastric ulcer, it can easily lead to misdiagnosis, and therefore more emphasis should be placed on histopathological analysis.

The value of 18F-FDG PET/CT and carbohydrate antigen 19-9 in predicting lymph node micrometastases of pancreatic cancer.

PURPOSE: This study aimed to assess the value of 18F-FDG PET/CT and carbohydrate antigen 19-9 (CA 19-9) levels in predicting lymph node micrometastases in patients with pancreatic cancer. PATIENTS AND METHODS: A total of 160 patients with pancreatic carcinoma were included in the study from 2012 to 2017. All patients underwent surgical treatment and PET/CT scans as well as tests to measure CA 19-9 levels before surgery. The PET/CT scans were evaluated by 2 nuclear medicine physicians who were blinded to the clinical information and were compared to the postsurgical pathological findings. Logistic regression analysis was performed to determine the variables that could predict lymph node micrometastases. Receiver operating characteristic (ROC) curves were utilized to find the best cutoff value of the variables related to predicting lymph node micrometastases. RESULTS: The maximum standardized uptake value (SUVmax) of the primary tumor and CA 19-9 level were potent predictors for determining the lymph node status. The best SUVmax and CA 19-9 cutoff values for predicting lymph node micrometastases were 7.05 (sensitivity = 71.2%, specificity = 76.6%) and 240.55 U/ml (sensitivity = 62.1%, specificity = 79.8%), respectively. CONCLUSION: Patients with pancreatic cancer with a tumor SUVmax ≥ 7.05 or a CA 19-9 value ≥ 240.55 are likely to have lymph node micrometastases.