Granulomatosis With Polyangiitis of Spinal Dura Presenting With "Bottle Brush Sign" on 18 F-FDG PET/CT.

ABSTRACT: A 72-year-old man with fever and weakness in both lower limbs underwent thoracolumbar MRI and 18 F-FDG PET/CT. The PET/CT scan revealed diffused FDG uptake along the spinal dura mater from T7 to S2 level like a "bottle brush." Pathologic examination after biopsy of spinal canal lesions manifested granulomatous inflammation. The blood test showed cytoplasmic antineutrophil cytoplasmic antibody (ANCA) and myeloperoxidase-ANCAs were positive, whereas the perinuclear ANCA was negative. Eventually, he was diagnosed with granulomatosis with polyangiitis.

Intracranial Phosphaturic Mesenchymal Tumor Detected by 68 Ga-DOTATATE PET/CT.

ABSTRACT: A 68 Ga-DOTATATE PET/CT scan was conducted to locate the causative tumor responsible for suspected tumor-induced osteomalacia in a 56-year-old woman. The PET/CT images showed a focus in the right occipital region. Subsequent MRI showed an extra-axial nodule in the right occipital region, mimicking a meningioma. Although rare, an intracranial phosphaturic mesenchymal tumor was still suspected because of the typical clinical settings. Finally, phosphaturic mesenchymal tumor was confirmed by the postoperative pathology.

Predicting in-hospital mortality in ICU patients with sepsis using gradient boosting decision tree.

ABSTRACT: Sepsis is a leading cause of mortality in the intensive care unit. Early prediction of sepsis can reduce the overall mortality rate and cost of sepsis treatment. Some studies have predicted mortality and development of sepsis using machine learning models. However, there is a gap between the creation of different machine learning algorithms and their implementation in clinical practice.This study utilized data from the Medical Information Mart for Intensive Care III. We established and compared the gradient boosting decision tree (GBDT), logistic regression (LR), k-nearest neighbor (KNN), random forest (RF), and support vector machine (SVM).A total of 3937 sepsis patients were included, with 34.3% mortality in the Medical Information Mart for Intensive Care III group. In our comparison of 5 machine learning models (GBDT, LR, KNN, RF, and SVM), the GBDT model showed the best performance with the highest area under the receiver operating characteristic curve (0.992), recall (94.8%), accuracy (95.4%), and F1 score (0.933). The RF, SVM, and KNN models showed better performance (area under the receiver operating characteristic curve: 0.980, 0.898, and 0.877, respectively) than the LR (0.876).The GBDT model showed better performance than other machine learning models (LR, KNN, RF, and SVM) in predicting the mortality of patients with sepsis in the intensive care unit. This could be used to develop a clinical decision support system in the future.

Anti-proliferative and apoptosis-inducible activity of labdane and abietane diterpenoids from the pulp of Torreya nucifera in HeLa cells.

Two abietane and one labdane type diterpenoids were isolated from the methanol extracts of Torreya nucifera pulp and investigated for their ability to inhibit the growth of human cancer cells. Among the three compounds, the labdane compound kayadiol was found to have the most effective inhibitory effect against a wide variety of human cancer cells. Using the MTT assay, kayadiol was determined to have an IC50 (50% inhibition concentration) of 30 µM in HeLa cells, and also to exhibit anti-proliferative effects towards six other human cancer cell lines, with IC50 values of 30-50 µM. Kayadiol treatment of HeLa cells resulted in a dose-dependent generation of apoptotic events, including DNA laddering (≤100 µM). Moreover, kayadiol-treated HeLa cells showed activation of caspases-3 and -9, as well as an increase in the depolarization of mitochondrial membrane potential and the Bax/Bcl-2 ratio. These results indicate that a mitochondria-related apoptotic pathway is involved in the kayadiol-induced death of HeLa cells. Kayadiol is therefore a promising novel anti-proliferative agent and merits further investigation.

Luteolin increases the expression of GRP94 in HeLa cells.

We found increased expression of GRP94, the 94-kDa glucose-regulated protein, in HeLa cells 24 h after treatment with luteolin. Luteolin increased the levels of GRP94 mRNA and protein, but it did not increase the expression of unfolded protein response (UPR)-regulated genes. In addition, luteolin also enhanced GRP94 promoter activity, suggesting that it enhances the expression of GRP94 at the transcriptional level, not via the UPR signaling pathway.