Glycopatterns and Glycoproteins Changes in MCN and SCN: A Prospective Cohort Study

Changes in DNA Methylation and mRNA Expression in Lung Tissue after Long-Term Supplementation with an Increased Dose of Cholecalciferol

Maintaining an appropriate concentration of vitamin D is essential for the proper functioning of the body, regardless of age. Nowadays, there are more and more indications that vitamin D supplementation at higher than standard doses may show protective and therapeutic effects. Our study identified differences in the body's response to long-term supplementation with cholecalciferol at an increased dose. Two groups of pigs were used in the experiment. The first group received a standard dose of cholecalciferol (grower, 2000 IU/kg feed, and finisher, 1500 IU/kg feed), and the second group received an increased dose (grower, 3000 IU/kg feed, and finisher, 2500 IU/kg feed). After slaughter, lung samples were collected and used for RRBS and mRNA sequencing. Analysis of the methylation results showed that 2349 CpG sites had significantly altered methylation patterns and 1116 (47.51%) identified DMSs (Differentially Methylated Sites) were related to genes and their regulatory sites. The mRNA sequencing results showed a significant change in the expression of 195 genes. The integrated analysis identified eleven genes with DNA methylation and mRNA expression differences between the analyzed groups. The results of this study suggested that an increased vitamin D intake may be helpful for the prevention of lung cancer and pulmonary fibrosis. These actions may stem from the influence of vitamin D on the expression of genes associated with collagen production, such as SHMT1, UGT1A6, and ITIH2.The anti-cancer properties of vitamin D are also supported by changes in KLHL3 and TTPA gene expression.

Etodolac improves collagen induced rheumatoid arthritis in rats by inhibiting synovial inflammation, fibrosis and hyperplasia

Synovial hyperplasia is the main cause of chronic rheumatoid arthritis (RA), but the mechanism of synovial hyperplasia is still unclear. Etodolac (ETD) is a selective COX-2 inhibitor for relieving pain and stiffness in RA, but the disease modifying effect is still lack of evidence. Proteomics method was used to study the differential proteome of synovial tissue in collagen induced arthritis (CIA) in rats. With the help of STRING analysis, the upregulated proteins enriched in the cluster of complement and coagulation cascades and platelet degranulation were highlighted, these proteins with fibrogenic factors Lum, CIV, CXI and Tgfbi participated in the synovial inflammation, fibrosis and hyperplasia in CIA. Based on KOG function class analysis, the proteins involved in the events of the central dogma was explored. They might be hyperplasia related proteins for most of them are related to the proliferation of cancer. ETD significantly attenuated synovial inflammation, fibrosis and hyperplasia in CIA rats by downregulating these proteins. Several proteins have not been observed in RA so far, such as Tmsb4x, Pura, Nfic, Ruvbl1, Snrpd3, U2af2, Srrm2, Srsf7, Elavl1, Hnrnph1, Wars, Yars, Bzw2, Mcts1, Eif4b, Ctsh, Lamp1, Dpp7, Ptges3, Cdc37 and Septin9, they might be potentials targets for RA. Blood biochemistry tests showed the safety of 7 months use of ETD on rats. In conclusion, present study displayed a comprehensive mechanism of synovial hyperplasia in CIA rats, on this basis, the clinical value of ETD in the treatment of RA was well confirmed.

Deep Learning-Based Differentiation between Mucinous Cystic Neoplasm and Serous Cystic Neoplasm in the Pancreas Using Endoscopic Ultrasonography

Mucinous cystic neoplasms (MCN) and serous cystic neoplasms (SCN) account for a large portion of solitary pancreatic cystic neoplasms (PCN). In this study we implemented a convolutional neural network (CNN) model using ResNet50 to differentiate between MCN and SCN. The training data were collected retrospectively from 59 MCN and 49 SCN patients from two different hospitals. Data augmentation was used to enhance the size and quality of training datasets. Fine-tuning training approaches were utilized by adopting the pre-trained model from transfer learning while training selected layers. Testing of the network was conducted by varying the endoscopic ultrasonography (EUS) image sizes and positions to evaluate the network performance for differentiation. The proposed network model achieved up to 82.75% accuracy and a 0.88 (95% CI: 0.817–0.930) area under curve (AUC) score. The performance of the implemented deep learning networks in decision-making using only EUS images is comparable to that of traditional manual decision-making using EUS images along with supporting clinical information. Gradient-weighted class activation mapping (Grad-CAM) confirmed that the network model learned the features from the cyst region accurately. This study proves the feasibility of diagnosing MCN and SCN using a deep learning network model. Further improvement using more datasets is needed.

Extracellular Vesicle Analysis Allows for Identification of Invasive IPMN

BACKGROUND AND AIMS

Advances in cross-sectional imaging have resulted in increased detection of intraductal papillary mucinous neoplasms (IPMNs), and their management remains controversial. At present, there is no reliable noninvasive method to distinguish between indolent and high risk IPMNs. We performed extracellular vesicle (EV) analysis to identify markers of malignancy in an attempt to better stratify these lesions.

METHODS

Using a novel ultrasensitive digital extracellular vesicle screening technique (DEST), we measured putative biomarkers of malignancy (MUC1, MUC2, MUC4, MUC5AC, MUC6, Das-1, STMN1, TSP1, TSP2, EGFR, EpCAM, GPC1, WNT-2, EphA2, S100A4, PSCA, MUC13, ZEB1, PLEC1, HOOK1, PTPN6, and FBN1) in EV from patient-derived cell lines and then on circulating EV obtained from peripheral blood drawn from patients with IPMNs. We enrolled a total of 133 patients in two separate cohorts: a clinical discovery cohort (n = 86) and a validation cohort (n = 47).

RESULTS

From 16 validated EV proteins in plasma samples collected from the discovery cohort, only MUC5AC showed significantly higher levels in high-grade lesions. Of the 11 patients with invasive IPMN (inv/HG), 9 had high MUC5AC expression in plasma EV of the 11 patients with high-grade dysplasia alone, only 1 had high MUC5AC expression (sensitivity of 82%, specificity of 100%). These findings were corroborated in a separate validation cohort. The addition of MUC5AC as a biomarker to imaging and high-riskstigmata allowed detection of all cases requiring surgery, whereas imaging and high-risk stigmata alone would have missed 5 of 14 cases (36%).

CONCLUSIONS

MUC5AC in circulating EV can predict the presence of invasive carcinoma within IPMN. This approach has the potential to improve the management and follow-up of patients with IPMN including avoiding unnecessary surgery.