N-[4-(β-d-Allopyranos­yloxy)benzyl­idene]methyl­amine

The title compound, C14H19NO6, was synthesized by the condensation reaction between hecilid (4-formyl­phenl-β-d-allopyran­oside) and methyl­amine in methanol. In the crystal structure, the pyran ring adopts a chair conformation and adjacent mol­ecules are linked by inter­molecular O—H⋯O and O—H⋯N hydrogen bonds, forming a three-dimensional network

Ammonium 2-(2,4-dichloro­phen­oxy)acetate hemihydrate

The title compound, NH4 +·C8H7Cl2O6 −·0.5H2O, was prepared by the reaction of 2-(2,4-dichloro­phen­oxy)­acetic acid and ammonia in water at 367 K. The mol­ecular structure and packing are stabilized by N—H⋯O and O—H⋯O inter­molecular hydrogen-bond inter­actions

Novel Gemini cationic lipids with carbamate groups for gene delivery

Novel Gemini cationic lipids were investigated to show superior gene delivery properties and promising applications in the future

Downregulation of connective tissue growth factor inhibits the growth and invasion of gastric cancer cells and attenuates peritoneal dissemination

<p>Abstract</p> <p>Background</p> <p>Connective tissue growth factor (CTGF) has been shown to be implicated in tumor development and progression. However, the role of CTGF in gastric cancer remains largely unknown.</p> <p>Results</p> <p>In this study, we showed that CTGF was highly expressed in gastric cancer tissues compared with matched normal gastric tissues. The CTGF expression in tumor tissue was associated with histologic grade, lymph node metastasis and peritoneal dissemination (P < 0.05). Patients with positive CTGF expression had significantly lower cumulative postoperative 5 year survival rate than those with negative CTGF expression (22.9% versus 48.1%, P < 0.001). We demonstrated that knockdown of CTGF expression significantly inhibited cell growth of gastric cancer cells and decreased cyclin D₁expression. Moreover, knockdown of CTGF expression also markedly reduced the migration and invasion of gastric cancer cells and decreased the expression of matrix metalloproteinase (MMP)-2 and MMP-9. Animal studies revealed that nude mice injected with the CTGF knockdown stable cell lines featured a smaller number of peritoneal seeding nodules than the control cell lines.</p> <p>Conclusions</p> <p>These data suggest that CTGF plays an important role in cell growth and invasion in human gastric cancer and it appears to be a potential prognostic marker for patients with gastric cancer.</p

Prediction model of cervical lymph node metastasis based on clinicopathological characteristics of papillary thyroid carcinoma: a dual-center retrospective study

BackgroundThe overall prevalence of papillary thyroid carcinoma (PTC) patients is expanding along with an ongoing increase in thyroid cancer incidence. Patients with PTC who have lymph node metastases have a poor prognosis and a high death rate. There is an urgent need for indicators that can predict lymph node metastasis (LNM) before surgery as current imaging techniques, such as ultrasonography, do not have sufficient sensitivity to detect LNM. To predict independent risk factors for Central lymph node metastasis (CLNM) or Lateral lymph node metastasis (LLNM), we therefore developed two nomograms based on CLNM and LLNM, separately.MethodsIn two centers, the Second Affiliated Hospital of Nanchang University and Yichun People’s Hospital, we retrospectively analyzed clinicopathological characteristics of PTC patients. We utilized multivariate analysis to screen for variables that might be suspiciously related to CLNM or LLNM. Furthermore, we developed nomograms to graphically depict the independent risk valuables connected to lymph node metastasis in PTC patients.ResultUltimately, 6068 PTC patients in all were included in the research. Six factors, including age&lt;45, male, mETE, TSH&gt;1.418, tumor size&gt;4cm, and location (multicentric and lobe), were observed to be related to CLNM. Age&lt;45, male, mETE (minimal extrathyroidal extension), multifocality, TSH≥2.910, CLNM positive, and tumor size&gt;4cm were regarded as related risk factors for LLNM. The two nomograms developed subsequently proved to have good predictive power with 0.706 and 0.818 and demonstrated good clinical guidance functionality with clinical decision curves and impact curves.ConclusionBased on the successful establishment of this dual-institution-based visual nomogram model, we found that some clinical features are highly correlated with cervical lymph node metastasis, including CLNM and LLNM, which will better help clinicians make individualized clinical decisions for more effectively rationalizing managing PTC patients

Prediction of intraoperative cerebrospinal fluid leaks in endoscopic endonasal transsphenoidal pituitary surgery based on a deep neural network model trained with MRI images: a pilot study

ObjectiveThis study aimed to investigate the reliability of a deep neural network (DNN) model trained only on contrast-enhanced T1 (T1CE) images for predicting intraoperative cerebrospinal fluid (ioCSF) leaks in endoscopic transsphenoidal surgery (EETS).Methods396 pituitary adenoma (PA) cases were reviewed, only primary PAs with Hardy suprasellar Stages A, B, and C were included in this study. The T1CE images of these patients were collected, and sagittal and coronal T1CE slices were selected for training the DNN model. The model performance was evaluated and tested, and its interpretability was explored.ResultsA total of 102 PA cases were enrolled in this study, 51 from the ioCSF leakage group, and 51 from the non-ioCSF leakage group. 306 sagittal and 306 coronal T1CE slices were collected as the original dataset, and data augmentation was applied before model training and testing. In the test dataset, the DNN model provided a single-slice prediction accuracy of 97.29%, a sensitivity of 98.25%, and a specificity of 96.35%. In clinical test, the accuracy of the DNN model in predicting ioCSF leaks in patients reached 84.6%. The feature maps of the model were visualized and the regions of interest for prediction were the tumor roof and suprasellar region.ConclusionIn this study, the DNN model could predict ioCSF leaks based on preoperative T1CE images, especially in PAs in Hardy Stages A, B, and C. The region of interest in the model prediction-making process is similar to that of humans. DNN models trained with preoperative MRI images may provide a novel tool for predicting ioCSF leak risk for PA patients

Preoperative Alfa-Fetoprotein and Fibrinogen Predict Hepatocellular Carcinoma Recurrence After Liver Transplantation Regardless of the Milan Criteria: Model Development with External Validation

Background/Aims: Patient selection is critically important in improving the outcomes of liver transplantation for hepatocellular carcinoma. The aim of the current study was to identify biochemical measures that could affect patient prognosis after liver transplantation. Methods: A total of 119 patients receiving liver transplantation for hepatocellular carcinoma were used to construct a model for predicting recurrence. The results were validated using an independent sample of 109 patients from independent hospitals. All subjects in both cohorts met the Hangzhou criteria. Results: Analysis of the discovery cohort revealed an association of recurrence with preoperative fibrinogen and AFP levels. A mathematical model was developed for predicting probability of recurrence within 5 years: Y = logit(P) = -4.595 + 0.824 ×fibrinogen concentration (g/L) + 0.641 × AFP score (1 for AFP&#x3c;=20ng/ml, 2 for 20&#x3c;AFP&#x3c;=100ng/ml, 3 for 100&#x3c;AFP&#x3c;=200ng/ml, 4 for 200&#x3c;AFP&#x3c;=400ng/ml, 5 for AFP&#x3e; 400ng/ml). At a cutoff score of -0.85, the area under the curve (AUC) was 0.819 in predicting recurrence (vs. 0.655 when using the Milan criteria). In the validation cohort, this model had reasonable performance in predicting 5-year overall survival (68.8% vs. 28.1% in using the -0.85 cutoff, p&#x3c; 0.001) and disease-free survival (65.7% vs. 25.9%, p&#x3c; 0.001). The sensitivity and specificity were 77.0% and 62.5%, respectively. The AUC of this newly developed model was similar to that with the Milan criteria (0.698 vs. 0.678). Surprisingly, the DFS in patients with score &#x3c;= -0.85 under this model but not meeting the Milan criteria was similar to that in patients meeting the Milan criteria (53.8% vs. 60.0%, p=0.380). Conclusions: Preoperative AFP and fibrinogen are useful in predicting recurrence of hepatocellular carcinoma after liver transplantation