[Value of (18)F-FDG PET-CT imaging to predict epidermal growth factor receptor mutations in patients with lung squamous cell carcinoma].

Objective: To investigate the value of (18)F-fluorodeoxy glucose ((18)F-FDG) positron emission tomography/computed tomography (PET-CT) in predicting the epidermal growth factor receptor (EGFR) mutations in patients with lung squamous cell carcinoma. Methods: We retrospectively analyzed the clinical data and (18)F-FDG PET-CT imaging data of 206 patients with lung squamous cell carcinoma confirmed by pathology and underwent EGFR mutation test in the First Affiliated Hospital of Nanjing Medical University from June 2013 to October 2018. Receiver operating characteristic (ROC) curve analysis was performed to quantify the predictive value of maximum standard uptake value (SUV(max)), metabolic tumor volume (MTV), total lesion glycolysis (TLG). The Chi-squared test was used to assess the difference in PET parameters. A multivariate Logistic regression analysis was performed to yield the parameters with statistic difference. Results: All of 206 patients with lung squamous cell carcinoma showed a high (18)F-FDG uptake. The median of SUV(max), MTV and TLG were 19.14, 37.69 cm(3) and 291.73, respectively. Among the 206 patients, EGFR mutations were identified in 14 cases, including 7 with exon 21 (L858R) mutation, 6 with exon 19 mutation and 1 with exon 20 mutation. ROC curve showed that the AUC of SUV(max), MTV and TLG were 0.624 (95% CI=0.454-0.794, P=0.122), 0.892 (95% CI=0.811-0.973, P<0.001) and 0.860 (95% CI=0.768-0.952, P<0.001), respectively. The median SUV(max) (19.14) was used as the cutoff points due to the small value of AUC. The cutoff point of MTV was 20.09 cm(3), the cutoff point of TLG was 211.07. Univariate analysis showed that the sex, smoking history, M stage, MTV and TLG were associated with EGFR mutations (all P<0.05). Logistic multivariate analysis showed that the sex, smoking history and TLG were the independent predictors of EGFR mutation (all P<0.05). Conclusion: TLG detected by (18)F-FDG PET/CT is an independent factor for predicting EGFR mutation in patients with lung squamous cell carcinoma, and has certain reference value for predicting EGFR mutation.

Conjunctiva reconstruction by induced differentiation of human amniotic epithelial cells.

In this study, we aimed to investigate the feasibility of directed differentiation of human amniotic epithelial cells into conjunctival epithelium under specific conditions as well as of constructing tissue-engineered conjunctiva for ocular surface reconstruction. Human amniotic epithelial cells were cultured with induced denuded conjunctival matrix and conjunctival homogenate. Immunohistochemistry of cytokeratin-4, cytokeratin-13, and muc5ac as well as PAS staining were performed. The concentration of muc5ac at different times was measured using ELISA. The differentiated cells with quantum dots were transferred onto a denuded amniotic membrane to establish tissue-engineered conjunctiva and transplanted into a rabbit model with a conjunctival defect. After induction of human amniotic epithelial cells, differentiated cells showed conjunctival epithelium phenotype, while trace amounts of mu5ac in the culture medium measured by ELISA increased gradually within 1 to 7 days. Successfully tissue-engineered conjunctiva had similar structure as normal conjunctiva and was transplanted into a rabbit model with conjunctiva defect. After 2 weeks post-surgery, conjunctiva grafts survived and were integrated. Immunohistochemistry showed conjunctival epithelium phenotype, positive cells were found in PAS staining. Thus, human amniotic epithelial cells could differentiate into conjunctival epithelium-like cells and goblet cells with partially physiological function, and we successfully restored ocular surface integrity in the rabbit model using tissue-engineered conjunctiva.

Outbreak of Occupational Brucellosis at a Pharmaceutical Factory in Southeast China.

Brucellosis is an occupational disease affecting workers in butcher shops, the milking and dairy product industry, causing more than 500 000 new cases around the world. As a national statutory B infectious disease in China, morbidity of brucellosis is rapidly increasing in recent years. We report an occupational outbreak of brucellosis infection in a pharmaceutical factory. Exposure was a result of manual operation in the process line, close contact with sheep placentas, insufficient disinfection and repeated using of protective suits and infected by aerosol dissemination. Improved preventive methods, appropriate public health measures and spread of health education would be helpful to prevent the occupational outbreak of brucellosis in future.

A lazy learning-based QSAR classification study for screening potential histone deacetylase 8 (HDAC8) inhibitors.

Histone deacetylases 8 (HDAC8) is an enzyme repressing the transcription of various genes including tumour suppressor gene and has already become a target of human cancer treatment. In an effort to facilitate the discovery of HDAC8 inhibitors, two quantitative structure-activity relationship (QSAR) classification models were developed using K nearest neighbours (KNN) and neighbourhood classifier (NEC). Molecular descriptors were calculated for the data set and database compounds using ADRIANA.Code of Molecular Networks. Principal components analysis (PCA) was used to select the descriptors. The developed models were validated by leave-one-out cross validation (LOO CV). The performances of the developed models were evaluated with an external test set. Highly predictive models were used for database virtual screening. Furthermore, hit compounds were subsequently subject to molecular docking. Five hits were obtained based on consensus scoring function and binding affinity as potential HDAC8 inhibitors. Finally, HDAC8 structures in complex with five hits were also subjected to 5 ns molecular dynamics (MD) simulations to evaluate the complex structure stability. To the best of our knowledge, the NEC classification model used in this study is the first application of NEC to virtual screening for drug discovery.