Establishment and Validation of a Nomogram for Tonsil Squamous Cell Carcinoma: A Retrospective Study Based on the SEER Database.

This study aimed to establish and validate a comprehensive nomogram for predicting the cause-specific survival (CSS) probability in tonsillar squamous cell carcinoma (TSCC). We screened and extracted data from the SEER (Surveillance, Epidemiology, and End Results) database for the period 2004 to 2016. We randomly divided the 7243 identified patients into a training cohort (70%) for constructing the model and a validation cohort (30%) for evaluating the model using R software. Multivariate Cox stepwise regression was used to select predictive variables. The concordance index (C-index), the area under the time-dependent receiver operating characteristics curve (AUC), the net reclassification improvement (NRI), the integrated discrimination improvement (IDI), calibration plotting, and decision-curve analysis (DCA) were used to evaluate the model. The multivariate Cox stepwise regression analysis successfully established a nomogram for the 1-, 3-, and 5-year CSS probabilities for TSCC patients. The C-index, AUC, NRI, and IDI were all showed that the model has good discrimination. The calibration plots were very close to the standard lines, indicating that the model has a good degree of calibration, and the DCA curve further illustrated that the model has good clinical validity. We have established the first nomogram for predicting the 1-, 3-, and 5-year CSS probabilities for TSCC based on a large retrospective sample. Our rigorous validation and evaluation indicated that the model can provide useful guidance to clinical workers making clinical decisions about individual patients.

A method developed for determination of heptachlor and its metabolites from pork.

A new method has been developed to determine heptachlor and its metabolites heptachlor-exo-epoxide and heptachlor-endo-epoxide in pork. The pork samples were extracted with acetone-n-hexane (2:8, V:V) and cleaned up by gel permeation chromatography and florisil solid-phase extraction cartridge. The extract was then determined by gas chromatography equipped with electron capture detector (GC-ECD), followed by validation using gas chromatography-mass spectrometry (GC-MS) with negative chemical ionization. Linearity of calibration curves ranged from 0.01 to 0.5 mg L(-1), with correlation coefficients of more than 0.9980 for GC-ECD and GC-MS, respectively. At spiked concentrations of 0.01, 0.05, and 0.1 mg kg(-1), the average recovery and relative standard deviation values were 87.1-102.2 and 4.0-11.3%, respectively. The limit of quantification for each analyte was 0.01 mg kg(-1), which satisfied the current maximum residue limit permitted in pork. Our results showed that the method developed was successfully used to determine heptachlor and heptachlor epoxide residues in real pork samples.

[Identification of Nanfeng mandarin from different origins by using near infrared spectroscopy coupled with principal components analysis].

The identification of Nanfeng mandarins from different origins was developed by using near infrared spectroscopy (NIR) and principal components analysis (PCA). Mandarins from different places in one orchard, and from different orchards in three towns of Nanfeng county were studied. Also differences among Shaowu, Liucheng and Nanfeng were investigated. Furthermore, the effect of storage time of mandarins on the PCA model was considered. The results demonstrate that there was no clear diversity of the mandarins in one origin but great differences existed among different ones. And the storage time of mandarins played little role in the discrimination model. The method of multiple scattering correction (MSC) coupled with second derivative was selected to build PCA discrimination models compared with other data pretreatment methods. The proposed model would be a reference method for origin identification of Nanfeng mandarin.

Determination of beta-lactamase residues in milk using matrix-assisted laser desorption/ionization Fourier transform mass spectrometry.

A selective, fast, and effective enzyme assay based on matrix-assisted laser desorption/ionization Fourier transform mass spectrometry (MALDI-FTMS) for quantifying beta-lactamase, an illegal additive in milk products, has been reported. The strengths of the mass spectrometric assay are its response to all substrate and products, simple and direct detection of the conversion of substrate, and facile determination of enzyme activity. Also, MALDI MS is tolerant to many buffer salts and reagents without the requirement of complicated sample pretreatment procedures. In this study, the approach was used to detect the presence of beta-lactamases (BLA) in milk samples. The amount of BLA that could be determined in a milk sample is 6 x 10(-3) U x mL(-1) by this approach. To test the strategy, it has been applied to the fortified milk (adding a BLA product known as an antimicrobial destroyer). It is then tested whether the pasteurization procedure of the milk process affects the activity of BLA in milk samples. This study offers a perspective into the utility of MALDI-FTMS as an alternative detection tool for BLA screening in milk.

Prognostic factors in patients with gallbladder adenocarcinoma identified using competing-risks analysis: A study of cases in the SEER database.

A competing-risks model was developed in this study to identify the significant prognostic factors and evaluate the cumulative incidence of cause-specific death in gallbladder adenocarcinoma (GBAC), with the aim of providing guidance on effective clinical treatments.All patients with GBAC in the Surveillance, Epidemiology, and End Results (SEER) database during 1973 to 2015 were identified. The potential prognostic factors were identified using competing-risks analyses implemented using the R and SAS statistical software packages. We calculated the cumulative incidence function (CIF) for cause-specific death and death from other causes at each time point. The Fine-Gray proportional-subdistribution-hazards model was then applied in univariate and multivariate analyses to test the differences in CIF between different groups and identify independent prognostic factors.This study included 3836 eligible patients who had been enrolled from 2004 to 2015 in the SEER database. The univariate analysis indicated that age, race, AJCC stage, RS, tumor size, SEER historic stage, grade, surgery, radiotherapy, chemotherapy and adjuvant therapy (RCT, SRT, SCT and SRCT) were significant factors affecting the probability of death due to GBAC. The multivariate analysis indicated that age, race, AJCC stage, RS status, tumor size, grade and SRT were independent prognostic factors affecting GBAC cancer-specific death. A nomogram model was constructed based on multivariate models for death related to GBAC.We have constructed the first competing-risks nomogram for GBAC. The model was found to perform well. This novel validated prognostic model may facilitate the choosing of beneficial treatment strategies and help when predicting survival.