The modeling of micro-level food demand patterns requires not only allowing for household heterogeneity, but also addressing the problem of censoring. In this article, we present a variation of the Amemiya-Tobin framework for estimating a censored demand system that allows for household heterogeneity. The unique aspect of our approach is the use of a procedure that ensures the adding up of both latent and observed expenditure shares and also imposes expenditure share nonnegativity. This system is applied to an analysis of food demand based on a random sample of urban Mexican households. Copyright 2004, Oxford University Press.

The present study aimed to investigate the clinicopathological significance of programmed cell death ligand-1 (PD-L1) and programmed cell death protein 1 (PD-1) expression in extrahepatic cholangiocarcinoma (ECC). PD-L1 and PD-1 expression was detected by immunohistochemical methods in 70 ECC formalin-fixed, paraffin-embedded tissue specimens and 50 para-carcinoma tissue specimens. The associations of PD-L1 and PD-1 expression with clinicopathological characteristics and prognosis of ECC patients were explored. Positive rates of PD-L1 and PD-1 expression were increased in ECC tissues compared with those in the corresponding para-carcinoma tissues. Besides, the expression of PD-L1 was correlated with the expression of PD-1 (P<0.05). Statistical analysis revealed that the expression of PD-L1 and PD-1 in ECC tissues exhibited no correlation with patient age, sex or histological grade, but was significantly correlated with tumor-node-metastasis (TNM) stage and lymphatic metastasis. Univariate analysis demonstrated that PD-L1 expression, PD-1 expression, TNM stage and lymphatic metastasis were significantly associated with the survival time of patients. Further multivariate analysis revealed the PD-L1 expression was an independent prognostic factor of patients with ECC. These preliminary results suggested that PD-L1 or PD-1 immunodetection may be a valuable prognostic marker for ECC patients, and that PD-L1 immunodetection may be used as an independent factor to evaluate the prognosis of ECC patients.

Radiomics has become an area of interest for tumor characterization in 18F-Fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) imaging. The aim of the present study was to demonstrate how imaging phenotypes was connected to somatic mutations through an integrated analysis of 115 non-small cell lung cancer (NSCLC) patients with somatic mutation testings and engineered computed PET/CT image analytics. A total of 38 radiomic features quantifying tumor morphological, grayscale statistic, and texture features were extracted from the segmented entire-tumor region of interest (ROI) of the primary PET/CT images. The ensembles for boosting machine learning scheme were employed for classification, and the least absolute shrink age and selection operator (LASSO) method was used to select the most predictive radiomic features for the classifiers. A radiomic signature based on both PET and CT radiomic features outperformed individual radiomic features, the PET or CT radiomic signature, and the conventional PET parameters including the maximum standardized uptake value (SUVmax), SUVmean, SUVpeak, metabolic tumor volume (MTV), and total lesion glycolysis (TLG), in discriminating between mutant-type of epidermal growth factor receptor (EGFR) and wild-type of EGFR- cases with an AUC of 0.805, an accuracy of 80.798%, a sensitivity of 0.826 and a specificity of 0.783. Consistently, a combined radiomic signature with clinical factors exhibited a further improved performance in EGFR mutation differentiation in NSCLC. In conclusion, tumor imaging phenotypes that are driven by somatic mutations may be predicted by radiomics based on PET/CT images.

PurposeProgrammed death 1 (PD-1) receptor and its ligand, programmed death ligand-1 (PD-L1), play critical roles in the immune invasion of various tumors. This study aimed to explore the clinical significance of PD-L1/PD-1 expression in the progression of pulmonary neuroendocrine tumors (PNETs).MethodsThe expression of PD-L1 and PD-1 in 80 patients diagnosed with PNETs were investigated. Immunohistochemical analysis was performed on 80 formalin-fixed paraffin-embedded tissue specimens from PNETs and 20 corresponding cancer-adjacent tissue specimens.ResultsTissues from PNETs had higher levels of PD-L1 (58.8%) and PD-1 (51.3%) compared to the cancer-adjacent tissues (25% and 20%, respectively). Meanwhile, PD-L1 expression was associated with PD-1 expression (P=0.007). PD-L1 expression was significantly associated with histological type (P=0.014) and tumor stage (P=0.014). Univariate analyses showed that the overall survival time of PNETs patients was significantly associated with PD-L1 expression in cancer cells (P=0.003), PD-1 expression in tumor-infiltrating lymphocytes (P=0.001), tumor node metastasis stage (P<0.05), and distant metastasis (P<0.001). Additionally, multivariate analysis revealed that PD-L1 expression, PD1 expression, and distant metastasis of PNETs were independently associated with survival time. Moreover, Kaplan–Meier survival curves analysis revealed that patients with negative PD-L1 and PD-1 expression had better prognoses.ConclusionData suggested that PD-L1 and PD-1 can be useful prognostic biomarkers for survival and can pave the way toward new immunotherapy regimens against PNETs through targeting the PD-L1/PD-1 pathway.

A procedure that corrects for selectivity bias is proposed to estimate demand functions using cross-sectional data under the assumption that prices vary across households. This procedure, which extends the work by Cox and Wohlgenant, includes a two-equation system of expenditure and unit value functions. This model is applied to household expenditures for beef steaks and roasts. Copyright 1998, Oxford University Press.