Gene/Protein Disease Symptom Drug Enzyme Compound
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Query: UMLS:C0546837 (esophageal cancer)
 8,907 document(s) hit in 31,850,051 MEDLINE articles (0.00 seconds)

The results of 63 patients with advanced malignant tumors treated by combined chemotherapy including high-dose cisplatin (HD-DDP) (single dose 50-100 mg/m2) are reported. The remission rates and duration of the remission for various malignant tumors were: 40% (10 PR out of 25 patients) and 3-8 months for non-small cell lung cancer (NSCLC) treated by PMFV (DDP, MMC, 5FU and VCR) regimen; 87% (4 CR and 9 PR out of 15) and 3-14 months for breast cancer treated by PCMF (DDP, CTX, MTX and 5FU) regimen; 100% (1 CR and 3 PR out of 4) and 3-10 months for testicular cancer treated by PPV (DDP, Pingyangmycin and VCR) regimen; 57% (1CR and 3 PR out of 7) and 5-12 months for malignant melanoma treated by PBDV (DDP, BCNU, DTIC and VCR) regimen; 33% (2 PR out of 6) and 5 months for esophageal cancer treated by PPV regimen. In 6 patients with other malignant tumors, the remission rate was 50% (3 PR). The results show that the combined regimens including HD-DDP in the treatment of breast cancer and NSCLC (remission rate 87% and 40%, respectively) are better than that including low-dose DDP (17% and 7%) (P less than 0.001, P less than 0.01) and that including adriamycin (30% and 13%) (P less than 0.001, P less than 0.05). In the treatment, obvious gastrointestinal reaction, leukopenia, thrombocytopenia and mild functional damage of the liver and kidney were observed.
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PMID:[Evaluation of combined chemotherapy including high-dose cisplatin in the treatment of malignant tumors]. 282 Jun 83

In the fields of medicine and public health, a common application of areal data models is the study of geographical patterns of disease. When we have several measurements recorded at each spatial location (for example, information on p>/= 2 diseases from the same population groups or regions), we need to consider multivariate areal data models in order to handle the dependence among the multivariate components as well as the spatial dependence between sites. In this article, we propose a flexible new class of generalized multivariate conditionally autoregressive (GMCAR) models for areal data, and show how it enriches the MCAR class. Our approach differs from earlier ones in that it directly specifies the joint distribution for a multivariate Markov random field (MRF) through the specification of simpler conditional and marginal models. This in turn leads to a significant reduction in the computational burden in hierarchical spatial random effect modeling, where posterior summaries are computed using Markov chain Monte Carlo (MCMC). We compare our approach with existing MCAR models in the literature via simulation, using average mean square error (AMSE) and a convenient hierarchical model selection criterion, the deviance information criterion (DIC; Spiegelhalter et al., 2002, Journal of the Royal Statistical Society, Series B64, 583-639). Finally, we offer a real-data application of our proposed GMCAR approach that models lung and esophagus cancer death rates during 1991-1998 in Minnesota counties.
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PMID:Generalized hierarchical multivariate CAR models for areal data. 1640 Dec 68