Gas development projects face growing challenges from increasingly sour resources with relatively high levels of CO2, H2S, Mercaptans and COS, and tighter sales specifications and stricter environmental emission standards. The removal of trace components such as COS and RSH can have detrimental effect on project value as considerable CAPEX and OPEX investments have to be made.
Predominantly mercaptans are either removed in Acid Gas Removal Unit (AGRU) with the use of hybrid solvents such as Shell's Sulfinol solvent, or slipped to downstream dehydration/mercaptan removal adsorption unit using molsieves. While the former line-up involves a single process unit to remove all sulphur compounds (H2S, RSH and COS) and CO2, the latter process line-up involves multiple process steps including an AGRU absorber with aqueous amines to remove H2S and CO2, and a molecular sieve downstream unit to dehydrate and remove mercaptans in combination with a dedicated physical or hybrid solvent for removal of mercaptans from molsieve regeneration gas.
There is the perception that the use of hybrid solvents reduces revenue through hydrocarbon absorption losses. Simple hydrocarbon-loss percentages around AGRU absorber provide some insight, but this data must be used cautiously, as this data alone will not tell the full story.
Previous work has shown that, in most cases, higher hydrocarbon losses through co-absorption with hybrid solvents compared with using aqueous amines such as methyl diethanolamine (MDEA) are offset by savings in reboiler energy consumption. This paper goes a step further by modelling the full life-cycle costs of the two conventionally used process line-ups so that informed solvent-selection and capital investment decisions can be made. This paper also discussed capital- and additional operating-cost savings associated with hybrid solvents, which further tip the economic balance in favour of hybrid solvent systems.
Pancreatic cancer, especially pancreatic ductal adenocarcinoma (PDAC), has for long remained a deadly form of cancer characterized by high mortality rates resulting from metastasis to multiple organs. Several factors, including the late manifestation of the disease, partly amplified by lack of efficient screening methods, have hampered the drive to design an effective therapeutic strategy to treat this deadly cancer. Understanding the biology of PDAC progression and identifying critical genes regulating these processes are essential to overcome the barriers toward effective treatment. Metastasis suppressor genes have been shown to inhibit multiple steps in the metastatic cascade without affecting primary tumor formation and are considered to hold promise for treating metastatic cancers. In this review, we catalog the bona fide metastasis suppressor genes reported in PDAC and discuss their known mechanism of action.
Introduction: Components of immune system communicate extensively in tumour micro environment. Normally, immune system engages with tumours to inhibit its further progression. Simultaneously, cancer cells learn cues derived from immune system to its own growth advantage. Cytokines are cell signaling messengers that affect disease pathogenesis, non-specific response to infection, specific response to antigen, etc. A battery of cytokines are produced in the tumour microenvironment, when released in response to infections and inflammations, can function to inhibit tumour development and progression. Cancer cells also release cytokines that promote growth, extenuate apoptosis and perform invasion and metastasis.Hypothesis: Alterations in cytokine signaling genes might help tumour to misguide immune system. The aim of the study is to identify such genomic alterations in cytokine genes that may drive major human cancers.
Methods:We did extensive literature survey to prepare a list of known cytokine genes (n=776) which were validated in multiple databases. To know the baseline DNA variation in cytokine genes, we analyzed DNA variations in healthy human population from the 1000 Genome project dataset. Somatic mutational landscape for cytokine genes were analyzed in 32 different human cancer types (TCGA data). Significantly mutated genes were detected using MutSig2CV and Oncodrive FM analysis. Genes found significant in both analysis were tabulated. Standard statistical and bioinformatics analysis were done further to identify putative driver events.
Result:We detected 9 significantly mutated cytokine genes across major tumor types. EDN1 was found to be most significantly mutated, in multiple tumour types; apart from genes like CDH1,
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