Tumor survival,genomic stability,and therapy resistance are dictated by the DNA damage response(DDR).Although poly(ADP-ribose)polymerase(PARP)inhibitors have established the DDR as a therapeutic target,many tumors eva...Tumor survival,genomic stability,and therapy resistance are dictated by the DNA damage response(DDR).Although poly(ADP-ribose)polymerase(PARP)inhibitors have established the DDR as a therapeutic target,many tumors evade first-generation drugs by rewiring their adaptive repair pathways and imposing microenvironmental constraints.This review synthesizes recent discoveries in key DDR pathways,such as PARP,ataxia telangiectasia and Rad3-related kinase(ATR),ataxia telangiectasia mutated kinase(ATM),checkpoint kinase 1(CHK1),WEE1 G2 checkpoint kinase(WEE1),and DNA-dependent protein kinase(DNA-PK),and describes the next-generation inhibitors designed to increase selectivity and circumvent resistance.We also analyze the role of hypoxia,stromal remodeling,inflammatory cytokines,and immune-cell plasticity in the tumor microenvironment in determining DDR dependency and response.Special attention is paid to cGAS-STING,immunogenic signaling via damage-associated molecular patterns(DAMPs),and mechanisms that convert a cold tumor into a hot one.Lastly,we touch upon the new nanocarrier-based delivery approaches that enhance pharmacokinetics,target resistant tumor niches,and expand the possibilities for combinatorics with immunotherapy and radiotherapy.Collectively,these findings provide a guide to the implementation of next-generation DDR inhibitors and nanomedicines to deliver a more accurate,durable,and context-specific cancer therapy.展开更多
Support Vector Regression (SVR) has already been proved to be one of the mostreferred and used machine learning technique in various fields. In this study, wehave addressed a predictive-cum-prescriptive analysis for f...Support Vector Regression (SVR) has already been proved to be one of the mostreferred and used machine learning technique in various fields. In this study, wehave addressed a predictive-cum-prescriptive analysis for finalizing fundallocations by the Government at center to the schemes under Central Plan andto the schemes under States and Union Territories Plan, with a goal to maximizeGross Value Added (GVA) at factor cost. Here, we have proposed a hybridmachine learning model comprising of OFS (Orthogonal Forward Selection),TLBO (Teaching Learning Based Optimization) and SVR for the prediction ofGVA at factor cost. In this model, referred as OFS–TLBO–SVR hybrid model,SVR is at the core of prediction mechanism, OFS is for identifying the relevantfeatures, and TLBO is to support in optimizing the free parameters of SVR andagain TLBO is used for optimizing the governable attributes of data.展开更多
文摘Tumor survival,genomic stability,and therapy resistance are dictated by the DNA damage response(DDR).Although poly(ADP-ribose)polymerase(PARP)inhibitors have established the DDR as a therapeutic target,many tumors evade first-generation drugs by rewiring their adaptive repair pathways and imposing microenvironmental constraints.This review synthesizes recent discoveries in key DDR pathways,such as PARP,ataxia telangiectasia and Rad3-related kinase(ATR),ataxia telangiectasia mutated kinase(ATM),checkpoint kinase 1(CHK1),WEE1 G2 checkpoint kinase(WEE1),and DNA-dependent protein kinase(DNA-PK),and describes the next-generation inhibitors designed to increase selectivity and circumvent resistance.We also analyze the role of hypoxia,stromal remodeling,inflammatory cytokines,and immune-cell plasticity in the tumor microenvironment in determining DDR dependency and response.Special attention is paid to cGAS-STING,immunogenic signaling via damage-associated molecular patterns(DAMPs),and mechanisms that convert a cold tumor into a hot one.Lastly,we touch upon the new nanocarrier-based delivery approaches that enhance pharmacokinetics,target resistant tumor niches,and expand the possibilities for combinatorics with immunotherapy and radiotherapy.Collectively,these findings provide a guide to the implementation of next-generation DDR inhibitors and nanomedicines to deliver a more accurate,durable,and context-specific cancer therapy.
文摘Support Vector Regression (SVR) has already been proved to be one of the mostreferred and used machine learning technique in various fields. In this study, wehave addressed a predictive-cum-prescriptive analysis for finalizing fundallocations by the Government at center to the schemes under Central Plan andto the schemes under States and Union Territories Plan, with a goal to maximizeGross Value Added (GVA) at factor cost. Here, we have proposed a hybridmachine learning model comprising of OFS (Orthogonal Forward Selection),TLBO (Teaching Learning Based Optimization) and SVR for the prediction ofGVA at factor cost. In this model, referred as OFS–TLBO–SVR hybrid model,SVR is at the core of prediction mechanism, OFS is for identifying the relevantfeatures, and TLBO is to support in optimizing the free parameters of SVR andagain TLBO is used for optimizing the governable attributes of data.
