The original purpose of this article was to modify the original SIR equations to allow for a direct source of infection (without which the original equations would have no solutions unless one starts with an already infected population) and also to see to what extent one could obtain multiple outbreaks of an infectious disease. In the course of developing the basic ideas several other factors arose to take prominent roles.Perhaps one of the more salient factors is the point that choosing an arbitrary time to change conditions from say a lock-down for the population to a less stringent social behavior, such as allowing partial or complete opening of businesses and schools, etc. should be based on knowledge of the disease and its evolution. Such decisions are usually made by politicians who have less than full information concerning the consequences of their actions. Several examples are given to illustrate these points.
BackgroundTriple-negative breast cancer (TNBC) is the most aggressive histologic and the only subtype of breast cancer responding to immunotherapy. The immunologic portrait of TNBC is featured with unique microenvironment of high levels of tumour lymphocyte infiltration (TILs) and PD-L1 expression.1-3 In our previous work, we demonstrated that CD39+CD8+T cells represent tumour antigen-specific CD8+T cells as well as a potential biomarker that predicts response to PD-1/PD-L1 blockade in treatment-naive non-small cell lung cancer.4 From these perspectives, we hypothesized that accurate quantitation of CD39+CD8+T cell subset would predict prognosis in TNBC.5-7MethodsTumour specific T cells and CD39+CD8+T cells were detected with flow cytometry in peripheral blood mononuclear cells (PBMCs) and TILs from 4T1.2 tumour-bearing mice as well as TNBC patients respectively. 315 Asian TNBC cases recruited from Singapore General Hospital were comprised in our cohort and multiplex immunohistochemistry/immunofluorescence (mIHC/IF) was used to detect the expression of CD39, CD8 and other immune lineage biomarkers. Associations between overall survival (OS), disease free survival (DFS) and biomarkers expression were investigated.ResultsIn 4T1.2 mice model, tumour-specific CD8+T cells (gp70 tetramer+) expressed significantly higher CD39 in PBMCs and TILs, especially after anti-PD1 and anti-CD137 immunization. CD39+CD8+T cells were also detected in TNBC patients9 PBMCs and TILs. Furthermore, CD39+CD8+T cell and other CD39+ sub-population immune infiltrates including CD39+CD68+ macrophages and CD39+FOXP3+ regulatory T cells were detected in TNBC tumour samples subjected to mIHC/IF. Multivariate analysis showed that tumours with high CD39+ immune infiltrates harboured significantly increased OS (HR 0.41, P=0.0029) and DFS (HR 0.59, P=0.0243), and the increase of OS was significant even after adjusting for clinicopathological parameters (HR 0.25, P=0.0154). In addition, the proportion of CD39+CD8+ T cells (HR 0.50, P=0.0317), but not CD39-CD8+T cells (HR 0.62, P=0.1420), was associated with improved OS and the prognostic significance was independent of clinicopathological parameters. Notably, multivariate analysis demonstrated that CD39+CD8+T cell proportion was similarly associated with improved OS (HR=0.49; 95% CI 0.27-0.90; P=0.0181) and DFS (HR=0.62; 95% CI 0.41-0.96; P=0.0287). For other sub-populations of CD39+ immune infiltrates, CD39+CD68+ and CD39+FOXP3+ immune infiltrates were not associated with improved OS or DFS.ConclusionsOur present study provided further support for previous findings that CD39+CD8+T cells may serve as a surrogate marker of tumour-specific T cells in TNBC. High CD39+CD8+T cells proportion is associated with improved clinical outcomes in TNBC. Further studies will quantitate and monitor the trajectory changes of these tumour-specific T cells in an immunotherapy treated TNBC cohort.ReferencesBareche Y, Buisseret L, Gruosso T, Girard E, Venet D, Dupont F, Desmedt C, Larsimont D, Park M, Rothé F, Stagg J, Sotiriou C. Unraveling triple-negative breast cancer tumor microenvironment heterogeneity: towards an optimized treatment approach. J Natl Cancer Inst. 2020;112(7):708–719. Telli ML, Vinayak S. Future of checkpoint blockade in triple-negative breast cancer: combination strategies to lead the way. Ann Oncol 2019;30(3):347–348. Hammerl D, Martens JWM, Timmermans M, Smid M, Trapman-Jansen AM, Foekens R, Isaeva OI, Voorwerk L, Balcioglu HE, Wijers R, Nederlof I, Salgado R, Horlings H, Kok M, Debets R. Spatial immunophenotypes predict response to anti-PD1 treatment and capture distinct paths of T cell evasion in triple negative breast cancer. Nat Commun 2021;12(1):5668. doi:10.1038/s41467-021-25962-0. Yeong J, Suteja L, Simoni Y, Lau KW, Tan AC, Li HH, Lim S, Loh JH, Wee FYT, Nerurkar SN, Takano A, Tan EH, Lim TKH, Newell EW, Tan DSW. Intratumoral CD39+CD8+ T Cells Predict Response to Programmed Cell Death Protein-1 or Programmed Death Ligand-1 Blockade in Patients With NSCLC. J Thorac Oncol. 2021;16(8):1349–1358. Simoni Y, Becht E, Fehlings M, Loh CY, Koo SL, Teng KWW, Yeong JPS, Nahar R, Zhang T, Kared H, Duan K, Ang N, Poidinger M, Lee YY, Larbi A, Khng AJ, Tan E, Fu C, Mathew R, Teo M, Lim WT, Toh CK, Ong BH, Koh T, Hillmer AM, Takano A, Lim TKH, Tan EH, Zhai W, Tan DSW, Tan IB, Newell EW. Bystander CD8+ T cells are abundant and phenotypically distinct in human tumour infiltrates. Nature. 2018;557(7706):575–579. Liu J, Blake SJ, Yong MC, Harjunpää H, Ngiow SF, Takeda K, Young A, O9Donnell JS, Allen S, Smyth MJ, Teng MW. Improved efficacy of neoadjuvant compared to adjuvant immunotherapy to eradicate metastatic disease. Cancer Discov. 2016;6(12):1382–1399. Yeong J, Lim JCT, Lee B, Li H, Ong CCH, Thike AA, Yeap WH, Yang Y, Lim AYH, Tay TKY, Liu J, Wong SC, Chen J, Lim EH, Iqbal J, Dent R, Newell EW, Tan PH. Prognostic value of CD8?+?PD-1+ immune infiltrates and PDCD1 gene expression in triple negative breast cancer. J Immunother Cancer 2019;7(1):34.
Bitcoin and the blockchain technology on which it is based are the key drivers behind the accelerated pace of Fourth Industrial Revolution in the domain of Finance. The offshoots of this technology however are not limited and are rapidly spreading in other domains such as oil market. This paper investigates the causality or influences that both markets, Bitcoin price (BP) and oil price (OP) have on each other by applying the bootstrap Granger causal relationship tests considering full as well as sub-samples. Our analysis reveals that shocks originated in OP and transmitted towards BP can be both positive or negative. The positive impact indicates that Bitcoin can be viewed as an asset helpful in avoiding the risks of the high OP, which also indicates that Bitcoin and oil are in the same boat, however, the negative effects cannot support this view. The negative influence of OP on BP can be explained by the burst of the Bitcoin bubble which has weakened its hedging ability. In turn, there is also a negative influence or reverse causality running from BP to OP, highlighting that the demand for oil by investors can be threatened by the increasing BP. Keeping in view the more integrated and complexed financial dynamics which are the results of Fourth Industrial Revolution, investors can benefit from this interrelationship to diversify the risks and optimize their investment by building a more balanced portfolio. Also, governments could promote and protect the healthy development of the Bitcoin and energy market by preventing the Bitcoin bubbles and understanding the reasons of oil price volatility.
Biological invasions are among the main threats to biodiversity, and pollinators are not exempt from such threat. Here, we review the mechanisms through which animal and plant invasive species may affect pollinators, either directly or indirectly. We classified these mechanisms in four groups: (a) changes in habitat quantity or quality, (b) predation, (c) pathogen spread and (d) genetic introgression. We review the impacts of these mechanisms on the behaviour, demography and evolution of native pollinators and their communities. In addition, we highlight some current knowledge gaps about the effect of biological invasions on pollinators and suggest some measures to minimize such effects.
