Black hole thermodynamics in the Sharma-Mittal generalized entropy formalism

Using the Sharma-Mittal entropy, we study some properties of the Schwarzschild and Schwarzschild-de Sitter black holes. The results are compared with those obtained by attributing the Bekenstein entropy bound to the mentioned black holes. Our main results show that while the Schwarzschild black hole is always stable in the micro-canonical ensemble, it can be stable in the canonical ensemble if its mass is bigger than the mass of the coldest Schwarzschild black hole. A semi-classical analysis has also been used to find an approximate relation between the entropy free parameters. Throughout the paper, we use units $c=G=\hbar=k_B=1$, where $k_B$ denotes the Boltzmann constant.Comment: 5 pages, 5 figure

An mRNA processing pathway suppresses metastasis by governing translational control from the nucleus

Cancer cells often co-opt post-transcriptional regulatory mechanisms to achieve pathologic expression of gene networks that drive metastasis. Translational control is a major regulatory hub in oncogenesis; however, its effects on cancer progression remain poorly understood. Here, to address this, we used ribosome profiling to compare genome-wide translation efficiencies of poorly and highly metastatic breast cancer cells and patient-derived xenografts. We developed dedicated regression-based methods to analyse ribosome profiling and alternative polyadenylation data, and identified heterogeneous nuclear ribonucleoprotein C (HNRNPC) as a translational controller of a specific mRNA regulon. We found that HNRNPC is downregulated in highly metastatic cells, which causes HNRNPC-bound mRNAs to undergo 3′ untranslated region lengthening and, subsequently, translational repression. We showed that modulating HNRNPC expression impacts the metastatic capacity of breast cancer cells in xenograft mouse models. In addition, the reduced expression of HNRNPC and its regulon is associated with the worse prognosis in breast cancer patient cohorts

Quercetin Impact in Pancreatic Cancer: An Overview on Its Therapeutic Effects

Pancreatic cancer (PC) is a lethal malignancy cancer, and its mortality rates have been increasing worldwide. Diagnosis of this cancer is complicated, as it does not often present symptoms, and most patients present an irremediable tumor having a 5-year survival rate after diagnosis. Regarding treatment, many concerns have also been raised, as most tumors are found at advanced stages. At present, anticancer compounds-rich foods have been utilized to control PC. Among such bioactive molecules, flavonoid compounds have shown excellent anticancer abilities, such as quercetin, which has been used as an adjunctive or alternative drug to PC treatment by inhibitory or stimulatory biological mechanisms including autophagy, apoptosis, cell growth reduction or inhibition, EMT, oxidative stress, and enhancing sensitivity to chemotherapy agents. The recognition that this natural product has beneficial effects on cancer treatment has boosted the researchers’ interest towards more extensive studies to use herbal medicine for anticancer purposes. In addition, due to the expensive cost and high rate of side effects of anticancer drugs, attempts have been made to use quercetin but also other flavonoids for preventing and treating PC. Based on related studies, it has been found that the quercetin compound has significant effect on cancerous cell lines as well as animal models. Therefore, it can be used as a supplementary drug to treat a variety of cancers, particularly pancreatic cancer. This review is aimed at discussing the therapeutic effects of quercetin by targeting the molecular signaling pathway and identifying antigrowth, cell proliferation, antioxidative stress, EMT, induction of apoptotic, and autophagic features.The authors acknowledge the Molecular Medicine Research Center, Bio-Medicine Institute, Tabriz University of Medical Sciences, and the Clinical Research Development Unit of Sina Educational, Research and Treatment Center, Tabriz University of Medical Sciences, Tabriz, Iran. This work was supported and funded by Tabriz University of Medical Sciences, Tabriz, Iran (grant number: 68344)

Estimation in a Competing Risks Proportional Hazards Model Under Length-biased Sampling With Censoring

International audienceWhat population does the sample represent? The answer to this question is of crucial importance when estimating a survivor function in duration studies. As is well-known, in a stationary population, survival data obtained from a cross-sectional sample taken from the population at time $t_0$ represents not the target density $f(t)$ but its length-biased version proportional to $tf(t)$, for $t>0$. The problem of estimating survivor function from such length-biased samples becomes more complex, and interesting, in presence of competing risks and censoring. This paper lays out a sampling scheme related to a mixed Poisson process and develops nonparametric estimators of the survivor function of the target population assuming that the two independent competing risks have proportional hazards. Two cases are considered: with and without independent consoring before length biased sampling. In each case, the weak convergence of the process generated by the proposed estimator is proved. A well-known study of the duration in power for political leaders is used to illustrate our results. Finally, a simulation study is carried out in order to assess the finite sample behaviour of our estimators

Highly multiplexed immune repertoire sequencing links multiple lymphocyte classes with severity of response to COVID-19

BACKGROUND: Disease progression of subjects with coronavirus disease 2019 (COVID-19) varies dramatically. Understanding the various types of immune response to SARS-CoV-2 is critical for better clinical management of coronavirus outbreaks and to potentially improve future therapies. Disease dynamics can be characterized by deciphering the adaptive immune response. METHODS: In this cross-sectional study we analyzed 117 peripheral blood immune repertoires from healthy controls and subjects with mild to severe COVID-19 disease to elucidate the interplay between B and T cells. We used an immune repertoire Primer Extension Target Enrichment method (immunoPETE) to sequence simultaneously human leukocyte antigen (HLA) restricted T cell receptor beta chain (TRB) and unrestricted T cell receptor delta chain (TRD) and immunoglobulin heavy chain (IgH) immune receptor repertoires. The distribution was analyzed of TRB, TRD and IgH clones between healthy and COVID-19 infected subjects. Using McFadden's Adjusted R2 variables were examined for a predictive model. The aim of this study is to analyze the influence of the adaptive immune repertoire on the severity of the disease (value on the World Health Organization Clinical Progression Scale) in COVID-19. FINDINGS: Combining clinical metadata with clonotypes of three immune receptor heavy chains (TRB, TRD, and IgH), we found significant associations between COVID-19 disease severity groups and immune receptor sequences of B and T cell compartments. Logistic regression showed an increase in shared IgH clonal types and decrease of TRD in subjects with severe COVID-19. The probability of finding shared clones of TRD clonal types was highest in healthy subjects (controls). Some specific TRB clones seems to be present in severe COVID-19 (Figure S7b). The most informative models (McFadden´s Adjusted R2=0.141) linked disease severity with immune repertoire measures across all three cell types, as well as receptor-specific cell counts, highlighting the importance of multiple lymphocyte classes in disease progression. INTERPRETATION: Adaptive immune receptor peripheral blood repertoire measures are associated with COVID-19 disease severity