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Conodont Sequences and Their Lineages in the Permian-Triassic Boundary Strata at the Meishan Section, South China
Published in Wang Naiwen, J. Remane, Stratigraphy, 2020
Ding Meihua, Lai Xulong, Zhang Kexin
Hindeodus parvus (Kozur and Pjatakova) is a species between Isarcicella and Hindeodus. The form of the parvus Pa element is very similar to the species of Hindeodus and no ramiform of parvus has been published that seems very like to belong to a species of Isarcicella. The attribution of H. parvus (Kozur and Pjatakova) is unsettled. In this paper, H. parvus (Kozur and Pjatakova) is attributed to Hindeodus. H. parvus (Kozur and Pjatakova) ranges from the basal Triassic, and is earlier than Isarcicella isarciea(Huckriede). Thus, H. parvus is very significant in the Permian-Triassic boundary strata and in the evolution of the Hindeodus-Isarcicella lineage. But now, Hindeodus latidentatus (Kozur, Mostler and Rahimi-Yazd) is found in Bed 25 (white clay) of the top of the Permian at section B of Meishan, Changxing. We consider that H. latidentatus is a forerunner of the Isarcicella stock. The Pa element of H. latidentatus and the Pa element of H. parvus are very similar, except that the height of the denticles of H. parvus is higher than the denticles of H.latidentatus. Orchard[8] assigned Hindeodus latidentatus from Bed 25 in section B at Meishan to H. aff H. parvus. But, in our view H. latidentatus extended from the Late Changxingian to the Early Griesbachian, and H. parvus appeared only in the Griesbachian. The specimen from Bed 25 of section B at Meishan has a closer affinity to H. latidentatus than to H. parvus. According to the conodont data of the Meishan section, Changxing, the evolution of the Hindeodus-Isatcicella lineage is discussed as follows (Fie. 31:
IDH1 and IDH2 Mutations as Novel Therapeutic Targets in Acute Myeloid Leukemia (AML): Current Perspectives
Published in Peter Grunwald, Pharmaceutical Biocatalysis, 2020
Angelo Paci, Mael Heiblig, Christophe Willekens, Sophie Broutin, Mehdi Touat, Virginie Penard-Lacronique, Stéphane de Bottona
D-2HG can also alter DNA repair due to deficiency in homologous recombination (HR) (Inoue et al., 2016; Sulkowsky et al., 2017), seemingly making IDH-mutant cells susceptible to poly(adenosine 5’-diphosphate-ribose) polymerase (PARP) inhibition (Sulkowsky et al., 2017; Molenaar et al., 2018). Furthermore, myeloid lineage-specific IDH1 R132H conditional knock-in mice demonstrated an age-dependent accumulation of DNA damage, related to the downregulation of the DNA damage response protein ATM by altering histone methylation (Inoue et al., 2016).
Clinical and epidemiological context of COVID-19
Published in Sanjeeva Srivastava, Multi-Pronged Omics Technologies to Understand COVID-19, 2022
Viswanthram Palanivel, Akanksha Salkar, Radha Yadav, Renuka Bankar, Om Shrivastav, Arup Acharjee
Genomic epidemiology has come of age during the current COVID-19. The pandemic has also ushered scientists in tracking genomic changes to a virus at a speed and scale never seen before (Cyranoski 2021). The coronavirus has a mutation rate that is tenfold lower than that of other RNA viruses, owing to the proofreading activity of nsp14. It is known to have a mutation rate of about 8 × 10−4 nucleotides/genome per year. The researchers have used these mutations to stratify the virus to a particular lineage or clade. Global Initiative on Sharing All Influenza Data (GISAID) hosts the largest curated international repositories of SARS-CoV-2 sequence data (Hemrajata 2021). Around 3,913 genomes have been collected and uploaded in GSAID SARS-CoV-2 Genomic Epidemiology (EpiCoV) platform between September 2020 and April 2021. This data sheds light on strain diversity and putative intra- and intercontinental transmissions by providing a concurrent summary of the distribution of SARS-CoV-2 clades across geographical reasons. The United Kingdom and Denmark constitute 45% and 7% of genomes on the database (COG-UK 2020; Cyranoski 2021). One such study undertaken in North California revealed that WA1 strain associated with Washington state and six other strains were introduced cryptically into North California (Deng et al. 2020). Another study from the Netherlands demonstrated how a combination of WGS and epidemiology helped understand the transmission and strengthen the evidence base for public health decision-making to implement strict measures (Oude Munnink et al. 2020). In another study from Japan, genome sequencing was performed for returnees or travelers arriving in Japan at airport quarantining stations (Sekizuka et al. 2021). The results demonstrated that testing and genome sequencing should be performed efficiently to monitor the introduction of new strains into the community. Sequencing for Public Health Emergency Response, Epidemiology and Surveillance (SPHERES), a SARS-CoV-2 sequencing initiative, is established under CDC’s advanced molecular detection program. It aims at providing sequence data in real time for investigating COVID-19 cases and clusters. Despite these global efforts, there are still some gaps in the surveillance that need to be filled. These can be due to the lack of national-level consortium in many countries that have intense transmission.
Influence of extracellular cues of hydrogel biomaterials on stem cell fate
Published in Journal of Biomaterials Science, Polymer Edition, 2022
Haley Barnett, Mariya Shevchuk, Nicholas A. Peppas, Mary Caldorera-Moore
The ability of stem cells to differentiate into various lineages has laid much of the foundation for tissue engineering and regenerative medicine research. Tissue engineering research seeks to combine knowledge of biomaterials, stem cells, and physiochemical cues to repair, replace, or enhance tissue or organ function (Figure 1). Despite recent advances in the tissue engineering field, many challenges remain in precisely controlling stem cell fate due to the natural complexity of stem cell behavior. Cells rely on multiple signals from their environment that dictate their ability to adhere, spread, proliferate, and differentiate [1,2]. Thus, the difficulty in using stem cells in therapeutic applications lies in reproducing the stem cell environment or ‘niche’.