The original version of this Article contained an error in the spelling of the author James C. Mulloy, which was incorrectly given as James Mulloy. This has now been corrected in both the PDF and HTML versions of the Article.
Effective therapy of acute myeloid leukemia (AML) remains an unmet need. DNA methylcytosine dioxygenase Ten-eleven translocation 1 (TET1) is a critical oncoprotein in AML. Through a series of data analysis and drug screening, we identified two compounds (i.e., NSC-311068 and NSC-370284) that selectively suppress TET1 transcription and 5-hydroxymethylcytosine (5hmC) modification, and effectively inhibit cell viability in AML with high expression of TET1 (i.e., TET1-high AML), including AML carrying t(11q23)/MLL-rearrangements and t(8;21) AML. NSC-311068 and especially NSC-370284 significantly repressed TET1-high AML progression in vivo. UC-514321, a structural analog of NSC-370284, exhibited a more potent therapeutic effect and prolonged the median survival of TET1-high AML mice over three fold. NSC-370284 and UC-514321 both directly target STAT3/5, transcriptional activators of TET1, and thus repress TET1 expression. They also exhibit strong synergistic effects with standard chemotherapy. Our results highlight the therapeutic potential of targeting the STAT/TET1 axis by selective inhibitors in AML treatment.
Formation pore pressure is one of the most important basic data in petroleum exploration and development. The traditional prediction model of formation pore pressure relies on artificial experience and is highly subjective, so it seldom considers the influence multiple variables characteristics of formation pore pressure. In recent years, machine learning has been applied in lithology, reservoir, complex drilling conditions, formation pore pressure, and other fields. This article introduces the research progress and general process of machine learning algorithm in formation pore pressure prediction in recent years. In this study, the intelligent optimization algorithm is used to optimize the machine learning model and realize the intelligent prediction method of formation pore pressure. The results show that support vector regression (SVR) obtained the best prediction performance with the determination coefficient of 0.996. At the same time, this study reflects the importance of intelligent optimization algorithm to machine learning model optimization accuracy. This method provides a new idea for using multiple variables formation pore pressure in the future.
Since the outbreak of Coronavirus Disease 2019 (COVID-19) in December 2019, various digestive symptoms have been frequently reported in patients infected with the virus. In this study, we aimed to further investigate the prevalence and outcomes of COVID-19 patients with digestive symptoms.In this descriptive, cross-sectional, multicenter study, we enrolled confirmed patients with COVID-19 who presented to 3 hospitals from January 18, 2020, to February 28, 2020. All patients were confirmed by real-time polymerase chain reaction and were analyzed for clinical characteristics, laboratory data, and treatment. Data were followed up until March 18, 2020.In the present study, 204 patients with COVID-19 and full laboratory, imaging, and historical data were analyzed. The average age was 52.9 years (SD ± 16), including 107 men and 97 women. Although most patients presented to the hospital with fever or respiratory symptoms, we found that 103 patients (50.5%) reported a digestive symptom, including lack of appetite (81 [78.6%] cases), diarrhea (35 [34%] cases), vomiting (4 [3.9%] cases), and abdominal pain (2 [1.9%] cases). If lack of appetite is excluded from the analysis (because it is less specific for the gastrointestinal tract), there were 38 total cases (18.6%) where patients presented with a gastrointestinal-specific symptom, including diarrhea, vomiting, or abdominal pain. Patients with digestive symptoms had a significantly longer time from onset to admission than patients without digestive symptoms (9.0 days vs 7.3 days). In 6 cases, there were digestive symptoms, but no respiratory symptoms. As the severity of the disease increased, digestive symptoms became more pronounced. Patients with digestive symptoms had higher mean liver enzyme levels, lower monocyte count, longer prothrombin time, and received more antimicrobial treatment than those without digestive symptoms.We found that digestive symptoms are common in patients with COVID-19. Moreover, these patients have a longer time from onset to admission, evidence of longer coagulation, and higher liver enzyme levels. Clinicians should recognize that digestive symptoms, such as diarrhea, are commonly among the presenting features of COVID-19 and that the index of suspicion may need to be raised earlier in at-risk patients presenting with digestive symptoms. However, further large sample studies are needed to confirm these findings.
MLL-rearranged acute myeloid leukemia (AML) remains a fatal disease with a high rate of relapse and therapeutic failure due to chemotherapy resistance. In analysis of our Affymetrix microarray profiling and chromatin immunoprecipitation (ChIP) assays, we found that ALOX5 is especially down-regulated in MLL-rearranged AML, via transcription repression mediated by Polycomb repressive complex 2 (PRC2). Colony forming/replating and bone marrow transplantation (BMT) assays showed that Alox5 exhibited a moderate anti-tumor effect both in vitro and in vivo. Strikingly, leukemic cells with Alox5 overexpression showed a significantly higher sensitivity to the standard chemotherapeutic agents, i.e., doxorubicin (DOX) and cytarabine (Ara-C). The drug-sensitizing role of Alox5 was further confirmed in human and murine MLL-rearranged AML cell models in vitro, as well as in the in vivo MLL-rearranged AML BMT model coupled with treatment of "5 + 3" (i.e. DOX plus Ara-C) regimen. Stat and K-Ras signaling pathways were negatively correlated with Alox5 overexpression in MLL-AF9-leukemic blast cells; inhibition of the above signaling pathways mimicked the drug-sensitizing effect of ALOX5 in AML cells. Collectively, our work shows that ALOX5 plays a moderate anti-tumor role and functions as a drug sensitizer, with a therapeutic potential, in MLL-rearranged AML.
Accumulating mutations in the SARS-CoV-2 Spike (S) protein can increase the possibility of immune escape, challenging the present COVID-19 prophylaxis and clinical interventions. Here, 3 receptor binding domain (RBD) specific monoclonal antibodies (mAbs), 58G6, 510A5 and 13G9, with high neutralizing potency blocking authentic SARS-CoV-2 virus display remarkable efficacy against authentic B.1.351 virus. Surprisingly, structural analysis has revealed that 58G6 and 13G9 both recognize the steric region S470-495 on the RBD, overlapping the E484K mutation presented in B.1.351. Also, 58G6 directly binds to another region S450-458 in the RBD. Significantly, 58G6 and 510A5 both demonstrate prophylactic efficacy against authentic SARS-CoV-2 and B.1.351 viruses in the transgenic mice expressing human ACE2 (hACE2), protecting weight loss and reducing virus loads. Together, we have evidenced 2 potent neutralizing Abs with unique mechanism targeting authentic SARS-CoV-2 mutants, which can be promising candidates to fulfill the urgent needs for the prolonged COVID-19 pandemic.
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