Transcriptional regulation of human osteopontin promoter by histone deacetylase inhibitor, trichostatin A in cervical cancer cells

<p>Abstract</p> <p>Background</p> <p>Trichostatin A (TSA), a potent inhibitor of histone deacetylases exhibits strong anti-tumor and growth inhibitory activities, but its mechanism(s) of action is not completely understood. Osteopontin (OPN) is a secreted glycoprotein which has long been associated with tumor metastasis. Elevated OPN expression in various metastatic cancer cells and the surrounding stromal cells often correlates with enhanced tumor formation and metastasis. To investigate the effects of TSA on OPN transcription, we analyzed a proximal segment of OPN promoter in cervical carcinoma cells.</p> <p>Results</p> <p>In this paper, we for the first time report that TSA suppresses PMA-induced OPN gene expression in human cervical carcinoma cells and previously unidentified AP-1 transcription factor is involved in this event. Deletion and mutagenesis analyses of OPN promoter led to the characterization of a proximal sequence (-127 to -70) that contain AP-1 binding site. This was further confirmed by gel shift and chromatin immunoprecipitation (ChIP) assays. Western blot and reverse transcription-PCR analyses revealed that TSA suppresses c-jun recruitment to the OPN promoter by inhibiting c-jun levels while c-fos expression was unaffected. Silencing HDAC1 followed by stimulation with PMA resulted in significant decrease in OPN promoter activity suggesting that HDAC1 but not HDAC3 or HDAC4 was required for AP-1-mediated OPN transcription. TSA reduces the PMA-induced hyperacetylation of histones H3 and H4 and recruitment of RNA pol II and TFIIB, components of preinitiation complex to the OPN promoter. The PMA-induced expression of other AP-1 regulated genes like cyclin D1 and uPA was also altered by TSA. Interestingly, PMA promoted cervical tumor growth in mice xenograft model was significantly suppressed by TSA.</p> <p>Conclusions</p> <p>In conclusion, these findings provide new insights into mechanisms underlying anticancer activity of TSA and blocking OPN expression at transcriptional level by TSA may act as novel therapeutic strategy for the management of cervical cancer.</p

HEiMDaL: Highly Efficient Method for Detection and Localization of wake-words

Streaming keyword spotting is a widely used solution for activating voice assistants. Deep Neural Networks with Hidden Markov Model (DNN-HMM) based methods have proven to be efficient and widely adopted in this space, primarily because of the ability to detect and identify the start and end of the wake-up word at low compute cost. However, such hybrid systems suffer from loss metric mismatch when the DNN and HMM are trained independently. Sequence discriminative training cannot fully mitigate the loss-metric mismatch due to the inherent Markovian style of the operation. We propose an low footprint CNN model, called HEiMDaL, to detect and localize keywords in streaming conditions. We introduce an alignment-based classification loss to detect the occurrence of the keyword along with an offset loss to predict the start of the keyword. HEiMDaL shows 73% reduction in detection metrics along with equivalent localization accuracy and with the same memory footprint as existing DNN-HMM style models for a given wake-word

Regulation of Monoamine Oxidase A (MAO-A) Expression, Activity, and Function in IL-13–Stimulated Monocytes and A549 Lung Carcinoma Cells

Monoamine oxidase A (MAO-A) is a mitochondrial flavoen-zyme implicated in the pathogenesis of atherosclerosis and inflammation and also in many neurological disorders. MAO-A also has been reported as a potential therapeutic target in prostate cancer. However, the regulatory mechanisms controlling cytokine-induced MAO-A expression in immune or cancer cells remain to be identified. Here, we show that MAO-A expression is co-induced with 15-lipoxygenase (15-LO) in interleukin 13 (IL-13)-activated primary human monocytes and A549 nonsmall cell lung carcinoma cells. We present evidence that MAO-A gene expression and activity are regulated by signal transducer and activator of transcription 1, 3, and 6 (STAT1, STAT3, and STAT6), early growth response 1 (EGR1), and cAMP-responsive element– binding protein (CREB), the same transcription factors that control IL-13– dependent 15-LO expression. We further established that in both primary monocytes and in A549 cells, IL-13–stimulated MAO-A expression, activity, and function are directly governed by 15-LO. In contrast, IL-13– driven expression and activity of MAO-A was 15-LO–independent in U937 promonocytic cells. Furthermore, we demonstrate that the 15-LO– dependent transcriptional regulation of MAO-A in response to IL-13 stimulation in monocytes and in A549 cells is mediated by peroxisome proliferator–activated receptor (PPAR) and that signal transducer and activator of transcription 6 (STAT6) plays a crucial role in facilitating the transcriptional activity of PPAR. We further report that the IL-13–STAT6 – 15-LO–PPAR axis is critical for MAO-A expression, activity, and function, including migration and reactive oxygen species generation. Altogether, these results have major implications for the resolution of inflammation and indicat

A Composition Theorem for Randomized Query Complexity

Let the randomized query complexity of a relation for error probability epsilon be denoted by R_epsilon(). We prove that for any relation f contained in {0,1}^n times R and Boolean function g:{0,1}^m -> {0,1}, R_{1/3}(f o g^n) = Omega(R_{4/9}(f).R_{1/2-1/n^4}(g)), where f o g^n is the relation obtained by composing f and g. We also show using an XOR lemma that R_{1/3}(f o (g^{xor}_{O(log n)})^n) = Omega(log n . R_{4/9}(f) . R_{1/3}(g))$, where g^{xor}_{O(log n)} is the function obtained by composing the XOR function on O(log n) bits and g

Production of He-4 and (4) in Pb-Pb collisions at root(NN)-N-S=2.76 TeV at the LHC

Results on the production of He-4 and (4) nuclei in Pb-Pb collisions at root(NN)-N-S = 2.76 TeV in the rapidity range vertical bar y vertical bar <1, using the ALICE detector, are presented in this paper. The rapidity densities corresponding to 0-10% central events are found to be dN/dy4(He) = (0.8 +/- 0.4 (stat) +/- 0.3 (syst)) x 10(-6) and dN/dy4 = (1.1 +/- 0.4 (stat) +/- 0.2 (syst)) x 10(-6), respectively. This is in agreement with the statistical thermal model expectation assuming the same chemical freeze-out temperature (T-chem = 156 MeV) as for light hadrons. The measured ratio of (4)/He-4 is 1.4 +/- 0.8 (stat) +/- 0.5 (syst). (C) 2018 Published by Elsevier B.V.Peer reviewe

Global, regional, and national burden of disorders affecting the nervous system, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

BackgroundDisorders affecting the nervous system are diverse and include neurodevelopmental disorders, late-life neurodegeneration, and newly emergent conditions, such as cognitive impairment following COVID-19. Previous publications from the Global Burden of Disease, Injuries, and Risk Factor Study estimated the burden of 15 neurological conditions in 2015 and 2016, but these analyses did not include neurodevelopmental disorders, as defined by the International Classification of Diseases (ICD)-11, or a subset of cases of congenital, neonatal, and infectious conditions that cause neurological damage. Here, we estimate nervous system health loss caused by 37 unique conditions and their associated risk factors globally, regionally, and nationally from 1990 to 2021.MethodsWe estimated mortality, prevalence, years lived with disability (YLDs), years of life lost (YLLs), and disability-adjusted life-years (DALYs), with corresponding 95% uncertainty intervals (UIs), by age and sex in 204 countries and territories, from 1990 to 2021. We included morbidity and deaths due to neurological conditions, for which health loss is directly due to damage to the CNS or peripheral nervous system. We also isolated neurological health loss from conditions for which nervous system morbidity is a consequence, but not the primary feature, including a subset of congenital conditions (ie, chromosomal anomalies and congenital birth defects), neonatal conditions (ie, jaundice, preterm birth, and sepsis), infectious diseases (ie, COVID-19, cystic echinococcosis, malaria, syphilis, and Zika virus disease), and diabetic neuropathy. By conducting a sequela-level analysis of the health outcomes for these conditions, only cases where nervous system damage occurred were included, and YLDs were recalculated to isolate the non-fatal burden directly attributable to nervous system health loss. A comorbidity correction was used to calculate total prevalence of all conditions that affect the nervous system combined.FindingsGlobally, the 37 conditions affecting the nervous system were collectively ranked as the leading group cause of DALYs in 2021 (443 million, 95% UI 378–521), affecting 3·40 billion (3·20–3·62) individuals (43·1%, 40·5–45·9 of the global population); global DALY counts attributed to these conditions increased by 18·2% (8·7–26·7) between 1990 and 2021. Age-standardised rates of deaths per 100 000 people attributed to these conditions decreased from 1990 to 2021 by 33·6% (27·6–38·8), and age-standardised rates of DALYs attributed to these conditions decreased by 27·0% (21·5–32·4). Age-standardised prevalence was almost stable, with a change of 1·5% (0·7–2·4). The ten conditions with the highest age-standardised DALYs in 2021 were stroke, neonatal encephalopathy, migraine, Alzheimer's disease and other dementias, diabetic neuropathy, meningitis, epilepsy, neurological complications due to preterm birth, autism spectrum disorder, and nervous system cancer.InterpretationAs the leading cause of overall disease burden in the world, with increasing global DALY counts, effective prevention, treatment, and rehabilitation strategies for disorders affecting the nervous system are needed

Global burden of 288 causes of death and life expectancy decomposition in 204 countries and territories and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

BACKGROUND Regular, detailed reporting on population health by underlying cause of death is fundamental for public health decision making. Cause-specific estimates of mortality and the subsequent effects on life expectancy worldwide are valuable metrics to gauge progress in reducing mortality rates. These estimates are particularly important following large-scale mortality spikes, such as the COVID-19 pandemic. When systematically analysed, mortality rates and life expectancy allow comparisons of the consequences of causes of death globally and over time, providing a nuanced understanding of the effect of these causes on global populations. METHODS The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 cause-of-death analysis estimated mortality and years of life lost (YLLs) from 288 causes of death by age-sex-location-year in 204 countries and territories and 811 subnational locations for each year from 1990 until 2021. The analysis used 56 604 data sources, including data from vital registration and verbal autopsy as well as surveys, censuses, surveillance systems, and cancer registries, among others. As with previous GBD rounds, cause-specific death rates for most causes were estimated using the Cause of Death Ensemble model-a modelling tool developed for GBD to assess the out-of-sample predictive validity of different statistical models and covariate permutations and combine those results to produce cause-specific mortality estimates-with alternative strategies adapted to model causes with insufficient data, substantial changes in reporting over the study period, or unusual epidemiology. YLLs were computed as the product of the number of deaths for each cause-age-sex-location-year and the standard life expectancy at each age. As part of the modelling process, uncertainty intervals (UIs) were generated using the 2·5th and 97·5th percentiles from a 1000-draw distribution for each metric. We decomposed life expectancy by cause of death, location, and year to show cause-specific effects on life expectancy from 1990 to 2021. We also used the coefficient of variation and the fraction of population affected by 90% of deaths to highlight concentrations of mortality. Findings are reported in counts and age-standardised rates. Methodological improvements for cause-of-death estimates in GBD 2021 include the expansion of under-5-years age group to include four new age groups, enhanced methods to account for stochastic variation of sparse data, and the inclusion of COVID-19 and other pandemic-related mortality-which includes excess mortality associated with the pandemic, excluding COVID-19, lower respiratory infections, measles, malaria, and pertussis. For this analysis, 199 new country-years of vital registration cause-of-death data, 5 country-years of surveillance data, 21 country-years of verbal autopsy data, and 94 country-years of other data types were added to those used in previous GBD rounds. FINDINGS The leading causes of age-standardised deaths globally were the same in 2019 as they were in 1990; in descending order, these were, ischaemic heart disease, stroke, chronic obstructive pulmonary disease, and lower respiratory infections. In 2021, however, COVID-19 replaced stroke as the second-leading age-standardised cause of death, with 94·0 deaths (95% UI 89·2-100·0) per 100 000 population. The COVID-19 pandemic shifted the rankings of the leading five causes, lowering stroke to the third-leading and chronic obstructive pulmonary disease to the fourth-leading position. In 2021, the highest age-standardised death rates from COVID-19 occurred in sub-Saharan Africa (271·0 deaths [250·1-290·7] per 100 000 population) and Latin America and the Caribbean (195·4 deaths [182·1-211·4] per 100 000 population). The lowest age-standardised death rates from COVID-19 were in the high-income super-region (48·1 deaths [47·4-48·8] per 100 000 population) and southeast Asia, east Asia, and Oceania (23·2 deaths [16·3-37·2] per 100 000 population). Globally, life expectancy steadily improved between 1990 and 2019 for 18 of the 22 investigated causes. Decomposition of global and regional life expectancy showed the positive effect that reductions in deaths from enteric infections, lower respiratory infections, stroke, and neonatal deaths, among others have contributed to improved survival over the study period. However, a net reduction of 1·6 years occurred in global life expectancy between 2019 and 2021, primarily due to increased death rates from COVID-19 and other pandemic-related mortality. Life expectancy was highly variable between super-regions over the study period, with southeast Asia, east Asia, and Oceania gaining 8·3 years (6·7-9·9) overall, while having the smallest reduction in life expectancy due to COVID-19 (0·4 years). The largest reduction in life expectancy due to COVID-19 occurred in Latin America and the Caribbean (3·6 years). Additionally, 53 of the 288 causes of death were highly concentrated in locations with less than 50% of the global population as of 2021, and these causes of death became progressively more concentrated since 1990, when only 44 causes showed this pattern. The concentration phenomenon is discussed heuristically with respect to enteric and lower respiratory infections, malaria, HIV/AIDS, neonatal disorders, tuberculosis, and measles. INTERPRETATION Long-standing gains in life expectancy and reductions in many of the leading causes of death have been disrupted by the COVID-19 pandemic, the adverse effects of which were spread unevenly among populations. Despite the pandemic, there has been continued progress in combatting several notable causes of death, leading to improved global life expectancy over the study period. Each of the seven GBD super-regions showed an overall improvement from 1990 and 2021, obscuring the negative effect in the years of the pandemic. Additionally, our findings regarding regional variation in causes of death driving increases in life expectancy hold clear policy utility. Analyses of shifting mortality trends reveal that several causes, once widespread globally, are now increasingly concentrated geographically. These changes in mortality concentration, alongside further investigation of changing risks, interventions, and relevant policy, present an important opportunity to deepen our understanding of mortality-reduction strategies. Examining patterns in mortality concentration might reveal areas where successful public health interventions have been implemented. Translating these successes to locations where certain causes of death remain entrenched can inform policies that work to improve life expectancy for people everywhere. FUNDING Bill & Melinda Gates Foundation

Synthesis of Novel Heterocycles via Palladium Catalyzed Reactions

All solvents were distilled prior to use. Petroleum ether refers to fraction boiling in the range 60-80 °C. DMF and DCM were dried over CaH2, distilled, and stored over 3Å molecular sieves in sealed container. THF was distilled over sodium and benzophenone. All the reactions were carried out under argon or nitrogen or oxygen atmosphere and anhydrous conditions unless otherwise stated. Analytical thin-layer chromatography (TLC) was performed on Silica gel 60 F254 aluminium TLC sheets. Visualization of the developed chromatogram was performed by UV absorbance or iodine exposure. For purification, column chromatography was performed using 100-200 mesh or 230-400 mesh silica gels. All the reagents including PdCl2, Pd(OAc)2, PdCl2(PPh3)2, Pd(PPh3)4 were purchased from Aldrich, Merck, Alfa-Aesar etc. 1H and 13C NMR spectra were recorded using a Brüker Advance 300 or 600 MHz using tetramethylsilane (TMS) as internal standard. Chemical shifts (δ) were given from TMS (δ = 0.00) in parts per million (ppm) with the residual protons of deuterated solvent used [CDCl3: 1H NMR δ = 7.26 ppm (s); 13C NMR δ = 77.16 ppm (t)]. Coupling constants (J) were expressed in Hertz (Hz) and spin multiplicities were given as s (singlet), d (doublet), dd (double doublet), t (triplet), m (multiplet) and br (broad). All 13C NMR spectra were obtained with complete proton decoupling. Mass spectra were recorded in ESITOF or JEOL JMS600 or GCMS-SHIMADZU-QP5050A DI-EI mass spectrometer. Infrared spectra were obtained on JASCO FT/IR-4200 infrared spectrometer as neat or KBr plate. Crystallographic data were obtained using Brüker Kuppa Apex 2 instrument