The NF-kappa B inhibitor, celastrol, could enhance the anti-cancer effect of gambogic acid on oral squamous cell carcinoma

<p>Abstract</p> <p>Background</p> <p>Gambogic acid (GA) is a major active ingredient of gamboge, a widely used traditional Chinese medicine that has been reported to be a potent cytotoxic agent against some malignant tumors. Many studies have shown that the NF-kappa B signaling pathway plays an important role in anti-apoptosis and the drug resistance of tumor cells during chemotherapy. In this study, the effects and mechanisms of GA and the NF-kappa B inhibitor celastrol on oral cancer cells were investigated.</p> <p>Methods</p> <p>Three human oral squamous cell carcinoma cell lines, Tca8113, TSCC and NT, were treated with GA alone, celastrol alone or GA plus celastrol. Cytotoxicity was assessed by MTT assay. The rate of apoptosis was examined with annexin V/PI staining as well as transmission electronic microscopy in Tca8113 cells. The level of constitutive NF-kappa B activity in oral squamous cell carcinoma cell lines was determined by immunofluorescence assays and nuclear extracts and electrophoretic mobility shift assays (EMSAs) <it>in vitro</it>. To further investigate the role of NF-kappa B activity in GA and celastrol treatment in oral squamous cell carcinoma, we used the dominant negative mutant SR-IκBα to inhibit NF-kappa B activity and to observe its influence on the effect of GA.</p> <p>Results</p> <p>The results showed that GA could inhibit the proliferation and induce the apoptosis of the oral squamous cell carcinoma cell lines and that the NF-kappa B pathway was simultaneously activated by GA treatment. The minimal cytotoxic dose of celastrol was able to effectively suppress the GA-induced NF-kappa B pathway activation. Following the combined treatment with GA and the minimal cytotoxic dose of celastrol or the dominant negative mutant SR-IκBα, proliferation was significantly inhibited, and the apoptotic rate of Tca8113 cells was significantly increased.</p> <p>Conclusion</p> <p>The combination of GA and celastrol has a synergistic antitumor effect. The effect can be primarily attributed to apoptosis induced by a decrease in NF-kappa B pathway activation. The NF-kappa B signaling pathway plays an important role in this process. Therefore, combining GA and celastrol may be a promising modality for treating oral squamous cell carcinoma.</p

Analytical validation of a standardised scoring protocol for Ki67 immunohistochemistry on breast cancer excision whole sections: an international multicentre collaboration

Aims The nuclear proliferation marker Ki67 assayed by immunohistochemistry has multiple potential uses in breast cancer, but an unacceptable level of interlaboratory variability has hampered its clinical utility. The International Ki67 in Breast Cancer Working Group has undertaken a systematic programme to determine whether Ki67 measurement can be analytically validated and standardised among laboratories. This study addresses whether acceptable scoring reproducibility can be achieved on excision whole sections. Methods and results Adjacent sections from 30 primary ER+ breast cancers were centrally stained for Ki67 and sections were circulated among 23 pathologists in 12 countries. All pathologists scored Ki67 by two methods: (i) global: four fields of 100 tumour cells each were selected to reflect observed heterogeneity in nuclear staining; (ii) hot-spot: the field with highest apparent Ki67 index was selected and up to 500 cells scored. The intraclass correlation coefficient (ICC) for the global method [confidence interval (CI) = 0.87; 95% CI = 0.799-0.93] marginally met the prespecified success criterion (lower 95% CI >= 0.8), while the ICC for the hot-spot method (0.83; 95% CI = 0.74-0.90) did not. Visually, interobserver concordance in location of selected hot-spots varies between cases. The median times for scoring were 9 and 6 min for global and hot-spot methods, respectively. Conclusions The global scoring method demonstrates adequate reproducibility to warrant next steps towards evaluation for technical and clinical validity in appropriate cohorts of cases. The time taken for scoring by either method is practical using counting software we are making publicly available. Establishment of external quality assessment schemes is likely to improve the reproducibility between laboratories further

Analytical validation of a standardized scoring protocol for Ki67: phase 3 of an international multicenter collaboration

Pathological analysis of the nuclear proliferation biomarker Ki67 has multiple potential roles in breast and other cancers. However, clinical utility of the immunohistochemical (IHC) assay for Ki67 immunohistochemistry has been hampered by unacceptable between-laboratory analytical variability. The International Ki67 Working Group has conducted a series of studies aiming to decrease this variability and improve the evaluation of Ki67. This study tries to assess whether acceptable performance can be achieved on prestained core-cut biopsies using a standardized scoring method. Sections from 30 primary ER+ breast cancer core biopsies were centrally stained for Ki67 and circulated among 22 laboratories in 11 countries. Each laboratory scored Ki67 using three methods: (1) global (4 fields of 100 cells each); (2) weighted global (same as global but weighted by estimated percentages of total area); and (3) hot-spot (single field of 500 cells). The intraclass correlation coefficient (ICC), a measure of interlaboratory agreement, for the unweighted global method (0.87; 95% credible interval (CI): 0.81–0.93) met the prespecified success criterion for scoring reproducibility, whereas that for the weighted global (0.87; 95% CI: 0.7999–0.93) and hot-spot methods (0.84; 95% CI: 0.77–0.92) marginally failed to do so. The unweighted global assessment of Ki67 IHC analysis on core biopsies met the prespecified criterion of success for scoring reproducibility. A few cases still showed large scoring discrepancies. Establishment of external quality assessment schemes is likely to improve the agreement between laboratories further. Additional evaluations are needed to assess staining variability and clinical validity in appropriate cohorts of samples

Transcriptome Analysis of the Model Protozoan, Tetrahymena thermophila, Using Deep RNA Sequencing

Background: The ciliated protozoan Tetrahymena thermophila is a well-studied single-celled eukaryote model organism for cellular and molecular biology. However, the lack of extensive T. thermophila cDNA libraries or a large expressed sequence tag (EST) database limited the quality of the original genome annotation. Methodology/Principal Findings: This RNA-seq study describes the first deep sequencing analysis of the T. thermophila transcriptome during the three major stages of the life cycle: growth, starvation and conjugation. Uniquely mapped reads covered more than 96 % of the 24,725 predicted gene models in the somatic genome. More than 1,000 new transcribed regions were identified. The great dynamic range of RNA-seq allowed detection of a nearly six order-of-magnitude range of measurable gene expression orchestrated by this cell. RNA-seq also allowed the first prediction of transcript untranslated regions (UTRs) and an updated (larger) size estimate of the T. thermophila transcriptome: 57 Mb, or about 55 % of the somatic genome. Our study identified nearly 1,500 alternative splicing (AS) events distributed over 5.2 % of T. thermophila genes. This percentage represents a two order-of-magnitude increase over previous EST-based estimates in Tetrahymena. Evidence of stage-specific regulation of alternative splicing was also obtained. Finally, our study allowed us to completely confirm about 26.8 % of the genes originally predicted by the gene finder, to correct coding sequence boundaries an

Prevalence of the rs1801282 single nucleotide polymorphism of the PPARG gene in patients with metabolic syndrome

Objective: this study aimed to get the genotypic and allelic frequencies of rs1801282 in 179 volunteer donors and 154 patients with Metabolic syndrome (MetS) in Brasilia, Brazil and also examine the association with anthropometric, biochemical and hemodynamic variables in the latter group. MetS comprises a group of diseases resulting from insulin resistance, in-creased risk of type 2 diabetes and atherosclerotic cardiovascular disease. MetS is defined by the presence of increased visceral fat, atherogenic dyslipidemia (elevated triglycerides (TGL)), with decreased high density lipoprotein (HDL) and increased low density lipoprotein (LDL) levels, hypertension (BPH) and disturbances in glucose homeostasis representing a significant burden across the world due to the alarming increase in the incidence over the last decades besides their significant morbidity and mortality. Peroxisome proliferator activated receptor-gamma (PPARg) has been mentioned as a candidate gene for determining the risk of MetS. It is a member of the nuclear receptors superfamily and a ligand-activated transcription factor, which regulates the expression of genes involved in the network lipogenesis and adipogenesis, insulin sensitivity, energy balance, inflammation, angiogenesis and atherosclerosis. Among the PPARG genetic variants, single nucleotide polymorphism rs1801282 has been the most extensively studied one since it was first described by Yen and cols. in 1997. This polymorphism is characterized by the replacement of a proline (CCC) to an alanine (GCA) at codon 12 of exon B, due to the exchange of a cytosine with a guanine. The Ala allele frequency varies in different ethnic groups. Materials and methods: DNA was extracted using Chelex-100 method and determinations of genotypes were performed by allele-specific chain reaction. Results: the distribution of genotype frequency of the MetS group was not statistically different from the frequency in the donor population at large. In the first group, genotype frequency was CC to 0.869 and 0.103 for CG, while allelic frequencies were 0.948 for C and 0.052 for G allele. In the group of donors, the genotype and allele frequencies were 0.882 for CC, 0.117 to CG; and 0.941 to 0.059 for G and C, respectively. GG genotype was not found in any of the two groups. The genotype distribution and allele frequencies were in Hardy-Weinberg equilibrium. No marker could be detected from the analysis of anthropometric, biochemical and hemodynamic variables in the MetS group. Conclusion: our data suggest that this polymorphism is not correlated with predisposition to MetS. The results obtained on a small sample of the population of Brasilia, corroborate the data reported in the literature on the prevalence of this polymorphism in PPAR in populations of different ethnic origins

Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks