Aged garlic extract potentiates doxorubicin cytotoxicity in human breast cancer cells

Purpose: To investigate the potential chemo-sensitizing effect of aged garlic extract (AGE) on doxorubicin (DOX) in breast cancer cells (MCF-7), and the possible underlying mechanisms.Methods: Human breast cancer cell line (MCF-7) was treated with AGE and DOX. The cytotoxic effects of AGE and DOX were investigated via cell cycle analysis and apoptosis induction, using flow cytometry. Mechanistic studies involved the determination of cellular uptake of DOX and  p-glycoprotein (P-gp) activity.Results: Combined treatment of MCF7 cells with AGE and DOX produced no significant effect at AGE dose of 10 mg/mL. However, co-treatment with AGE at doses of 50 and 93 mg/mL enhanced the cytotoxicity of DOX on MCF-7 cells, with IC50 values of 0.962 and 0.999 μM, respectively, whencompared with 1.85 μM DOX alone. Moreover, Annexin V-FITC and PI techniques showed that AGE significantly increased percentage of cells in late apoptosis. Besides, AGE-DOX treatment significantly increased cellular uptake of DOX and inhibited P-gp activity, when compared with DOX alone (p < 0.05).Conclusion: AGE enhances the cytotoxic effect of DOX on MCF-7 cells, most likely due to cell cycle distribution, stimulation of apoptosis, increased uptake of DOX by MCF7, and inhibition of P-gp activity. Keywords: Aged garlic extract, Doxorubicin, Breast cancer, MCF-7 cell line, P-glycoprotein, Apoptosis, Cell cycl

Upregulation of Twist2 in Non-Muscle Invasive Urothelial Carcinoma of the Bladder Correlate with Response to Treatment and Progression

BACKGROUND: Twist2 is a transcription factor and an epithelial-to-mesenchymal transition that plays an important role in cell polarity, cell adhesion, and has a role in tumour invasion and metastases.AIM: In this study, we examined the expression of Twist2 in non-muscle invasive bladder carcinoma (NMIBC) and correlated the expression with response to treatment and tumour progression.METHODS: Data of 305 patients with NMIBC of Ta, T1 were retrieved from hospitals archives. Twist2 expression was examined in tissue samples by immunohistochemistry at initial diagnosis and final follow-up, normal control was 10 normal urothelium, 10 patients with muscle-invasive bladder cancer (MIBC) were a positive control. Treatment of NMIBC was implemented according to the European Association of Urology guidelines on NMIBC. The descriptive statistical analysis included means, standard deviation, p-value; Univariate and multivariate Cox regression analyses.RESULTS: Twist2 expression score was identified as negative, low (1-15%); medium (15-40%); and high (40-100%). Patients who had low or low medium scores at the initial diagnosis had a good response and a favourable prognosis. Expression of a high score of Twist2 in patients having high-grade T1 tumours showed non-responsiveness to repeated courses of intravesical bacillus Calmette Guerin (BCG) therapy and was upstaged to MIBC.CONCLUSION: Twist2 expression in tissue samples of NMIBC would indicate the tumour response to therapy, upgrading and upstaging in the follow up after intravesical BCG therapy

The evolving SARS-CoV-2 epidemic in Africa: Insights from rapidly expanding genomic surveillance

INTRODUCTION Investment in Africa over the past year with regard to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) sequencing has led to a massive increase in the number of sequences, which, to date, exceeds 100,000 sequences generated to track the pandemic on the continent. These sequences have profoundly affected how public health officials in Africa have navigated the COVID-19 pandemic. RATIONALE We demonstrate how the first 100,000 SARS-CoV-2 sequences from Africa have helped monitor the epidemic on the continent, how genomic surveillance expanded over the course of the pandemic, and how we adapted our sequencing methods to deal with an evolving virus. Finally, we also examine how viral lineages have spread across the continent in a phylogeographic framework to gain insights into the underlying temporal and spatial transmission dynamics for several variants of concern (VOCs). RESULTS Our results indicate that the number of countries in Africa that can sequence the virus within their own borders is growing and that this is coupled with a shorter turnaround time from the time of sampling to sequence submission. Ongoing evolution necessitated the continual updating of primer sets, and, as a result, eight primer sets were designed in tandem with viral evolution and used to ensure effective sequencing of the virus. The pandemic unfolded through multiple waves of infection that were each driven by distinct genetic lineages, with B.1-like ancestral strains associated with the first pandemic wave of infections in 2020. Successive waves on the continent were fueled by different VOCs, with Alpha and Beta cocirculating in distinct spatial patterns during the second wave and Delta and Omicron affecting the whole continent during the third and fourth waves, respectively. Phylogeographic reconstruction points toward distinct differences in viral importation and exportation patterns associated with the Alpha, Beta, Delta, and Omicron variants and subvariants, when considering both Africa versus the rest of the world and viral dissemination within the continent. Our epidemiological and phylogenetic inferences therefore underscore the heterogeneous nature of the pandemic on the continent and highlight key insights and challenges, for instance, recognizing the limitations of low testing proportions. We also highlight the early warning capacity that genomic surveillance in Africa has had for the rest of the world with the detection of new lineages and variants, the most recent being the characterization of various Omicron subvariants. CONCLUSION Sustained investment for diagnostics and genomic surveillance in Africa is needed as the virus continues to evolve. This is important not only to help combat SARS-CoV-2 on the continent but also because it can be used as a platform to help address the many emerging and reemerging infectious disease threats in Africa. In particular, capacity building for local sequencing within countries or within the continent should be prioritized because this is generally associated with shorter turnaround times, providing the most benefit to local public health authorities tasked with pandemic response and mitigation and allowing for the fastest reaction to localized outbreaks. These investments are crucial for pandemic preparedness and response and will serve the health of the continent well into the 21st century

Applying Reduction Rules to ECATNets

Workshop co-located with the European Joint Conferences on Theory and Practice of Software (ETAPS'06

Implémentation des Régles de Réduction des ECATNets dans MAUDE

International audienc

Modeling Workflows with Recursive ECATNets

A major limitation of current workflow management systems appears in (1) their lack of support for flexible workflows whose structures can be modified dynamically during the execution and (2) in their failure in dealing, efficiently, with the most advanced workflow patterns. In this paper, we propose a new model which we call recursive ECATNets (RECATNets) to model workflow processes with dynamic structure and, particularly, to handle the most complex workflow patterns, in a concise way. The RECATNets extend classical ECATNets (Extended Concurrent Algebraic Term Nets) with the recursion concept firstly introduced in the recursive Petri nets. We define the semantics of RECATNets in the conditional rewriting logic framework. Rewriting logic is a true concurrency and operational semantics which allows rapid prototyping using rewriting techniques and the system Maude in particular.For further information, please visit this web site

Workflow Specification and Analysis Using Recursive ECATNets

International audienceWorkflow Specification and Analysis Using Recursive ECATNet

DKEMA: GPU-based and dynamic key-dependent efficient message authentication algorithm

International audienceRecently, benefiting from the advancement in the Graphic Processing Unit (GPU) technology, there is an increased interest in implementing and designing new efficient cryptographic schemes. Existing cryptographic algorithms, especially the Message Authentication Algorithms (MAAs) such as Hash Message Authentication Code (HMAC) and Ciphered Message Authentication Code (CMAC), are not designed to benefit from the GPU characteristics, which results in degraded performance of their GPU implementations. This gives rise to a trade-off between the design concept and the performance level. In this paper, a new MAA, called ’DKEMA’, is proposed to better suit the GPU functionality. This scheme is basedon the dynamic key-dependent scheme with one round of substitution and diffusion operations. The experimental results show that the proposed solution is highly effective on Tesla V100 and A100 GPUs, and the throughput is, respectively, more than 400GB/s and 500GB/s. Therefore, DKEMA can be considered as a promising MAA candidate for GPU implementation, achieving the desired cryptographic properties such as high randomness, collision tolerance in addition to message and key avalanche effect. The experimental results show that theproposed solution, based on the dynamic key approach, is immune towards well-known authentication and cryptanalysis attacks. In addition, DKEMA, consisting of one round compression function, presents an enhancement in terms of performance compared to existing algorithms (e.g. AES and SHA)

A deep learning scheme for efficient multimedia IoT data compression

International audienceMultimedia Internet of Things (MIoT) devices and networks will face many power and communication overhead constraints given the volume of multimedia sensed data. Oneclassic approach to overcoming the difficulty of large-scale data is to use lossy compression. However, current lossy compression algorithms require a limited compression rate to maintain acceptable perceived image quality. This is commonly referred to as the image quality-compression ratio trade-off. Motivated by current breakthroughs in computer vision, this article proposes recovering high-quality decompressed images at the application server level using a deep learning-based super-resolution model. As a result, this paper proposes ignoring the trade-off betweenimage quality and size and increasing the reduction size further by using a lossy compressor with downscaling to conserve energy. The experimental study demonstrates that the proposed technique effectively improves the visual quality of compressed and downscaled images. The proposed solution was evaluated on resource-constrained microcontrollers. The obtained results show that the transmission latency and energy consumption can be decreased by up to 10% compared to conventional lossy compression techniques