Explore the vast range of titles available.

Triple-negative breast cancer (TNBC) is cancer that tested as negative for estrogen receptors (ER), progesterone receptors (PR), and excess human epidermal growth factor receptor 2 (HER2) protein which accounts for 15%–20% of all breast cancer cases. TNBC is considered to be a poorer prognosis than other types of breast cancer, mainly because it involves more aggressive phenotypes that are similar to stem cell–like cancer cells (cancer stem cell, CSC). Thus, targeted treatment of TNBC remains a major challenge in clinical practice. This review article surveys the latest evidence concerning the role of genomic alteration in current TNBC treatment responses, current clinical trials and potential targeting sites, CSC and drug resistance, and potential strategies targeting CSCs in TNBC. Furthermore, the role of insulin-like growth factor 1 receptor (IGF-1R) and nicotinic acetylcholine receptors (nAChR) in stemness expression, chemoresistance, and metastasis in TNBC and their relevance to potential treatments are also discussed and highlighted.

A layered description of the structure of turbulent flame brushes is provided for situations featuring large but finite values of the Damköhler number, which correspond to the wrinkled flame regime of turbulent premixed combustion. One special focus of this study is placed on the description of the leading edge of the turbulent flame brush, the role of which is known to be essential with respect to propagation, transport and stabilization issues. On the basis of rather simple and well-identified working hypotheses, the influence of slight increases in the Karlovitz number values is revealed. The phenomenology and associated statistics are also investigated analytically, which leads to a mathematical description of the leading edge internal structure. With respect to the progress variable statistics, i.e. probability density function, this leading edge can indeed be thought of as the inner part of a boundary layer where the influence of the finite thickness of laminar flamelets can no longer be neglected. From the proposed description, standard fast-chemistry closures, which are currently used to perform the numerical simulation of turbulent combustion, may easily be generalized to account for the finite-rate chemistry effects occurring in this sublayer, thus emphasizing the interest of the present analysis for turbulent combustion theory and modelling.

Possible drug–food constituent interactions (DFIs) could change the intended efficiency of particular therapeutics in medical practice. The increasing number of multiple-drug prescriptions leads to the rise of drug–drug interactions (DDIs) and DFIs. These adverse interactions lead to other implications, e.g., the decline in medicament’s effect, the withdrawals of various medications, and harmful impacts on the patients’ health. However, the importance of DFIs remains underestimated, as the number of studies on these topics is constrained. Recently, scientists have applied artificial intelligence-based models to study DFIs. However, there were still some limitations in data mining, input, and detailed annotations. This study proposed a novel prediction model to address the limitations of previous studies. In detail, we extracted 70,477 food compounds from the FooDB database and 13,580 drugs from the DrugBank database. We extracted 3780 features from each drug–food compound pair. The optimal model was eXtreme Gradient Boosting (XGBoost). We also validated the performance of our model on one external test set from a previous study which contained 1922 DFIs. Finally, we applied our model to recommend whether a drug should or should not be taken with some food compounds based on their interactions. The model can provide highly accurate and clinically relevant recommendations, especially for DFIs that may cause severe adverse events and even death. Our proposed model can contribute to developing more robust predictive models to help patients, under the supervision and consultants of physicians, avoid DFI adverse effects in combining drugs and foods for therapy.

Entrepreneurship has been viewed as a critical contributor and an economic engine in a country for creating new jobs and it is crucial for graduates to alter their mindset to become self-employed. Thus, it is necessary to synthesize the factors that impact the entrepreneurial intentions (EI) of students at tertiary level. The aim of this research is twofold; first to identify the factors which have been most studied in the literature and second, to determine which factors are less explored to measure the EI of students. This research adopts the systematic review approach to identify various studies conducted between 2005 to June 2022. The paper further adopted citation analysis and identified the 36 most impactful studies in this area of research. Next, the thematic analysis was conducted and seven main themes (factors) (cognitive, personality, environmental, social, educational, contextual and demographic) of EI determinants were identified. The analysis of the papers clearly demonstrated that the TPB model and cognitive factors dominate this area of research. Furthermore, over half of the studies are conducted in Asia, hence it is important to explore other regions such as Africa, America and Europe and other comparative studies between various regions. The study offers avenues for future research and practical implications of the study for the practitioners.

Information security is a fundamental and urgent issue in the digital transformation era. Cryptographic techniques and digital signatures have been applied to protect and authenticate relevant information. However, with the advent of quantum computers and quantum algorithms, classical cryptographic techniques have been in danger of collapsing because quantum computers can solve complex problems in polynomial time. Stemming from that risk, researchers worldwide have stepped up research on post-quantum algorithms to resist attack by quantum computers. In this review paper, we survey studies in recent years on post-quantum cryptography (PQC) and provide statistics on the number and content of publications, including a literature overview, detailed explanations of the most common methods so far, current implementation status, implementation comparisons, and discussion on future work. These studies focused on essential public cryptography techniques and digital signature schemes, and the US National Institute of Standards and Technology (NIST) launched a competition to select the best candidate for the expected standard. Recent studies have practically implemented the public key encryption/key encapsulation mechanism (PKE/KEM) and digital signature schemes on different hardware platforms and applied various optimization measures based on other criteria. Along with the increasing number of scientific publications, the recent trend of PQC research is increasingly evident and is the general trend in the cryptography industry. The movement opens up a promising avenue for researchers in public key cryptography and digital signatures, especially on algorithms selected by NIST.

Hepatitis B remains one of the major global health problems more than 40 years after the identification of human hepatitis B virus (HBV) as the causative agent. A critical turning point in combating this virus was the development of a preventative vaccine composed of the HBV surface (envelope) protein (HBsAg) to reduce the risk of new infections. The isolation of HBsAg sub-viral particles (SVPs) from the blood of asymptomatic HBV carriers as antigens for the first-generation vaccines, followed by the development of recombinant HBsAg SVPs produced in yeast as the antigenic components of the second-generation vaccines, represent landmark advancements in biotechnology and medicine. The ability of the HBsAg SVPs to accept and present foreign antigenic sequences provides the basis of a chimeric particulate delivery platform, and resulted in the development of a vaccine against malaria (RTS,S/AS01, MosquirixTM), and various preclinical vaccine candidates to overcome infectious diseases for which there are no effective vaccines. Biomedical modifications of the HBsAg subunits allowed the identification of strategies to enhance the HBsAg SVP immunogenicity to build potent vaccines for preventative and possibly therapeutic applications. The review provides an overview of the formation and assembly of the HBsAg SVPs and highlights the utilization of the particles in key effective vaccines.

Recent advances in the isolation and stacking of monolayers of van der Waals materials have provided approaches for the preparation of quantum materials in the ultimate two-dimensional limit 1 , 2 . In van der Waals heterostructures formed by stacking two monolayer semiconductors, lattice mismatch or rotational misalignment introduces an in-plane moiré superlattice 3 . It is widely recognized that the moiré superlattice can modulate the electronic band structure of the material and lead to transport properties such as unconventional superconductivity 4 and insulating behaviour driven by correlations 5 – 7 ; however, the influence of the moiré superlattice on optical properties has not been investigated experimentally. Here we report the observation of multiple interlayer exciton resonances with either positive or negative circularly polarized emission in a molybdenum diselenide/tungsten diselenide (MoSe 2 /WSe 2 ) heterobilayer with a small twist angle. We attribute these resonances to excitonic ground and excited states confined within the moiré potential. This interpretation is supported by recombination dynamics and by the dependence of these interlayer exciton resonances on twist angle and temperature. These results suggest the feasibility of engineering artificial excitonic crystals using van der Waals heterostructures for nanophotonics and quantum information applications. Multiple interlayer exciton resonances in a MoSe 2 /WSe 2 heterobilayer with a small twist angle are attributed to excitonic ground and excited states confined within the moiré potential.

The prognosis and treatment plans for patients diagnosed with low-grade gliomas (LGGs) may significantly be improved if there is evidence of chromosome 1p/19q co-deletion mutation. Many studies proved that the codeletion status of 1p/19q enhances the sensitivity of the tumor to different types of therapeutics. However, the current clinical gold standard of detecting this chromosomal mutation remains invasive and poses implicit risks to patients. Radiomics features derived from medical images have been used as a new approach for non-invasive diagnosis and clinical decisions. This study proposed an eXtreme Gradient Boosting (XGBoost)-based model to predict the 1p/19q codeletion status in a binary classification task. We trained our model on the public database extracted from The Cancer Imaging Archive (TCIA), including 159 LGG patients with 1p/19q co-deletion mutation status. The XGBoost was the baseline algorithm, and we combined the SHapley Additive exPlanations (SHAP) analysis to select the seven most optimal radiomics features to build the final predictive model. Our final model achieved an accuracy of 87% and 82.8% on the training set and external test set, respectively. With seven wavelet radiomics features, our XGBoost-based model can identify the 1p/19q codeletion status in LGG-diagnosed patients for better management and address the drawbacks of invasive gold-standard tests in clinical practice.

The Silk Road Economic Belt (SREB), a major 21st-century initiative, aims to revive the historic Silk Road by connecting Asia, Europe, and Africa through a network of trade and cultural exchange routes. This study aims to assess sustainable development across sixteen countries situated in South Asia, West Asia, and Africa—regions that are central to the SREB but face diverse environmental and socio-economic challenges. To achieve this, a novel hybrid multi-criteria decision-making approach is proposed, combining the Method based on the Removal Effects of Criteria (MEREC) and Operational Competitiveness Ratings Analysis (OCRA). The MEREC method is used to determine objective weights for sustainability indicators by evaluating the impact of each criterion’s exclusion, while OCRA is employed to evaluate and rank countries based on both beneficial and non-beneficial indicators. The findings reveal significant disparities in sustainability performance across the studied countries. Israel ranked highest in sustainability, followed by Sri Lanka and Nepal, while India showed the lowest performance. These results provide valuable benchmarks and strategic insights for regional policy planning and sustainable development efforts within the SREB framework.