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Breast cancer is a multifaceted and prevalent malignancy, impacting a considerable proportion of women globally. Numerous signaling pathways intricately regulate cellular functions such as growth, proliferation, and survival. Among the various regulators, lncRNAs have emerged as significant players despite their inability to encode proteins. An expanding body of literature underscores the pivotal roles lncRNAs play in cancer biology, particularly in the context of breast cancer. Autophagy, the cellular process dedicated to the degradation and recycling of cellular components, is now recognized as a crucial factor in cancer initiation and progression. The interplay between lncRNAs, various signaling pathways, and autophagy in the pathophysiology of breast cancer remains an active area of investigation. Researchers have identified specific lncRNAs that are dysregulated in breast cancer patients, influencing the modulation of key signaling pathways. Using experimental methodologies and bioinformatics approaches, multiple lncRNAs have been elucidated, providing deeper insights into their contributions to breast cancer pathogenesis and metastatic processes. In summary, the pathophysiological landscape of breast cancer is characterized by the complex interactions involving lncRNA-mediated autophagy. This understanding paves the way for identifying novel therapeutic targets, prognostic markers, and diagnostic markers, ultimately contributing to improved treatment outcomes in breast cancer management., (© 2025 International Federation of Cell Biology.)

The messenger RNA (mRNA) vaccines have progressed from a theoretical concept to a clinical reality over the last few decades. Compared to conventional vaccination methods, these vaccines have a number of benefits, such as substantial potency, rapid growth, inexpensive production, and safe administration. Nevertheless, their usefulness was restricted up to now due to worries about the erratic and ineffective circulation of mRNA in vivo. Thankfully, these worries have largely been allayed by recent technological developments, which have led to the creation of multiple mRNA vaccination platforms for cancer and viral infections. The mRNA vaccines have been demonstrated as a powerful alternative to traditional conventional vaccines because of their high potency, safety and efficacy, capacity for rapid clinical development, and potential for rapid, low-cost manufacturing. The paper will examine the present status of mRNA vaccine technology and suggest future paths for the advancement and application of this exciting vaccine platform as a common therapeutic choice., (© 2024 John Wiley & Sons Ltd.)

Electrochemical techniques are commonly used to analyze and screen various environmental pathogens. When used in conjunction with other optical recognition methods, it can extend the sensing range, lower the detection limit, and offer mutual validation. Nowadays, electrochemical-optical dual-mode biosensors have ensured the accuracy of test results by integrating two signals into one, indicating their potential use in primary food safety quantitative assays and screening tests. Particularly, visible optical signals from electrochemical/colorimetric dual-mode biosensors could meet the demand for real-time screening of microbial pathogens. While electrochemical-optical dual-mode probes have been receiving increasing attention, there is limited emphasis on the design approaches for sensors intended for microbial pathogens. Here, we review the recent progress in the merging of optical and electrochemical techniques, including fluorescence, colorimetry, surface plasmon resonance (SPR), and surface enhanced Raman spectroscopy (SERS). This study particularly emphasizes the reporting of various sensing performances, including sensing principles, types, cutting-edge design approaches, and applications. Finally, some concerns and upcoming advancements in dual-mode probes are briefly outlined.

Mycotoxin contamination in foodstuffs and agricultural products has posed a serious hazard to human health and raised international concern. The progress of cost-effective, facile, rapid and reliable analytical tools for mycotoxin determination is in urgent need. In this regard, the potential utility of metal-organic frameworks (MOFs) as a class of crystalline porous materials has sparked immense attention due to their large specific surface area, adjustable pore size, nanoscale framework structure and good chemical stability. The amalgamation of MOFs with high-affinity aptamers has resulted in the progress of advanced aptasensing methods for clinical and food/water safety diagnosis. Aptamers have many advantages over classical approaches as exceptional molecular recognition constituents for versatile bioassays tools. The excellent sensitivity and selectivity of the MOF-aptamer biocomposite nominate them as efficient lab-on-chip tools for portable, label-free, cost-effective and real-time screening of mycotoxins. Current breakthroughs in the concept, progress and biosensing applications of aptamer functionalized MOFs-derived electrochemical and optical sensors for mycotoxins have been discussed in this study. We first highlighted an overview part, which provides some insights into the functionalization mechanisms of MOFs with aptamers, offering a foundation to create MOFs-based aptasensors. Then, we discuss various strategies to design high-performance MOFs-based aptamer scaffolds, which serve as either signal nanoprobe carriers or signal nanoprobes and their applications. We perceived that applications of optical aptamers are in their infancy in comparison with electrochemical MOFs-derived aptasensors. Finally, current challenges and prospective trends of MOFs-aptamer sensors are discussed.

Membrane-based separation processes has been recently of significant global interest compared to other conventional separation approaches due to possessing undeniable advantages like superior performance, environmentally-benign nature and simplicity of application. Computational simulation of fluids has shown its undeniable role in modeling and simulation of numerous physical/chemical phenomena including chemical engineering, chemical reaction, aerodynamics, drug delivery and plasma physics. Definition of fluids can be occurred using the Navier-Stokes equations, but solving the equations remains an important challenge. In membrane-based separation processes, true perception of fluid's manner through disparate membrane modules is an important concern, which has been significantly limited applying numerical/computational procedures such s computational fluid dynamics (CFD). Despite this noteworthy advantage, the optimization of membrane processes using CFD is time-consuming and expensive. Therefore, combination of artificial intelligence (AI) and CFD can result in the creation of a promising hybrid model to accurately predict the model results and appropriately optimize membrane processes and phase separation. This paper aims to provide a comprehensive overview about the advantages of commonly-employed ML-based techniques in combination with the CFD to intelligently increase the optimization accuracy and predict mass transfer and the unfavorable events (i.e., fouling) in various membrane processes. To reach this objective, four principal strategies of AI including SL, USL, SSL and ANN were explained and their advantages/disadvantages were discussed. Then after, prevalent ML-based algorithm for membrane-based separation processes. Finally, the application potential of AI techniques in different membrane processes (i.e., fouling control, desalination and wastewater treatment) were presented., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved.)