Shadi Houshyar

Most antimicrobial drug candidates currently in development are derivatives of established antibiotic classes. In contrast, antimicrobial heteroatom-doped carbon quantum dot (CǪD) nanoparticles vastly differ from their chemical antibiotic counterparts and exhibit potent antibacterial activity and favourable biocompatibility, representing a promising alternative strategy, particularly for topical applications. Here, we report the incorporation of cobalt-doped carbon quantum dots (Co-CǪDs) into injectable, biocompatible hydrogels capable of both sensing pH and eliminating bacteria. Ultrasmall Co-CǪDs demonstrated broad-spectrum activity against gram-positive Methicillin-resistant Staphylococcus aureus (MRSA) and Gram-negative Pseudomonas aeruginosa (PAO1), mediated by membrane hyperpolarisation and reactive oxygen species (ROS) induced membrane damage. The particles showed negligible effect on primary fibroblast and endothelial cell viability at concentrations that were bactericidal to MRSA. Polymeric hydrogels were fabricated via electrospinning of chitosan, polyvinylpyrrolidone (PVP), and polyvinyl alcohol (PVA) polymer blends incorporating Co-CǪD and pH-responsive HPTS particles. This approach provided accurate measurement of environmental pH within the physiological range observed across healthy and chronic wounds. In vivo , the injectable hydrogels exhibited robust antimicrobial efficacy against MRSA without impairing wound closure relative to untreated controls, while also reducing inflammatory immune responses in infected tissues. Collectively, these findings demonstrate the potential of ultrasmall metal-doped CǪDs for infection control and their integration into 3D matrices as multifunctional theragnostic platforms. ToC (<60 words describe the main results): Ultrasmall antimicrobial carbon nanoparticles were incorporated into polymeric hydrogels containing a pH-responsive probe. This platform enabled detection across a physiologically relevant pH range of 5.0–6.5, spanning conditions associated with both healthy healing and chronically infected wounds. The hydrogel demonstrated strong antibacterial activity by generating damaging reactive oxygen species, and, in mice, effectively controlled infection while reducing pro-inflammatory immune responses. Abstract Figure: Scheme 1: Antimicrobial nanoparticle-doped hydrogel glows in alkaline wound conditions which are representative of chronic infections. Once in the wound bed, the hydrogel removes all MRSA infection, reducing inflammatory macrophage (iNOS) and neutrophil (MPO) populations, and restores healthy wound collagen deposition.

The textiles industry is a major contributor to global carbon dioxide (CO ₂₎ emissions, and it is projected to account for up to 26% of global emissions by 2050. Beyond emission reduction strategies, textiles themselves present an underexplored opportunity for carbon capture due to their hierarchical porosity, flexibility, large surface are and scalability. This review critically examines recent advances in textile-based materials for carbon capture, covering both functionalized textile substrates and sorbents derived from solid textile waste. Textile platforms incorporating porous carbon, metal-organic frameworks, amines, enzymes and biological systems are analyzed of adsorption or absorption mechanisms, capture capacity, regeneration behaviour, durability and operating conditions. In parallel, the transformation of textile waste into activated carbons and functional sorbents is evaluated from a circular economy perspective. Across reported studies, CO ₂ uptake capacities typically range from < 1 to ~ < 5mmol g ^− 1 under ambient conditions, with higher values achieved through activated carbon textiles and amine-functionalized systems, while regeneration stability and energy demand remain key challenges. This review highlights the advantages and limitations of textile-based CO ₂ capture technologies compared with conventional powder or pellet sorbents, identifies critical research gaps related to durability, regeneration and scalability and outlines future directions for developing efficient, low-energy and sustainable textile-based carbon capture technologies.

Antimicrobial peptides (AMPs) offer promising alternatives to conventional antibiotics, yet most predictive models fail to account for chemical modifications that influence real-world efficacy. Among these, C-terminal amidation is a widely adopted and effective strategy that improves structural stability, membrane interaction, and protease resistance. In this study, we established an integrated framework for the design and prediction of C-terminal amidated AMPs targeting Escherichia coli. Our approach combined a design-oriented model based on an interpretable Explainable Boosting Machine (EBM), which extracts actionable sequence-level design rules, together with a reliable deployment model, built on a fine-tuned ESM2 deep learning architecture. The resulting tool, CAmidPred, enables both predictive classification and amino acid pattern analysis with outputs examined in relation to published alanine-scanning experiments. Using these models, we identified a pardaxin variant with improved activity against E. coli, demonstrating the practical utility of the dual-model framework in targeted AMP design.

Integration of functional materials has significantly advanced the development of smart textiles, attracting increasing attention from researchers across disciplines. Among textile-forming techniques, knitting stands out due to its inherent structural versatility and dynamic loop configurations, offering great opportunities for responsive and adaptive textile systems. Recent innovations in materials science and structural engineering have further driven the development of smart knitted fabrics, especially for wearable electronics and thermal comfort management applications. Despite this progress, a comprehensive review focusing on the role of knitted structural parameters, such as stitch variations and combinations, remains limited. This review addresses the gap by systematically investigating the influence of knitted structures on smart textile performances. Key aspects, including sensing and thermal mechanisms, stitch design, materials selection and knitting techniques, are discussed in detail. Insights presented offer a roadmap for designing next-generation smart knitted textiles with multifunctional capabilities, aiming to inspire further research and innovation in this rapidly evolving field.

The pH levels in biofluids, including sweat, wound exudates, tears, and saliva, indicate hydration status, dermatological conditions, and underlying health issues such as kidney disease. The rapid development of wearable technology in health monitoring has led to considerable interest in textile-based wearable sensors, owing to their flexibility, comfort, breathability, biodegradability and diverse fabrication methodologies. Although there are numerous reviews on textile-based wearable sensors, a thorough review specifically addressing wearable textile-based pH sensors is lacking. The objective of this review is to present a comprehensive analysis of the current status of research on textile-based colorimetric pH sensors, highlighting their prospective applications in non-invasive health monitoring. Key textile substrates, pH-sensitive dyes, surface functionalization techniques, dye immobilization strategies, and innovative fabrication methods are discussed. Possible limitations of colorimetric sensors, including dye leaching, stability challenges, accuracy issues, and inadequate reversibility are summarized, and research initiatives aimed at improving sensor performance, durability, and wearability, such as encapsulation, multi-layer coatings, sol-gel methods, hydrogels, printing, chemical and physical bonding, and printing, are included. Besides, novel techniques such as nanofiber-based dye encapsulation and sensor miniaturization, and enhancement of sensor sensitivity, dye retention, and response time, are highlighted. This work will shed light on future development of textile-based pH sensors for wearable, non-invasive and reliable medical applications.

Platelet‐rich plasma (PRP) is a promising therapeutic approach in regenerative medicine that has gained considerable attention due to its capability to enhance tissue repair and regeneration. Initially introduced for managing thrombocytopenia, PRP now has broad clinical applications across various biomedical fields, ranging from dermatology to neurology. Its therapeutic effects rely on delivering concentrated platelets that release critical growth factors and cytokines, thereby enhancing natural healing processes such as cellular proliferation, extracellular matrix remodeling, and angiogenesis. Despite promising clinical outcomes, PRP therapy faces challenges due to inconsistencies in preparation methods, limiting its standardization and clinical efficacy. Unlike previous studies that focus narrowly on individual applications, this comprehensive review presents a multidisciplinary overview that integrates the latest advancements in PRP preparation methods and their implications for broader clinical translation, such as orthopedics, nerve regeneration, dermatology, dentistry, and skin aging treatment. Additionally, the article addresses ongoing issues related to standardizing PRP use, ethical considerations, and identifies critical areas for future research. Through this evaluation, the review aims to provide valuable insights for clinicians and researchers, contributing to improved clinical outcomes and broader therapeutic utilization of PRP in regenerative medicine.

The relentless expansion of sensor networks demands energy solutions that are autonomous and environmentally benign. Triboelectric nanogenerators (TENGs) have emerged as a game-changing solution due to their ability to convert small-scale mechanical vibrations, including geological movements, vibrations, and water waves, also known as blue energy, into electricity. However, most existing blue energy harvesters rely on per- and polyfluoroalkyl-substance (PFAS)-based polymers, which pose serious environmental risks and hinder sustainable device development. In this study, a PFAS-free TENG using a polydimethylsiloxane-cellulose-MgAl layered double hydroxide (PCL) nanocomposite is developed and designed for efficient mechanical and blue energy harvesting. With an optimized 3 wt % of LDH loading, the device achieved an output voltage of ∼69 V and power density of 23.26 μWcm^-2 at a load resistance of 1 MΩ. The fabricated TENG demonstrated improved performance in low-temperature aqueous environments, underscoring the importance of evaluating thermal effects in blue energy devices. The device exhibits excellent durability and long-term output stability, reliably charging a 10 μF capacitor. This work underscores the potential of PFAS-free nanocomposites as sustainable alternatives in energy harvesting technologies. Moreover, the lightweight, flexible, and robust nanocomposite film design allows scalable deployment on water surfaces for practical blue energy harvesting applications. The research findings pave the way for eco-friendly and high-performance energy solutions that meet the rigorous demands of modern sensor networks and beyond.

Antimicrobial peptides (AMPs) offer promising alternatives to conventional antibiotics, yet most predictive models fail to account for chemical modifications that influence real-world efficacy. Among these, C-terminal amidation is a widely adopted and effective strategy that improves structural stability, membrane interaction, and protease resistance. To address the limitations of existing models, we developed an integrated framework for the design and prediction of C-terminal amidated AMPs targeting Escherichia coli. Our approach combined a design-oriented model, based on an interpretable Explainable Boosting Machine (EBM), which extracts actionable sequence-level design rules, alongside with a high-accuracy deployment model, built on a fine-tuned ESM2 deep learning architecture. The resulting tool, CAmidPred, enables both predictive classification and amino acid pattern analysis, with outputs validated against published alanine-scanning experiments. This dual-model approach bridges computational modeling and wet-lab discovery, offering a robust and practical pipeline for the rational design of the targeted AMP design pipelines