Biological puncture systems span microorganisms, plants, invertebrates, and vertebrates, yet all must solve a common mechanical problem: creating a localized fracture in a protective barrier while avoiding failure of the penetrating structure and minimizing damage to the target. Living systems therefore provide diverse natural solutions to the same fundamental mechanical challenges faced by microneedles, making them useful comparators for bioinspired puncture design. This review examines representative biological puncture systems-including bacterial secretion needles, jellyfish nematocysts, insect stylets, snake fangs, plant trichomes, and drilling gastropods-through a unified mechanics-based framework encompassing biological context, geometry, materials, insertion dynamics, and functional integration. The comparative synthesis identifies that the apparent biological diversity collapses into a recurring mechanical design space governed by four recurring requirements: stress concentration, structural stability, interface control, and reduction of target resistance. Across taxa, successful puncture is achieved through convergent strategies including high-aspect-ratio geometries, localized reinforcement, material-property gradients, dynamic insertion mechanisms, lubrication, and multicomponent architectures. Building on a mechanical definition of pain in which nociceptor activation arises primarily from tissue deformation, displacement, and shear rather than barrier fracture itself, we identify bioinspired principles for painless microneedle design. By comparing how biological systems achieve barrier breach while controlling structural failure and target disturbance, the review identifies design principles that can be translated to microneedles. The analysis suggests that the fundamental objective of biological puncture systems is not to maximize insertion force or penetration depth, but to create the required barrier breach while minimizing the volume of mechanically disturbed tissue. Key design implications include maximizing stress concentration, minimizing prefracture indentation, maintaining insertion stability, reducing friction, and limiting penetration to the depth necessary for barrier traversal. These findings provide a unifying biomechanical framework for translating biological puncture strategies into next-generation microneedles for minimally invasive drug delivery and diagnostics.

The preservation of cultural heritage sites and objects faces critical challenges due to natural aging, environmental degradation, and human-induced damage such as vandalism and graffiti. This review article explores recent advancements in protective strategies for heritage materials including stone, concrete, ceramic, glass, metal, wood, and textiles. Special attention is given to the development and application of superhydrophobic and superoleophobic coatings, which offer promising defense against moisture, pollutants, and oily substances. These functional surfaces, often based on coatings consisting of polymeric, ceramic, and composite materials, can provide durable, non-invasive protection tailored to specific substrate weaknesses and exposure environments (indoor and outdoor). Objective of this review article is to critically examine the most recent studies and materials innovations relevant to cultural heritage site preservation. First the assessment of substrate vulnerabilities and environmental threats is presented, followed by a detailed analysis of coating types and compositions. It concludes with emerging trends, challenges, and future perspectives, offering a valuable resource for researchers, conservators, and materials scientists committed to the long-term safeguarding of historical artifacts and monuments.

ICME employs computational modelling to analyse manufacturing processes and predict product performance. Materials science has evolved into a transformative discipline supported by initiatives such as MGI, Industry 4.0, and Society 5.0. Through ICME, new materials will be discovered and developed faster while costs and timelines are reduced. Data-driven ICME approaches that integrate CALPHAD with high-throughput first-principles calculations have recently been developed, providing insight into bonding charge density and the mechanisms of material strengthening. Photodetectors and LEDs are designed using high-throughput density functional theory (DFT) screening in addition to efficient exploration of optoelectronic semiconductors and energy materials. The use of computational modelling, including ab initio, DFT, semiempirical, and simulation methods (Monte Carlo, molecular dynamics, quantum mechanics, finite element methods, and multiscale simulations), allows material properties to be predicted, synthesis optimised, and structure-property relationships to be optimised. As a result of these tools, innovation has been accelerated, and theoretical models and experimental validation have been brought together to accelerate materials research.

Nanogels (NGs) are presently the focus of extensive research because of their special qualities, including minimal particle size, excellent encapsulating efficacy, and minimizing the breakdown of active compounds. As a result, NGs are great candidates for drug delivery systems. Cross-linked nanoparticles (NPs) called stimulus-responsive NGs are comprised of synthetic, natural, or a combination of natural and synthetic polymers. These NPs can swell in response to large amounts of solvent, but their structural makeup prevents them from dissolving. Furthermore, in response to (i) physical stimuli like temperatures, ion strength, and magnetized or electrical fields; (ii) chemical stimuli like the pH level, molecules, or ions; (iii) biological stimuli like the enzymatic substrate or affinity ligand, they transform into a hard particle (collapsed form) from a polymer solution (swell form). Over the past decade, there has been a major advancement in the creation of "smart" NGs in applications related to therapeutics and diagnosis, involving nucleic acid and intracellular drug delivery, photodynamic/photothermal treatment, biological imaging, and its detection. The nanogels reviewed in this article rely only on temperatures, pH, light, magnetic fields, and combinations of those variables. Developing a targeted delivery vehicle will greatly benefit from the presented information, especially when used for Core-shell multi-sensitive photo-sensitive nanogels.

Superoleophobic-superhydrophilic surfaces have been used for applications in the fabrication of antioil-fouling smart textiles and for immiscible oil-water mixtures and emulsions separation. Typically, these surfaces are constructed by depositing coatings with selective superwetting properties on porous structures. The facile electrospun technique involves the deposition of polymer solutions or melts through an electric field, resulting in nanometer-sized fibers that form nanofibrous membranes with hierarchical structures and a large specific surface area. In this study, superoleophobic-superhydrophilic nanofibrous membranes were prepared by blending perfluorooctanoic acid and polyacrylonitrile polymers and fluorinated TiO2 nanoparticles and electrospinning them. The prepared nanofibrous membranes exhibit superoleophobic and superhydrophilic properties, along with antioil-fouling characteristics. These membranes can separate immiscible oil-water mixtures and oil-in-water emulsions. This superoleophobic-superhydrophilic nanofibrous membranes have broad applications in industry and for environmental protection.

Considerably, the most severe illness threatening human health today is cancer, which ranks as the second most significant cause of death around the globe. Modern medical technologies are very helpful for cancer patients in improving their quality of life, thanks to procedures like chemotherapy, radiation therapy, and surgery. Nevertheless, the lives of cancer patients are not always prolonged by these treatments. Concurrently, these methods are frequently associated with several unfavorable outcomes, including the development of side effects and a higher chance of relapse. Therefore, the creation of innovative cancer-eradication approaches remains necessary. The potential of nanomedicine as a technology provides new paradigms to get around the restrictions of traditional cancer treatments. Due to their many unique advantages, such as their easy functionalization, multiple and beneficial physicochemical properties, and customizable size and shape, gold nanoparticles (AuNPs) have attracted much attention. These features have led to the exploration of numerous therapeutic and diagnostic uses for AuNPs, especially in the case of malignant tumors. These uses include drug payload and genetic material delivery, computed tomography imaging, photodynamic therapy, and photothermal therapy based on X-rays. To fully utilize the potential of AuNPs, these applications require a thorough and detailed overview. Consequently, this review addressed recent developments in AuNPs for anticancer applications more systematically. To provide a basis for future research, the current state of clinical trials is also thoroughly discussed, along with potential challenges in implementing certain fundamental discoveries in clinical settings.

The benzimidazole scaffold is a promising nucleus for developing novel therapeutic agents for ulcer treatment. Its unique chemical structure provides desirable pharmacological properties, such as excellent bioavailability, metabolic stability, and low toxicity, making it an attractive candidate for ulcer treatment. Several benzimidazole derivatives have shown significant anti-ulcer activity in preclinical and clinical studies, acting through multiple pathways, including inhibition of gastric acid secretion, suppression of gastric inflammation, and promotion of mucosal protection. Some benzimidazole derivatives have also demonstrated anti-Helicobacter pylori activity, suggesting their potential for eradicating bacteria associated with ulcer formation. However, challenges such as poor solubility and limited selectivity remain. Various approaches, such as prodrug design and formulation optimization, have been explored to overcome these issues and improve the therapeutic profile of benzimidazole derivatives. Overall, the benzimidazole scaffold holds great promise as a nucleus for developing novel anti-ulcer agents. Further research and optimization efforts are needed to harness its full potential and translate it into effective treatments for ulcers. With continued advancements in medicinal chemistry and drug design, benzimidazole-based compounds may offer new therapeutic options for patients suffering from ulcers and related gastrointestinal disorders. Hence, this review highlights the knowledge about benzimidazole scaffold, the mechanism of ulcer formation, and various benzimidazole derivatives with anti-ulcer activity, which can be further studied in pre-clinical and clinical trials.

Maize is a highly versatile crop holding significant importance in global food, feed and nutritional security. Grain yield is a complex trait and difficult to improve without targeting the improvement of grain yield attributing traits, which are relatively less complex in nature. Hence, considering the erosion in genetic diversity, there is an urgent need to use wild relatives for genetic diversification and unravel the genomic regions for grain yield attributing traits in maize. Thus, the current study aimed to identify quantitative trait loci (QTLs) linked with grain yield and yield attributing traits. Two BC2F2 populations developed from the cross of LM13 with Zea parviglumis (population 1) and LM14 with Zea parviglumis (population 2) were genotyped and phenotyped in field conditions in the kharif season. BC2F2:3 lines in both populations were phenotyped again for grain yield and attributing traits in the spring season. In total, three QTLs each for ear height (EH), two QTLs for flag leaf length (FLL) and one QTL each for ear diameter (ED), plant height, flag leaf length (FLL), flag leaf width and 100 kernel-weight were identified in population 1. In population 2, two QTLs for kernel row per ear (KRPE) and one QTL for FLL were detected in. QTLs for EH, FLL and KPRE showed consistency across seasons. Among the identified QTLs, six QTLs were found to be co-localized near identified genomic regions in previous studies, validating their potential in contributing to trait expression. The identified QTLs can be utilized for marker assisted selection, transferring favorable alleles from wild relatives in modern maize.

Enzyme inhibition is a crucial mechanism for regulating biological processes and developing therapeutic interventions. This pharmacological review summarizes recent advances in enzyme inhibition, focusing on key developments and their implications for drug discovery and therapeutic strategies. It explains basic ideas, including the different kinds of inhibitors and how they work, and looks at recent advances in small-molecule inhibitor design, fragment-based drug discovery, and virtual screening techniques. The review also highlights the advances in targeting specific enzyme families, explaining the structural basis of enzyme-inhibitor interactions, optimizing inhibitor potency, selectivity, and pharmacokinetic properties, and new trends in enzyme inhibition. The clinical implications of recent advances in enzyme inhibition include the development of novel therapeutic agents for diseases like cancer, infectious diseases, and neurological disorders. The review addresses challenges and future directions in the field, such as optimizing drug safety, resistance mechanisms, and personalized medicine approaches. Overall, the insights provided in this review may inspire further research and collaborations to accelerate the translation of enzyme inhibitors into effective clinical treatments.

Identifying genomic regions under selection is essential for understanding the genetic mechanisms driving species evolution and adaptation. Traditional methods often fall short in detecting complex, spatially varying selection signals. Recent advances in deep learning, however, present promising new approaches for uncovering subtle selection signals that traditional methods might miss. In this study, we utilized the deep learning framework DeepGenomeScan to detect spatially varying selection signatures across 15 bovine populations worldwide. Our analysis uncovered novel insights into selective sweep hotspots within the bovine genome, revealing key genes associated with physiological and adaptive traits that were previously undetected. We identified significant quantitative trait loci linked to milk protein and fat percentages. By comparing the selection signatures identified in this study with those reported in the Bovine Genome Variation Database, we discovered 38 novel genes under selection that were not identified through traditional methods. These genes are primarily associated with milk and meat yield and quality. Our findings enhance our understanding of spatially varying selection's impact on bovine genomic diversity, laying a foundation for future research in genetic improvement and conservation. This is the first deep learning-based study of selection signatures in cattle, offering new insights for evolutionary and livestock genomics research.

From an economic standpoint, reproductive characteristics are fundamental for sustainable production, particularly for monotocous livestock like cattle. A longer inter-calving interval is indicative of low reproductive capacity. This issue changes the dynamics of current and future lactations since it necessitates more inseminations, veterinary care, and hormone interventions. Various reproductive phenotypes, including ovulation, mating, fertility, pregnancy, embryonic growth, and calving-related traits, are observed in dairy cattle, and these traits have been associated with several QTLs. Calving ease, age at puberty, scrotal circumference, and inseminations per conception have been associated with 4437, 10623, 10498, and 2476 Quantitative Trait Loci (QTLs), respectively. This data offers valuable insights into enhancing and comprehending reproductive traits in livestock breeding. Studying QTLs associated with reproductive traits has far-reaching implications across various fields, from agriculture and animal husbandry to human health, evolutionary biology, and conservation. It provides the foundation for informed breeding practices, advances in biotechnology, and a deeper understanding of the genetic underpinnings of reproduction.