Programmable hydrogels have emerged as a new generation of intelligent biomaterials capable of integrating stimuli-responsiveness, biocompatibility, and regenerative functions for precise drug delivery. Unlike traditional passive hydrogels, these systems utilize dynamic covalent and supramolecular crosslinking to achieve reversible adaptability and spatiotemporal regulation of therapeutic release. This review systematically summarizes the molecular design principles, stimuli-responsive mechanisms (pH, redox, enzyme, thermal, mechanical, and light), and crosslinking strategies that enable programmability and biodegradability within hydrogel networks. The crucial design approaches, including hybrid network architectures, molecular imprinting, and bioresponsive linkers, are highlighted as central to achieving selective and adaptive functionality. Particular focus is placed on multi-stimuli and feedback-controlled systems that coordinate drug release with biological signals to enable autonomous, context-specific therapy. Recent progress shows the integration of molecular imprinting, bioresponsive linkers, and hybrid structures that improve structural stability while maintaining responsiveness. Applications in wound healing, angiogenesis, and tissue regeneration emphasize the role of programmable hydrogels as bio-instructive matrices that modulate the immune response, preserve redox balance, and promote scar-free healing. Furthermore, emerging technologies such as AI-guided material design, 4D bioprinting, and bioelectronic integration are accelerating the development of closed-loop and patient-specific therapeutic platforms. Despite these advances, significant challenges remain, particularly regarding scalability, reproducibility, long-term stability, and the predictability of in vivo performance, which continue to limit clinical translation. Overall, programmable hydrogels mark a significant shift from static carriers to dynamic, self-regulating biomaterials for advanced regenerative medicine.
Purpose-The study ascertains the mothers' empowerment level and investigates its influence on the intergenerational transmission of educational achievements. Design/methodology/approach-The study uses primary data from 432 households, and the mothers' empowerment index is constructed with six distinct dimensions using principal component analysis. The impact of mothers' empowerment on the educational achievement of children is evaluated using multinomial logistic regression. Findings-Women in the sample show a moderately high progressive attitude, and there is positive cooperation between husband and wife; the empowerment index indicates a modest level of empowerment. Results indicate that empowered women positively influence their children's education; however, a daughter's educational accomplishments are more impacted by maternal empowerment than a son's. Originality/value-This study introduces two important novel aspects of women's empowerment that are absent in existing literature, i.e. husband-wife cooperation and women's progressive attitudes; both aspects have a substantial influence on the overall level of empowerment.
Early distinction between oral squamous cell carcinoma (OSCC) and possibly malignant disorders (PMDs) has been one of the most important diagnostic issues that may involve invasive biopsies. Therefore, it is of interest to assess the usefulness of salivary lactate dehydrogenase (LDH) and matrix metalloproteinase-9 (MMP-9) as non-invasive biomarkers of early detection. Spectrophotometry and ELISA were used in analyzing saliva samples of 135 participants (50 OSCC, 45 PMDs, 40 controls). The level of LDH and MMP-9 was much greater in OSCC than in PMDs and controls and demonstrated very high diagnostic accuracy (AUC > 0.89). Simultaneous biomarker measurement had better sensitivity and specificity indicating their possible use as well-grounded, non-invasive measures of screening oral cancer in its early stages.
In education, AI has become a disruptive phenomenon, reshaping learning techniques, results, and applications. Institutions of learning can leverage technology and digital tools to enable modern teaching methods with the desired student engagement. By allowing for adaptive software such as automatic tutoring systems and student targeted learning materials, AI can increase technical problem-solving within the TPACK framework. The data was collected from 516 educators of various technology and non-technology courses during the first 3 months of 2025. The analysis was done using the Smart PLS—SEM and ANN approach to confirm the proposed models significant predictors influencing classroom engagement. The results of the study suggest that academics should use artificial intelligence combined with subject matter expertise and innovative teaching practices to foster creativity, trust, and understanding in today’s classrooms which naturally enhance student engagement. Rather than a passive response to educational inputs, student engagement is conceived as an active mindset supported by relevant knowledge bases, engaging methodologies, and immersive learning settings—complexities enabled through AI-empowered pedagogy. The results will provide useful tools for academic organizations and academics to utilise AI as a tool for them to be efficient in new teaching techniques. AI learning tools can promote creativity, expand the range of methods enhance student engagement and be a new concept to a game-changer educational engagement.
In this paper, we investigate a class of q-convex functions that generalizes the classical convex functions by incorporating q-calculus and subordination to the q-exponential function. The primary objective is to determine sharp bounds for the initial coefficients, the Fekete–Szegö functional, coefficient differences, and certain orders of the Toeplitz determinant associated inverse of function in the class mentioned.
