Plants are continuously exposed to diverse environmental stresses, including drought, salinity, temperature extremes, and nutrient limitations, which significantly constrain agricultural productivity and ecosystem stability. Understanding how plants perceive, integrate, and respond to these stresses at the genetic level has become a central focus of modern plant science. Recent advances in genomics, transcriptomics, and functional genetics have revealed that plant adaptation to environmental stress is governed by complex, multilayered regulatory networks rather than single stress-responsive genes. These networks involve stress-sensing mechanisms, signal transduction pathways, transcriptional reprogramming, and post-transcriptional regulation, collectively shaping plant performance under adverse conditions. Genetic variation within and among plant species provides a critical resource for stress tolerance, enabling plants to optimize growth, metabolism, and reproductive success in fluctuating environments. Moreover, emerging tools such as genome-wide association studies, epigenetic profiling, and genome editing technologies have accelerated the identification of key genetic determinants underlying stress resilience. Integrating genetic insights with physiological and ecological perspectives has enhanced our understanding of how plants balance stress tolerance with growth and yield. This trade-off is particularly relevant under climate change scenarios. This review synthesizes recent progress in elucidating the genetic mechanisms that drive plant adaptation and performance under environmental stress. It highlights major stress-responsive gene families, regulatory networks, and adaptive strategies, and discusses how these insights can be translated into crop improvement programs. By bridging fundamental genetic research with applied plant breeding, this review underscores the potential of gene integration to develop resilient plant systems capable of sustaining productivity in increasingly challenging environments.

Artificial intelligence (AI) is transforming diagnostic decision-making across the life sciences, yet evidence remains fragmented across human, veterinary, plant, environmental, and microbial domains. We conducted a PRISMA-ScR scoping review (protocol preregistered on OSF; details in Supplement) and bibliometric analysis covering 2015–2025. Searches in PubMed/MEDLINE, Scopus, Web of Science, and IEEE Xplore (plus arXiv/bioRxiv tagging) identified 28,541 records and 68 preprints; after de-duplication and dual screening, 689 primary studies met inclusion criteria (with 42 preprints analyzed descriptively but excluded from citation-based bibliometrics). Human medicine dominated the corpus (81.3%), followed by veterinary (6.2%), plant (5.1%), environmental (4.2%), and microbial diagnostics (3.2%). Modalities were led by medical imaging (65.0%), then omics (18.0%), time-series (8.1%), spectra (4.1%), text (2.9%), and eDNA (1.9%). Reported performance was high (median AUROC 0.94), but external validity and transparency were limited: only 28.0% performed external validation, 9.0% used prospective designs, and 5.2% reported probability calibration. Reproducibility signals were weak (code availability 22.9%, data availability 18.0%, explicit preregistration rare), and fairness/bias assessments appeared in 7.0% of studies, concentrated in human health. Bibliometrics showed rapid year-on-year growth, with the United States (32.1%) and China (28.4%) leading output and collaborations. Trends indicate a shift from task-specific CNNs to multimodal/foundation-model approaches and early data-fusion gains, but consistent gaps persist in leakage controls, calibration, subgroup reporting, and regulatory alignment. We recommend domain-aware, leakage-resistant splits; at least one independent, real-world evaluation; prevalence-aware metrics with calibration and decision-utility; open datasheets/model cards; and federated/external benchmarking to probe generalization. These practices can convert impressive internal results into dependable, equitable diagnostics that work across clinics, farms, rivers, and labs.

Background: Immunization during pregnancy, specifically with Tdap and Influenza vaccines, is vital for reducing maternal and neonatal morbidity and mortality. Despite global recommendations, vaccination coverage remains suboptimal due to knowledge gaps, safety concerns, and negative attitudes among expectant mothers. Aim: This review evaluates the current state of knowledge and attitudes regarding Tdap and Influenza vaccination among pregnant women and assesses the impact of educational interventions on vaccine uptake. Methods: A systematic literature search was conducted across PubMed, Cochrane, MEDLINE, and Google Scholar for peer-reviewed studies published between 2020 and 2024. The search utilized keywords related to pregnancy vaccination, Tdap, influenza, and educational interventions. A total of 27 studies (24 quantitative, 1 qualitative, and 2 mixed-methods) were included in the final synthesis. Results: Findings reveal significant global variations in vaccine awareness. Studies from Saudi Arabia, India, and China reported low levels of knowledge regarding vaccine safety and the benefits of maternal immunization. In contrast, European studies generally showed higher awareness, though misconceptions regarding fetal risks persisted. Across all regions, the primary barriers to vaccination included fear of side effects, lack of healthcare provider (HCP) recommendations, and misinformation. Conversely, educational interventions—including tailored mobile applications and provider-led health education—demonstrated a marked increase in vaccine acceptance and uptake post-intervention. Conclusion: Knowledge gaps and lack of HCP encouragement are critical barriers to maternal immunization. Structured educational interventions and strong provider-patient communication are essential strategies to demystify vaccinations and improve uptake. Future research should focus on longitudinal, culturally tailored interventional studies to develop effective public health campaigns.

The persistent increase in atmospheric CO₂ levels presents a dual challenge of environmental mitigation and sustainable energy generation. This study introduces a unified nano-engineered platform combining nanostructured metals, plasmonic nanoparticles, quantum dots, and defect-rich TiO₂ to drive selective CO₂ conversion into CO, CH₄, and CH₃OH. By leveraging synergistic nano-interfaces, this work integrates catalytic activity with optoelectronic functionality, enabling simultaneous energy harvesting and chemical transformation. Nanostructured metals provide tailored surface states for CO₂ adsorption, while plasmonic nanoparticles induce hot-electron injection, and quantum dots facilitate directional charge transfer. Defective TiO₂ layers introduce oxygen vacancies that localize charges and modulate reaction pathways. Comprehensive material characterization using TEM, XRD, XPS, PL, and UV–Vis spectroscopy confirms controlled interface formation, defect density, and optical enhancement. CO₂ conversion experiments under gas-phase and photo-assisted modes demonstrate tunable product selectivity via defect engineering and electrical bias application. The hybrid platform achieves enhanced Faradaic efficiency, turnover number, and operational stability compared to conventional systems. Mechanistic insights reveal that defect-plasmon-quantum dot interactions govern charge localization and transfer, providing a predictive framework for reaction steering. Integration with photovoltaic and optoelectronic modules showcases the feasibility of combined chemical and energy conversion, offering a pathway toward scalable, smart CO₂-to-fuel system. These findings provide a transformative approach to CO₂ utilization, highlighting the potential for decentralized renewable energy generation and sustainable fuel production. The methodology and insights reported herein establish a foundation for designing multi-functional catalytic systems with controllable reaction pathways and integrated energy recovery.

Background: Transitioning to full oral feeding is a critical developmental milestone for preterm infants and a primary requirement for hospital discharge. The Premature Infant Oral Motor Intervention (PIOMI) is a novel, evidence-based technique designed to strengthen oral muscles and decrease the length of hospital stays. Despite its clinical benefits, the successful implementation of PIOMI depends heavily on the competence and attitudes of neonatal intensive care unit (NICU) nurses. Objective: This review aimed to appraise the current state of knowledge and practices among neonatal nurses regarding the implementation of PIOMI. Method: A systematic approach was employed to search electronic databases including PubMed, Ovid (Medline), and Elsevier. The search focused on peer-reviewed articles published within the last five years using specific keywords such as "PIOMI," "oral feeding practices," and "neonatal nurses' knowledge." Strict inclusion criteria ensured the selection of empirical studies focused on NICU settings and nurse-led interventions. Result: Seventeen studies met the inclusion criteria. The review of literature reveals that while PIOMI significantly improve feeding efficiency and reduce hospital stays, a notable disparity exists between nurses' positive attitudes and their practical competence. Several studies highlighted that structured training programs significantly improve nurses' knowledge levels and the speed at which infants achieve independent feeding. Conclusion: The literature underscores the efficacy of PIOMI in optimizing developmental outcomes for preterm infants. However, the gap between nurse enthusiasm and clinical skill necessitates the integration of standardized, competency-based educational programs into NICU protocols. Collaborative efforts to involve parents in feeding interventions and the adoption of standardized assessment scales are crucial for fostering the successful transition to oral feeding and improving the overall quality of neonatal care.

Infectious diseases remain a major threat to enhanced poultry production, resulting in significant financial losses and disruptions to productivity and food security. This study tested the efficacy of breeding for improving resistance to infectious bird diseases and compared the immune responses and survival of immigrants. The birds used in this study included an indigenous breed (Local Desi), an improved dual-purpose breed (Rhode Island Red), a commercial layer breed (White Leghorn), a commercial broiler strain, and a CRISPR-mediated MHC-enhanced line. The birds in the controlled challenge were 300, while the replication was 20 per breed × disease combination. The two-way analysis of variance indicated significant effects of breed, disease challenge, and the breed-by-disease interaction on mortality, survival, and antibody titer. The enhanced CRISPR line revealed the lowest average mortality of ≈12–15% while the highest antibody titers were ≈7.5–8.2 log₂ units. The broiler type’s average percentage was the highest at ≈32–38%. The indigenous birds were average as well; they had a stronger immune response than the commercial birds. Pearson’s correlation analysis showed a significant negative association between titer and mortality. r = −0.72, P < 0.001, and a positive association between titer and survival. With r = 0.76 and P < 0.001, a high heterophil-to-lymphocyte ratio correlated positively with mortality, indicating stress-related vulnerability. These results demonstrated substantial genetic variability in resistance characteristics and supported the implementation of genomic and gene-editing methods to promote immune efficiency inquisitiveness in breeding. Also, it helps to increase sustainability.