Background and objective: The present study provides valuable insights into the three-dimensional positioning of impacted maxillary canines and the associated mesiodistal dimensions of maxillary incisors in female patients. The objective of this study is to investigate the potential correlation between the displacement of impacted maxillary canines and the dimensions of the adjacent incisors. Material and methods: The sample consisted of pretreatment CBCT images of 28 females Yemeni, with palatal canine or buccal canine displacement (PDC or BDC) unilateral or bilateral, females with mean for aged 23.3± 2.1 years. An independent samples t-test was conducted to examine whether there is statistically significant difference between the means of two independent groups on two different variables. Results: The findings indicate that a significant correlation exists between the positioning of impacted canines and the dimensions of adjacent incisors. Specifically, patients with buccally displaced canines (BDC) exhibited larger mesiodistal crown sizes of maxillary incisors, suggesting a potential predictive marker for this type of canine displacement. In contrast, those with palatally displaced canines (PDC) demonstrated a significant reduction in incisor width, indicating a trend towards smaller tooth dimensions. This observation challenges the prevailing notion that spatial limitations are the primary cause of palatal impaction, as these cases often occur in individuals with adequate arch space. Conclusion: The contrasting incisor dimensions between the BDC and PDC groups underscore the importance of early morphological assessments in predicting canine eruption patterns. These insights can enhance diagnostic accuracy and inform individualized treatment planning in orthodontics, particularly for female patients who are more susceptible to canine impaction. Future research should further explore the implications of these findings on treatment outcomes and the underlying biological mechanisms influencing canine eruption.

Introduction: KMC has been documented as a safe and effective alternative method of care of low birth weight (LBW) babies in developing as well as developed countries to meet the baby’s need for warmth, breast feeding, protection from infection, stimulation, safety and love; improving maternal confidence and lactation and promoting early hospital discharge. [1] In terms of cost and impact on neonatal survival, it has comparative advantages over the conventional method of care (CMC). [2] But still KMC is not a widely practiced method of care of LBW babies in India. This study aimed at reviewing the evidence concerning the progress of KMC implementation and its health benefits especially in India. Methods: A prospective cohort study including inborn babies with birth weight <1800g with their mothers/ care givers was conducted at SNCU, Medical College, Kolkata over a period of 1 year from January to December 2023. Babies with hemodynamic instability and major congenital anomalies were excluded from the study. Results: 480 LBW (<1800g) babies were followed till discharge or 40 week post menstrual age, which ever was earlier. The babies having KMC had significant reduction of hypothermia (p value 0.03) and incidence of sepsis (p value 0.003). KMC was found to be associated with a significant reduction in the risk of mortality (RR 0.7). KMC group had earlier initiation of breastfeeding (mean difference 3 days), increased rate of daily weight gain (mean difference 5.61 g/day), earlier regaining of birth weight (mean difference 5.22 days) and decrease in duration of hospital stay (mean difference 2 days). Conclusion: This study supports the use of KMC in LBW (<1800g) as an adjunct to conventional neonatal care mainly in resource limited settings. KMC has significant positive impact on initiation and sustaining breast feeding, growth parameters and mother infant bonding.

Antibiotic resistance in pathogenic bacteria is a growing concern in clinical dentistry, particularly in the management of gingivitis and periodontal diseases. Porphyromonas gingivalis, a key periodontal pathogen, has demonstrated increasing resistance to commonly used antibiotics, partly due to efflux pump mechanisms. This study aimed to investigate the phenotypic and genotypic evidence of efflux pump-mediated resistance in P. gingivalis isolates obtained from gingivitis patients. A total of 48 P. gingivalis isolates were collected from 150 gingivitis patients and subjected to antibiotic susceptibility testing. High resistance rates were observed for tetracycline (70.8%) and erythromycin (58.3%), while lower resistance was noted for ciprofloxacin (31.3%) and amoxicillin-clavulanate (25.0%). Phenotypic detection of efflux pump activity was performed using the ethidium bromide (EtBr) cartwheel assay, revealing that 62.5% (30/48) of isolates exhibited efflux activity. The addition of the efflux pump inhibitor carbonyl cyanide m-chlorophenyl hydrazone (CCCP) significantly reduced the minimum inhibitory concentrations (MICs) in 73.3% of these isolates, confirming efflux-mediated resistance. Genotypic analysis via real-time PCR (qPCR) quantified the expression levels of two major efflux pump gene systems, acrAB-tolC and mexAB-oprM, in resistant isolates. High expression (≥5-fold increase) of acrAB-tolC was detected in 60% of isolates, while 40% exhibited high expression of mexAB-oprM. Statistical analysis revealed a strong positive correlation between efflux activity and acrAB-tolC expression (Pearson’s r = 0.82, p < 0.001), and a moderate correlation with mexAB-oprM expression (r = 0.65, p = 0.002). Overexpression of acrAB-tolC was significantly associated with tetracycline (p = 0.003) and erythromycin resistance (p = 0.01), whereas mexAB-oprM overexpression correlated with ciprofloxacin resistance (p = 0.02). These findings underscore the critical role of efflux pumps in antibiotic resistance among P. gingivalis isolates from gingivitis patients. The study highlights the need for alternative therapeutic strategies, such as efflux pump inhibitors, to combat resistance. Further research should explore the clinical applicability of targeting efflux mechanisms to improve treatment outcomes in periodontal infections.

The intricate and bidirectional relationship between nutrition and oral health is a cornerstone of modern preventive medicine. Diet is a primary etiological factor in the most prevalent oral diseases, including dental caries, periodontal disease, and dental erosion, while compromised oral health status directly impairs nutritional intake and systemic well-being. Despite overwhelming scientific evidence and consensus from global health organizations, a significant gap persists between the recognized importance of nutrition and its integration into dental education curricula worldwide. This review provides an evidence-based analysis of the current state of nutrition education in dental schools, its impact on clinical practice, and the future directions for reform. A comprehensive review of the scientific foundations reveals the specific roles of macro- and micronutrients in oral tissue homeostasis and the pathophysiology of oral diseases. A global analysis of dental curricula indicates a widespread deficiency in dedicated, clinically relevant nutrition instruction, characterized by insufficient hours, a lack of standardized competencies, and a shortage of faculty with expertise in applied nutrition. This educational deficit translates into a dental workforce that, while acknowledging the importance of nutrition, often lacks the confidence and skills to provide effective patient counseling. Clinician, patient, and system-level barriers further impede the implementation of nutritional interventions in practice. This review examines evidence-based pedagogical models poised to address these deficiencies, including competency-based frameworks, spiral curricula, and interprofessional education (IPE) programs that foster collaboration between dentistry, dietetics, and other health disciplines. Technology and artificial intelligence are also emerging as powerful tools to enhance both education and patient communication. Based on this synthesis, a series of actionable recommendations are proposed for educational institutions, accreditation bodies, clinicians, and researchers. The central conclusion is that the effective integration of nutrition into dental curricula is not merely an addition to an existing program but a fundamental paradigm shift necessary to equip future oral health professionals for a role in comprehensive, preventive, and integrated health care.

The convergence of nanotechnology and precision agriculture is redefining the future of sustainable food systems. As global agricultural systems face mounting pressures from climate volatility, resource depletion, and population growth, nanosensors engineered at the molecular scale offer a revolutionary toolkit for real-time, high-resolution monitoring of soil nutrients, crop physiological status, and water dynamics. Despite burgeoning research, a critical synthesis of how these nanoscale devices functionally integrate across the agro-ecosystem from rhizosphere to canopy, from lab to field remains absent. This review fills that void by providing a transdisciplinary analysis of nanosensor platforms, deployment architectures, and data ecosystems tailored for precision agriculture. We evaluate cutting-edge materials including plasmonic nanostructures, electrochemical nanowires, enzyme-functionalized quantum dots, and molecularly imprinted polymers for their sensitivity, environmental stability, and field-deployable form factors. Novel insights are presented on overlooked challenges: nanomaterial aging under UV/soil pH, biofouling interference, energy autonomy for remote sensing, and regulatory fragmentation across jurisdictions. Beyond technology, we examine socio-technical adoption barriers and propose scalable manufacturing and farmer-engagement models. This review does not merely catalog innovations it constructs a unified framework for evaluating “agricultural nanosensor readiness,” identifying critical gaps and accelerators for real-world impact. By bridging materials science, agronomy, data engineering, and policy, we chart a course toward intelligent, self-regulating farms where nanosensors serve as the nervous system of sustainable agriculture transforming data into decisions, and innovation into resilience.

Background: Stroke stands as a prominent contributor to enduring disability, inflicting motor and functional limitations upon survivors, significantly impacting their quality of life. Mirror therapy, a cost-effective and easy-to-use method, is increasingly employed in stroke rehabilitation to alleviate sensory-motor impairments and expedite limb recovery. This promising technique harnesses visual feedback to enhance neuroplasticity and boost post-stroke motor function. Method s: A randomized controlled trial was conducted among thirty stroke survivors; participants were assigned to either a mirror therapy (MT) group (n=15) or a standard rehabilitation group (n=15). The MT group underwent five 15-minute daily sessions for 14 days. Baseline data, including Barthel Index and Rivermead Mobility assessments, were gathered pre-intervention. Three-week post-intervention assessments targeted to investigate outcomes of MT in motor mobility and daily living activities in functional independence among stroke survivors. Results: A notable age difference was observed between the intervention (59±7.78) and comparison groups (58.8±6.50). The intervention group showed slightly more effects in Gross Function (d=0.162 vs. 0.132), Leg & Trunk (d=0.191 vs. 0.219), and Arm Function (d=0.323 vs. 0.205). Barthel Index effect size increased from small (d=0.261, CI 0.217–1.393) to large (d=0.172, CI 1.850–4.339). ANCOVA indicated no heteroscedasticity (F=0.704, p>0.05). A strong correlation (r=0.98) was found between daily functioning and motor function gains. Conclusion: MT effectively enhances daily activities in stroke survivors. However, statistical analysis showed no significant difference between groups in Rivermead scores (t = 0.17, p = 0.87). Yet, additional longitudinal studies are needed to thoroughly assess its impact on motor function improvement.

Objectives: The study aimed to develop and evaluate floating tablets of Boerhavia diffusa extract to enhance gastric retention and provide controlled release for effective management of diabetes mellitus. Methods: Floating tablets were prepared using Trigonella foenum-graecum mucilage and HPMC K100M by wet granulation. Pre-formulation studies, FTIR compatibility tests, and phytochemical screening were performed. The tablets were evaluated for pre- and post-compression parameters, in-vitro buoyancy, swelling index, dissolution, kinetic modeling, and stability studies as per ICH guidelines. Results: All formulations showed acceptable micromeritic properties and mechanical strength. The swelling index increased progressively up to 8 h, with formulation F5 exhibiting the highest swelling capacity. In-vitro buoyancy tests confirmed floating lag times of less than 1 min and sustained flotation for more than 10 h. Dissolution studies demonstrated drug release in the range of 70.61–89.56% over 12 h, with F5 showing the most controlled release profile. Kinetic modeling indicated zero-order release with non-Fickian diffusion. Stability testing over three months confirmed no significant changes in hardness, drug content, or release characteristics. Conclusion: The optimized formulation (F5) demonstrated desirable swelling, buoyancy, and sustained release properties, establishing Boerhavia diffusa floating tablets as a promising gastro-retentive delivery system with potential therapeutic benefits in diabetes management.

Preeclampsia is a major contributor to maternal and perinatal morbidity and mortality worldwide. Effective community-based strategies can significantly mitigate its impact. This review highlights evidence-based interventions for preeclampsia prevention, emphasizing early risk detection, lifestyle and dietary modifications, health education, and the role of local healthcare policies. A multidisciplinary approach that integrates the efforts of healthcare professionals, policy-makers, and communities is essential to reduce the burden of this condition and improve maternal and neonatal outcomes.

In this study, an adsorbent ZnO/GO/Zeolite-A nanocomposite was synthesized and characterized using standard methods for the removal of copper (Cu), iron (Fe), and chromium (Cr) from pharmaceutical effluents. The synthesis involved a multi-step approach comprising hydrothermal synthesis of Zeolite-A, sol-gel formation of ZnO nanoparticles, and incorporation of graphene oxide via ultrasonic dispersion to enhance surface area and functionality. The composite was characterized using X-ray diffraction (XRD), Fourier-transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM), Brunauer–Emmett–Teller (BET) surface analysis, and energy-dispersive X-ray spectroscopy (EDX). The results confirmed a well-integrated, porous nanostructure with high surface area and active functional groups suitable for sorption. Batch sorption experiments were conducted to evaluate the influence of contact time, pH, and temperature. The nanocomposite showed rapid and high sorption efficiency, with maximum removal rates observed at pH 5–6 and equilibrium reached within 60 minutes. The composite exhibits a steady increase from 52.6 % to 100 % efficiency removal of Fe, attributed to its superior adsorption capacity and large specific surface area. The zeolite-A/ZnO/GO consistently shows the best performance compared to individual treatments at all temperatures with Cu, Fe and Cr, showing removal efficiencies of 65.15 % at 50 °C, 75.52 % at 60 °C, and 82.15 % at 70 °C, with synergistic effects becoming more pronounced at elevated temperatures. Thermodynamic studies indicated that the sorption process was spontaneous and endothermic. The integration of ZnO and GO significantly enhanced the adsorption capacity of Zeolite-A due to synergistic effects, making ZnO/GO/Zeolite-A a promising candidate for sustainable treatment of heavy metal-laden pharmaceutical wastewater, contributing to environmental protection and public health improvement.

Salmonella, major food-borne illness among human and animals where poultry being primary source of infection. Current strategies, vaccination, antibiotics, feed additives, help to reduce the infection in poultry but insufficient for long-term protection. This study's aim to compare the efficacy of three antibiotics used for salmonellosis treatment in poultry. Experimentally, rabbits (n=12) 8 weeks old, were divided into four (4) groups (A, B, C and D), three animals in each group. Animals initially kept for five days in their respective wooden cages, fed on commercial diet. The blood samples from typhoid infected human patients (n=5) were collected from Jinnah hospital Lahore to isolate bacteria by culturing on blood agar media. Culture filtrate (5 mL) of salmonella typhi was injected to experimental rabbits except control group. After 48 hours collected blood samples of three antibiotics treated groups were subjected for genomic DNA isolation, PCR amplification of flipC gene. For experiment trail, the experimental groups were subjected on three antibiotics treatments with oral dose (50mg/kg) for ten days. Group-A (control) untreated, group-B (ciprofloxacin), group-C (azithromycin) and group-D (cefotaxime). During drug delivery, feces of rabbits were collected on 1st, 4th, and 7th day for comparative analysis of drug efficacy by calculating CFU/mL grown on blood agar medium. Body weight analysis showed an increase in weight of untreated group while gradual decrease for experimental groups, which indicated the effect of infection and poor absorption of nutrients. Salmonella infection was confirmed through PCR gene mapping test which was observed in all infected animals. Lesser CFU/mL (68.33) with grey-white colonies were observed in animal of group-B, 107 CFU/mL with opaque colored colonies (group-C) and 89 CFU/mL with moist, circular, smooth convex surface colonies (group-D). Thus ciprofloxacin (group-B) revealed as most effective antibiotic against Salmonella infection with more efficacy. These findings would be helpful for the farmers to use this antibiotic at poultry flocks against salmonellosis.

Green synthesis of silver nanoparticles (AgNPs) has gained attention as an eco-friendly and sustainable approach to nanomaterial production, particularly in the search for alternatives to conventional antimicrobials amid rising resistance. This systematic review, conducted in accordance with PRISMA 2020 guidelines, identified 17 in vitro experimental studies that investigated the antimicrobial potential of green-synthesized AgNPs. Biological sources included plants (n = 11), fungi (n = 2), a polysaccharide (n = 1), a cyanobacterium (n = 1), and a succulent (n = 1). Reported nanoparticle sizes ranged from 8 to 150 nm, with smaller particles (<30 nm) generally exhibiting superior antimicrobial efficacy. Antimicrobial activity was demonstrated against Gram-positive bacteria in 15 studies, Gram-negative bacteria in 14 studies, and fungi in 5 studies, with zones of inhibition ranging from 7 mm to 37 mm. Only six studies reported minimum inhibitory or bactericidal concentrations, underscoring a lack of standardized quantitative data. The predominant mechanisms of action were attributed to reactive oxygen species (ROS) generation, oxidative stress, membrane disruption, protein inactivation, and DNA interference. Cytotoxicity was assessed in six studies, suggesting biocompatibility at lower concentrations but potential dose-dependent toxicity. Overall, green-synthesized AgNPs demonstrate consistent antimicrobial potential, but future research must focus on standardized synthesis protocols, robust MIC/MBC testing, and systematic toxicity evaluation to support clinical translation.

The Shakambhari Hills in the Sikar region of Rajasthan host a rich and diverse entomofauna, yet have remained largely unexplored in terms of systematic entomological studies. This study presents a comprehensive inventory of insect species recorded from three distinct locations Kalakhet, Sakarai, and Bhagova by conducting random field surveys between 2021 and 2024. A total of 8,631 individuals belonging to 148 genera across 10 orders and 61 families were identified. Coleoptera was the most diverse and abundant order, followed by Lepidoptera and Hymenoptera. Presence of large number of insects from this region clearly indicates this region to comprise of tremendous diversity of insects and quite rich in flora which serve as host plants. These findings highlight the ecological significance of the Shakambhari Hills and underscore the need for conservation and further ecological research.

Artificial intelligence (AI) is reshaping aesthetic dentistry by improving diagnostic precision, treatment planning, outcome predictability, and overall patient satisfaction. This review aims to systematically analyze the role of AI in aesthetic dentistry, highlighting its applications, advantages, limitations, and future directions. A comprehensive literature search was conducted using PubMed, Scopus, Web of Science, and Google Scholar, covering studies published between 2018 and 2024. Search terms included “AI in dentistry,” “aesthetic dentistry,” “machine learning,” “prosthodontics,” and “orthodontics.” The review includes 28 peer-reviewed articles encompassing systematic reviews, clinical studies, narrative analyses, and expert consensus papers. Evidence shows that AI technologies such as convolutional neural networks (CNNs), generative adversarial networks (GANs), support vector machines (SVMs), and fuzzy logic systems have enhanced dental imaging, tooth segmentation, digital smile design, implant planning, prosthetic design, and personalized treatment simulations. AI facilitates real-time visualization, streamlines CAD/CAM workflows, and improves efficiency in clinical and administrative tasks. Moreover, AI enables predictive modeling of treatment outcomes and fosters patient-centered care through individualized approaches. However, significant challenges remain, including the need for high-quality datasets, ethical concerns about privacy and bias, lack of interpretability in AI decision-making, and high costs of implementation. The findings suggest broad consensus on AI’s transformative potential, but controversies persist regarding transparency, reliability, and accessibility. Future directions include explainable AI, integration with robotics, advanced biomaterials, and interdisciplinary collaborations. Overall, AI is revolutionizing modern aesthetic dentistry, paving the way for more predictable, minimally invasive, and patient-centered treatments that align with global digital healthcare trends.

Critical infrastructures, such as transportation, healthcare, and energy systems, are becoming increasingly interconnected, creating an urgent need for secure and efficient data sharing between agencies. However, the complexity of inter-agency collaboration is heightened by significant challenges, including privacy concerns, regulatory constraints, and inherent security risks. To address these concerns, Federated Learning (FL), a machine learning technique that facilitates the collaborative training of models across decentralized data sources without the need to transfer sensitive data, has emerged as a highly promising solution. FL ensures that agencies can jointly leverage the power of data-driven insights while ensuring privacy preservation. This paper investigates the potential of federated learning as a means to enable secure, scalable data collaboration between agencies in critical infrastructure sectors. We propose a novel federated learning framework tailored specifically for these sectors, taking into account sector-specific data requirements, regulatory frameworks, and security needs. Additionally, we discuss the effectiveness, challenges, and limitations of the proposed framework, as well as explore its potential for future applications and advancements. This paper aims to contribute to the growing body of research on privacy-preserving machine learning solutions in high-stakes, sensitive environments.

The increasing unpredictability of environmental events due to climate change has amplified the need for more resilient infrastructure and supply chains. Integrating climate-aware decision intelligence into planning processes can significantly improve the ability of organizations and industries to manage these risks effectively. This paper explores the crucial role of incorporating environmental risk assessments into infrastructure and supply chain planning. We propose a decision intelligence framework that combines real-time climate data, predictive modeling, and dynamic simulation techniques to inform decision-making. This approach aims to enhance the adaptability and sustainability of infrastructure and supply chains in response to climate-related challenges. The paper also reviews existing methodologies in environmental risk management and highlights case studies that demonstrate the practical application and success of such frameworks. By integrating predictive analytics and climate risk data, decision-makers can identify potential disruptions and make more informed decisions to mitigate these risks. The proposed solution not only improves resilience but also enables organizations to proactively adjust to changing environmental conditions, ensuring long-term operational stability. In this context, climate-aware decision intelligence becomes an essential tool for organizations seeking to future-proof their infrastructure and supply chain operations against the growing threat of climate change. This paper outlines the benefits and applications of the proposed framework and suggests future directions for research in this evolving field.