Current Medical and Drug Research (ISSN: 2581-5008) (Abbreviation: Curr Med Drug Res) is an international research journal dedicated to publish all type of Review articles, Min-review articles, Research articles, Short communications, Case reports, Letter to Editor, Book reviews and Commentaries in the all areas of Medical Sciences including anatomy, physiology, surgery, cardiology, pulmonary medicine, drug research, pharmacology, pharmaceutics, pharmacognosy, pharmacodynamics, etc. The journal aims to publish high-quality papers in all areas of medical sciences and other relevant areas to promote the recent advances in medical science for humanity.
Nanoparticle-Based Liquid Biopsy Biosensors in Cancer
Nanoparticle liquid biopsy biosensors have become a disruptive technology in cancer diagnostics, potentially substituting traditional tissue biopsies in offering a non-invasive, high sensitivity and real-time diagnostic method in a cancer patient. These biosensors hold the physicochemical characteristics of nanoparticles with that, high-surface-area, tunable optical properties, and magnetic responsiveness allow such biosensors to detect circulating tumor biomarkers, including, but not limited to, circulating tumor DNA (ctDNA), microRNAs (miRNAs), circulating tumor cells (CTCs), and extracellular vesicles Nanomaterials, especially gold nanoparticles, magnetic nanoparticles, and quantum dots, have recently been employed and improved the ability of biosensing platforms to operate by offering increased signal amplification and bio-specific recognition. Optical, electrochemical, and subsequent lateral flow detection systems have been integrated to make its use fast and point-of-care, which has enabled early cancer detection and constant monitoring of the disease. In spite of these developments, issues like reproducibility, mass production, and medical standardization continue to be key impediments to mass adoption. Continued advances in nanoparticle-based engineering and biosensor development, however, are extending the potential of liquid biopsy technologies by making them a foundational pillar to precision oncology and personalized medicine.
Nanoparticles; Liquid biopsy; Biosensors; Cancer diagnostics; Circulating tumor DNA (ctDNA); MicroRNA (miRNA); Extracellular vesicles.
Lipid Nanoparticles for RNA Delivery Beyond the Liver
Lipid nanoparticles (LNPs) have transformed the field of RNA therapies by facilitating the targeted delivery of nucleic acids to hepatocytes, but a key challenge is to broaden their use to other tissues. The presented review discusses logistical planning approaches, mechanistic understanding, and translational advantage of LNPs adjusted to be delivered to other tissues other than the liver. The issue of critical targeting of organs (such as lipid composition, particle size, surface charge, and selective organ targeting, SORT), is examined. Preclinical data demonstrate successful delivery to lungs, spleen, and kidneys, and overcome biological barriers, immune clearance and off-target effects. The design principles of rational design, quality-by-design and future of clinical translation are also addressed in the review. The increase in the multi-organ RNA delivery versatility among LNPs is also promising to widen the applicability of therapy in genetic, inflammatory, as well as infectious diseases.
Lipid nanoparticles, RNA delivery, extrahepatic targeting, selective organ targeting, mRNA therapeutics, nanoparticle design
A Scoping Review of Integrating Epidemiologic Evidence and Health Literacy to Support Community Action
Background: Epidemiologic evidence can inform community action only when individuals and communities are able to access, comprehend, critically appraise, and apply relevant information, and when public health systems minimize avoidable demands on these capabilities. Health literacy therefore functions as a critical link between the interpretation of epidemiologic evidence, the development of guidance, risk communication, community engagement, and the material and institutional conditions necessary for action. However, these interrelated processes are often examined within separate bodies of literature. Objective:This study aims to map the processes through which epidemiologic evidence is translated into health-literacy-responsive guidance and communication, identify the mechanisms and barriers that shape its progression toward community action, and develop integrated evidence–health literacy–action framework. Methods: A scoping review followed JBI guidance and PRISMA-ScR reporting principles. PubMed and authoritative public health repositories were searched for English-language empirical studies, reviews, frameworks, and guidance addressing epidemiologic evidence, evidence-informed guidance, health literacy, knowledge translation, risk communication, community engagement, implementation, or community behavior. Data were charted by pathway stage, actors, mechanisms, context, equity considerations, and outcomes, then synthesized narratively through a health-literacy lens. Results: Twenty-two sources were included: 17 peer-reviewed publications and 5 institutional documents. Five linked stages were identified: epidemiologic signal and community information needs; evidence synthesis and accessible interpretation; health-literacy-responsive guidance development; community translation and supported implementation; and action, outcomes, and feedback. Across stages, health literacy operated as a relational capacity shaped by the clarity, accessibility, credibility, cultural relevance, and actionability of information and by the resources available to act. Evidence-to-action was strengthened by triangulating evidence, communicating uncertainty, involving communities early, using trusted intermediaries, adapting language and delivery, pairing messages with accessible services or products, and maintaining feedback loops. Recurrent barriers were technical or inconsistent guidance, one-way communication, low trust, high comprehension or numeracy demands, language and digital exclusion, inequitable access, and evaluation limited to reach. Conclusions: The translation of epidemiologic evidence into community action is negotiated and iterative rather than a linear transmission of facts. Health literacy should be treated as a shared responsibility of individuals, communities, communicators, and institutions. Equitable action is more likely when evidence is interpreted transparently, guidance is understandable and actionable, lived experience shapes delivery, and practical resources and continuous learning are integrated within an accountable pathway.
epidemiology; health literacy; community action; evidence-informed decision-making; knowledge translation; risk communication; community engagement; implementation
Digital Twin Dentures Adapting Occlusion Based on Chewing Behavior Collected Through Embedded Sensors
Digital twin dentures represent an emerging advancement in prosthodontics by combining digital modeling, embedded sensor technology, and artificial intelligence to create adaptive removable prostheses capable of responding to an individual’s chewing behavior. Unlike conventional dentures, these smart prostheses continuously collect functional data such as bite force, chewing frequency, occlusal contacts, and mandibular movement through integrated microsensors. The acquired information is transmitted to a virtual digital twin that replicates the clinical performance of the denture, enabling continuous assessment of occlusal function and personalized treatment planning. Advanced data analytics facilitate the identification of functional imbalances, excessive loading, and parafunctional habits, allowing clinicians to optimize occlusion and improve prosthesis performance over time. This adaptive approach has the potential to enhance masticatory efficiency, patient comfort, prosthesis stability, and long-term durability while supporting preventive maintenance and reducing the frequency of manual adjustments. Furthermore, integration with cloud-based monitoring systems and digital dentistry workflows may enable remote follow-up and evidence-based clinical decision-making. Although challenges remain regarding sensor reliability, data security, manufacturing complexity, and clinical validation, digital twin dentures offer a promising direction for personalized prosthodontic care by transforming static prostheses into intelligent systems capable of continuous functional adaptation and improved patient-centered outcomes.
Digital twin, smart dentures, adaptive occlusion, embedded sensors, chewing behavior, artificial intelligence, prosthodontics, digital dentistry, occlusal analysis, personalized prosthetic care
Development of Self-healing Bioactive Endodontic Sealers Containing Microcapsules that Release Calcium Silicate after Microcrack Formation
The development of self-healing bioactive endodontic sealers represents an innovative approach to improving the long-term success of root canal treatment. Conventional sealers are susceptible to degradation, shrinkage, and microcrack formation over time, which may compromise the apical seal and permit bacterial reinfection. Incorporating microcapsules containing calcium silicate into bioactive sealer formulations offers a promising strategy for autonomous repair of these defects. When microcracks develop within the sealer matrix, the embedded microcapsules rupture and release calcium silicate, initiating a mineralization process that fills the cracks and restores sealing integrity. In addition to self-repair, calcium silicate contributes to bioactivity by promoting hydroxyapatite formation, stimulating periapical tissue healing, and enhancing the biological compatibility of the material. The integration of antibacterial nanoparticles and advanced encapsulation technologies may further improve resistance to microbial colonization while maintaining favorable mechanical properties. This emerging biomaterial concept combines regenerative potential with intelligent material design to enhance the durability and clinical performance of endodontic sealers. Although current findings indicate considerable promise, further optimization of microcapsule characteristics, healing efficiency, and long-term mechanical stability, together with comprehensive laboratory investigations and well-designed clinical studies, is required before these self-healing bioactive sealers can be widely adopted in routine endodontic practice.
Self-healing endodontic sealer, bioactive sealer, calcium silicate, microcapsules, root canal therapy, microcrack repair, biomineralization, endodontic biomaterials, controlled release, regenerative endodontics.
Continuous Wearable Salivary Biosensors for Real-time Periodontal Inflammation Monitoring
Periodontal inflammation remains one of the most prevalent oral health conditions, often progressing silently until significant tissue destruction has occurred. Conventional diagnostic methods rely on periodic clinical examinations, limiting the ability to detect dynamic changes in disease activity. Continuous wearable salivary biosensors represent an emerging approach that enables real-time, non-invasive monitoring of inflammatory biomarkers directly from saliva. These wearable devices integrate advanced biosensing technologies with miniaturized electronics to continuously measure biochemical indicators associated with periodontal disease, providing immediate insights into disease progression and treatment response. The incorporation of flexible materials, wireless communication, and portable analytical platforms allows seamless data acquisition and transmission, supporting personalized oral healthcare and remote patient monitoring. Continuous assessment of salivary biomarkers may facilitate earlier intervention, improve treatment planning, enhance patient compliance, and reduce the long-term burden of periodontal disease. Furthermore, integration with digital health systems and artificial intelligence-based analytical tools has the potential to improve diagnostic accuracy and support predictive disease management. Although technical challenges such as sensor stability, biomarker variability, and regulatory considerations remain, ongoing advances in wearable biosensor design continue to improve clinical feasibility. Continuous wearable salivary biosensors therefore represent a promising innovation for precision periodontology, offering a practical strategy for early detection, continuous disease surveillance, and individualized management of periodontal inflammation.
Wearable salivary biosensors; Periodontal inflammation; Salivary biomarkers; Real-time monitoring; Point-of-care diagnostics; Lab-on-a-chip; Precision dentistry; Digital oral health.
Influence of Circadian Rhythm on Postoperative Endodontic Pain and Inflammatory Cytokine Expression
Postoperative pain remains one of the most common complications following endodontic treatment and is primarily influenced by inflammatory responses within the periapical tissues. Recent evidence suggests that circadian rhythm, the body’s intrinsic biological timing system, plays a significant role in regulating pain perception, immune activity, hormonal secretion, and inflammatory mediator production. Variations in the expression of pro-inflammatory and anti-inflammatory cytokines throughout the day may contribute to differences in the intensity and duration of postoperative endodontic pain among patients. Circadian regulators, including melatonin and clock-related genes, have demonstrated protective effects by modulating oxidative stress, enhancing immune balance, and promoting tissue repair. Understanding these biological mechanisms provides new opportunities for optimizing treatment timing, improving postoperative pain control, and supporting faster healing. Furthermore, advances in digital dentistry and artificial intelligence offer promising approaches for integrating patient-specific biological rhythms with clinical decision-making to develop personalized pain management strategies. This review examines the influence of circadian rhythm on postoperative endodontic pain and inflammatory cytokine expression, highlighting the underlying molecular pathways, clinical implications, and emerging technologies that may enhance therapeutic outcomes. A chronobiology-based approach has the potential to improve patient comfort, reduce inflammatory complications, and contribute to more predictable and individualized endodontic care.
Circadian rhythm, postoperative endodontic pain, inflammatory cytokines, melatonin, BMAL1, chronobiology, oxidative stress, immune response, periapical healing, artificial intelligence, endodontics.
AI-based Prediction of Root Canal Failure Using Smartphone Photographs of Temporary Restorations Combined with Patient-reported Symptoms
Root canal treatment is a highly successful procedure when proper diagnosis, disinfection, and coronal sealing are achieved. However, failure may occur because of temporary restoration breakdown, coronal leakage, persistent microbial infection, or delayed recognition of postoperative complications. Conventional follow-up often depends on scheduled clinical visits, which may postpone the detection of early signs of treatment failure. Recent advances in artificial intelligence and smartphone technology offer an opportunity for remote monitoring through automated analysis of clinical photographs and patient-reported symptoms. This approach combines visual assessment of temporary restorations with information such as pain intensity, swelling, biting discomfort, and sensitivity to identify patients at increased risk of treatment failure. Deep learning algorithms can recognize restoration defects, discoloration, fractures, and soft tissue changes from smartphone images, while machine learning models integrate these findings with symptom data to generate individualized risk predictions. Such systems have the potential to improve tele-endodontic care by supporting timely clinical intervention, reducing unnecessary emergency visits, and enhancing patient engagement during the healing period. Although challenges remain regarding image standardization, data privacy, and model validation, AI-assisted remote surveillance represents a promising adjunct to conventional postoperative assessment. Continued refinement of predictive algorithms and mobile health applications may contribute to more efficient follow-up strategies and improved long-term outcomes in endodontic practice.
Artificial intelligence, root canal failure, smartphone photography, temporary restoration, deep learning, tele-endodontics, patient-reported symptoms, mobile health, machine learning, remote monitoring.
Strategic Approaches to Women’s Health: A Systematic Review of Intervention Strategies for Increasing Breast and Gynecological Cancer Screening Uptake
Although effective screening methods exist to enable early diagnosis and enhance outcome, breast and gynecological cancers are still leading causes of morbidity and mortality in women of the world. But many individual, socioeconomic, cultural and health care system barriers remain barriers to participation in screening programs. This systematic review is an analysis of data on interventions to promote breast and gynecological cancer screening among various female populations. The review is based on the outcomes from studies examining education programs, reminder systems, provider recommendations, community outreach, culturally targeted education, and organized, population-based screening initiatives. Multi-component interventions over the years have shown to be more effective in increasing screening participation than single interventions because they help to address awareness, accessibility, and behavioral issues. Further disparities can be reduced and equitable access to preventive health services can be achieved by targeting interventions to underserved populations, such as ethnic minorities and women from lower socioeconomic backgrounds. The review also emphasizes the need to integrate evidence-based implementation strategies into public health policies and clinical practice to support and improve participation in screening and improve women’s health outcomes. In summary, the results offer a solid basis for the healthcare sector, researchers, and health policy makers to design sustainable, population-based health programs that contribute to increased uptake of breast and gynecological cancer screening and better cancer detection at an early stage to decrease the burden of preventable cancer.
Breast cancer screening; Gynecological cancer screening; Women's health; Systematic review; Intervention strategies; Screening uptake; Patient navigation; Community outreach; Health promotion; Preventive healthcare.
Comparison of minimally invasive access cavity designs using finite element analysis and long-term clinical outcomes
Minimally invasive access cavity designs have emerged as an important advancement in endodontics, aiming to preserve maximum tooth structure while maintaining adequate access for effective root canal treatment. Various designs, including conservative, ninja, and truss access cavities, have been proposed to enhance the biomechanical integrity of treated teeth without compromising clinical outcomes. Finite element analysis (FEA) has become a valuable computational tool for evaluating stress distribution, fracture resistance, and structural behavior under simulated functional loading conditions. This review examines the biomechanical performance of different minimally invasive access cavity designs through FEA and compares these findings with available long-term clinical outcomes. The relationship between tooth structure preservation, canal accessibility, instrumentation efficiency, and restorative success is critically discussed. Clinical evidence indicates that while conservative access designs may improve fracture resistance by preserving pericervical dentin, their effectiveness depends on appropriate case selection, operator expertise, and the ability to achieve thorough canal debridement and disinfection. Long-term treatment success is influenced by multiple factors, including restoration quality, occlusal loading, and patient-specific characteristics, in addition to cavity design. Integrating computational modeling with clinical follow-up provides a more comprehensive understanding of the strengths and limitations of minimally invasive approaches, supporting evidence-based decision-making and guiding future innovations in endodontic treatment planning and restorative strategies.
cavity design, finite element analysis, fracture resistance,
conservative accesscavity, ninja access cavity, truss access cavity, tooth preservation, root canal treatment, long-term clinical outcomes.
3D bioprinting of pulp tissue using dental pulp stem cells and bioactive hydrogels
Three-dimensional (3d) bioprinting has emerged as a transformative strategy in regenerative endodontics by enabling the fabrication of biomimetic pulp tissue constructs that closely replicate the native dental pulp microenvironment. This approach combines dental pulp stem cells (dpscs), which possess high proliferative, angiogenic, neurogenic, and odontogenic potential, with bioactive hydrogels that serve as supportive bioinks for cell encapsulation and tissue development. Advances in hydrogel engineering have improved scaffold biocompatibility, printability, and biodegradability while promoting cell survival, proliferation, and differentiation into functional pulp-like tissues. Modern bioprinting technologies also allow precise spatial organization of cells and biomaterials, facilitating the formation of vascularized and structurally stable tissue constructs suitable for regenerative applications. Despite significant progress, challenges remain, including optimization of vascular integration, mechanical stability, long-term functionality, and translation from laboratory research to routine clinical practice. Continued innovations in biomaterials, biofabrication techniques, and personalized scaffold design are expected to accelerate the development of clinically applicable pulp regeneration therapies. Overall, 3d bioprinting using dental pulp stem cells and bioactive hydrogels represents a promising frontier for restoring pulp vitality, preserving natural tooth structure, and advancing minimally invasive regenerative dental treatments.
dental pulp stem cells; regenerative endodontics;
bioactive hydrogels; tissue engineering; pulp regeneration; bioinks; vascularization.
Artificial Intelligence-Enabled Clinical Decision Support for Chronic Disease Management in Primary Care: Implications for Family Nurse Practitioner Practice
Artificial intelligence-enabled clinical decision support is increasingly being applied to chronic disease management in primary care. This structured integrative review examined how AI supports risk prediction, complication detection, patient prioritisation, screening, education, and care coordination, with emphasis on implications for family nurse practitioner practice. PubMed was used as the principal information source, with studies limited to English-language, human, free full-text publications from January 2018 to July 2026. Seventy-one records were identified, one duplicate was removed, and 70 unique records were screened. Seventeen publications were retained, including 15 AI-related studies and two contextual digital decision-support studies. The evidence showed that AI can improve case finding for diabetes, predict chronic kidney disease and cardiovascular risk, identify complications in free-text records, support diabetic-retinopathy screening, and assist with patient segmentation and self-management education. However, performance varied across settings, and several models were limited by poor calibration, internal validation, workflow burden, and uncertain transferability to primary care. Direct evidence involving family nurse practitioners was limited, but the findings were highly relevant to screening, monitoring, referral, patient education, and care coordination. AI should therefore be used as a support tool rather than an independent decision-maker. Safe implementation requires clinical oversight, local validation, transparency, privacy protection, equity monitoring, and appropriate professional training.
Artificial intelligence; clinical decision support; chronic disease management; family nurse practitioner; primary care; machine learning; health equity
Copyright (c) 2026 Current Medical and Drug Research
Machine learning analysis of root canal anatomy in different ethnic populations using CBCT
Root canal anatomy exhibits considerable variation among individuals and ethnic populations, presenting significant challenges for accurate diagnosis and successful endodontic treatment. Cone-beam computed tomography (CBCT) has transformed the evaluation of root canal morphology by providing high-resolution three-dimensional images that overcome many limitations of conventional radiography. The integration of machine learning (ML) with CBCT imaging has further enhanced the ability to automatically identify, segment, classify, and predict complex root canal configurations with improved speed, consistency, and diagnostic accuracy. ML algorithms, particularly deep learning models, can analyze large CBCT datasets to detect subtle anatomical variations and support personalized treatment planning across diverse populations. Understanding ethnicity-related differences in root canal morphology enables the development of more robust and generalizable predictive models while reducing the risk of missed canals and procedural complications. Despite promising advances, challenges remain regarding dataset diversity, annotation quality, algorithm bias, and clinical validation. Continued research involving multi-ethnic datasets and standardized imaging protocols is expected to improve the reliability and clinical applicability of AI-assisted endodontic diagnosis. Overall, the combination of machine learning and CBCT represents a significant advancement toward precision endodontics and individualized patient care.
Machine learning, artificial intelligence, cone-beam computed tomography, CBCT, root canal anatomy, ethnic populations, deep learning, endodontics, dental imaging, root canal morphology.
Patient-specific 3D-printed biodegradable scaffolds for regenerative endodontic therapy
Regenerative endodontic therapy has emerged as a promising biologically based approach for restoring the vitality and function of damaged pulp–dentin tissues. However, conventional scaffold materials often lack the ability to conform precisely to the complex anatomy of individual root canal systems, limiting their regenerative potential. Patient-specific three-dimensional (3D)-printed biodegradable scaffolds represent an innovative solution by integrating advanced imaging, digital design, and additive manufacturing to produce customized constructs that closely match patient-specific anatomical features. These scaffolds provide a favorable microenvironment for stem cell attachment, proliferation, differentiation, vascularization, and controlled tissue regeneration while gradually degrading as new tissue forms. Advances in biomaterials, bioactive molecule incorporation, and scaffold surface modification have further enhanced their biological performance and clinical applicability. Despite encouraging preclinical and early clinical findings, challenges remain regarding material optimization, manufacturing standardization, cost, regulatory approval, and long-term clinical validation. Continued integration of digital dentistry, tissue engineering, and regenerative medicine is expected to accelerate the translation of personalized scaffold technologies into routine endodontic practice. Patient-specific 3D-printed biodegradable scaffolds therefore represent a significant advancement toward predictable, minimally invasive, and biologically driven regenerative endodontic treatment.
Regenerative endodontic therapy, three-dimensional printing, 3D printing, biodegradable scaffolds, patient-specific scaffolds, tissue engineering, dental pulp regeneration, biomaterials, additive manufacturing, personalized dentistry.
Predicting endodontic flare-ups using wearable physiological data (heart rate variability, stress, sleep quality)
Endodontic flare-ups remain one of the most challenging postoperative complications following root canal treatment, often resulting in severe pain, swelling, and unexpected emergency visits. Traditional prediction of flare-up risk primarily relies on clinical findings and patient history, which may not adequately capture dynamic physiological changes associated with stress and recovery. Recent advances in wearable health technologies provide an opportunity to continuously monitor physiological parameters such as heart rate variability, heart rate, stress levels, and sleep quality, offering valuable insights into autonomic nervous system activity and patient well-being. When integrated with artificial intelligence and predictive analytics, these real-time data streams may enable early identification of patients at increased risk of postoperative complications and facilitate personalized postoperative care. This review examines the physiological basis linking wearable-derived biomarkers with endodontic flare-ups, explores the potential application of machine learning models for risk prediction, and discusses the opportunities and challenges associated with incorporating wearable technologies into endodontic practice. Although current evidence remains limited, wearable physiological monitoring represents a promising adjunct to conventional clinical assessment and may contribute to more proactive, patient-centered management of postoperative endodontic outcomes.
Endodontic flare-ups; wearable technology; heart rate variability; stress monitoring; sleep quality; artificial intelligence; machine learning; predictive analytics; digital dentistry; postoperative pain.
Developing Interpretable Bayesian Machine Learning Model for Early Sepsis Prediction and Clinical Decision making in U.S. Healthcare Systems
Sepsis is one of the major contributors to morbidity and mortality in the health care system and can be diagnosed and treated rapidly enough in intensive care units and emergency departments to enhance patient outcomes. Classical machine learning methods have shown to be effective for predicting sepsis onset, but lack interpretability and difficulty in modelling predictive uncertainty, thereby limiting their clinical uptake. This study suggests building an interpretable Bayesian machine learning model to predict sepsis early and to make clinical decisions in healthcare systems across the United States. The proposed framework uses EHR data such as vital signs, laboratory measurements, demographics and comorbidity profiles to calculate real-time patient-specific sepsis risk. The model makes probabilistic predictions and offers uncertainty estimates to improve transparency and trust by clinicians. To improve understanding of model recommendations, mechanisms of explainability have been incorporated, to help identify the most influential clinical variables involved in the onset of sepsis. The framework is assessed based on the performance benchmarks: accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve (AUROC) and is compared with the existing machine learning and deep learning methods. The results validate that interpretable Bayesian models can provide strong prediction performance, yet provide clinically relevant explanations and uncertainty quantification. The proposed solution can help advance the early detection of sepsis, support evidence-based clinical decisions, and, ultimately, positively impact patient outcomes in different healthcare systems in the United States.
Sepsis Prediction, Bayesian Machine Learning, Interpretable Artificial Intelligence, Explainable AI, Clinical Decision Support, Electronic Health Records, Early Detection, Healthcare Analytics, Predictive Modeling, U.S. Healthcare Systems.
Leadership strategies for community women's health outreach programs
Community women's health outreach programs play a pivotal role in improving access to preventive, promotive, and primary healthcare services, particularly among underserved and vulnerable populations. However, the effectiveness and sustainability of these programs are often influenced by the quality of leadership guiding their planning, implementation, and evaluation. This study examines leadership strategies that enhance the performance of community women's health outreach initiatives by exploring how leadership practices influence stakeholder collaboration, community engagement, resource mobilization, and service delivery outcomes. Drawing on contemporary leadership theories and empirical evidence, the study adopts a mixed-methods research design that combines quantitative survey data with qualitative interviews involving healthcare professionals, community leaders, outreach coordinators, and program beneficiaries. The findings indicate that transformational, servant, and participatory leadership approaches significantly improve community trust, volunteer motivation, inter-organizational collaboration, and program sustainability. Effective communication, culturally responsive leadership, capacity building, and data-driven decision-making also emerged as critical determinants of successful outreach implementation. Conversely, inadequate funding, limited leadership training, fragmented stakeholder coordination, and policy constraints continue to impede program effectiveness. The study concludes that adaptive and collaborative leadership is essential for strengthening women's health outreach programs and improving health equity at the community level. It recommends sustained investment in leadership development, multisector partnerships, community participation, and evidence-based management practices to enhance the reach, quality, and long-term impact of women's health outreach services. The findings contribute to the growing body of knowledge on public health leadership and provide practical guidance for policymakers, healthcare administrators, and community organizations seeking to improve women's health outcomes through effective leadership.
Community women's health, health outreach programs, leadership strategies, transformational leadership, servant leadership, community engagement, public health leadership.
Tagetes erecta (Marigold) - A review on its phytochemical and medicinal properties
Medicinal plants extract and secondary metabolites are becoming popular all over the world as a natural alternative to synthetically produced chemicals both in Traditional and Allopathic system of medicine. This article discusses the medicinal values of Tagetes erecta (Compositae), also known as Genda Phul (marigold), reported during the period between 2006 and 2014. Different parts of the plant are useful in fevers, astringent, carminative, stomachic, scabies and liver complaints, diseases of the eyes, purify the blood, bleeding piles, rheumatism, colds and bronchitis. The plant Tagetes erecta contains various important phytochemical constituents from the different part of the plant. It shows different pharmacological activities like anti-nociceptive, anti-inflammatory, antioxidant, insecticidal, larvicidal, hepatoprotective, antipyretic, wound healing, antibacterial, antimicrobial, antiepileptic and antifungal. This review discusses the investigation made by various workers related to chemical constituents, pharmacological action and toxicological studies, traditional and non-pharmacological uses of this plant for years.
Genda Marigold Tagetes erecta Traditional medicine
Chemistry and biological activities of 2-azetidinone derivatives – A mini-review
2-Azetidinone skeleton is well established as the pharmacophore of β-lactam antibiotics. β-lactam antibiotics are the most widely employed class of antibiotics. The structural diversity of biologically active β-lactam antibiotics led to the development of many novel methods for the construction of appropriately substituted azetidine with attendant control of functional group and stereochemistry. Azetidine derivatives are reported to show a variety of antimicrobial, antitubercular, anticonvulsant, anti-inflammatory and cardiovascular activities. These activities showed that the minor change in the substitution pattern activities of azetidine derivatives have enhanced dramatically.
2-Azetidinone Anti-inflammatory activity Antimicrobial activity Antitubercular activity
Cotrimoxazole and teicoplanin in the management of Covid-19: Pleiotropic effects, shadows and lights
At present, coronavirus disease 2019 (Covid-19), caused by SARS-CoV-2 infection, has turned into a global pandemic. Comparable to the H7N9 virus, SARS-CoV-2 causes soaring mortality in elderly patients who have pre-existing chronic diseases. SARS-CoV-2 infects human epithelial cells by binding to human angiotensin-converting enzyme2 (ACE2). Multi-organ failure has been observed in some patients with severe COVID-19. TheACE2 expression profile suggests that SARS-CoV-2 infection may initiate or even directly cause organ failure. In addition to direct infection, evidence shows that an elevated inflammatory immune response is also involved in the pathogenesis of SARS-CoV-2 infection. The immunomodulatory and anti-inflammatory activity of the cotrimoxazole (CTX) and teicoplanin are seen in several studies that show the lower concentrations of plasma pro-inflammatory markers like CRP, IL6 in continuous CTX prophylaxis, suggesting its role as anti-inflammatory and immunomodulation. The present review concludes that both teicoplanin and CTX are effective in the management of Covid-19 through anti-SARS-CoV-2 and anti-inflammatory effects as well as controlling of secondary bacterial infections.
Coronavirus Cotrimoxazole Covid-19 Teicoplanin
Baicalein inhibits KB oral cancer cells by inducing apoptosis via modulation of ROS
The theme of the current examination was to evaluate the anticancer activity of baicalein (5,6,7-trihydroxyflavone) in human oral cancer KB-cells by observing cell restraint intercellular ROS movement and apoptotic changes using 3-(4,5-dimethylthiazol-2-yl)- 2,5-diphenyltetrazolium bromide (MTT) assay and 2,7-dichlorodihydrofluorescein diacetate (DCFH-DA) test. The concentration of baicalein at 80 μM caused 100% cancer cell inhibition. Therefore, lower concentrations i.e. 10, 20 and 40 μM were selected in the present study. Baicalein at 40 μM significantly hindered human oral cancer KB cells. In the interim, incendiary and apoptosis were seen through expanded ROS action that actuated apoptotic cell death. The results of the present study suggested that the anticancer effect of baicalein is likely due to its potential to improve ROS level and hence the increased apoptotic activity. This study suggested that baicalein might be a potential candidate for the treatment of oral cancer.
Phytochemical, antimicrobial and antioxidant studies of the leaf extract of Hamelia patens
This study aimed to characterise the different extracts of the leaves of Hamelia patens Jacq. (Rubiaceae) and to evaluate their antibacterial and antioxidant activities in vitro. Phytochemical screening of the leaf extract revealed the presence of alkaloids, flavonoids, sterols, tannins and carotenoids. The extracts were further characterised with the help of FTIR and UV-VIS spectroscopy. The antibacterial activity was evaluated by agar well diffusion method and the zone of inhibition was measured. 1,1-diphenyl-2- picrylhydrazyl (DPPH) assay was used to evaluate the antioxidant capability of the extract. The extract was found effective in inhibiting the growth of both E. coli and S. aureus at 5 μg/mL concentration. The methanolic extract was also found to scavenge DPPH radicals. Although the extract is found effective in the present study, however, further research is required to evaluate the biological activity in higher experimental models.
Hamelia patens Phytochemical test DPPH Antibacterial activity
Antimicrobial activity of the water-soluble ash extract from the invasive weed Parthenium hysterophorus L.
Parthenium hysterophorus L., also known as congress grass or carrot grass, is a widely distributed invasive weed which is a big challenge for sustainable agriculture. For the past several years, a number of attempts have been made to evaluate its medicinal properties. The present study aimed to evaluate the antimicrobial activity of the water-soluble ash extract of P. hysterophorus against different bacterial and fungal pathogens. The disc diffusion method was used to measure zone of inhibition (ZOI) whereas broth microdilution method was used to evaluate minimum inhibitory concentration (MIC) of the extract. The extract was found most effective against Staphylococcus aureus with a ZOI value of 33.5 mm at 33 mg/mL concentration when compared with ofloxacin (ZOI = 39.5 mm at 5 μg/disc). The extract was also found active against Salmonella enterica, Staphylococcus epidermidis and Escherichia coli with ZOI values of 25.5, 32.5 and 25.5 mm, respectively. The present study revealed that the ash extract of P. hysterophorus has potential to inhibit various bacterial species. However, the tested fungal pathogens like Candida albicans were found unaffected after treating with the ash extract.
Antimicrobial activity Compositae Gajar ghas Invasive weed Klebsiella pneumonia Parthenium hysterophorus
Brain injury and SARS-CoV-2 infection: Bidirectional pathways
Coronavirus infection disease (COVID-19) is a recent pandemic infectious disease caused by severe acute respiratory syndrome coronavirus (SARS-CoV-2). COVID-19 is associated with different neurological manifestations, as one-third of COVID-19 patients have some neurological disorders, including paraesthesia, headache, cold extremities and disturbances of consciousness, that are more evident in severely affected patients. The entry of SARS-CoV-2 into human host cells is mediated mainly by the cellular receptor angiotensin-converting enzyme 2 (ACE2), which is expressed at very low levels in the CNS under normal conditions. The dissemination of COVID-19 in the systemic circulation or across the cribriform plate of the ethmoid bone during an early or later phase of the infection can lead to cerebral involvement as has been reported in the past for SARS-CoV affected patients. COVID‐19‐related CNS dysfunction results from direct viral injury, indirect consequences of immune‐mediated disease, systemic effects of infection, or local effects of the virus is still a matter of debate and these hypotheses are not mutually exclusive.
CNS dysfunction Coronavirus COVID-19 SARS-CoV-2
Galenic Hospital Laboratory during COVID-19 emergency - A practical experience in an advanced country
In the actual Covid-19 emergency, as pandemic disease, in many countries at the same time, there was the rapid need to use preventive and therapeutic measures to control the diffusion of infection. In PC Area (Italy North) during the period between March and May 2020, there were observed about 1000 deaths related to Covid-19 (in March 2020 +271% death vs 2019). Between all the measures submitted by a public international institution like WHO, OMS, CDC and many others, the deeply use of disinfectants product became a crucial fact in safety procedure and protocols. The high amount of these disinfectants and antiseptic was needed especially in hospital settings or assimilates structure (named as the Covid-19 hospital) but also for territorial healthcare need. So, it was needed to buy these products from industries but also to start an internal production in Galenic Laboratories. This was because of pharmaceutical industries were not provided, in some cases, the requested amount of these safe life products. In this work, we reported a practical experience in a public hospital, PC area related galenic extra-ordinary production of disinfectants and antiseptics. The result of this local experience can be easily translated to other advanced and developing countries in the world.
Coronavirus Covid-19 Disinfectants Emergency Galenic High production Laboratory Preventive measure Tempestivity
Current and future role of immobilized enzymes in medical field
Although the history of immobilized enzymes and their applications in different fields are traced back to the second half of the twentieth century, their importance in bioreactors and biosensors highlighted at the turn of the current century is under active consideration in these days for broad-spectrum applications in different medical fields. Thus, this article presents a review of the literature concerning the current and future role of the immobilized enzymes in different medical fields. As the author and his supervised research groups have also been actively involved in research on immobilized enzymes, he has also made some input with a recommendation to revise the current classification of immobilized enzymes basically into “Naturally Immobilized” and “Artificially Immobilized Enzymes”, keeping the sub-classification intact. He has also suggested the development of some medical products based on the results of his research groups.
Anti-inflammatory agents Artificially immobilized enzymes Immunosensors Matrix entrapped enzymes
COVID-19 and risk of cardiomyocyte injury: The prevailing scenario
The novel coronavirus which is also called severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is responsible for COVID-19 (coronavirus disease 2019). SARS-CoV-2 is known to cause substantial pulmonary disease, including pneumonia and acute respiratory distress syndrome (ARDS), clinicians have observed many extra-pulmonary manifestations of COVID-19. SARS‐CoV‐2 infection is associated with a variety of pro‐inflammatory mediators that may play important roles in the pathophysiology of cardiac and arrhythmic complications. Systemic inflammatory response syndrome (cytokine storm) is another putative mechanism of myocardial injury. In addition to lung damage, there may be significant cardiac involvement in patients with COVID-19, which is responsible for worsening the clinical condition of the host. The main cardiac manifestations can be oedema, pericarditis, cardiac fibrosis, myocarditis, impairment of contractile function and cardiac electrophysiology. The cardiac status of patients with ongoing SARS-CoV-2 infection of surviving patients in convalescence period should be carefully monitored.
Cardiac injury Coronavirus 2 Novel Coronavirus Severe Acute Respiratory Syndrome
Elucidation of the origin of autoimmune diseases via computational multiscale mechanobiology and extracellular matrix remodeling: theories and phenomenon of immunodominance
Experimental practices of computational multiscale mechanobiology were applied to explicate different topographies of mechanobiology and to predict the fine minutiae of mechanosensing and mechanotransduction in a cellular system accurately. Autoimmune diseases denoted as biologically implausible. Recently, scientific and medical communities investigated autoimmune diseases and associated settings to conclude the disarrays of autoimmune as earlier as possible. Therefore, an urgent need is there to evaluate ocular immunology and the route of growth of autoimmune diseases. Scientific investigations play a significant role to detect interrelated cures and bearing research for probing treatments for autoimmune diseases, which are yet undiscovered. Extracellular matrix remodeling followed in the cellular microenvironment dynamically to manage remodelling events. Immunodominance deals with the immunodominance mechanism evolved in response to clearing any type of infection and not yet distinguished entirely.
Autoimmune diseases Computational multiscale mechanobiology Extracellular matrix remodelling Immunodominance