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Lactobacillus Reuteri Probiotic Consumption Reduced Various Virulence Gene Expression in Dental Plaque of Fixed Orthodontic Subjects

The struggle of preserving self-esteem and professional identity: A grounded theory study of specialists’ experiences serving in the public health system in Rajasthan, India

This study aimed to explore and describe the experiences of specialist physicians at rural community health centers (CHCs) and determine the factors accounting for their retention and attrition in Rajasthan, India. Twenty-one medical professionals from different public health facilities in Rajasthan, selected through purposive sampling, were interviewed in depth. Strauss and Corbin’s grounded theory approach was used to develop a theory/model. Open, axial and selective coding were used to identify the major themes/categories and develop the core category of the model. Strauss and Corbin’s paradigm model was employed to establish linkages among the categories structured in terms of conditions, action-interaction strategies and consequences to explore the experiences of specialist physicians. The central theme of the model, ‘The struggle of preserving Self-esteem and Professional identity’, captures the experiences of specialist physicians working at the rural CHCs in Rajasthan’s public healthcare system. The disadvantages of an underdeveloped rural setting, local political climate and various healthcare system factors challenge the specialists’ self-esteem and professional identity culminating in their decision to remain or quit the system. This further leads to their acute shortage in the system which affects the quality and provision of rural healthcare services in the state. The state government and essential stakeholders must collaborate to enact policies addressing specialist physicians’ personal and professional needs, which include improving developmental infrastructure, healthcare system processes, local governance and accountability. Such policy packages can generate sustainable solutions to mitigate the high attrition rate of specialists in rural CHCs and strengthen rural healthcare services in Rajasthan.

Evaluation of the Effect of Various Surface Conditioning Methods and Attachment of Platelet-rich Fibrin on Dental Implant Surface: An In Vitro Study

Retraction: Blocking TLR2 Activity Attenuates Pulmonary Metastases of Tumor

Oral Health of Roma Children in Dendropotamos, Greece: A Cross-sectional Study

Long-Range Resonant Charge Transport through Open-Shell Donor–Acceptor Macromolecules

Correction: An efficient interpretable framework for unsupervised low, very low and extreme birth weight detection

Tooth Dimension as a Distinguishing Trait of Sexual Dimorphism: An Odontometric Study on Kannur Population

Disparities in Medicaid and Medicare physician reimbursements for ophthalmic procedures

Evaluation of Microtensile Bond Strength to Dentin of a Self-adhesive Bulk-fill Resin Composite Restorative Material after Aging (In Vitro Study)

Optimizing the train timetable in a high-speed rail corridor: The implications on departure time, fare cost and seat preference of passengers

High-speed railway timetables are typically based on origin-destination (OD) passenger demand, establishing departure times and intervals for trains. Utilizing this data, operators systematically develop daily train timetables that are consistent across a defined operational cycle. However, this approach often overlooks individual passenger preferences for departure times, fares, and seat classes, leading to low occupancy rates for some trains while others remain difficult to book. In this article, with the number of trains predetermined and considering the diverse demands of passengers, we addresses these challenges by analyzing passenger preferences and optimizing train stopping patterns and adjacent train departure intervals. We propose a time-space-state three-dimensional network (TSSN) that integrates preferences for travel time, fares, and seat classes. Impedance functions for various network arcs are developed, incorporating these three key attributes of travel demand and transforming the passenger travel choice issue into a path selection problem within the TSSN. A bi-level programming model is formulated: the upper level optimizes train operations and fare structures, while the lower level employs user equilibrium (UE) theory to distribute OD passenger demands across trains. Using the Lanzhou-Xi’an high-speed railway corridor as a case study, we apply a genetic algorithm combined with a nested Frank-Wolfe method to solve the model. The resulting timetable balances the interests of high-speed rail operators and passengers, incorporating non-uniform departure intervals to better meet diverse travel needs. Ultimately, this approach enhances the scientific rigor and practicality of high-speed railway scheduling while accommodating passenger preferences effectively.

Effect of Air Abrasion Techniques vs Tungsten Carbide Burs on Enamel Surface after Orthodontic Adhesive Remnant Removal

A novel ABC fractional-order mathematical model for malaria transmission dynamics incorporating treatment-seeking behavior

Malaria remains a significant global health challenge, particularly in developing countries. This study introduces a novel ABC fractional-order model to analyze malaria transmission dynamics, incorporating treatment-seeking behavior, which includes both treatment at professional health facilities and interventions through indigenous traditional medicine. We conducted a comprehensive analysis of the model, examining the existence and uniqueness of solutions and performing numerical simulations using various mathematical techniques. Our findings reveal that fractional-order effects significantly influence malaria transmission dynamics; specifically, higher fractional orders result in slower increases in susceptible and exposed human populations while leading to more rapid changes in the dynamics of infected populations. Furthermore, the model demonstrates that increasing the rate of treatment at health facilities can substantially reduce the infected population and decrease the reproduction number, thereby facilitating the elimination of the disease within a shorter time frame. Additionally, the study highlights that reliance on traditional medicine without clinical validation may lead to temporary recovery but not complete elimination of the malaria parasite, increasing the risk of relapse and further disease spread. The finding suggests that public health initiatives should encourage collaboration between traditional medicine practitioners and professional healthcare providers to reduce non-standardized treatment risks and improve the effectiveness of malaria management strategies. These contribute to the broader field of epidemiological modeling, offering a robust framework for understanding and mitigating malaria transmission in resource-limited settings.

Natural soap is clinically effective and less toxic and more biodegradable in aquatic organisms and human skin cells than synthetic detergents

In the era of COVID-19, concerns about and consumption of soaps and detergents have increased. The environmental effects, along with their direct impacts on the human body, are being simultaneously considered to ensure safety and support healthy living. Natural soap compounds are considered readily biodegradable and unlikely to produce hazardous waste, while artificial detergents are composed of synthetic surfactants, plasticizers, binders, and additives. This study aimed to investigate representative natural soap compounds consisting of fatty acid salts and compare them with synthetic detergents, such as sodium dodecylbenzene sulfonate (SDB) and sodium lauryl sulfate (SLS). Environmental assays recommended by the OECD, as well as human keratinocyte assays for toxicity and biodegradability, were utilized. The major components of natural soap were found to be less toxic and more biodegradable in aquatic environments—assessed using algae, crustaceans, and fish—compared to synthetic detergents. Additionally, in the human keratinocyte assay, natural soap compounds were significantly less toxic and demonstrated higher viability than SLS after a 48 h culture and a 5 min exposure. The half-maximal inhibitory concentration (IC50) obtained from the viability assay revealed values of 7.82 mM for potassium laurate (C12K), 7.56 mM for potassium oleate (C18:1K), and 0.604 mM for SLS. Therefore, natural soap appears to be valuable due to its lower toxicity, greater biodegradability in aquatic environments, enhanced safety for human cells, and potential efficiency in clinical applications.

Advancing breast cancer prediction: Comparative analysis of ML models and deep learning-based multi-model ensembles on original and synthetic datasets

Breast cancer is a significant global health concern with rising incidence and mortality rates. Current diagnostic methods face challenges, necessitating improved approaches. This study employs various machine learning (ML) algorithms, including KNN, SVM, ANN, RF, XGBoost, ensemble models, AutoML, and deep learning (DL) techniques, to enhance breast cancer diagnosis. The objective is to compare the efficiency and accuracy of these models using original and synthetic datasets, contributing to the advancement of breast cancer diagnosis. The methodology comprises three phases, each with two stages. In the first stage of each phase, stratified K-fold cross-validation was performed to train and evaluate multiple ML models. The second stage involved DL-based and AutoML-based ensemble strategies to improve prediction accuracy. In the second and third phases, synthetic data generation methods, such as Gaussian Copula and TVAE, were utilized. The KNN model outperformed others on the original dataset, while the AutoML approach using H2OXGBoost using synthetic data also showed high accuracy. These findings underscore the effectiveness of traditional ML models and AutoML in predicting breast cancer. Additionally, the study demonstrated the potential of synthetic data generation methods to improve prediction performance, aiding decision-making in the diagnosis and treatment of breast cancer.

Net-Coded Organic Building Blocks for the Reticular Assembly of High-Connectivity Metal–Organic Frameworks

Relationships of corticosterone and thyroxine with mortality, mass gain, feeding and activity in Kemp’s ridley sea turtles (Lepidochelys kempii) recovering from cold-stunning

Mass strandings of juvenile Kemp’s ridley sea turtles (Lepidochelys kempii) occur annually on the shores of Cape Cod, Massachusetts, USA, during the months of Oct-Dec. Strandings have increased from dozens to hundreds per year in the past two decades, challenging recovery and management of this critically endangered species. Most stranded turtles are suffering from “cold-stunning”, a life-threatening hypothermia-like condition, and are brought to nearby marine animal veterinary clinics for treatment and rehabilitation. Though most individuals survive, some mortality does occur, and even among surviving turtles there can be prolonged deficits in health and behavior. Previous studies have indicated that upon admission, the adrenal stress hormone corticosterone is elevated approximately an order of magnitude above presumed baseline, while plasma thyroxine is often undetectable, suggesting that these two hormones show promise as markers of recovery from cold-stunning. In this prospective study, 106 cold-stunned Kemp’s ridleys were monitored during rehabilitation, with serial blood sampling at 0, 3, 7, 18, 30, 60 and 80 days post-admission to compare plasma concentrations of corticosterone and thyroxine to mortality, mass gain, feeding and activity. Corticosterone and thyroxine normalized in 88% of turtles by approximately day 18, but 12% showed persistent elevations of corticosterone (typically 2-3x above baseline), and persistently low thyroxine. Elevated corticosterone at day 18 was found to be predictive of mortality after day 18. The endocrine profile of high corticosterone and low thyroxine is also associated with lower rates of gain in body mass over time and reduced feeding. As prolonged deficits in growth affect body size at release, low mass gain may affect the predation risk on these juvenile turtles subsequent to release. These results suggest that endocrine biomarkers are useful for monitoring recovery of turtles in rehabilitation, and that growth rates and mass gains during rehabilitation may warrant further investigation.

Editing metacaspase (StMC7) gene enhances late blight resistance in Russet Burbank potato

Plants induce hypersensitive response programmed cell death (HR-PCD), upon biotrophic pathogen infection, to contain the pathogen to the point of infection. Apoptotic-like PCD (AL-PCD) has been reported upon prolonged hemibiotrophic and necrotrophic pathogen infection in potato, to feed on the dead cells for their growth. In potato, silencing of the gene StHRC lead to the suppression of AL-PCD, thus increasing resistance to blights in potato. This was also associated with a significant reduction in the expression of the metacaspase gene StMC7. Accordingly, the gene StMC7 was silenced in potato cultivar ‘Russet Burbank’ using CRISPR-Cas9 to improve disease resistance against late blight of potato caused by Phytophthora infestans. Following pathogen infection, the disease severity, pathogen biomass and StMC7 gene expression was lower in Stmc7 mutants as compared to wild type. Disease severity was also decreased in Alternaria solani inoculated Stmc7 mutants, compared to the wild type, suggesting possible multiple disease resistance in the Stmc7 knockdown mutants. This confirms that the silencing of StMC7 improves late blight disease resistance in potato.

Spectroscopic and Biochemical Characterization of the Noncanonical Radical SAM Enzyme ArsL, Involved in Arsinothricin Biosynthesis

Impact of conditional cash transfer on households food security in Akwa Ibom State, Nigeria

The Nigerian government introduced the Household Upliftment Program (HUP) which is a conditional cash transfer scheme to help households improve consumption levels, reduce poverty and therefore prevent vulnerable households from becoming poorer. This paper investigated the impact of Condition Cash Transfer of HUP on household food security in Akwa Ibom State, Nigeria. The objectives were to assess the impact of CCT on protecting the beneficiary’s basic level of food consumption from becoming food insecure and also determine if CCT of HUP facilitate the beneficiary to invest in human and other productive assets. The paper utilized descriptive statistics, food security index, Likert scale and propensity score technique to analyse the research objectives. The findings showed that on average, benefitting and non-benefitting households spent about N31,917.78 ($76.46) and N34,898.67 ($83.60) respectively on food per month. Food insecurity was higher among benefiting households compared to non-benefiting households. The Average Treatment Effect on the Treated (ATET) estimator value was −0.2610. The negative value suggests that, on average, benefiting households spend 26.10% less on food consumption than non-benefitting households. The results imply that the current cash transfer program may not be effectively addressing food insecurity among beneficiaries. However, the CCT program facilitated the beneficiaries’ ability to invest in human and productive assets, enhanced their financial and social inclusion, opened doors to financial services previously inaccessible to many and fostered a sense of community among them. The paper concludes that while conditional cash transfer programs have the potential to positively impact beneficiaries, the government must reassess and potentially adjust the program to better address the current inflationary pressures and ensure its positive impact on the food security of the beneficiaries.