Modeling Host-Microbe Interactions in Periodontal Disease: A GCNN Vs GAT Approach

ABSTRACT Introduction: Periodontitis is an inflammatory condition that affects the tooth supporting structures and causes significant tissue destruction. It is not caused by merely poor oral hygiene but by a complex interplay between microbial agents and the host\u27s immune response, which triggers an inflammatory response. Periodontitis severity and progression vary among individuals, with genetic predisposition, systemic health issues, and environmental factors playing crucial roles. People with compromised immune systems and chronic inflammation are at higher risk. Understanding the relationship between host immune responses and microbial factors is vital for developing effective prevention and treatment strategies. This gap presents an opportunity for future research, potentially leading to advancements in understanding host-bacterial interactions and health management implications. Methods: The PHI-base Pathogen-Host Interactions Dataset provides detailed information on pathogen-host interactions, including protein and gene data, enhancing our understanding of plant diseases. The initial phase involves data collection from a periodontal pathogen virulence database organized in tab-delimited text files. This data includes gene information, pathogen species, phenotypes, and functional annotations, providing insights into periodontal disease roles. It is then subjected to graph convoluted neural networks for analysis. Results: Graph Convolutional Neural Network (GCNN) and Graph Attention Network (GAT) models demonstrated high precision metrics and confidence distributions in predicting reduced virulence. They achieved a rate of 84.62% accuracy, with GCNN showing a higher prediction confidence at 86.19% compared to GAT\u27s 84.82%. Both models performed well in predicting the majority class, characterized by reduced virulence, yielding a precision of 0.846, a recall of 1.0, and an F1-score of 0.917. However, they faced challenges in minority classes, particularly those indicating increased virulence and unaffected states. The GAT model reached a final loss of 0.5213, suggesting better performance. Both models achieved an accuracy of 0.8462, indicating they can effectively capture relevant patterns within the trained data. Conclusion: The study demonstrates the effectiveness of machine learning in predicting host virulence interactions with periodontal inflammation, highlighting the need for future research for improved clinical outcomes

Global age-sex-specific all-cause mortality and life expectancy estimates for 204 countries and territories and 660 subnational locations, 1950–2023: a demographic analysis for the Global Burden of Disease Study 2023

Background: Comprehensive, comparable, and timely estimates of demographic metrics—including life expectancy and age-specific mortality—are essential for evaluating, understanding, and addressing trends in population health. The COVID-19 pandemic highlighted the importance of timely and all-cause mortality estimates for being able to respond to changing trends in health outcomes, showing a strong need for demographic analysis tools that can produce all-cause mortality estimates more rapidly with more readily available all-age vital registration (VR) data. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) is an ongoing research effort that quantifies human health by estimating a range of epidemiological quantities of interest across time, age, sex, location, cause, and risk. This study—part of the latest GBD release, GBD 2023—aims to provide new and updated estimates of all-cause mortality and life expectancy for 1950 to 2023 using a novel statistical model that accounts for complex correlation structures in demographic data across age and time. Methods: We used 24 025 data sources from VR, sample registration, surveys, censuses, and other sources to estimate all-cause mortality for males, females, and all sexes combined across 25 age groups in 204 countries and territories as well as 660 subnational units in 20 countries and territories, for the years 1950–2023. For the first time, we used complete birth history data for ages 5–14 years, age-specific sibling history data for ages 15–49 years, and age-specific mortality data from Health and Demographic Surveillance Systems. We developed a single statistical model that incorporates both parametric and non-parametric methods, referred to as OneMod, to produce estimates of all-cause mortality for each age-sex-location group. OneMod includes two main steps: a detailed regression analysis with a generalised linear modelling tool that accounts for age-specific covariate effects such as the Socio-demographic Index (SDI) and a population attributable fraction (PAF) for all risk factors combined; and a non-parametric analysis of residuals using a multivariate kernel regression model that smooths across age and time to adaptably follow trends in the data without overfitting. We calibrated asymptotic uncertainty estimates using Pearson residuals to produce 95% uncertainty intervals (UIs) and corresponding 1000 draws. Life expectancy was calculated from age-specific mortality rates with standard demographic methods. For each measure, 95% UIs were calculated with the 25th and 975th ordered values from a 1000-draw posterior distribution. Findings: In 2023, 60·1 million (95% UI 59·0–61·1) deaths occurred globally, of which 4·67 million (4·59–4·75) were in children younger than 5 years. Due to considerable population growth and ageing since 1950, the number of annual deaths globally increased by 35·2% (32·2–38·4) over the 1950–2023 study period, during which the global age-standardised all-cause mortality rate declined by 66·6% (65·8–67·3). Trends in age-specific mortality rates between 2011 and 2023 varied by age group and location, with the largest decline in under-5 mortality occurring in east Asia (67·7% decrease); the largest increases in mortality for those aged 5–14 years, 25–29 years, and 30–39 years occurring in high-income North America (11·5%, 31·7%, and 49·9%, respectively); and the largest increases in mortality for those aged 15–19 years and 20–24 years occurring in Eastern Europe (53·9% and 40·1%, respectively). We also identified higher than previously estimated mortality rates in sub-Saharan Africa for all sexes combined aged 5–14 years (87·3% higher in GBD 2023 than GBD 2021 on average across countries and territories over the 1950–2021 period) and for females aged 15–29 years (61·2% higher), as well as lower than previously estimated mortality rates in sub-Saharan Africa for all sexes combined aged 50 years and older (13·2% lower), reflecting advances in our modelling approach. Global life expectancy followed three distinct trends over the study period. First, between 1950 and 2019, there were considerable improvements, from 51·2 (50·6–51·7) years for females and 47·9 (47·4–48·4) years for males in 1950 to 76·3 (76·2–76·4) years for females and 71·4 (71·3–71·5) years for males in 2019. Second, this period was followed by a decrease in life expectancy during the COVID-19 pandemic, to 74·7 (74·6–74·8) years for females and 69·3 (69·2–69·4) years for males in 2021. Finally, the world experienced a period of post-pandemic recovery in 2022 and 2023, wherein life expectancy generally returned to pre-pandemic (2019) levels in 2023 (76·3 [76·0–76·6] years for females and 71·5 [71·2–71·8] years for males). 194 (95·1%) of 204 countries and territories experienced at least partial post-pandemic recovery in age-standardised mortality rates by 2023, with 61·8% (126 of 204) recovering to or falling below pre-pandemic levels. There were several mortality trajectories during and following the pandemic across countries and territories. Long-term mortality trends also varied considerably between age groups and locations, demonstrating the diverse landscape of health outcomes globally. Interpretation: This analysis identified several key differences in mortality trends from previous estimates, including higher rates of adolescent mortality, higher rates of young adult mortality in females, and lower rates of mortality in older age groups in much of sub-Saharan Africa. The findings also highlight stark differences across countries and territories in the timing and scale of changes in all-cause mortality trends during and following the COVID-19 pandemic (2020–23). Our estimates of evolving trends in mortality and life expectancy across locations, ages, sexes, and SDI levels in recent years as well as over the entire 1950–2023 study period provide crucial information for governments, policy makers, and the public to ensure that health-care systems, economies, and societies are prepared to address the world's health needs, particularly in populations with higher rates of mortality than previously known. The estimates from this study provide a robust framework for GBD and a valuable foundation for policy development, implementation, and evaluation around the world

Burden of 375 diseases and injuries, risk-attributable burden of 88 risk factors, and healthy life expectancy in 204 countries and territories, including 660 subnational locations, 1990–2023: a systematic analysis for the Global Burden of Disease Study 2023

Background: For more than three decades, the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) has provided a framework to quantify health loss due to diseases, injuries, and associated risk factors. This paper presents GBD 2023 findings on disease and injury burden and risk-attributable health loss, offering a global audit of the state of world health to inform public health priorities. This work captures the evolving landscape of health metrics across age groups, sexes, and locations, while reflecting on the remaining post-COVID-19 challenges to achieving our collective global health ambitions. Methods: The GBD 2023 combined analysis estimated years lived with disability (YLDs), years of life lost (YLLs), and disability-adjusted life-years (DALYs) for 375 diseases and injuries, and risk-attributable burden associated with 88 modifiable risk factors. Of the more than 310 000 total data sources used for all GBD 2023 (about 30% of which were new to this estimation round), more than 120 000 sources were used for estimation of disease and injury burden and 59 000 for risk factor estimation, and included vital registration systems, surveys, disease registries, and published scientific literature. Data were analysed using previously established modelling approaches, such as disease modelling meta-regression version 2.1 (DisMod-MR 2.1) and comparative risk assessment methods. Diseases and injuries were categorised into four levels on the basis of the established GBD cause hierarchy, as were risk factors using the GBD risk hierarchy. Estimates stratified by age, sex, location, and year from 1990 to 2023 were focused on disease-specific time trends over the 2010–23 period and presented as counts (to three significant figures) and age-standardised rates per 100 000 person-years (to one decimal place). For each measure, 95% uncertainty intervals [UIs] were calculated with the 2·5th and 97·5th percentile ordered values from a 250-draw distribution. Findings: Total numbers of global DALYs grew 6·1% (95% UI 4·0–8·1), from 2·64 billion (2·46–2·86) in 2010 to 2·80 billion (2·57–3·08) in 2023, but age-standardised DALY rates, which account for population growth and ageing, decreased by 12·6% (11·0–14·1), revealing large long-term health improvements. Non-communicable diseases (NCDs) contributed 1·45 billion (1·31–1·61) global DALYs in 2010, increasing to 1·80 billion (1·63–2·03) in 2023, alongside a concurrent 4·1% (1·9–6·3) reduction in age-standardised rates. Based on DALY counts, the leading level 3 NCDs in 2023 were ischaemic heart disease (193 million [176–209] DALYs), stroke (157 million [141–172]), and diabetes (90·2 million [75·2–107]), with the largest increases in age-standardised rates since 2010 occurring for anxiety disorders (62·8% [34·0–107·5]), depressive disorders (26·3% [11·6–42·9]), and diabetes (14·9% [7·5–25·6]). Remarkable health gains were made for communicable, maternal, neonatal, and nutritional (CMNN) diseases, with DALYs falling from 874 million (837–917) in 2010 to 681 million (642–736) in 2023, and a 25·8% (22·6–28·7) reduction in age-standardised DALY rates. During the COVID-19 pandemic, DALYs due to CMNN diseases rose but returned to pre-pandemic levels by 2023. From 2010 to 2023, decreases in age-standardised rates for CMNN diseases were led by rate decreases of 49·1% (32·7–61·0) for diarrhoeal diseases, 42·9% (38·0–48·0) for HIV/AIDS, and 42·2% (23·6–56·6) for tuberculosis. Neonatal disorders and lower respiratory infections remained the leading level 3 CMNN causes globally in 2023, although both showed notable rate decreases from 2010, declining by 16·5% (10·6–22·0) and 24·8% (7·4–36·7), respectively. Injury-related age-standardised DALY rates decreased by 15·6% (10·7–19·8) over the same period. Differences in burden due to NCDs, CMNN diseases, and injuries persisted across age, sex, time, and location. Based on our risk analysis, nearly 50% (1·27 billion [1·18–1·38]) of the roughly 2·80 billion total global DALYs in 2023 were attributable to the 88 risk factors analysed in GBD. Globally, the five level 3 risk factors contributing the highest proportion of risk-attributable DALYs were high systolic blood pressure (SBP), particulate matter pollution, high fasting plasma glucose (FPG), smoking, and low birthweight and short gestation—with high SBP accounting for 8·4% (6·9–10·0) of total DALYs. Of the three overarching level 1 GBD risk factor categories—behavioural, metabolic, and environmental and occupational—risk-attributable DALYs rose between 2010 and 2023 only for metabolic risks, increasing by 30·7% (24·8–37·3); however, age-standardised DALY rates attributable to metabolic risks decreased by 6·7% (2·0–11·0) over the same period. For all but three of the 25 leading level 3 risk factors, age-standardised rates dropped between 2010 and 2023—eg, declining by 54·4% (38·7–65·3) for unsafe sanitation, 50·5% (33·3–63·1) for unsafe water source, and 45·2% (25·6–72·0) for no access to handwashing facility, and by 44·9% (37·3–53·5) for child growth failure. The three leading level 3 risk factors for which age-standardised attributable DALY rates rose were high BMI (10·5% [0·1 to 20·9]), drug use (8·4% [2·6 to 15·3]), and high FPG (6·2% [–2·7 to 15·6]; non-significant). Interpretation: Our findings underscore the complex and dynamic nature of global health challenges. Since 2010, there have been large decreases in burden due to CMNN diseases and many environmental and behavioural risk factors, juxtaposed with sizeable increases in DALYs attributable to metabolic risk factors and NCDs in growing and ageing populations. This long-observed consequence of the global epidemiological transition was only temporarily interrupted by the COVID-19 pandemic. The substantially decreasing CMNN disease burden, despite the 2008 global financial crisis and pandemic-related disruptions, is one of the greatest collective public health successes known. However, these achievements are at risk of being reversed due to major cuts to development assistance for health globally, the effects of which will hit low-income countries with high burden the hardest. Without sustained investment in evidence-based interventions and policies, progress could stall or reverse, leading to widespread human costs and geopolitical instability. Moreover, the rising NCD burden necessitates intensified efforts to mitigate exposure to leading risk factors—eg, air pollution, smoking, and metabolic risks, such as high SBP, BMI, and FPG—including policies that promote food security, healthier diets, physical activity, and equitable and expanded access to potential treatments, such as GLP-1 receptor agonists. Decisive, coordinated action is needed to address long-standing yet growing health challenges, including depressive and anxiety disorders. Yet this can be only part of the solution. Our response to the NCD syndemic—the complex interaction of multiple health risks, social determinants, and systemic challenges—will define the future landscape of global health. To ensure human wellbeing, economic stability, and social equity, global action to sustain and advance health gains must prioritise reducing disparities by addressing socioeconomic and demographic determinants, ensuring equitable health-care access, tackling malnutrition, strengthening health systems, and improving vaccination coverage. We live in times of great opportunity

Emergence of information processing in biological systems and the origin of life

As every life form is composed of cells, elements of consciousness, namely memory and sentience, must be grounded in mechanisms that are integral to unicellular organisms. Earlier studies indicated that cellular cytoskeletal structures consisting of excitable, flexible, and oscillating polymers such as microtubules, along with quantum events, are potentially responsible for information processing and thus consciousness. This work attempts to solve the unknown, that is, how, at the spark of life, the phenomenon of cellular information processing first appears. This study posits that the spatially distributed wave energy of the molecules of an incepting cell interacts with space and generates a rotating bioinformation field, forming a vortex. This vortex, the local energy maximum, whose inbound and outbound energy fluxes represent signal reception and dispersal, is a critical step in the spark of life responsible for information storage, and with incremental wave superpositions, exhibits information processing. The vorticity of the rotating field is computed, and the obtained field characteristics indicated the emergence of a prebiotic complex to initiate information processing. Furthermore, the developed system model explains how perturbations from the environment are converted into response signals for the emanation of sense, locomotion, nutrition, and asexual reproduction, the fundamental evolutionary building blocks of prokaryotes. Further research directions include explaining how the energy potential available in the bio-information field and the vortex leads to the first formation of genetic material, emergence of cytoskeleton, and extension of bio-information field to multi-cellular organisms

Cell-based therapy in the management of Class III Miller's recession – A case report with 45-month follow-up

Miller's Class III gingival recessions (GRs) have always posed a challenge to the clinicians in terms of achieving complete root coverage (CRC). In the present case, a cell-based therapy with autologous fibroblasts seeded onto a Type 1 collagen membrane, through an in-vitro culturing method was utilized. The fibroblasts-seeded membrane was surgically placed under a laterally repositioned flap. The patient presented with a CRC, which was stable even at the postoperative period of 45 months. In addition, a 3-mm substantial gain in the width of keratinized tissue was achieved and maintained throughout the postoperative period. Hence, the results of the cell-seeded therapy emphasize that it can serve as an effective alternative method for the management of Miller's Class III GRs

Antiviral Essential Oil Components Against SARS-CoV-2 in Pre-procedural Mouth Rinses for Dental Settings During COVID-19: A Computational Study

COVID-19 mainly spreads through cough or sneeze droplets produced by an infected person. The viral particles are mostly present in the oral cavity. The risk of contracting COVID-19 is high in the dental profession due to the nature of procedures involved that produce aerosols. Along with other measures to limit the risk of infection, pre-procedural mouth rinses are beneficial in reducing the viral particles in the oral cavity. In this study, the antiviral efficacy of essential oil components has been determined specifically against SARS-CoV-2 by molecular docking and conceptual DFT approach. Based on the binding affinities of the components against the receptor binding domain of the S1 glycoprotein, cuminal, carvacrol, myrtanol, and pinocarveol were found to be highly active. The molecular descriptor values obtained through conceptual DFT also indicated the above-mentioned components to be active based on the correlation between the structure and the activity of the compounds. Therefore, pre-procedural mouth rinses with these components included may be specifically suitable for dental procedures during the COVID-19 period.</jats:p

Prediction of interactomic hub genes in PBMC cells in type 2 diabetes mellitus, dyslipidemia, and periodontitis

Background and objective: In recent years, the complex interplay between systemic health and oral well-being has emerged as a focal point for researchers and healthcare practitioners. Among the several important connections, the convergence of Type 2 Diabetes Mellitus (T2DM), dyslipidemia, chronic periodontitis, and peripheral blood mononuclear cells (PBMCs) is a remarkable example. These components collectively contribute to a network of interactions that extends beyond their domains, underscoring the intricate nature of human health. In the current study, bioinformatics analysis was utilized to predict the interactomic hub genes involved in type 2 diabetes mellitus (T2DM), dyslipidemia, and periodontitis and their relationships to peripheral blood mononuclear cells (PBMC) by machine learning algorithms. Materials and Methods: Gene Expression Omnibus datasets were utilized to identify the genes linked to type 2 diabetes mellitus(T2DM), dyslipidemia, and Periodontitis (GSE156993).Gene Ontology (G.O.) Enrichr, Genemania, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were used for analysis for identification and functionalities of hub genes. The expression of hub D.E.G.s was confirmed, and an orange machine learning tool was used to predict the hub genes. Result: The decision tree, AdaBoost, and Random Forest had an A.U.C. of 0.982, 1.000, and 0.991 in the R.O.C. curve. The AdaBoost model showed an accuracy of (1.000). The findings imply that the AdaBoost model showed a good predictive value and may support the clinical evaluation and assist in accurately detecting periodontitis associated with T2DM and dyslipidemia. Moreover, the genes with p-value &lt; 0.05 and A.U.C.&gt;0.90, which showed excellent predictive value, were thus considered hub genes. Conclusion: The hub genes and the D.E.G.s identified in the present study contribute immensely to the fundamentals of the molecular mechanisms occurring in the PBMC associated with the progression of periodontitis in the presence of T2DM and dyslipidemia. They may be considered potential biomarkers and offer novel therapeutic strategies for chronic inflammatory diseases