Search Results (220)

Plaque erosion (PE) is now recognized as a common and clinically significant cause of acute coronary syndromes (ACSs), accounting for up to 40% of cases. Unlike plaque rupture (PR), PE involves superficial endothelial loss over an intact fibrous cap and occurs in a low-inflammatory setting, typically affecting younger patients, women, and smokers with fewer traditional risk factors. The growing recognition of PE has been driven by high-resolution intracoronary imaging, particularly optical coherence tomography (OCT), which enables in vivo differentiation from PR. Identifying PE with OCT has opened the door to personalized treatment strategies, as explored in recent trials evaluating the safety of deferring stent implantation in selected cases in favor of intensive medical therapy. Given its unexpectedly high prevalence, PE is now recognized as a common pathophysiological mechanism in ACS, rather than a rare exception. This growing awareness underscores the importance of its accurate identification through OCT in clinical practice. Early recognition and a deeper understanding of PE are essential steps toward the implementation of precision medicine, allowing clinicians to move beyond “one-size-fits-all” models toward “mechanism-based” therapeutic strategies. This narrative review aims to offer an integrated overview of PE, tracing its epidemiology, elucidating the molecular and pathophysiological mechanisms involved, outlining its clinical presentations, and placing particular emphasis on diagnostic strategies with OCT, while also discussing emerging therapeutic approaches and future directions for personalized cardiovascular care. Full article

Drug resistance in Candida may result in either a fitness cost or a fitness advantage. Candida auris, whose intrinsic drug resistance remains unclear, has emerged as a significant human pathogen. We aimed to investigate whether Candida fitness, including early interaction with the host innate immune system, depends on the antifungal susceptibility phenotype and putative-associated resistance mutations. We compared interleukin-1β, interleukin-6, interleukin-8, and tumor necrosis factor α production by human colorectal adenocarcinoma cells stimulated by fluconazole-susceptible and fluconazole-resistant strains of Candida albicans, C. parapsilosis, C. tropicalis, and C. glabrata, as well as fluconazole-resistant C. auris strains. Sensitive Candida strains induced lower cytokine levels compared with C. auris and resistant strains, except for TNF a. Resistant strains induced cytokine levels like C. auris, except for higher IL-1β and lower TNF-α. Susceptible strains exhibited cytokine profiles distinct from those of resistant strains. C. auris induced cytokine levels comparable to resistant strains but displayed profiles resembling those of susceptible strains. This study highlights the relationship among antifungal susceptibility, fungal fitness and host early immunity. C. auris behavior appears to be between fluconazole-sensitive and fluconazole-resistant strains. Understanding these dynamics may enhance the knowledge of the survival and reproduction of resistant Candida and the epidemiology of fungal infections. Full article

In 2022, consecutive sweeps of highly transmissible SARS-CoV-2 Omicron-derived lineages (B.1.1.529*) maintained viral transmission despite extensive antigen exposure from both vaccinations and infections. To better understand Omicron variant emergence in the context of the dynamic fitness landscape of 2022, we aimed to explore putative drivers behind SARS-CoV-2 lineage replacements. Variant fitness is determined through its ability to either outrun previously dominant lineages or more efficiently circumvent host immune responses to previous infections and vaccinations. By analyzing data collected through our local genomic surveillance program from Connecticut, USA, we compared emerging Omicron lineages’ growth rates, estimated infections, effective reproductive rates, average viral copy numbers, and likelihood for causing infections in recently vaccinated individuals. We find that newly emerging Omicron lineages outcompeted dominant lineages through a combination of enhanced viral shedding or advanced immune escape depending on the population-level exposure state. This analysis integrates individual-level sequencing data with demographic, vaccination, laboratory, and epidemiological data and provides further insights into host–pathogen dynamics beyond public aggregate data. Full article

Objectives: Shoulder injury prevalence appears to be the highest among all injuries in CrossFit (CF) athletes. Nevertheless, there is no evidence deriving from prospective studies to explain this phenomenon. The purpose of this study was to document shoulder injury incidence in CF participants over a 12-month period and prospectively investigate the risk factors associated with their demographic, epidemiological, and functional characteristics. Methods: The sample comprised 109 CF athletes in various levels. Participants’ data were collected during the baseline assessment, using a specially designed questionnaire, as well as active range of motion, muscle strength, muscle endurance, and sport-specific tests. Non-parametric statistical tests and inferential statistics were employed, and in addition, linear and regression models were created. Logistic regression models incorporating the study’s continuous predictors to classify injury occurrence in CF athletes were developed and evaluated using the Area Under the ROC Curve (AUC) as the performance metric. Results: A shoulder injury incidence rate of 0.79 per 1000 training hours was recorded. Olympic weightlifting (45%) and gymnastics (35%) exercises were associated with shoulder injury occurrence. The most frequent injury concerned rotator cuff tendons (45%), including lesions and tendinopathies, exhibiting various severity levels. None of the examined variables individually showed a statistically significant correlation with shoulder injuries. Conclusions: This is the first study that has investigated prospectively shoulder injuries in CrossFit, creating a realistic profile of these athletes. Despite the broad spectrum of collected data, the traditional statistical approach failed to identify shoulder injury predictors. This indicates the necessity to explore this topic using more sophisticated techniques, such as advanced machine learning approaches. Full article

Narrative review synthesizes the most current literature on the SARS-CoV-2 XEC variant, focusing on its genomic evolution, immune evasion characteristics, epidemiological dynamics, and public health implications. To achieve this, we conducted a structured search of the literature of peer-reviewed articles, preprints, and official surveillance data from 2023 to early 2025, prioritizing virological, clinical, and immunological reports related to XEC and its parent lineages. Defined by the distinctive spike protein mutations, T22N and Q493E, XEC exhibits modest reductions in neutralization in vitro, although current evidence suggests that mRNA booster vaccines, including those targeting JN.1 and KP.2, retain cross-protective efficacy against symptomatic and severe disease. The XEC strain of SARS-CoV-2 has drawn particular attention due to its increasing prevalence in multiple regions and its potential to displace other Omicron subvariants, although direct evidence of enhanced replicative fitness is currently lacking. Preliminary analyses also indicated that glycosylation changes at the N-terminal domain enhance infectivity and immunological evasion, which is expected to underpin the increasing prevalence of XEC. The XEC variant, while still emerging, is marked by a unique recombination pattern and a set of spike protein mutations (T22N and Q493E) that collectively demonstrate increased immune evasion potential and epidemiological expansion across Europe and North America. Current evidence does not conclusively associate XEC with greater disease severity, although additional research is required to determine its clinical relevance. Key knowledge gaps include the precise role of recombination events in XEC evolution and the duration of cross-protective T-cell responses. New research priorities include genomic surveillance in undersampled regions, updated vaccine formulations against novel spike epitopes, and long-term longitudinal studies to monitor post-acute sequelae. These efforts can be augmented by computational modeling and the One Health approach, which combines human and veterinary sciences. Recent computational findings (GISAID, 2024) point to the potential of XEC for further mutations in under-surveilled reservoirs, enhancing containment challenges and risks. Addressing the potential risks associated with the XEC variant is expected to benefit from interdisciplinary coordination, particularly in regions where genomic surveillance indicates a measurable increase in prevalence. Full article

Background: Dog agility is a rapidly growing sport involving a partnership between a dog and the handler, running through an obstacle course. Despite its increasing popularity and physical benefits, research on handler injuries remains limited. This study aimed to assess injury epidemiology of athletes practicing dog agility. Methods: This cross-sectional study was conducted using a comprehensive online survey consisting of 124 items, available in both English and Italian. The questionnaire was divided into four sections: Introduction collected demographic data and medical history; Materials and Methods focused on agility-related activities; Results explored injuries sustained in the past 12 months; Discussion examined training habits unrelated to agility. Results: Among 389 participants, the most represented age group ranged between 30 and 40 years old. Overall, 7% reported upper limb injuries, while 27% experienced at least one lower limb injury. Additionally, 20% of participants used medication, and 25% reported at least one chronic illness. On average, handlers trained twice per week and competed in two events per month. Lower limb injuries were predominantly muscular (49%) or ligamentous (14%) and most commonly occurred on grass pitches (56%). These injuries were more common in participants with a higher BMI, those using dynamic handling styles, and those competing at higher levels. Conclusions: This cross-sectional study highlighted the importance of identifying risk factors associated with dog agility handlers. Lower limb injuries were the most common, often associated with increased physical demands and handling styles involving intensive running and correlated with reduced physical fitness. Athletic conditioning, including structured warm-up and cool-down practices, might help decline injury risks. Full article

Exercise interventions are increasingly recognized as effective non-pharmacological strategies to improve clinical outcomes in patients with breast cancer. This review provides a comprehensive framework linking physical activity with breast cancer risk reduction, disease progression, and survivorship. We first outline the robust epidemiological evidence demonstrating that regular exercise significantly reduces breast cancer incidence, recurrence, and disease-specific mortality. The review then delves into the molecular mechanisms by which exercise exerts its protective effects, including modulation of sex hormones, metabolic hormones, systemic inflammation, oxidative stress, circulating microRNAs, and breast cancer-related DNA methylation. Furthermore, we summarize findings from clinical trials evaluating the effects of exercise on cardiorespiratory fitness, functional capacity, and quality of life in breast cancer patients. Emerging research on the synergistic potential of exercise with conventional cancer treatments and bioactive dietary components, particularly polyphenols such as saffron and curcumin, is also discussed. Finally, we present evidence-based exercise recommendations tailored to breast cancer patients, emphasizing the importance of individualized prescriptions to optimize safety and therapeutic benefit. Collectively, this review highlights the multifaceted role of exercise in breast cancer prevention, treatment, and survivorship. Full article

Uncomplicated hypertension (UH) during pregnancy represents a common condition, worsening maternal and fetal prognosis. However, no single biomarker has proven optimal for determining the risk of UH. We developed an early risk multivariate model for UH, integrating hemodynamics with biochemistry, focusing on the relationship between blood pressure (BP) indices, uric acid (UA), and angiogenesis-related factors (AF). We collected and analyzed data on 24 h ambulatory BP monitoring, demographic, epidemiological, clinical, and laboratory variables from 132 pregnancies. The main predictors were BP indices and serum UA and AF levels. Uncomplicated hypertension, defined as the presence of gestational hypertension or worsening of essential hypertension beyond the 20th week, was the main outcome. The combined second-degree polynomial transformation of UA and the AF (sFlt-1/PIGF) ratio, called the UA-AF Index, consistently showed a positive association with UH. The models incorporating nighttime BP indices combined with the UA-AF Index outperformed the others, with the best-performing model based on the nocturnal systolic BP (SBP). Specifically, in the best-fitting model (nighttime SBP + UA-AF Index as predictors), each 1 mmHg increase in nocturnal SBP was associated with a 10% higher risk of UH, while each one-unit increase in the UA-AF Index raised the likelihood of UH by more than twofold (accuracy: 0.830, AUC 0. 874, SE 0.032, p-value < 0.001, 95%CI 0.811–0.938). The combination of nighttime blood pressure indices, serum uric acid, and angiogenesis-related factors may provide added value in the assessment of uncomplicated hypertension during pregnancy. Full article

Compositional Data Analysis (CoDA) is a powerful statistical approach for analyzing 24 h time-use data, effectively addressing the interdependence of sleep, sedentary behavior, and physical activity. Unlike traditional methods that struggle with perfect multicollinearity, CoDA handles time use as proportions of a whole, providing biologically meaningful insights into how daily activity patterns relate to health. Applications in epidemiology have linked variations in time allocation between behaviors to key health outcomes, including adiposity, cardiometabolic health, cognitive function, fitness, quality of life, glycomics, clinical psychometrics, and mental well-being. Research consistently shows that reallocating time from sedentary behavior to sleep or moderate-to-vigorous physical activity (MVPA) improves health outcomes. Importantly, CoDA reveals that optimal activity patterns vary across populations, supporting the need for personalized, context-specific recommendations rather than one-size-fits-all guidelines. By overcoming challenges in implementation and interpretation, CoDA has the potential to transform healthcare analytics and deepen our understanding of lifestyle behaviors’ impact on health. Full article

Periodontal disease is a chronic inflammatory condition that extends beyond the oral cavity and is increasingly recognized as a significant contributor to cardiovascular disease, the leading cause of global mortality. Systemic inflammation serves as the key link between the two, with elevated biomarkers such as C-reactive protein and interleukin-6 observed in individuals with PD, driving endothelial dysfunction and atherogenesis. Epidemiological and mechanistic studies consistently demonstrate that PD not only heightens the risk of major adverse cardiovascular events but is also independently associated with increased all-cause and cardiovascular mortality. In parallel, emerging research underscores the protective role of regular physical activity and improved cardiorespiratory fitness, which attenuate systemic inflammation and may reduce the burden of both PD and CVD. This review integrates current evidence on the shared inflammatory mechanisms linking PD and CVD, highlights the systemic impact of poor oral health, and advocates for incorporating exercise-based interventions and oral health assessments into comprehensive cardiovascular risk management. A multidisciplinary approach—bridging cardiology, dentistry, and preventive medicine—may offer a novel strategy to reduce inflammation-driven morbidity and mortality. Full article

Probability models are instrumental in a wide range of applications by being able to accurately model real-world data. Over time, numerous probability models have been developed and applied in practical scenarios. This study introduces the AAP-X family of distributions—a novel, flexible framework for continuous data analysis named after authors Aadil Ajaz and Parvaiz. The proposed family effectively accommodates both symmetric and asymmetric characteristics through its shape-controlling parameter, an essential feature for capturing diverse data patterns. A specific subclass of this family, termed the “AAP Exponential” (AAPEx) model is designed to address the inflexibility of classical exponential distributions by accommodating versatile hazard rate patterns, including increasing, decreasing and bathtub-shaped patterns. Several fundamental mathematical characteristics of the introduced family are derived. The model parameters are estimated using six frequentist estimation approaches, including maximum likelihood, Cramer–von Mises, maximum product of spacing, ordinary least squares, weighted least squares and Anderson–Darling estimation. Monte Carlo simulations demonstrate the finite-sample performance of these estimators, revealing that maximum likelihood estimation and maximum product of spacing estimation exhibit superior accuracy, with bias and mean squared error decreasing systematically as the sample sizes increases. The practical utility and symmetric–asymmetric adaptability of the AAPEx model are validated through five real-world applications, with special emphasis on cancer survival times, COVID-19 mortality rates and reliability data. The findings indicate that the AAPEx model outperforms established competitors based on goodness-of-fit metrics such as the Akaike Information Criteria (AIC), Schwartz Information Criteria (SIC), Akaike Information Criteria Corrected (AICC), Hannan–Quinn Information Criteria (HQIC), Anderson–Darling (A*) test statistic, Cramer–von Mises (W*) test statistic and the Kolmogorov–Smirnov (KS) test statistic and its associated p-value. These results highlight the relevance of symmetry in real-life data modeling and establish the AAPEx family as a powerful tool for analyzing complex data structures in public health, engineering and epidemiology. Full article

Campylobacteriosis has been described as an ever-changing disease and health issue that is rather dangerous for different population groups all over the globe. The World Health Organization (WHO) reports that 33 million years of healthy living are lost annually, and nearly one in ten persons have foodborne illnesses, including Campylobacteriosis. This explains why there is a need to develop new policies and strategies in the management of diseases at the intergovernmental level. Within this framework, an advanced stochastic fractional delayed model for Campylobacteriosis includes new stochastic, memory, and time delay factors. This model adopts a numerical computational technique called the Grunwald–Letnikov-based Nonstandard Finite Difference (GL-NSFD) scheme, which yields an exponential fitted solution that is non-negative and uniformly bounded, which are essential characteristics when working with compartmental models in epidemic research. Two equilibrium states are identified: the first is an infectious Campylobacteriosis-free state, and the second is a Campylobacteriosis-present state. When stability analysis with the help of the basic reproduction number $R_0$ is performed, the stability of both equilibrium points depends on the $R_0$ value. This is in concordance with the actual epidemiological data and the research conducted by the WHO in recent years, with a focus on the tendency to increase the rate of infections and the necessity to intervene in time. The model goes further to analyze how a delay in response affects the band of Campylobacteriosis spread, and also agrees that a delay in response is a significant factor. The first simulations of the current state of the system suggest that certain conditions can be achieved, and the eradication of the disease is possible if specific precautions are taken. The outcomes also indicate that enhancing the levels of compliance with the WHO-endorsed SOPs by a significant margin can lower infection rates significantly, which can serve as a roadmap to respond to this public health threat. Unlike most analytical papers, this research contributes actual findings and provides useful recommendations for disease management approaches and policies. Full article

The phenomenological universalities (PU) approach is employed to derive the Richards growth function in the unknown hyperbolic representation. The formula derived can be applied in theoretical modeling of sigmoid and involuted growth of biological systems. In the model proposed, the exponent in the Richards function has the following clear biological meaning: it describes the number of cells doubling, leading to an increase in a biomass of the system from $m_0$ (birth or hatching mass) to the limiting value $m_{\infty }$ (mass at maturity). The generalized form of the universal growth function is derived. It can be employed in fitting the weight–age data for a variety of biological systems, including copepods, tumors, fish, birds, mammals and dinosaurs. Both the PU methodology and the Richards model can be effectively applied in the theoretical modeling of infectious disease outbreaks. To substantiate this assertion, the simplest PU-SIR (Susceptible–Infective–Removed) epidemiological model is considered. In this approach, it is assumed that the number of births is approximately equal to the number of deaths, while the impact of recovered (quarantined) individuals on the dynamics of the infection is negligible. Full article

Phenomenological models are highly effective tools for forecasting disease dynamics using real-world data, particularly in scenarios where detailed knowledge of disease mechanisms is limited. However, their reliability depends on the model parameters’ structural and practical identifiability. In this study, we systematically analyze the identifiability of six commonly used growth models in epidemiology: the generalized growth model (GGM), the generalized logistic model (GLM), the Richards model, the generalized Richards model (GRM), the Gompertz model, and a modified SEIR model with inhomogeneous mixing. To address challenges posed by non-integer power exponents in these models, we reformulate them by introducing additional state variables. This enables rigorous structural identifiability analysis using the StructuralIdentifiability.jl package in JULIA. We validated the structural identifiability results by performing parameter estimation and forecasting using the GrowthPredict MATLAB Toolbox. This toolbox is designed to fit and forecast time series trajectories based on phenomenological growth models. We applied it to three epidemiological datasets: weekly incidence data for monkeypox, COVID-19, and Ebola. Additionally, we assessed practical identifiability through Monte Carlo simulations to evaluate parameter estimation robustness under varying levels of observational noise. Our results confirm that all six models are structurally identifiable under the proposed reformulation. Furthermore, practical identifiability analyses demonstrate that parameter estimates remain robust across different noise levels, though sensitivity varies by model and dataset. These findings provide critical insights into the strengths and limitations of phenomenological models to characterize epidemic trajectories, emphasizing their adaptability to real-world challenges and their role in informing public health interventions. Full article

Robust epidemiological models are essential for managing COVID-19, especially in diverse urban settings. In this study, we present a fractional advection–diffusion–reaction model to analyze COVID-19 spread in three major Turkish cities: Ankara, Istanbul, and Izmir. The model employs a Caputo-type time-fractional derivative, with its order dynamically determined by the Hurst exponent, capturing the memory effects of disease transmission. A nonlinear reaction term models self-reinforcing viral spread, while a Gaussian forcing term simulates public health interventions with adjustable spatial and temporal parameters. We solve the resulting fractional PDE using an implicit finite difference scheme that ensures numerical stability. Calibration with weekly case data from February 2021 to March 2022 reveals that Ankara has a Hurst exponent of 0.4222, Istanbul 0.1932, and Izmir 0.6085, indicating varied persistence characteristics. Distribution fitting shows that a Weibull model best represents the data for Ankara and Istanbul, whereas a two-component normal mixture suits Izmir. Sensitivity analysis confirms that key parameters, including the fractional order and forcing duration, critically influence outcomes. These findings provide valuable insights for public health policy and urban planning, offering a tailored forecasting tool for epidemic management. Full article