Search Results (12,843)

Entropic soft-min operators are widely used to obtain smooth approximations of minimum and argmin mechanisms in optimization, machine learning, and reinforcement learning. The quality of this approximation is controlled by an inverse temperature parameter that governs the trade-off between smoothness and fidelity, yet its selection is usually based on global heuristics or worst-case bounds that do not account for the geometry of the candidate cost vector. This study investigates the calibration of the inverse temperature parameter from a geometry-aware perspective, with explicit guarantees on the approximation error between the entropic soft-min and the exact minimum value. After establishing the structural properties of the relaxation error, including monotonicity with respect to the inverse temperature and its dependence on the geometry of the near-optimal set, we introduce a conformal calibration rule that selects the smallest inverse temperature, ensuring that a prescribed upper quantile of the approximation error remains below a target tolerance with distribution-free finite-sample validity. The resulting selector adapts to the geometry distribution represented in the calibration population and provides a principled alternative to mean-based and worst-case tuning rules. Numerical experiments, including geometry-controlled benchmarks and a contextual bandit setting illustrating the impact of geometry-aware calibration on decision-making under estimated action values, show that the proposed method accurately tracks oracle calibration temperatures, preserves the desired operator-level coverage, and makes explicit how geometric heterogeneity governs the effective sharpness required by the soft-min approximation. Additional shifted evaluations illustrate the role of exchangeability in the validity guarantee and the consequences of transferring temperatures across populations with different near-optimal geometries. Full article

Quercus pyrenaica (Willd.), a sub-Mediterranean oak, is expected to experience substantial distribution shifts under climate change, with some populations in Portugal at risk. Beyond climate-driven pressures, long-standing anthropogenic pressures have likely contributed to the species’ current vulnerability. This work aims to characterize the current status of closed-canopy Q. pyrenaica forests by providing a spatio-temporal assessment of forest fragmentation and its recent evolution. Using multispectral bands from Sentinel-2 time-series data, vegetation indices, embedding vectors generated by Google’s AlphaEarth foundational model, and topographic variables, we applied a machine learning Random Forest classifier to map Q. pyrenaica forests in 2019 and 2024 and to analyze their spatial configuration patterns. The findings indicate robust predictive performance (spatial cross-validation OA of 95.1%, Kappa of 83.7%, and F1 of 86.9%) and reveal the prominent role of AlphaEarth embedding features in the RF classifier, suggesting that these features are well-suited for classifying forest habitats of conservation importance. Quercus pyrenaica occurs predominantly at mid-elevations (~820 m a.s.l.), on gentle slopes (~9°), topographically neutral terrain, and northwestern-facing aspects, consistently across both years. Between 2019 and 2024, the Q. pyrenaica forest area showed an increasing signal. However, the results point to a landscape in an initial phase of forest recovery, constrained by land-use legacies, with cover increasing predominantly through the sprawl of small, geometrically complex, and poorly connected patches. Together, these results provide a baseline to track recent changes in Q. pyrenaica distribution and fragmentation, highlighting a contrast between apparent area expansion and declining overall structural integrity. In the future, patch connectivity and full recovery of secondary succession should be a priority for policymakers and forest owners. Full article

Background: In the Philippines, studies have shown that availability and access to healthcare varies widely. Although the shortage of psychiatrists in the country has been recognized for many years, no published study to date has described their distribution across the regions. This study aimed to describe the distribution of psychiatrists in the country using publicly available data on the Internet. Methods: This was a cross-sectional study, analyzing publicly available data from the Philippine Psychiatric Association (PPA) web directory, the Philippine Health Insurance Corporation (PhilHealth) web database of accredited psychiatrists, and the Philippine Statistics Authority. Information on location of practice, sex, PPA membership, PhilHealth accreditation, regional gross domestic product (GDP), and regional population were collated. Results: Information on 409 psychiatrists was available online, with 68% being female and 53% holding PhilHealth accreditation. There were a total of 417 declared locations of practice, with six psychiatrists practicing in more than one location. The National Capital Region accounted for 53.5% of the declared practice locations, while no psychiatrist declared practicing in the Bangsamoro region. Conclusions: This study highlights the maldistribution of psychiatrists across the Philippines. Policies to incentivize and encourage practice in low-access regions and investment in technology, such as telemedicine, may help reduce the access gap. Full article

Air pollution remains a critical environmental and public health threat, particularly in highly populated urban areas such as the Lisbon Metropolitan Area (LMA). This study provides a refined and detailed assessment of the spatial distribution of air pollution and associated attributable mortality across the LMA. High-resolution (1 km²) annual mean concentrations of key pollutants (PM_2.5, PM₁₀ and NO₂) for 2022 and 2023 were estimated by integrating outputs from the URBAIR dispersion model with ground-based monitoring observations using advanced geostatistical data-fusion techniques. Air pollutant concentrations were combined with gridded population data and age-stratified baseline mortality rates within a Geographic Information System framework to quantify spatial variations in health impacts. Using the World Health Organization AirQ+ framework and established concentration–response functions, we estimated a total of 3195 air-pollution-attributable deaths across the Lisbon Metropolitan Area (LMA) in 2022, increasing to 4010 deaths in 2023. Fine particulate matter (PM_2.5) was identified as the dominant contributor, accounting for more than 40% of the total health burden. At a high spatial resolution (1 km² grid), estimated mortality exhibited substantial variability, ranging from 0 to 29 deaths per cell in 2022 and from 0 to 36 deaths per cell in 2023. These results highlight the importance of fine-scale spatial analysis, revealing intra-urban disparities that are not captured by aggregated estimates of total attributable mortality. The proposed methodological framework, integrating dispersion modelling, data fusion, and spatially explicit health impact assessment at fine spatial scales, provides a robust and transferable approach to support evidence-based air quality management and urban health policy development in European metropolitan contexts. This integrated approach enhances comparability, improves exposure assessment accuracy, and strengthens the scientific basis for designing targeted mitigation strategies that could prevent hundreds of premature deaths annually while addressing documented spatial inequalities in pollution exposure. Full article

Floodplain forests in central Amazonia are structured along a marked flooding gradient that influences species distribution, performance, and survival. This study evaluated the demographic structure, survival, and growth responses of two co-occurring tree species across contrasting várzea environments differing in inundation regimes. Field surveys quantified seedlings, juveniles, and adults in low- and high-floodplain forests, while a field experiment assessed survival and growth under conditions with and without interspecific interaction. Repeated-measures ANOVA revealed that temporal variation and forest type significantly affected growth parameters, with species-specific responses to flooding intensity. In the field experiment, mortality of Crateva tapia L. differed significantly among treatments (χ² = 24.96, p < 0.001), with the highest mortality observed in high-várzea (up to 75% under interspecific interaction), while Hura crepitans L. showed 100% survival across all treatments. Non-parametric analyses detected no significant treatment effects on selected morphological traits. The results support the stress-gradient hypothesis, suggesting that plant–plant interactions may shift along the flooding gradient, with facilitative processes becoming more relevant under higher stress conditions. Overall, differential flood tolerance appears to be a key driver of habitat preference and population structure in these Amazonian wetlands. Full article

Subway-hub blocks are critical areas where the pressures of metropolitan populations and environmental quality are closely interconnected. This study constructs a “pressure–context–carrier–response” (PCRC) framework (F1–F7) to systematically reveal the correlations between built-environment characteristics and environmental performance. The results demonstrate that resource allocation (F7) and comprehensive response (F5) display notable “asymmetric differentiation”. The socio-economic environment (F2, F3) considerably influences the concentration of green-space resource allocations (F7) (p < 0.01), with affluent blocks demonstrating a clear advantage in resource distribution. The thermo-ecological composite response (F5), which includes NDVI and LST, demonstrates “statistical convergence” (p = 0.894) across various block types, indicating that resource inputs cannot be linearly transformed into environmental efficiency. This disconnection is ascribed to two physical limitations: firstly, the stochastic nature of spatial distribution (Global Moran’s I ≈ 0) restricts the scale effects of green spaces; secondly, the nonlinear limitations of the physical medium indicate that under conditions of high pressure load (F1) and elevated spatial capacity (F6), the regulatory effectiveness of greening demonstrates a significant diminishing marginal return effect. Therefore, intervention planning must shift from controlling macro-level indicators to optimising micro-level accuracy to address ecological performance constraints in densely populated metropolitan areas. Full article

Precise, spatially explicit sub-provincial GDP estimates are essential for regional planning, especially in mountainous areas where official economic data remain spatially coarse and unevenly distributed. This study develops a multisource county-level GDP spatialization framework for Sichuan Province, China, integrating corrected NPP/VIIRS nighttime-light (NTL) data with Points of Interest (POIs), land-use structure indicators (proportion of farmland (PFL); proportion of construction land (PCL)), elevation, precipitation, accessibility and population density within a unified indicator system. Two regression approaches—Ordinary Least Squares (OLS) as a global benchmark and Geographically Weighted Regression (GWR) as the spatially adaptive primary model—are calibrated on county-level cross-sectional data for 2020 (n = 183) and evaluated using R², adjusted R², AICc and residual spatial diagnostics. The multisource GWR model achieves R² = 0.882 (adjusted R² = 0.872, AICc = 5712.26), substantially outperforming both the global OLS benchmark (R² = 0.801) and NTL-only GWR baseline (R² = 0.662), confirming that spatial nonstationarity is an intrinsic feature of the GDP–proxy relationship and that integrating complementary geospatial proxies is the primary pathway to improved estimation accuracy in topographically heterogeneous regions. The GWR-based GDP surface exhibits a pronounced basin–plateau contrast: high-value clusters concentrate along the Chengdu Plain and adjacent city corridors, while extensive low-value zones prevail across the western highlands (global Moran’s I = 0.33, Z = 14.26, p < 0.001). Spatially varying GWR coefficients reveal that elevation and precipitation constrain GDP most strongly in high-altitude counties, construction land exerts a consistently positive but spatially graded effect, and the influences of accessibility and population density are context-dependent and locally differentiated. These findings support differentiated territorial development policies: plateau counties require accessibility-first strategies; hill counties benefit from targeted small-city industrialization; and basin cores need managed growth to balance agglomeration advantages against congestion pressures. The framework relies exclusively on globally or nationally available data and is portable to other mountainous regions, though cross-regional validation and extension to multi-year panels using geographically weighted panel regression remain important directions for future work. Full article

Rapid urbanization in megacities has exacerbated the spatial mismatch between employment and housing, necessitating effective spatial optimization strategies. However, classical optimization models often rely on the idealized assumption of “proximity maximization,” failing to account for the complex, nonlinear regularities of actual human mobility. To address this disconnect between theoretical modeling and real-world behavior, this study establishes a job–housing balance optimization framework integrated with empirical commuting patterns. Using Shenzhen as a case study, we analyze citywide commuting big data since 2024 to characterize the power law relationship between commuting population size and distance. We propose a novel optimization model that partitions residential areas into “commuting rings” on the basis of observed distance-decay functions rather than simple Euclidean proximity. We applied the proposed method to current and future planning scenarios and successfully generated spatial regulation schemes that decentralize employment functions to peripheral areas while strategically densifying residential zones. By respecting the “heavy-tailed” nature of commuting distributions, this approach offers urban planners a more robust tool for reducing aggregate commuting burdens without violating the behavioral realities of the workforce. Full article

Background/Objectives: Several trials have highlighted the importance of PARP inhibitors (PARPi) in the treatment of BRCA-associated breast cancers (BC), initiating changes in practice. However, data on the real-life outcomes of PARPi therapy is limited. In this study, we characterized the clinical characteristics and outcomes of patients with advanced BC and germline BRCA pathogenic variants (PVs) who received PARPi therapy. Methods: We conducted a retrospective single-institution cohort study of patients with advanced BC and germline BRCA1/2 PVs treated with PARPi. Outcomes included objective response rate (ORR), progression-free survival (PFS), and overall survival (OS). Survival was estimated using Kaplan–Meier methods, and prognostic factors were evaluated using Cox regression analysis. Results: Of the 107 patients treated with PARPi, 48 (44.9%) and 59 (55.1%) had BRCA1 and BRCA2 PVs, respectively. Ninety-seven patients (90.7%) had invasive ductal carcinoma and 42 (39.3%) had triple-negative BC. Nineteen (17.8%) patients had de novo metastatic BC. Sixty-two (57.9%) patients received at least one line of systemic therapy before PARPi; 24 (22.4%) patients received prior platinum. ORR was 62.6%, and the median duration of response (DoR) was 7 months (range, 2.1–96.2). The median PFS was 9 months (95% CI, 6.9–10.5) and median OS was 25.8 months (95% CI, 18.7–31.5). In multivariable models for PFS, bone metastases (HR = 2.25; 95% CI, 1.40–3.61; p = 0.0008) and lung metastases (HR = 2.40; 95% CI, 1.45–3.98; p = 0.0007) were independently associated with increased risk of progression or death. In multivariable models for OS, brain metastases (HR = 3.54; 95% CI, 1.59–7.90; p = 0.0020), bone metastases (HR = 2.22; 95% CI, 1.27–3.88; p = 0.0050), and lung metastases (HR = 2.38; 95% CI, 1.38–4.11; p = 0.0018), were independently associated with increased risk of death. Conclusions: The clinical outcomes of our real-world patients are similar to those reported in previous clinical trials. In addition, metastatic site distribution was independently prognostic for survival outcomes and may support baseline risk stratification at the time of PARPi initiation. Further studies of predictive markers of response and resistance, as well as sequencing with platinums and combinations with other targeted agents, are needed to optimize the benefits of PARPi in this patient population. Full article

Background/Objectives: Vaccine hesitancy remains a significant public health challenge worldwide, yet nationally representative data from Central Asia are scarce. Evidence on the multidimensional structure of vaccination attitudes and their social patterning in Kazakhstan is limited. The study aimed to assess the distribution of anti-vaccination attitudes among adults in Kazakhstan and to examine their associations with socio-demographic, behavioural, clinical, and territorial characteristics. Methods: We conducted a cross-sectional, nationally representative survey of adults aged 18–69 years across all 17 regions of Kazakhstan between May and October 2025 (n = 6712). A multistage, stratified cluster sampling design was applied, and analyses incorporated sampling weights and design-based corrections. Vaccination attitudes were measured using the 12-item Vaccination Attitudes Examination (VAX) scale, comprising four subscales: mistrust of vaccine benefit, worries about unforeseen future effects, concerns about commercial profiteering, and preference for natural immunity. Internal consistency and confirmatory factor analysis were performed. Design-adjusted linear regression models were used to identify factors independently associated with each subscale and the overall VAX score. Results: The weighted mean overall VAX score was 3.70 (95% CI 3.67–3.73) on a 1–6 scale. The highest scores were observed for worries about unforeseen future effects (4.12; 95% CI 4.10–4.14), followed by preference for natural immunity (3.93; 95% CI 3.87–3.98), concerns about commercial profiteering (3.49; 95% CI 3.45–3.52), and mistrust of vaccine benefit (3.27; 95% CI 3.23–3.31). Internal consistency was high for the overall scale (Cronbach’s α = 0.861), and the four-factor structure demonstrated acceptable fit (CFI = 0.965; TLI = 0.952; RMSEA = 0.071). In multivariable design-adjusted models, age showed a generally consistent gradient, with lower scores in younger groups and the clearest differences observed among the youngest respondents. Married/cohabiting respondents had lower adjusted scores than single respondents across all subscales and for the overall VAX score. Men had lower adjusted worries scores than women, but sex was not independently associated with the overall VAX score. Diabetes was associated with higher adjusted mistrust, concerns about commercial profiteering, and overall VAX score, but not with worries or preference for natural immunity. Territorial differences were domain-specific: urban residence was associated with lower mistrust and higher worries, while macro-region was significant at the factor level only for worries. Conclusions: Anti-vaccination attitudes in Kazakhstan exhibit a multidimensional structure and clear socio-demographic patterning. Concerns about long-term safety were the most prominent attitudinal domain, whereas mistrust of vaccine benefit was comparatively less pronounced. Territorial differences were domain-specific rather than uniform, supporting the need for targeted communication strategies tailored to specific attitudinal domains and population subgroups. Full article

Net primary productivity (NPP) is a critical indicator of carbon sequestration and biomass accumulation in terrestrial ecosystems, directly reflecting ecosystem carbon sink capacity. Existing NPP studies have primarily emphasized climate-driven interannual variability. Spatially explicit analyses that jointly quantify multi-factor driving mechanisms, thresholds, and land-use transition risks remain limited. Here, we develop an integrated multi-method analytical workflow (DRIBS) that integrates Distributional Response, Informative Boundary constraints, and Spatial Interpretability Optimization, and apply it to the Jiziwan region in the Yellow River Basin, one of China’s major ecological restoration hotspot regions. From 2000 to 2020, the annual increasing rate of NPP was 5.80 gC·m⁻²·yr⁻¹, and 78% of the area showed a significant increasing trend. Among them, grasslands and croplands in the eastern and western parts exhibited strong fluctuations and low long-term stability. Evapotranspiration (ET) and fractional vegetation cover (FVC) were the dominant drivers of NPP spatial heterogeneity, and precipitation around ~220 mm marked a critical water-stress threshold. Population density and nighttime lights showed a non-linear “ecological adaptation window”, implying both disturbance and management potential. Land-use transitions exhibited divergent risk signatures: grassland/cropland-to-forest transitions produced stable enhancement (priority restoration zones), whereas cropland/unused-to-urban transitions were associated with degradation risk (urgent management). Overall, DRIBS provides an interpretable “change-mechanism-threshold-risk” assessment to support carbon-sink regulation and restoration prioritization in arid and semi-arid regions. Full article

Accurate forecasting of respiratory virus-associated hospitalization rates per 100,000 population is essential for healthcare capacity planning, yet remains challenging during the COVID-19 era due to abrupt distribution shifts and symptom overlap among influenza-like illnesses caused by multiple pathogens. We propose a two-stage deep learning framework that disentangles stable pre-pandemic seasonal dynamics from COVID-19-induced excess hospitalizations. A lightweight GRU is first trained on pre-pandemic surveillance data to model baseline influenza/RSV-driven seasonality, after which an excess model learns from the residual series and integrates multiple online search trends (flu, COVID-19, and fever) using a standard multi-head self-attention mechanism. While we use COVID-19-era data as a case study, the proposed baseline–excess decomposition is not disease-specific and is intended to generalize to future large-scale respiratory outbreaks or pandemics that induce abrupt regime shifts. Experiments on U.S. weekly respiratory hospitalization rate data curated from CDC surveillance networks (AME) show that the proposed approach achieves strong accuracy on a chronological COVID-era split (2020–2025), reaching $R^2=0.907$ with MAPE = 19.22%. Beyond point forecasts, we further evaluate an expanding-window rolling-origin protocol and report calibrated prediction intervals via split conformal prediction, supporting deployment-oriented uncertainty quantification. By decoupling baseline and excess components and fusing behavioral trend signals in a disciplined manner, this framework improves predictive performance under regime shift while providing interpretable excess estimates for timely situational awareness and healthcare resource planning. Full article

Genetic diversity is fundamental for the conservation and sustainable utilization of plant species. Triadica cochinchinensis, a tree species native to southern China, is an important ornamental and nectar-producing plant with considerable economic value. However, the levels of genetic diversity and the patterns of population differentiation across its natural populations remain unexplored. Here, we developed 24 highly polymorphic SSR markers and used them to assess the genetic diversity and differentiation among 280 individuals collected from 10 natural populations of T. cochinchinensis. The results showed that the average expected heterozygosity (He) revealed by the SSR markers was 0.774, and the average Shannon diversity index (I) was 1.660, indicating a high level of genetic diversity at the species level of T. cochinchinensis. Analysis using SSR markers revealed a low average observed heterozygosity (Ho = 0.323) and a relatively high average inbreeding coefficient within populations (F = 0.466). These findings suggest that inbreeding is likely occurring, which may contribute to a loss of heterozygosity within the studied populations. Notably, not all populations had high genetic diversity. For example, the He of SC2 population (0.490), QY population (0.568), and SC1 population (0.585) were all below the mean He (0.607), suggesting that attention should be given to protecting populations with low genetic diversity. The results further showed that the average genetic differentiation coefficient (F_ST) between populations was 0.094, and the average gene flow (Nm) was 2.278, indicating that the natural populations of T. cochinchinensis had low genetic differentiation and relatively high gene flow. AMOVA indicated that 74% of the total variation was distributed within populations. Notably, populations SC1 and SC2 exhibited higher genetic differentiation from all others (F_ST > 0.1), which is likely attributed to mountain barriers restricting gene flow. Therefore, it is recommended to enhance in situ conservation efforts while also facilitating assisted gene flow, such as through artificial introduction. For the first time, this study reveals the genetic information of natural populations of T. cochinchinensis at the molecular level, thereby offering a valuable reference for the conservation and utilization of its germplasm resources. Full article

Objectives: Given the limitations of body mass index (BMI) as a metric and the lack of data on the relationship between various anthropometric indices of obesity and immune fitness (IF), this study aimed at exploring the possible association between various anthropometric indicators and the immune fitness among an adult sample of the Saudi population residing in Makkah. Methods: A structured self-reported questionnaire, with questions covering age, sex, anthropometric and immune fitness assessment data, was distributed online to a convenience sample of target population. The Immune Status Questionnaire (ISQ) was utilized to assess respondents’ IF over the past 12 months, while perceived momentary immune fitness (PMIF) was measured using a single-item scale. A total of 1135 responses were included in the study. Results: Overall, 530 male (46.7%) and 605 female (53.3%) respondents were included in the analysis. Of these, 478 (42.1%) had a normal BMI, and 343 (30.2%) were classified as overweight, 184 (16.2%) as obese, and 130 (11.5%) as underweight. Participants with reduced ISQ score (<6) were more likely to be underweight (p < 0.001), have a high weight-adjusted waist index (WWI) (p = 0.035), and exhibit an increased conicity index (C index) (p = 0.037) compared to those with an ISQ score ≥ 6. After controlling for age and sex, weight (p = 0.003), height (p < 0.001), and WWI (p = 0.01) were found to have significant correlations with past-year IF, while only height (p = 0.004) showed a significant positive correlation with PMIF. A multiple linear regression analysis revealed that sex and height and waist circumference (WC) were significant predictors of IF. Specifically, males and those who were taller had higher IF scores. Whereas individuals with high-risk WC values reported lower IF scores than those with low-risk WC. Conclusions: Sex (male) and anthropometric measures (lower WC, and taller height) were the most informative predictors of higher IF scores. The findings highlight the association between anthropometric measures and IF. A deeper understanding of these associations can inform the development of targeted interventions aimed at improving IF and overall health outcomes. Full article

Background: The global demographic shift towards an aging population has driven a steady, exponential increase in the utilization of cardiac implantable electronic devices (CIEDs). Consequently, device-related infectious complications have emerged as a leading cause of morbidity and healthcare expenditure. Patients in their eighth decade of life—octogenarians (aged 80–90 years)—represent an exceptionally high-risk demographic due to the compounding factors of physiological frailty, immunosenescence, and complex multi-morbidity. Despite this growing demographic, their specific clinical presentations, microbiological profiles, and procedural outcomes following infection remain poorly defined in the current literature. This study aimed to comprehensively compare the clinical characteristics, pathogen distribution, and in-hospital outcomes of CIED infections in an octogenarian cohort against a younger patient population. Methods: We conducted a robust retrospective cohort analysis of 383 consecutive patients treated for confirmed CIED infections at one major tertiary referral center (Heidelberg University Hospital) between January 2002 and December 2022. The cohort was stratified by age into octogenarians (n = 76) and a younger control group (n = 307). We systematically extracted and compared data regarding baseline clinical presentation, chronic comorbidities, detailed microbiological cultures (pocket, blood, and extracted leads), and definitive in-hospital outcomes, primarily mortality and length of stay. Results: The octogenarian cohort exhibited a significantly heavier comorbidity burden, notably higher rates of coronary artery disease (51.3% vs. 29.6%, p < 0.001), systemic hypertension (55.3% vs. 38.1%, p = 0.007), and chronic obstructive pulmonary disease (7.9% vs. 1.6%, p = 0.003). Furthermore, therapeutic systemic anticoagulant use was substantially more prevalent in the elderly group (60.5% vs. 45.0%, p = 0.015). Octogenarians presented overwhelmingly with localized generator pocket infections (73.0% vs. 30.0%, p < 0.001) but paradoxically also demonstrated higher rates of systemic bacteremia and sepsis (26.3% vs. 15.0%, p = 0.019). Microbiological analysis revealed a unique pathogen profile, with Staphylococcus capitis found with significantly higher frequency in the generator pockets of the elderly cohort. Remarkably, despite possessing a higher average lead burden (2.1 vs. 1.2 leads) and extreme comorbidity profiles, octogenarians demonstrated no statistically significant differences in in-hospital mortality (3.9% vs. 4.2%, p = 1.000) or overall length of hospital stay (14.7 vs. 17.2 days, p = 0.386) when compared to the younger cohort. Conclusions: Octogenarians suffering from CIED infections display highly distinct clinical and microbiological profiles, characterized predominantly by elevated rates of localized pocket infections, specific opportunistic pathogens, and a severe underlying comorbidity burden. Crucially, our findings indicate that with the application of modern extraction and management protocols, advanced age alone does not intrinsically correlate with increased in-hospital mortality. Future prevention and perioperative management strategies tailored to this rapidly expanding demographic must heavily prioritize the mitigation of pocket-related complications, particularly considering the high prevalence of concurrent anticoagulation therapy. Full article