Abstract Background: Deep vein thrombosis (DVT) is a major cause of morbidity and mortality. Proximal DVT carries a higher risk of pulmonary embolism (PE), but the distinct inflammatory profiles of proximal and distal DVT remain unclear. Objectives: This study aims to compare the distribution of next-generation systemic inflammatory indices (SII, SIRI, AISI, NLR, PLR, MLR) and conventional biomarkers (CRP, D-dimer) and to evaluate the diagnostic and prognostic value of these parameters in predicting PE. Additionally, the effects of demographic and etiological differences between groups on the systemic inflammatory response were analyzed; the findings were validated using logistic regression models and ROC curve metrics to identify independent predictive markers. Methods: In this retrospective case-control study, 750 patients (2019–2025) with suspected lower extremity DVT were classified into proximal DVT (n=250), distal DVT (n=250), and Doppler-negative control group (n=250). Inflammatory indices [SII, SIRI, AISI, NLR, PLR, MLR], conventional biomarkers (CRP, D-dimer), and biochemical parameters were evaluated. Group differences were tested using ANOVA/Kruskal–Wallis, while logistic regression and ROC analyses identified predictors of proximal DVT and PE. Results: Patients with proximal DVT had significantly higher inflammatory indices, CRP, and D-dimer values compared to those with distal DVT and controls (all p

Dynamical behavior for the nonautonomous logistic equation

Ecological determinants and indicator-based analysis of Aedes albopictus expansion in a Central European metropolis: implications for urban sustainability.

Objective To assess the diagnostic value of serum NLRP3 inflammasome and occludin levels in predicting hemorrhagic transformation (HT) and functional outcomes in acute ischemic stroke (AIS). Methods AIS patients and matched controls were enrolled between June and November 2021. Serum biomarkers were measured and correlated with infarct volume, stroke severity, HT occurrence, and prognosis. Predictive performance was evaluated using logistic regression and ROC curves. Results A total of 156 AIS patients and 55 controls were included. AIS patients showed significantly elevated serum NLRP3 (56.59 pg/mL vs. 33.24 pg/mL) and occludin levels (97.42 ng/mL vs. 44.54 ng/mL) (both p <0.001). Both biomarkers correlated positively with infarction volume and NIHSS scores. HT occurred in 19.2% of patients, who exhibited markedly higher NLRP3 and occludin levels (both p<0.001). NLRP3 levels differed significantly between HI and PH subtypes (p = 0.042). Logistic regression identified infarct volume, creatinine, reperfusion therapy, and NLRP3 as independent predictors of HT. Poor functional outcomes were associated with older age, atrial fibrillation, HT, larger infarcts, higher NIHSS scores, and elevated serum biomarkers (all p <0.05). Age, NIHSS, LDL-C, and NLRP3 independently predicted prognosis. The optimal NLRP3 cut-off for predicting HT was 80.86 pg/mL (AUC 0.911), and for poor prognosis 82.75 pg/mL (AUC 0.663). Combining NLRP3 with NIHSS significantly enhanced prognostic accuracy (AUC 0.903). Conclusions Elevated serum NLRP3 inflammasome levels represent a promising biomarker for predicting HT and unfavorable outcomes in AIS patients.

Introduction: Child malnutrition remains a major public health issue in Benin. This study assessed its prevalence, clinical forms, and modeled associated risk factors among children in Aplahoué. Methods: An analytical, observational, and cross-sectional study was conducted in 2020 among 313 children aged 6 to 59 months, selected through twostage cluster sampling. Anthropometric measurements were performed according to WHO (2006) standards. Statistical analyses, including multivariate logistic regressions and ROC curve assessment, were used to identify determinants of malnutrition. The final model integrated relevant sociodemographic and anthropometric variables, with collinearity control and validation through the Hosmer-Lemeshow test. Results: The prevalence of acute, underweight, and chronic malnutrition was 29.4%, 31.7%, and 48.9%, respectively. Severe forms predominated (marasmus: 17.7%). The multivariate predictive modelincluding sex, age, birth weight, maternal BMI, and education levelshowed a modest but statistically significant discriminative ability (AUC = 0.581; p = 0.0206). At the optimal cutoff (0.263), sensitivity reached 76%, indicating a potential utility for early identification of children at risk in a screening context. Conclusion: Child malnutrition remains a major concern in Aplahoué; integrated interventions targeting socio-economic and maternal determinants are essential.

To examine the determinants of hospitalisation outcomes in patients with traumatic pelvic fractures accompanied by shock through the establishment of a retrospective cohort study. analyseanalyse the influencing factors of hospitalisation. To examine the factors influencing the outcome of hospitalisation treatment for patients with traumatic pelvic fracture combined with shock, we retrospectively analysed the clinical data of 134 patients in our hospital from July 2021 to July 2024. We classified the patients into survival and death groups based on the outcome of hospitalisation, gathered and compared the pertinent data of the research subjects, and performed logistic regression analysis for the items with differences. We classified. The covariance analysis showed that there was no covariance among Glasgow Coma Score (GCS), Shock Index (SI), Mean Arterial Pressure (MAP), Blood Lactic Acid (Lac), and Fibrinogen (Fib) levels in 134 patients with traumatic pelvic fracture combined with shock, of whom 108 survived hospitalization (80.60%) and were included in the survival group, and 26 patients died (19.40%) and were included in the death group; Plotting the receiver operating curve (ROC) revealed that each of the factors above and the joint prediction of Area Under Curve (AUC) values were 0.689, 0.673, 0.832, 0.681, 0.670, and 0.930, respectively. The logistic regression equation also showed that GCS score, SI index, MAP, Lac, and Fib levels were all significant factors in the death of hospitalised patients with traumatic pelvic fracture combined with shock. GCS score, SI index, MAP, Lac and Fib levels are all influential factors in the hospitalization outcome of patients with traumatic pelvic fracture combined with shock, and their joint prediction accuracy is high, and the clinic can combine the above factors to strengthen the risk monitoring, and formulate the relevant program to improve the hospitalization outcome of patients.

Background — Chronic heart failure (CHF) is one of the leading causes of mortality. Many factors can influence the risk of mortality in patients with CHF. Therefore, it is necessary to clarify the predictors of mortality in patients with CHF. The goal of our study was to identify predictors of adverse prognosis in patients with CHF. Methods — The study included 591 patients with CHF at 60 medical facilities registered in Samara Region CHF Registry during one month in 2022. Their median age was 71.0 (64.0-80.0) years, and 339 (57.4%) of them were men. The follow-up period lasted 18 months, during which 198 (33.5%) patients died. Results — According to the results of multivariate analysis, prognostic factors associated with mortality in patients with CHF were age (OR 1.024, 95% confidence interval [CI] 1.007-1.042, p=0.006), NYHA functional class IV (OR 2.226, 95% CI 1.358-3.649, p=0.002), pleural effusion (OR 1.423, 95% CI 0.973-2.083, p=0.069), oxygen therapy in outpatient settings (2.401, 95% CI 0.963-5.988, p=0.06), inotropic therapy in a hospital settings (OR 1.559, 95% CI 0.924-2.630, p=0.096), and left ventricular ejection fraction (LVEF) &lt; 40% (OR 1.580, 95% CI 1.066-2.342, p=0.023). Previous cardiac surgery was inversely associated with the probability of death (OR 0.476, 95% CI 0.3-0.755, p=0.002). The area under the ROC curve, corresponding to the relationship between mortality and the value of the logistic regression function, was 0.691 (p=0.023) (95% CI 0.645-0.736). Conclusion — Predictors of mortality in patients with CHF over an 18-month follow-up period include age, NYHA functional class IV CHF, pleural effusion, oxygen therapy in outpatient settings, inotropic therapy in a hospital setting, and LVEF &lt; 40%. Previous cardiovascular surgeries had a favorable effect on mortality in these patients.

The purpose of this paper is to give sufficient conditions for the existence and uniqueness of positive solutions to a rather general type of elliptic system of the Dirichlet problem on a bounded domain \(\Omega\) in \(R^{n}\). Also considered are the effects of perturbations on the coexistence state and uniqueness. The techniques used in this paper are super-sub solutions method, eigenvalues of operators, maximum principles, spectrum estimates, inverse function theory, and general elliptic theory. The arguments also rely on some detailed properties for the solution of logistic equations. These results yield an algebraically computable criterion for the positive coexistence of species of animals with predator-prey relation in many biological models.

BackgroundIdentifying cognitive markers for early detection of neurodegenerative disorders is crucial for understanding pathological aging. Given the early impact of information acquisition, consolidation, and retrieval processes in Alzheimer's disease (Jutten et al., 2020), this study aims to determine whether immediate or delayed recall processes at baseline can effectively differentiate between individuals with stable mild cognitive impairment (MCI) and those who convert to Alzheimer's Clinical Syndrome (ACS) after two years.MethodA cohort of 140 older adults diagnosed with MCI (62.4% female, mean age = 76.27, SD = 5.71) underwent baseline neuropsychological assessment, including the Spanish version of the California Verbal Learning Test (CVLT‐TAVEC), the Rey–Osterrieth Complex Figure Test (ROCF), and Verbal Paired Associates (Wechsler Memory Scale). After a two‐year follow‐up, 24 participants progressed to ACS (MCI‐converters). Binary logistic regression and ROC curve analyses were performed to determine whether immediate or delayed recall measures at baseline were more effective in predicting conversion.ResultBinary logistic regression revealed that immediate recall performance on both verbal (CVLT‐TAVEC) and visual (ROCF) memory tests significantly predicted conversion to ACS within two years (p < 0.001). Participants who later developed ACS showed significantly lower baseline scores on short‐term recall tasks compared to those who remained stable (p < 0.001). ROC curve analyses demonstrated that CVLT‐TAVEC immediate recall (AUC = 0.815, p < 0.000) was a stronger predictor of conversion than long‐term recall (AUC = 0.804, p < 0.000).ConclusionThese findings highlight immediate consolidation deficits as a potential early marker of prodromal Alzheimer's disease, emphasizing their role in the preclinical trajectory of AD. The results suggest that immediate recall impairments, rather than delayed recall deficits, may better capture the earliest cognitive dysfunctions associated with disease progression. Incorporating short‐term recall measures into clinical assessments could improve the early identification of high‐risk individuals and refine predictive models for AD. Further research is needed to explore the underlying neural mechanisms linking immediate consolidation to Alzheimer's pathology.

BackgroundOur group has demonstrated that the subtype 3 metabotropic glutamate receptor (mGlu3R) expressed in astrocytes exerts neuroprotective functions and promotes both non‐amyloidogenic cleavage of APP and Aβ clearance. In turn, we showed that mGlu3R levels progressively decreased with age in the hippocampus of PDAPP‐J20 murine AD model. The goal of this study was to investigate whether these changes reflect glial dysfunction and are also present in AD patients.MethodBrain hemispheres‐ derived glial cells from young adult (2 months‐old) PDAPP‐J20 mice or non‐transgenic littermates were cultured after isolation in a Percoll gradient. After 8‐10 days in vitro, cells were lysed to obtain proteins for determination of mGlu3R, GLT‐1, and SR‐A levels for western blot. We also performed bioinformatics analysis of 6 RNASeq databases from brain tissue from patients with AD or controls, using the NCBI online tool GEO2R, in order to study mRNA expression of mGlu3R and other proteins involved in mGlu3R pathway, such as GLT‐1, SRA‐1, and BDNF. TPM (transcripts per million kilobase) data were used to perform logistic regression and ROC curves. For the SNPs analysis, we used RStudio to perform regression and clustering models.ResultIn accordance with reduced hippocampal expression of mGlu3R in PDAPP‐J20 mice, glial mGlu3R levels were early diminished in these animals. This was accompanied with reduced levels of GLT‐1 and increased expression of SR‐A. By analyzing RNASeq databases from AD or control brains, we observed a significant decrease in mGlu3R, GLT1, and BDNF levels in AD patients; while SRA was increased. ROC curve analysis yielded significant results for both mGlu3R and the 4‐gene panel as predictors for AD. Moreover, mGlu3R reduced mRNA levels were mainly linked to the peri‐plaque regions. However, neither GRM3 nor FOLH1 SNPs were correlated to AD diagnostic or to cognitive impairment, after analysis of GWAS data from an Argentinian cohort.ConclusionWe suggest that early changes in glial mGlu3R expression in AD deprive the brain of the neuroprotective and anti‐amyloidogenic mechanisms executed by the receptor, and this may be involved in the etiology of the disease.

ABSTRACT As climate change continues to reshape global ecosystems, accurately monitoring and understanding shifts in land surface phenology (LSP) has become increasingly critical for detecting vegetation responses, assessing ecosystem resilience, and improving predictions of climate–biosphere interactions. While several moderate-to-fine spatial resolution global LSP datasets exist, most are constrained to post−2000 records due to the availability of MODIS era observations, leaving a significant gap in long-term phenological information and limiting our ability to investigate multi decadal trends. To address this, we developed the first global 1 km resolution LSP dataset spanning 1982–2018. By integrating MODIS and AVHRR NDVI data through the enhanced Flexible Spatiotemporal Data Fusion (cuFSDAF) model, we reconstructed pre−2000 MODIS-like NDVI time series with improved temporal consistency and reduced cross sensor bias. Using logistic functions and third-order derivatives, we extracted four key phenological metrics, including the start of the growing season (SOS), maturity, senescence, and the end of the growing season (EOS), enabling detailed characterization of vegetation development stages across diverse biomes. In the Northern Hemisphere (NH), SOS, maturity, and EOS advanced significantly from 1982 to 2018, while senescence was delayed, indicating a widespread lengthening of the growing season. In contrast, the Southern Hemisphere (SH) experienced delayed SOS, maturity, and senescence, with EOS occurring earlier, suggesting a shortening of the growing season and revealing contrasting hemispheric sensitivities to climate forcing. These metrics were validated against ground-based observations from multiple networks, demonstrating strong consistency, high accuracy, and reliable reconstruction of long term phenological signals. As the first dataset of its kind at 1 km resolution over such an extended period, it provides an invaluable resource for studying global vegetation dynamics and serves as a crucial tool for ecological monitoring, climate change research, and terrestrial ecosystem modeling.

BackgroundInterreader variability in ureteral stone size measurements affect the predicted probability of spontaneous stone passage (SSP), especially in proximal ureteral stones. Window settings have been shown to influence interreader variability.PurposeTo investigate interreader variability of ureteral stone size measurements in four different window settings.Material and MethodsPatients with a unilateral proximal ureteral stone ≥2.0 mm detected during emergency computed tomography (CT) were included in this single-center study. Five observers measured each stone in three dimensions in a soft-tissue window, bone window, and two half-value windows (based on the mean [half-value MEAN] or maximum attenuation of the stone [half-value MAX]). Limits of agreement of the mean (LOAM) for stone size in each window setting were assessed. Logistic regression curves were created for predicted probability of SSP.ResultsIn total, 124 patients (87 men, 37 women; mean age = 52 years; age range = 22-82 years) were retrospectively evaluated. LOAM: bone window (±1.6 mm, 95% confidence interval [CI]=1.24-4.90), soft-tissue window (±0.4 mm, 95% CI=0.37-0.82), half-value MEAN window (±0.3 mm, 95% CI=0.24-0.40), half-value MAX window (±0.2 mm, 95% CI=0.14-0.30). Prediction curves aligned and shifted to the left as mean stone size decreased in the half-value window settings.ConclusionThe bone window is unsatisfactory for ureteral stone size measurements. The interreader variability in soft-tissue and half-value windows is on a sub-mm magnitude, with no expected impact on clinical decision-making. The half-value MAX window had the smallest interreader variability and should be considered for reproducible and semiautomated ureteral stone size measurements.

Hopf bifurcation and control in a delayed HCV infection model incorporating cellular diffusion and logistic growth

Background: according to official statistics, the general incidence of mental disorders among adolescents during 1992–2022 was significantly higher than among the entire Russian population. This difference was already 82.4% in 2022. During 30 yars, the general incidence rate of adolescents increased by 48.4%, while the number of adolescent population decreased by 27.9%. The aim was to find the main factors related to the dynamics of the indicator of the general incidence of mental disorders in the adolescent population of Russia and the construction of population mathematical models (linear and nonlinear) for this indicator based on data related to the period 1992–2022. Material and Methods: the work used official data from statistical reports (Rosstat), materials from research institutions, and published results of epidemiological studies of the mental health of adolescents and the entire Russian population. When constructing linear models for the indicator of overall morbidity, correlation and regression analysis were used in the framework of MS Excel (the “Data Analysis” add-in), and the formation of a nonlinear model was carried out using a logistic function, the parameters of which were selected using the least squares method in the framework of MS Excel (the “Solver” add-in). Results: using the correlation analysis of the initial data, the main factors influencing the dynamics of the overall incidence of mental disorders in the adolescent (15–17 years old) population of Russia in the period 1992–2022 were identified. The main factors for the period under review are: 1) the human resource of the adolescent mental health service; 2) the number of the adolescent population. Linear regression models with a high explanatory power have been obtained, which allow for operational monitoring of the overall morbidity of adolescents. Conclusions: the results of study made it possible to develop epidemiological models with a high explanatory power (linear and nonlinear) to assess the indicator of the general incidence of mental disorders depending on the human resource of the adolescent psychiatric service and the number of adolescents. Using the obtained models it would be possible to monitor and predict the mental health of adolescents in both the short and medium term, as well as develop the necessary psychosocial assistance programs and plan appropriate resources for their implementation, which is important for the organizers of psychiatric care.

The possibility of using the L1-regularization to obtain logistic classification equations of quantitative/qualitative structure-activity/property relationships (QSAR/QSPR) have been investigated. The least angle regression (LARS) of least absolute shrinkage and selection operator (LASSO) variant has been implemented in the logistic regression. The method was used for building simple classification functions for three tasks: to evaluate basicity of different organic compounds towards Li+ cation, to study binding affinity to the estrogen receptor of various organic molecules, and to predict activity against COVID-19 main protease. The obtained simple classification functions have satisfactory prognostic properties. The obtained results provide a foundation for the investigation of the electronic and spatial structures of potential ligands exhibiting the desired activity. A comparative analysis of chemoinformatics approaches facilitates the optimization of lead identification methodologies.

Different approaches have been developed to assess the phenological dynamics of ecosystems. However, diverse data sources and extraction methods for assessing ecosystem phenology can result in discrepant and inaccurate results, especially across different types of vegetation under various climate classifications. Based on the phenology of dominant plant species (Pheplant) obtained from ground monitoring in an alpine shrub meadow at Haibei Station (HBS) on the Qinghai-Tibetan Plateau and in a broad-leaved Korean pine forest at Changbai Mountain (CBF) in Northeastern China, we extracted vegetation phenology from the Normalized Difference Vegetation Index (PheNDVI) and photosynthetic phenology from gross primary productivity (PheGPP) using five common methods. These methods included Gaussian fitting, single logistic function fitting, double logistic function fitting, and smoothing techniques combined with fixed threshold and derivative-based determination approaches. There was no consistent interannual trend in either plant phenology or environmental factors at the two sites. Among the three types of plant phenology, a similar interannual pattern in the start of the growing season (SOS) was observed, whereas the interannual patterns for the end of the growing season (EOS) and the growing season length (GSL) were asynchronous. Compared to Pheplant, both PheNDVI and PheGPP exhibited an earlier SOS, a delayed EOS, and consequently an extended GSL. The SOS derived from both PheNDVI and PheGPP was advanced by increasing spring temperatures at both sites, while the relationship between EOS and air temperature was relatively weak. The discrepancy between PheNDVI and PheGPP was more pronounced at CBF than at HBS, likely due to the complex vegetation composition and structure of the mixed forest. The different extraction methods produced more consistent and less variable estimates of SOS compared to EOS and GSL at both sites. Among the five methods, the dynamic threshold approach showed a relatively small difference between PheNDVI and PheGPP, suggesting that it could provide a more consistent estimate of plant phenology across the two sites. This study clearly reveals the inherent discrepancies associated with using different types of phenological data and the influence of extraction methods on phenology across different plant functional types. More attention should be given to improving the accuracy of EOS and understanding the influence of vegetation composition on phenological variation in future studies.

We derive the first conditionally consistent estimators for a class of parametric Markov population models with logistic growth, which are suitable for modeling endangered populations in restricted habitats with a carrying capacity. We focus on discrete-time parametric population-size-dependent branching processes, for which we propose a new class of weighted least-squares estimators based on a single trajectory of population size counts. We establish the consistency and asymptotic normality of our estimators, conditional on non-extinction up to time n, as n → ∞ . Since Markov population models with a carrying capacity become extinct almost surely under general conditions, our proofs rely on arguments distinct from those in the existing literature. Our results are motivated by conservation biology, where endangered populations are often studied precisely because they are still alive, leading to an observation bias. Through simulated examples, we show that our conditionally consistent estimators generally reduce this bias for key quantities such as a habitat’s carrying capacity. We apply our methodology to estimate the carrying capacity of the Chatham Island black robin, a population reduced to a single breeding female in the 1970’s, which has since recovered but has yet to reach the island’s carrying capacity. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.

Pteridium aquilinum is a medicinally important fern with a limited range in northern Iran, increasingly threatened by climate change. Using morphological, genetic, and environmental data, we assessed differentiation, adaptive capacity, and vulnerability across 11 populations. Factor analysis of mixed data (FAMD) identified stipe indument, pinnule shape, and pinnae number as key traits distinguishing populations. Redundancy and association analyses (RDA/CCA) revealed strong links between both morphological and genetic variation and climatic gradients, particularly temperature and humidity, indicating local adaptation. Several SCoT loci were detected as adaptive outliers. Spatial PCA showed that variation is shaped by both global and local spatial factors, forming clines and local variants. Populations varied in sensitivity and adaptive capacity; populations 2, 3, 7, and 8 exhibited the lowest adaptive indices and highest vulnerability. Connectivity modeling suggested that while some populations (e.g., 2, 4, and 6) may maintain or slightly improve connectivity, others risk isolation under future climates. Structural equation modeling (SEM) indicated a positive genetic contribution to adaptation, while differential equation modeling (DEM) predicted logistic growth with temporary instability and genetic decline in vulnerable groups. Overall, findings highlight spatially uneven adaptive responses and recommend targeted conservation through connectivity enhancement, assisted gene flow, and ex-situ preservation of adaptive genotypes.

Machine learning simulations of open quantum dynamics often rely on recursive predictors that accumulate error. We develop nonrecursive convolutional neural networks (CNNs) that map system parameters and a redundant time encoding directly to excitation energy transfer (EET) populations in the Fenna-Matthews-Olson (FMO) complex. The encoding-modified logistic plus tanh function normalizes time and resolves fast, transitional, and quasi-steady regimes, while physics-informed labels enforce population conservation and intersite consistency. Trained only on 0-7 ps reference trajectories generated with a Lindblad model in QuTiP, the network accurately predicts 0-100 ps dynamics across a range of reorganization energies, bath rates, and temperatures. Beyond 20 ps, the absolute relative error remains below 0.05, demonstrating stable long-time extrapolation. By avoiding step-by-step recursion, the method suppresses error accumulation and generalizes across time scales. These results show that redundant time encoding enables data-efficient inference of long-time quantum dissipative dynamics in realistic pigment-protein complexes and may aid the data-driven design of light-harvesting materials.

Mathematical models are routinely applied to interpret biological data, with common goals that include both prediction and parameter estimation. A challenge in mathematical biology, in particular, is that models are often complex and non-identifiable, while data are limited. Rectifying identifiability through simplification can seemingly yield more precise parameter estimates, albeit, as we explore in this perspective, at the potentially catastrophic cost of introducing model misspecification and poor accuracy. We demonstrate how uncertainty in model structure can be propagated through to uncertainty in parameter estimates using a semi-parametric Gaussian process approach that delineates parameters of interest from uncertainty in model terms. Specifically, we study generalised logistic growth with an unknown crowding function, and a spatially resolved process described by a partial differential equation with a time-dependent diffusivity parameter. Allowing for structural model uncertainty yields more robust and accurate parameter estimates, and a better quantification of remaining uncertainty. We conclude our perspective by discussing the connections between identifiability and model misspecification, and alternative approaches to dealing with model misspecification in mathematical biology.