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Objective To create and verify a machine learning model that integrates social determinants of health (SDoH) for assessing post-stroke depression (PSD) and examining the association between SDoH and disease outcomes. Methods Data were acquired from the National Health and Nutrition Examination Survey. Logistic regression was employed to analyse the association between SDoH and PSD, whereas Cox regression was utilized to assess the correlation between SDoH and all-cause mortality in PSD. The Boruta algorithm was employed for feature selection, and four machine learning models were constructed (CatBoost, Logistic, Multilayer Perceptron, and Random Forest) to evaluate the predictive effectiveness, calibration, and clinical applicability of these ML models. SHAP values were computed to ascertain the predictive significance of each feature in the model that exhibited the highest predictive performance. Results Logistic regression analysis revealed a significant positive correlation between SDoH and PSD prevalence( p for trend < 0.0001). Compared to the other three models, CatBoost (AUC = 0.966) demonstrated the best overall predictive performance. Moreover, the decision curve analysis (DCA) and calibration curve findings demonstrated that the CatBoost model possessed considerable clinical utility and consistent predictive efficacy. The ten-fold cross-validation method further confirmed the model’s robustness and generalization ability. Conclusions A linear relationship exists between SDoH and PSD, with CatBoost demonstrating the best performance in predicting PSD. SHAP values emphasize the importance of SDoH.

Objective The optimal management of a small intracranial aneurysm (sIA) remains a challenge due to the lack of a size-specific risk predictive model for aneurysm rupture. We aimed to develop and validate a nomogram-based risk predictive model for sIA. Methods A total of 382 patients harboring 215 ruptured and 167 unruptured small intracranial aneurysms (uSIAs) (≤ 7 mm) were recruited and divided into training and validation cohorts. Risk factors for the construction of a nomogram were selected from clinical and aneurysmal features by least absolute shrinkage and selection operator (LASSO) and multivariate logistic regression. The nomogram for risk of rupture was evaluated in both the training and validation cohorts for discrimination, calibration, and clinical usefulness. Results Hyperlipidemia (odds ratio (OR)=2.74, 95% confidence interval (CI)=1.322~5.956, P=0.008), the presence of a daughter dome (OR=3.068, 95%CI=1.311~7.598, P=0.012), larger size-to-neck ratio (SN) (OR=1.807, 95%CI=1.131~3.063, P=0.021) and size ratio (SR) (OR=2.221, 95%CI=1.262~4.025, P=0.007) were selected as independent risk factors for sIA rupture and used for construction of nomogram. Internal validation by bootstrap sampling showed the Concordance index (C index) of 0.756 for the nomogram. The calibration by the Hosmer-Lemeshow test showed a P value of 0.847, indicating the model was well-fitted. Additionally, decision curve analysis (DCA) demonstrated that the predictive model has good clinical usefulness, providing net benefits across a range of threshold probabilities, thus supporting its application in clinical decision-making. Conclusion The risk prediction model can reliably predict the risk of sIA rupture, which may provide an important reference for optimizing the therapeutic strategy.

The epidemiological association between frailty and insomnia is well established, yet the presence of a common genetic etiology is still uncertain. Further exploration is needed to ascertain the causal relationship between frailty and insomnia. Utilizing data obtained from genome-wide association studies (GWAS) summaries, we utilized the linkage disequilibrium score regression (LDSC) to determine the genetic correlation existing between frailty and insomnia. The determination of causality was achieved through the application of two-sample Mendelian randomization. We investigated the enrichment of single nucleotide polymorphism (SNP) at various tissue types utilizing stratified LD score regression (S-LDSC) and multimarker analysis of genome annotation (MAGMA). Common risk SNPs were identified using Multi-Trait Analysis of GWAS (MTAG) and Cross-Phenotype Association (CPASSOC). We further investigated the expression profiles of risk genes in tissues using Summary-data-based Mendelian randomization(SMR) based on pooled data, to explore potential functional genes. Our findings indicated a significant genetic correlation between frailty and insomnia, highlighting SNPs sharing risk (rs34290943, rs10865954), with a pronounced correlation in the localized genomic region 3p21.31. Partitioned genetic analysis revealed 24 functional elements significantly associated with both frailty and insomnia. Furthermore, mendelian randomization revealed a causal connection between frailty and insomnia. The genetic correlation between frailty and insomnia showed enrichment in 11 brain regions (S-LDSC) and 9 brain regions (MAGMA), where four functional genes (RMB6, MST1R, RF123, and FAM212A) were identified. This study suggests the existence of a genetic correlation and common risk genes between frailty and insomnia, contributing to a deeper comprehension of their pathogenesis and assists in identifying potential therapeutic targets.

Immunity and neuroinflammation play crucial roles in the pathogenesis of Parkinson's disease (PD). Nonetheless, prior investigations into the correlation between immune inflammation and PD have produced varying results. Identifying specific immune cell phenotypes that are truly associated with PD is challenging, and the causal relationship between immune cells and PD remains elusive. This study conducted a comprehensive two-sample Mendelian randomization (MR) analysis, employing five distinct analytical approaches, to clarify the causal connection between immune cell characteristics and the risk of PD. Utilizing GWAS data, we investigated the causal relationship between 731 immune cell traits and PD. These immune cell phenotypes encompass absolute cell (AC) counts, median fluorescence intensity (MFI), and relative cell (RC) counts for B cells, cDCs, mature stage T cells, monocytes, myeloid cells, TBNK (T cells, B cells, and natural killer cells), and Tregs, as well as the logistic parameter (MP) for cDCs and TBNK. The inverse variance weighted (IVW) analysis indicated that Myeloid DCs (  = 0.004), HVEM expression on CD45RA- CD4+ T cells (  = 0.007), CD62L- CD86+ Myeloid DCs (  = 0.015), and HLA DR expression on monocytes (  = 0.019) were associated with a reduced risk of PD. CD14+ CD16+ monocytes (  = 0.005), HLA DR+ NK cells within CD3- lymphocytes (  = 0.023), and CD28 expression on activated & secreting Tregs (  = 0.032) were associated with an increased risk of PD. This study establishes a causal link between immune cell phenotype and the pathogenesis of PD, identifying several specific immune cell characteristics associated with PD. This could inspire researchers to delve into the pathogenesis of PD at the cellular subtype level, and aid in the identification of potential pharmacological protein targets for PD.

In this current paper, we developed a new predator-prey model accompanying delay based on the earlier works. By applying inequality strategies, fixed point theorem, and a suitable function, we got new necessary conditions for the existence, uniqueness, nonnegativeness, and boundedness of the solution to the developed delayed predator-prey model. The bifurcation behavior and stability nature of the defined delayed predator-prey model were investigated by using stability and bifurcation theory of delayed differential equations. We have modified the Hopf bifurcation's appearance time and stability domain by building two distinct hybrid delayed feedback controllers for the delayed predator-prey model. The time of Hopf bifurcation appearance and stability domain of the model were explored. Matlab experiment diagrams were given to support the learned important results. The derived outcomes in this paper were original and have significant theoretical implications for maintaining equilibrium between the densities of the three species.

Accurate intracranial artery segmentation from Digital Subtraction Angiography is critical for the diagnosis and interventional treatment of cerebrovascular diseases. However, traditional methods struggle to capture dynamic spatiotemporal dependencies, often leading to vascular discontinuity and the loss of fine distal vessels due to background interference and resolution loss. In this study, we propose the Wavelet-Enhanced Spatiotemporal Connectivity-Preserving Network (WESCP-Net), a novel framework designed to synergize physical priors with frequency-domain feature extraction. Specifically, we introduce a Physically-Guided Spatiotemporal Enhancement module that explicitly exploits hemodynamic flow variance to differentiate active vascular signals from static artifacts. To address the loss of high-frequency spatial details in standard downsampling, we incorporate a Wavelet-Integrated Encoder and a Topology-Aware Reconstruction module, which utilize discrete wavelet transforms to preserve sharp vessel boundaries and restore structural connectivity. Experimental results on the DIAS dataset demonstrate that WESCP-Net achieves state-of-the-art performance, yielding a Dice Similarity Coefficient of 0.7982 and an Intersection over Union score of 0.6422. Notably, its connectivity-preserving mechanism achieves a clDice metric of 0.7135, improving the continuity of vascular terminals. WESCP-Net provides a robust technological paradigm for precise cerebrovascular segmentation, facilitating reliable surgical navigation and quantitative diagnosis.

Delayed dynamical models demonstrate significant application value in depicting interactions and internal dynamics among different biological populations. Therefore, they have garnered significant interest from numerous scholars in both biology and mathematics. Based on previous studies, this article construct a novel delayed predator-prey model. By utilizing fixed point theory, inequality methods, and appropriate functions, this article examined the desirable properties of the solutions of the constructed delayed predator-prey system, including existence and uniqueness, boundedness, and non-negativity. This paper determines the parameter conditions for system stability and the occurrence of bifurcations by employing bifurcation theory and the stability theory of delayed differential equations. Using two control strategies, namely the mixed controller and the extended delay feedback controller, this paper effectively adjusts the stability domain of the delayed predator-prey systems and controls the time of bifurcation onset. The research explores how delays affect the stabilization of system and the adjustment of bifurcation. This paper provides computer simulation photos supporting the main obtained findings. The outcomes of this paper are groundbreaking and can provide critical guidance for the control and regulation of predator and prey population densities.

Subarachnoid hemorrhage (SAH) is a rare neurological complication of cardiac myxoma and is associated with poor outcomes. Previous reports have shown that myxoma-associated SAH was contributed by rupture of myxomatous intracerebral aneurysm. Here, we present an unusual case of angiographic-negative SAH in a young patient with left atrial myxoma. A 28-year-old male was admitted for SAH. He had a history of magnetic resonance imaging (MRI)-confirmed ischemic stroke one year ago. The digital subtraction angiography (DSA) performed on next day revealed no intracerebral aneurysm or vascular malformation. Transthoracic echocardiography (TTE) showed a left atrial mass measuring 5.09 * 3.34 cm, indicating a diagnosis of atrial myxoma, which was confirmed by pathological examination. The cardiac tumor was excised and the patient's symptoms improved completely. No intracerebral aneurysm was found by brain computed tomographic angiography (CTA) performed on day 24 after onset and one year after discharge. The patient remained asymptomatic during the one-year following-up. The result suggests that SAH may be more commonly associated with cardiac myxoma than previously expected. And, mechanisms other than rupture of myxomatous intracerebral aneurysm involve in SAH associated with cardiac myxoma. Prolonged length of following-up using novel imaging technique should be applied to identify and monitor the change of source bleeding.Subarachnoid hemorrhage (SAH) is a rare neurological complication of cardiac myxoma and is associated with poor outcomes. Previous reports have shown that myxoma-associated SAH was contributed by rupture of myxomatous intracerebral aneurysm. Here, we present an unusual case of angiographic-negative SAH in a young patient with left atrial myxoma. A 28-year-old male was admitted for SAH. He had a history of magnetic resonance imaging (MRI)-confirmed ischemic stroke one year ago. The digital subtraction angiography (DSA) performed on next day revealed no intracerebral aneurysm or vascular malformation. Transthoracic echocardiography (TTE) showed a left atrial mass measuring 5.09 * 3.34 cm, indicating a diagnosis of atrial myxoma, which was confirmed by pathological examination. The cardiac tumor was excised and the patient's symptoms improved completely. No intracerebral aneurysm was found by brain computed tomographic angiography (CTA) performed on day 24 after onset and one year after discharge. The patient remained asymptomatic during the one-year following-up. The result suggests that SAH may be more commonly associated with cardiac myxoma than previously expected. And, mechanisms other than rupture of myxomatous intracerebral aneurysm involve in SAH associated with cardiac myxoma. Prolonged length of following-up using novel imaging technique should be applied to identify and monitor the change of source bleeding.

Stroke remains a major cause of disability worldwide, and population-level indicators that integrate multidimensional vulnerability with physiological reserve may provide useful perspectives for characterizing cerebrovascular health in aging populations. This cross-sectional study examined the association between the Frailty-to-estimated Cardiorespiratory Fitness Ratio (FCR) and self-reported prevalent stroke among middle-aged and older adults using NHANES 2011–2014 data. FCR was calculated as a 23-item modified Frailty Index divided by estimated cardiorespiratory fitness, providing an interpretable load-to-reserve measure that linked accumulated health deficits with estimated cardiorespiratory reserve. The final analytical sample included 3511 participants aged 45 years or older. Survey-weighted logistic regression models, sensitivity analyses, exploratory subgroup analyses, and spline models were used to evaluate the association. The weighted prevalence of self-reported prevalent stroke increased across FCR quartiles, from 2.2% in Q1 to 12.9% in Q4. In the conventional clinical adjustment model, participants in the highest FCR quartile had greater odds of self-reported prevalent stroke than those in the lowest quartile (OR = 6.13, 95% CI: 2.97–12.66; p<0.001), with similar findings in the overlap-aware model (OR = 6.31, 95% CI: 3.07–12.97; p<0.001). Log-transformed FCR was also consistently associated with greater odds of self-reported prevalent stroke across adjustment models, and spline analysis suggested a generally increasing association. These findings support FCR, particularly when modeled using quartiles or log-transformed values, as an interpretable integrative load-to-reserve construct associated with self-reported prevalent stroke, and suggest its potential relevance for population-based characterization of cerebrovascular health in aging adults.

Rosacea is significantly associated with dementia, particularly Alzheimer’s disease (AD). However, the common underlying molecular mechanism connecting these two diseases remains limited. This study aimed to reveal the common molecular regulatory networks and identify the potential therapeutic drugs for rosacea and AD. There were 747 overlapped DEGs (ol-DEGs) that were detected in AD and rosacea, enriched in inflammation-, metabolism-, and apoptosis-related pathways. Using the TF regulatory network analysis, 37 common TFs and target genes were identified as hub genes. They were used to predict the therapeutic drugs for rosacea and AD using the DGIdb/CMap database. Among the 113 predicted drugs, melatonin (MLT) was co-associated with both RORA and IFN-γ in AD and rosacea. Subsequently, network pharmacology analysis identified 19 pharmacological targets of MLT and demonstrated that MLT could help in treating AD/rosacea partly by modulating inflammatory and vascular signaling pathways. Finally, we verified the therapeutic role and mechanism of MLT on rosacea in vivo and in vitro . We found that MLT treatment significantly improved rosacea-like skin lesion by reducing keratinocyte-mediated inflammatory cytokine secretion and repressing the migration of HUVEC cells. In conclusion, this study contributes to common pathologies shared by rosacea and AD and identified MLT as an effective treatment strategy for rosacea and AD via regulating inflammation and angiogenesis.