Search Results (1,765)

Background: Poor clinical control in Chronic Obstructive Pulmonary Disease (COPD) is prevalent, yet the interplay of disease severity, modifiable factors, and clinician perception remains poorly understood. This study aimed to determine the frequency of poor control, identify its independent determinants, and characterize the heterogeneity of the poorly controlled population receiving maintenance inhaled therapy with various devices in primary care. Methods: In a multicenter, cross-sectional analysis of 988 patients from the Study SIMPLIFY, clinical control of COPD was classified using the objective RADAR score. We used multivariable logistic regression and Machine Learning (Random Forest with SHAP analysis) to identify determinants of poor control (RADAR ≥ 4) and k-medoids cluster analysis to characterize the poorly controlled subgroup (n = 452). Results: Nearly half the cohort (45.7%, n = 452) had poor clinical control. Agreement between physician-assessed control (five categories) and RADAR classification was 49.3%, with overestimation in 34.0% and underestimation in 16.7% of cases (Cohen’s κ = −0.081; weighted κ = −0.037). The strongest independent determinants were the exacerbator phenotypes (eosinophilic aOR 6.85; non-eosinophilic aOR 4.91). Key modifiable factors included active smoking (aOR 1.92), lower TAI-12 adherence score (per point; aOR 0.96), high dosing frequency (≥4 inhalations/day; aOR 1.54) and high inhaler burden (≥3 devices; aOR 1.84). Machine learning analysis identified clinical phenotype and adherence behavior as the top two scale-independent predictors of poor control. Cluster analysis of the poorly controlled group revealed five reproducible and clinically meaningful phenotypes (C0–C4), primarily separated by treatment complexity, comorbidities, and adherence. Conclusions: Poor clinical control is common and critically under-recognized in primary care patients with COPD on maintenance inhaled therapy. This is driven by a profound clinician perception gap and a failure to address key modifiable determinants, such as high dosing frequency, regimen complexity, and poor adherence, which likely drives therapeutic inertia. Our findings underscore the need to integrate objective tools to unmask poor control and highlight the importance of treatment simplification. The identification of distinct clinical phenotypes provides a roadmap toward a more personalized, evidence-based standard of care. Full article

Lumpy Skin Disease (LSD) is a transboundary bovine viral disease. It has a significant economic impact and is caused by the Lumpy Skin Disease Virus (LSDV). Effective surveillance tools are essential for the early detection of infection, outbreak control, and assessment of vaccination coverage in endemic regions such as India. In this study, an in-house ELISA based on inactivated whole-virus antigen (WVA) was developed, optimized, and validated for the detection of LSDV antibodies in cattle. Its field applicability was assessed through a cross-sectional seroprevalence survey conducted across five Indian states. A local field isolate of LSDV (strain 5-Chitra) was cultured in MDBK cells, inactivated using binary ethylenimine (BEI), and used as the antigen source. The assay was optimized by checkerboard titration and evaluated against the Serum Neutralization Test (SNT). Diagnostic sensitivity and specificity were evaluated using the receiver operating characteristic (ROC) curve and area under the curve (AUC) analyses, while cross-reactivity was assessed using sera positive for HS, IBR, BQ, MCF, GTP, SPP, CE, FMD, and Brucellosis. Assay reproducibility was confirmed through inter- and intra-laboratory validation. For the seroprevalence study, 3230 cattle serum samples were collected using a stratified random sampling design across five Indian states, and logistic regression analysis of a subset of 1302 samples was performed to assess the influence of age and sex on LSDV seropositivity. Checkerboard titration identified optimal ELISA conditions at 50 ng of antigen per well, a 1:150 serum dilution, and a 1:10,000 dilution of anti-bovine HRP-conjugated secondary antibody. The WVA-ELISA demonstrated excellent diagnostic performance, with 100% sensitivity, 95% specificity, and no cross-reactivity with other ruminant bacterial or viral pathogens, and showed high laboratory reproducibility (κ > 0.96). Seroprevalence ranged from 50.6% to 71.1% across the five states, indicating widespread exposure to LSDV. Risk factor analysis revealed significantly higher odds of seropositivity among calves (≤1 year old) and female cattle, suggesting age- and sex-dependent susceptibility. Full article

Background/Objectives: Human papillomavirus (HPV) infection remains the principal etiologic factor for cervical intraepithelial neoplasia (CIN) and cervical cancer. This longitudinal cohort study aimed to characterize the dynamics of cytological and histopathological changes over a two-year follow-up, focusing on post-treatment reduction in lesion grade, persistence, and progression in relation to HPV genotype distribution and smoking status. Methods: A total of 351 women aged 20–76 years were included, with cervical samples collected at the “Elena Doamna” Clinical Hospital, Iași, Romania. Cytology was categorized according to the Bethesda System, while colposcopy and conization served as diagnostic confirmation methods. HPV genotyping identified both high-risk (HR) and low-risk (LR) viral subtypes. Longitudinal assessments were performed at baseline, one-year, and two-year intervals to evaluate temporal patterns of disease evolution. Results: At baseline, HSIL represented the predominant cytologic category (51.3%, n = 180), followed by ASC-US (19.1%), ASC-H (15.1%), and LSIL (14.5%). Negative cytology increased from 62.4% at one year to 71.8% at two years, indicating substantial post-treatment reduction in lesion grade. Downgrading of lesion severity after treatment occurred in 26.2%, persistence in 11.1%, and progression in 11.1% of cases. Concordance between colposcopy and conization was moderate but statistically significant (κ = 0.345), with the highest agreement observed for HSIL with equivocal features between CIN II and CIN III lesions. Smoking showed a significant association with lesion persistence at two years (OR = 3.07; 95% CI: 1.16–8.08) but no statistically significant association with HR-HPV persistence. HR-HPV genotypes 16, 18, 31, and 33 were most frequently linked to progression, whereas HPV 35, 59, and 68 were associated with persistence. Conclusions: Over two years, most cervical lesions regressed or normalized, demonstrating effective management and follow-up. Persistent infection with HR-HPV types and smoking were the primary determinants of unfavorable outcomes. These findings highlight the clinical relevance of sustained surveillance, HPV genotyping, and smoking cessation as integral components of evidence-based cervical disease prevention and management strategies. Full article

Background: Rapid and accurate fracture detection is critical in emergency departments (EDs), where high patient volume and time pressure increase the risk of diagnostic error, particularly in radiographic interpretation. Multimodal large language models (LLMs) with image-recognition capability have recently emerged as general-purpose tools for clinical decision support, but their diagnostic performance within routine emergency department imaging workflows in orthopedic trauma remains unclear. Methods: In this retrospective diagnostic accuracy study, we included 1136 consecutive patients referred from the ED to orthopedics between 1 January and 1 June 2025 at a single tertiary center. Given the single-center, retrospective design, the findings should be interpreted as hypothesis-generating and may not be fully generalizable to other institutions. Emergency radiographs and clinical data were processed by a multimodal LLM (2025 version) via an official API using a standardized, deterministic prompt. The model’s outputs (“Fracture present”, “No fracture”, or “Uncertain”) were compared with final diagnoses established by blinded orthopedic specialists, which served as the reference standard. Diagnostic agreement was analyzed using Cohen’s kappa (κ), sensitivity, specificity, accuracy, and 95% confidence intervals (CIs). False-negative (FN) cases were defined as instances where the LLM reported “no acute fracture” but the specialist identified a fracture. The evaluated system is a general-purpose multimodal LLM and was not trained specifically on orthopedic radiographs. Results: Overall, the LLM showed good diagnostic agreement with orthopedic specialists, with concordant results in 808 of 1136 patients (71.1%; κ = 0.634; 95% CI: 68.4–73.7). The model achieved balanced performance with sensitivity of 76.9% and specificity of 66.8%. The highest agreement was observed in knee trauma (91.7%), followed by wrist (78.8%) and hand (69.6%). False-negative cases accounted for 184 patients (16.2% of the total cohort), representing 32.4% of all LLM-negative assessments. Most FN fractures were non-displaced (82.6%), and 17.4% of FN cases required surgical treatment. Ankle and foot regions showed the highest FN rates (30.4% and 17.4%, respectively), reflecting the anatomical and radiographic complexity of these areas. Positive predictive value (PPV) and negative predictive value (NPV) were 69.4% and 74.5%, respectively, with likelihood ratios indicating moderate shifts in post-test probability. Conclusions: In an emergency department-to-orthopedics consultation cohort reflecting routine clinical workflow, a multimodal LLM demonstrated moderate-to-good diagnostic agreement with orthopedic specialists, broadly within the range reported in prior fracture-detection AI studies; however, these comparisons are indirect because model architectures, training strategies, datasets, and endpoints differ across studies. However, its limited ability to detect non-displaced fractures—especially in anatomically complex regions like the ankle and foot—carries direct patient safety implications and confirms that specialist review remains indispensable. At present, such models may be explored as hypothesis-generating triage or decision-support tools, with mandatory specialist confirmation, rather than as standalone diagnostic systems. Prospective, multi-center studies using high-resolution imaging and anatomically optimized algorithms are needed before routine clinical adoption in emergency care. Full article

Maqui (Aristotelia chilensis) is a berry native to southern Chile, recognized for its high content of phenolic compounds, particularly delphinidin-type anthocyanins, which confer strong antioxidant and anti-inflammatory properties and have generated growing interest as a functional food. Its scientific relevance has increased due to advances in understanding its biological mechanisms, including the Nrf2 signaling pathway, modulation of systemic inflammation, improvement in mitochondrial function, and potential applications in cardiometabolic, renal, and vascular health. Objective: The objective of this study is to analyze the available evidence on maqui in relation to its nutritional composition, bioactive profile, antioxidant and anti-inflammatory mechanisms, bioavailability, and emerging clinical applications in the prevention and/or treatment of chronic non-communicable diseases. Main findings: Maqui is rich in delphinidins, dietary fiber, and antioxidant micronutrients and modulates key oxidative stress and inflammatory pathways, including Nrf2-HO-1 and NF-κB. Preclinical and early clinical evidence supports its cardiometabolic and nephroprotective effects, with improvements in glycemic control, lipid metabolism, oxidative stress, and endothelial function. Conclusions: Maqui shows considerable potential as a Chilean functional food with antioxidant and anti-inflammatory effects relevant to human health. However, robust clinical trials and formulations with enhanced bioavailability are required to consolidate its therapeutic application. Full article

Breast cancer remains a major global health challenge despite advances in chemotherapy, endocrine therapy, targeted therapy, and radiotherapy, which are frequently constrained by therapeutic resistance, cumulative toxicity, and high costs. Accumulating preclinical and translational evidence demonstrates that plant-derived natural compounds can synergistically enhance the efficacy of conventional treatments, improve tumor response, and potentially reduce adverse effects. This review critically synthesizes in vitro, in vivo, and emerging clinical studies from 2015 to 2025, focusing on key phytochemicals, including curcumin, epigallocatechin-3-gallate, resveratrol, kaempferol, genistein, and other bioactive molecules as stand alone agents and as mechanistically validated adjuvants to chemotherapy, hormonal therapy, and radiotherapy. These compounds exert complementary actions, including the inhibition of PI3K/Akt/mTOR and NF-κB signaling, induction of apoptosis and cell-cycle arrest, suppression of epithelial–mesenchymal transition, and modulation of drug resistance pathways. Preclinical studies consistently show that combination strategies enhance tumor inhibition and may permit cytotoxic dose reduction, mitigating systemic and cardiotoxic effects. Nanocarrier-based delivery systems further optimize solubility, bioavailability, and tumor targeting. Despite robust preclinical evidence, clinical translation is limited by variability in raw materials, lack of standardization, regulatory barriers, and scarce large-scale trials. This review emphasizes both the therapeutic promise and translational challenges of integrating natural compounds as synergistic adjuvants in evidence-based breast cancer therapy. Full article

Background/Objectives: Multimodal large language models (MLLMs) offer potential for automated fracture detection, yet their diagnostic stability under repeated inference remains underexplored. This study evaluates the diagnostic accuracy, stability, and intra-model consistency of four MLLMs in detecting hand fractures on plain radiographs. Methods: In total, images of hand radiographs of 65 adult patients with confirmed hand fractures (30 phalangeal, 30 metacarpal, 5 scaphoid) were evaluated by four models: GPT-5 Pro, Gemini 2.5 Pro, Claude Sonnet 4.5, and Mistral Medium 3.1. Each image was independently analyzed five times per model using identical zero-shot prompts (1300 total inferences). Diagnostic accuracy, inter-run reliability (Fleiss’ κ), case-level agreement profiles, subgroup performance, and exploratory demographic inference (age, sex) were assessed. Results: GPT-5 Pro achieved the highest accuracy (64.3%) and consistency (κ = 0.71), followed by Gemini 2.5 Pro (56.9%, κ = 0.57). Mistral Medium 3.1 exhibited high agreement (κ = 0.88) despite low accuracy (38.5%), indicating systematic error (“confident hallucination”). Claude Sonnet 4.5 showed low accuracy (33.8%) and consistency (κ = 0.33), reflecting instability. While phalangeal fractures were reliably detected by top models, scaphoid fractures remained challenging. Demographic analysis revealed poor capabilities, with age estimation errors exceeding 12 years and sex prediction accuracy near random chance. Conclusions: Diagnostic accuracy and consistency are distinct performance dimensions; high intra-model agreement does not imply correctness. While GPT-5 Pro demonstrated the most favorable balance of accuracy and stability, other models exhibited critical failure modes ranging from systematic bias to random instability. At present, MLLMs should be regarded as experimental diagnostic reasoning systems rather than reliable standalone tools for clinical fracture detection. Full article

This study proposes a stage-aware governance framework for large language models (LLMs) that structures human oversight and accountability across different decision stages in AI-assisted literature review systems. Large language models (LLMs) are increasingly embedded in systematic review workflows, yet how human oversight and accountability should be structured across different decision stages remains unclear. This study evaluates three LLMs in a controlled two-stage literature review workflow—title-and-abstract screening and eligibility assessment—using identical evidence inputs and fixed inclusion criteria, with outputs benchmarked against expert consensus under fully reproducible conditions with standardized prompts and comprehensive logging. While LLMs closely matched expert decisions during screening (precision 0.83–0.91; F1 up to 0.89; Cohen’s κ 0.65–0.85), performance degraded substantially at the eligibility stage (F1 0.58–0.65; κ 0.52–0.62), indicating increased epistemic uncertainty when fine-grained criteria must be inferred from abstract-level information. Importantly, disagreements clustered in borderline cases rather than random error, supporting a stage-aware governance approach in which LLMs automate high-throughput screening while inter-model disagreement is operationalized as an actionable uncertainty signal that triggers human oversight in more consequential decision stages. These findings highlight the need for explicit oversight thresholds, responsibility allocation, and auditability in the responsible deployment of AI-assisted decision systems for evidence synthesis. Full article

High Mobility Group ^{Box 1} (HMGB1) and S100 proteins are major ligands of Receptor for Advanced Glycation End-products (RAGE) and have causal roles in endometriosis lesions. Yet the AGE–RAGE pathway that unifies Advanced Glycation End-products (AGEs) with these ligands has not been assessed in endometriosis. In diabetes, atherosclerosis, and chronic kidney disease, AGE–RAGE links insulin resistance and oxidative stress to inflammation, fibrosis, and organ harm. Endometriosis shares key drivers of AGE accumulation, including insulin resistance, oxidative stress, and chronic inflammation. Endometriosis is also linked to higher vascular risk and arterial stiffness. We asked whether AGE–RAGE could bridge metabolic stress to pelvic lesions and systemic risk. We did a focused review of mechanisms and an evidence map of studies on AGEs, RAGE, or known RAGE ligands in endometriosis. We grouped findings as most consistent with a driver, amplifier, consequence, or parallel role. We included 29 studies across human samples, cell systems, and animal models. Few studies measured AGE adducts directly. Most work tracked RAGE ligands (mainly HMGB1 and S100 proteins) and downstream immune and angiogenic programs. Across models, this pattern fits best with a self-reinforcing loop after lesions form. RAGE expression often aligned with lesion remodeling, especially fibrosis. Blood and skin readouts of AGE burden were mixed and varied by cohort and sample type. A central gap is receptor proof. Many models point to shared Toll-like receptor 4 (TLR4)/ nuclear factor kappa B (NF-κB) signaling, but few test RAGE dependence. Overall, current evidence supports AGE–RAGE as a disease-amplifying loop involved in chronic inflammation and fibrosis rather than an initiating trigger. Its effects likely vary by stage and site. Priorities now include direct lesion AGE measurement, paired systemic–pelvic sampling over time, receptor-level studies, and trials testing diet or drug interventions against clear endpoints. Outcomes could include fibrosis, angiogenesis, immune state, pain, and oocyte and follicle function. Full article

Tongue-rolling behavior (TR) is commonly observed in dairy cows and is considered a stereotypic behavior indicative of compromised welfare. Chronic stress can impair lactation and immune function, yet the interaction between behavior (TR vs. normal behavior (NB)) and chronic stress (high vs. low) remains unclear. In this study, hair cortisol concentration (HCC) was used to assess stress levels in cows. The cows were first classified into high- and low-stress cows using K-means clustering. Subsequently, cows exhibiting high levels of TR and those exhibiting NB (i.e., no stereotypic behaviors) were selected from both stress categories to establish four groups (n = 8 per group): high-stress TR (HT), high-stress NB (HN), low-stress TR (LT), and low-stress NB (LN). We analyzed milk protein composition, milk proteome, and plasma immune-inflammatory indicators. Behavior (TR vs. NB) and chronic stress (high vs. low) showed significant interaction effects on plasma tumor necrosis factor-α (p = 0.046), interleukin-6 (p = 0.002), and proteomic profiles, involving multiple guanosine triphosphate-binding proteins (p < 0.05), transferrin (p = 0.001), and complement factors (p < 0.05). In addition, TR cows had significantly lower levels of αs1-casein (p = 0.019), β-casein (p < 0.001), κ-casein (p = 0.016), lactoferrin (p = 0.003), and plasma immunoglobulin A (p = 0.002). These results indicate that, under different chronic stress levels, TR cows differ markedly from NB cows in milk protein expression, immune function, and inflammatory responses. Moreover, milk from TR cows showed reduced quality, and immune dysfunction and inflammation were exacerbated under high stress. Overall, this study provides new insights into the physiological consequences of stereotypic behavior in dairy cows. These findings may help dairy farmers identify cows exhibiting TR as at risk of reduced milk quality and immune dysfunction, allowing for early management interventions to improve animal welfare and productivity. Full article

Background: We describe the post-mortem analysis of a CARD11 variant allele, p.Ser995Leu, identified in fraternal twins who died in early infancy with no identifiable cause of death. CARD11 variants through varied inheritance models can alter immune function through loss- or gain-of-function mechanisms, involving distinct protein domains; yet the significance of GUK domain variants remains poorly characterized. Twin autopsies showed non-specific findings, such as pulmonary macrophage accumulation and splenic white pulp expansion, but without infection or structural abnormalities. Methods: Whole-exome sequencing, performed as part of molecular autopsies, identified the shared CARD11 p.Ser995Leu variant, previously classified as a variant of uncertain significance (VUS). We assessed evolutionary conservation across CARD family proteins and species and predicted functional impact using in silico tools, which estimate the likelihood that a variant is deleterious. AlphaFold-based structural modeling emphasized qualitative biophysical assessment. Using epidemiological data, population allele frequency, and Bayesian ACMG variant classification, we assessed competing hypotheses under an autosomal dominant model. Results: The p.Ser995Leu substitution affects a conserved, surface-exposed β-sheet within the GUK domain. While CADD scores exceeded 20, other predictive algorithms offered only partial support of pathogenicity. Structural modeling suggested a potential GUK domain destabilization. Integrating genetic, pathologic, immunologic, and probabilistic modeling, we propose a biologically plausible model in which the variant, like other GUK variants, may alter NF-κB or other signaling pathways and is likely pathogenic. Conclusions: While the CARD11 p.Ser995Leu variant’s contribution to disease is uncertain without functional validation or parental testing, and phenotypic findings are non-specific, the presence of an ultra-rare GUK domain variant in both twins, combined with in silico and statistical modeling, supports its interpretation as likely pathogenic or high risk. The results highlight the challenges of data-limited post-mortem variant interpretation. Full article

Toxoplasma gondii is a globally distributed zoonotic parasite that infects a wide range of warm-blooded animals and poses serious threats to many endangered species. Effective serological detection is critical for disease surveillance and control, yet the accuracy of commercial assays varies significantly across host species. Validated serological methods for the Chinese red panda (Ailurus styani) remain scarce, limiting effective monitoring and conservation efforts. A total of 57 serum samples were collected from captive red pandas. Three commercial serological kits—the modified agglutination test (MAT), indirect hemagglutination assay (IHA), and enzyme-linked immunosorbent assay (ELISA)—were used to detect T. gondii antibodies. Statistical analyses included agreement assessment (Cohen’s kappa), McNemar’s test, and receiver operating characteristic (ROC) curve analysis, with MAT as the reference standard. MAT detected T. gondii antibodies in 36.84% (21/57; 95% CI: 24.32–49.36) of samples, while ELISA identified 42.11% (24/57; 95% CI: 29.29–54.92). IHA failed to detect any positive samples (0%). MAT and ELISA showed almost perfect agreement (κ = 0.817; 95% CI: 0.66–0.97) with no significant difference (p > 0.05). Compared to MAT, ELISA exhibited 95.2% sensitivity and 88.9% specificity. The combined MAT/ELISA approach with retesting of discordant samples yielded a final seroprevalence of 35.09% (20/57), with 8.77% (5/57) indeterminate results. MAT and ELISA are reliable methods for serological surveillance of T. gondii in red pandas, while IHA is unsuitable. We recommend MAT for initial screening followed by ELISA confirmation. The high seroprevalence highlights the need for integrated control strategies, including management of stray felids and rodents in captive environments to reduce transmission risks. Full article

Liver cirrhosis (LC) is a complex pathological condition characterized by extensive transcriptomic reprogramming, yet the regulatory role of non-coding RNAs in disease progression remains poorly understood. This study aimed to systematically investigate long non-coding RNA (lncRNA)-messenger RNA (mRNA) interaction networks in LC through weighted gene co-expression network analysis (WGCNA). Gene expression profiles from datasets GSE197406, GSE107170, and GSE17548 were retrieved from the Gene Expression Omnibus (GEO) database, and differentially expressed lncRNAs and mRNAs were identified. Co-expression modules were constructed using WGCNA. Furthermore, functional enrichment analyses were conducted and drug repurposing opportunities were evaluated. Additionally, lncRNA-mRNA co-expression networks and lncRNA-mRNA-pathway networks were constructed to identify key regulatory relationships. Molecular docking simulations were subsequently performed to validate potential drug–target interactions. The results revealed several co-expression modules significantly associated with LC, particularly the turquoise module (r = 0.81). Genes within this module were enriched in several biological pathways, including the PI3K-Akt signaling pathway, NF-κB signaling pathway, and chemokine signaling pathway. The hub lncRNA in the turquoise module, NONHSAT134945.2, was found to be co-expressed with mRNAs involved in inflammasome-mediated pyroptosis and hepatocyte activation, such as CSF1R, HCK, and CASP1. Based on this hub gene signature, AB-1010, GW768505A, and Dasatinib were identified as potential therapeutic candidates. Molecular docking analysis confirmed that these compounds exhibit high binding affinity to CSF1R and HCK, with all interatomic distances maintained below 3.5 Å. These findings provide new insights into the molecular mechanisms underlying LC and suggest that the NONHSAT134945.2–CSF1R/HCK axis may serve as a valuable target for future translational research and therapeutic development. Full article

Background/Objectives: Cerebral angiography is a cornerstone diagnostic and therapeutic procedure for cerebrovascular diseases; however, its potential effects on vascular integrity and cellular homeostasis remain incompletely elucidated. This systematic review aims to comprehensively evaluate endothelial and histopathological alterations induced by cerebral angiographic procedures, with particular emphasis on oxidative stress, inflammation, endothelial dysfunction, and blood–brain barrier disruption. Methods: This systematic review was conducted in accordance with the PRISMA 2020 guidelines. PubMed, Scopus, and Web of Science databases were systematically searched for studies published between 1981 and 2025 using predefined keywords related to cerebral angiography, endothelial injury, oxidative stress, inflammation, and histopathological changes. A total of 1142 records were identified, and 216 duplicates were removed. Following title and abstract screening, 312 full-text articles were assessed for eligibility, of which 112 were excluded due to irrelevance or insufficient endothelial or histopathological data. Ultimately, 200 studies were included in the qualitative synthesis. The literature identification, screening, and selection process are summarized in the manuscript. The review protocol was not prospectively registered. Results: The included studies demonstrated that cerebral angiographic procedures induce endothelial and microvascular alterations through both mechanical and contrast-mediated mechanisms. Iodinated contrast agents were consistently associated with increased reactive oxygen species production, reduced endothelial nitric oxide bioavailability, mitochondrial dysfunction, and activation of pro-inflammatory signaling pathways, including nuclear factor kappa B (NF-κB). Histopathological findings revealed endothelial swelling, vacuolization, apoptosis, microthrombus formation, inflammatory cell infiltration, and disruption of endothelial junctions, leading to increased vascular permeability and blood–brain barrier impairment. Mechanical factors related to catheter manipulation and high-pressure contrast injection further exacerbated endothelial injury by altering shear stress and promoting leukocyte adhesion. The severity of endothelial damage and inflammatory responses was consistently greater in patients with comorbid conditions such as diabetes mellitus, hypertension, and atherosclerotic disease. Conclusions: Cerebral angiography may induce endothelial dysfunction and histopathological vascular injury predominantly through oxidative and inflammatory mechanisms. Optimization of contrast agent selection, refinement of procedural techniques, and implementation of endothelial-protective strategies may mitigate vascular injury and improve procedural safety. Further translational and clinical studies are warranted to identify biomarkers and protective interventions targeting angiography-induced endothelial damage. Full article

Orchard wind-assisted spraying technology relies on auxiliary airflow to disturb the canopy and improve droplet deposition uniformity. However, there are few effective means of quantitatively assessing the dynamic response of fruit tree leaves to airflow or the changes in airflow patterns within the canopy in real time. To address this, this study proposed an online monitoring method for the aerodynamic characteristics of fruit tree leaves using strain gauge sensors. The flexible strain gauge was affixed to the midribs of leaves from peach, pear and apple trees. Leaf deformations were captured with high-speed video recording (100 fps) alongside electrical signals in controlled wind fields. Bartlett low-pass filtering and Fourier transform were used to extract frequency-domain features spanning between 0 and 50 Hz. The AdaBoost decision tree model was used to evaluate classification performance across frequency bands. The results demonstrated high accuracy in identifying wind exposure (98%) for pear leaf and classifying the three leaf types (κ = 0.98) within the 4–6 Hz band. A comparison with the frame analysis of high-speed video recordings revealed a time error of 2 s in model predictions. This study confirms that strain gauge sensors combined with machine learning could efficiently monitor fruit tree leaf responses to external airflow in real time. It provides novel insights for optimizing wind-assisted spray parameters, reconstructing internal canopy wind field distributions and achieving precise pesticide application. Full article