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Inspiratory muscle training (IMT) is widely used in rehabilitation, yet the accuracy of mechanical threshold devices has been only partially characterised. The aim of this study was to evaluate the validity and stability of the pressure delivered by the Threshold IMT&#xae; device (Philips Respironics) using independent calibrated instrumentation, with the measurement chain itself validated against a weighted-plunger reference method. Three units were tested on a bench equipped with a calibrated differential pressure transducer (YOKOGAWA EJA110E; full-scale (FS) error &#x2264;0.055%), an ISO 5167-3-referenced differential flow meter (uncertainty <5%), and an industrial fan. Five measurements per nominal pressure level (9, 25, and 41 cmH2O) were collected for each device across a range of inspiratory flow rates. The pressure reported by the bench corresponds to the total differential developed at the patient side, i.e. the load the inspiratory muscles must overcome at the mouth. A two-way ANOVA was used to assess precision and inter-device reproducibility. The measurement chain reproduced the weighted-plunger reference pressures to within 2.5%, confirming its accuracy. The patient-side pressures were systematically lower than the nominal scale values at all three levels, corresponding to approximately 40-50% of the set value (linear regression slope = 0.49; R2 &#x2248; 0.99). Reproducibility was good (no significant device main effect: F[2,36] = 0.24; p = 0.79), indicating a design-inherent characteristic rather than a manufacturing artefact. The pressure-flow relationship followed a logarithmic curve for each device and threshold level. For the device tested, the nominal scale value did not correspond to the pressure measured at the patient side. These findings have implications for how Threshold IMT&#xae; settings are interpreted when designing and reporting IMT protocols.

To identify serum metabolic biomarkers that distinguish corticosteroid and cyclosporin A (CS & CsA) resistant pediatric idiopathic uveitis (PIU) patients from sensitive counterparts. Serum samples were collected from 32 CS & CsA-sensitive PIU patients and 24 CS & CsA-resistant PIU patients, respectively. UHPLC-OE-MS was employed for comprehensive metabolic profiling of the serum samples. Bioinformatic analyses were performed to identify differentially expressed metabolites (DEMs) between the two patient groups. A machine learning-based classification model was constructed using the identified DEMs as predictive features. For validation purposes, an independent internal cohort of 16 CS & CsA-sensitive and 10 CS & CsA-resistant patients was recruited to evaluate the model's stability. Compared with the CS & CsA-sensitive PIU patients, serum samples from CS & CsA-resistant PIU patients displayed significant metabolic reprogramming. Among the identified differential metabolites, lipids were the most prominently dysregulated class, accounting for 72.47% of all differential metabolites. A machine learning based multivariate feature selection approach including NNET, LASSO, and XGBoost identified 4 candidate metabolite biomarkers. ROC analysis showed that three of these biomarkers (MG 15:0, PI-Cer 28:0;3O, and SPB 20:0;2O) exhibited AUC values of 0.934, 0.953, and 0.904, respectively, and were all upregulated in CS & CsA resistant patients. In contrast, N-acetylaspartic acid showed an AUC of 0.934 and was downregulated in CS & CsA resistant patients. The combined classification model incorporating these 4 metabolites achieved an AUC of 1.0. Validation in an independent internal cohort confirmed the model's excellent performance, with AUC values of 0.971 for NNET, 0.971 for LASSO, and 0.957 for XGBoost. We have established a classification model capable of effectively discriminating CS & CsA-resistant from -sensitive PIU patients. The machine learning model leveraging metabolic biomarkers demonstrates exceptional classification accuracy and generalizability, offering potential for clinical subtype classification.

Cardiometabolic heart failure with preserved ejection fraction (HFpEF) is a high-risk phenotype primarily driven by metabolic syndrome, with a significantly increased incidence and risk of adverse outcomes. A fundamental reason for this is the lack of early clinical diagnosis. As a tool capable of accurately capturing pathophysiological states, metabolomics provides a critical entry point for addressing this issue; however, studies focusing on the metabolic characteristics of this population remain limited. This study integrated a clinical cohort and untargeted metabolomics to compare serum metabolic profiles between patients with cardiometabolic HFpEF and those with metabolic syndrome (MetS). Baseline characteristics were balanced using propensity score matching (PSM). Differential metabolites were identified by untargeted metabolomics, followed by KEGG pathway enrichment analysis. Machine-learning approaches were further applied to screen candidate metabolites with potential diagnostic efficacy, and weighted gene co-expression network analysis (WGCNA) together with SHapley Additive exPlanations (SHAP) were used to evaluate phenotype association and feature contribution. In an independent clinical cohort, total sphingomyelin (SM) levels were assessed by ELISA as an external evaluation strategy based on clinical applicability. Differential metabolites between the two groups were mainly enriched in sphingolipid metabolism and glycerophospholipid metabolism pathways. Through multi-method screening, C24:1 Sphingomyelin was identified as a candidate metabolite with potential diagnostic efficacy. The co-expression module containing C24:1 Sphingomyelin was significantly correlated with NT-proBNP, a key biomarker of heart failure, and SHAP analysis indicated that C24:1 Sphingomyelin contributed substantially to the classification model. In the external cohort, total SM levels were associated with disease status, suggesting the potential clinical association of sphingolipid-related signals. This study preliminarily characterized the metabolic features distinguishing cardiometabolic HFpEF from MetS alone, suggesting that sphingolipid dysregulation is associated with the development and progression of this phenotype. Among the identified metabolites, C24:1 Sphingomyelin was identified as a candidate metabolite with potential diagnostic performance, and SM showed potential clinical applicability. These findings provide new clues for biomarker discovery and preliminary clinical translational exploration in cardiometabolic HFpEF.

By comparing conventional ultrasonography (US) and contrast-enhanced ultrasonography (CEUS) features of liver metastases of melanoma (LMM) with those of other common liver metastases from non-melanoma malignancies (non-LMM), a differential diagnosis nomogram was developed and internally validated to explore its clinical application value. This single-center, retrospective, case-control study enrolled 108 patients with LMM (109 lesions) and 95 patients with non-LMM (109 lesions) at our institution from January 2017 to April 2025. All cases were confirmed by pathological diagnosis. Univariate analysis was performed to compare clinical characteristics and US/CEUS features between groups. Variables with P < 0.05 were entered into a stepwise bidirectional regression model (based on the Akaike information criterion (AIC)) to identify independent risk factors and assess collinearity (using variance inflation factor (VIF)). A nomogram was developed using logistic regression and generalized estimating equations (GEE) and evaluated by the area under the receiver operating characteristic curve (AUC-ROC), bootstrapping (1000 resamples), calibration curves, and decision curve analysis (DCA). The independent risk factors included in the nomogram model were morphology, posterior acoustic enhancement, number, enhancement pattern and wash-out time. The model demonstrated an AUC of 0.892 (95% CI: 0.849-0.935) and a bias-corrected AUC of 0.873 (95% CI: 0.805-0.933) by bootstrapping. Both calibration curves and DCA demonstrated the nomogram's favorable calibration and clinical utility (Hosmer-Lemeshow test, P > 0.05). The LMM differential diagnosis nomogram based on US/CEUS features complements clinical screening and facilitates precise diagnosis while aiding in subsequent treatment evaluation and follow-up.

Nocturnal hypertension (NH) is a high-risk, underdiagnosed blood pressure (BP) phenotype strongly associated with cardiovascular morbidity. Obstructive sleep apnea (OSA), which is common in patients with NH, promotes vascular injury through sympathetic activation, inflammation, and endothelial dysfunction; however, the molecular mechanisms underlying this association in patients with NH remain poorly defined. This study explored these mechanisms using targeted proteomics and endothelial cell models. Adults undergoing polysomnography (PSG) with NH, defined as nighttime BP &#x2265;120/70mmHg at the time of 24-hour ambulatory BP monitoring, were included. Fasting blood samples were collected after PSG. Participants were classified as controls, defined as an apnea-hypopnea index (AHI) <15eventsh-1, or severe OSA, defined as AHI &#x2265;30eventsh-1; patients with moderate OSA, defined as AHI between 15 and 30eventsh-1, were excluded to maximize contrast between groups. Participants were matched for age, sex, BMI, and nocturnal mean arterial pressure. The Olink&#xae; platform was used to quantify proteins. Differential abundance was assessed using linear models, and sparse partial least squares discriminant analysis was used to identify OSA-associated protein signatures. Endothelial integrity was assessed in cells exposed to extracellular vesicles derived from a subset of participants (n=8 controls; n=8 severe OSA). A total of 58 matched participants were included, with 29 participants per group. Overall, 70.7% were men, the median age was 48 years, and the median BMI was 29.5kg/m2. Twenty-one proteins were differentially abundant and were enriched in pathways related to cell adhesion maintenance and extracellular vesicle composition. A 13-protein signature associated with OSA showed interconnectivity and enrichment for endothelial regulatory pathways. Extracellular vesicles from patients with OSA showed a trend toward increased endothelial barrier disruption compared with controls. In patients with NH, severe OSA appears to be associated with molecular alterations indicative of endothelial dysfunction, providing preliminary mechanistic insight into elevated cardiovascular risk that may help refine risk stratification.

Rising global energy demand and increasing decarbonization requirements have intensified the need for intelligent building energy management capable of handling nonlinear dynamics and multi-objective operational trade-offs. Conventional discrete-time and simulation-dependent control strategies often struggle to maintain temporal continuity, adaptive responsiveness, and consistent performance across heterogeneous building environments. Addressing these limitations, NODE-RL-BEM (Neural Ordinary Differential Equation Reinforcement Learning for Building Energy Management) introduces a unified continuous-time optimization paradigm that jointly models system dynamics and learns adaptive control policies. The approach integrates heterogeneous operational data, temporal state embeddings, neural differential equation modeling, and multi-objective reinforcement learning within a cohesive architecture designed for predictive and responsive energy optimization. Performance evaluation conducted on the ASHRAE Great Energy Predictor III dataset and the Intelligent Indoor Environment Dataset demonstrates the effectiveness of the proposed framework, achieving 42-48% energy savings, maintaining comfort violations below 0.5%, and improving indoor air quality by 28-35%. The framework further achieves a generalization score of 0.91 across diverse building operational scenarios, confirming strong transferability and stability. Continuous-time dynamics learning improves predictive fidelity and ensures smooth state evolution, while adaptive reinforcement learning enables robust decision-making under dynamic environmental and occupancy variations. Scalable applicability to multi-zone building environments highlights practical deployment feasibility. This work establishes a novel continuous-time dynamic-policy learning paradigm that integrates predictive modeling with real-time adaptive control, advancing data-driven intelligent building operation toward sustainable and autonomous energy management.

Idiopathic epilepsy (IE) is the most common chronic nervous system disorder of dogs, and its cause is poorly understood. Emerging evidence suggests that microbiome alterations can occur with IE via the microbiota-gut-brain axis. Therefore, we analyzed the fecal microbiomes of 98 dogs (49 IE, 49 control) in a pairwise case-control observational study using 16S rRNA gene sequencing. Although the microbial community was mostly similar between groups, IE was associated with a modest but significant shift in weighted UniFrac distance (p&#x2009;=&#x2009;0.042). We used six differential abundance (DA) methods to identify differentially abundant amplicon sequencing variants (ASVs) between IE and control groups. Notably, one Collinsella ASV was found to be significantly more abundant in IE dogs by all six methods. The gut microbial compositions varied drastically across households (accounting for about 69% of the total variation), but did not have significant differences between sex, age, or breed. Phenobarbital administration in IE dogs had a significant effect on seizure control, and was not associated with changes in the microbiome. Our findings suggest a relationship between gut microbiomes and IE. However, the specific mechanism needs to be further investigated.

Iron (Fe) and zinc (Zn) deficiencies frequently limit nutrient acquisition and grain micronutrient accumulation in rice grown under direct-seeded rice (DSR) systems due to reduced micronutrient availability in aerobic soils. Understanding the genetic architecture controlling micronutrient uptake and its association with root system architecture (RSA) is critical for developing nutrient-efficient rice varieties. In this study, a diverse panel of 290 rice genotypes was evaluated for RSA, agronomic traits, and grain Fe and Zn concentration under DSR conditions across three years. Genome-wide association analysis using 18,639 high-quality SNP markers identified 118 significant marker-trait associations distributed across the rice genome. Several loci exhibited pleiotropic effects, linking RSA traits with grain micronutrient accumulation and yield-related traits. Notably, multiple genomic regions co-localized with previously reported QTLs and key genes involved in metal homeostasis, including OsIRO2, OsNAS, OsYSL, OsZIP, OsHMA2, and OsVIT1, suggesting conserved regulatory mechanisms controlling Fe and Zn uptake. Expression profiling under Fe and Zn deficiency further revealed differential regulation of transcription factors and metal transporters between nutrient-efficient and inefficient genotypes, indicating genotype-specific adaptive responses to micronutrient stress. These findings provide insights into the genetic basis of micronutrient uptake and identify promising donors, genomic regions and candidate genes for marker-assisted breeding of nutrient-efficient rice varieties adapted to DSR cultivation systems.

Identifying diagnostic and prognostic biomarkers and therapeutic targets for hepatocellular carcinoma (HCC) is essential to improve risk stratification, guide individualized treatment, and enhance therapeutic efficacy.The expression of SAMM50 (Sorting and Assembly Machinery Component 50) was initially analyzed in publicly accessible curated genomic and proteomic databases, such as the Cancer Cell Line Encyclopedia, the Human Protein Atlas, and other HCC-specific repositories. This analysis revealed differential expression patterns between HCC and non-neoplastic liver tissue. Subsequently, clinicopathological data and tissue specimens were collected from 200 HCC patients who underwent treatment at our institution. The protein and transcript levels of SAMM50 were experimentally measured in paired HCC and adjacent non-tumorous tissues using immunohistochemistry (IHC) and quantitative reverse transcription polymerase chain reaction (qRT-PCR). The association between SAMM50 expression and key clinicopathological features was further evaluated. Univariate and multivariate Cox proportional hazards analyses were performed to determine the independent prognostic value of SAMM50 expression in HCC. Based on these results, a reproducible and clinically applicable nomogram, supported by a forest plot, was constructed to facilitate prognostic prediction and support individualized therapeutic decision-making. Finally, in vitro and in vivo experiments were conducted to characterize the phenotypic alterations in HCC cells after SAMM50 knockdown, thereby confirming its involvement in critical oncogenic behaviors.This research demonstrated that the mRNA and protein levels of SAMM50 in HCC tissues were elevated compared to those in normal liver and adjacent tissues. Immunohistochemistry findings confirmed that SAMM50 protein levels were persistently higher in HCC tissues than in paired adjacent tissues. High expression of SAMM50 was correlated with unfavorable clinicopathological factors, encompassing pretreatment alpha-fetoprotein (AFP) levels, tumor size, T stage, American Joint Committee on Cancer (AJCC) stage, histological grade, and worse overall survival.Specifically, high expression of SAMM50 was linked to shorter overall survival (OS), progression-free survival (PFS), and disease-free survival (DFS). Moreover, univariate and multivariate Cox analyses were conducted to investigate the association between SAMM50 expression and clinicopathological features in HCC patients and to identify independent prognostic factors. The area under the receiver operating characteristic (ROC) curve (AUC) for SAMM50 was 0.863, suggesting its potential as a diagnostic marker for HCC, though further validation in independent cohorts is needed. Silencing of SAMM50 inhibited HCC cell proliferation, migration, and invasion, promoted apoptosis in vitro, and suppressed HCC growth in vivo.This research demonstrates that SAMM50 shows potential diagnostic value for HCC, though this observation requires further validation in larger, independent, and prospective cohorts. The results of this study not only contribute to the evaluation of baseline data and risk stratification in HCC but also offer novel approaches for the development of precise treatment strategies and targeted therapies.

Allergic rhinitis (AR) is a prevalent chronic condition, yet the cellular and molecular changes associated with its pathogenesis remain incompletely understood. We sought to construct a comprehensive cellular atlas of the nasal mucosa in AR and non-allergic rhinitis (NAR) and elucidate disease-associated transcriptional and epigenetic alterations. We performed single-cell RNA sequencing and single-cell ATAC sequencing on nasal mucosa samples from 39 subjects (AR, n=24; NAR, n=15). Differential expressed gene analysis, differentially accessible peaks analysis, cell-cell communication, trajectory inference, and gene regulatory network reconstruction were applied. A deep learning framework was developed to integrate multi-omics data for disease prediction. We profiled 1,024,146 cells, constructing a comprehensive nasal mucosa atlas. The AR epithelium exhibited aberrant differentiation with suppressed maturation of basal and club cells, while fibroblasts displayed inflammatory activation and matrix remodeling signatures. Epithelial-stromal crosstalk was enhanced in the AR group. Cell subset-specific epigenetic alterations were also observed. Single-cell Multi-omics for Allergic Rhinitis Integrative Analysis (scMARIA) can simultaneously predict AR risk and clinically relevant disease parameters, and prioritize putative cell type-specific regulatory linkages. This multi-omics study establishes a comprehensive molecular framework of the nasal mucosa, revealing dysregulated epithelial-stromal interactions and gene regulatory networks that are correlated with AR status.

Early postnatal growth is a critical determinant of meat production efficiency and long-term genetic improvement in goats; however, the molecular mechanisms underlying individual variation in growth performance remain poorly understood. In this study, a total of 123 Hechuan white goats were included. First, a genome-wide association study (GWAS) for average daily gain (ADG) was performed using all 123 individuals. Subsequently, based on the coefficient of variation of ADG (CV&#x2009;=&#x2009;65.6%), an extreme phenotype sampling (EPS) strategy was applied to select 39 individuals with extreme growth phenotypes for subsequent metabolomic, microbiome, and integrated mGWAS analyses.The results showed that ADG approximately followed a normal distribution across the 123 goats. GWAS identified 22 loci significantly associated with ADG, mapping to genes including DLK1, NCAPG2, LCORL, CNTNAP2, and SLC8A1, which are involved in pathways related to skeletal muscle development, cell cycle regulation, ion transport, and immune function. Metabolomic profiling detected 1,589 putative metabolites, revealing differential enrichment of lipid, amino acid, and bile acid metabolic pathways between fast- and slow-growing goats. Gut microbiome analysis demonstrated that Christensenellaceae_R-7_group and Monoglobus were significantly enriched in fast-growing individuals, whereas Desulfovibrio was more abundant in slow-growing goats.Integrated mGWAS analysis further revealed extensive effects of host genetic variation on gut microbiota and fecal metabolites. Specifically, 11 bacterial genera were significantly associated with host genomic variants, among which Desulfovibrio exhibited the highest number of associated loci. Integration of multiple variant types consistently linked Desulfovibrio, Eubacterium_hallii_group, and Candidatus_Saccharimonas with genes such as ARHGAP24 and IGF2BP2. In addition, 14 metabolites were significantly associated with host genetic variants, with Lysopc(14:1(9Z)/0:0) and glycocholic acid showing the strongest associations. Notably, the peak signal for Lysopc was located within HMGA2.Collectively, these findings define a coordinated host genome-gut microbiota-metabolite network underlying early growth variation in goats and provide a mechanistic foundation for precision breeding and targeted nutritional strategies in goat production systems.

This study presents the design, simulation, fabrication, and experimental characterization of a single-axis MEMS capacitive accelerometer fabricated using a wet bulk-micromachining process. The proposed structure employs an enlarged proof mass and a simplified differential capacitive architecture to improve noise performance while maintaining low nonlinearity and low cross-axis sensitivity. Unlike many previously reported MEMS accelerometers that rely on complex comb-drive structures, silicon-on-insulator (SOI) wafers, or deep reactive ion etching (DRIE), the proposed device is implemented using standard silicon wafers and conventional wet etching, thereby reducing fabrication complexity. Finite-element simulations were performed in COMSOL Multiphysics to optimize the electromechanical behavior and structural parameters of the sensor. The fabricated accelerometer demonstrated a resonant frequency of 3620&#xa0;Hz, nonlinearity below 0.9% full scale, cross-axis sensitivity below 0.03%, and a dynamic range of &#xb1;&#x2009;10&#xa0;g. The measured noise spectral density reached 18&#xa0;&#xb5;g/&#x221a;Hz in the mid-band region, while the scale-factor sensitivity was approximately 200&#xa0;mV/g with a resolution of 0.6&#xa0;mg at 1&#xa0;Hz bandwidth. The device also showed stable operation over a temperature range of -&#x2009;20&#xa0;&#xb0;C to +&#x2009;80&#xa0;&#xb0;C. In addition, finite-element simulations indicated that the structure can tolerate shock loads up to 5000&#xa0;g without exceeding the silicon stress limit. The results suggest that wet bulk micromachining can enable an enlarged proof mass and reduced noise floor while preserving acceptable linearity and mechanical safety margins. Furthermore, simulations showed that the dynamic range of the proposed architecture can be tuned from &#xb1;&#x2009;2&#xa0;g to &#xb1;&#x2009;200&#xa0;g by modifying the suspension beam thickness without changing the overall device topology. These findings suggest that the proposed accelerometer is a practical and cost-effective candidate for capacitive sensing applications requiring simplified fabrication and competitive performance.

In this paper, a novel Disturbance Observer-Based Adaptive Sliding Mode Control (DOB-ASMC) architecture for a medium-size tricopter unmanned aerial vehicle (UAV) equipped with variable dihedral arms and dual-mode yaw vectoring is suggested with focus on the design, mathematical modeling, and experimental validation. The proposed tricopter architecture comprises mechanically reconfigurable dihedral angles (0&#xb0;-30&#xb0;) on its three arms and a hybrid tail rotor system capable of both conventional servo-deflection yaw control and thrust-differential yaw generation. This dual-mode yaw technique significantly improves torque bandwidth and agility in degraded conditions. The tightly coupled, nonlinear six-degree-of-freedom (6-DOF) dynamics coming from dihedral reconfiguration and rotor interaction with DOB-ASMC are presented. A nonlinear disturbance observer (NDO) is introduced for the estimation of time-varying external disturbances, including wind gusts, motor asymmetry, and structural flexibility, feeding compensatory signals into an adaptive sliding mode control law whose switching gain self-tunes based on estimated disturbance magnitude. A proof of Lyapunov stability analysis with finite-time convergence is presented for a sliding surface and ultimate boundedness of tracking error. Comprehensive simulation studies in MATLAB/Simulink with aerodynamic disturbance injection and hardware-in-loop (HIL) experiments on a custom 1.2&#xa0;kg prototype have been done to show superior attitude tracking, robust yaw performance, and graceful degradation relative to classical PID, standard SMC, and backstepping controllers. The studies demonstrate that root mean square (RMS) tracking error is reduced by up to 63% and chattering is attenuated by 47% compared to conventional SMC under severe wind disturbances of 8&#xa0;m/s.

Breast cancer is one of the most common cancers among women worldwide, and metastasis plays an important role in its lethality. Silymarin (SLY) is a natural compound that has exhibited potential anticancer effects. However, its effects on the metastatic properties of different breast cancer cell lines remain unknown. Therefore, this study aimed to investigate the effects of SLY on the metastatic properties of two breast cancer cell lines (MCF-7 and MDA-MB-231) and evaluate differences between two-dimensional (2D) and three-dimensional (3D) cell culture models. This study used 2D and 3D cell culture systems to evaluate the effects of SLY on cell proliferation, migration, and epithelial-mesenchymal transition (EMT). The effective dose of SLY was determined using 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide cell viability assays in 2D and 3D cultures. In 2D cultures, wound healing assays, polymerase chain reaction (PCR) to evaluate mRNA levels and immunohistochemical analyses to evaluate protein expression. In 3D cultures, spheroids were formed, and Transwell migration assays, PCR for mRNA levels, and immunohistochemical analyses for protein expression were conducted. SLY effectively modulated proliferation, migration, and EMT in both cell lines. Notably, the 3D microenvironment exerted differential effects on drug sensitivity: MCF-7 cells showed increased sensitivity to SLY in 3D culture, whereas MDA-MB-231 cells exhibited increased resistance compared to 2D models. These findings suggest that SLY exhibits promising antimetastatic potential in vitro, warranting further in vivo and clinical investigations to evaluate its efficacy as a supportive therapeutic candidate for breast cancer treatment. This study emphasizes the importance of 3D culture models for accurately evaluating the efficacy of anticancer drugs. Notably, SLY's effect on MMP-9 expression was found to be environment-dependent, showing a significant reduction specifically in the 3D spheroid model, which highlights its potential antimetastatic efficacy in more biomimetic settings.

Chloroplast development is a fundamental process underlying photosynthesis and plant growth, yet its molecular regulatory mechanisms remain to be fully elucidated. In this study, we identified an albino rice mutant, which exhibits drastically reduced chlorophyll content and defective chloroplast ultrastructure. Through map-based cloning, coupled with CRISPR/Cas9-mediated gene editing and complementation assays, we verified that the target gene encodes a zinc metalloprotease of the FtsH protein family, OsFtsH1. The expression of OsFtsH1 is light-inducible, and its encoded protein localizes specifically to chloroplasts. RNA-sequencing (RNA-seq) analysis revealed broad differential expression of photosynthesis-related genes in osftsh1 mutants. qRT-PCR further demonstrated that plastid-encoded genes involved in chloroplast biogenesis are markedly downregulated at the transcriptional level. Functional assays indicated that OsFtsH1 sustains the homeostasis of D1, D2 and CP43, core proteins of the photosystem II (PSII), and collaboratively regulates chloroplast development through interactions with three key proteins: the chloroplast signaling protein OsCPL1, the PSII oxygen-evolving complex component OsPsbO, and the photosynthetic electron transport protein OsFd1. Additionally, the expression of OsFtsH1 is induced by indole-3-acetic acid (IAA) and abscisic acid (ABA), and its overexpression markedly enhances rice sensitivity to these two phytohormones. Collectively, our findings unravel the multifaceted and crucial functions of OsFtsH1 in orchestrating chloroplast development, photosynthetic machinery homeostasis, and stress response in rice.

Aortic dissection (AD) is a life-threatening vascular condition characterized by acute inflammation and structural deterioration of the aortic wall. This study aimed to delineate the immune landscape, particularly T cell-mediated responses, and identify conserved inflammatory mechanisms driving AD pathogenesis across human and murine models. Ascending aortic tissues and plasma were collected from patients with AD and normal controls. CD45&#x207a; immune cells were isolated using magnetic-activated cell sorting, followed by single-cell RNA sequencing (scRNA-seq) via the 10x Genomics Chromium platform. Data processing and downstream bioinformatics analyses were performed using Cell Ranger and Seurat pipelines, including cell clustering, differential expression, and pathway enrichment analyses. To validate transcriptomic findings, a &#x3b2;-aminopropionitrile (BAPN)-induced AD mouse model was established, and aortic tissues were subjected to eukaryotic transcriptome sequencing. Differentially expressed genes (DEGs) were identified and functionally annotated via GO and KEGG analyses, while transcription factor-target networks were constructed to reveal key regulators. Cross-species integration of human and murine transcriptomic datasets was conducted to identify conserved DEGs and signaling pathways. Quantitative PCR and ELISA were performed on human samples to validate transcriptomic results and assess systemic inflammatory responses. scRNA-seq analysis revealed a distinct immune landscape in AD tissues characterized by significant T cell enrichment and activation. Disease-associated T cell clusters exhibited elevated expression of cytotoxic and inflammatory genes such as GZMA, GZMB, IFIT1, and IFI6, indicating enhanced adaptive immune activity within the aortic wall. Bulk transcriptomic analysis further demonstrated upregulation of pro-inflammatory mediators (IL1B, CXCL8, CCL2, NFKBIA) and enrichment in immune-related pathways, including TNF, NF-&#x3ba;B, and IL-17 signaling. In the murine AD model, transcriptomic profiling identified 4,427 DEGs, primarily involved in inflammatory responses, leukocyte migration, extracellular matrix remodeling, and apoptosis. Transcription factor analysis highlighted Nfkb1, Jun, Fos, and Stat3 as key regulatory hubs orchestrating these processes. Cross-species integration revealed 51 conserved DEGs between human and mouse datasets, predominantly enriched in IL-17 and cytokine-cytokine receptor interaction pathways. qRT-PCR validation in human aortic tissues confirmed significant upregulation of IL1B, IL6, MAPK10, MAPK12, MMP9, MMP13, S100A8, and S100A9 in AD samples, while ELISA demonstrated elevated serum levels of IFN-&#x3b3;, IL-10, IL-6, TNF-&#x3b1;, IL-17&#xa0;A, and IL-8, indicative of a systemic pro-inflammatory state. Collectively, our integrative multi-omics analyses demonstrate that aortic dissection is driven by coordinated immune remodeling involving both adaptive and innate immune cells, accompanied by conserved inflammatory transcriptional programs across human and murine datasets. Despite limited changes in circulating cytokines, tissue-level analyses reveal robust activation of TNF, NF-&#x3ba;B, and IL-17-associated pathways, underscoring the importance of local vascular immune responses in AD pathogenesis.