As the first defense of respiratory system,airway epi-thelial cells(AECs)play an important role in separating the re-spiratory internal and external environment.They are essential for the natural immune function.Small airway lesions are an im-portant early pathology of chronic obstructive pulmonary disease(COPD),when AECs are exposed to harmful particles or gases for a long time,the epithelial barrier is damaged,and the signa-ling pathways which involved in differentiation,repair,and in-flammatory are disordered,resulting in epithelial cell cycle stag-nation and accelerated aging.A number of studies have sugges-ted that AECs of COPD patients express high levels of aging markers,suggesting that senescence of AECs is closely related to COPD.This review discusses the potential mechanisms of AECs senescence in COPD,the impact of AECs senescence on the de-velopment and severity of the disease,and highlights potential targets for modulating cellular senescence in airway epithelium as a therapeutic approach in COPD.

As the first defense of respiratory system,airway epi-thelial cells(AECs)play an important role in separating the re-spiratory internal and external environment.They are essential for the natural immune function.Small airway lesions are an im-portant early pathology of chronic obstructive pulmonary disease(COPD),when AECs are exposed to harmful particles or gases for a long time,the epithelial barrier is damaged,and the signa-ling pathways which involved in differentiation,repair,and in-flammatory are disordered,resulting in epithelial cell cycle stag-nation and accelerated aging.A number of studies have sugges-ted that AECs of COPD patients express high levels of aging markers,suggesting that senescence of AECs is closely related to COPD.This review discusses the potential mechanisms of AECs senescence in COPD,the impact of AECs senescence on the de-velopment and severity of the disease,and highlights potential targets for modulating cellular senescence in airway epithelium as a therapeutic approach in COPD.

Objective:To evaluate the value of ultrasound radiomics combined with machine learning for early diagnosis of seronegative Hashimoto’s thyroiditis (SN-HT) .Methods:This retrospective study included 164 patients from Liaoning Provincial People’s Hospital , Lixin County People’s Hospital, Linghai Dalinghe Hospital, Fengcheng Phoenix Hospital, who underwent thyroidectomy for solitary nodules with normal thyroid function between Nov. 2016 and Jan. 2024. Postoperative pathology confirmed Hashimoto’s thyroiditis (HT) in some cases, who were further categorized into antibody-positive and antibody-negative groups based on serum antibody status. Patients without Hashimoto’s thyroiditis served as the control group. A total of 298 ultrasound images were analyzed. Radiomics features were extracted from hypoechoic non-nodular areas within 0.5 cm surrounding the tumor. Two senior pathologists and two senior ultrasound physicians independently assessed lymphocytic infiltration, eosinophilic changes of follicular epithelium, and the proportion of hypoechoic areas in pathology and ultrasound images, respectively. A machine learning model, CCH-NET, was developed using linear regression and t-distributed stochastic neighbor embedding (t-SNE) techniques. The dataset was divided into a training set (80%) and a validation set (20%) to compare the diagnostic accuracy of CCH-NET with that of senior ultrasound physicians. Results:In internal validation, CCH-NET achieved a diagnostic accuracy of 88.89% for both antibody-positive and antibody-negative groups, significantly higher than the 66.67% accuracy of senior ultrasound physicians ( P<0.01). In external validation, CCH-NET achieved 75.00% and 66.67% accuracy for the two groups, compared to 50.00% by senior ultrasound physicians. For the control group, both methods achieved 93.33% accuracy. The AUC of CCH-NET was 0.848, outperforming senior ultrasound physicians (0.681) ,demonstrating superior diagnostic performance. Conclusion:The radiomics-based CCH-NET model, using non-nodular hypoechoic areas as a specific indicator, can accurately identify early SN-HT in euthyroid patients. It significantly outperforms senior ultrasound physicians, improving diagnostic accuracy and reducing missed diagnoses.

Aim To investigate the anti-Zika virus(ZIKV)mechanism of diterpenoid compound 9 from Euphorbia helioscopia in vitro.Methods The cytotox-icity of compound 9 was evaluated using the CCK-8 as-say.A ZIKV-infected Vero cell model was established,and the antiviral activity was assessed through RT-qPCR,plaque assay,Western blot,and immunofluores-cence.Furthermore,the mechanism of action was elu-cidated using multi-cell line validation,nanoparticle tracking analysis,cellular thermal shift assay,and mo-lecular docking.Results In Vero cells,compound 9 exhibited an EC50 of(3.95±0.15)μmol·L-1 and a CC50 of(272.12±8.56)μmol·L-1,demonstrating significantly higher antiviral efficacy than the positive control drug ribavirin(RBV).Its virus inactivation effect was time-dependent and could significantly re-duce viral load and plaque formation.Studies revealed that compound 9 altered the physicochemical properties of ZIKV particles,including reducing surface charge and increasing particle size distribution.Additionally,it significantly enhanced the thermal stability of the prM protein.Molecular docking analysis indicated that compound 9 formed a high-affinity interaction with the prM protein(binding energy:-38.52 kJ·mol-1)and stabilized its structure through hydrophobic interac-tions.Conclusion Compound 9 exerts in vitro anti-ZIKV activity by directly inactivating the virus,disrup-ting viral particle integrity,and targeting the prM pro-tein.

Objective To develop a deep learning model based on fundus retinal images to improve the detection rate of mild cognitive impairment(MCI)in patients with coronary heart disease,achieve early intervention and improve prognosis.Methods The study was a single-center cross-sectional study that retrospectively included patients diagnosed with coronary heart disease(CHD)by coronary angiography(≥50％ stenosis of at least one coronary vessel)from Beijing Anzhen Hospital between November 2021 and December 2022.The whole data set was randomly divided into the training set and the testing set according to the ratio of 8∶2 for model development.After that,the patient data of the same center from January 2023 to April 2023 were included in the time verification method to verify the model.The diagnostic criteria for MCI were MMSE＜27 or MoCA＜26.Four kinds of convolutional neural network(CNN)architectures were used to train fundus images,and a comprehensive vision model of MCI detection was established through model integration.The area under the curve(AUC),sensitivity and specificity of the receiver operating curve(ROC)were used to evaluate the performance of the AI model.Results We collected 5 880 eligible fundus images from 3 368 CHD patients.Based on the results of the MMSE scale,the algorithm was labeled,including 2 898 males and 527 MCI patients.The AUC of the deep learning model in the test group is 0.733(95％CI 0.688-0.778),and the sensitivity of the algorithm in the test group is 0.577(95％CI 0.528-0.625)by using the operating point with the maximum sum of sensitivity and specificity.With a specificity of 0.758(95％CI 0.714-0.802),corresponding to a validated AUC of 0.710(95％CI 0.601-0.818).Based on the results of the MoCA scale,the algorithm labels 2 437 males and 1 626 MCI patients.The AUC of the deep learning model in the test group was 0.702(95％CI 0.671-0.733).The operating point with the maximum sum of sensitivity and specificity was selected,and the sensitivity of the algorithm was 0.749(95％CI 0.719-0.778)and the specificity was 0.561(95％CI 0.527-0.595),corresponding to the AUC value of the verification group was 0.674(95％CI 0.622-0.726).Conclusions The deep learning algorithm model based on fundus images has good diagnostic performance,and may be used as a new non-invasive,convenient and rapid screening method for MCI in CHD population.

Objective To establish and evaluate a mouse model of chronic obstructive pulmonary disease(COPD)induced by cigarette smoke(CS).Methods Forty BALB/c mice were divided randomly into a control group and a CS group.Mice in the CS group were subjected to passive smoking for 20 weeks and a COPD model was established.Morphological changes in the organs and lung,heart,liver,and kidney fibrosis were observed by hematoxylin-eosin(HE)and Masson staining.Lung,cardiac,and brain cognitive function were evaluated by pulmonary function testing,small-animal ultrasound,and Morris water maze trials.Tumor necrosis factor-α(TNF-α),interleukin(IL)-6 and IL-1β levels in lung and brain tissues were detected by ELISA.Liver and renal functions were measured by biochemical method.Results The alveolar septum was narrowed or broken in mice in the CS group,and the adjacent alveolar cavity was enlarged and fused,consistent with the pathological changes of COPD.Neuronal degeneration and necrosis were observed in the hippocampus,but there were no significant morphological changes in other organs.Masson staining showed no obvious fibrosis in the lung,heart,liver,or kidney in CS-group mice.The result of pulmonary function tests showed that the forced expiratory volume in 0.1 second/forced vital capacity(FEV 0.1/FVC)and dynamic compliance were significantly decreased in the CS group compared with the control group,while airway resistance was obviously increased.Cognitive impairment in mice in the CS group was confirmed in the Morris water maze trial.TNF-α,IL-6,and IL-1β levels in lung and brain tissues were higher in the CS group compared with the control group.There were no significant differences in cardiac,liver,and renal functions between the groups.Conclusions A mouse model of COPD can be established by CS exposure for 20 weeks.Lung histomorphology,lung function,brain cognitive function,and levels of inflammatory factors can be used as indicators to evaluate the success of the model.

Objective:To evaluate the value of ultrasound radiomics combined with machine learning for early diagnosis of seronegative Hashimoto’s thyroiditis (SN-HT) .Methods:This retrospective study included 164 patients from Liaoning Provincial People’s Hospital , Lixin County People’s Hospital, Linghai Dalinghe Hospital, Fengcheng Phoenix Hospital, who underwent thyroidectomy for solitary nodules with normal thyroid function between Nov. 2016 and Jan. 2024. Postoperative pathology confirmed Hashimoto’s thyroiditis (HT) in some cases, who were further categorized into antibody-positive and antibody-negative groups based on serum antibody status. Patients without Hashimoto’s thyroiditis served as the control group. A total of 298 ultrasound images were analyzed. Radiomics features were extracted from hypoechoic non-nodular areas within 0.5 cm surrounding the tumor. Two senior pathologists and two senior ultrasound physicians independently assessed lymphocytic infiltration, eosinophilic changes of follicular epithelium, and the proportion of hypoechoic areas in pathology and ultrasound images, respectively. A machine learning model, CCH-NET, was developed using linear regression and t-distributed stochastic neighbor embedding (t-SNE) techniques. The dataset was divided into a training set (80%) and a validation set (20%) to compare the diagnostic accuracy of CCH-NET with that of senior ultrasound physicians. Results:In internal validation, CCH-NET achieved a diagnostic accuracy of 88.89% for both antibody-positive and antibody-negative groups, significantly higher than the 66.67% accuracy of senior ultrasound physicians ( P<0.01). In external validation, CCH-NET achieved 75.00% and 66.67% accuracy for the two groups, compared to 50.00% by senior ultrasound physicians. For the control group, both methods achieved 93.33% accuracy. The AUC of CCH-NET was 0.848, outperforming senior ultrasound physicians (0.681) ,demonstrating superior diagnostic performance. Conclusion:The radiomics-based CCH-NET model, using non-nodular hypoechoic areas as a specific indicator, can accurately identify early SN-HT in euthyroid patients. It significantly outperforms senior ultrasound physicians, improving diagnostic accuracy and reducing missed diagnoses.

China has become the country with the highest global burden of heart failure(HF).Despite the widespread use of prognostic-improving medications today,the mortality rate of HF remains high,reaching 13.7％at one year-particularly among patients with heart failure with reduced ejection fraction(HFrEF).HF interventional device therapy(structural intervention)targets the structural factors underlying HF,including atrial pressure,ventricular remodeling,and valvular intervention.It leverages the heart's intrinsic physiological properties and pathological progression mechanisms to deliver treatments through interventions without external active forces,achieving anatomical or functional repair.This field has emerged as a rapidly growing area and plays an increasingly critical role in HF management.This article provides a comprehensive review and summary of the latest advancements in HF and cardiomyopathy interventional therapy over the past year.It covers various novel technologies and products currently in the research phase,aiming to provide an in-depth analysis of the current status and future directions of HF interventional therapy,and further advance the development of this discipline.

To investigate the effects and mechanisms of the water extract from Sorbus tianschanica(STE) on asthmatic airway inflammation, the mice were randomly divided into six groups, including a control group, a model group, a positive drug dexamethasone group(2 mg·kg~(-1)), a low-dose STE group(1 g·kg~(-1)), a medium-dose STE group(2 g·kg~(-1)), and a high-dose STE group(4 g·kg~(-1)). Except for the control group, all groups were subjected to ovalbumin induction to establish an asthma mouse model. The anti-inflammatory effects of STE were evaluated by examining pathological changes in lung tissue and measuring the levels of interleukin(IL)-4 and IL-5 in bronchoalveolar lavage fluid(BALF). Transcriptomic and proteomic methods were further employed to analyze differentially expressed genes and proteins, as well as their associated signaling pathways in lung tissue. Subsequently, the expression changes of key genes were verified by reverse transcription-quantitative polymerase chain reaction(RT-qPCR), and immunohistochemistry and Western blot methods were used to explore the regulatory mechanisms of STE in the pathogenesis of asthma in mice. Molecular docking was performed by using AutoDock Vina software to evaluate the binding affinity of the main active components in STE with the target proteins, including phosphatidylinositol-3-kinase catalytic subunit α(PIK3CA), Toll-like receptor 4(TLR4), protein kinase B1(Akt1), and matrix metallopeptidase 9(MMP9). The results showed significant inflammatory cell infiltration and fibrous tissue proliferation in the lung tissue of mice in the model group. However, these pathological changes were markedly reduced following STE intervention. Compared with those of the control group, the expression levels of IL-4 and IL-5 in the BALF of the model group were significantly increased but notably decreased following STE intervention. Transcriptomic and proteomic analyses identified key genes and proteins associated with allergic asthma, including tumor necrosis factor(TNF), IL-6, TLR4, PIK3CA, and MMP9. RT-qPCR validation revealed that high-dose STE intervention significantly downregulated the expressions of PIK3CA, IL-6, Akt1, MMP9, IL-13, nuclear factor-kappa B(NF-κB), TNF, CXC motif chemokine ligand 1(CXCL1), and TLR4 mRNAs and significantly upregulated the expression of signal transducer and activator of transcription 1(STAT1) mRNA. Western blot and immunohistochemical analyses confirmed that STE significantly downregulated the expressions of MMP9, TLR4, PIK3CA, and phosphorylated protein kinase B(p-Akt) in lung tissue of asthmatic mice. Moreover, molecular docking demonstrated that kaempferol-3,7-diglucoside, isoquercitrin, quercetin-3-gentiobioside, and hyperoside in STE exhibited stable binding affinities with PIK3CA, TLR4, Akt1, and MMP9, suggesting that the active components may exert anti-inflammatory effects by targeting and modulating asthma-related signaling pathways. In summary, STE exerts anti-asthmatic effects by inhibiting the expressions of PIK3CA, MMP9, p-Akt, and TLR4 and regulating the TLR4/PI3K/Akt/MMP9 signaling pathway.
Animals ; Asthma/metabolism* ; Toll-Like Receptor 4/metabolism* ; Signal Transduction/drug effects* ; Mice ; Phosphatidylinositol 3-Kinases/metabolism* ; Proto-Oncogene Proteins c-akt/metabolism* ; Matrix Metalloproteinase 9/metabolism* ; Mice, Inbred BALB C ; Drugs, Chinese Herbal/administration & dosage* ; Female ; Humans ; Lung/immunology* ; Male

Optical imaging is highly valued for its superior temporal and spatial resolution. This is particularly important in near-infrared II (NIR-II, 1 000-3 000 nm) imaging, which offers advantages such as reduced tissue absorption, minimal scattering, and low autofluorescence. These characteristics make NIR-II imaging especially suitable for deep tissue visualization, where high contrast and minimal background interference are critical for accurate diagnosis and monitoring. Currently, inorganic fluorescent probes—such as carbon nanotubes, rare earth nanoparticles, and quantum dots—offer high brightness and stability. However, they are hindered by ambiguous structures, larger sizes, and potential accumulation toxicity in vivo. In contrast, organic fluorescent probes, including small molecules and polymers, demonstrate higher biocompatibility but are limited by shorter emission wavelengths, lower quantum yields, and reduced stability. Recently, gold clusters have emerged as a promising class of nanomaterials with potential applications in biocatalysis, fluorescence sensing, biological imaging, and more. Water-soluble gold clusters are particularly attractive as fluorescent probes due to their remarkable optical properties, including strong photoluminescence, large Stokes shifts, and excellent photostability. Furthermore, their outstanding biocompatibility—attributed to good aqueous stability, ultra-small hydrodynamic size, and high renal clearance efficiency—makes them especially suitable for biomedical applications. Gold clusters hold significant potential for NIR-II fluorescence imaging. Atomic-precision gold clusters, typically composed of tens to hundreds of gold atoms and measuring only a few nanometers in diameter, possess well-defined three-dimensional structures and clear spatial coordination. This atomic-level precision enables fine-tuned structural regulation, further enhancing their fluorescence properties. Variations in cluster size, surface ligands, and alloying elements can result in distinct physicochemical characteristics. The incorporation of different atoms can modulate the atomic and electronic structures of gold clusters, while diverse ligands can influence surface polarity and steric hindrance. As such, strategies like alloying and ligand engineering are effective in enhancing both fluorescence and catalytic performance, thereby meeting a broader range of clinical needs. In recent years, gold clusters have attracted growing attention in the biomedical field. Their application in NIR-II imaging has led to significant progress in vascular, organ, and tumor imaging. The resulting high-resolution, high signal-to-noise imaging provides powerful tools for clinical diagnostics. Moreover, biologically active gold clusters can aid in drug delivery and disease diagnosis and treatment, offering new opportunities for clinical therapeutics. Despite the notable achievements in fundamental research and clinical translation, further studies are required to address challenges related to the standardized synthesis and complex metabolic behavior of gold clusters. Resolving these issues will help accelerate their clinical adoption and broaden their biomedical applications.