BACKGROUND: The association of systemic inflammatory response index (SIRI) with prognosis of coronary artery disease (CAD) patients has never been investigated in a large sample with long-term follow-up. This study aimed to explore the association of SIRI with all-cause and cause-specific mortality in a nationally representative sample of CAD patients from United States. METHODS: A total of 3386 participants with CAD from the National Health and Nutrition Examination Survey (NHANES) 1999-2018 were included in this study. Cox proportional hazards model, restricted cubic spline (RCS), and receiver operating characteristic curve (ROC) were performed to investigate the association of SIRI with all-cause and cause-specific mortality. Piece-wise linear regression and sensitivity analyses were also performed. RESULTS: During a median follow-up of 7.7 years, 1454 all-cause mortality occurred. After adjusting for confounding factors, higher lnSIRI was significantly associated with higher risk of all-cause (HR = 1.16, 95% CI: 1.09-1.23) and CVD mortality (HR = 1.17, 95% CI: 1.05-1.30) but not cancer mortality (HR = 1.17, 95% CI: 0.99-1.38). The associations of SIRI with all-cause and CVD mortality were detected as J-shaped with threshold values of 1.05935 and 1.122946 for SIRI, respectively. ROC curves showed that lnSIRI had robust predictive effect both in short and long terms. CONCLUSIONS SIRI was independently associated with all-cause and CVD mortality, and the dose-response relationship was J-shaped. SIRI might serve as a valid predictor for all-cause and CVD mortality both in the short and long terms.

Interfering hepatitis B virus (HBV) capsid assembly holds promise as a therapeutic approach for chronic hepatitis B (CHB). Novel anti-HBV agents are urgently needed to overcome drug resistance challenges, with targeted protein degradation (TPD) emerging as a hopeful strategy. Herein, we report the first degradation of HBV core protein (HBC), a multifunctional structural protein, using small-molecule degraders developed by hydrophobic tagging (HyT) technology. Structure-activity relationship (SAR) analysis identified compound HyT-S7, featuring an adamantyl group, exhibiting potent inhibitory activity (EC₅₀ = 0.46 μmol/L, HepAD38 cells) and degradation ability (DC₅₀ = 3.02 ± 0.54 μmol/L) in a dose- and time-dependent manner. Mechanistic studies demonstrated that the autophagy-lysosome pathway was a potential driver of HyT-S7-induced HBC degradation. Remarkably, HyT-S7 effectively degraded 11 drug-resistant mutants, including highly resistant strains P25G and T33N, to Phase III drug GLS4. Furthermore, cellular thermal shift assay, surface plasmon resonance assay, and molecular dynamics simulations revealed the precise mode of HyT-S7 binding to HBC with the adamantyl group potentially mimicking protein misfolding to facilitate HBC degradation. This first proof-of-concept study highlights the potential of HyT-mediated TPD in HBC as a promising avenue for discovering novel HBV and other antiviral agents with favorable drug resistance profiles.

Numerous studies on the formation and consolidation of memory have shown that memory processes are characterized by phase-dependent and dynamic regulation. Memory retrieval, as the only representation of memory content and an active form of memory processing that induces memory reconsolidation, has attracted increasing attention in recent years. Although the molecular mechanisms specific to memory retrieval-induced reconsolidation have been gradually revealed, an understanding of the time-dependent regulatory mechanisms of this process is still lacking. In this study, we applied a transcriptome analysis of memory retrieval at different time points in the recent memory stage. Differential expression analysis and Short Time-series Expression Miner (STEM) depicting temporal gene expression patterns indicated that most differential gene expression occurred at 48 h, and the STEM cluster showing the greatest transcriptional upregulation at 48 h demonstrated the most significant difference. We then screened the differentially-expressed genes associated with that met the expression patterns of those cluster-identified genes that have been reported to be involved in learning and memory processes in addition to dipeptidyl peptidase 9 (DPP9). Further quantitative polymerase chain reaction verification and pharmacological intervention suggested that DPP9 is involved in 48-h fear memory retrieval and viral vector-mediated overexpression of DPP9 countered the 48-h retrieval-induced attenuation of fear memory. Taken together, our findings suggest that temporal gene expression patterns are induced by recent memory retrieval and provide hitherto undocumented evidence of the role of DPP9 in the retrieval-induced reconsolidation of fear memory.
Animals ; Fear/physiology* ; Male ; Dipeptidyl-Peptidases and Tripeptidyl-Peptidases/genetics* ; Memory Consolidation/physiology* ; Time Factors ; Mental Recall/drug effects* ; Mice ; Gene Expression Profiling

Mutations in the cyclin-dependent kinase-like 5 gene (CDKL5) cause a severe neurodevelopmental disorder, yet the impact of truncating mutations remains unclear. Here, we introduce the Cdkl5^492stop mouse model, mimicking C-terminal truncating mutations in patients. 492stop/Y mice exhibit altered dendritic spine morphology and spontaneous seizure-like behaviors, alongside other behavioral deficits. After creating cell lines with various Cdkl5 truncating mutations, we found that these mutations are regulated by the nonsense-mediated RNA decay pathway. Most truncating mutations result in CDKL5 protein loss, leading to multiple disease phenotypes, and offering new insights into the pathogenesis of CDKL5 disorder.
Animals ; Disease Models, Animal ; Mice ; Protein Serine-Threonine Kinases/deficiency* ; Mutation/genetics* ; Epileptic Syndromes/genetics* ; Humans ; Dendritic Spines/pathology* ; Spasms, Infantile/genetics* ; Male ; Seizures/genetics* ; Mice, Inbred C57BL

OBJECTIVE: Humans are exposed to complex mixtures of environmental chemicals and other factors that can affect their health. Analysis of these mixture exposures presents several key challenges for environmental epidemiology and risk assessment, including high dimensionality, correlated exposure, and subtle individual effects. METHODS: We proposed a novel statistical approach, the generalized functional linear model (GFLM), to analyze the health effects of exposure mixtures. GFLM treats the effect of mixture exposures as a smooth function by reordering exposures based on specific mechanisms and capturing internal correlations to provide a meaningful estimation and interpretation. The robustness and efficiency was evaluated under various scenarios through extensive simulation studies. RESULTS: We applied the GFLM to two datasets from the National Health and Nutrition Examination Survey (NHANES). In the first application, we examined the effects of 37 nutrients on BMI (2011-2016 cycles). The GFLM identified a significant mixture effect, with fiber and fat emerging as the nutrients with the greatest negative and positive effects on BMI, respectively. For the second application, we investigated the association between four pre- and perfluoroalkyl substances (PFAS) and gout risk (2007-2018 cycles). Unlike traditional methods, the GFLM indicated no significant association, demonstrating its robustness to multicollinearity. CONCLUSION GFLM framework is a powerful tool for mixture exposure analysis, offering improved handling of correlated exposures and interpretable results. It demonstrates robust performance across various scenarios and real-world applications, advancing our understanding of complex environmental exposures and their health impacts on environmental epidemiology and toxicology.
Humans ; Environmental Exposure/analysis* ; Linear Models ; Nutrition Surveys ; Environmental Pollutants ; Body Mass Index

A portable molecularly imprinted(MIP)surface-enhanced Raman scattering(SERS)chip was fabricated via a green electrochemical approach for highly selective detection of bisphenol A(BPA).This MIP-AuNP/UIO-66/SPE sensor was fabricated through a single-step co-deposition process.The process involved electropolymerization onto a UIO-66 modified screen-printed electrode(SPE),by usingo-phenylenediamine(OPD)as functional monomer and BPA as template,and simultaneously electro-reduction generated gold nanoparticles(AuNPs),which served as the SERS-active substrate.Ultimately,this one-step method formed a three-dimensional porous architecture on the electrode surface.Under 785 nm laser excitation,the sensing chip exhibited a highly sensitive SERS response towards BPA.The intensity of its characteristic peak at 850 cm-1 showed a good linear relationship with logarithm of BPA concentration in the range of 1.0×10-10 to 1.0×10-6 mol/L,with a detection limit of 1.0×10-12 mol/L.More importantly,the fabricated chips maintained highly selective binding affinity for BPA in water samples even in the presence of structural analogs bisphenol F(BPF)and bisphenol S(BPS).When the chip was applied to detection of BPA in water samples from plastic bottle and paper cup,the recovery rates ranged from 94.0%to 103.0%with relative standard deviations(RSD)less than 4.7%.The developed chip offered a highly sensitive and selective solution for detection of trace BPA in complex water samples.

Objective To explore a deep learning-based automatic bone age estimation model for elbow joint X-ray images of Chinese Han adolescents and children and evaluate its performance.Methods A total of 943(517 males and 426 females)elbow joint frontal view X-ray images of Chinese Han ado-lescents and children aged 6.00 to<16.00 years were collected from East,South,Central and North-west China.Three experimental schemes were adopted for bone age estimation.Scheme 1:Directly in-put preprocessed images into the regression model;Scheme 2:Train a segmentation network using"key elbow joint bone annotations"as labels,then input segmented images into the regression model;Scheme 3:Train a segmentation network using"full elbow joint bone annotations"as labels,then in-put segmented images into the regression model.For segmentation,the optimal model was selected from U-Net,UNet++and TransUNet.For regression,VGG16,VGG19,InceptionV2,InceptionV3,ResNet34,ResNet50,ResNet101 and DenseNet121 models were selected for bone age estimation.The dataset was randomly split into 80%(754 samples)for training and validation for model fitting and hyperparameter tuning,and 20%(189 samples)as an internal test set to test the performance of the trained model.An additional 104 elbow joint X-ray images from the same demographic and age group were col-lected and used as an external test set.Model performance was evaluated by comparing the mean ab-solute error(MAE),root mean square error(RMSE),accuracies within±0.7 years(P±0.7 years)and±1.0 years(P±1.0 years)between the estimated age and the actual age,and by drawing radar charts,scat-ter plots,and heatmaps.Results When segmented with Scheme 3,the UNet++model achieved good segmentation performance with a segmentation loss of 0.000 4 and an accuracy of 93.8%at a learning rate of 0.000 1.In the internal test set,the DenseNet121 model with Scheme 3 yielded the best results with MAE,P±0.7 years and P±1.0 years being 0.83 years,70.03%,and 84.30%,respectively.In the external test set,the DenseNet121 model with Scheme 3 also performed best,with an average MAE of 0.89 years and an average RMSE of 1.00 years.Conclusion When performing automatic bone age estima-tion using elbow joint X-ray images in Chinese Han adolescents and children,it is recommended to use the UNet++model for segmentation.The DenseNet121 model with Scheme 3 achieves optimal per-formance.Using segmentation networks,especially that trained with annotation areas encompassing the full elbow joint including the distal humerus,proximal radius,and proximal ulna,can improve the ac-curacy of bone age estimation based on elbow joint X-ray images.

Objective To explore a deep learning network model suitable for bone age estimation using shoulder joint X-ray images in Chinese Han adolescents.Methods A retrospective collection of 1 286 shoulder joint X-ray images of Chinese Han adolescents aged 12.0 to<18.0 years(708 males and 578 females)was conducted.Using random sampling,approximately 80%of the samples(1 032 cases)were selected as the training and validation sets for model learning,selection and optimization,and the other 20%samples(254 cases)were used as the test set to evaluate the model's generalization ability.The original single-channel shoulder joint X-ray images and dual-channel inputs combining original images with segmentation labels(manually annotated shoulder joint regions multiplied pixel-by-pixel with original images,followed by segmentation via the U-Net++network to retain only key shoulder joint region information)were respectively input into four network models,namely VGG16,ResNet18,ResNet50 and DenseNet121 for bone age estimation.Additionally,manual bone age estimation was con-ducted on the test set data,and the results were compared with the four network models.The mean absolute error(MAE),root mean square error(RMSE),coefficient of determination(R2),and Pear-son correlation coefficient(PCC)were used as main evaluation indicators.Results In the test set,the bone age estimation results of the four models with dual-channel input of shoulder joint X-ray images outperformed those with single-channel input in all four evaluation indicators.Among them,DenseNet121 with dual-channel input achieved best results with MAE of 0.54 years,RMSE of 0.82 years,R2 of 0.76,and PCC(r)of 0.88.Manual estimation yielded an MAE of 0.82 years,ranking second only to dual-channel DenseNet121.Conclusion The DenseNet121 model with dual-channel input combined with original images and segmentation labels is superior to manual evaluation results,and can effectively estimate the bone age of Chinese Han adolescents.

Objective:To investigate the regulatory effects of transcutaneous auricular vagus nerve stimulation (taVNS) on functional connectivity (FC) of thalamic subregions in patients with premenstrual syndrome (PMS).Methods:This study was a cross-sectional investigation. Clinical, laboratory, and imaging data were retrospectively collected from 56 PMS patients (PMS group) and 66 healthy controls (control group) recruited from various universities and hospitals in Nanning between November 2021 and June 2024. Resting-state functional MRI (fMRI) data and fMRI data during taVNS immediate stimulation (2 Hz, 25 Hz) were acquired from subjects during their late luteal phase. Using thalamic subregions (anterior thalamic nucleus, lateral nucleus, ventral nucleus, medial nucleus, central nucleus, posterior nucleus) as seeds, two-sample t-tests or paired t-tests were employed to analyze alterations in thalamic subregion FC in PMS patients and the regulatory effects of taVNS on these changes. Independent samples t-test were used to compare the differences in clinical and laboratory indicators between the PMS group and the control group. The relationship between taVNS regulation of thalamic subregion FC in PMS patients and thalamic internal functional connectivity were analyzed using mediation effect analysis. Results:Compared to the control group, patients in the PMS group showed increased scores on the Daily Record of Severity of Problems, Pittsburgh Sleep Quality Index, Self-Rating Anxiety Scale, Self-Rating Depression Scale, Hamilton Anxiety Rating Scale 17, and Hamilton Depression Rating Scale 14 during the late luteal phase ( P<0.05). At baseline, PMS patients exhibited higher FC between the left thalamic lateral nucleus and the left insula, and lower FC between the left medial nucleus, posterior nucleus, and ventral nucleus of the thalamus and the right middle frontal gyrus (MFG) compared to the control group (GRF corrected, voxel-level P<0.001, cluster-level P<0.05). During 2 Hz taVNS immediate stimulation in PMS group, FC between the left thalamic medial nucleus, posterior nucleus, ventral nucleus and the right MFG, as well as the FC between the left thalamic ventral nucleu and the left MFG increased compared to baseline levels; meanwhile, FC between the left thalamic posterior nucleus, ventral nucleus and the left insula decreased compared to baseline levels (GRF corrected, voxel-level P<0.001, cluster-level P<0.05). During 25 Hz taVNS immediate stimulation, the FC between the left thalamic ventral nucleus and the right MFG decreased compared to the baseline level (GRF corrected, voxel-level P<0.001, cluster-level P<0.05). Mediation effect analysis showed that the FC between the left thalamic posterior nucleus and the left lateral nucleus mediated part of the association between the FC of the left lateral thalamic nucleus-left insula and the FC of the left ventral thalamic nucleus-left putamen/insula; there were significant direct effects between the FC of the left lateral thalamic nucleus-the left posterior nucleus and FC of the left lateral thalamic nucleus-the left insula, as well as between the FC of the left ventral thalamic nucleus-the left MFG and FC of the left ventral thalamic nucleus-the right MFG. Conclusions:taVNS can modulate abnormal FC of the left thalamic subregions in PMS patients, restoring it toward normalization. The regulatory effects of 2 Hz stimulation are more pronounced than those of 25 Hz stimulation. This modulation primarily operates through two pathways: the left thalamic lateral nucleus-left insula-left thalamic ventral nucleus pathway and the left MFG-left thalamic ventral nucleus-right MFG.