Preparation of immunomagnetic composite nanostructures with bifunctional four-arm PEG derivatives as linkers for the ultrafast enrichment of zearalenone and its metabolites.

It is challenging to prepare sample pretreatment materials with simple use, strong selectivity and satisfactory enrichment performance. In this study, the antibody (3D4) that can specifically recognize zearalenone (ZEN) and its metabolites was immobilized on the surface of gold-coated magnetic Fe3O4 nanoparticles (GMN) by streptavidin (SA)-biotin interaction using GMN as the substrate and our designed four-arm PEG derivative (HS-4ARMPEG10K-(CM)3) as the linker. The immunomagnetic nanoparticles (GMN-4ARMPEG10K-SA-3D4) prepared by this strategy can achieve rapid enrichment (only 5 min) of analytes directly in the matrix, and higher enrichment capacity compared with the previous immunomagnetic particles. The sensitive and accurate analysis of ZEN and its metabolites can be achieved coupled with HPLC-MS/MS. The LODs and LOQs were 0.02-0.05 µg/kg and 0.05-0.10 µg/kg, respectively. The recoveries were 84.13%-112.67%, and the RSDs were 1.09%-9.39%. The method can provide a powerful tool for highly sensitive and rapid monitoring of mycotoxins in complex matrices due to its' strong selectivity and resistance to matrix interference.

ANN-GA optimized composite color protectant combined with magnetic field assisted freezing: Effects on the quality of mushroom (Agaricus bisporus).

Due to their high water content, frozen mushrooms (Agaricus bisporus) were greatly affected by ice crystal formation, which can lead to the destruction of tissue structure, serious browning, high juice loss, and difficulty in maintaining good sensory characteristics. In order to improve the quality of frozen Agaricus bisporus, this study employed Artificial neural network and genetic algorithm (ANN-GA) to optimize the amount of composite color protectant, and identified the optimal freezing conditions for freezing Agaricus bisporus by determining the freezing curves under different magnetic field-assisted freezing conditions, the color variance, texture and structure, drip loss, and distribution of moisture. Furthering, using X-ray µCT three dimensional images were taken to characterize the microstructure of the samples. Among them, the 6 mT magnetic field-assisted freezing treatment group was significantly better than the control group, and the results showed that the magnetic field-assisted freezing combined with chemical color protectant as a composite processing technology improved the quality of frozen Agaricus bisporus. This provides a theoretical basis and technical support for enhanced processing of frozen Agaricus bisporus.

A Spectroscopic Method for Distinguishing Two Novel Sandwich-Type Tungsten Oxide Cluster Compounds.

This study introduces two novel sandwich-type tungsten-oxygen cluster compounds synthesized by hydrothermal methods, H4(C6H12N2H2)3{Na(H2O)2[Mn2(H2O)(GeW9O34)]}2 (Compound 1) and H2(C6H12N2H2)3.5{Na3(H2O)4[Co2(H2O)(GeW9O34)]2}·17H2O (Compound 2). The two compounds comprise cluster anions [GeW9O34]10- coordinated with transition metal atoms, either Mn or Co, and are stabilized by organic ligands. These compounds are crystallized in the hexagonal crystal system and P63/m space group. The two compounds were characterized through various techniques. Fourier transform infrared (IR) spectroscopy showed absorption peaks of anionic backbone vibrations of the Keggin cluster at 500-1000 cm-1, IR spectral peaks of δ(N-H) and νas(C-N) of the ligand triethylenediamine at 1000-2000 cm-1, and IR spectral peaks of the ligand νas(N-H) and νas(O-H) of water at 3000-3500 cm-1. Despite similar one-dimensional (1D) IR spectra due to the same cluster anions and similar molecular structures, the two compounds exhibited distinct responses in two-dimensional correlation spectroscopy with IR under magnetic and thermal perturbations. Under magnetic perturbation, Compound 1 showed a strong response peak for νas(W-Ob-W), while Compound 2 exhibited a strong response peak for νas(W=Od), possibly linked to differing magnetic particles. Similarly, Compound 1 displayed a strong response peak under thermal perturbation for νas(W-Oc-W). In contrast, Compound 2 showed a strong response peak for νas(W=Od); these results may be attributed to the different hydrogen bonding connections between the two compounds, which affect the groups in distinct ways through vibration and transmit these vibrations to the W-O bonds. The research presented in this paper expands the theoretical and experimental data of 2D correlation IR spectroscopy.

Poly(vinyl alcohol)s and Their Glycoderivatives as Efficient Shell-Builders of Nanocapsules by Nanoprecipitation.

Poly(vinyl alcohol)s (PVAs) are very popular dispersants for the construction of colloids and common shell-constituents of microcapsules but remain mostly unexplored as building blocks for the design of nanocapsules through nanoprecipitation or other processes. Herein, we first show that model commercial PVAs and oils can be concomitantly engaged in solvent-shifting procedures to give rise to oil-filled nanocapsules in one step. Next, we report the synthesis of precisely defined water-soluble glyco-PVAs by reversible addition-fragmentation chain transfer (RAFT) copolymerization of 6-O-vinyladipoyl-d-glucopyranose and vinyl chloroacetate and selective alcoholysis reactions. We finally demonstrate that these glycopolymers are excellent candidates for the straightforward conception of oil- and drug-filled, surface- and/or core-tagged, stealth, and degradable nanocapsules by nanoprecipitation.

A Novel Multiarmed Bifunctional PEG Derivative for the Preparation of Mass Spectrometry Ion Sources with Antifouling Property and High Selectivity.

It is challenging to prepare a highly selective mass spectrometry (MS) ion source for the rapid and highly sensitive detection of analytes, especially mycotoxins. In this study, an amino and tetrazine bifunctionalized multiarm PEG derivative (NH2HCl-4armPEG10K-(MTz)3), which can be easily immobilized on the substrate by the addition reaction between amino and polydopamine, was used for the preparation of MS ionization substrate. NH2HCl-4armPEG10K-(MTz)3 can also be used as a linker to immobilize sufficient streptavidin (SA) on the surface of the substrate by a click reaction. The process further promotes the immobilization of broad-spectrum antibodies (3D4), which were used as the recognition element for ZEN and its metabolites. The prepared SSS-Au-PDA-4armPEG10K-SA-3D4 not only can rapidly enrich ZEN and its metabolites with high selectivity but also shows good antifouling properties in the matrix. After simple sample preparation, the prepared SSS-Au-PDA-4armPEG10K-SA-3D4 can be directly coupled with MS to achieve high sensitivity (LODs: 0.18-0.66 ng/mL, LOQs: 0.5-1.0 ng/mL) and selective detection of ZEN and its metabolites in the matrix. At the same time, satisfactory recoveries (83.60-97.80%) and precision (RSD: 2.80-9.10%) can also be obtained. The prepared SSS-Au-PDA-4armPEG10K-SA-3D4 is expected to provide a powerful tool for the rapid and highly sensitive determination of multiple targets by MS.

Receptor Ligand-Free Mesoporous Silica Nanoparticles: A Streamlined Strategy for Targeted Drug Delivery across the Blood-Brain Barrier.

Mesoporous silica nanoparticles (MSNs) represent a promising avenue for targeted brain tumor therapy. However, the blood-brain barrier (BBB) often presents a formidable obstacle to efficient drug delivery. This study introduces a ligand-free PEGylated MSN variant (RMSN25-PEG-TA) with a 25 nm size and a slight positive charge, which exhibits superior BBB penetration. Utilizing two-photon imaging, RMSN25-PEG-TA particles remained in circulation for over 24 h, indicating significant traversal beyond the cerebrovascular realm. Importantly, DOX@RMSN25-PEG-TA, our MSN loaded with doxorubicin (DOX), harnessed the enhanced permeability and retention (EPR) effect to achieve a 6-fold increase in brain accumulation compared to free DOX. In vivo evaluations confirmed the potent inhibition of orthotopic glioma growth by DOX@RMSN25-PEG-TA, extending survival rates in spontaneous brain tumor models by over 28% and offering an improved biosafety profile. Advanced LC-MS/MS investigations unveiled a distinctive protein corona surrounding RMSN25-PEG-TA, suggesting proteins such as apolipoprotein E and albumin could play pivotal roles in enabling its BBB penetration. Our results underscore the potential of ligand-free MSNs in treating brain tumors, which supports the development of future drug-nanoparticle design paradigms.

Dual-Track Multifunctional Bimetallic Metal-Organic Frameworks for Antibiotic Enrichment and Detection.

The improper use and overuse of antibiotics have led to significant burdens and detrimental effects on the environment, food supply, and human health. Herein, a magnetic solid-phase extraction program and an optical immunosensor based on bimetallic Ce/Zr-UiO 66 for the detection of antibiotics are developed. A magnetic Fe3O4@SiO2@Ce/Zr-UiO 66 metal-organic framework (MOF) is prepared to extract and enrich chloramphenicol from fish, wastewater, and urine samples, and a horseradish peroxidase (HRP)-Ce/Zr-UiO 66@bovine serum protein-chloramphenicol probe is used for the sensitive detection of chloramphenicol based on the dual-effect catalysis of Ce and HRP. In this manner, the application of Ce/Zr-UiO 66 in integrating sample pretreatment and antibiotic detection is systematically investigated and the associated mechanisms are explored. It is concluded that Ce/Zr-UiO 66 is a versatile dual-track material exhibiting high enrichment efficiency (6.37 mg g-1) and high sensitivity (limit of detection of 51.3 pg mL-1) for chloramphenicol detection and serving as a multifunctional MOF for safeguarding public health and hygiene.

LGCDA: Predicting CircRNA-Disease Association Based on Fusion of Local and Global Features.

CircRNA has been shown to be involved in the occurrence of many diseases. Several computational frameworks have been proposed to identify circRNA-disease associations. Despite the existing computational methods have obtained considerable successes, these methods still require to be improved as their performance may degrade due to the sparsity of the data and the problem of memory overflow. We develop a novel computational framework called LGCDA to predict circRNA-disease associations by fusing local and global features to solve the above mentioned problems. First, we construct closed local subgraphs by using k-hop closed subgraph and label the subgraphs to obtain rich graph pattern information. Then, the local features are extracted by using graph neural network (GNN). In addition, we fuse Gaussian interaction profile (GIP) kernel and cosine similarity to obtain global features. Finally, the score of circRNA-disease associations is predicted by using the multilayer perceptron (MLP) based on local and global features. We perform five- fold cross validation on five datasets for model evaluation and our model surpasses other advanced methods. The code is available at https://github.com/lanbiolab/LGCDA.

Deep Imputation Bi-stochastic Graph Regularized Matrix Factorization for Clustering Single-cell RNA-sequencing Data.

By generating massive gene transcriptome data and analyzing transcriptomic variations at the cell level, single-cell RNA-sequencing (scRNA-seq) technology has provided new way to explore cellular heterogeneity and functionality. Clustering scRNA-seq data could discover the hidden diversity and complexity of cell populations, which can aid to the identification of the disease mechanisms and biomarkers. In this paper, a novel method (DSINMF) is presented for single cell RNA sequencing data by using deep matrix factorization. Our proposed method comprises four steps: first, the feature selection is utilized to remove irrelevant features. Then, the dropout imputation is used to handle missing value problem. Further, the dimension reduction is employed to preserve data characteristics and reduce noise effects. Finally, the deep matrix factorization with bi-stochastic graph regularization is used to obtain cluster results from scRNA-seq data. We compare DSINMF with other state-of-the-art algorithms on nine datasets and the results show our method outperformances than other methods.

Proteomic Analyses in Diverse Populations Improved Risk Prediction and Identified New Drug Targets for Type 2 Diabetes.

OBJECTIVE: Integrated analyses of plasma proteomics and genetic data in prospective studies can help assess the causal relevance of proteins, improve risk prediction, and discover novel protein drug targets for type 2 diabetes (T2D). RESEARCH DESIGN AND METHODS: We measured plasma levels of 2,923 proteins using Olink Explore among ∼2,000 randomly selected participants from China Kadoorie Biobank (CKB) without prior diabetes at baseline. Cox regression assessed associations of individual protein with incident T2D (n = 92 cases). Proteomic-based risk models were developed with discrimination, calibration, reclassification assessed using area under the curve (AUC), calibration plots, and net reclassification index (NRI), respectively. Two-sample Mendelian randomization (MR) analyses using cis-protein quantitative trait loci identified in a genome-wide association study of CKB and UK Biobank for specific proteins were conducted to assess their causal relevance for T2D, along with colocalization analyses to examine shared causal variants between proteins and T2D. RESULTS: Overall, 33 proteins were significantly associated (false discovery rate <0.05) with risk of incident T2D, including IGFBP1, GHR, and amylase. The addition of these 33 proteins to a conventional risk prediction model improved AUC from 0.77 (0.73-0.82) to 0.88 (0.85-0.91) and NRI by 38%, with predicted risks well calibrated with observed risks. MR analyses provided support for the causal relevance for T2D of ENTR1, LPL, and PON3, with replication of ENTR1 and LPL in Europeans using different genetic instruments. Moreover, colocalization analyses showed strong evidence (pH4 > 0.6) of shared genetic variants of LPL and PON3 with T2D. CONCLUSIONS: Proteomic analyses in Chinese adults identified novel associations of multiple proteins with T2D with strong genetic evidence supporting their causal relevance and potential as novel drug targets for prevention and treatment of T2D.

MRUNet-3D: A multi-stride residual 3D UNet for lung nodule segmentation.

Obtaining an accurate segmentation of the pulmonary nodules in computed tomography (CT) images is challenging. This is due to: (1) the heterogeneous nature of the lung nodules; (2) comparable visual characteristics between the nodules and their surroundings. A robust multi-scale feature extraction mechanism that can effectively obtain multi-scale representations at a granular level can improve segmentation accuracy. As the most commonly used network in lung nodule segmentation, UNet, its variants, and other image segmentation methods lack this robust feature extraction mechanism. In this study, we propose a multi-stride residual 3D UNet (MRUNet-3D) to improve the segmentation accuracy of lung nodules in CT images. It incorporates a multi-slide Res2Net block (MSR), which replaces the simple sequence of convolution layers in each encoder stage to effectively extract multi-scale features at a granular level from different receptive fields and resolutions while conserving the strengths of 3D UNet. The proposed method has been extensively evaluated on the publicly available LUNA16 dataset. Experimental results show that it achieves competitive segmentation performance with an average dice similarity coefficient of 83.47 % and an average surface distance of 0.35 mm on the dataset. More notably, our method has proven to be robust to the heterogeneity of lung nodules. It has also proven to perform better at segmenting small lung nodules. Ablation studies have shown that the proposed MSR and RFIA modules are fundamental to improving the performance of the proposed model.

The palliative care needs and experiences of patients with advanced Parkinson's disease: a qualitative scoping review.

Aim: To determine the experiences and needs of palliative care in patients with advanced Parkinson's disease (PD). Methods: A scoping literature review methodology, as described by the Joanna Briggs Institute, was employed to search for relevant literature. An electronic search of studies published in English was conducted across five databases from inception to 10 September 2023. Results: The search yielded a total of 1,205 articles, with 20 meeting the inclusion criteria. The findings were organized into four themes: (1) unmet emotional and informational needs; (2) needs for effective coordination of care; (3) planning for the future; and (4) symptom management. This scoping review highlights the intricate nature of palliative care for patients with PD and sheds light on issues within current palliative care healthcare systems. The findings emphasize the necessity for individualized interventions and services to address the diverse unmet palliative care needs of people with PD. Conclusion: The study reveals the complex landscape of palliative care for individuals with advanced PD, emphasizing the inadequacies within existing healthcare systems. The identified themes underscore the importance of tailored interventions to address the varied unmet palliative care needs of this population.

DeepKEGG: a multi-omics data integration framework with biological insights for cancer recurrence prediction and biomarker discovery.

Deep learning-based multi-omics data integration methods have the capability to reveal the mechanisms of cancer development, discover cancer biomarkers and identify pathogenic targets. However, current methods ignore the potential correlations between samples in integrating multi-omics data. In addition, providing accurate biological explanations still poses significant challenges due to the complexity of deep learning models. Therefore, there is an urgent need for a deep learning-based multi-omics integration method to explore the potential correlations between samples and provide model interpretability. Herein, we propose a novel interpretable multi-omics data integration method (DeepKEGG) for cancer recurrence prediction and biomarker discovery. In DeepKEGG, a biological hierarchical module is designed for local connections of neuron nodes and model interpretability based on the biological relationship between genes/miRNAs and pathways. In addition, a pathway self-attention module is constructed to explore the correlation between different samples and generate the potential pathway feature representation for enhancing the prediction performance of the model. Lastly, an attribution-based feature importance calculation method is utilized to discover biomarkers related to cancer recurrence and provide a biological interpretation of the model. Experimental results demonstrate that DeepKEGG outperforms other state-of-the-art methods in 5-fold cross validation. Furthermore, case studies also indicate that DeepKEGG serves as an effective tool for biomarker discovery. The code is available at https://github.com/lanbiolab/DeepKEGG.

Correlates and consequences of atrial fibrillation in a prospective study of 25 000 participants in the China Kadoorie Biobank.

Aims: The prevalence of atrial fibrillation (AF) is positively correlated with prior cardiovascular diseases (CVD) and CVD risk factors but is lower in Chinese than Europeans despite their higher burden of CVD. We examined the prevalence and prognosis of AF and other electrocardiogram (ECG) abnormalities in the China Kadoorie Biobank. Methods and results: A random sample of 25 239 adults (mean age 59.5 years, 62% women) had a 12-lead ECG recorded and interpreted using a Mortara VERITAS™ algorithm in 2013-14. Participants were followed up for 5 years for incident stroke, ischaemic heart disease, heart failure (HF), and all CVD, overall and by CHA2DS2-VASc scores, age, sex, and area. Overall, 1.2% had AF, 13.6% had left ventricular hypertrophy (LVH), and 28.1% had ischaemia (two-thirds of AF cases also had ischaemia or LVH). The prevalence of AF increased with age, prior CVD, and levels of CHA2DS2-VASc scores (0.5%, 1.3%, 2.1%, 2.9%, and 4.4% for scores <2, 2, 3, 4, and ≥5, respectively). Atrial fibrillation was associated with two-fold higher hazard ratios (HR) for CVD (2.15; 95% CI, 1.71-2.69) and stroke (1.88; 1.44-2.47) and a four-fold higher HR for HF (3.79; 2.21-6.49). The 5-year cumulative incidence of CVD was comparable for AF, prior CVD, and CHA2DS2-VASc scores ≥ 2 (36.7% vs. 36.2% vs. 37.7%, respectively) but was two-fold greater than for ischaemia (19.4%), LVH (18.0%), or normal ECG (14.1%), respectively. Conclusion: The findings highlight the importance of screening for AF together with estimation of CHA2DS2-VASc scores for prevention of CVD in Chinese adults.

A metal-organic framework signaling probe-mediated immunosensor for the economical and rapid determination of enrofloxacin in milk.

Ensuring the safety of animal-derived foods requires the reliable and swift identification of enrofloxacin residues to monitor the presence of antibiotics. In this regard, we synthesized, tuned, and investigated the optical properties of a bimetallic metal-organic framework (Ce/Zr-UiO 66). The investigation was facilitated by employing a polydopamine-coated pipette tip with high adsorption efficiency, serving as an immunoreactive carrier. Subsequently, an immunofunctionalized variant of Ce/Zr-UiO 66, referred to as Ce/Zr-UiO 66@ Bovine serum albumin-enrofloxacin, was developed as an optical probe for the rapid and sensitive identification of enrofloxacin across a variety of samples. The method can accurately detect enrofloxacin at concentrations as low as 0.12 ng/mL, with a determination time of under 15 min; furthermore, it demonstrates exceptional efficacy when applied to food, environmental, and clinical samples. The implementation of this methodology offers a valuable means for cost-effective, rapid, and on-site enrofloxacin determination.

Overcoming the Blood-Brain Tumor Barrier with Docetaxel-Loaded Mesoporous Silica Nanoparticles for Treatment of Temozolomide-Resistant Glioblastoma.

While temozolomide (TMZ) has been a cornerstone in the treatment of newly diagnosed glioblastoma (GBM), a significant challenge has been the emergence of resistance to TMZ, which compromises its clinical benefits. Additionally, the nonspecificity of TMZ can lead to detrimental side effects. Although TMZ is capable of penetrating the blood-brain barrier (BBB), our research addresses the need for targeted therapy to circumvent resistance mechanisms and reduce off-target effects. This study introduces the use of PEGylated mesoporous silica nanoparticles (MSN) with octyl group modifications (C8-MSN) as a nanocarrier system for the delivery of docetaxel (DTX), providing a novel approach for treating TMZ-resistant GBM. Our findings reveal that C8-MSN is biocompatible in vitro, and DTX@C8-MSN shows no hemolytic activity at therapeutic concentrations, maintaining efficacy against GBM cells. Crucially, in vivo imaging demonstrates preferential accumulation of C8-MSN within the tumor region, suggesting enhanced permeability across the blood-brain tumor barrier (BBTB). When administered to orthotopic glioma mouse models, DTX@C8-MSN notably prolongs survival by over 50%, significantly reduces tumor volume, and decreases side effects compared to free DTX, indicating a targeted and effective approach to treatment. The apoptotic pathways activated by DTX@C8-MSN, evidenced by the increased levels of cleaved caspase-3 and PARP, point to a potent therapeutic mechanism. Collectively, the results advocate DTX@C8-MSN as a promising candidate for targeted therapy in TMZ-resistant GBM, optimizing drug delivery and bioavailability to overcome current therapeutic limitations.

Predictive value of 8-year blood pressure measures in intracerebral hemorrhage risk over 5 years.

AIMS: The relationships between long-term blood pressure (BP) measures and intracerebral hemorrhage (ICH), as well as their predictive ability on ICH, were unclear. We aimed to investigate the independent associations of multiple BP measures with subsequent 5-year ICH risk, as well as the incremental value of these measures over a single-point BP measurement in ICH risk prediction. METHODS: We included 12,398 participants from the China Kadoorie Biobank (CKB) who completed three surveys every four to five years. The following long-term BP measures were calculated: mean, minimum, maximum, standard deviation, coefficient of variation, average real variability, and cumulative BP exposure (cumBP). Cox proportional hazard models were used to examine the associations between these measures and ICH. The potential incremental value of these measures in ICH risk prediction was assessed using Harrell's C statistics, continuous net reclassification improvement (cNRI), and relative integrated discrimination improvement (rIDI). RESULTS: The hazard ratios (95% confidence intervals) of incident ICH associated with per SD increase in cumSBP and cumDBP were 1.62 (1.25, 2.10) and 1.59 (1.23, 2.07), respectively. When cumBP was added to the conventional 5-year ICH risk prediction model, the C-statistic change was 0.009 (-0.001, 0.019), the cNRI was 0.267 (0.070, 0.464), and the rIDI was 18.2% (5.8%, 30.7%). Further subgroup analyses revealed a consistent increase in cNRI and rIDI in men, rural residents, and participants without diabetes. Other long-term BP measures showed no statistically significant associations with incident ICH and generally did not improve model performance. CONCLUSION: The nearly 10-year cumBP was positively associated with an increased 5-year risk of ICH and could significantly improve risk reclassification for the ICH risk prediction model that included single-point BP measurement.

This prospective cohort study of Chinese adults investigated the independent associations of multiple blood pressure (BP) measures with subsequent 5-year intracerebral hemorrhage (ICH) risk, as well as the incremental value of these measures over a single-point BP measurement in ICH risk prediction. The cumulative BP exposure (cumBP) was positively associated with subsequent 5-year risk of ICH, independent of the recent single-point SBP and DBP levels.The cumBP could improve the risk reclassification of the conventional 5-year ICH risk prediction model that included single-point BP measurement for all participants, as well as for men, rural residents, and participants without diabetes.

Dietary factors and patterns in relation to risk of later-onset ulcerative colitis in Chinese: A prospective study of 0.5 million people.

BACKGROUND: There is limited evidence on the associations of dietary factors and patterns with risk of later-onset ulcerative colitis (UC) in Chinese adults. AIMS: To investigate the associations of dietary factors and patterns with risk of later-onset UC in Chinese. METHODS: The prospective China Kadoorie Biobank cohort study recruited 512,726 participants aged 30-79. Dietary habits were assessed using food frequency questionnaires. Dietary patterns were derived by factor analysis with a principal component method. Cox regression analysis was used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs). RESULTS: During a median follow-up of 12.1 years, 312 cases of newly diagnosed UC were documented (median age of diagnosis 60.1 years). Egg consumption was associated with higher risk of UC (HR for daily vs. never or rarely: 2.29 [95% CI: 1.26-4.16]), while spicy food consumption was inversely associated with risk of UC (HR: 0.63 [0.45-0.88]). The traditional northern dietary pattern, characterised by high intake of wheat and low intake of rice, was associated with higher risk of UC (HR for highest vs. lowest quartile of score: 2.79 [1.93-4.05]). The modern dietary pattern, characterised by high intake of animal-origin foods and fruits, was associated with higher risk of UC (HR: 2.48 [1.63-3.78]). Population attributable fraction was 13.04% (7.71%-19.11%) for daily/almost daily consumption of eggs and 9.87% (1.94%-18.22%) for never/rarely consumption of spicy food. CONCLUSIONS: The findings highlight the importance of evaluating dietary factors and patterns in the primary prevention of later-onset UC in Chinese adults.

Segregation of morphogenetic regulatory function of Shox2 from its cell fate guardian role in sinoatrial node development.

Shox2 plays a vital role in the morphogenesis and physiological function of the sinoatrial node (SAN), the primary cardiac pacemaker, manifested by the formation of a hypoplastic SAN and failed differentiation of pacemaker cells in Shox2 mutants. Shox2 and Nkx2-5 are co-expressed in the developing SAN and regulate the fate of the pacemaker cells through a Shox2-Nkx2-5 antagonistic mechanism. Here we show that simultaneous inactivation of Nkx2-5 in the SAN of Shox2 mutants (dKO) rescued the pacemaking cell fate but not the hypoplastic defects, indicating uncoupling of SAN cell fate determination and morphogenesis. Single-cell RNA-seq revealed that the presumptive SAN cells of Shox2-/- mutants failed to activate pacemaking program but remained in a progenitor state preceding working myocardium, while both wildtype and dKO SAN cells displayed normal pacemaking cell fate with similar cellular state. Shox2 thus acts as a safeguard but not a determinant to ensure the pacemaking cell fate through the Shox2-Nkx2-5 antagonistic mechanism, which is segregated from its morphogenetic regulatory function in SAN development.