Ziresovir in Hospitalized Infants with Respiratory Syncytial Virus Infection.

BACKGROUND: Respiratory syncytial virus (RSV) is a leading cause of severe illness in infants, with no effective treatment. Results of a phase 2 trial suggested that ziresovir may have efficacy in the treatment of infants hospitalized with RSV infection. METHODS: In a phase 3, multicenter, double-blind, randomized, placebo-controlled trial conducted in China, we enrolled participants 1 to 24 months of age who were hospitalized with RSV infection. Participants were randomly assigned, in a 2:1 ratio, to receive ziresovir (at a dose of 10 to 40 mg, according to body weight) or placebo, administered twice daily, for 5 days. The primary end point was the change from baseline to day 3 (defined as 48 hours after the first administration) in the Wang bronchiolitis clinical score (total scores range from 0 to 12, with higher scores indicating greater severity of signs and symptoms). The intention-to-treat population included all the participants with RSV-confirmed infection who received at least one dose of ziresovir or placebo; the safety population included all the participants who received at least one dose of ziresovir or placebo. RESULTS: The intention-to-treat population included 244 participants, and the safety population included 302. The reduction from baseline in the Wang bronchiolitis clinical score at day 3 was significantly greater with ziresovir than with placebo (-3.4 points [95% confidence interval {CI}, -3.7 to -3.1] vs. -2.7 points [95% CI, -3.1 to -2.2]; difference, -0.8 points [95% CI, -1.3 to -0.3]; P = 0.002). The reduction in the RSV viral load at day 5 was greater in the ziresovir group than in the placebo group (-2.5 vs. -1.9 log10 copies per milliliter; difference, -0.6 log10 copies per milliliter [95% CI, -1.1 to -0.2]). Improvements were observed in prespecified subgroups, including in participants with a baseline bronchiolitis score of at least 8 and in those 6 months of age or younger. The incidence of adverse events related to the drug or placebo was 16% with ziresovir and 13% with placebo. The most common adverse events that were assessed by the investigator as being related to the drug or placebo were diarrhea (in 4% and 2% of the participants, respectively), an elevated liver-enzyme level (in 3% and 3%, respectively), and rash (in 2% and 1%). Resistance-associated mutations were identified in 15 participants (9%) in the ziresovir group. CONCLUSIONS: Ziresovir treatment reduced signs and symptoms of bronchiolitis in infants and young children hospitalized with RSV infection. No safety concerns were identified. (Funded by Shanghai Ark Biopharmaceutical; AIRFLO ClinicalTrials.gov number, NCT04231968.).

Axin1 regulates tooth root development by inhibiting AKT1-mTORC1 activation and Shh translation in Hertwig's epithelial root sheath.

Hertwig's epithelial root sheath (HERS) interacts with dental apical mesenchyme and guides development of the tooth root, which is an integral part for the function of the whole tooth. However, the key genes in HERS essential for root development are understudied. Here we show that Axin1, a scaffold protein that negatively regulates canonical Wnt signaling, is strongly expressed in the HERS. Axin1 ablation in the HERS of mice leads to defective root development but in a manner independent of canonical Wnt signaling. Further studies reveal that Axin1 in the HERS negatively regulates the AKT1-mTORC1 pathway through binding to AKT1, leading to inhibition of ribosomal biogenesis and mRNA translation. Sonic hedgehog (Shh) protein, a morphogen essential for root development, is over synthesized by upregulated mTORC1 activity upon Axin1 inactivation. Importantly, either haploinsufficiency of mTORC1 subunit Raptor or pharmacologic inhibition of Shh signaling can rescue the root defects in Axin1 mutant mice. Collectively, our data suggest that, independent of canonical Wnt signaling, Axin1 controls ribosomal biogenesis and selective mRNA translation programs via AKT1-mTORC1 signaling during tooth root development.

CHIP inhibits odontoblast differentiation through promoting DLX3 polyubiquitylation and degradation.

Dentin is the major hard tissue of teeth formed by differentiated odontoblasts. How odontoblast differentiation is regulated remains enigmatic. Here, we report that the E3 ubiquitin ligase CHIP is highly expressed in undifferentiated dental mesenchymal cells and downregulated after differentiation of odontoblasts. Ectopic expression of CHIP inhibits odontoblastic differentiation of mouse dental papilla cells, whereas knockdown of endogenous CHIP has opposite effects. Chip (Stub1) knockout mice display increased formation of dentin and enhanced expression of odontoblast differentiation markers. Mechanistically, CHIP interacts with and induces K63 polyubiquitylation of the transcription factor DLX3, leading to its proteasomal degradation. Knockdown of DLX3 reverses the enhanced odontoblastic differentiation caused by knockdown of CHIP. These results suggest that CHIP inhibits odontoblast differentiation by targeting its tooth-specific substrate DLX3. Furthermore, our results indicate that CHIP competes with another E3 ubiquitin ligase, MDM2, that promotes odontoblast differentiation by monoubiquitylating DLX3. Our findings suggest that the two E3 ubiquitin ligases CHIP and MDM2 reciprocally regulate DLX3 activity by catalyzing distinct types of ubiquitylation, and reveal an important mechanism by which differentiation of odontoblasts is delicately regulated by divergent post-translational modifications.

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.

DNA Methylation Age Mediates Effect of Metabolic Profile on Cardiovascular and General Aging.

BACKGROUND: Alterations in lipid metabolism and DNA methylation are 2 hallmarks of aging. Connecting metabolomic, epigenomic, and aging outcomes help unravel the complex mechanisms underlying aging. We aimed to assess whether DNA methylation clocks mediate the association of circulating metabolites with incident atherosclerotic cardiovascular disease (ASCVD) and frailty. METHODS: The China Kadoorie Biobank is a prospective cohort study with a baseline survey from 2004 to 2008 and a follow-up period until December 31, 2018. We used the Infinium Methylation EPIC BeadChip to measure the methylation levels of 988 participants' baseline blood leukocyte DNA. Metabolite profiles, including lipoprotein particles, lipid constituents, and various circulating metabolites, were measured using quantitative nuclear magnetic resonance. The pace of DNA methylation age acceleration (AA) was calculated using 5 widely used epigenetic clocks (the first generation: Horvath, Hannum, and Li; the second generation: Grim and Pheno). Incident ASCVD was ascertained through linkage with local death and disease registries and national health insurance databases, supplemented by active follow-up. The frailty index was constructed using medical conditions, symptoms, signs, and physical measurements collected at baseline. RESULTS: A total of 508 incident cases of ASCVD were documented during a median follow-up of 9.5 years. The first generation of epigenetic clocks was associated with the risk of ASCVD (P<0.05). For each SD increment in LiAA, HorvathAA, and HannumAA, the corresponding hazard ratios for ASCVD risk were 1.16 (1.05-1.28), 1.10 (1.00-1.22), and 1.17 (1.04-1.31), respectively. Only LiAA mediated the association of various metabolites (lipids, fatty acids, histidine, and inflammatory biomarkers) with ASCVD, with the mediating proportion reaching up to 15% for the diameter of low-density lipoprotein (P=1.2×10-2). Regarding general aging, a 1-SD increase in GrimAA was associated with an average increase of 0.10 in the frailty index (P=2.0×10-3), and a 33% and 63% increased risk of prefrailty and frailty at baseline (P=1.5×10-2 and 5.8×10-2), respectively; this association was not observed with other clocks. GrimAA mediated the effect of various lipids, fatty acids, glucose, lactate, and inflammatory biomarkers on the frailty index, with the mediating proportion reaching up to 22% for triglycerides in very small-sized very low-density lipoprotein (P=6.0×10-3). CONCLUSIONS: These findings suggest that epigenomic mechanisms may play a role in the associations between circulating metabolites and the aging process. Different mechanisms underlie the first and second generations of DNA methylation age in cardiovascular and general aging.

DNA cytosine methyltransferases differentially regulate genome-wide hypermutation and interhomolog recombination in Trichoderma reesei meiosis.

Trichoderma reesei is an economically important enzyme producer with several unique meiotic features. spo11, the initiator of meiotic double-strand breaks (DSBs) in most sexual eukaryotes, is dispensable for T. reesei meiosis. T. reesei lacks the meiosis-specific recombinase Dmc1. Rad51 and Sae2, the activator of the Mre11 endonuclease complex, promote DSB repair and chromosome synapsis in wild-type and spo11Δ meiosis. DNA methyltransferases (DNMTs) perform multiple tasks in meiosis. Three DNMT genes (rid1, dim2 and dimX) differentially regulate genome-wide cytosine methylation and C:G-to-T:A hypermutations in different chromosomal regions. We have identified two types of DSBs: type I DSBs require spo11 or rid1 for initiation, whereas type II DSBs do not rely on spo11 and rid1 for initiation. rid1 (but not dim2) is essential for Rad51-mediated DSB repair and normal meiosis. rid1 and rad51 exhibit a locus heterogeneity (LH) relationship, in which LH-associated proteins often regulate interconnectivity in protein interaction networks. This LH relationship can be suppressed by deleting dim2 in a haploid rid1Δ (but not rad51Δ) parental strain, indicating that dim2 and rid1 share a redundant function that acts earlier than rad51 during early meiosis. In conclusion, our studies provide the first evidence of the involvement of DNMTs during meiotic initiation and recombination.

Single-cell transcriptomic signatures and gene regulatory networks modulated by Wls in mammalian midline facial formation and clefts.

Formation of highly unique and complex facial structures is controlled by genetic programs that are responsible for the precise coordination of three-dimensional tissue morphogenesis. However, the underlying mechanisms governing these processes remain poorly understood. We combined mouse genetic and genomic approaches to define the mechanisms underlying normal and defective midfacial morphogenesis. Conditional inactivation of the Wnt secretion protein Wls in Pax3-expressing lineage cells disrupted frontonasal primordial patterning, cell survival and directional outgrowth, resulting in altered facial structures, including midfacial hypoplasia and midline facial clefts. Single-cell RNA sequencing revealed unique transcriptomic atlases of mesenchymal subpopulations in the midfacial primordia, which are disrupted in the conditional Wls mutants. Differentially expressed genes and cis-regulatory sequence analyses uncovered that Wls modulates and integrates a core gene regulatory network, consisting of key midfacial regulatory transcription factors (including Msx1, Pax3 and Pax7) and their downstream targets (including Wnt, Shh, Tgfß and retinoic acid signaling components), in a mesenchymal subpopulation of the medial nasal prominences that is responsible for midline facial formation and fusion. These results reveal fundamental mechanisms underlying mammalian midfacial morphogenesis and related defects at single-cell resolution.

Benchmarking of computational methods for predicting circRNA-disease associations.

Accumulating evidences demonstrate that circular RNA (circRNA) plays an important role in human diseases. Identification of circRNA-disease associations can help for the diagnosis of human diseases, while the traditional method based on biological experiments is time-consuming. In order to address the limitation, a series of computational methods have been proposed in recent years. However, few works have summarized these methods or compared the performance of them. In this paper, we divided the existing methods into three categories: information propagation, traditional machine learning and deep learning. Then, the baseline methods in each category are introduced in detail. Further, 5 different datasets are collected, and 14 representative methods of each category are selected and compared in the 5-fold, 10-fold cross-validation and the de novo experiment. In order to further evaluate the effectiveness of these methods, six common cancers are selected to compare the number of correctly identified circRNA-disease associations in the top-10, top-20, top-50, top-100 and top-200. In addition, according to the results, the observation about the robustness and the character of these methods are concluded. Finally, the future directions and challenges are discussed.

Downregulated BMP-Smad1/5/8 signaling causes emphysema via dysfunction of alveolar type II epithelial cells.

Bone morphogenetic protein (BMP)-Smad1/5/8 signaling plays a crucial regulatory role in lung development and adult lung homeostasis. However, it remains elusive whether BMP-Smad1/5/8 signaling is involved in the pathogenesis of emphysema. In this study, we downregulated BMP-Smad1/5/8 signaling by overexpressing its antagonist Noggin in adult mouse alveolar type II epithelial cells (AT2s), resulting in an emphysematous phenotype mimicking the typical pathological features of human emphysema, including distal airspace enlargement, pulmonary inflammation, extracellular matrix remodeling, and impaired lung function. Dysregulation of BMP-Smad1/5/8 signaling in AT2s leads to inflammatory destruction dominated by macrophage infiltration, associated with reduced secretion of surfactant proteins and inhibition of AT2 proliferation and differentiation. Reactivation of BMP-Smad1/5/8 signaling by genetics or chemotherapy significantly attenuated the morphology and pathophysiology of emphysema and improved the lung function in Noggin-overexpressing lungs. We also found that BMP-Smad1/5/8 signaling was downregulated in cigarette smoke-induced emphysema, and that enhancing its activity in AT2s prevented or even reversed emphysema in the mouse model. Our data suggest that BMP-Smad1/5/8 signaling, located at the top of the signaling cascade that regulates lung homeostasis, represents a key molecular regulator of alveolar stem cell secretory and regenerative function, and could serve as a potential target for future prevention and treatment of pulmonary emphysema. © 2023 The Pathological Society of Great Britain and Ireland.

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.

A high-resolution haplotype-resolved Reference panel constructed from the China Kadoorie Biobank Study.

Precision medicine depends on high-accuracy individual-level genotype data. However, the whole-genome sequencing (WGS) is still not suitable for gigantic studies due to budget constraints. It is particularly important to construct highly accurate haplotype reference panel for genotype imputation. In this study, we used 10 000 samples with medium-depth WGS to construct a reference panel that we named the CKB reference panel. By imputing microarray datasets, it showed that the CKB panel outperformed compared panels in terms of both the number of well-imputed variants and imputation accuracy. In addition, we have completed the imputation of 100 706 microarrays with the CKB panel, and the after-imputed data is the hitherto largest whole genome data of the Chinese population. Furthermore, in the GWAS analysis of real phenotype height, the number of tested SNPs tripled and the number of significant SNPs doubled after imputation. Finally, we developed an online server for offering free genotype imputation service based on the CKB reference panel (https://db.cngb.org/imputation/). We believe that the CKB panel is of great value for imputing microarray or low-coverage genotype data of Chinese population, and potentially mixed populations. The imputation-completed 100 706 microarray data are enormous and precious resources of population genetic studies for complex traits and diseases.

Redesigned Guide DNA Enhanced Clostridium butyricum Argonaute Activity for Amplification-Free and Multiplexed Detection of Pathogens.

Clostridium butyricum (CbAgo)-based bioassays are popular due to their programmability and directional cleavage capabilities. However, the relatively compact protein structure of CbAgo limits its cleavage activity (even at the optimal temperature), thus restricting its wider application. Here, we observed that guide DNA (gDNA) with specific structural features significantly enhanced CbAgo cleavage efficiency. Then, we invented a novel gDNA containing DNAzyme segments (gDNAzyme) that substantially enhanced the CbAgo cleavage efficency (by 100%). Using a molecular dynamics simulation system, we found that the augmented cleavage efficiency might be attributed to the large-scale global movement of the PIWI domain of CbAgo and an increased number of cleavage sites. Moreover, this gDNAzyme feature allowed us to create a biosensor that simultaneously and sensitively detected three pathogenic bacteria without DNA extraction and amplification. Our work not only dramatically expands applications of the CbAgo-based biosensor but also provides unique insight into the protein-DNA interactions.

Development and evaluation of a polygenic risk score for lung cancer in never-smoking women: A large-scale prospective Chinese cohort study.

The proportion of lung cancer in never smokers is rising, especially among Asian women, but there is no effective early detection tool. Here, we developed a polygenic risk score (PRS), which may help to identify the population with higher risk of lung cancer in never-smoking women. We first performed a large GWAS meta-analysis (8595 cases and 8275 controls) to systematically identify the susceptibility loci for lung cancer in never-smoking Asian women and then generated a PRS using GWAS datasets. Furthermore, we evaluated the utility and effectiveness of PRS in an independent Chinese prospective cohort comprising 55 266 individuals. The GWAS meta-analysis identified eight known loci and a novel locus (5q11.2) at the genome-wide statistical significance level of P < 5 × 10-8 . Based on the summary statistics of GWAS, we derived a polygenic risk score including 21 variants (PRS-21) for lung cancer in never-smoking women. Furthermore, PRS-21 had a hazard ratio (HR) per SD of 1.29 (95% CI = 1.18-1.41) in the prospective cohort. Compared with participants who had a low genetic risk, those with an intermediate (HR = 1.32, 95% CI: 1.00-1.72) and high (HR = 2.09, 95% CI: 1.56-2.80) genetic risk had a significantly higher risk of incident lung cancer. The addition of PRS-21 to the conventional risk model yielded a modest significant improvement in AUC (0.697 to 0.711) and net reclassification improvement (24.2%). The GWAS-derived PRS-21 significantly improves the risk stratification and prediction accuracy for incident lung cancer in never-smoking Asian women, demonstrating the potential for identification of high-risk individuals and early screening.

Infectious pathogens and risk of esophageal, gastric and duodenal cancers and ulcers in China: A case-cohort study.

Infection by certain pathogens is associated with cancer development. We conducted a case-cohort study of ~2500 incident cases of esophageal, gastric and duodenal cancer, and gastric and duodenal ulcer and a randomly selected subcohort of ~2000 individuals within the China Kadoorie Biobank study of >0.5 million adults. We used a bead-based multiplex serology assay to measure antibodies against 19 pathogens (total 43 antigens) in baseline plasma samples. Associations between pathogens and antigen-specific antibodies with risks of site-specific cancers and ulcers were assessed using Cox regression fitted using the Prentice pseudo-partial likelihood. Seroprevalence varied for different pathogens, from 0.7% for Hepatitis C virus (HCV) to 99.8% for Epstein-Barr virus (EBV) in the subcohort. Compared to participants seronegative for the corresponding pathogen, Helicobacter pylori seropositivity was associated with a higher risk of non-cardia (adjusted hazard ratio [HR] 2.73 [95% CI: 2.09-3.58]) and cardia (1.67 [1.18-2.38]) gastric cancer and duodenal ulcer (2.71 [1.79-4.08]). HCV was associated with a higher risk of duodenal cancer (6.23 [1.52-25.62]) and Hepatitis B virus was associated with higher risk of duodenal ulcer (1.46 [1.04-2.05]). There were some associations of antibodies again some herpesviruses and human papillomaviruses with risks of gastrointestinal cancers and ulcers but these should be interpreted with caution. This first study of multiple pathogens with risk of gastrointestinal cancers and ulcers demonstrated that several pathogens are associated with risks of gastrointestinal cancers and ulcers. This will inform future investigations into the role of infection in the etiology of these diseases.

Associations of diabetes, circulating protein biomarkers, and risk of pancreatic cancer.

BACKGROUND: Type 2 diabetes (T2D) is associated with higher risk of pancreatic cancer (PC), but the underlying mechanisms are not fully understood. METHODS: We conducted a case-subcohort study involving 610 PC cases and 623 subcohort participants with 92 protein biomarkers measured in baseline plasma samples. Genetically-instrumented T2D was derived using 86 single-nucleotide polymorphisms (SNPs), including insulin resistance (IR) SNPs. RESULTS: In observational analyses of 623 subcohort participants (mean age, 52 years; 61% women), T2D was positively associated with 13 proteins (SD difference: IL6: 0.52 [0.23-0.81]; IL10: 0.41 [0.12-0.70]), of which 8 were nominally associated with incident PC. The 8 proteins potentially mediated 36.9% (18.7-75.0%) of the association between T2D and PC. In MR, no associations were observed for genetically-determined T2D with proteins, but there were positive associations of genetically-determined IR with IL6 and IL10 (SD difference: 1.23 [0.05-2.41] and 1.28 [0.31-2.24]). In two-sample MR, fasting insulin was associated with both IL6 and PC, but no association was observed between IL6 and PC. CONCLUSIONS: Proteomics were likely to explain the association between T2D and PC, but were not causal mediators. Elevated fasting insulin driven by insulin resistance might explain the associations of T2D, proteomics, and PC.

Long-Term Exposure to Fine Particulate Matter and Incidence of Esophageal Cancer: A Prospective Study of 0.5 Million Chinese Adults.

BACKGROUND & AIMS: Evidence is sparse and inconclusive on the association between long-term fine (≤2.5 µm) particulate matter (PM2.5) exposure and esophageal cancer. We aimed to assess the association of PM2.5 with esophageal cancer risk and compared the esophageal cancer risk attributable to PM2.5 exposure and other established risk factors. METHODS: This study included 510,125 participants without esophageal cancer at baseline from China Kadoorie Biobank. A high-resolution (1 × 1 km) satellite-based model was used to estimate PM2.5 exposure during the study period. Hazard ratios (HR) and 95% CIs of PM2.5 with esophageal cancer incidence were estimated using Cox proportional hazard model. Population attributable fractions for PM2.5 and other established risk factors were estimated. RESULTS: There was a linear concentration-response relationship between long-term PM2.5 exposure and esophageal cancer. For each 10-µg/m3 increase in PM2.5, the HR was 1.16 (95% CI, 1.04-1.30) for esophageal cancer incidence. Compared with the first quarter of PM2.5 exposure, participants in the highest quarter had a 1.32-fold higher risk for esophageal cancer, with an HR of 1.32 (95% CI, 1.01-1.72). The population attributable risk because of annual average PM2.5 concentration ≥35 µg/m3 was 23.3% (95% CI, 6.6%-40.0%), higher than the risks attributable to lifestyle risk factors. CONCLUSIONS: This large prospective cohort study of Chinese adults found that long-term exposure to PM2.5 was associated with an elevated risk of esophageal cancer. With stringent air pollution mitigation measures in China, a large reduction in the esophageal cancer disease burden can be expected.

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.

Mesophilic Argonaute-Mediated Polydisperse Droplet Biosensor for Amplification-Free, One-Pot, and Multiplexed Nucleic Acid Detection Using Deep Learning.

Detection of nucleic acids from a single multiplexed and amplification-free test is critical for ensuring food safety, clinical diagnostics, and environmental monitoring. In this study, we introduced a mesophilic Argonaute protein from Clostridium butyricum (CbAgo), which exhibits nucleic acid endonuclease activity, to achieve a programmable, amplification-free system (PASS) for rapid nucleic acid quantification at ambient temperatures in one pot. By using CbAgo-mediated binding with specific guide DNA (gDNA) and subsequent targeted cleavage of wild-type target DNAs complementary to gDNA, PASS can detect multiple foodborne pathogen DNA (<102 CFU/mL) simultaneously. The fluorescence signals were then transferred to polydisperse emulsions and analyzed by using deep learning. This simplifies the process and increases the suitability of polydisperse emulsions compared to traditional digital PCR, which requires homogeneous droplets for accurate detection. We believe that PASS has the potential to become a next-generation point-of-care digital nucleic acid detection method.

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.

KGANCDA: predicting circRNA-disease associations based on knowledge graph attention network.

Increasing evidences have proved that circRNA plays a significant role in the development of many diseases. In addition, many researches have shown that circRNA can be considered as the potential biomarker for clinical diagnosis and treatment of disease. Some computational methods have been proposed to predict circRNA-disease associations. However, the performance of these methods is limited as the sparsity of low-order interaction information. In this paper, we propose a new computational method (KGANCDA) to predict circRNA-disease associations based on knowledge graph attention network. The circRNA-disease knowledge graphs are constructed by collecting multiple relationship data among circRNA, disease, miRNA and lncRNA. Then, the knowledge graph attention network is designed to obtain embeddings of each entity by distinguishing the importance of information from neighbors. Besides the low-order neighbor information, it can also capture high-order neighbor information from multisource associations, which alleviates the problem of data sparsity. Finally, the multilayer perceptron is applied to predict the affinity score of circRNA-disease associations based on the embeddings of circRNA and disease. The experiment results show that KGANCDA outperforms than other state-of-the-art methods in 5-fold cross validation. Furthermore, the case study demonstrates that KGANCDA is an effective tool to predict potential circRNA-disease associations.