Noninvasive Prenatal Genetic Screening of Cell-Free Fetal DNA for Early Prediction of ß-Thalassemia Using Fiber Optic Nanogold-Linked Sorbent Assay.

ß-Thalassemia is a prevalent type of severe inherited chronic anemia, primarily identified in developing countries. The identification of single nucleotide polymorphisms (SNPs) plays a vital role in the early diagnosis of genetic diseases. Here, we reported the development of an amplification-free fiber optic nanogold-linked sorbent assay method using a fiber optic particle plasmon resonance (FOPPR) biosensor for rapid and ultrasensitive detection of SNPs. Herein, MutS protein was selected as the biorecognition capture probe and immobilized on the sensing region to capture the target mutant DNA, which was hybridized with a single-base mismatched single-stranded DNA labeled by a gold nanoparticle (AuNP). The AuNP acts as a signaling agent to be detected by the FOPPR biosensor when it is bound on the fiber core surface. The method effectively differentiates mismatched double-stranded DNA by MutS protein from perfectly matched/complementary dsDNA. It exhibits an impressively low detection limit for the detection of SNPs at approximately 10-16 M using low-cost sensor chips and devices. By determination of the ratio of mutant DNA to normal DNA in cell-free genomic DNA from blood samples, this method is promising for diagnosing ß-thalassemia in fetuses without invasive testing techniques.

Adaptive carrier-phase-noise-canceled LiDAR for range-Doppler imaging beyond hundreds of laser coherence length.

Carrier-phase noise limits both the performance and the maximum operation range of coherent LiDAR. To address this issue, we propose a carrier-phase-noise-canceled LiDAR based on an auxiliary interferometer and adaptive filters. Compared to previous methods, this approach is calibration-free and offers higher compensation accuracy, as well as applicability of dynamic target detection. Experiments of range-Doppler imaging for stationary targets and rotating extended targets have been performed, and the detection results close to the theoretical resolution were obtained at the round trip distance to the target beyond 981 times and 106 times coherence length, respectively.

Nutritional risk and adverse health outcomes in Chinese community-dwelling older adults: A study based on the Elderly Nutritional Indicators for Geriatric Malnutrition Assessment (ENIGMA).

OBJECTIVES: Malnutrition and nutritional risk are risk factors for many adverse health outcomes in older adults, but they have rarely been assessed in China. The aim of this study was to evaluate the availability of Elderly Nutritional Indicators for Geriatric Malnutrition Assessment (ENIGMA), a nutritional scale originally developed to predict mortality, in assessing nutritional risks and predicting adverse health outcomes in Chinese community-dwelling older adults. METHODS: This was a population-based longitudinal cohort study (Chinese Longitudinal Healthy Longevity Survey), with a 4-y follow-up of 2063 community-dwelling adults aged 65 y or older. Nutritional risks were assessed via the use of ENIGMA and Geriatric Nutritional Risk Index (GNRI) at baseline (the 2014 wave). Cognitive impairment, functional limitation, and frailty were evaluated using the Chinese version of the Mini-Mental State Examination, Instrumental Activities of Daily Living/Instrumental Activities of Daily Living scale, and Frailty Index, respectively, at baseline and 4-y follow-up (the 2018 wave). Mortality was measured by survival status and duration of exposure to death from baseline to follow-up. The associations of nutritional risks with prevalent/incident cognitive impairment, functional limitation and frailty, and 4-y mortality were estimated using logistic regression and Cox proportional hazards regression models, adjusting for confounders. The discriminatory accuracy of ENIGMA and GNRI for these adverse health outcomes were compared by receiver operating characteristic analyses. RESULTS: According to ENIGMA, 48.6% of the Chinese community-dwelling older adults (age: 86.5±11.3 y) showed moderate and high nutritional risk. Nutritional risks defined by the ENIGMA were significantly associated with increased prevalence and incidence of cognitive impairment, functional limitation, and frailty (odds ratio ranging from 1.79 to 89.6, values ranging from P < 0.001 to 0.048) but were mostly insignificant for that defined by GNRI. With respect to 4-y mortality, nutritional risks as defined by GNRI showed better prediction effects than those defined by ENIGMA. Receiver operating characteristic analyses indicated that nutritional risks defined by ENIGMA had better discriminatory accuracy than those defined by GNRI for prevalent and incident cognitive impairment (C = 0.73 vs 0.64, P < 0.001; C = 0.65 vs 0.59, P = 0.015, respectively), functional limitation (C = 0.74 vs 0.63, P < 0.001 at baseline; C = 0.61 vs 0.56, P = 0.016 at follow-up), frailty (C = 0.85 vs 0.67, P < 0.001 at baseline; C = 0.64 vs 0.55, P < 0.001 at follow-up), and even 4-y mortality (C = 0.68 vs 0.64, P = 0.020). CONCLUSIONS: ENIGMA could serve as a nutritional risk screening tool that has a robust role in predicting cognitive impairment, functional limitation, and frailty in Chinese community-dwelling older adults. It may be recommended for early nutritional risk screening and has the potential to guide early nutritional intervention in communities and primary care settings in China.

Integrating multi-omics data of childhood asthma using a deep association model.

Childhood asthma is one of the most common respiratory diseases with rising mortality and morbidity. The multi-omics data is providing a new chance to explore collaborative biomarkers and corresponding diagnostic models of childhood asthma. To capture the nonlinear association of multi-omics data and improve interpretability of diagnostic model, we proposed a novel deep association model (DAM) and corresponding efficient analysis framework. First, the Deep Subspace Reconstruction was used to fuse the omics data and diagnostic information, thereby correcting the distribution of the original omics data and reducing the influence of unnecessary data noises. Second, the Joint Deep Semi-Negative Matrix Factorization was applied to identify different latent sample patterns and extract biomarkers from different omics data levels. Third, our newly proposed Deep Orthogonal Canonical Correlation Analysis can rank features in the collaborative module, which are able to construct the diagnostic model considering nonlinear correlation between different omics data levels. Using DAM, we deeply analyzed the transcriptome and methylation data of childhood asthma. The effectiveness of DAM is verified from the perspectives of algorithm performance and biological significance on the independent test dataset, by ablation experiment and comparison with many baseline methods from clinical and biological studies. The DAM-induced diagnostic model can achieve a prediction AUC of 0.912, which is higher than that of many other alternative methods. Meanwhile, relevant pathways and biomarkers of childhood asthma are also recognized to be collectively altered on the gene expression and methylation levels. As an interpretable machine learning approach, DAM simultaneously considers the non-linear associations among samples and those among biological features, which should help explore interpretative biomarker candidates and efficient diagnostic models from multi-omics data analysis for human complex diseases.

Towards bridging thermo/electrocatalytic CO oxidation: from nanoparticles to single atoms.

Proton exchange membrane fuel cells (PEMFCs), as a feasible alternative to replace the traditional fossil fuel-based energy converter, contribute significantly to the global sustainability agenda. At the PEMFC anode, given the high exchange current density, Pt/C is deemed the catalyst-of-choice to ensure that the hydrogen oxidation reaction (HOR) occurs at a sufficiently fast pace. The high performance of Pt/C, however, can only be achieved under the premise that high purity hydrogen is used. For instance, in the presence of trace level carbon monoxide, a typical contaminant during H2 production, Pt is severely deactivated by CO surface blockage. Addressing the poisoning issue necessitates for either developing anti-poisoning electrocatalysts or using pre-purified H2 obtained via a thermo-catalysis route. In other words, the CO poisoning issue can be addressed by either thermal-catalysis from the H2 supply side or electrocatalysis at the user side, respectively. In spite of the distinction between thermo-catalysis and electro-catalysis, there are high similarities between the two routes. Essentially, a reduction in the kinetic barrier for the combination of CO to oxygen containing intermediates is required in both techniques. Therefore, bridging electrocatalysis and thermocatalysis might offer new insight into the development of cutting edge catalysts to solve the poisoning issue, which, however, stands as an underexplored frontier in catalysis science. This review provides a critical appraisal of the recent advancements in preferential CO oxidation (CO-PROX) thermocatalysts and anti-poisoning HOR electrocatalysts, aiming to bridge the gap in cognition between the two routes. First, we discuss the differences in thermal/electrocatalysis, CO oxidation mechanisms, and anti-CO poisoning strategies. Second, we comprehensively summarize the progress of supported and unsupported CO-tolerant catalysts based on the timeline of development (nanoparticles to clusters to single atoms), focusing on metal-support interactions and interface reactivity. Third, we elucidate the stability issue and theoretical understanding of CO-tolerant electrocatalysts, which are critical factors for the rational design of high-performance catalysts. Finally, we underscore the imminent challenges in bridging thermal/electrocatalytic CO oxidation, with theory, materials, and the mechanism as the three main weapons to gain a more in-depth understanding. We anticipate that this review will contribute to the cognition of both thermocatalysis and electrocatalysis.

Ginger essential oil prevents NASH progression by blocking the NLRP3 inflammasome and remodeling the gut microbiota-LPS-TLR4 pathway in mice.

BACKGROUND: Diet and gut microbiota contribute to non-alcoholic steatohepatitis (NASH) progression. High-fat diets (HFDs) change gut microbiota compositions, induce gut dysbiosis, and intestinal barrier leakage, which facilitates portal influx of pathogen-associated molecular patterns including lipopolysaccharides (LPS) to the liver and triggers inflammation in NASH. Current therapeutic drugs for NASH have adverse side effects; however, several foods and herbs that exhibit hepatoprotection could be an alternative method to prevent NASH. METHODS: We investigated ginger essential oil (GEO) against palm oil-containing HFDs in LPS-injected murine NASH model. RESULTS: GEO reduced plasma alanine aminotransferase levels and hepatic pro-inflammatory cytokine levels; and increased antioxidant catalase, glutathione reductase, and glutathione levels to prevent NASH. GEO alleviated hepatic inflammation through mediated NLR family pyrin domain-containing 3 (NLRP3) inflammasome and LPS/Toll-like receptor four (TLR4) signaling pathways. GEO further increased beneficial bacterial abundance and reduced NASH-associated bacterial abundance. CONCLUSION: This study demonstrated that GEO prevents NASH progression which is probably associated with the alterations of gut microbiota and inhibition of the LPS/TLR4/NF-κB pathway. Hence, GEO may offer a promising application as a dietary supplement for the prevention of NASH.

Multimaterial Metamaterials: Customize Targeted Thermal Deformation Modes Responding to Time- and Space-Variant Temperature Stimuli.

Customizing the engineering targeted thermal deformations is of vital significance for dimensional stability or shape morphing in materials and structures. However, current metamaterials are designed solely in the homogeneous form to respond only to the time-variant temperature (TVT) stimuli, far behind the practical engineering scenario and demands. Here, a new strategy is originally proposed and experimentally verified to design a series of both homogeneous and inhomogeneous multimaterial metamaterials, which uniquely output various thermal deformation modes, responding to time-variant and space-variant temperature (SVT) stimuli. Specifically, in addition to the regular isotropic thermal deformations, the metamaterials could exclusively output the entirely different positive and negative thermal deformations along the two orthotropic directions. Besides, stimulated by both TVT and SVT, the metamaterials provide more flexibility to customize the targeted thermal deformations. That is, the uniform thermal deformations could be well customized by the metamaterials stimulated by either TVT or SVT. More importantly, the customizability is remarkably broadened, as the nonuniform, specifically, mathematicized linear and nonlinear thermal deformations, are elaborately customized. Overall, these originally devised metamaterials open a new avenue for the purpose of customizing the engineering targeted thermal deformations in various modes under both TVT and SVT stimuli.

The regulation and function of Nrf2 signaling in ferroptosis-activated cancer therapy.

Ferroptosis is an iron-dependent programmed cell death process that involves lipid oxidation via the Fenton reaction to produce lipid peroxides, causing disruption of the lipid bilayer, which is essential for cellular survival. Ferroptosis has been implicated in the occurrence and treatment response of various types of cancer, and targeting ferroptosis has emerged as a promising strategy for cancer therapy. However, cancer cells can escape cellular ferroptosis by activating or remodeling various signaling pathways, including oxidative stress pathways, thereby limiting the efficacy of ferroptosis-activating targeted therapy. The key anti-oxidative transcription factor, nuclear factor E2 related factor 2 (Nrf2 or NFE2L2), plays a dominant role in defense machinery by reprogramming the iron, intermediate, and glutathione peroxidase 4 (GPX4)-related network and the antioxidant system to attenuate ferroptosis. In this review, we summarize the recent advances in the regulation and function of Nrf2 signaling in ferroptosis-activated cancer therapy and explore the prospect of combining Nrf2 inhibitors and ferroptosis inducers as a promising cancer treatment strategy.

Impact of Esophageal Motility on Microbiome Alterations in Symptomatic Gastroesophageal Reflux Disease Patients With Negative Endoscopy: Exploring the Role of Ineffective Esophageal Motility and Contraction Reserve.

Background/Aims: Ineffective esophageal motility (IEM) is common in patients with gastroesophageal reflux disease (GERD) and can be associated with poor esophageal contraction reserve on multiple rapid swallows. Alterations in the esophageal microbiome have been reported in GERD, but the relationship to presence or absence of contraction reserve in IEM patients has not been evaluated. We aim to investigate whether contraction reserve influences esophageal microbiome alterations in patients with GERD and IEM. Methods: We prospectively enrolled GERD patients with normal endoscopy and evaluated esophageal motility and contraction reserve with multiple rapid swallows during high-resolution manometry. The esophageal mucosa was biopsied for DNA extraction and 16S ribosomal RNA gene V3-V4 (Illumina)/full-length (Pacbio) amplicon sequencing analysis. Results: Among the 56 recruited patients, 20 had normal motility (NM), 19 had IEM with contraction reserve (IEM-R), and 17 had IEM without contraction reserve (IEM-NR). Esophageal microbiome analysis showed a significant decrease in microbial richness in patients with IEM-NR when compared to NM. The beta diversity revealed different microbiome profiles between patients with NM or IEM-R and IEM-NR (P = 0.037). Several esophageal bacterial taxa were characteristic in patients with IEM-NR, including reduced Prevotella spp. and Veillonella dispar, and enriched Fusobacterium nucleatum. In a microbiome-based random forest model for predicting IEM-NR, an area under the receiver operating characteristic curve of 0.81 was yielded. Conclusions: In symptomatic GERD patients with normal endoscopic findings, the esophageal microbiome differs based on contraction reserve among IEM. Absent contraction reserve appears to alter the physiology and microbiota of the esophagus.

Deep self-reconstruction driven joint nonnegative matrix factorization model for identifying multiple genomic imaging associations in complex diseases.

OBJECTIVE: Comprehensive analysis of histopathology images and transcriptomics data enables the identification of candidate biomarkers and multimodal association patterns. Most existing multimodal data association studies are derived from extensions of the joint nonnegative matrix factorization model for identifying complex data associations, which can make full use of clinical prior information. However, the raw data were usually taken as the input without considering the underlying complex multi-subspace structure, influencing the subsequent integration analysis results. METHODS: This study proposed a deep-self reconstructed joint nonnegative matrix factorization (DSRJNMF) model to use self-expressive properties to reconstruct the raw data to characterize the similarity structure associated with clinical labels. Then, the sparsity, orthogonality, and regularization constraints constructed from prior information are added to the DSRJNMF model to determine the sparse set of biologically relevant features across modalities. RESULTS: The algorithm has been applied to identify the imaging genetic association of triple negative breast cancer (TNBC). Multilevel experimental results demonstrate that the proposed algorithm better estimates potential associations between pathological image features and miRNA-gene and identifies consistent multimodal imaging genetic biomarkers to guide the interpretation of TNBC. CONCLUSION: The propose method provides a novel idea of data association analysis oriented to complex diseases.

Direct and specific detection of methyl-paraoxon using a highly sensitive fluorescence strategy combined with phosphatase-like nanozyme and molecularly imprinted polymer.

Methyl paraoxon (MP) is a highly toxic, efficient and broad-spectrum organophosphorus pesticide, which poses significant risks to ecological environment and human health. Many detection methods for MP are based on the enzyme catalytic or inhibition effect. But natural biological enzymes are relatively expensive and easy to be inactivated with a short service life. As a unique tool of nanotechnology with enzyme-like characteristics, nanozyme has attracted increasing concern. However, a large proportion of nanozymes lack the intrinsic specificity, becoming a main barrier of constraining their use in biochemical analysis. Here, we use a one-pot reverse microemulsion polymerization combine the gold nanoclusters (AuNCs) with molecularly imprinted polymers (MIPs), polydopamine (PDA) and hollow CeO2 nanospheres to synthesize the bright red-orange fluorescence probe (CeO2@PDA@AuNCs-MIPs) with high phosphatase-like activity for selective detection of MP. The hollow structure possesses a specific surface area and porous matrix, which not only increases the exposure of active sites but also enhances the efficiency of mass and electron transport. Consequently, this structure significantly enhances the catalytic activity by reducing transport distances. The introduced MIPs provide the specific recognition sites for MP. And Ce (III) can excite aggregation induced emission of AuNCs and enhance the fluorescent signal. The absolute fluorescence quantum yield (FLQY) of CeO2@PDA@AuNCs-MIPs (1.41 %) was 12.8-fold higher than that of the GSH-AuNCs (0.11 %). With the presence of MP, Ce (IV)/Ce (III) species serve as the active sites to polarize and hydrolyze phosphate bonds to generate p-nitrophenol (p-NP), which can quench the fluorescent signal through the inner-filter effect. The as-prepared CeO2@PDA@AuNCs-MIPs nanozyme-based fluorescence method for MP detection displayed superior analytical performances with wide linearities range of 0.45-125 nM and the detection limit of 0.15 nM. Furthermore, the designed method offers satisfactory practical application ability. The developed method is simple and effective for the in-field detection.

Universal relation between the conditional auto-correlation function and the cross-correlation function of biphotons.

We systematically studied the relation between the conditional auto-correlation function (CACF) and cross-correlation function (CCF) of biphotons or pairs of single photons. The biphotons were generated from a heated atomic vapor via the spontaneous four-wave mixing (SFWM) process. In practical usage, one single photon of a pair is utilized as the heralding photon, and another is employed as the heralded photon. Motivated by the data of CACF of the heralded photons versus CCF, we proposed a universal formula to predict the CACF. The derived formula was based on general theory and is also valid for the biphoton generation process of spontaneous parametric down-conversion (SPDC). With the formula, we utilized the experimentally determined parameters to predict CACFs, which can well agree with the measured CACFs. The proposed formula enables one to quantitatively know the CACF of heralded single photons without the measurement of Hanbury-Brown-Twiss-type three-fold coincidence count. This study provides a better understanding of biphoton generation using the SFWM or SPDC process. Our work demonstrates a valuable tool for analyzing a vital property of how the heralded photons are close to Fock-state single photons.

Assessment of the herpes zoster risk among renal transplant recipients administered the influenza vaccine.

BACKGROUND: Reactivation of the latent varicella-zoster virus can cause herpes zoster (HZ) infection, and renal transplant recipients undergoing immunosuppressive therapy are particularly susceptible to this condition. This study aims to evaluate the potential increase in HZ incidence following influenza vaccination among this specific patient population. METHODS: This study was a population-based, retrospective, self-controlled case series. Data were retrieved from Taiwan's National Health Insurance Research Database spanning the years 2008 to 2017. Patients diagnosed with HZ within a 6-month period before and after receiving the influenza vaccine were eligible for inclusion. Two distinct time intervals were defined for analysis: the initial 15 days and 30 days following vaccination were categorized as risk intervals, while all other periods served as control intervals. Incidence rate ratios (IRRs) were computed to compare HZ incidence during the risk intervals with that during the control intervals. RESULTS: This study encompassed a cohort of 4,222 renal transplant recipients who had received the influenza vaccine. Among this group, 67 recipients were subsequently diagnosed with HZ. The IRR during both the initial 15 days (IRR = 0.63; 95 % CI, 0.23-1.89) and the first 30 days (IRR = 1.50; 95 % CI, 0.71-3.16) following influenza vaccination did not demonstrate a statistically significant increase when compared to the post-exposure observation times. Comparable results were also observed when comparing these IRR values to the pre-exposure observation times. The subgroup analysis, stratified by age, sex, and underlying medical conditions (including cancer and autoimmune diseases), revealed that the IRRs did not exhibit statistically significant differences. CONCLUSIONS: No significant association between the influenza vaccine and an elevated risk of HZ was detected. The administration of annual influenza vaccines appears to be a reasonable practice for renal transplant recipients.

Comprehensive assessment of combined inorganic and organic fertilization strategies on cotton cultivation: implications for sustainable agriculture.

BACKGROUND: The integration of inorganic and organic fertilizers is increasingly being recommended to address the demand for sustainable cotton cultivation and to mitigate the ecological impacts of reliance on inorganic fertilizers. However, the nuanced effects of this combined fertilization approach on soil quality, cotton growth, yield, and their interaction mechanisms, remain unclear. METHOD: To elucidate this, a 2-year field trial (2022-2023) was conducted, incorporating five fertilization treatments: low inorganic fertilizer (BI1), high inorganic fertilizer (BI2), organic fertilizer (BO), combined low inorganic and organic fertilizer (BIO1), and combined high inorganic and organic fertilizer (BIO2). This study aimed to evaluate the influence of these treatments on soil quality, cotton growth, and yield. RESULTS: The results indicate that the BO treatment significantly enhanced plant height growth rate, and BIO1 treatment increased leaf area index and dry matter accumulation growth rate. Critical soil parameters such as alkali-hydrolyzed nitrogen and available potassium emerged as pivotal determinants of soil quality over the trial period, corresponding to soil quality index (SQI) values of 0.482 and 0.478, and yields of 7506.19 kg ha-1 and 6788.02 kg ha-1, respectively. Water productivity reached optimum levels at SQI values of 0.461 and 0.462, with corresponding efficiencies of 13.31 kg (ha mm)-1 and 12.16 kg (ha mm)-1. Partial least squares path modeling revealed that integrating organic fertilizer with reduced inorganic fertilizer usage significantly boosts cotton yield by enhancing soil quality (path coefficient: 0.842). CONCLUSION: In conclusion, this integrated fertilization strategy not only improves soil health but also increases agricultural productivity. It presents a promising approach for optimizing crop yields while fostering sustainable agricultural practices. © 2024 Society of Chemical Industry.

The mechanism of using magnetized-ionized water in combination with organic fertilizer to enhance soil health and cotton yield.

Addressing critical challenges in sustainable agriculture, notably water scarcity and soil degradation, necessitates innovative irrigation and fertilization methods. This investigation thoroughly assessed the effects of combining inorganic and organic fertilizers under brackish water irrigation, particularly focusing on magnetized-ionized brackish water-a promising solution for these challenges. The study shows that the integration of inorganic and organic fertilizers notably enhances soil water retention and salt leaching when applied with magnetized-ionized brackish water irrigation (MIBIO treatment), with water storage rate and salt accumulation rate observed at -0.454 and -0.075, respectively. Additionally, soil microbial diversity and uniformity witnessed significant improvement, positively influencing cotton growth rates, particularly noting a dry matter accumulation rate of 9.3262 kg·(ha·°C)-1. Transcriptomic analysis revealed that the MIBIO treatment elevated gene expression during the boll period, with notable enrichment in pathways such as the MAPK signaling pathway-plant and amino sugar and nucleotide sugar metabolism. Furthermore, the partial least squares path modeling indicated that soil alkali-hydrolyzed nitrogen (AN) and available potassium (AK) positively impact cotton leaf transcription and yield, with path coefficients of 0.613 and 0.428, respectively. Specifically, AN and AK contribute to enhancing cotton growth and affect the expression of metabolism genes in cotton leaves, thereby increasing cotton yield. Our study highlights the crucial role of irrigation and fertilization in influencing the soil environment and cotton growth. We recommend the use of magnetized-ionized water irrigation in combination with organic fertilizers as a strategy to boost agricultural productivity. Through the development of these strategies, our goal is to offer farmers practical guidance that can be readily implemented to enhance crop production efficiency, reduce environmental impact, and adhere to the principles of sustainable agriculture.

Common dietary emulsifiers promote metabolic disorders and intestinal microbiota dysbiosis in mice.

Dietary emulsifiers are linked to various diseases. The recent discovery of the role of gut microbiota-host interactions on health and disease warrants the safety reassessment of dietary emulsifiers through the lens of gut microbiota. Lecithin, sucrose fatty acid esters, carboxymethylcellulose (CMC), and mono- and diglycerides (MDG) emulsifiers are common dietary emulsifiers with high exposure levels in the population. This study demonstrates that sucrose fatty acid esters and carboxymethylcellulose induce hyperglycemia and hyperinsulinemia in a mouse model. Lecithin, sucrose fatty acid esters, and CMC disrupt glucose homeostasis in the in vitro insulin-resistance model. MDG impairs circulating lipid and glucose metabolism. All emulsifiers change the intestinal microbiota diversity and induce gut microbiota dysbiosis. Lecithin, sucrose fatty acid esters, and CMC do not impact mucus-bacterial interactions, whereas MDG tends to cause bacterial encroachment into the inner mucus layer and enhance inflammation potential by raising circulating lipopolysaccharide. Our findings demonstrate the safety concerns associated with using dietary emulsifiers, suggesting that they could lead to metabolic syndromes.