Unsupervised disentanglement strategy for mitigating artifact in photoacoustic tomography under extremely sparse view.

Traditional methods under sparse view for reconstruction of photoacoustic tomography (PAT) often result in significant artifacts. Here, a novel image to image transformation method based on unsupervised learning artifact disentanglement network (ADN), named PAT-ADN, was proposed to address the issue. This network is equipped with specialized encoders and decoders that are responsible for encoding and decoding the artifacts and content components of unpaired images, respectively. The performance of the proposed PAT-ADN was evaluated using circular phantom data and the animal in vivo experimental data. The results demonstrate that PAT-ADN exhibits excellent performance in effectively removing artifacts. In particular, under extremely sparse view (e.g., 16 projections), structural similarity index and peak signal-to-noise ratio are improved by ∼188 % and ∼85 % in in vivo experimental data using the proposed method compared to traditional reconstruction methods. PAT-ADN improves the imaging performance of PAT, opening up possibilities for its application in multiple domains.

Temporal Dynamics and Contribution of Phage Community to the Prevalence of Antibiotic Resistance Genes in a Full-Scale Sludge Anaerobic Digestion Plant.

Anaerobic digestion (AD) is an important biological resource recovery process, where microorganisms play key roles for material transformation. There has been some knowledge about the prokaryotic community and antibiotic resistance genes (ARGs) in AD, but there has been very limited knowledge of phages. In this study, samples from a full-scale AD plant were collected over 13 months, sequenced, and analyzed for viral and prokaryotic metagenomes. Totally, 3015 viral operational taxonomic units (vOTUs) were detected, mostly assigned to Caudoviricetes. The phage community had faster temporal variation than the prokaryotic community. Warm seasons harbored a higher abundance of both temperate phages and broad host-range phages. Seven ARGs of 6 subtypes were carried by 20 vOTUs, a representative ermT gene was synthesized and expressed, and the resistance activity in the host was examined, confirming the real activity of virus-carried ARGs in the AD process. Some of the ARGs were horizontally transferred between the phage and prokaryotic genomes. However, phage infection was not found to contribute to ARG transfer. This study provided an insight into the ecological patterns of the phage community, confirmed the antibiotic resistance activity of virus-carried ARGs, evaluated the contribution of phages on the ARG prevalence, and laid the foundation for the control strategies of the community and antibiotic resistance in the AD process.

Fast and sensitive CRISPR detection by minimized interference of target amplification.

Despite the great potential of CRISPR-based detection, it has not been competitive with other market diagnostics for on-site and in-home testing. Here we dissect the rate-limiting factors that undermine the performance of Cas12b- and Cas13a-mediated detection. In one-pot testing, Cas12b interferes with loop-mediated isothermal amplification by binding to and cleaving the amplicon, while Cas13a directly degrades the viral RNA, reducing its amplification. We found that the protospacer-adjacent motif-interacting domain engineered Cas12b accelerated one-pot testing with 10-10,000-fold improved sensitivity, and detected 85 out of 85 SARS-CoV-2 clinical samples with a sensitivity of 0.5 cp µl-1, making it superior to wild-type Cas12b. In parallel, by diminishing the interference of Cas13a with viral RNA, the optimized Cas13a-based assay detected 86 out of 87 SARS-CoV-2 clinical samples at room temperature in 30 min with a sensitivity of 0.5 cp µl-1. The relaxed reaction conditions and improved performance of CRISPR-based assays make them competitive for widespread use in pathogen detection.

Effects of exercise on muscle fiber conversion, muscle development and meat quality of Sunit sheep.

This study aimed to investigate the effects of exercise on muscle fiber conversion, muscle development and meat quality in the biceps femoris (BF) muscle of Sunit sheep. Twelve Sunit sheep with similar body weight were divided into two groups: control group (C group) and exercise group (E group), E group lambs underwent 6 km of exercise training per day for 90 d. The findings revealed that compared with the C group, exercise training enhanced the expression of MyHC IIa mRNA, decreased the number ratio of type IIB muscle fibers and the expression of MyHC IIb mRNA (P < 0.05). Furthermore, the E group lamb displayed higher creatine kinase (CK) activity, and lactic acid levels (P < 0.05), while glycogen content and lactic dehydrogenase (LDH) activity showed opposite trends (P < 0.05). Exercise significantly up-regulated the mRNA expression of AMP-activated protein kinase α1 (AMPKα1), sirtuin1 (SIRT1), peroxisome proliferator-activated receptor gamma coactivator 1-α (PGC-1α), cytochrome c oxidase IV (COX IV), protein kinase B (Akt), mammalian target of rapamycin (mTOR) and p70 Ribosomal S6 Kinase 1 (p70s6k1) (P < 0.05), suggesting exercise promoted muscle fiber conversion by mediating AMPK/PGC-1α pathway, and improved skeletal muscle development via Akt/mTOR pathway. Besides, backfat thickness and pH45min value in the E group decreased significantly, while the pH24, a*, and shear force value increased significantly (P < 0.05). To conclude, this study suggested that exercise training can be used to alter muscle fiber characteristics and muscle development in lamb production.

An explainable unsupervised risk early warning framework based on the empirical cumulative distribution function: Application to dairy safety.

Efficient food safety risk assessment significantly affects food safety supervision. However, food detection data of different types and batches show different feature distributions, resulting in unstable detection results of most risk assessment models, lack of interpretability of risk classification, and insufficient risk traceability. This study aims to explore an efficient food safety risk assessment model that takes into account robustness, interpretability and traceability. Therefore, the Explainable unsupervised risk Warning Framework based on the Empirical cumulative Distribution function (EWFED) was proposed. Firstly, the detection data's underlying distribution is estimated as non-parametric by calculating each testing indicator's empirical cumulative distribution. Next, the tail probabilities of each testing indicator are estimated based on these distributions and summarized to obtain the sample risk value. Finally, the "3σ Rule" is used to achieve explainable risk classification of qualified samples, and the reasons for unqualified samples are tracked according to the risk score of each testing indicator. The experiments of the EWFED model on two types of dairy product detection data in actual application scenarios have verified its effectiveness, achieving interpretable risk division and risk tracing of unqualified samples. Therefore, this study provides a more robust and systematic food safety risk assessment method to promote precise management and control of food safety risks effectively.

Big data-driven spatio-temporal heterogeneity analysis of Beijing's catering service industry during the COVID-19 pandemic.

The Catering Service Industry (CSI) experienced profound impacts due to the COVID-19 pandemic. However, the long-term and multi-timepoint analysis using big data remained limited, influencing governmental decision-making. We applied Kernel Density Estimation, Shannon Diversity Index, and the Geographic detector to explore the spatial heterogeneity and determinants of the CSI in Beijing during the pandemic, with monthly granularity. The temporal-spatial dynamics of the CSI presented a "W"-shaped trend from 2018 to 2023, with pivotal shifts aligning with key pandemic stages. Spatial characteristics exhibited heterogeneity, with greater stability in the city center and more pronounced shifts in peripheral urban zones. Districts facing intricate outbreaks showed lower catering income, and Chinese eateries exhibited heightened resilience compared to others. The CSI displayed strong interconnections with living service sectors. Development in each district was influenced by economic level, population distribution, service facilities convenience, and the risk of the COVID-19 pandemic. Dominant factors included total retail sales of consumer goods, permanent population, average Baidu Heat Index, density of transportation and catering service facilities, infection cases and the consecutive days with confirmed cases existing. Consequently, we suggested seizing post-pandemic recovery as an avenue to unlock the CSI's substantial potential, ushering a fresh phase of growth.

Long-term occurrence, resistance risk and chaotic characteristics of antibiotic resistance genes in sludge anaerobic digestion system.

The long-term occurrence, dynamics and risk of antibiotic resistance genes (ARGs) in anaerobic digestion (AD) of excess sludge (ES) are not fully understood. Therefore, 13-month metagenomic monitoring was carried out in a full-scale AD plant. The highest ARG abundance and risk scores were observed in spring. AD achieved a 35 % removal rate for the total ARG abundance, but the risk score of AD sludge was not always lower than ES samples, because of the higher proportion of Rank I ARGs in AD sludge. ARGs showed less obvious patterns under linear models compared with microbial community, implying their chaotic dynamics, which was further confirmed by nonlinearity tests. Empirical dynamic modeling performed better than the autoregressive integrated moving average model for ARG dynamics, especially for those with simple and nonlinear dynamics. This study highlighted spring for its higher ARG abundance and risk, and recommended nonlinear models for revealing the dynamics of ARGs.

The crucial regulatory role of type I interferon in inflammatory diseases.

Type I interferon (IFN-I) plays crucial roles in the regulation of inflammation and it is associated with various inflammatory diseases including systemic lupus erythematosus (SLE), rheumatoid arthritis (RA), and periodontitis, impacting people's health and quality of life. It is well-established that IFN-Is affect immune responses and inflammatory factors by regulating some signaling. However, currently, there is no comprehensive overview of the crucial regulatory role of IFN-I in distinctive pathways as well as associated inflammatory diseases. This review aims to provide a narrative of the involvement of IFN-I in different signaling pathways, mainly mediating the related key factors with specific targets in the pathways and signaling cascades to influence the progression of inflammatory diseases. As such, we suggested that IFN-Is induce inflammatory regulation through the stimulation of certain factors in signaling pathways, which displays possible efficient treatment methods and provides a reference for the precise control of inflammatory diseases.

Proteomic and single-cell analysis shed new light on the anti-inflammatory role of interferonß in chronic periodontitis.

Periodontitis, a condition that results in periodontal attachment loss and alveolar bone resorption, contributes to the global burden of oral disease. The underlying mechanism of periodontitis involves the dysbiosis and dyshomeostasis between host and oral microbes, among which the macrophage is one of the major innate immune cell players, producing interferon ß (IFNß) in response to bacterial infection. The objective of this research was to examine the interaction of macrophages with periodontitis and the role and mechanism of IFNß on macrophages. IFNß has been shown to have the potential to induce the differentiation of M1 to M2 macrophages, which are stimulated by low levels of lipopolysaccharide (LPS). Additionally, IFNß has been demonstrated to promote the production of ISG15 by macrophages, which leads to the inhibition of the innate immune response. Moreover, our investigation revealed that IFNß has the potential to augment the secretion of ISG15 and its downstream cytokine, IL10, in LPS-stimulated macrophages. Single-cell analysis was conducted on the gingival tissues of patients with periodontitis, which revealed a higher proportion of macrophages in the periodontitis-diseased tissue and increased expression of IFNß, ISG15, and IL10. Gene Set Enrichment Analysis indicated that bacterial infection was associated with upregulation of IFNß, ISG15, and IL10. Notably, only IL10 has been linked to immunosuppression, indicating that the IFNß-ISG15-IL10 axis might promote an anti-inflammatory response in periodontitis through IL10 expression. It is also found that macrophage phenotype transitions in periodontitis involve the release of higher levels of IFNß, ISG15, and IL10 by the anti-inflammatory M2 macrophage phenotype compared to the pro-inflammatory M1 phenotype and myeloid-derived suppressor cells (MDSCs). This implies that the IFNß-induced production of IL10 might be linked to the M2 macrophage phenotype. Furthermore, cell communication analysis demonstrated that IL10 can promote fibroblast proliferation in periodontal tissues via STAT3 signaling.

Whole genome sequence-based characterization of virulence and antimicrobial resistance gene profiles of Staphylococcus aureus isolated from food poisoning incidents in eastern China.

Staphylococcus aureus is an opportunistic foodborne pathogen occasionally isolated from diarrhea patients. In recent years, increasing studies have reported the detection of S. aureus in food poisoning incidents due to food contamination in the North and South of China. However, the epidemiology and genetic characteristics of S. aureus from food poisoning incidents in Eastern China remain unknown. The present study examined the genetic characteristics, antimicrobial resistance, and virulent factors of multidrug-resistant S. aureus isolated from 22 food poisoning incidents reported by the hospitals and health centers in Eastern China from 2011 to 2021. A total of 117 resistant and enterotoxigenic S. aureus isolates were collected and sequenced, among which 20 isolates were identified as methicillin resistant. Genetic analysis revealed 19 distinct CC/ST types, with CC6, CC22, CC59, CC88, and CC398 being the most frequent variants in methicillin-resistant S. aureus (MRSA). A considerable shift in CC types from CC1 to CC398 between 2011 and 2021 was observed in this study, indicating that CC398 may be the main epidemic strain circulating in the current food poisoning incidents. Additionally, genes for enterotoxins were detected in 55 isolates, with a prevalence of 27.8% (27/97) for methicillin-sensitive variants and 35.0% (7/20) for MRSA. The scn gene was detected in 59.0% of the isolates, demonstrating diverse contaminations of S. aureus among livestock-to-human transmission. Of the 117 isolates, only ten isolates displayed multi-drug resistance (MDR) to penicillin, tetracycline, and macrolides. None of the 117 foodborne S. aureus isolates tested positive for vanA in this study. Together, the present study provided phylogenetic characteristics of S. aureus from food poisoning incidents that emerged in Eastern China from 2011 to 2021. Our results suggested that these diarrhea episodes were hypotonic and merely transient low-MDR infections, however, further research for continued surveillance given the detection of virulence and antimicrobial resistance determinants is required to elucidate the genomic characteristics of pathogenic S. aureus in food poisoning incidents in the context of public health.

Facile Synthesis of a SiOx-Graphite Composite toward Practically Accessible High-Energy-Density Lithium-Ion Battery Anodes.

SiOx-based material is a promising candidate for lithium-ion batteries (LIBs) owing to its high theoretical capacity. The inherent disadvantages of poor electronic conductivity and large volume variation can be solved by constructing the outermost carbon layer and reserving internal voids. However, the practical application of SiOx/C composites remains a great challenge due to the unsatisfactory energy density. Herein, we propose a facile synthetic approach for fabricating SNG/H-SiOx@C composites, which are constructed by amorphous carbon, hollow SiOx (H-SiOx), and spherical natural graphite (SNG). H-SiOx alleviates volume expansion, while amorphous carbon promotes Li+ migration and stable solid electrolyte interphase (SEI) formation. The as-prepared SNG/H-SiOx@C demonstrates a high reversible capacity (465 mAh g-1), excellent durability (93% capacity retention at 0.5C after 500 cycles), lower average delithiation potential than SNG (0.143 V after 500 cycles), and a 14% gravimetric energy density improvement at a loading level of 4.5 mg cm-2. Even at a compacted density of 1.5 g cm-3, the SNG/H-SiOx@C anode presents a modest volume deformation of 14.3% after 100 cycles at 0.1C.

Predicting the retention time of Synthetic Cannabinoids using a combinatorial QSAR approach.

Background: Abuse of Synthetic Cannabinoids (SCs) has become a serious threat to public health. Due to the various structural and chemical group modified by criminals, their detection is a major challenge in forensic toxicological identification. Therefore, rapid and efficient identification of SCs is important for forensic toxicology and drug bans. The prediction of an analyte's retention time in liquid chromatography is an important index for the qualitative analysis of compounds and can provide informatics solutions for the interpretation of chromatographic data. Methods: In this study, experimental data from high-resolution mass spectrometry (HRMS) are used to construct a regression model for predicting the retention time of SCs using machine learning methods. The prediction ability of the model is improved by adopting a strategy that combines different descriptors in different independent machine-learning methods. Results: The best model was obtained with a method that combined Substructure Fingerprint Count and Finger printer features and the support vector regression (SVR) method, as it exhibited an R2 value of 0.81 for the validation set and 0.83 for the test set. In addition, 4 new SCs were predicted by the optimized model, with a prediction error within 3%. Conclusions: Our study provides a model that can predict the retention time of compounds and it can be used as a filter to reduce false-positive candidates when used in combination with LC-HRMS, especially in the absence of reference standards. This can improve the confidence of identification in non-targeted analysis and the reliability of identifying unknown substances.

CSGNN: Contamination Warning and Control of Food Quality via Contrastive Self-Supervised Learning-Based Graph Neural Network.

Effective contamination warning and control of food quality can significantly reduce the likelihood of food quality safety incidents. Existing food contamination warning models for food quality rely on supervised learning, do not model the complex feature associations between detection samples, and do not consider the unevenness of detection data categories. In this paper, To overcome these limitations, we propose a Contrastive Self-supervised learning-based Graph Neural Network framework (CSGNN) for contamination warning of food quality. Specifically, we structure the graph for detecting correlations between samples and then define the positive and negative instance pairs for contrastive learning based on attribute networks. Further, we use a self-supervised approach to capture the complex relationships between detection samples. Finally, we assessed each sample's contamination level based on the absolute value of the subtraction of the prediction scores from multiple rounds of positive and negative instances obtained by the CSGNN. Moreover, we conducted a sample study on a batch of dairy product detection data in a Chinese province. The experimental results show that CSGNN outperforms other baseline models in contamination assessment of food quality, with AUC and recall of unqualified samples reaching 0.9188 and 1.0000, respectively. Meanwhile, our framework provides interpretable contamination classification for food detection. This study provides an efficient early warning method with precise and hierarchical contamination classification for contamination warning of food quality work.

Single-cell analysis reveals that cancer-associated fibroblasts stimulate oral squamous cell carcinoma invasion via the TGF-ß/Smad pathway.

Although substantial progress has been made in cancer biology and treatment, the prognosis of oral squamous cell carcinoma (OSCC) is still not satisfactory because of local tumor invasion and frequent lymph node metastasis. The tumor microenvironment (TME) is a potential target in which cancer-associated fibroblasts (CAFs) are of great significance due to their interactions with cancer cells. However, the exact mechanism is still unclear. Therefore, we focus on the crosstalk between cancer cells and CAFs and discover that CAFs are the main source of TGF-ß1. Transwell assays and western blot analysis further prove that CAFs activate the TGF-ß1/Smad pathway to promote OSCC invasion. Through survival analysis, we confirm that CAF overexpression is correlated with poor overall survival in OSCC. To further elucidate the origin and role of CAFs in OSCC, we analyze single-cell RNA sequencing (scRNA-seq) data from 14 OSCC tumor samples and identify four distinct cell types, including CAFs, in the TME, indicating high intratumoral heterogeneity. Then, two subtypes of CAFs, namely, myofibroblasts (mCAFs) and inflammatory CAFs (iCAFs), are further distinguished. Based on the differentially upregulated genes of mCAFs and iCAFs, GO enrichment analysis reveals their different roles in OSCC progression. Furthermore, the gene expression pattern is dynamically altered across pseudotime, potentially taking part in the transformation from epithelial to mCAFs or iCAFs through the epithelial to mesenchymal transition.

Comprehensive analysis of INHBA: A biomarker for anti-TGFß treatment in head and neck cancer.

Inhibin subunit ßA (INHBA) is a protein-coding gene belonging to the transforming growth factor ß (TGFß) superfamily, which is associated with the development of a variety of cancers. However, the role of INHBA in head and neck squamous cell carcinoma (HNSC) remains unclear. The expression profile and prognostic significance of INHBA in HNSC were assessed using a variety of informatics methods. The level of INHBA expression was significantly higher in patients with HNSC, and it was correlated with sex, tumor-node-metastasis (TNM) stage, histological grade, and human papillomavirus (HPV) status. Kaplan-Meier (K-M) analysis indicated that poor overall survival (OS) and disease-free survival (DFS) were significantly associated with INHBA upregulation in HNSC. INHBA overexpression was validated as an independent poor prognostic factor by multivariate Cox regression, and including INHBA expression level in the prognostic model could increase prediction accuracy. In addition, copy number alterations (CNAs) of INHBA and miR-217-5p downregulation are potential mechanisms for elevated INHBA expression in HNSC. In conclusion, INHBA may represent a promising predictive biomarker and candidate target for anti-TGFß therapy in HNSC.

Necroptotic TNFα-Syndecan 4-TNFα Vicious Cycle as a Therapeutic Target for Preventing Temporomandibular Joint Osteoarthritis.

Temporomandibular joint osteoarthritis (TMJOA) is a chronic degenerative disease for which the underlying mechanism still remains unclear. Compared with apoptosis and autophagy, necroptosis causes greater harm to tissue homeostasis by releasing damage-associated molecular patterns (DAMPs). However, the role of necroptosis and downstream key DAMPs in TMJOA is unknown. Here, rodent models of TMJOA were established by the unilateral anterior crossbite (UAC). Transmission electron microscopy (TEM) and immunohistochemistry of receptor interacting protein kinase 3 (RIPK3)/phosphorylation of mixed lineage kinase domain-like protein (pMLKL) were conducted to evaluate the occurrence of necroptosis in vivo. The therapeutic effects of blocking necroptosis were achieved by intra-articularly injecting RIPK3 or MLKL inhibitors and using RIPK3 or MLKL knockout mice. In vitro necroptosis of condylar chondrocyte was induced by combination of tumor necrosis factor alpha (TNFα), second mitochondria-derived activator of caspases (SMAC) mimetics and carbobenzoxy-valyl-alanyl-aspartyl-[O-methyl]- fluoromethylketone (z-VAD-fmk). The possible DAMPs released by necroptotic chondrocytes were screened by quantitative proteomics and blocked by specific antibody. Translucent cytosol, swollen organelles, and ruptured cell membranes, features of necroptosis, were frequently manifested in chondrocytes at the early stage of condylar cartilage degeneration in TMJOA, which was accompanied by upregulation of RIPK3/pMLKL. Inhibiting or knocking out RIPK3/MLKL significantly prevented cartilage degeneration. DAMPs released by necroptotic condylar chondrocytes, such as syndecan 4 (SDC4) and heat shock protein 90 (HSP90), were verified. Furthermore, blocking the function of SDC4 significantly attenuated the expression of TNFα in cartilage and synovium, and accordingly increased cartilage thickness and reduced synovial inflammation. Thus, the necroptotic vicious cycle of TNFα-SDC4-TNFα contributes to cartilage degeneration and synovitis, and can serve as a potential therapeutic target for treating TMJOA. © 2022 American Society for Bone and Mineral Research (ASBMR).

Optimal sites and angles for the insertion of orthodontic mini-implants at infrazygomatic crest: a cone beam computed tomography (CBCT)-based study.

BACKGROUND AND OBJECTIVES: Both cortical and overall bone thicknesses of the infrazygomatic crest (IZC) were measured to determine the optimal areas for mini-implants into the IZC. The impact of insertion sites, heights and angles, sex and age on bone thickness were evaluated. MATERIALS AND METHODS: In this study, cone beam computed tomography (CBCT) images of 32 patients were included. The cortical bone thickness (CBT) and overall bone thicknesses (OBT) of IZC were measured at different insertion sites between the maxillary first and second molars (site 61, 62, 63, 67, 71, 72 and 73), different heights (0 to 12 mm from alveolar bone crest) and different angles (0 to 90 degrees from the reference line). RESULTS: OBT was the thickest at site 63, followed by site 73. For each site, the insertion height where OBT was the thickest decreased with the increase of angle CBT and OBT were significantly influenced by sex and age. The percentage of root contact was significantly influenced by insertion heights and angles, not by sites. The recommended regions in the IZC for mini-implants were mapped. CONCLUSIONS: Both CBT and OBT in the infrazygomatic crest were influenced by insertion sites, heights, and angles. Sex and age had an impact on CBT and OBT. The optimal insertion heights and angles were 12 mm to 18 mm from the occlusal plane, and 40 to 70 degrees for mini-implants at IZC.

Seasonal dynamics of the microbial community in two full-scale wastewater treatment plants: Diversity, composition, phylogenetic group based assembly and co-occurrence pattern.

The optimal operation and functional stability of a wastewater treatment plant (WWTP) strongly depend on the properties of its microbial community. However, a knowledge gap remains regarding the seasonal dynamics of microbial community properties, especially phylogenetic group based assembly and co-occurrence patterns. Accordingly, in this study, influent and activated sludge (AS) samples were weekly collected from 2 full-scale WWTPs for one year (89 influent and 103 AS samples in total) and examined by high-throughput Illumina-MiSeq sequencing. The results suggested that the microbial community diversity and composition in the influent fluctuated substantially with season, while those in the AS had a relatively more stable pattern throughout the year. The phylogenetic group based assembly mechanisms of AS community were identified by using "Infer Community Assembly Mechanisms by Phylogenetic-bin-based null model (iCAMP)". The results showed that drift accounted for the largest proportion (52.8%), while homogeneous selection (18.2%) was the most important deterministic process. Deterministic processes dominated in 47 microbial groups (bins), which were also found (~40%) in the AS core taxa dataset. Moreover, the results suggested that Nitrospira were more susceptible to stochastic processes in winter, which may provide a possible explanation for nitrification failure in winter. Network analysis results suggested that the network structure of the AS community could be more stable in summer and autumn. In addition, there were no identical keystone taxa found in different networks (constructed from different plants, sources, and seasons), which supported the context dependency theory. The results of this study deepened our understanding of the microbial ecology in AS systems and provided a foundation for further studies on the community regulation strategy of WWTPs.

Gold standard for nutrition: a review of human milk oligosaccharide and its effects on infant gut microbiota.

Human milk is the gold standard for nutrition of infant growth, whose nutritional value is mainly attributed to human milk oligosaccharides (HMOs). HMOs, the third most abundant component of human milk after lactose and lipids, are complex sugars with unique structural diversity which are indigestible by the infant. Acting as prebiotics, multiple beneficial functions of HMO are believed to be exerted through interactions with the gut microbiota either directly or indirectly, such as supporting beneficial bacteria growth, anti-pathogenic effects, and modulation of intestinal epithelial cell response. Recent studies have highlighted that HMOs can boost infants health and reduce disease risk, revealing potential of HMOs in food additive and therapeutics. The present paper discusses recent research in respect to the impact of HMO on the infant gut microbiome, with emphasis on the molecular basis of mechanism underlying beneficial effects of HMOs.