Design and testing of a humanized porcine donor for xenotransplantation.

Recent human decedent model studies1,2 and compassionate xenograft use3 have explored the promise of porcine organs for human transplantation. To proceed to human studies, a clinically ready porcine donor must be engineered and its xenograft successfully tested in nonhuman primates. Here we describe the design, creation and long-term life-supporting function of kidney grafts from a genetically engineered porcine donor transplanted into a cynomolgus monkey model. The porcine donor was engineered to carry 69 genomic edits, eliminating glycan antigens, overexpressing human transgenes and inactivating porcine endogenous retroviruses. In vitro functional analyses showed that the edited kidney endothelial cells modulated inflammation to an extent that was indistinguishable from that of human endothelial cells, suggesting that these edited cells acquired a high level of human immune compatibility. When transplanted into cynomolgus monkeys, the kidneys with three glycan antigen knockouts alone experienced poor graft survival, whereas those with glycan antigen knockouts and human transgene expression demonstrated significantly longer survival time, suggesting the benefit of human transgene expression in vivo. These results show that preclinical studies of renal xenotransplantation could be successfully conducted in nonhuman primates and bring us closer to clinical trials of genetically engineered porcine renal grafts.

Assessing and engineering the IscB-ωRNA system for programmed genome editing.

OMEGA RNA (ωRNA)-guided endonuclease IscB, the evolutionary ancestor of Cas9, is an attractive system for in vivo genome editing because of its compact size and mechanistic resemblance to Cas9. However, wild-type IscB-ωRNA systems show limited activity in human cells. Here we report enhanced OgeuIscB, which, with eight amino acid substitutions, displayed a fourfold increase in in vitro DNA-binding affinity and a 30.4-fold improvement in insertion-deletion (indel) formation efficiency in human cells. Paired with structure-guided ωRNA engineering, the enhanced OgeuIscB-ωRNA systems efficiently edited the human genome across 26 target sites, attaining up to 87.3% indel and 62.2% base-editing frequencies. Both wild-type and engineered OgeuIscB-ωRNA showed moderate fidelity in editing the human genome, with off-target profiles revealing key determinants of target selection including an NARR target-adjacent motif (TAM) and the TAM-proximal 14 nucleotides in the R-loop. Collectively, our engineered OgeuIscB-ωRNA systems are programmable, potent and sufficiently specific for human genome editing.

Explainable Deep Learning-Assisted Photochromic Sensor for ß-Lactam Antibiotic Identification.

Photochromic sensors have the advantages of diverse isomers for multi-analysis, providing more sensing information and possessing more recognition units and more sensitivity to external stimulations, but they present enormous complexity with various stimulations as well. Deep learning (DL) algorithms contribute a huge advantage at analyzing nonlinear and multidimensional data, but they suffer from nontransparent inner networks, "black-boxes". In this work, we employed the explainable DL approach to process and explicate photochromic sensing. Spirooxazine metallic complexes were adopted to prepare a multi-state analysis array for ß-Lactams identification and quantitation. A dataset of 2520 unduplicated fluorescence intensity images was collected for convolutional neural network (CNN) operation. The method clearly discriminated six ß-Lactams with 97.98% prediction accuracy and allowed rapid quantification with a concentration range from 1 to 100 mg/L. The photochromic sensing mechanism was verified via molecular simulation and class activation mapping, which explicated how the CNN model assesses the importance of photochromic sensor states and makes a discrimination decision. The explainable DL-assisted analysis method establishes an end-to-end strategy to ascertain and verify the complicated sensing mechanism for device optimization and even new scientific discovery.

Explainable Deep Learning-Assisted Fluorescence Discrimination for Aminoglycoside Antibiotic Identification.

The complexity and multivariate analysis of biological systems and environment are the drawbacks of the current high-throughput sensing method and multianalyte identification. Deep learning (DL) algorithms contribute a big advantage in analyzing the nonlinear and multidimensional data. However, most DL models are data-driven black boxes suffering from nontransparent inner workings. In this work, we developed an explainable DL-assisted visualized fluorometric array-based sensing method. Based on a data set of 8496 fluorometric images of various target molecule fingerprint patterns, two typical DL algorithms and eight machine learning algorithms were investigated for the efficient qualitative and quantitative analysis of six aminoglycoside antibiotics (AGs). The convolutional neural network (CNN) approached 100% prediction accuracy and 1.34 ppm limit of detection of six AG analysis in domestic, industrial, medical, consumption, or aquaculture water. The class activation mapping assessment explicates how the CNN model assesses the importance of sensor elements and makes the discrimination decision. The feedback mechanism guides the sensor array evolution for less material using a simplified operation or efficient data acquisition. The explainable DL-assisted analysis method establishes an "end-to-end" strategy to resolve the black box of the DL algorithm, promote hardware design or principle optimization, and contribute facile indicators for environment monitoring, disease diagnosis, and even new scientific discovery.

Acute pancreatitis precedes chronic pancreatitis in the majority of patients: Results from the NAPS2 consortium.

INTRODUCTION: The mechanistic definition of chronic pancreatitis (CP) identifies acute pancreatitis (AP) as a precursor stage. We hypothesized that clinical AP frequently precedes the diagnosis of CP and is associated with patient- and disease-related factors. We describe the prevalence, temporal relationship and associations of AP in a well-defined North American cohort. METHODS: We evaluated data from 883 patients with CP prospectively enrolled in the North American Pancreatitis Studies across 27 US centers between 2000 and 2014. We determined how often patients had one or more episodes of AP and its occurrence in relationship to the diagnosis of CP. We used multivariable logistic regression to determine associations for prior AP. RESULTS: There were 624/883 (70.7%) patients with prior AP, among whom 161 (25.8%) had AP within 2 years, 115 (18.4%) within 3-5 years, and 348 (55.8%) >5 years prior to CP diagnosis. Among 504 AP patients with available information, 436 (86.5%) had >1 episode. On multivariable analyses, factors associated with increased odds of having prior AP were a younger age at CP diagnosis, white race, abdominal pain, pseudocyst(s) and pancreatic duct dilatation/stricture, while factors associated with a lower odds of having prior AP were exocrine insufficiency and pancreatic atrophy. When compared with patients with 1 episode, those with >1 AP episode were diagnosed with CP an average of 5 years earlier. CONCLUSIONS: Nearly three-quarters of patients were diagnosed with AP prior to CP diagnosis. Identifying which AP patients are at-risk for future progression to CP may provide opportunities for primary and secondary prevention.

The electronic structure and interfacial contact with metallic borophene of monolayer ScSX (X = I, Br, and Cl).

Two-dimensional (2D) semiconductors with excellent electronic and optical properties provide a great prospect for the fundamental research and application for the next-generation devices. Exploring the contact properties between 2D semiconductors and metal electrodes for improving the performance of nanodevices is of utmost importance. Motivated by the successful synthesis of bulk ScSI experimentally in a recent work [A. M. Ferrenti, M. A. Siegler, S. Gao, N. Ng and T. M. McQueen, Chem. Mater., 2022, 34, 12, 5443-5451], here we systematically investigate the intrinsic structural, electronic and optical properties of the novel monolayer ScSX (X = I, Br, and Cl) and their interfacial contact properties with the metal electrode of borophene using first-principles calculations. Interestingly, halogen X atoms with different electronegativities not only influence the intrinsic properties of monolayer ScSX, but also affect the interlayer coupling between monolayer ScSX and metallic borophene. The ScSI/borophene heterostructure forms a p-type Schottky contact, while both ScSBr/borophene and ScSCl/borophene heterostructures form a n-type Schottky contact. Moreover, our calculations demonstrate that strain engineering and applying an external electric field are effective strategies to regulate the Schottky barrier and contact types at the interface of ScSX/borophene. These findings provide a very promising path for designing tunable Schottky nanodevices with high-performance based on monolayer ScSX.

Magnesium supplementation for glycemic status in women with gestational diabetes: a systematic review and meta-analysis.

OBJECTIVE: The effect of magnesium supplementation on glycemic status in women with gestational diabetes remains controversial and this meta-analysis aims to explore the efficacy of magnesium supplementation for gestational diabetes. METHODS: We have searched PubMed, Excerpta Medica database, Web of science, Elton B. Stephens. Company, and Cochrane library databases. The meta-analysis included randomized controlled trials (RCTs) assessing the effect of magnesium supplementation for gestational diabetes and was performed using the random-effect model. RESULTS: Four RCTs were included in the meta-analysis. Overall, compared with placebo in gestational diabetes, magnesium supplementation was associated with significantly reduced fasting plasma glucose (standard mean difference [SMD] = -0.99; 95% confidence interval [CI] = -1.28 to -0.70; p < .00001), serum insulin (SMD = -0.75; 95% CI = -1.24 to -0.26; p = .003), homeostasis model assessment of insulin resistance (SMD = -0.74; 95% CI = -1.10 to -0.39; p < .0001) and increased quantitative insulin sensitivity check index (SMD = 0.47; 95% CI = 0.12 to 0.82; p = .008). In addition, low-density lipoprotein-cholesterol (SMD = -0.39; 95% CI = -0.73 to -0.04; p = .03) and total cholesterol (SMD = -0.62; 95% CI = -0.97 to -0.27; p = .0005) were also obviously decreased in the magnesium group than those in the control group. CONCLUSION: Magnesium supplementation benefits glycemic control for gestational diabetes.

A doctor-nurse-patient mobile health management system effectively controls blood glucose in chinese patients with type 2 diabetes mellitus: a prospective study.

BACKGROUND: Coronavirus-2019 pandemic in China aroused increasing interest in telemedicine-supported glycaemic control. We hypothesize that age might influence usage and efficacy of telemedicine-supported glycaemic control. This study aims to measure the effects of a doctor-nurse-patient Mobile Health Management System (MHMS) for fasting plasma glucose (FPG) control in patients with type 2 diabetes mellitus (T2DM). METHODS: Four hundred sixty four patients with T2DM were recruited. A one-hour diabetes education provided to each patient and subsequent follow-ups arranged in the 1st, 2nd, 4th, 8th, and 12th week after enrollment were recorded in MHMS. The effectiveness of MHMS was defined as the proportion of patients achieving FPG target (below 126 mg/dL or 7.0mml/L). RESULTS: Among the enrolled 464 patients (age: 55.0 ± 13.7 years) who were divided into three groups: young (18-40 years), middle-aged (41-65 years) and elderly (> 65 years), 424 ones completed all follow-ups of 12 weeks. FPG decreased from 178.38 ± 95.04 to 117.90 ± 14.22 mg/dL in the young group, from 180.00 ± 91.08 to 122.94 ± 37.95 mg/dL in the middle-aged group, and from 174.24 ± 80.64 to 128.88 ± 23.4 mg/dL in the elderly group. The proportion of FPG-target-achieved patients increased from 46.2 to 90.4% in the young group, from 32.6 to 82.8% in the middle-aged group, and from 29.5 to 73.3% in the elderly group. The proportion of FPG-target-achieved patients between three age groups were statistically significant (P < 0.001). And the changes of proportion of FPG-target-achieved patients at different follow-up times were statistically significant (P = 0.037). Compared with the young group, the elderly group achieved poorer FPG level (P = 0.032). CONCLUSION: MHMS can help patients with T2DM lower FPG and improve proportion of FPG-target-achieved patients. Younger patients may achieve better glycaemic control than older patients. MHMS may serve multitudinous patients with T2DM to achieve adequate FPG self-management.

Network pharmacology prediction and molecular docking-based strategy to explore the pharmacodynamic substances and mechanism of "Mung Bean" against bacterial infection.

OBJECTIVE: The network pharmacology approach combined the technologies of molecular docking and in vitro bacteriostatic validation to explore the active compounds, core targets, and mechanism of Mung Bean against bacterial infection. METHODS: A Mung Bean target and anti-bacterial infection-related gene set was established using TCMSP and GeneCards databases. Gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis and protein-protein interaction network were performed using DAVID and STRING database. The combination of core targets and active compounds was predicted by molecular docking. The bacteriostatic experiment in vitro was performed to verify the antibacterial activity of the active compounds. RESULT: 32 potential targets and 5 active compounds of Mung Bean against bacterial infection were obtained by bioinformatics analysis. SRC, EGFR, and MAPK8 might be the candidate targets of Mung Bean. There were 137 GO items (p < 0.05) and 60 signaling pathways (p < 0.05) in GO and KEGG enrichment analysis. The PI3K-AKT pathway, TNF signaling pathway, MAPK signaling pathway might play a significant role in Mung Bean against bacterial infection. Molecular docking results showed that sitosterol and vitamin-e had a high binding affinity with the core targets, which might be the key compounds of Mung bean. In vitro bacteriostatic experimental verified that vitamin-e had a significant bacteriostatic effect. CONCLUSION: Sitosterol and vitamin-E in Mung bean might act on MAPK1, regulate inflammation and immune response to play a role in anti-bacterial infection.

Robust Self-Testing of Four-Qubit Symmetric States.

Quantum verification has been highlighted as a significant challenge on the road to scalable technology, especially with the rapid development of quantum computing. To verify quantum states, self-testing is proposed as a device-independent concept, which is based only on the observed statistics. Previous studies focused on bipartite states and some multipartite states, including all symmetric states, but only in the case of three qubits. In this paper, we first give a criterion for the self-testing of a four-qubit symmetric state with a special structure and the robustness analysis based on vector norm inequalities. Then we generalize the idea to a family of parameterized four-qubit symmetric states through projections onto two subsystems.

Two Zn(II)-based Coordination Polymers: Fe3+ Ion and Prevention Activity Detection On Postpartum Hemorrhage By Regulating Anticoagulant Factor Activity.

Two new Zn(II)-based coordination polymers {[Zn3(L1)6(H2O)]∙(H2O)4}n (1, HL1 = 4-(tetrazol-5-yl)phenyl-4,2':6',4″-terpyridine) and [Zn2Cl2(L2)2H2O]n (2, HL2 = 4-([2,2':6',2″'-terpyridin]-4'-yl)benzoic acid) have been successfully prepared using two similar organic ligands with distinct donor groups under similar reaction conditions. The distinct structural features and donor atoms make the two complexes show different water stability, and the complex 1 with good water stability, which can be utilized as the sensor for Fe3+ ion detection in water. The value of Stern-Volmer quenching constant of 1 to the Fe3+ is 5.77 × 104 M- 1, which lies in the top region of the reported CP-based sensors. The mechanism investigation reveals that the energy transfer of resonance from the complex 1 to the Fe3+ ion can account for its fluorescent quenching behavior. The treatment activity of compounds 1 and 2 on the postpartum hemorrhage (PPH) was assessed. First, the cytotoxicity of compounds 1 and 2 on human umbilical vein endothelial cells was assessed with Cell Counting Kit-8 detection kit. Then, to evaluate the prevention of compounds 1 and 2 on the PPH, we conducted the Lowry method and detected the clotting factor IX and anticoagulant factor III contents after the indicated treatment. Finally, the inflammatory response in mice was determined by ELISA method, and the IL-6 and IL-8 levels were determined.

Improved Resource State for Verifiable Blind Quantum Computation.

Recent advances in theoretical and experimental quantum computing raise the problem of verifying the outcome of these quantum computations. The recent verification protocols using blind quantum computing are fruitful for addressing this problem. Unfortunately, all known schemes have relatively high overhead. Here we present a novel construction for the resource state of verifiable blind quantum computation. This approach achieves a better verifiability of 0.866 in the case of classical output. In addition, the number of required qubits is 2N+4cN, where N and c are the number of vertices and the maximal degree in the original computation graph, respectively. In other words, our overhead is less linear in the size of the computational scale. Finally, we utilize the method of repetition and fault-tolerant code to optimise the verifiability.

cBinderDB: a covalent binding agent database.

Motivation: Small molecule drug candidates with attractive toxicity profiles that modulate target proteins through non-covalent interactions are usually favored by scientists and pharmaceutical industry. In the past decades, many non-covalent binding agents have been developed for different diseases. However, an increasing attention has been paid to covalent binding agents in pharmaceutical fields during recent years. Many covalent binding agents entered clinical trials and exerted significant advantages for diseases such as infection, cancers, gastrointestinal disorders, central nervous system or cardiovascular diseases. It has been recognized that covalent binding ligands can be attractive drug candidates. But, there is lack of resource to support covalent ligand discovery. Results: Hence, we initiated a covalent binder database (cBinderDB). To our best knowledge, it is the first online database that provides information on covalent binding compound structures, chemotypes, targets, covalent binding types and other biological properties. The covalent binding targets are annotated with biological functions, protein family and domains, gene information, modulators and receptor-ligand complex structure. The data in the database were collected from scientific publications by combining a text mining method and manual inspection processes. cBinderDB covers covalent binder's data up to September 2016. Availability and Implementation: cBinderDB is freely available at www.rcdd.org.cn/cbinderdb/. Contact: guqiong@mail.sysu.edu.cn or junxu@biochemomes.com . Supplementary information: Supplementary data are available at Bioinformatics online.

Polymer monolithic capillary column fabricated by using monodisperse iron oxide nanocrystal template to enhance the electrochromatographic separation of small molecules.

Monodisperse iron oxide nanocrystals and organic solvents were utilized as coporogens in monolithic poly(glycidyl methacrylate-co-ethylene glycol dimethacrylate) capillary columns to afford stationary phases with enhanced electrochromatographic performance of small molecules. While the conventional monoliths using organic solvents only as a porogen exhibited poor resolution (Rs) <1.0 and low efficiency of 40 000-60 000 plates/m, addition of a small amount of nanocrystals to the polymerization mixture provided increased resolution (Rs > 3.0) and high efficiency ranged from 60 000 to 100 000 plates/m at the same linear velocity of 0.856 mm/s. It was considered that the mesopores introduced by the nanocrystals played an important role in the improvement of the monolith performance. This new strategy expanded the application range of the hydrophobic monoliths in the separation of polar alkaloids and narcotics. The successful applications demonstrated that the glycidyl methacrylate based monoliths prepared by using nanocrystal template are a good alternative for enhanced separation efficiency of small molecules.

RETRACTED: Ai et al. The Effects of Environmental Regulations on Medical Expenses: Evidence from China. Int. J. Environ. Res. Public Health2022, 19, 7567.

The authors and journal retract the following article: "The Effects of Environmental Regulations on Medical Expenses: Evidence from China" [...].

Homocysteine Facilitates the Formation of Carotid Atherosclerotic Plaque Through Inflammatory and Noninflammatory Mechanisms.

Background: Elevated homocysteine (Hcy) was considered a significant risk factor in the development and progression of carotid atherosclerosis (CAS), which involves a combination of inflammatory and noninflammatory mechanisms. However, epidemiological surveys have presented conflicting results. In this study, we aim to offer an epidemiological viewpoint on how elevated Hcy impacts CAS and its potential mechanisms. Methods: Levels of high-sensitivity C-reactive protein (hsCRP) were measured to assess the inflammatory status. The estimation of CAS events was performed by assessing carotid intima-media thickness using Doppler ultrasonography. Univariate analysis was conducted to investigate the variations in biochemical parameters among three groups: normal, carotid atherosclerotic thickening (CAT), and carotid atherosclerotic plaque (CAP) formation. Logistic regression analysis was conducted to identify the risk factors associated with the progression of CAT and CAP. In addition, multivariate linear regression analysis was conducted to identify the independent factors that correlated with hsCRP levels. Results: The study encompassed 3897 participants, with 2992 (76.8%) being males and 905 (23.2%) being females. The incidence of CAT and CAP rose with higher Hcy levels, with an overall odds ratio (OR) of 2.04 [95% confidence intervals (CI) 1.69-2.40] for CAT and 2.68 (95% CI 2.32-3.05) for CAP. After adjusting for gender, age, and blood markers, the OR for CAT and CAP decreased, with an overall OR of 1.05 (95% CI 0.81-1.28) and OR of 1.24 (95% CI 1.02-1.46), respectively. CAP risk independently increased when Hcy level exceeded 19.7 µmol/L (P = 0.030), but not CAT risk (P = 0.299). The impact of hsCRP on CAS events is similar to that of Hcy, and a multiple linear analysis found a significant independent correlation between hsCRP and Hcy (P = 0.001). Conclusions: Elevated Hcy levels can facilitate the formation of CAP through both inflammatory and noninflammatory processes, but it does not independently influence CAT.

Learning to Learn Variational Quantum Algorithm.

Variational quantum algorithms (VQAs) use classical computers as the quantum outer loop optimizer and update the circuit parameters to obtain an approximate ground state. In this article, we present a meta-learning variational quantum algorithm (meta-VQA) by recurrent unit, which uses a technique called "meta-learner." Motivated by the hybrid quantum-classical algorithms, we train classical recurrent units to assist quantum computing, learning to find approximate optima in the parameter landscape. Here, aiming to reduce the sampling number more efficiently, we use the quantum stochastic gradient descent method and introduce the adaptive learning rate. Finally, we deploy on the TensorFlow Quantum processor within approximate quantum optimization for the Ising model and variational quantum eigensolver for molecular hydrogen (H2), lithium hydride (LiH), and helium hydride cation (HeH+). Our algorithm can be expanded to larger system sizes and problem instances, which have higher performance on near-term processors.

Chronic Inflammation Plays a Role of a Bridge Between Cardiovascular Disease and Hyperglycemia.

Background: Chronic inflammation is believed to play a key role in managing cardiovascular disease (CVD) and glycometabolism, but the specific effects remain unclear. The subclinical features of CVD events and hyperglycemia linked to inflammatory status were evaluated in this study. In addition, independent factors associated with inflammatory status were identified. Methods: Inflammatory status was measured by high-sensitivity C-reactive protein (hs-CRP), CVD events estimated by carotid intima-media thickness (cIMT), and hyperglycemia determined by glycated hemoglobin (HbA1c). Univariate analysis was performed to identify the characteristics of HbA1c-defined normoglycemia, prediabetes, and diabetes, whereas multivariate linear regression analysis was conducted to identify independent factors that correlated with hs-CRP levels. Results: Compared with HbA1c-defined normoglycemia, individuals with prediabetes and diabetes had significantly higher risks of cIMT thickening [risk ratio (RR) was 2.21 and 2.40, respectively], carotid atherosclerosis (RR was 2.29 and 3.04, respectively), and carotid plaque (RR was 2.15 and 2.63, respectively). Diabetes had higher risks of carotid atherosclerosis (RR was 1.33) and carotid plaque (RR was 1.22) than prediabetes. Increasing prevalence of cIMT thickening, atherosclerosis, and plaque was correlated with hs-CRP levels rising. There was a notable linear relationship between HbA1c and hs-CRP levels (R2 = 0.8685). In addition, both men and women showed an independent correlation of hs-CRP levels with HbA1c and low-density lipoprotein cholesterol, whereas men also had thyroid-stimulating hormone and women had age as an independent factor. Conclusions: Chronic inflammation links hyperglycemia to CVD events, and the relevant risk factors would be potential targets for alleviating inflammation and delaying the progression of the atherogenic process.

Study on Anti-inflammatory Mechanism of Blueberry based on Network Pharmacology and Molecular Docking Technology.

The Batman-TCM research platform based on network pharmacology was used to predict the reverse targets of 11 active components of blueberry. The anti-inflammatory target genes of these components were extracted by comparing them with the anti-inflammatory drug target genes in the GeneCards database. GO enrichment and KEGG pathway, as well as protein interaction analysis of these anti-inflammatory target genes, were carried out using the String database. The antiinflammatory component-target-action pathway map of blueberry was constructed using the Cytoscape software. The molecular docking between seven components and two targets was validated using the Autodock-vina program. The results showed that 7 components had anti-inflammatory activity and acted on 84 anti-inflammatory targets. KEGG and GO analysis showed that the main active components of blueberry could inhibit inflammation by inhibiting the production of inflammatory factors and enhancing immunity. Network analysis revealed that the main anti-inflammatory targets of blueberry active components were TNF, ESR1, AGTR1, and IGF1. Based on molecular docking analysis, the main components of blueberry integrate with 2 important targets in inflammatory networks. Collectively, we characterized the anti-inflammatory effect of blueberry by multi-component, multi-target, and multi-pathway. The molecular mechanism of the multi-target effect of blueberry was preliminarily expounded, thereby providing a scientific basis for exploring the material basis and mechanism of the anti- inflammatory action of blueberry. BACKGROUND: Non-steroidal anti-inflammatory drugs, such as aspirin, have beneficial effects in the treatment of inflammation but they often have undesired side effects. In contrast, various natural remedies, with their unique natural, safe and effective ingredients, have achieved good effects in the treatment of inflammation and become widely used for anti-inflammatory medication. OBJECTIVE: To provide scientific basis for exploring the material basis and mechanism of antiinflammatory action of blueberry. METHODS: The anti-inflammatory target genes of these components were extracted by comparing them with the anti-inflammatory drug target genes in the GeneCards database. GO enrichment and KEGG pathway, as well as protein interaction analysis of these anti-inflammatory target genes, were carried out by using the String database. The anti-inflammatory component-target-action pathway map of blueberry was constructed using the Cytoscape software. The molecular docking between seven components and two targets was validated using the Autodock-vina program. The results showed that 7 components had anti-inflammatory activity and acted on 84 anti-inflammatory targets. RESULTS: 7 components had anti-inflammatory activity and acted on 84 anti-inflammatory targets. KEGG and GO analysis showed that the main active components of blueberry could inhibit inflammation by inhibiting the production of inflammatory factors and enhancing immunity. Network analysis revealed that the main anti-inflammatory targets of blueberry active components were TNF, ESR1, AGTR1 and IGF1. Based on molecular docking analysis, the main components of blueberry integrate with 2 important targets in inflammatory networks. CONCLUSION: The molecular mechanism of the multi-target effect of blueberry was preliminarily expounded, thereby providing a scientific basis for exploring the material basis and mechanism of antiinflammatory action of blueberry.

The composition of environmental microbiota in three tree fruit packing facilities changed over seasons and contained taxa indicative of L. monocytogenes contamination.

BACKGROUND: Listeria monocytogenes can survive in cold and wet environments, such as tree fruit packing facilities and it has been implicated in outbreaks and recalls of tree fruit products. However, little is known about microbiota that co-occurs with L. monocytogenes and its stability over seasons in tree fruit packing environments. In this 2-year longitudinal study, we aimed to characterize spatial and seasonal changes in microbiota composition and identify taxa indicative of L. monocytogenes contamination in wet processing areas of three tree fruit packing facilities (F1, F2, F3). METHODS: A total of 189 samples were collected during two apple packing seasons from floors under the washing, drying, and waxing areas. The presence of L. monocytogenes was determined using a standard culturing method, and environmental microbiota was characterized using amplicon sequencing. PERMANOVA was used to compare microbiota composition among facilities over two seasons, and abundance-occupancy analysis was used to identify shared and temporal core microbiota. Differential abundance analysis and random forest were applied to detect taxa indicative of L. monocytogenes contamination. Lastly, three L. monocytogenes-positive samples were sequenced using shotgun metagenomics with Nanopore MinION, as a proof-of-concept for direct detection of L. monocytogenes' DNA in environmental samples. RESULTS: The occurrence of L. monocytogenes significantly increased from 28% in year 1 to 46% in year 2 in F1, and from 41% in year 1 to 92% in year 2 in F3, while all samples collected from F2 were L. monocytogenes-positive in both years. Samples collected from three facilities had a significantly different microbiota composition in both years, but the composition of each facility changed over years. A subset of bacterial taxa including Pseudomonas, Stenotrophomonas, and Microbacterium, and fungal taxa, including Yarrowia, Kurtzmaniella, Cystobasidium, Paraphoma, and Cutaneotrichosporon, were identified as potential indicators of L. monocytogenes within the monitored environments. Lastly, the DNA of L. monocytogenes was detected through direct Nanopore sequencing of metagenomic DNA extracted from environmental samples. CONCLUSIONS: This study demonstrated that a cross-sectional sampling strategy may not accurately reflect the representative microbiota of food processing facilities. Our findings also suggest that specific microorganisms are indicative of L. monocytogenes, warranting further investigation of their role in the survival and persistence of L. monocytogenes. Video Abstract.