Postbiotics in inflammatory bowel disease: efficacy, mechanism, and therapeutic implications.

Inflammatory bowel disease (IBD) is one of the most challenging diseases in the 21st century, and more than 10 million people around the world suffer from IBD. Because of the limitations and adverse effects associated with conventional IBD therapies, there has been increased scientific interest in microbial-derived biomolecules, known as postbiotics. Postbiotics are defined as the preparation of inanimate microorganisms and/or their components that confer a health benefit on the host, comprising inactivated microbial cells, cell fractions, metabolites, etc. Postbiotics have shown potential in enhancing IBD treatment by reducing inflammation, modulating the immune system, stabilizing intestinal flora and maintaining the integrity of intestinal barriers. Consequently, they are considered promising adjunctive therapies for IBD. Recent studies indicate that postbiotics offer distinctive advantages, including spanning clinical (safe origin), technological (easy for storage and transportation) and economic (reduced production costs) dimensions, rendering them suitable for widespread applications in functional food/pharmaceutical. This review offers a comprehensive overview of the definition, classification and applications of postbiotics, with an emphasis on their biological activity in both the prevention and treatment of IBD. © 2024 Society of Chemical Industry.

Marinobacter sediminicola sp. nov. and Marinobacter xiaoshiensis sp. nov., Isolated from Coastal Sediment.

Two Gram-stain-negative, facultative anaerobic, rod-shaped, motile bacterial strains, designated F26243T and F60267T were isolated from coastal sediment in Weihai, China. Strains F26243T and F60267T were grown at 4-40 °C (optimum 33 °C), pH 7.0-9.5 and pH 6.5-9.5 (optimum at pH 7.0), in the presence of 1.0-7.0% (w/v) NaCl (optimum 2.5%) and 1.0-12.0% (w/v) NaCl (optimum 2.0%), respectively. The 16S rRNA gene sequences phylogenetic analysis showed that strains F26243T and F60267T are closely related to the genus Marinobacter and exhibited the highest sequence similarities to Marinobacter salexigens HJR7T (97.7% and 98.0%, respectively), the similarity between two isolates was 96.7%. Strains F26243T and F60267T displayed genomic DNA G + C content of 53.6% and 53.8%, respectively. When compared to the M. salexigens HJR7T, the average nucleotide identity (ANI) values were 83.7% and 84.1%, and the percentage of conserved proteins (POCP) values were 79.9% and 84.6%, respectively. Ubiquinone 9 (Q-9) was the only respiratory quinone detected in both isolates. The major cellular fatty acids (> 10.0%) were summed feature 3 (comprising C16:1ω7c and/or C16:1ω6c), C16:0 and C18:1ω9c. The polar lipid profiles of strains F26243T and F60267T contained diphosphatidylglycerol, phosphatidylethanolamine, phosphatidyldimethylethanolamine, phosphatidylglycerol, aminophospholipid and one unidentified phospholipid. Based on genomic characteristics, phenotypic and chemotaxonomic, strains F26243T and F60267T represent two novel species of the genus Marinobacter, for which the names Marinobacter sediminicola sp. nov. and Marinobacter xiaoshiensis sp. nov. are proposed, the type strains are F26243T (= KCTC 92640T = MCCC 1H01345T) and F60267T (= KCTC 92638T = MCCC 1H01346T).

Experimental and Numerical Investigation Integrated with Machine Learning (ML) for the Prediction Strategy of DP590/CFRP Composite Laminates.

This study unveils a machine learning (ML)-assisted framework designed to optimize the stacking sequence and orientation of carbon fiber-reinforced polymer (CFRP)/metal composite laminates, aiming to enhance their mechanical properties under quasi-static loading conditions. This work pioneers the expansion of initial datasets for ML analysis in the field by uniquely integrating the experimental results with finite element simulations. Nine ML models, including XGBoost and gradient boosting, were assessed for their precision in predicting tensile and bending strengths. The findings reveal that the XGBoost and gradient boosting models excel in tensile strength prediction due to their low error rates and high interpretability. In contrast, the decision trees, K-nearest neighbors (KNN), and random forest models show the highest accuracy in bending strength predictions. Tree-based models demonstrated exceptional performance across various metrics, notably for CFRP/DP590 laminates. Additionally, this study investigates the impact of layup sequences on mechanical properties, employing an innovative combination of ML, numerical, and experimental approaches. The novelty of this study lies in the first-time application of these ML models to the performance optimization of CFRP/metal composites and in providing a novel perspective through the comprehensive integration of experimental, numerical, and ML methods for composite material design and performance prediction.

ZrB2-Copper-Graphite Composite for Electric Brushes: Positive Effect of ZrB2 Addition on Composite Properties.

A ZrB2-copper-graphite composite was produced through powder metallurgy and was tested as a new electric brush material. The aim of this paper was to study the effect of ZrB2 addition on the composite's properties. Besides its physical properties such as density and resistivity, its mechanical properties, such as hardness, bending strength and wear resistance, were studied. A scanning electron microscope (SEM) was used to study the morphology of the wear surface, and a configured energy-dispersive spectrometer (EDS) was used to research the chemical composition of the samples. The results showed that, with the addition of ZrB2, the composite's properties such as density, resistivity, hardness, and bending strength improved significantly. Compared with samples without ZrB2, samples with the addition of 4% ZrB2 achieved a hardness of 87.5 HRA, which was improved by 45.8%, and a bending strength of 53.1 MPa, which was increased by nearly 50.0%. Composites with 1% content of ZrB2 showed the best wear resistance under non-conductive friction; however, under conductive friction, composites with 4% content of ZrB2 showed better wear resistance.

Buried interface modification and light outcoupling strategy for efficient blue perovskite light-emitting diodes.

Perovskite light-emitting diodes (PeLEDs) exhibit remarkable potential in the field of displays and solid-state lighting. However, blue PeLEDs, a key element for practical applications, still lag behind their green and red counterparts, due to a combination of strong nonradiative recombination losses and unoptimized device structures. In this report, we propose a buried interface modification strategy to address these challenges by focusing on the bottom-hole transport layer (HTL) of the PeLEDs. On the one hand, a multifunctional molecule, aminoacetic acid hydrochloride (AACl), is introduced to modify the HTL/perovskite interface to regulate the perovskite crystallization. Experimental investigations and theoretical calculations demonstrate that AACl can effectively reduce the nonradiative recombination losses in bulk perovskites by suppressing the growth of low-n perovskite phases and also the losses at the bottom interface by passivating interfacial defects. On the other hand, a self-assembly nanomesh structure is ingeniously developed within the HTLs. This nanomesh structure is meticulously crafted through the blending of poly-(9,9-dioctyl-fluorene-co-N-(4-butyl phenyl) diphenylamine) and poly (n-vinyl carbazole), significantly enhancing the light outcoupling efficiency in PeLEDs. As a result, our blue PeLEDs achieve remarkable external quantum efficiencies, 20.4% at 487 nm and 12.5% at 470 nm, which are among the highest reported values. Our results offer valuable insights and effective methods for achieving high-performance blue PeLEDs.

A thiolated oxidized guar gum and sodium lginate dual-network microspheres with enhanced gastric acid resistance and mucoadhesion for delivery of probiotics.

Probiotics offer numerous beneficial functions for human bodies, while the low survival rate under gastric acid and short retention time in the intestine are the major obstacles to their utilization. To address these issues, we designed a novel dual-network hydrogel microsphere that combines gastric acid resistance with enhanced mucoadhesion, aiming for the targeted delivery of probiotics. Thiolated oxidized guar gum (SOGG) was disulfide-linked to form the first network, and sodium alginate (SA) was cross-linked with Ca2+ to form the second network. Under the protection of the interpenetrating dual network microspheres, a much higher viability of Lactobacillus rhamnosus (LGG) (8.73 log CFU/mL) was achieved in simulated gastric fluid, compared to the zero-survival rate of free LGG. Mucoadhesion tests showed that the adhesion rate of SOGG/SA microspheres to the intestinal mucosa was 1.75 times higher than that of thiol-free microspheres. In vivo studies revealed that LGG-loaded microspheres significantly enhanced intestinal barrier function, remodeled the gut microbiome, and alleviated DSS-induced colitis in mice. Overall, SOGG/SA microspheres provide an effective strategy to the challenges of probiotic reduction in the stomach and rapid expulsion from the intestines, enhancing their health benefits.

Protocol to study CCT-mediated folding of Gß5 by single-particle cryo-EM.

The chaperonin CCT mediates folding of many cytosolic proteins, including G protein ß subunits (Gßs). Here, we present a protocol for isolating Gß5 bound to CCT and its co-chaperone PhLP1 and determining the CCT-mediated folding trajectory of Gß5 using single-particle cryoelectron microscopy (cryo-EM) techniques. We describe steps for purifying CCT-Gß5-PhLP1 from human cells, stabilizing the closed CCT conformation, preparing and imaging cryo-EM specimens, and processing data to recover multiple Gß5 folding intermediates. This protocol permits visualization of protein folding by CCT. For complete details on the use and execution of this protocol, please refer to Sass et al.1.

Postbiotics-peptidoglycan, lipoteichoic acid, exopolysaccharides, surface layer protein and pili proteins-Structure, activity in wounds and their delivery systems.

Chronic wound healing is a pressing global public health concern. Abuse and drug resistance of antibiotics are the key problems in the treatment of chronic wounds at present. Postbiotics are a novel promising strategy. Previous studies have reported that postbiotics have a wide range of biological activities including antimicrobial, immunomodulatory, antioxidant and anti-inflammatory abilities. However, several aspects related to these postbiotic activities remain unexplored or poorly known. Therefore, this work aims to outline general aspects and emerging trends in the use of postbiotics for wound healing, such as the production, characterization, biological activities and delivery strategies of postbiotics. In this review, a comprehensive overview of the physiological activities and structures of postbiotic biomolecules that contribute to wound healing is provided, such as peptidoglycan, lipoteichoic acid, bacteriocins, exopolysaccharides, surface layer proteins, pili proteins, and secretory proteins (p40 and p75 proteins). Considering the presence of readily degradable components in postbiotics, potential natural polymer delivery materials and delivery systems are emphasized, followed by the potential applications and commercialization prospects of postbiotics. These findings suggest that the treatment of chronic wounds with postbiotic ingredients will help provide new insights into wound healing and better guidance for the development of postbiotic products.

Symmetry breaking of highly symmetrical nanoclusters for triggering highly optical activity.

Developing new approaches to fulfill the enantioseparation of nanocluster racemates and construct cluster-based nanomaterials with optical activity remains highly desired in cluster science, because it is an essential prerequisite for fundamental research and extensive applications of these nanomaterials. We herein propose a strategy termed "active-site exposing and partly re-protecting" to trigger the symmetry breaking of highly symmetrical nanoclusters and to render cluster crystals optically active. The vertex PPh3 of the symmetrical Ag29(SSR)12(PPh3)4 (SSR = 1, 3-benzenedithiol) nanocluster was firstly dissociated in the presence of counterions with large steric hindrance, and then the exposed Ag active sites of the obtained Ag29(SSR)12 nanocluster were partly re-protected by Ag+, yielding an Ag29(SSR)12-Ag2 nanocluster with a symmetry-breaking construction. Ag29(SSR)12-Ag2 followed a chiral crystallization mode, and its crystal displayed strong optical activity, derived from CD and CPL characterizations. Overall, this work presents a new approach (i.e., active-site exposing and partly re-protecting) for the symmetry breaking of highly symmetrical nanoclusters, the enantioseparation of nanocluster racemates, and the achievement of highly optical activity.

Development of a clinical diagnostic model for Bell's palsy in patients with facial muscle weakness.

Early diagnosis of Bell's palsy is crucial for effective patient management in primary care settings. This study aimed to develop a simplified diagnostic tool to enhance the accuracy of identifying Bell's palsy among patients with facial muscle weakness. Data from 240 patients were analyzed using seven potential clinical evaluation indicators. Two diagnostic benchmarks were established: one based on clinical assessment and the other incorporating magnetic resonance imaging (MRI) findings. A multivariate logistic regression model was developed based on these benchmarks, resulting in the construction of a predictive tool evaluated through latent class models. Both models retained four key clinical indicators: absence of forehead wrinkles, accumulation of food and saliva inside the mouth on the affected side, presence of vesicular rash in the ear or pharynx, and lack of pain or symptoms associated with tick exposure, rash, or joint pain. The first model demonstrated excellent discriminative ability (area under the curve [AUC] = 0.96, 95% confidence interval [CI] 0.94 - 0.99) and calibration (P < 0.001), while the second model also showed good performance (AUC = 0.88, 95% CI 0.83 - 0.92) and calibration (P = 0.005). Bootstrap validation indicated no significant overfitting. The latent class defined by the first model significantly aligned with the clinical diagnosis group, while the second model showed lower consistency.

Discovering of Atomically Precise Metal Nanoclusters by High-Throughput Syntheses Platform.

Understanding the complete structure of noble metal nanoclusters is both academically and practically significant. However, progress has been hindered by the low synthetic efficiency of many nanocluster syntheses. In this study, we present the first high-throughput syntheses of homo-gold, homo-copper, and gold-copper alloy nanoclusters in dichloromethane at room temperature. Through high-throughput screening, we successfully obtained three nanoclusters in a single reaction: Au18(SC6H11)14, [Au41Cu66(SC6H11)44](SbF6)3, and an unidentified copper cluster (referred to as Au18, Au41Cu66 , and Cu-NC). The optimized synthesis route was achieved with the assistance of machine learning for experimental data analysis, which also guided the synthesis of other metal nanoclusters such as Au40Cu34(4-S-PhF)40 (Au40Cu34), [Au6Cu6(SPh)12]n ([Au6Cu6]n), and Au18Cu32(3,5-C8H9S)36 (Au18Cu32)). This research demonstrates that high-throughput screening can be a valuable tool in accelerating the development of nanocluster syntheses.

The novel regulator HdrR controls the transcription of the heterodisulfide reductase operon hdrBCA in Methanosarcina barkeri.

Methanogenic archaea play a key role in the global carbon cycle because these microorganisms remineralize organic compounds in various anaerobic environments. The microorganism Methanosarcina barkeri is a metabolically versatile methanogen, which can utilize acetate, methanol, and H2/CO2 to synthesize methane. However, the regulatory mechanisms underlying methanogenesis for different substrates remain unknown. In this study, RNA-seq analysis was used to investigate M. barkeri growth and gene transcription under different substrate regimes. According to the results, M. barkeri showed the best growth under methanol, followed by H2/CO2 and acetate, and these findings corresponded well with the observed variations in genes transcription abundance for different substrates. In addition, we identified a novel regulator, MSBRM_RS03855 (designated as HdrR), which specifically activates the transcription of the heterodisulfide reductase hdrBCA operon in M. barkeri. HdrR was able to bind to the hdrBCA operon promoter to regulate transcription. Furthermore, the structural model analyses revealed a helix-turn-helix domain, which is likely involved in DNA binding. Taken together, HdrR serves as a model to reveal how certain regulatory factors control the expression of key enzymes in the methanogenic pathway.IMPORTANCEThe microorganism Methanosarcina barkeri has a pivotal role in the global carbon cycle and contributes to global temperature homeostasis. The consequences of biological methanogenesis are far-reaching, including impacts on atmospheric methane and CO2 concentrations, agriculture, energy production, waste treatment, and human health. As such, reducing methane emissions is crucial to meeting set climate goals. The methanogenic activity of certain microorganisms can be drastically reduced by inhibiting the transcription of the hdrBCA operon, which encodes heterodisulfide reductases. Here, we provide novel insight into the mechanisms regulating hdrBCA operon transcription in the model methanogen M. barkeri. The results clarified that HdrR serves as a regulator of heterodisulfide reductase hdrBCA operon transcription during methanogenesis, which expands our understanding of the unique regulatory mechanisms that govern methanogenesis. The findings presented in this study can further our understanding of how genetic regulation can effectively reduce the methane emissions caused by methanogens.

Extracellular vesicles in endometriosis: role and potential.

Endometriosis is a chronic inflammatory gynecological disease, which profoundly jeopardizes women's quality of life and places a significant medical burden on society. The pathogenesis of endometriosis remains unclear, posing major clinical challenges in diagnosis and treatment. There is an urgent demand for the development of innovative non-invasive diagnostic techniques and the identification of therapeutic targets. Extracellular vesicles, recognized for transporting a diverse array of signaling molecules, have garnered extensive attention as a novel mode of intercellular communication. A burgeoning body of research indicates that extracellular vesicles play a pivotal role in the pathogenesis of endometriosis, which may provide possibility and prospect for both diagnosis and treatment. In light of this context, this article focuses on the involvement of extracellular vesicles in the pathogenesis of endometriosis, which deliver information among endometrial stromal cells, macrophages, mesenchymal stem cells, and other cells, and explores their potential applications in the diagnosis and treatment, conducing to the emergence of new strategies for clinical diagnosis and treatment.

An Artificial Intelligence-Driven Approach for Automatic Evaluation of Right-to-Left Shunt Grades in Saline-Contrasted Transthoracic Echocardiography.

BACKGROUND: Intracardiac or pulmonary right-to-left shunt (RLS) is a common cardiac anomaly associated with an increased risk of neurological disorders, specifically cryptogenic stroke. Saline-contrasted transthoracic echocardiography (scTTE) is often used for RLS diagnosis. However, the identification of saline microbubbles in the left heart can be challenging for novice residents, potentially leading to a delay in diagnosis and treatment. In this study, we proposed an artificial intelligence (AI)-based algorithm designed to automatically detect microbubbles in scTTE images and evaluate right-to-left shunt grades. This tool aims to support residency training and decrease the workload of cardiologists. METHODS: A dataset of 23,665 scTTE images obtained from 174 individuals was included in this study. This dataset was partitioned into a training set (n = 20,475) and an internal validation set (n = 3,190) on a patient-level basis. An additional cohort of 33 patients diagnosed with cryptogenic ischemic stroke was enrolled as an external validation set. The proposed algorithm for right-to-left shunt degree classification employed the EfficientNet-b4 model, and the model's performance was evaluated using the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity, and compared to the performance of residents. RESULTS: Our AI model demonstrated robust performance with an accuracy of 0.926, sensitivity of 0.827, and specificity of 0.951 on the internal testing dataset. In the external validation set, our AI model exhibited diagnostic accuracy, sensitivity, and specificity of 0.864, 0.818, and 0.909, respectively. In comparison, residents achieved values of 0.727, 0.636, and 0.818, respectively. CONCLUSION: Our AI model provides a swift, precise, and easily deployable methodology for grading the degree of right-to-left shunt in scTTE, carrying substantial implications for routine clinical practice. Residents can benefit from our artificial intelligence-based algorithm, enhancing both the accuracy and efficiency of RLS diagnosis.

Polystyrene nanoplastics with different functional groups and charges have different impacts on type 2 diabetes.

BACKGROUND: Increasing attention is being paid to the environmental and health impacts of nanoplastics (NPs) pollution. Exposure to nanoplastics (NPs) with different charges and functional groups may have different adverse effects after ingestion by organisms, yet the potential ramifications on mammalian blood glucose levels, and the risk of diabetes remain unexplored. RESULTS: Mice were exposed to PS-NPs/COOH/NH2 at a dose of 5 mg/kg/day for nine weeks, either alone or in a T2DM model. The findings demonstrated that exposure to PS-NPs modified by different functional groups caused a notable rise in fasting blood glucose (FBG) levels, glucose intolerance, and insulin resistance in a mouse model of T2DM. Exposure to PS-NPs-NH2 alone can also lead the above effects to a certain degree. PS-NPs exposure could induce glycogen accumulation and hepatocellular edema, as well as injury to the pancreas. Comparing the effect of different functional groups or charges on T2DM, the PS-NPs-NH2 group exhibited the most significant FBG elevation, glycogen accumulation, and insulin resistance. The phosphorylation of AKT and FoxO1 was found to be inhibited by PS-NPs exposure. Treatment with SC79, the selective AKT activator was shown to effectively rescue this process and attenuate T2DM like lesions. CONCLUSIONS: Exposure to PS-NPs with different functional groups (charges) induced T2DM-like lesions. Amino-modified PS-NPs cause more serious T2DM-like lesions than pristine PS-NPs or carboxyl functionalized PS-NPs. The underlying mechanisms involved the inhibition of P-AKT/P-FoxO1. This study highlights the potential risk of NPs pollution on T2DM, and provides a new perspective for evaluating the impact of plastics aging.

An Au5Ag12(SR)9(dppf)4 alloy nanocluster: structural determination and optical property and photothermal conversion investigation.

Photothermal conversion has garnered significant attention due to its potential for efficient energy conversion and application in targeted therapies. However, controlling photothermal properties at the atomic level remains a challenge in current materials synthesis. In this study, we report the synthesis and structural determination of a phosphine and mercaptan co-protected Au5Ag12(SR)9(dppf)4 (Au5Ag12) nanocluster with an extremely low quantum yield (∼0%). For comparative purposes, we synthesized three alloy nanoclusters of similar size. Notably, Au5Ag12 demonstrates a remarkably superior photothermal conversion performance, significantly outperforming the other clusters. We investigated this variance from both absorption and emission perspectives. This research not only opens new avenues for the application of clusters with extremely low quantum yields, but also provides experimental evidence for understanding the photothermal conversion properties of cluster materials at the atomic level.

Understanding the decomposition process of the Pt1Ag24(SPhCl2)18 nanocluster at the atomic level.

We report the decomposition of the Pt1Ag24(SPhCl2)18 nanocluster into a crown-like Pt1Ag4(SR)8 (SR = 2,4-SPhCl2 and 4-SPhBr) complex. UV-vis spectra and single crystal X-ray diffraction were used to track the structure-conversion process. Based on the total structure, the differences in ligand exchange rates at different sites and the effects on the stability were mapped out. This work can not only help us understand the ligand exchange behavior of the clusters, but also provide experimental support for the design of stable metal clusters.

Spacer Conformation Induced Multiple Hydrogen Bonds in 2D Perovskite toward Highly Efficient Optoelectronic Devices.

Two-dimensional (2D) Dion-Jacobson (DJ) perovskites typically outperform Ruddlesden-Popper (RP) analogs in terms of photodetection (PD). However, the mechanism behind this enhanced performance remains elusive. Theoretical calculations for elucidating interlayer spacer conformation-induced multiple hydrogen bonds in 2D perovskite are presented, along with the synthesis of DPAPbBr4 (DPB) single crystals (SCs) and their PD properties under X-ray/ultraviolet (UV) excitation. The high-quality DPB SC enhances PD with exceptional photoresponse attributes, including a high on/off ratio (4.89 × 104), high responsivity (2.44 A W⁻1), along with large dynamic linear range (154 dB) and low detection limit (7.1 nW cm⁻2), which are currently the best results among 2D perovskite SC detectors, respectively. Importantly, high-resolution images are obtained under UV illumination with weak light levels. The SC X-ray detector exhibits a high sensitivity of 663 µC Gyair⁻1 cm-2 at 10 V and a detection limit of 1.44 µGyair s⁻1. This study explores 2D DJ perovskites for efficient and innovative optoelectronic applications.

Gorilla optimization algorithm combining sine cosine and cauchy variations and its engineering applications.

To address the issues of lacking ability, loss of population diversity, and tendency to fall into the local extreme value in the later stage of optimization searching, resulting in slow convergence and lack of exploration ability of the artificial gorilla troops optimizer algorithm (AGTO), this paper proposes a gorilla search algorithm that integrates the positive cosine and Cauchy's variance (SCAGTO). Firstly, the population is initialized using the refractive reverse learning mechanism to increase species diversity. A positive cosine strategy and nonlinearly decreasing search and weight factors are introduced into the finder position update to coordinate the global and local optimization ability of the algorithm. The follower position is updated by introducing Cauchy variation to perturb the optimal solution, thereby improving the algorithm's ability to obtain the global optimal solution. The SCAGTO algorithm is evaluated using 30 classical test functions of Test Functions 2018 in terms of convergence speed, convergence accuracy, average absolute error, and other indexes, and two engineering design optimization problems, namely, the pressure vessel optimization design problem and the welded beam design problem, are introduced for verification. The experimental results demonstrate that the improved gorilla search algorithm significantly enhances convergence speed and optimization accuracy, and exhibits good robustness. The SCAGTO algorithm demonstrates certain solution advantages in optimizing the pressure vessel design problem and welded beam design problem, verifying the superior optimization ability and engineering practicality of the SCAGTO algorithm.