Predicting molecular properties based on the interpretable graph neural network with multistep focus mechanism.

Graph neural networks based on deep learning methods have been extensively applied to the molecular property prediction because of its powerful feature learning ability and good performance. However, most of them are black boxes and cannot give the reasonable explanation about the underlying prediction mechanisms, which seriously reduce people's trust on the neural network-based prediction models. Here we proposed a novel graph neural network named iteratively focused graph network (IFGN), which can gradually identify the key atoms/groups in the molecule that are closely related to the predicted properties by the multistep focus mechanism. At the same time, the combination of the multistep focus mechanism with visualization can also generate multistep interpretations, thus allowing us to gain a deep understanding of the predictive behaviors of the model. For all studied eight datasets, the IFGN model achieved good prediction performance, indicating that the proposed multistep focus mechanism also can improve the performance of the model obviously besides increasing the interpretability of built model. For researchers to use conveniently, the corresponding website (http://graphadmet.cn/works/IFGN) was also developed and can be used free of charge.

Comprehensive assessment of protein loop modeling programs on large-scale datasets: prediction accuracy and efficiency.

Protein loops play a critical role in the dynamics of proteins and are essential for numerous biological functions, and various computational approaches to loop modeling have been proposed over the past decades. However, a comprehensive understanding of the strengths and weaknesses of each method is lacking. In this work, we constructed two high-quality datasets (i.e. the General dataset and the CASP dataset) and systematically evaluated the accuracy and efficiency of 13 commonly used loop modeling approaches from the perspective of loop lengths, protein classes and residue types. The results indicate that the knowledge-based method FREAD generally outperforms the other tested programs in most cases, but encountered challenges when predicting loops longer than 15 and 30 residues on the CASP and General datasets, respectively. The ab initio method Rosetta NGK demonstrated exceptional modeling accuracy for short loops with four to eight residues and achieved the highest success rate on the CASP dataset. The well-known AlphaFold2 and RoseTTAFold require more resources for better performance, but they exhibit promise for predicting loops longer than 16 and 30 residues in the CASP and General datasets. These observations can provide valuable insights for selecting suitable methods for specific loop modeling tasks and contribute to future advancements in the field.

Real-Time pH Sensor in Bacterial Microenvironments Using Liquid Crystal Core-Shell Microspheres.

It is well-known that the bacterial microenvironment imposes restrictions on the growth and behavior of bacteria. The localized monitoring of microenvironmental factors is appreciated when consulting bacterial adaptation and behavior in the presence of chemical or mechanical stimuli. Herein, we developed a novel liquid crystal (LC) biosensor in a microsphere configuration for real-time 3D monitoring of the bacteria microenvironment, which was implemented by a microfluidic chip. As a proof of concept, a LC gel (LC-Gel) microsphere biosensor was prepared and employed in the localized pH changes of bacteria by observing the configuration change of LC under polarized optical microscopy. Briefly, the microsphere biosensor was constructed in core-shell configuration, wherein the core contained LCE7 (a nematic LC) doped with 4-pentylbiphenyl-4'-carboxylic acid (PBA), and the shell encapsulated the bacteria. The protonation of carboxyl functional groups of the PBA induced a change in charge density on the surface of LCE7 and the orientation of E7 molecules, resulting in the transitions of the LC nucleus from axial to bipolar. The developed LC-Gel microspheres pH sensor exhibited its dominant performance on localized pH real-time sensing with a resolution of 0.1. An intriguing observation from the prepared pH biosensor was that the diverse bacteria impelled distinct acidifying or alkalizing effects. Overall, the facile LC-Gel microsphere biosensor not only provides a versatile tool for label-free, localized pH monitoring but also opens avenues for investigating the effects of chemical and mechanical stimuli on cellular metabolism within bacterial microenvironments.

Microfluidic Engineering of Addressable Multicompartmental Microspheres for Multicellular Systems.

With the advantages of high-throughput manufacturing and customizability, on-microsphere construction of in vitro multicellular analytical systems has garnered significant attention. However, achieving a precise, biocompatible cell arrangement and spatial signal analysis in hydrogel microspheres remains challenging. In this work, a microfluidic method is reported for the biocompatible generation of addressable supersegmented multicompartmental microspheres. Additionally, these microspheres are developed as novel label-free multicellular systems. In the microfluidic approach, controllable microfluidics is used to finely tune the internal microstructure of the microspheres, and the gas ejector ensures the biocompatibility of the preparation process. As a proof of concept, six- and twenty-compartment microspheres were obtained without the addition of any biohazardous reagents. For microsphere decoding, the visualization of two basic compartments can provide clues for identifying label-free cells due to the structural regularity of the microspheres. Finally, by encapsulating cells of different types, these microspheres as multicellular systems were successfully used for cell coculture and drug testing. These biocompatible, scalable, and analyzable microspheres will open up new prospects for biomedical analysis.

Secoisolariciresinol diglucoside attenuates neuroinflammation and cognitive impairment in female Alzheimer's disease mice via modulating gut microbiota metabolism and GPER/CREB/BDNF pathway.

BACKGROUND: Gender is a significant risk factor for late-onset Alzheimer's disease (AD), often attributed to the decline of estrogen. The plant estrogen secoisolariciresinol diglucoside (SDG) has demonstrated anti-inflammatory and neuroprotective effects. However, the protective effects and mechanisms of SDG in female AD remain unclear. METHODS: Ten-month-old female APPswe/PSEN1dE9 (APP/PS1) transgenic mice were treated with SDG to assess its potential ameliorative effects on cognitive impairments in a female AD model through a series of behavioral and biochemical experiments. Serum levels of gut microbial metabolites enterodiol (END) and enterolactone (ENL) were quantified using HPLC-MS. Correlation analysis and broad-spectrum antibiotic cocktail (ABx) treatment were employed to demonstrate the involvement of END and ENL in SDG's cognitive improvement effects in female APP/PS1 mice. Additionally, an acute neuroinflammation model was constructed in three-month-old C57BL/6J mice treated with lipopolysaccharide (LPS) and subjected to i.c.v. injection of G15, an inhibitor of G protein-coupled estrogen receptor (GPER), to investigate the mediating role of the estrogen receptor GPER in the cognitive benefits conferred by SDG. RESULTS: SDG administration resulted in significant improvements in spatial, recognition, and working memory in female APP/PS1 mice. Neuroprotective effects were observed, including enhanced expression of CREB/BDNF and PSD-95, reduced ß-amyloid (Aß) deposition, and decreased levels of TNF-α, IL-6, and IL-10. SDG also altered gut microbiota composition, increasing serum levels of END and ENL. Correlation analysis indicated significant associations between END, ENL, cognitive performance, hippocampal Aß-related protein mRNA expression, and cortical neuroinflammatory cytokine levels. The removal of gut microbiota inhibited END and ENL production and eliminated the neuroprotective effects of SDG. Furthermore, GPER was found to mediate the inhibitory effects of SDG on neuroinflammatory responses. CONCLUSION: These findings suggest that SDG promotes the production of gut microbial metabolites END and ENL, which inhibit cerebral ß-amyloid deposition, activate GPER to enhance CREB/BDNF signaling pathways, and suppress neuroinflammatory responses. Consequently, SDG exerts neuroprotective effects and ameliorates cognitive impairments associated with AD in female mice.

Polystyrene-Based Matrix to Enhance the Fluorescence of Aggregation-Induced Emission Luminogen for Fluorescence-Guided Surgery.

Achieving ultrabright fluorogens is a key issue for fluorescence-guided surgery (FGS). Fluorogens with aggregation-induced emission (AIEgens) are potential agents for FGS on the benefit of the bright fluorescence in physiological conditions. Herein, the fluorescence brightness of AIEgen is further improved by preparing the nanoparticle using a polystyrene-based matrix and utilizing it for tumor FGS with a high signal-to-background ratio. After encapsulating AIEgen into polystyrene-poly (ethylene glycol) (PS-PEG), the fluorescence intensity of the prepared AIE@PS-PEG nanoparticles is multiple times that of nanoparticles in 1, 2-distearoyl-sn-glycero-3-phosphoethanolamine-poly (ethylene glycol) (DSPE-PEG), a commonly used polymer matrix for nanoparticle preparation. Molecular dynamics simulations suggest that higher free energy is required for the outer rings of AIEgen to rotate in polystyrene than in the DSPE, indicating that the benzene rings in polystyrene can restrict the intramolecular motions of AIEgen better than the alkyl chain in DSPE-PEG. Fluorescence correlation microscopy detections suggest that the triplet excited state of AIEgens is less in PS-PEG than in DSPE-PEG. The restricted intramolecular motions and suppressed triplet excited state result in ultrabright AIE@PS-PEG nanoparticles, which are more conducive to illuminating tumor tissues in the intestine for FGS. The illumination of metastatic tumors in lungs by AIE@PS-PEG nanoparticles is also tried.

Sub-pixel target fine spatial feature extraction method based on aperture coding and micro-scanning imaging mechanism.

The small imaging size of targets over long distances results in the loss of geometry and spatial features. Current methods are subject to sampling limitations and cannot accurately capture the spatial features of sub-pixel targets. This paper proposes a method to accurately locate and extract the fine spatial features of sub-pixel targets through aperture coding and micro-scanning imaging. First, the formation mechanism of imaging features for sub-pixel targets is analyzed. Second, the optical aperture is anisotropically coded in different directions to modulate the spreading spots of the target. The primary spreading direction and the center of the anisotropic spreading spots are extracted. The contour and the location of the target are determined from the spreading length and the intersections of the primary spreading directions. Then, the target is sampled by different detector units through various micro-scanning offsets. The pixel units containing different sub-pixel components of the target after offset are determined based on the location results. The fine spatial distribution of the sub-pixel target is reconstructed based on the intensity variations in the pixel units containing the target. Finally, the accuracy of the sub-pixel target fine spatial feature extraction method is validated. The results show a sub-pixel localization error of less than 0.02 and an effective improvement of the sub-pixel target spatial resolution. This paper provides significant potential for improving the ability to capture spatial features of targets over long distances.

Imaging performance prediction of a hypersonic target in a geosynchronous orbit based on multi-dimensional information correlation of radiation, multi-spectral, and polarization.

Hypersonic target detection based on infrared intensity characteristics is easily disturbed by sea surface and cloud flares when detected by space-based optical systems, which results in a low detection rate, high false alarm, and difficulty in stable detection. This paper explores a method to improve target detection performance based on the correlation of infrared radiation, multi-spectral and polarization. Firstly, the comprehensive factors that influence complex ambient illumination, atmospheric transmission, and clutter background on spectral-polarization characteristics of hypersonic targets are analyzed. Based on the global radiation scattering theory, the temperature distribution model of the hypersonic target is established by using FLUENT. The polarization emission and pBRDF model of the target is established, and the radiation polarization transfer model is generated. Secondly, the sea surface temperature distribution is obtained by inversion of Landsat8 remote sensing data. The radiation polarization transfer model of the sea surface is established based on the Cox-Munk model combined with pBRDF and the polarization emission model. Thirdly, the polarization scattering effect of atmospheric particles on the upward radiation of the interaction of the target with the sunlight is considered comprehensively, and the 6SV radiative transfer model is used to calculate the polarization effect of atmospheric particles on the upward radiation transmission of the target and the background. Then, combined with the point diffusion of the optical system and the photoelectric conversion of the detector, the multi-dimensional full-chain imaging prediction model of the hypersonic target-sea background-ambient atmosphere-optical system-detector is established. The imaging characteristics and detection performance of the target in different imaging dimensions are simulated and analyzed with the signal-to-clutter ratio (SCR). The research shows that in the direction of reflected sunlight from the sea surface, the sea surface glare is suppressed and the target is highlighted through a target detection method of multi-dimensional information. This method has better detection results than the infrared multi-spectral detection method.

Quantitative characterization of micro-scanning imaging aliasing and optical parameter optimization.

Imaging aliasing is a common problem in the imaging domain. The aliasing of micro-scanning imaging is difficult to characterize accurately, and the matching relationship between the optical system and micro-scanning sampling is unclear. In this paper, a micro-scanning aliasing analysis model is proposed based on the property of sampling squeeze, in which the transfer functions of the optical system, detector, and digital filter are coupled with the micro-scanning sampling process. First, the imaging aliasing under different micro-scanning sampling modes is evaluated based on the constraint relationship of the transfer functions for each part. The stretch factor of the transfer function under micro-scanning sampling is calculated by utilizing the amount of aliasing. Second, the micro-scanning imaging transfer function under different optical parameters is predicted by the stretch factor, and the results indicate the existence of an optimal F-number that maximizes the micro-scanning performance improvement. Furthermore, the optimal micro-scanning imaging F-numbers for different fill factors are given, and the matching relationship between optical parameters, fill factors and micro-scanning mode is analyzed. Finally, a micro-scanning imaging simulation is performed based on the actual imaging transfer and micro-scanning sampling process. The simulation experiment verifies the accuracy of the micro-scanning aliasing model and gives the consistent test results of the optimal F-number. This paper can provide theoretical support for the matching relationship among micro-scanning imaging parameters, which is of great significance for the refined optimal design of micro-scanning imaging systems.

Exploration of ETV6::ABL1-positive AML with concurrent NPM1 and FLT3-ITD mutations.

ETV6::ABL1 is a rare fusion gene that found in MPN, ALL, and AML. It has a complex and diverse formation mechanism due to the reciprocal orientations of the ETV6 and ABL1 genes relative to the centromeres. NPM1 is frequently mutated in adult AML, often accompanied by FLT3-ITD, which suggests molecular synergisms in AML pathogenesis. Previous reports on ETV6::ABL1 mostly focus on FLT3-ITD. In this study, we present a case of AML with ETV6::ABL1, along with NPM1 and FLT3-ITD. The patient showed a rapid increase in primitive cells at the initial stage, along with the presence of immature granulocytes and erythrocytes. Through cytogenetic analysis, fluorescence in situ hybridization (FISH), and RNA-seq, we elucidated the mechanism behind the formation of the ETV6::ABL1 fusion gene. Despite conventional chemotherapy failure and rapid tumor proliferation, we attempted to add FLT3 inhibitor sorafenib to the treatment, along with chemotherapy bridging to haploidentical transplantation. After haplo-HSCT, a combination of sorafenib and dasatinib was administered as maintenance therapy. The patient achieved complete remission (CR) and maintained it for 11 months. The intricate genetic landscape observed in this case presents diagnostic dilemmas and therapeutic challenges, emphasizing the importance of a comprehensive understanding of its implications for disease classification, risk stratification, and treatment selection.

Cholinized-Polymer Functionalized Lipid-Based Drug Carriers Facilitate Liver Fibrosis Therapy via Ultrafast Liver-Targeting Delivery.

Here, we report novel cholinized-polymer functionalized lipid-based nanoparticles (CP-LNPs) for rapid and highly effective delivery of drugs to the liver, achieving targeting within 10 min and nearly 100% efficiency. In this study, CP-LNPs loaded with a promising antifibrotic agent curcumin (CP-LNPs/Cur) significantly improved the stability of curcumin under physiological conditions and its distribution in the liver. In vitro experiments demonstrated that CP-LNPs/Cur effectively suppressed the proliferation and migration of activated hepatic stellate cells (aHSCs), as evidenced by the decreased expression of α-SMA. Moreover, CP-LNPs/Cur attenuated oxidative stress levels in hepatocytes while improving mitochondrial physiological activity. In vivo antifibrosis studies have shown that CP-LNPs/Cur only require a low dose to significantly alleviate liver injury and collagen deposition, thereby preventing the progression of liver fibrosis. These findings indicated that CP-LNPs exhibit great potential in liver fibrosis therapy benefiting from the novel targeting strategy.

Machine learning-based predictive model for the development of thrombolysis resistance in patients with acute ischemic stroke.

BACKGROUND: The objective of this study was to establish a predictive model utilizing machine learning techniques to anticipate the likelihood of thrombolysis resistance (TR) in acute ischaemic stroke (AIS) patients undergoing recombinant tissue plasminogen activator (rt-PA) intravenous thrombolysis, given that nearly half of such patients exhibit poor clinical outcomes. METHODS: Retrospective clinical data were collected from AIS patients who underwent intravenous thrombolysis with rt-PA at the First Affiliated Hospital of Bengbu Medical University. Thrombolysis resistance was defined as ([National Institutes of Health Stroke Scale (NIHSS) at admission - 24-hour NIHSS] × 100％/ NIHSS at admission) ≤ 30%. In this study, we developed five machine learning models: logistic regression (LR), extreme gradient boosting (XGBoost), support vector machine (SVM), the least absolute shrinkage and selection operator (LASSO), and random forest (RF). We assessed the model's performance by using receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA), and presented the results through a nomogram. RESULTS: This study included a total of 218 patients with AIS who were treated with intravenous thrombolysis, 88 patients experienced TR. Among the five machine learning models, the LASSO model performed the best. The area under the curve (AUC) on the testing group was 0.765 (sensitivity: 0.767, specificity: 0.694, accuracy: 0.727). The apparent curve in the calibration curve was similar to the ideal curve, and DCA showed a positive net benefit. Key features associated with TR included NIHSS at admission, blood glucose, white blood cell count, neutrophil count, and blood urea nitrogen. CONCLUSION: Machine learning methods with multiple clinical variables can help in early screening of patients at high risk of thrombolysis resistance, particularly in contexts where healthcare resources are limited.

Comprehensive Evaluation of 10 Docking Programs on a Diverse Set of Protein-Cyclic Peptide Complexes.

Cyclic peptides have emerged as a highly promising class of therapeutic molecules owing to their favorable pharmacokinetic properties, including stability and permeability. Currently, many clinically approved cyclic peptides are derived from natural products or their derivatives, and the development of molecular docking techniques for cyclic peptide discovery holds great promise for expanding the applications and potential of this class of molecules. Given the availability of numerous docking programs, there is a pressing need for a systematic evaluation of their performance, specifically on protein-cyclic peptide systems. In this study, we constructed an extensive benchmark data set called CPSet, consisting of 493 protein-cyclic peptide complexes. Based on this data set, we conducted a comprehensive evaluation of 10 docking programs, including Rosetta, AutoDock CrankPep, and eight protein-small molecule docking programs (i.e., AutoDock, AudoDock Vina, Glide, GOLD, LeDock, rDock, MOE, and Surflex). The evaluation encompassed the assessment of the sampling power, docking power, and scoring power of these programs. The results revealed that all of the tested protein-small molecule docking programs successfully sampled the binding conformations when using the crystal conformations as the initial structures. Among them, rDock exhibited outstanding performance, achieving a remarkable 94.3% top-100 sampling success rate. However, few programs achieved successful predictions of the binding conformations using tLEaP-generated conformations as the initial structures. Within this scheme, AutoDock CrankPep yielded the highest top-100 sampling success rate of 29.6%. Rosetta's scoring function outperformed the others in selecting optimal conformations, resulting in an impressive top-1 docking success rate of 87.6%. Nevertheless, all the tested scoring functions displayed limited performance in predicting binding affinity, with MOE@Affinity dG exhibiting the highest Pearson's correlation coefficient of 0.378. It is therefore suggested to use an appropriate combination of different docking programs for given tasks in real applications. We expect that this work will offer valuable insights into selecting the appropriate docking programs for protein-cyclic peptide complexes.

Evaluation of AlphaFold2 Structures for Hit Identification across Multiple Scenarios.

The introduction of AlphaFold2 (AF2) has sparked significant enthusiasm and generated extensive discussion within the scientific community, particularly among drug discovery researchers. Although previous studies have addressed the performance of AF2 structures in virtual screening (VS), a more comprehensive investigation is still necessary considering the paramount importance of structural accuracy in drug design. In this study, we evaluate the performance of AF2 structures in VS across three common drug discovery scenarios: targets with holo, apo, and AF2 structures; targets with only apo and AF2 structures; and targets exclusively with AF2 structures. We utilized both the traditional physics-based Glide and the deep-learning-based scoring function RTMscore to rank the compounds in the DUD-E, DEKOIS 2.0, and DECOY data sets. The results demonstrate that, overall, the performance of VS on AF2 structures is comparable to that on apo structures but notably inferior to that on holo structures across diverse scenarios. Moreover, when a target has solely AF2 structure, selecting the holo structure of the target from different subtypes within the same protein family produces comparable results with the AF2 structure for VS on the data set of the AF2 structures, and significantly better results than the AF2 structures on its own data set. This indicates that utilizing AF2 structures for docking-based VS may not yield most satisfactory outcomes, even when solely AF2 structures are available. Moreover, we rule out the possibility that the variations in VS performance between the binding pockets of AF2 and holo structures arise from the differences in their biological assembly composition.

Assessing the performance of MM/PBSA and MM/GBSA methods. 10. Prediction reliability of binding affinities and binding poses for RNA-ligand complexes.

Ribonucleic acid (RNA)-ligand interactions play a pivotal role in a wide spectrum of biological processes, ranging from protein biosynthesis to cellular reproduction. This recognition has prompted the broader acceptance of RNA as a viable candidate for drug targets. Delving into the atomic-scale understanding of RNA-ligand interactions holds paramount importance in unraveling intricate molecular mechanisms and further contributing to RNA-based drug discovery. Computational approaches, particularly molecular docking, offer an efficient way of predicting the interactions between RNA and small molecules. However, the accuracy and reliability of these predictions heavily depend on the performance of scoring functions (SFs). In contrast to the majority of SFs used in RNA-ligand docking, the end-point binding free energy calculation methods, such as molecular mechanics/generalized Born surface area (MM/GBSA) and molecular mechanics/Poisson Boltzmann surface area (MM/PBSA), stand as theoretically more rigorous approaches. Yet, the evaluation of their effectiveness in predicting both binding affinities and binding poses within RNA-ligand systems remains unexplored. This study first reported the performance of MM/PBSA and MM/GBSA with diverse solvation models, interior dielectric constants (εin) and force fields in the context of binding affinity prediction for 29 RNA-ligand complexes. MM/GBSA is based on short (5 ns) molecular dynamics (MD) simulations in an explicit solvent with the YIL force field; the GBGBn2 model with higher interior dielectric constant (εin = 12, 16 or 20) yields the best correlation (Rp = -0.513), which outperforms the best correlation (Rp = -0.317, rDock) offered by various docking programs. Then, the efficacy of MM/GBSA in identifying the near-native binding poses from the decoys was assessed based on 56 RNA-ligand complexes. However, it is evident that MM/GBSA has limitations in accurately predicting binding poses for RNA-ligand systems, particularly compared with notably proficient docking programs like rDock and PLANTS. The best top-1 success rate achieved by MM/GBSA rescoring is 39.3%, which falls below the best results given by docking programs (50%, PLNATS). This study represents the first evaluation of MM/PBSA and MM/GBSA for RNA-ligand systems and is expected to provide valuable insights into their successful application to RNA targets.

Exploring the mechanisms and relationship between body mass index and asthma: findings from Mendelian randomization.

BACKGROUND: The mechanism linking BMI and asthma remains unclear. METHOD: Mendelian Randomization (MR) analysis was conducted using summary-level GWAS data from the FinnGen Biobank and the Open GWAS project. The analysis considering potential variables as mediators, including blood cell counts, blood pressure, and blood biomarkers. Three commonly used MR methods-the inverse-variance-weighted (IVW) method, weighted median (WM) method, and MR-Egger method-were employed to infer causal links. A two-step approach was used in mediation analysis to evaluate the causal links among BMI, candidate mediators, and asthma. RESULT: Elevated BMI demonstrated a substantial correlation with increased asthma risk. Thirteen biomarkers mediated the relationship between BMI and asthma, mainly including leukocyte count (5.070%), apolipoprotein A levels (7.395%), cystatin C levels (5.345%), urate levels (9.057%), diastolic blood pressure (7.365%), and albumin levels (10.888%). These factors collectively explained over 50% of the increased asthma risk associated with BMI elevation. Additionally, eosinophil count and C-reactive protein were also identified as important mediators using the WM method. CONCLUSION: This study highlights the complex relationship between obesity, blood biomarkers, and asthma risk. Additional studies are required to validate these results and investigate causal relationships in various populations.

Dried fruit, acetate, and asthma: a mediation Mendelian randomization analysis.

BACKGROUND: Asthma is a common chronic condition with increasing prevalence. Diet, including dried fruit consumption, has been linked to asthma risk, but the mechanisms remain unclear. This study investigates how dried fruit consumption affects asthma risk, focusing on acetate as a potential mediator. METHODS: We used Mendelian Randomization (MR) to analyze the relationships between dried fruit intake, acetate levels, and asthma. We applied three MR methods-Inverse-Variance Weighted (IVW), Weighted Median (WM), and MR-Egger-to determine causal effects. RESULTS: Dried fruit intake was inversely associated with asthma risk (IVW: ß = -0.506, p = 0.0135) and positively associated with acetate levels (IVW: ß = 0.269, p < 0.0001). Higher acetate levels were also linked to lower asthma risk (IVW: ß=-0.361, p < 0.0001). Mediation analysis showed that acetate mediates approximately 19.22% of the effect of dried fruit on asthma risk. CONCLUSION: Dried fruit consumption reduces asthma risk, partly through increasing acetate levels. This acetate-mediated pathway accounts for about 20% of the effect, suggesting potential for dietary strategies in asthma prevention and management. Further research could enhance the understanding and applicability of these findings.

Trimester-specific effect of maternal co-exposure to organophosphate esters and phthalates on preschooler cognitive development: The moderating role of gestational vitamin D status.

Organophosphate esters (OPEs) and phthalate acid esters (PAEs) are prevalent endocrine-disrupting chemicals (EDCs). Humans are often exposed to OPEs and PAEs simultaneously through multiple routes. Given that fetal stage is a critical period for neurodevelopment, it is necessary to know whether gestational co-exposure to OPEs and PAEs affects fetal neurodevelopment. However, accessible epidemiological studies are limited. The present study included 2, 120 pregnant women from the Ma'anshan Birth Cohort (MABC) study. The concentrations of tris (2-chloroethyl) phosphate (TCEP), 6 OPE metabolites and 7 PAE metabolites were measured in the first, second and third trimester using ultra-performance liquid chromatography-tandem mass spectrometry (LC-MS). Cognitive development of preschooler was assessed based on the Wechsler Preschool and Primary Scale of Intelligence-Fourth Edition (WPPSI-IV) of the Chinese version. Generalized estimating equations (GEEs), restricted cubic spline (RCS) and generalized additive models (GAMs) were employed to explore the associations between individual OPE exposure and preschooler cognitive development. The quantile-based g-computation (QGC) method was used to estimate the joint effect of PAEs and OPEs exposure on cognitive development. GEEs revealed significant adverse associations between diphenyl phosphate (DPHP) (ß: -0.58, 95% CI: -1.14, -0.01), bis (2-butoxyethyl) phosphate(BBOEP) (ß: -0.44, 95% CI: -0.85, -0.02), bis(1-chloro-2-propyl) phosphate (BCIPP) (ß: -0.81, 95%CI: -1.43, -0.20) and full-scale intelligence quotient (FSIQ) in the first trimester; additionally, TCEP and bis(2-ethylhexyl) phosphate (BEHP) in the second trimester, as well as DPHP in the third trimester, were negatively associated with cognitive development. Through the QGC analyses, mixture exposure in the first trimester was negatively associated with FSIQ scores (ß: -1.70, 95% CI: -3.06, -0.34), mono-butyl phthalate (MBP), BCIPP, and DPHP might be the dominant contributors after controlling for other OPEs and PAEs congeners. Additionally, the effect of OPEs and PAEs mixture on cognitive development might be driven by vitamin D deficiency.

MSC microvesicles loaded G-quadruplex-enhanced circular single-stranded DNA-9 inhibits tumor growth by targeting MDSCs.

BACKGROUND: Myeloid-derived suppressor cells (MDSCs) promote tumor growth, metastasis, and lead to immunotherapy resistance. Studies revealed that miRNAs are also expressed in MDSCs and promote the immunosuppressive function of MDSCs. Currently, few studies have been reported on inducible cellular microvesicle delivery of nucleic acid drugs targeting miRNA in MDSCs for the treatment of malignant tumors. RESULTS AND CONCLUSION: In this study, we designed an artificial DNA named G-quadruplex-enhanced circular single-stranded DNA-9 (G4-CSSD9), that specifically adsorbs the miR-9 sequence. Its advanced DNA folding structure, rich in tandem repeat guanine (G-quadruplex), also provides good stability. Mesenchymal stem cells (MSCs) were prepared into nanostructured vesicles by membrane extrusion. The MSC microvesicles-encapsulated G4-CSSD9 (MVs@G4-CSSD9) was delivered into MDSCs, which affected the downstream transcription and translation process, and reduced the immunosuppressive function of MDSCs, so as to achieve the purpose of treating melanoma. In particular, it provides an idea for the malignant tumor treatment.

Optimization of the Extraction Process and Biological Activities of Triterpenoids of Schisandra sphenanthera from Different Medicinal Parts and Growth Stages.

Schisandra sphenanthera Rehd. et Wils., as a traditional Chinese medicine, has important medicinal value. In the market, the availability of the fruit of S. sphenanthera mainly relies on wild picking, but many canes and leaves are discarded during wild collection, resulting in a waste of resources. The canes and leaves of S. sphenanthera contain various bioactive ingredients and can be used as spice, tea, and medicine and so present great utilization opportunities. Therefore, it is helpful to explore the effective components and biological activities of the canes and leaves to utilize S. sphenanthera fully. In this study, the response surface method with ultrasound was used to extract the total triterpenoids from the canes and leaves of S. sphenanthera at different stages. The content of total triterpenoids in the leaves at different stages was higher than that in the canes. The total triterpenoids in the canes and leaves had strong antioxidant and antibacterial abilities. At the same time, the antibacterial activity of the total triterpenoids against Bacillus subtilis and Pseudomonas aeruginosa was stronger than that against Staphylococcus aureus and Escherichia coli. This study provides the foundation for the development and utilization of the canes and leaves that would relieve the shortage of fruit resources of S. sphenanthera.