Classification of Laboratory Test Outcomes for Maintenance Hemodialysis Patients Using Cellular Bioelectrical Measurements.

Background: End-stage kidney disease (ESKD) patients often face complications like anemia, malnutrition, and cardiovascular issues. Serological tests, which are uncomfortable and not frequently conducted, assist in medical assessments. A non-invasive, convenient method for determining these test results would be beneficial for monitoring patient health. Objective: This study develops machine learning models to estimate key serological test results using non-invasive cellular bioelectrical impedance measurements, a routine procedure for ESKD patients. Methods: The study employs two machine learning models, Support Vector Machine (SVM) and Random Forest (RF), to determine key serological tests by classifying cell bioelectrical indicators. Data from 688 patients, comprising 3,872 biochemical-bioelectrical records, were used for model validation. Results: Both SVM and RF models effectively categorized key serological results (albumin, phosphorus, parathyroid hormone) into low, normal, and high. RF generally outperformed SVM, except in classifying calcium levels in women. Conclusion: The machine learning models effectively classified serological test results for maintenance hemodialysis patients using cellular bioelectrical indicators, therefore can help in making judgments about physicochemical indicators using electrical signals, thereby reducing the frequency of serological tests.

The relationship between future time perspective and suicide ideation in college students: Multiple mediating effects of anxiety and depression.

Background: Suicide ideation has high prevalence in adolescents, better future time perspective is considered a protective role for anxiety, depression, and suicide ideation. However, the impact of future time perspective on suicide ideation remains unclear, especially when anxiety and depression as mediating roles. Methods: A cross-sectional study of college students was performed in Chongqing, China. There are 851 students enrolled in this study and we distribute questionnaires through the WeChat platform to obtain data in 2023. We conducted Pearson correlation analysis and descriptive statistics. Model 6 in PROCESS 4.0 was used to test the multiple mediating effect. Results: College students who have higher future time perspective are associated with a lower risk of anxiety, depression, and suicide ideation. Future time perspective not only affects suicide ideation directly, but also influence it by means of two mediating pathways: ①depression, the mediation effect is 37.41 %; ②the multiple mediating effects of anxiety and depression with a mediating effect of 29.68 %. Conclusion: Higher future time perspective functions as a protective role in anxiety, depression, and suicide ideation; future time perspective can affect and predict the occurrence of suicide ideation by influencing anxiety and depression in college students. This conclusion will be a novel and insightful part of adolescent mental health research, and provide a new perspective to prevent college students from committing suicide in the future.

Rapid discrimination between wild and cultivated Ophiocordyceps sinensis through comparative analysis of label-free SERS technique and mass spectrometry.

Ophiocordyceps sinensis is a genus of ascomycete fungi that has been widely used as a valuable tonic or medicine. However, due to over-exploitation and the destruction of natural ecosystems, the shortage of wild O. sinensis resources has led to an increase in artificially cultivated O. sinensis. To rapidly and accurately identify the molecular differences between cultivated and wild O. sinensis, this study employs surface-enhanced Raman spectroscopy (SERS) combined with machine learning algorithms to distinguish the two O. sinensis categories. Specifically, we collected SERS spectra for wild and cultivated O. sinensis and validated the metabolic profiles of SERS spectra using Ultra-Performance Liquid Chromatography coupled with Orbitrap High-Resolution Mass Spectrometry (UPLC-Orbitrap-HRMS). Subsequently, we constructed machine learning classifiers to mine potential information from the spectral data, and the spectral feature importance map is determined through an optimized algorithm. The results indicate that the representative characteristic peaks in the SERS spectra are consistent with the metabolites identified through metabolomics analysis, confirming the feasibility of the SERS method. The optimized support vector machine (SVM) model achieved the most accurate and efficient capacity in discriminating between wild and cultivated O. sinensis (accuracy = 98.95%, 5-fold cross-validation = 98.38%, time = 0.89s). The spectral feature importance map revealed subtle compositional differences between wild and cultivated O. sinensis. Taken together, these results are expected to enable the application of SERS in the quality control of O. sinensis raw materials, providing a foundation for the efficient and rapid identification of their quality and origin.

Structural insights into human ABCD3-mediated peroxisomal acyl-CoA translocation.

Human ABC transporters ABCD1-3 are all localized on the peroxisomal membrane and participate in the ß-oxidation of fatty acyl-CoAs, but they differ from each other in substrate specificity. The transport of branched-chain fatty acids from cytosol to peroxisome is specifically driven by ABCD3, dysfunction of which causes severe liver diseases such as hepatosplenomegaly. Here we report two cryogenic electron microscopy (cryo-EM) structures of ABCD3 bound to phytanoyl-CoA and ATP at resolutions of 2.9 Å and 3.2 Å, respectively. A pair of phytanoyl-CoA molecules were observed in ABCD3, each binding to one transmembrane domain (TMD), which is distinct from our previously reported structure of ABCD1, where each fatty acyl-CoA molecule strongly crosslinks two TMDs. Upon ATP binding, ABCD3 exhibits a conformation that is open towards the peroxisomal matrix, leaving two extra densities corresponding to two CoA molecules deeply embedded in the translocation cavity. Structural analysis combined with substrate-stimulated ATPase activity assays indicated that the present structures might represent two states of ABCD3 in the transport cycle. These findings advance our understanding of fatty acid oxidation and the molecular pathology of related diseases.

Identification of hypermucoviscous Klebsiella pneumoniae strains via untargeted surface-enhanced Raman spectroscopy.

Klebsiella pneumoniae is one of the most common causes of hospital-acquired infections, especially due to the emergence of the hypervirulent K. pneumoniae (hvKp) strains. Multiple methods have been developed to discriminate hvKp strains from classical K. pneumoniae (cKp) strains, such as the presence of candidate genes (e.g., peg-344, iroB, and iucA), high level of siderophore production, hypermucoviscosity phenotype, etc. Although the string test is commonly used to confirm the hypermucoviscosity of K. pneumoniae strains, it is a method lacking rigidity and accuracy. Surface-enhanced Raman spectroscopy (SERS) coupled with machine learning algorithms has been widely used in discriminating bacterial pathogens with different phenotypes. However, the technique has not be applied to identify hypermucoviscous K. pneumoniae (hmvKp) strains. In this study, we isolated a set of K. pneumoniae strains from clinical samples, among which hmvKp strains (N = 10) and cKP strains (N = 10) were randomly selected to collect SESR spectra. Eight machine learning algorithms were recruited for model construction and spectral prediction in this study, among which support vector machine (SVM) outperforms all other algorithms with the highest prediction accuracy of hmvKp strains (5-fold cross validation = 99.07%). Taken together, this pilot study confirms that SERS, combined with machine learning algorithms, can accurately identify hmvKp strains, which can facilitate the fast recognition of hvKP strains when combined with relevant methods and biomarkers in clinical settings in the near future.

Asteraceae genome database: a comprehensive platform for Asteraceae genomics.

Asteraceae, the largest family of angiosperms, has attracted widespread attention for its exceptional medicinal, horticultural, and ornamental value. However, researches on Asteraceae plants face challenges due to their intricate genetic background. With the continuous advancement of sequencing technology, a vast number of genomes and genetic resources from Asteraceae species have been accumulated. This has spurred a demand for comprehensive genomic analysis within this diverse plant group. To meet this need, we developed the Asteraceae Genomics Database (AGD; http://cbcb.cdutcm.edu.cn/AGD/). The AGD serves as a centralized and systematic resource, empowering researchers in various fields such as gene annotation, gene family analysis, evolutionary biology, and genetic breeding. AGD not only encompasses high-quality genomic sequences, and organelle genome data, but also provides a wide range of analytical tools, including BLAST, JBrowse, SSR Finder, HmmSearch, Heatmap, Primer3, PlantiSMASH, and CRISPRCasFinder. These tools enable users to conveniently query, analyze, and compare genomic information across various Asteraceae species. The establishment of AGD holds great significance in advancing Asteraceae genomics, promoting genetic breeding, and safeguarding biodiversity by providing researchers with a comprehensive and user-friendly genomics resource platform.

Appetite hormones, neuropsychological function and methylphenidate use in children with attention-deficit/hyperactivity disorder.

Appetite hormones may play a significant role in neuronal excitability and synaptic plasticity and may also affect brain function development. This study aimed to explore the role of appetite hormones in attention deficit/hyperactivity disorder (ADHD), including aspects of pathophysiology, pharmacotherapy, and side effects. We recruited 119 patients with ADHD who were undergoing methylphenidate treatment (ADHD+MPH), 77 unmedicated ADHD patients (ADHD-MPH), and 87 healthy controls. Blood samples were collected from all participants to examine serum levels of orexin A, ghrelin, leptin, and adiponectin. Behavioral symptoms were assessed using the Swanson, Nolan, and Pelham Rating Scale, and visual and auditory attention were evaluated using computerized neuropsychological tests. The side effects of methylphenidate treatment were measured using Barkley's Side Effects Rating Scale. Orexin levels in the control group were significantly higher than in the ADHD-MPH (p=0.037) and ADHD+MPH (p<0.001) groups; additionally, orexin levels in the ADHD-MPH group were significantly higher than in the ADHD+MPH group (p=0.032). Leptin levels in both the ADHD+MPH (p=0.011) and ADHD-MPH (p=0.011) groups were significantly lower than in the control group. Ghrelin levels were positively associated with auditory attention across all ADHD groups (p=0.015). Furthermore, ghrelin levels were positively correlated with methylphenidate dosage (p=0.024), and negatively correlated with methylphenidate side effects (p=0.044) in the ADHD+MPH group. These findings provide further insight into the relationships between appetite hormones, pharmacotherapy, and ADHD. Orexin A and leptin are associated with the etiology of ADHD, while orexin A and ghrelin play important roles in attention deficits and methylphenidate usage in ADHD.

Exopolysaccharide from Leuconostoc mesenteroides XR1: Yield optimization, partial characterization and properties.

The production conditions of exopolysaccharide (EPS) from Leuconostoc mesenteroides XR1 were optimized by response surface methodology (RSM). Maximum EPS yield was 56.59 ± 0.51 g/L under fermentation conditions with 2.6 g/L ammonium citrate, initial pH 6.5 and temperature 23 °C, which was 6.21-fold greater than the EPS yield before optimization. Characterization of the chain conformation using Congo red test and circular dichroism (CD) showed that EPS exhibited a random coil structure in aqueous solution. The CD results revealed that the EPS concentration altered its hydrogen-bond interactions and chirality, but did not change its chain conformation. The average polydispersity index (PDI) of the EPS solution was only 27.16 %, indicating that it was uniformly distributed in the aqueous solution with high stability. The degradation temperature of EPS was 253.11 °C, indicating high thermal stability. EPS possessed the ability to scavenge activities of free radicals and was protective against oxidative stress-induced plasmid DNA damage. In addition, stable hydrogels could be formed at EPS concentrations above 5 % (w/v). These results collectively showed that EPS can be used commercially as an antioxidant and drug delivery carrier.

Efficacy and safety of telitacicept in IgA nephropathy: A retrospective, multicenter study.

Introduction The efficacy of Telitacicept treatment in reducing proteinuria in patients with IgA nephropathy (IgAN) was indicated in a phase II clinical trial with small sample size. In this study, we conducted a large multicenter retrospective study to explore the efficacy and safety of Telitacicept in patients with IgAN. Methods This study recruited patients with IgAN from 19 sites from China who were treated with Telitacicept and had been followed up at least once or with side effect reported, since April 1, 2021 to April 1, 2023. The primary outcomes of the study were the changing in proteinuria and eGFR over time. Results A cohort of 97 patients with IgAN who were treated with Telitacicept were recruited, with a median follow-up duration of 3 months. The median baseline proteinuria was 2.3 [1.3, 3.9] g/day and eGFR was 45.0 [26.8, 73.7] ml/min/1.73m2. There was a significant reduction of proteinuria at 2,4,6 months when compared with baseline (2.3 [1.5, 4.1] vs. 1.5 [0.8, 2.3] g/day; 2.3 [1.1, 3.7] vs. 1.1 [0.6, 1.9] g/day; 2.1 [1.0, 2.7] vs. 0.9 [0.5,1.7] g/day, all P values < 0.01). The level of eGFR were comparable between at the baseline and 2, 4, 6 months of follow-up time (41.5 [29.7, 72.0] vs. 42.5 [28.8, 73.3] ml/min/1.73m2; 41.0 [26.8, 67.7] vs. 44.7 [31.0, 67.8] ml/min/1.73m2; 33.7 [24.0, 58.5] vs. 32.6 [27.8, 57.5] ml/min/1.73m2, all P values > 0.26). Telitacicept was well tolerated in the patients. Conclusions This study indicates that Telitacicept alone or on top of steroids therapy can significantly and safely reduce proteinuria in patients with IgAN. The long-term kidney protection still need to be confirmed in large Phase III trial.

Radiogenomics based survival prediction of small-sample glioblastoma patients by multi-task DFFSP model.

In this paper, the multi-task dense-feature-fusion survival prediction (DFFSP) model is proposed to predict the three-year survival for glioblastoma (GBM) patients based on radiogenomics data. The contrast-enhanced T1-weighted (T1w) image, T2-weighted (T2w) image and copy number variation (CNV) is used as the input of the three branches of the DFFSP model. This model uses two image extraction modules consisting of residual blocks and one dense feature fusion module to make multi-scale fusion of T1w and T2w image features as backbone. Also, a gene feature extraction module is used to adaptively weight CNV fragments. Besides, a transfer learning module is introduced to solve the small sample problem and an image reconstruction module is adopted to make the model anatomy-aware under a multi-task framework. 256 sample pairs (T1w and corresponding T2w MRI slices) and 187 CNVs of 74 patients were used. The experimental results show that the proposed model can predict the three-year survival of GBM patients with the accuracy of 89.1 %, which is improved by 3.2 and 4.7 % compared with the model without genes and the model using last fusion strategy, respectively. This model could also classify the patients into high-risk and low-risk groups, which will effectively assist doctors in diagnosing GBM patients.

Changes in volatile flavors during the fermentation of tomato (Solanum lycopersicum L.) juice and its storage stabilization.

Flavor is a crucial indicators of the quality of fermented tomato juice; however, there has been limited research in this area. Herein, headspace solid-phase microextraction gas chromatography-mass spectrometry was used to analyze the volatile metabolites at different stages during FTJ fermentation. 131 volatile organic compounds (VOCs) were identified, with alcohols, acids, and esters as the main compounds. The content of superoxide dismutase (SOD) and lycopene (LYC) had a positive correlation with methyl salicylate, ethyl acetate, and linalyl acetate. Subsequently, the storage stability of FTJ was evaluated at temperatures of 4 °C, 25 °C, and 37 °C over a period of 45 d, revealing that the quality of FTJ decreased with increasing storage temperature. The shelf life of FTJ under different storage conditions was determined using SOD activity and LYC content as quality indicators. The final shelf life was 47 d at 37 °C, 69 d at 25 °C, and 123 d at 4 °C.

Optimizing domestic energy management with a wild Mice colony-inspired algorithm: Enhancing efficiency and coordination in smart grids through dynamic distributed energy storage.

Energy management (EM) is a critical strategy that spans the production and consumption of electricity, enhancing the stability of the electricity network. Smart grid technology significantly improves the electrical system's energy efficiency (EE), facilitating a transformation from a conventional power grid (PG) to a smart PG. This paper presents a key strategy for modeling the EE of the smart grid tailored to domestic demand, establishing smart coordination between domestic demand, energy production, and storage to reduce energy waste and costs. Our model integrates various energy sources, including renewable energy (RE), photovoltaic (PV) systems, wind power, and an energy storage system (ESS), interconnected with the PG. The model's structure ensures the coordinated flow of electricity in a residential house through an optimal control method (OCM). To develop a robust closed-loop control model, we employ Demand Response (DR) schemes within the Real-Time Electricity Pricing (RTEP) framework. We construct a dynamic model of the ESS to compute the System Performance Index (SPI), corresponding to energy costs. To enhance our model, we introduce a Dynamic Distributed Energy Storage Strategy (DDESS). Additionally, we introduce a novel optimization algorithm inspired by the behavioral patterns of wild mice, called the Wild Mice Colony (WMC). By analyzing the targeted and advantageous behaviors of wild mice in colonies, we propose that these behaviors can serve as a model for addressing complex, uncertain problems. This strategy is highly advantageous, capable of reducing total energy consumption (EC) from the main grid by over 100 % of the load demand, optimizing the energy system, and ensuring synchronization. The performance of DDESS optimizes energy flow (EF) during the repayment plan, leading to minimized EC costs from the PG.

Human neural stem cell-derived extracellular vesicles protect against ischemic stroke by activating the PI3K/AKT/mTOR pathway.

Human neural stem cell-derived extracellular vesicles exhibit analogous functions to their parental cells, and can thus be used as substitutes for stem cells in stem cell therapy, thereby mitigating the risks of stem cell therapy and advancing the frontiers of stem cell-derived treatments. This lays a foundation for the development of potentially potent new treatment modalities for ischemic stroke. However, the precise mechanisms underlying the efficacy and safety of human neural stem cell-derived extracellular vesicles remain unclear, presenting challenges for clinical translation. To promote the translation of therapy based on human neural stem cell-derived extracellular vesicles from the bench to the bedside, we conducted a comprehensive preclinical study to evaluate the efficacy and safety of human neural stem cell-derived extracellular vesicles in the treatment of ischemic stroke. We found that administration of human neural stem cell-derived extracellular vesicles to an ischemic stroke rat model reduced the volume of cerebral infarction and promoted functional recovery by alleviating neuronal apoptosis. The human neural stem cell-derived extracellular vesicles reduced neuronal apoptosis by enhancing phosphorylation of phosphoinositide 3-kinase, mammalian target of rapamycin, and protein kinase B, and these effects were reversed by treatment with a phosphoinositide 3-kinase inhibitor. These findings suggest that human neural stem cell-derived extracellular vesicles play a neuroprotective role in ischemic stroke through activation of phosphoinositide 3-kinase/protein kinase B/mammalian target of rapamycin signaling pathway. Finally, we showed that human neural stem cell-derived extracellular vesicles have a good in vivo safety profile. Therefore, human neural stem cell-derived extracellular vesicles are a promising potential agent for the treatment of ischemic stroke.

Extracellular microvesicle microRNAs, along with imaging metrics, improve detection of aggressive prostate cancer.

Prostate cancer is the most commonly diagnosed cancer in men worldwide. Early diagnosis of the disease provides better treatment options for these patients. Magnetic resonance imaging (MRI) provides an overall assessment of prostate disease. Quantitative metrics (radiomics) from the MRI provide a better evaluation of the tumor and have been shown to improve disease detection. Recent studies have demonstrated that plasma extracellular vesicle microRNAs (miRNAs) are functionally linked to cancer progression, metastasis, and aggressiveness. In our study, we analyzed a matched cohort with baseline blood plasma and MRI to access tumor morphology using imaging-based radiomics and cellular characteristics using miRNAs-based transcriptomics. Our findings indicate that the univariate feature-based model with the highest Youden's index achieved average areas under the receiver operating characteristic curve (AUC) of 0.76, 0.82, and 0.84 for miRNA, MR-T2W, and MR-ADC features, respectively, in identifying clinically aggressive (Gleason grade) disease. The multivariable feature-based model demonstrated an average AUC of 0.88 and 0.95 using combinations of miRNA markers with imaging features in MR-ADC and MR-T2W, respectively. Our study demonstrates combining miRNA markers with MRI-based radiomics improves predictability of clinically aggressive prostate cancer.

Human neural stem cell-derived artificial organelles to improve oxidative phosphorylation.

Oxidative phosphorylation (OXPHOS) in the mitochondrial inner membrane is a therapeutic target in many diseases. Neural stem cells (NSCs) show progress in improving mitochondrial dysfunction in the central nervous system (CNS). However, translating neural stem cell-based therapies to the clinic is challenged by uncontrollable biological variability or heterogeneity, hindering uniform clinical safety and efficacy evaluations. We propose a systematic top-down design based on membrane self-assembly to develop neural stem cell-derived oxidative phosphorylating artificial organelles (SAOs) for targeting the central nervous system as an alternative to NSCs. We construct human conditionally immortal clone neural stem cells (iNSCs) as parent cells and use a streamlined closed operation system to prepare neural stem cell-derived highly homogenous oxidative phosphorylating artificial organelles. These artificial organelles act as biomimetic organelles to mimic respiration chain function and perform oxidative phosphorylation, thus improving ATP synthesis deficiency and rectifying excessive mitochondrial reactive oxygen species production. Conclusively, we provide a framework for a generalizable manufacturing procedure that opens promising prospects for disease treatment.

Exploring the transactive relationships of influence factors for online asynchronous learning transactive memory system.

Transactive memory system (TMS) makes learning and transferring knowledge easy and efficient. This study has constructed a conceptual model of TMS to reveal the crucial factors influencing the formation of a TMS in online asynchronous learning to foster knowledge sharing and collaboration and enhance knowledge transfer effectiveness. The conceptual model of TMS is built upon theoretical foundations concerning the creation of TMS and the variables influencing them. The study has put forth a set of hypotheses to predict the expected effects of group factors, individual factors, task interdependence, the degree of intellectual silence, and knowledge management on the formation of TMS. In this study, a total of 229 questionnaire data were collected from undergraduate, master's, and doctoral students in Northeast China who had experience with an online asynchronous learning TMS. Structural equation modeling has been employed to identify the key indicators involved and their influence on the formation of TMS. The empirical study was carried out using statistical analysis of SPSS data, with the results indicating that each factor has varying impacts on knowledge management, ultimately affecting the formation of TMS. These findings provide a more nuanced understanding of how these factors shape online asynchronous learning environments.

Carbon and nitrogen addition-derived enzyme activities in topsoil but nitrogen availability in subsoil controls the response of soil organic carbon decomposition to warming.

Subsoil stores the majority of soil organic carbon (SOC), and plays a vital role in the global carbon cycle in terrestrial ecosystems and in regulating climate change. Response of SOC decomposition to temperature warming (TR) is a crucial parameter to predict SOC dynamics under global warming. However, it remains unknown how TR varies across the whole soil profile and responds to exogenous C and N inputs. To assess this, we designed a novel incubation system to measure SOC-derived CO2 efflux across the whole soil column (i.e., 60 cm length), allowing manual addition of 13C-labeled glucose and ammonium nitrate, and incubated it under ambient or warmed temperatures (+4 °C). We found that C addition significantly increased TR in 0-20 cm, 20-40 cm and 40-60 cm by 64.3 %, 68.1 % and 57.2 %, respectively. However, the combined addition of C and N decreased TR by 11.1 % - 15.3 % compared to without anything addition (CK) in the whole soil profile. The effect of N on TR ranged from -22.8 % to -40.4 % in the whole soil profile, and was significantly lower in topsoil than in subsoil. Furthermore, sole N addition significantly promoted TR compared to CK by 79.0 % and 94.7 % in 20-40 cm and 40-60 cm subsoil, only 9.8 % in 0-20 cm topsoil. These results together suggested that TR is sensitive to increasing C availability in the whole soil profile and increasing N availability in 20-60 cm subsoil. Random forest model indicated that soil enzyme activities (explained 21.3 % of the variance) and DOC (explained 11.1 % of the variance) dominantly governed TR in topsoil, but N availability displayed a predominant control of TR in subsoil. Overall, our results suggested that increased C and N availability under climate warming scenarios could further increase the risk of carbon loss especially in subsoil with substrate deficiency, but labile C (e.g., root exudation) input under climate warming and N enrichment could reduce SOC decomposition and benefit for C sequestration by decreasing TR.

Blueberry extract for the treatment of ischaemic stroke through regulating the gut microbiota and kynurenine metabolism.

Although the gut microbiota and kynurenine (KYN) metabolism have significant protective effects against ischaemic stroke (IS), the exact mechanism has yet to be fully elucidated. Combined serum metabolomics and 16S rRNA gene sequencing were used to reveal the differences between the gut microbiota and metabolites in rats treated with or without blueberry extract. Faecal microbiota transplantation (FMT) was employed to validate the protective role of the gut microbiota in IS. Furthermore, the interaction between Prevotella and IS was also confirmed in patients. Rats with IS experienced neurological impairments accompanied by an impaired intestinal barrier and disturbed intestinal flora, which further contributed to heightened inflammatory responses. Furthermore, Prevotella played a critical role in IS pathophysiology, and a positive correlation between Prevotella and KYN was detected. The role of KYN metabolism in IS was further demonstrated by the finding that IDO was significantly upregulated and that the use of the IDO inhibitor, attenuated KYN metabolic pathway activity and ameliorated neurological damage in rats with IS. Prevotella intervention also significantly improved stroke symptoms and decreasing KYN levels in rats with IS. FMT showed that the beneficial effects of blueberry extract on IS involve gut bacteria, especially Prevotella, which were confirmed by microbiological analyses conducted on IS patients. Moreover, blueberry extract led to significant changes in kynurenic acid levels and tryptophan and IDO levels through interactions with Prevotella. Our study demonstrates for the first time that blueberry extract could modulate "intestinal microecology-KYN metabolism" to improve IS.

Molecular interaction induced dual fibrils towards organic solar cells with certified efficiency over 20.

The nanoscale fibrillar morphology, featuring long-range structural order, provides abundant interfaces for efficient exciton dissociation and high-quality pathways for effective charge transport, is a promising morphology for high performance organic solar cells. Here, we synthesize a thiophene terminated non-fullerene acceptor, L8-ThCl, to induce the fibrillization of both polymer donor and host acceptor, that surpasses the 20% efficiency milestone of organic solar cells. After adding L8-ThCl, the original weak and less continuous nanofibrils of polymer donors, i.e. PM6 or D18, are well enlarged and refined, whilst the host acceptor L8-BO also assembles into nanofibrils with enhanced structural order. By adapting the layer-by-layer deposition method, the enhanced structural order can be retained to significantly boost the power conversion efficiency, with specific values of 19.4% and 20.1% for the PM6:L8-ThCl/L8-BO:L8-ThCl and D18:L8-ThCl/L8-BO:L8-ThCl devices, with the latter being certified 20.0%, which is the highest certified efficiency reported so far for single-junction organic solar cells.