Comprehensive quality evaluation of fermented-steaming Fructus Aurantii based on chemical composition, flavor characteristics, and intestinal microbial community.

Fructus Aurantii (FA) is an edible and medicinal functional food used worldwide that enhances digestion. Since raw FA (RFA) possesses certain side effects for some patients, processed FA (PFA) is commonly used in clinical practice. This study aimed to establish an objective and comprehensive quality evaluation of the PFA that employed the technique of steaming and fermentation. Combined with the volatile and non-volatile components, as well as the regulation of gut microbiota, the differentiation between RFA and PFA was analyzed. The results showed that the PFA considerably reduced the contents of flavonoid glycosides while increasing hesperidin-7-O-glucoside and flavonoid aglycones. The electronic nose and GC-MS (Gas chromatography/mass spectrometry) effectively detected the variation in flavor between RFA and PFA. Correlation analysis revealed that eight volatile components (relative odor activity value [ROAV] ≥ 0.1) played a key role in inducing odor modifications. The original floral and woody notes were subdued due to decreased levels of linalool, sabinene, α-terpineol, and terpinen-4-ol. After processing, more delightful flavors such as lemon and fruity aromas were acquired. Furthermore, gut microbiota analysis indicated a significant increase in beneficial microbial taxa. Particularly, Lactobacillus, Akkermansia, and Blautia exhibited higher abundance following PFA treatment. Conversely, a lower presence of pathogenic bacteria, including Proteobacteria, Flexispira, and Clostridium. This strategy contributes to a comprehensive analysis technique for the quality assessment of FA, providing scientific justifications for processing FA into high-value products with enhanced health benefits. PRACTICAL APPLICATION: This study provided an efficient approach to Fructus Aurantii quality evaluation. The methods of fermentation and steaming showed improved quality and safety.

Efficacy of Forsythia suspensa (Thunb.) Vahl on mouse and rat models of inflammation-related diseases: a meta-analysis.

Objective: To evaluate the efficacy of the fruits of the medicinal plant Forsythia suspensa (Thunb.) Vahl (FS), in treating inflammation-associated diseases through a meta-analysis of animal models, and also probe deeply into the signaling pathways underlying the progression of inflammation. Materials and methods: All data analyses were performed using Review Manager 5.3 and the results are presented as flow diagrams, risk-of-bias summaries, forest plots, and funnel plots. Summary estimates were calculated using a random- or fixed-effect model, depending on the value of I2. Results: Of the 710 records identified in the initial search, 11 were selected for the final meta-analysis. Each study extracted data from the model and treatment groups for analysis, and the results showed that FS alleviated the inflammatory cytokine levels in serum; oxidant indicator: reactive oxygen species; enzymes of liver function; endotoxin and regulatory cells in blood; and improved the antioxidant enzyme superoxide dismutase. Conclusion: FS effectively reversed the change in acute or chronic inflammation indicators in animal models, and the regulation of multiple channel proteins in inflammatory signaling pathways suggests that FS is a good potential drug for inflammatory disease drug therapy.

Corrigendum: Impact of intestinal microbiota on metabolic toxicity and potential detoxification of amygdalin.

[This corrects the article DOI: 10.3389/fmicb.2022.1030516.].

Safety and effectiveness of tigecycline combination therapy in renal transplant patients with infection due to carbapenem-resistant gram-negative bacteria.

Background: Carbapenem-resistant gram-negative bacterial (CRGNB) infections are increasing among kidney transplant recipients, and effective therapeutic options are limited. This study aimed to investigate the efficacy and adverse events associated with combination therapy tigecycline in renal transplant patients with CRGNB infections. Methods: This study retrospectively analyzed 40 Chinese patients with confirmed or suspected CRGNB infections who received tigecycline therapy. The patients' case features and clinical and microbiological data were analyzed. Results: A total of 40 renal transplant recipients received tigecycline therapy for a median duration of 9 (range, 3-25) days. CRGNB isolates were obtained from the organ preservation solution of the donor kidney in 28 patients, with confirmed transmission in 4 patients. Infections were detected in the bloodstream, urinary tract, sputum, and wound. The most prevalent isolates were Klebsiella pneumoniae (75%, 30/40), Acinetobacter baumannii (15%, 6/40), and Escherichia coli (10%, 4/40). A clinical response was observed in 32 (80%) patients. The 28-day all-cause mortality rate was 7.5% (3/40), while the one-year all-cause mortality rate was 2.5% (1/40). While one patient died owing to severe pancreatitis, no serious adverse events related to tigecycline therapy were reported. However, multiple indices of liver function and pancreatitis precursors increased after treatment with tigecycline compared to before treatment. Conclusion: Tigecycline therapy appears to be well tolerated in renal transplant recipients with multidrug-resistant bacterial infections. Nevertheless, attention should be paid to adverse reactions related to tigecycline therapy, especially gastrointestinal reactions, and the related laboratory tests should be closely monitored.

Corrigendum: Hydroxychloroquine attenuates autoimmune hepatitis by suppressing the interaction of GRK2 with PI3K in T lymphocytes.

[This corrects the article DOI: 10.3389/fphar.2022.972397.].

Fostering Athletes' Mental Resilience: Artistic Innovation and AI in Sports / Fomento de la resiliencia mental de los atletas: innovación artística e inteligencia artificial en los deportes

As artificial intelligence (AI) technology rapidly advances, the world of artistic innovation in sports paintings encounters both unprecedented challenges and exciting opportunities. AI's capacity to learn pushes the boundaries of traditional creative thinking within the realm of sports art, introducing a more diverse and intelligent approach to the creative process. However, the actual ecosystem for creativity and its application within this context lacks a robust management model, necessitating fundamental theoretical innovation and standardized discipline.This exploration embarks on a journey to elucidate the practical applications of AI technology in the creation of sports art. It delves into the pivotal roles played by AI professionals, creative artists, and viewers in shaping the future of sports art innovation. It paves the way for an innovative approach to sports painting and decorative art, grounded in AI intelligence.Emphasizing the symbiotic relationship between human and AI capabilities, intelligent product development, artistic creation, and the constraints on creative behavior, this inquiry dives into emerging application areas. Through this lens, it seeks to gain a deeper understanding of the essential cycles of innovation in the field of sports plastic art, driven by the transformative power of artificial intelligence.(AU)

Therapeutic effects of Siegesbeckia orientalis L. and its active compound luteolin in rheumatoid arthritis: network pharmacology, molecular docking and experimental validation.

ETHNOPHARMACOLOGICAL RELEVANCE: Rheumatoid arthritis (RA) is a common difficult disease with a high disability rate. Siegesbeckia orientalis L. (SO), a Chinese medicinal herb that is commonly used for treating RA in clinical practice. While, the anti-RA effect and the mechanisms of action of SO, as well as its active compound(s) have not been elucidated clearly. AIM OF THE STUDY: We aim to explore the molecular mechanism of SO against RA by using network pharmacology analysis, as well as the in vitro and in vivo experimental validations, and to explore the potential bioactive compound(s) in SO. METHODS: Network pharmacology is an advanced technology that provides us an efficient way to study the therapeutic actions of herbs with the underlying mechanisms of action delineated. Here, we used this approach to explore the anti-RA effects of SO, and then the molecular biological approaches were used to verify the prediction. We first established a drug-ingredient-target-disease network and a protein-protein interaction (PPI) network of SO-related RA targets, followed by the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses. Further, we used lipopolysaccharide (LPS)-stimulated RAW264.7 macrophages and vascular endothelial growth factor-A (VEGFA)-induced human umbilical vein endothelial cell (HUVEC) models, as well as adjuvant-induced arthritis (AIA) rat model to validate the anti-RA effects of SO. The chemical profile of SO was also determined by using the UHPLC-TOF-MS/MS analysis. RESULTS: Network pharmacology analysis highlighted inflammatory- and angiogenesis-related signaling pathways as promising pathways that mediate the anti-RA effects of SO. Further, in both in vivo and in vitro models, we found that the anti-RA effect of SO is at least partially due to the inhibition of toll like receptor 4 (TLR4) signaling. Molecular docking analysis revealed that luteolin, an active compound in SO, shows the highest degree of connections in compound-target network; moreover, it has a direct binding to the TLR4/MD-2 complex, which is confirmed in cell models. Besides, more than forty compounds including luteolin, darutoside and kaempferol corresponding to their individual peaks were identified tentatively via matching with the empirical molecular formulae and their mass fragments. CONCLUSION: We found that SO and its active compound luteolin exhibit anti-RA activities and potently inhibit TLR4 signaling both in vitro and in vivo. These findings not only indicate the advantage of network pharmacology in the discovery of herb-based therapeutics for treating diseases, but also suggest that SO and its active compound(s) could be developed as potential anti-RA therapeutic drugs.

Determination of Ten Flavonoids in the Raw and Fermented Fructus Aurantii by Quantitative Analysis of Multicomponents via a Single Marker (QAMS) Based on UPLC.

Fermented Fructus Aurantii (FFA) is widely used in South China for the treatment of functional dyspepsia. Naringin, neohesperidin, and other flavonoids are the main pharmacodynamic components of FFA. A new method is presented for the simultaneous determination of 10 flavonoids (including flavonoid glycosides and aglycones) in FFA using the quantitative analysis of multicomponents via a single marker (QAMS) approach and is used to investigate changes in flavonoids during fermentation. The viability and precision of QAMS were validated against the ultrahigh-performance liquid chromatography (UPLC), with various UPLC instruments and chromatographic conditions being evaluated. Differences between raw Fructus Aurantii (RFA) and FFA were examined using orthogonal partial least squares discrimination analysis (OPLS-DA) and content determination. The influence of various fermentation conditions on flavonoids was also investigated. There were no appreciable differences between the QAMS and the external standard method (ESM), demonstrating that QAMS is an improved method for the determination of FA and FFA. FFA and RFA can be readily distinguished based on OPLS-DA chemometric modelling and the corresponding chromatograms. In addition, the flavonoid changes after fermentation. Fermentation considerably reduced the contents of flavonoid glycosides, while increasing hesperidin-7-O-glucoside and flavonoid aglycones. Moreover, fermentation conditions impact multiple flavonoids in FA, so controlling these conditions is necessary for the quality control of fermented FA products. This QAMS approach is useful for detecting numerous components in RFA and FFA simply, quickly, and efficiently, thus strengthening the quality control of FA and its fermented products.

Construction and validation of prognostic models in critically Ill patients with sepsis-associated acute kidney injury: interpretable machine learning approach.

BACKGROUND: Acute kidney injury (AKI) is a common complication in critically ill patients with sepsis and is often associated with a poor prognosis. We aimed to construct and validate an interpretable prognostic prediction model for patients with sepsis-associated AKI (S-AKI) using machine learning (ML) methods. METHODS: Data on the training cohort were collected from the Medical Information Mart for Intensive Care IV database version 2.2 to build the model, and data of patients were extracted from Hangzhou First People's Hospital Affiliated to Zhejiang University School of Medicine for external validation of model. Predictors of mortality were identified using Recursive Feature Elimination (RFE). Then, random forest, extreme gradient boosting (XGBoost), multilayer perceptron classifier, support vector classifier, and logistic regression were used to establish a prognosis prediction model for 7, 14, and 28 days after intensive care unit (ICU) admission, respectively. Prediction performance was assessed using the receiver operating characteristic (ROC) curve and decision curve analysis (DCA). SHapley Additive exPlanations (SHAP) were used to interpret the ML models. RESULTS: In total, 2599 patients with S-AKI were included in the analysis. Forty variables were selected for the model development. According to the areas under the ROC curve (AUC) and DCA results for the training cohort, XGBoost model exhibited excellent performance with F1 Score of 0.847, 0.715, 0.765 and AUC (95% CI) of 0.91 (0.90, 0.92), 0.78 (0.76, 0.80), and 0.83 (0.81, 0.85) in 7 days, 14 days and 28 days group, respectively. It also demonstrated excellent discrimination in the external validation cohort. Its AUC (95% CI) was 0.81 (0.79, 0.83), 0.75 (0.73, 0.77), 0.79 (0.77, 0.81) in 7 days, 14 days and 28 days group, respectively. SHAP-based summary plot and force plot were used to interpret the XGBoost model globally and locally. CONCLUSIONS: ML is a reliable tool for predicting the prognosis of patients with S-AKI. SHAP methods were used to explain intrinsic information of the XGBoost model, which may prove clinically useful and help clinicians tailor precise management.

Identification and preliminary validation of synovial tissue-specific genes and their-mediated biological mechanisms in rheumatoid arthritis.

BACKGROUND: Rheumatoid arthritis (RA) is a chronic inflammatory autoimmune disease. It is well known that the formation of positive feedback between synovial hyperplasia and inflammatory infiltration is intimately associated with the occurrence and development of RA. However, the exact mechanisms still remain unknown, making the early diagnosis and therapy of RA difficult. This study was designed to identify prospective diagnostic and therapeutic biomarkers, as well as their-mediated biological mechanisms in RA. METHODS: Three microarray datasets (GSE36700, GSE77298 and GSE153015) and two RNA-sequencing datasets (GSE89408 and GSE112656) of synovial tissues, as well as three other microarray datasets (GSE101193, GSE134087 and GSE94519) of peripheral blood were downloaded for integrated analysis. The differently expressed genes (DEGs) were identified by "limma" package of R software. Then, weight gene co-expression analysis and gene set enrichment analysis were performed to investigate synovial tissue-specific genes and their-mediated biological mechanisms in RA. The expression of candidate genes and their diagnostic value for RA were verified by quantitative real-time PCR and receiver operating characteristic (ROC) curve, respectively. Relevant biological mechanisms were explored through cell proliferation and colony formation assay. The suggestive anti-RA compounds were discovered by CMap analysis. RESULTS: We identified a total of 266 DEGs, which were mainly enriched in cellular proliferation and migration, infection and inflammatory immune signaling pathways. Bioinformatics analysis and molecular validation revealed 5 synovial tissue-specific genes, which exhibited excellent diagnostic value for RA. The infiltration level of immune cells in RA synovial tissue was significantly higher than that in control individuals. Moreover, preliminary molecular experiments suggested that these characteristic genes may be responsible for the high proliferation potential of RA fibroblast-like synoviocytes (FLSs). Finally, 8 small molecular compounds with anti-RA potential were obtained. CONCLUSIONS: We have proposed 5 potential diagnostic and therapeutic biomarkers (CDK1, TTK, HMMR, DLGAP5, and SKA3) in synovial tissues that may contribute to the pathogenesis of RA. These findings may shed light on the early diagnosis and therapy of RA.

Gut microbiota and host cytochrome P450 characteristics in the pseudo germ-free model: co-contributors to a diverse metabolic landscape.

BACKGROUND: The pseudo germ-free (PGF) model has been widely used to research the role of intestinal microbiota in drug metabolism and efficacy, while the modelling methods and the utilization of the PGF model are still not standardized and unified. A comprehensive and systematic research of the PGF model on the composition and function of the intestinal microbiota, changes in host cytochrome P450 (CYP450) enzymes expression and intestinal mucosal permeability in four different modelling cycles of the PGF groups are provided in this paper. RESULTS: 16S rRNA gene amplicon sequencing was employed to compare and analyze the alpha and beta diversity, taxonomic composition, taxonomic indicators and predicted function of gut microbiota in the control and PGF groups. Bacterial richness and diversity decreased significantly in the PGF group beginning after the first week of establishment of the PGF model with antibiotic exposure. The PGF group exposed to antibiotics for 4-week-modelling possessed the fewest indicator genera. Moreover, increased intestinal mucosal permeability occurred in the second week of PGF model establishment, indicating that one week of antibiotic exposure is an appropriate time to establish the PGF model. The results of immunoblots revealed that CYP1A2, CYP2C19 and CYP2E1 expression was significantly upregulated in the PGF group compared with the control group, implying that the metabolic clearance of related drugs would change accordingly. The abundance of functional pathways predicted in the gut microbiota changed dramatically between the control and PGF groups. CONCLUSIONS: This study provides information concerning the microbial and CYP450 enzyme expression profiles as a reference for evaluating drug metabolism differences co-affected by gut microbiota and host CYP450 enzymes in the PGF model.

[Dryness-alleviating effect of processed Aurantii Fructus from Lingnan based on correlation of fecal metabolomics and dryness].

With the approach of untargeted metabolomics and correlation analysis, this study aimed to explore the mechanism of Aurantii Fructus from Lingnan region in alleviating dryness by analyzing the different effects of raw Aurantii Fructus(RAF) and processed Aurantii Fructus(PAF) on fecal endogenous metabolism in normal rats. Eighteen Sprague-Dawley(SD) rats were randomly divided into a control group(C), an RAF group(10 g·kg~(-1)), and a PAF group(10 g·kg~(-1)). After seven days of administration, the effects of RAF and PAF on dryness-related indexes were compared, including water intake, fecal water content, salivary secretion, the expression of AQP5, VIP, and 5-HT in the submandibular gland, as well as the expression of AQP3, VIP, and 5-HT in the colon. The fecal samples in each group were determined by LC-MS. Multivariate statistical analysis and Pearson correlation coefficient were used for screening the differential metabolites and metabolic pathways in alleviating dryness of RAF. The results indicated that both RAF and PAF showed certain dryness, and the dryness of RAF was more significant. Moreover, PAF could alleviate dryness of RAF to a certain extent by reducing the water intake, fecal water content, and the expression of AQP3, VIP, and 5-HT in the colon and increasing the salivary secretion and the levels of AQP5, VIP, and 5-HT in the submandibular gland. According to the analysis of fecal metabolomics, 99 and 58 metabolites related to dryness were found in RAF and PAF respectively, where 16 of them played an important role in alleviating dryness of RAF. Pathway analysis revealed that the mechanism of PAF in alleviating dryness of RAF was presumably related to the regulation of riboflavin metabolism, purine metabolism, arginine biosynthesis, pyrimidine metabolism, alanine metabolism, aspartate metabolism, glutamate metabolism, and retinol metabolism pathways. This study suggested that PAF might alleviate dryness of RAF by affecting the metabolic levels of the body, which provides a new basis for further clarifying the processing mechanism of PAF.

Colorimetric multi-channel sensing of metal ions and advanced molecular information protection based on fish scale-derived carbon nanoparticles.

Some nanosystems based on carbon nanomaterials have been used for fluorescent chemical/biosensing, elementary information processing, and textual coding. However, little attention has been paid to utilizing biowaste-derived carbon nanomaterials for colorimetric multi-channel sensing and advanced molecular information protection (including text and pattern information). Herein, fish scale-derived carbon nanoparticles (FSCN) were prepared and used for colorimetric detection of metal ions, encoding, encrypting and hiding text- and pattern-based information. The morphology and composition of FSCN were analyzed by TEM, XRD, FTIR, and XPS, and it was found that the FSCN-based multi-channel colorimetric sensing system can detect Cr6+ (detection limit of 56.59 nM and 13.32 nM) and Fe3+ (detection limit of 81.55 nM) through the changes of absorption intensity at different wavelengths (272, 370, and 310 nm). Moreover, the selective responses of FSCN to 20 kinds of metal ions can be abstracted into a series of binary strings, which can encode, hide, and encrypt traditional text-based and even two-dimensional pattern-based information. The preparation of carbon nanomaterials derived from waste fish scales can stimulate other researcheres' enthusiasm for the development and utilization of wastes and promoting resource recycling. Inspired by this work, more researches will continue to explore the world of molecular information technology.

Comprehensive bioinformatics analysis and molecular validation of lncRNAs-mediated ceRNAs network in schizophrenia.

AIMS: The present study aimed to investigate how Schizophrenia (SCZ)-specific long non-coding RNAs (lncRNAs) served as competing endogenous RNAs (ceRNAs) to modulate the biological functions and pathways involved in the pathogenesis of SCZ. MAIN METHODS: Microarray dataset (GSE54913) was obtained from Gene Expression Omnibus (GEO) database. Differently expressed (DE) lncRNAs and mRNAs were identified by "limma" package. The binding miRNAs of lncRNAs and target mRNAs of shared miRNAs were predicted by miRcode, miRDB, miRTarbase and targetscan databases. Following the ceRNAs theory, interaction network was established and visualized with the cytoscape. Functional enrichment analysis uncovered the concentrated functions and signaling pathways that may be associated with SCZ progression. Protein-protein interaction (PPI) analysis was utilized to determine hub genes. Quantitative real-time PCR (qRT-PCR) and receiver operating characteristic curve (ROC) were performed to evaluate the expression and diagnostic value of ceRNAs members, respectively. KEY FINDINGS: DElncRNAs and DEmRNAs were initially screened from GSE54913 to construct the SCZ-related ceRNAs network with 42 nodes and 53 edges. Functional enrichment analysis revealed that ceRNAs members appeared to be highly correlated with transcription factor activation, cell replication and tumor-related pathways. Once validated, a significant ceRNAs subnetwork was proposed as being implicated in the pathogenesis of SCZ. ROC analysis indicated that SCZ-related ceRNAs members may be sensitive diagnostic biomarkers for SCZ. SIGNIFICANCE: The significant SCZ-related ceRNAs subnetworks (lncRNA-C2orf48A/hsa-miR-20b-5p,-17-5p/KIF23, FOXJ2) may represent promising predictive and diagnostic biomarkers and provide novel insights into the mechanism by which lncRNAs act as microRNA sponges and contribute to the pathogenesis of SCZ.

Impact of intestinal microbiota on metabolic toxicity and potential detoxification of amygdalin.

Amygdalin (Amy) is metabolized into cyanide in vivo, which may lead to fatal poisoning after oral administration. The defense mechanisms against toxic cyanide have not yet been adequately studied. In this study, comparative toxicokinetics study of Amy was performed in normal and pseudo germ-free rats. The efficiency of cyanide release was significant higher in normal group when given a single oral dose of 440 mg/kg (50% median lethal dose). Thiocyanate, the detoxification metabolite, was firstly detected in feces, caecum, and intestinal microbiota incubation enzymic system. The results suggest intestinal microbiota is involved in bidirectional regulation of toxicity and detoxification of Amy. We further identified the species related to cyanogenesis of Amy with metagenomic sequencing, such as Bifidobacterium pseudolongum, Marvinbryantia formatexigens, and Bacteroides fragilis. Functional analysis of microbiota reveals the detoxification potential of intestinal microbiota for cyanide. Sulfurtransferase superfamily, such as rhodanese, considered as main detoxification enzymes for cyanide, are largely found in Coriobacteriaceae bacterium, Butyricicoccus porcorum, Akkermansia muciniphila, etc. Besides, cyanoamino acid metabolism pathway dominated by Escherichia coli may contribute to the detoxification metabolism of cyanide. In summary, intestinal microbiota may be the first line of defense against the toxicity induced by Amy.

Hydroxychloroquine attenuates autoimmune hepatitis by suppressing the interaction of GRK2 with PI3K in T lymphocytes.

Hydroxychloroquine (HCQ) is derivative of the heterocyclic aromatic compound quinoline, which has been used for the treatment of autoimmune diseases. The central purpose of this study was to investigate therapeutic effects and inflammatory immunological molecular mechanism of HCQ in experimental autoimmune hepatitis (AIH). Treatment with HCQ ameliorated hepatic pathologic damage, inflammatory infiltration, while promoted regulatory T cell (Treg) and down-regulated CD8+T cell differentiation in AIH mice induced by S-100 antigen. In vitro, HCQ also suppressed pro-inflammatory cytokine (IFN-γ, TNF-α, and IL-12) secretion, promoted anti-inflammatory cytokine (TGF-ß1) secretion. HCQ mainly impaired T cell lipid metabolism but not glycolysis to promote Treg differentiation and function. Mechanistically, HCQ down-regulated GRK2 membrane translocation in T cells, inhibited GRK2-PI3K interaction to reduce the PI3K recruiting to the membrane, followed by suppressing the phosphorylation of PI3K-AKT-mTOR signal. Pretreating T cells with paroxetine, a GRK2 inhibitor, disturbed HCQ effect to T cells. HCQ also reversed the activation of the PI3K-AKT axis by 740 Y-P (PI3K agonist). Meanwhile, HCQ inhibited the PI3K-AKT-mTOR, JAK2-STAT3-SOCS3 and increased the AMPK signals in the liver and T cells of AIH mice. In conclusion, HCQ exhibited specific and potent therapeutic effects on AIH and attendant liver injury, which was attributed to HCQ acted on GRK2 translocation, inhibited metabolism-related PI3K-AKT and inflammation-related JAK2-STAT3 signal in T lymphocytes, thereby modulating lipid metabolism of T cell function to regulate Treg differentiation and function.

PROK2, HRNR, and FIG4 as potential genetic biomarkers of high bleeding propensity in East Asian patients with acute coronary syndrome using ticagrelor.

STUDY OBJECTIVE: East Asians have a higher risk of bleeding than Europeans when treated with ticagrelor. This study aimed to explore genetic indicators related to the high bleeding propensity in East Asian patients with acute coronary syndrome (ACS) using ticagrelor. DESIGN: A multicenter prospective cohort study. SETTING: Four sub center hospitals participating the study. PATIENTS: Between March 2018 and July 2021, 208 patients with ACS were administered ticagrelor and underwent genetic testing. INVERTENTION: Patients were enrolled and followed up for bleeding events for 12 months. Single-nucleotide polymorphisms (SNPs) were detected using whole-exome sequencing. SNPs significantly associated with cumulative bleeding events within 1-, 6-, and 12-month follow-ups were selected (p < 0.01). Among these, SNPs showing a difference of ≥2 fold in their distribution frequency among East Asians and Europeans were selected. MEASUREMENTS AND MAIN RESULTS: Among all patients, 96.60% received ticagrelor plus aspirin or cilostazol, and 42.3% suffered from bleeding events during 12-month follow-up. Furthermore, 22 SNPs of 15 genes were found to have a significant association with cumulative bleeding events within 1-, 6-, and 12-month follow-ups. Among these SNPs, FIG4 rs2295837 (A>T) variant had the strongest association with bleeding events within 1 month (p = 1.28 × 10-4 ), with an increased risk of bleeding in T allele carriers (odds ratio [OR]: 3.07, 95% confidence interval [CI]: 1.68-5.63). PROK2 rs3796224 (C>T) variant was most strongly associated with cumulative bleeding events within 6 months (p = 4.57 × 10-4 ) with an increased risk of bleeding in T allele carriers (OR: 2.16, 95% CI: 1.20-3.89). Moreover, HRNR rs6662450 (C>T) variant showed the strongest relation with cumulative bleeding events within 12 months (p = 4.86 × 10-4 ) with a reduced risk of bleeding in T allele carriers (OR: 0.48, 95% CI: 0.24-0.95). CONCLUSION: Fifteen genes, including PROK2, HRNR, and FIG4, were potential biomarkers of high bleeding propensity in East Asian patients with ACS using ticagrelor.

A Dual-Input Neural Network for Online State-of-Charge Estimation of the Lithium-Ion Battery throughout Its Lifetime.

Online state-of-charge (SOC) estimation for lithium-ion batteries is one of the most important tasks of the battery management system in ensuring its operation safety and reliability. Due to the advantages of learning the long-term dependencies in between the sequential data, recurrent neural networks (RNNs) have been developed and have shown their superiority over SOC estimation. However, only time-series measurements (e.g., voltage and current) are taken as inputs in these RNNs. Considering that the mapping relationship between the SOC and the time-series measurements evolves along with the battery degradation, there still remains a challenge for RNNs to estimate the SOC accurately throughout the battery's lifetime. In this paper, a dual-input neural network combining gated recurring unit (GRU) layers and fully connected layers (acronymized as a DIGF network) is developed to overcome the above-mentioned challenge. Its most important characteristic is the adoption of the state of health (SOH) of the battery as the network input, in addition to time-series measurements. According to the experimental data from a batch of LiCoO2 batteries, it is validated that the proposed DIGF network is capable of providing more accurate SOC estimations throughout the battery's lifetime compared to the existing RNN counterparts. Moreover, it also shows greater robustness against different initial SOCs, making it more applicable for online SOC estimations in practical situations. Based on these verification results, it is concluded that the proposed DIGF network is feasible for estimating the battery's SOC accurately throughout the battery's lifetime against varying initial SOCs.

Genetic Polymorphisms in CYP2C19 Cause Changes in Plasma Levels and Adverse Reactions to Anlotinib in Chinese Patients With Lung Cancer.

Background: Anlotinib is a small molecular multi-targeting tyrosine kinase inhibitor. Growing evidence indicates that treatment efficacy, and toxicity varies considerably between individuals. Therefore, this study aimed to investigate the relationship between cytochrome P450 (CYP450) gene polymorphisms, drug concentrations, and their adverse reactions in anlotinib-treated patients with lung cancer. Methods: We enrolled 139 patients with lung cancer, treated with anlotinib. Twenty loci in the following five genes of the CYP450 family were genotyped: CYP450 family 3 subfamily A member 5 (CYP3A5), 3 subfamily A member 4 (CYP3A4), 2 subfamily C member 9 (CYP2C9), 2 subfamily C member 19 (CYP2C19), and 1 subfamily A member 2 (CYP1A2). Data on adverse reactions were collected from patients, and plasma anlotinib concentrations were measured. Results: There were significant variances in plasma trough concentration (3.95-52.88 ng/ml) and peak plasma concentration (11.53-42.8 ng/ml) following administration of 8 mg anlotinib. Additionally, there were significant differences in the plasma trough concentration (5.65-81.89 ng/ml) and peak plasma concentration (18.01-107.18 ng/ml) following administration of 12 mg anlotinib. Furthermore, for CYP2C19-rs3814637, the peak plasma concentrations of mutant allele T carriers (TT+CT) were significantly higher than those of wildtypes (CC). For CYP2C19-rs11568732, the peak plasma concentrations of the mutant allele G carriers (GT+GG) were significantly higher than those of the wild-type (TT). More importantly, the incidence rates of hypertension and hemoptysis (peripheral lung cancer) with TT+CT in rs3814637 and GT+GG in rs11568732 were significantly higher than those with CC and TT. Conclusions: The plasma trough and peak concentrations varied significantly for both 8 and 12 mg of anlotinib. Single-nucleotide polymorphisms in CYP2C19 are significantly associated with hypertension, hemoptysis, and anlotinib peak concentrations. Polymorphisms in CYP450 may explain inter-individual differences in anlotinib-related adverse reactions.

A Digital Twin-Driven Life Prediction Method of Lithium-Ion Batteries Based on Adaptive Model Evolution.

Accurate life prediction and reliability evaluation of lithium-ion batteries are of great significance for predictive maintenance. In the whole life cycle of a battery, the accurate description of the dynamic and stochastic characteristics of life has always been a key problem. In this paper, the concept of the digital twin is introduced, and a digital twin for reliability based on remaining useful cycle life prediction is proposed for lithium-ion batteries. The capacity degradation model, stochastic degradation model, life prediction, and reliability evaluation model are established to describe the randomness of battery degradation and the dispersion of the life of multiple cells. Based on the Bayesian algorithm, an adaptive evolution method for the model of the digital twin is proposed to improve prediction accuracy, followed by experimental verification. Finally, the life prediction, reliability evaluation, and predictive maintenance of the battery based on the digital twin are implemented. The results show the digital twin for reliability has good accuracy in the whole life cycle. The error can be controlled at about 5% with the adaptive evolution algorithm. For battery L1 and L6 in this case, predictive maintenance costs are expected to decrease by 62.0% and 52.5%, respectively.