Ccn2. Surprisingly, VERT co-treatment also prevented mTOR and survival signaling pathway alterations following chemotherapy exposure. These results suggest an interaction between the two main signaling pathways regulating follicle activation and a protective effect of VERT on 4HC-induced DNA damage.The Hippo pathway plays a crucial role in the regulation of follicular activation, which constitutes the first step of the folliculogenesis process. Disruption of this pathway occurs in several non-physiological contexts, after fragmentation for ovarian tissue cryopreservation procedures or chemotherapy exposure, leading to massive follicular growth and depletion. This study aimed to investigate the effect of controlling the Hippo pathway using verteporfin (VERT) during in vitro ovarian culture and to evaluate its potential preventive effects on chemotherapy-induced follicle activation using a mouse model. After exposure of cut ovaries to different concentrations of VERT for 3 h, a dose-dependent effect of VERT was observed that reached significant inhibition of YAP activity at 3 µmol/L. To assess the potential effect of controlling chemotherapy-induced Hippo pathway disruption, whole mouse ovaries were exposed to VERT alone or as a co-treatment with 4-hydroperoxycylophosphamide (4HC). VERT co-treatment prevented chemotherapy-induced YAP activation but had a limited impact on downstream effector gene,  This process is irreversible, leading to follicular atresia during growth or ovulation2. Adequate regulation of this process is crucial to maintenance of the follicular pool during reproductive life, but it can be disturbed by several non-physiological processes such as in vitro culture or chemotherapy exposure3. Follicle activation is regulated by two main intrafollicular signaling pathways, the phosphatidylinositol 3-kinase/protein kinase B/mammalian target of rapamycin (PI3K/AKT/MTOR) pathway and the Hippo pathway3. Studies have shown that the PI3K/AKT/MTOR signaling pathway plays a critical role in the control of activation and survival of primordial follicles4. Following KIT LIGAND (KL)-cKIT interactions, PI3K phosphorylates phosphatidylinositol-4,5-bisphosphate (PIP2), increasing membrane levels of phosphatidylinositol-3,4,5-trisphosphate (PIP3), and subsequently, inducing the activation of AKT5. These events are reversed by the dephosphorylation of PIP3 by the phosphatase and tensin homolog (PTEN) protein6. AKT promotes cell survival, and stimulates protein synthesis and cell growth, by activating the MTOR pathway through the inhibition of the heterotrimeric tuberous sclerosis complex 1/2 (TSC1/2)8. Activated MTOR Complex 1 (MTORC1) leads to the activation of downstream regulators, including 40S ribosomal protein S6 (RPS6) and eukaryotic translation initialization factor 4E (EIF4E), which directly affect protein synthesis and cell growth10.The primordial follicle (PF), that constitutes the ovarian reserve, is activated during the first step of follicular growth by transformation of granulosa cells (GCs) from flattened to cuboidal forms to reach the primary follicle stage11. This inhibitory pathway is a kinase-regulated network that includes the mammalian Ste20-like kinase 1/2 (MST1/2) and the large tumor suppressor homolog 1/2 (LATS1/2), the downstream effectors yes-associated protein (YAP) and transcriptional coactivator with PDZ-binding motif (TAZ)12. Hippo pathway activation results in the phosphorylation of the YAP/TAZ complex, which leads to its cytoplasmic retention and degradation. Consequently, YAP/TAZ nuclear activity is inhibited, and genes involved in cell proliferation are downregulated13. Conversely, disruption of the Hippo pathway leads to the accumulation of unphosphorylated YAP/TAZ in the nucleus which, in turn, stimulates the expression of downstream genes such as connective tissue growth factors (Ctgf or Ccn) and baculoviral inhibitors of apoptosis containing repeats (Birc)11. Moreover, ovary fragmentation leads to the polymerization of actin, resulting in the nuclear translocation of YAP followed by the production of CCN growth factors and anti-apoptotic BIRC, leading to stimulation of follicle growth14.The Hippo pathway is a highly conserved signaling pathway that controls organ development through regulation of cell proliferation and apoptosisBIRC1 and CCN2 during ovarian tissue processing for cryopreservation15. In human and mouse models, we also reported an increase in follicle activation in vitro through the upregulation of both the PI3K/AKT/MTOR and Hippo pathways16. Furthermore, a significant decrease of YAP activity was observed after inhibition of mTORC1 in mouse ovaries exposed to chemotherapy, suggesting a possible interaction between the two pathways16.Previous work from our laboratory highlighted the upregulation of Hippo pathway effectors 18. Therefore, regulation of the recruitment of quiescent follicles is crucial to preserving ovarian reserve but may further affect oocyte quality and follicular development. Moreover, inhibition of the PI3K/AKT/MTOR pathway has a limited impact on primordial follicle activation19, suggesting a possible interaction between signaling pathways in the ovary that may contribute to the complexity of follicle recruitment and growth in vitro.The accelerated follicular activation observed under non-physiological conditions may compromise ovarian reserve or oocyte quality14. VERT has been identified as an inhibitor of the interaction of YAP with transcriptional enhancer factor (TEAD) which, in turn, blocks transcriptional activation of targets downstream of YAP20. One study has shown that pretreatment of mice with VERT blocks Ccn2 expression induced by ovarian fragmentation14. Although not quantified, another study has reported that follicular growth was inhibited after incubating mouse ovaries for 7 days with VERT, suggesting that pharmacological inactivation of YAP in the mouse ovary disrupts follicle development12. However, this study reported a high percentage of apoptosis with VERT, highlighting the potential toxicity of this drug.Considering the limited effect of MTORC1 inhibition, the use of verteporfin (VERT) to inhibit the activity of YAP/TAZ might be an interesting approach since it has been demonstrated that this inhibition reduces follicle activation and growth in mouse ovariesThe aim of this study was to assess the efficacy and toxicity of in vitro exposure to VERT during the early phase of folliculogenesis using a mouse model. We also assessed the potential protective effect of this inhibitor on the chemotherapy-induced impairment of signaling pathways governing activation of ovarian follicles. Finally, we evaluated the possible interaction between the Hippo and PI3K/AKT/MTOR signaling pathways during in vitro follicle activation.16. Other studies have also shown that murine ovarian fragmentation leads to disruption of the Hippo pathway, resulting in upregulation of its downstream effectors, such as Ccn2,3,5,6 and Birc1,7 from 2 h post-sectioning14. In this study, Hippo pathway disruption was confirmed after 3 h of in vitro culture with a higher expression of Ccn2 (P < 0.001) and Cmyc (P < 0.001) in cut ovaries compared to whole controls, while Birc5 expression was not affected following sectioning (P = 0.437) , while Tsc1 (P = 0.946) and Mtor (P = 0.095) expression remained stable among groups . Similarly, gene expression levels of Bcl-2–associated X (Bax) (P = 0.096) and B-cell lymphoma 2 (Bcl2) (P = 0.095) were similar among groups .The impacts of in vitro culture on follicle activation were previously demonstrated using newborn mouse ovaries and human cortical tissuesCcn2 expression was observed in ovaries treated with 1.5 and 3 µmol/L VERT compared to controls  and to 0.2 µmol/L VERT  conditions   but the difference did not reach significance in the other concentrations. Tsc1 and Mtor levels were not significantly different among cultured groups . Expression levels of Bax and Bcl2 were stable following VERT treatment, irrespective of the concentration .To assess the ability of VERT to prevent the impact of sectioning, half-cut ovaries were cultured for 3 h and with different concentrations of VERT . A significant decrease in 16. To assess the potential effect of VERT during 4HC exposure to control signaling pathway alterations, the dose of 3 µmol/L of VERT was used. To preserve 4HC-induced follicle activation and limit its gonadotoxicity, a concentration of 10 µmol/L of chemotherapeutic agent was chosen for this study based on previous experiments21. The effect of VERT on chemotherapy-induced signaling pathways was investigated at 24 and 48 h of culture with 24-h exposure to treatments.Our previous study showed that 4HC exposure impacts both PI3K and Hippo signaling pathways in neonatal mouse ovaries, leading to follicle activationP = 0.002) and 48 (P = 0.004) hours of culture   and co-treatment with VERT significantly decreased its expression compared to 4HC alone (P = 0.006) , but no preventive effect of the co-treatment was observed  Fig. a,b. ExpoKl, Tsc1, and Mtor and observed no variations among the different conditions at both analysis times . The impact of co-treatment on PI3K proteins was then evaluated by western blotting for AKT and RPS6, as well as their phosphorylated forms . Ovaries exposed to 4HC alone had significantly higher pRPS6/RPS6 levels compared to controls at 24 (P = 0.02) and 48 (P = 0.012) hours of culture. When ovaries were exposed to VERT co-treatment with 4HC, we observed a significant decrease in this ratio compared to the 4HC alone condition    and 4HC (P < 0.001) conditions. VERT co-treatment appeared to prevent the 4HC-induced proliferation (P = 0.001)  . DNA damage staining analysis showed no deleterious impacts of VERT exposure in comparison with control conditions at both culture timepoints , VERT significantly prevented this effect (P = 0.002)  and VERT (P = 0.017) conditions  Fig. c,d. Howe P = 0.01 conditio16. Using a newborn mouse model, our laboratory reported that organ removal is sufficient to disrupt the Hippo signaling pathway16. Fragmentation of mouse ovaries has been reported to disrupt the Hippo pathway through modulation of actin polymerization and leads to increased expression of Ccn and Birc genes from 2 h post-sectioning14. Moreover, we have also reported that sectioning impairs both PI3K and Hippo signaling after 4 h of culture16. Non-physiological activation can compromise vital oocyte–somatic cell contacts and, consequently, oocyte quality17. A possible option to reduce this effect is the use of inhibitors to control signaling pathways involved in follicle activation. Therefore, we investigated the efficacy of pharmacological control of the Hippo signaling pathway using VERT, an inhibitor of YAP/TAZ activity22. Cell culture studies have reported significant repression of Ccn2 gene expression following exposure to 0.25 µmol/L VERT23. Therefore, we first exposed cut ovaries to 0.2, 1.5, and 3 µmol/L of VERT for 3 h. Following ovarian sectioning, we confirmed a disruption of Hippo signaling and reported a significant downregulation of Ccn2 and Cmyc gene expression in cut ovaries exposed to the highest tested concentration of VERT. Consistent with our previous study, Birc5 gene expression did not vary following fragmentation or VERT treatment, suggesting that this gene might be controlled by other transcription factors or signaling pathways in the ovary16. Notably, YAP/TAZ are known to also interact with mothers against decapentaplegic homologs (SMADs) and p73 factors26. Moreover, redundancies have been reported between Hippo activity and other signaling pathways such as the nuclear factor kappa-light-chain-enhancer of activated B cells (NFKB) pathway and the WNT-BETA-CATENIN pathway29. Interestingly, we observed that sectioning induced a significantly higher level of Kl that appeared to be prevented in VERT-treated ovaries, suggesting a potential link between the Hippo and PI3K pathways through gene modulation of the major trigger of follicle activation30.One of the main concerns regarding ovarian tissue processing remains the uncontrolled massive follicle activation associated with the process. Previous studies in mouse and human models have reported an increase in growing follicles following processing and in vitro cultureCcn2 expression was higher after chemotherapy exposure and downregulated when VERT was used in co-treatment with 4HC compared to 4HC alone. Interestingly, this preventive effect appeared to be present after 24 h of culture but was moderated at 48 h of culture, suggesting that VERT is poorly effective when culture-induced follicular activation has already occurred. We also reported that exposure to VERT alone did not impact the Hippo pathway when the ovaries were not fragmented. These results suggest that VERT activity is effective when Hippo is disrupted, due to sectioning or to 4HC exposure and to the culture. Studies performed in mouse models have reported an increased activation of PI3K signaling after cyclophosphamide injection or 4HC exposure32. Our previous study reported an induction of pAKT and pRPS6 after 4 h of exposure to 4HC at 3 and 20 µmol/L, that decreased with time, with pRPS6 levels still slightly increased after 48 h of culture16. Consistent with our previous report, we observed an increase in RPS6 activation in ovaries exposed to 10 µmol/L 4HC compared to controls at both culture timepoints.We also investigated the effect of chemotherapy on the two signaling pathways and the possible benefit of co-treatment with VERT. Hippo pathway analyses revealed an increase in YAP levels following 4HC exposure, as previously reported, and a significant prevention of this increase when ovaries when co-treated with VERT. Consistent with the protein activity, 33. Notably, we reported in an in vitro mouse model that co-treatment with a mTORC1 inhibitor, everolimus, prevents Hippo pathway disruption following chemotherapy exposure16. Interestingly here, VERT co-treatment resulted in a significant decrease in pRPS6/RPS6 levels in comparison with the 4HC condition. This result suggests a second interaction mediated by Hippo effectors on PI3K signaling, indicating a potential bi-directional crosstalk between these pathways.Previous studies have suggested regulation of Hippo signaling by PI3K pathway components34. Moreover, the gonadotoxicity of 4HC on mouse ovarian tissue has been well demonstrated31. Therefore, we also assessed the impact of VERT treatment on follicle growth and survival in our short-culture system. We first observed an acute induction of proliferation among healthy follicles following 4HC exposure, prevented by co-treatment with VERT. We then confirmed that DNA damage levels increased significantly after exposure to 4HC but only observed a slight protective effect of the co-treatment after 48 h of culture. This prevention of DNA damage appeared to be limited as the level of DNA breaks remained higher in the 4HC-treated condition than in the control condition. Altogether, these two results suggest an acute and deleterious follicle entrance into growth phase following culture with 4HC, that can partially be prevented with VERT.The PI3K and Hippo signaling pathways are known to control cellular fate and survivalOverall, this in vitro culture study confirmed that the PI3K and Hippo pathways are dysregulated by ovarian tissue sectioning and chemotherapy exposure. Our results underscore the fact that VERT treatment can control Hippo pathway disruption following sectioning, and can partially prevent the impairment of the MTOR and Hippo pathways following chemotherapy exposure at the concentrations we used. Our data further support an interaction between the two pathways to control follicle activation in non-physiological conditions. Despite the fact that a limited protective effect of VERT against chemotherapy damage was observed, the use of this inhibitor could be a promising approach in association with other inhibitors to effectively control both signaling pathways. The limited time of exposure and culture in our study should be considered and results should be interpreted with caution as the effect seems to be transitory.Adult CBA/Ca male and C57Bl/6 female mice from Janvier Laboratory were housed in a controlled environment . Experiments were performed using F1 mice (CBA/Ca x C57Bl/6) at day 3 or 4 of age. All experimental procedures were carried out with prior agreement of the Animal Welfare Ethics Committee of the Medicine Faculty at the Université libre de Bruxelles .16. To assess VERT efficacy on YAP function inhibition, whole and half-cut ovaries were exposed to 0.2, 1.5, and 3 µmol/L of VERT (Sigma) and cultured in vitro for 3 h. The effect of VERT on chemotherapy-induced follicle activation was evaluated after 24 or 48 h of culture. Intact ovaries were exposed for 24 h to 3 µmol/L of VERT alone or as a co-treatment with 4-hydroperoxycyclophosphamide , an active metabolite of cyclophosphamide, used at 10 µmol/L. For experiments at 48 h of culture, VERT and/or 4HC were added at 24 h with media replacement. During each experiment, a control condition was obtained by exposing ovaries to 0.1% dimethylsulfoxide (Sigma).Female post-natal day 3 or 4 (PND3/4) mice were sacrificed by decapitation and ovaries were collected and processed as previously described16. At least three sections per ovary from three different experiments were quantified. Morphologically healthy follicles containing at least one stained granulosa cell were considered as positive and reported on the total number of healthy follicles (n ≥ 180 follicles) per ovary. DNA fragmentation was assessed by TdT-mediated dUTP-biotin nick-end labelling (TUNEL) staining, using the In Situ Cell Death Detection Kit (Roche). Sections were deparaffinized, rehydrated, permeabilized with 20 μg/ml proteinase K (Qiagen) in 10 mmol/L Tris pH 7.4 and incubated, according to the manufacturer’s instructions, with TUNEL reagents and counterstained with 1 µg/mL Hoechst. Positive and negative controls were obtained by treating sections with 50 IU/mL DNase I (Invitrogen) and enzyme-free solution, respectively. Follicles were considered positive when containing at least one stained granulosa cell or a stained oocyte. To quantify apoptosis within ovaries, the ratio of positive follicles stained for TUNEL to the total number of follicles (n ≥ 40 follicles) per slide was calculated.Ovaries were fixed in 4% paraformaldehyde at 4 °C for 2 h after the culture. Samples were dehydrated in increasing concentrations of ethanol before paraffin embedding, then serially sectioned. Cell proliferation was analyzed by KI67 staining , as previously described16. Protein bands were revealed by chemiluminescence using SuperSignal West Femto Maximum Sensitivity Substrate (ThermoFisher) and ChemiDoc XRS + instrument (Bio-Rad). Visualization and quantification of protein bands were performed with Image Lab 3.0 software (Bio-Rad) and ImageJ software, respectively.For protein expression and quantification, 8 mouse ovaries were required per condition and protein lysates were obtained by mixing the ovaries with Laemmli buffer solution containing 10% glycerol, 5% beta-mercaptoethanol, 62.5 nmol/L Tris–HCl pH 6.8, 2% sodium dodecyl sulfate (SDS), and protease (Thermo Fisher) and phosphatase (Roche) inhibitors. A 5%-12% SDS–polyacrylamide gel electrophoresis was performed to separate proteins, followed by transfer to nitrocellulose membranes (Bio-Rad). After blocking of non-specific sites, membranes were incubated with 1:500 targeted primary antibodies  overnight and with 1:1000 horseradish peroxidase-conjugated secondary antibody35. Target gene transcripts were quantified using the comparative cycle threshold (Ct) method (ΔΔCt) through normalization of the mean Ct with housekeeping genes level  and control condition. Gene expression levels were evaluated based on the foldchange (2−ΔΔCt).RNA extraction was performed using RNAqueous-Micro Total RNA Isolation Kit (Life Technologies) according to the manufacturer’s instructions. RNA concentration was determined by spectrophotometry using NanoDrop 2000 (Thermo Scientific) and cDNA synthesis was carried out with GoScript Reverse Transcription Mix (Promega). Real-time qPCR was performed in 96-well plates on a 7500 Real-Time PCR System (Applied Biosystems) using Power SYBR Green Master Mix (Applied Biosystems). Primers used were previously validatedP-value < 0.05.All experiments were conducted at least three times. Depending on normality and homogeneity of variances of our data, parametric and non-parametric tests were performed by Student’s t-test or one-way ANOVA, and Wilcoxon-Mann–Whitney or Kruskal Wallis test, respectively. When required, multiple comparisons were performed with Tukey correction. Variables tested with parametric tests are shown with the mean and the standard deviation (SD). Median and interquartile range (IQR) are represented when non-parametric tests were performed. Statistical analyses were performed using SPSS Statistics 25 (IBM) and graphs were designed with Prism 6 (GraphPad). Statistical significance was considered when Supplementary Information.


Corona virus disease 2019 (COVID-19) is prone to induce multiple organ damage. The kidney is one of the target organs of SARS-CoV-2, which is susceptible to inducing acute kidney injury (AKI). Huanglian Jiedu Decoction (HLJDD) is one of the recommended prescriptions for COVID-19 with severe complications. We used network pharmacology and molecular docking to explore the therapeutic and protective effects of HLJDD on COVID-19-associated AKI. Potential targets related to “HLJDD,” “COVID-19,” and “Acute Kidney Injury/Acute Renal Failure” were identified from several databases. A protein–protein interaction (PPI) network was constructed and screened the core targets according to the degree value. The target genes were then enriched using gene ontology and Kyoto Encyclopedia of Genes and Genomes. The bioactive components were docked with the core targets. A total of 65 active compounds, 85 common targets for diseases and drugs were obtained; PPI network analysis showed that the core protein mainly involved JUN, RELA, and AKT1; functional analysis showed that these target genes were mainly involved in lipid and atherosclerosis signaling pathway and IL-17 signal pathway. The results of molecular docking showed that JUN, RELA, and AKT1 had good binding activity with the effective chemical components of HLJDD. In conclusion, HLJDD can be used as a potential therapeutic drug for COVID-19-associated AKI. EvidencIn the published pneumonia diagnosis and treatment protocol for novel coronavirus infection , Huanglian Jiedu Decoction (HLJDD) was officially formally included . This prNetwork pharmacology is based on the “disease–gene–target–drug” interaction network . ThroughIn this study, network pharmacology and molecular docking technologies were utilized to investigate the mechanism of HLJDD in the treatment of COVID-19-associated AKI and to provide a theoretical basis for its clinical application.22.1http://ibts.hkbu.edu.hk/LSP/tcmsp.php). For the Database, TCMSPTM was selected and the search entry was set to herb name. In the screening of the active chemical components of HLJDD, the drug composition of the compound was entered under the search entry: “Huanglian (Coptidis Rhizoma),” “Huangqin (Scutellariae Radix),” “Huangbo (Phellodendri Chinensis Cortex),” and “Zhizi (Gardeniae Fructus).” The screening conditions were set as oral bioavailability ≥30% and drug-likeness (DL) ≥0.18 [https://www.drugbank.ca/).The active chemical components and drug targets of HLJDD were obtained through the TCM System Network Pharmacology Database  (http://L) ≥0.18 . DL is cL) ≥0.18  (https:/2.2https://www.genecards. org/), Online Mendelian Inheritance in Man [https://www.ncbi.nlm.nih.gov/omim), TTD database (http://db.idrblab.net/ttd/), “Acute Kidney Injury (AKI)/Acute Renal Failure (ARF)” and “COVID-19” were searched as the keywords. The disease data were merged and de-duplicated, and the intersection of the two disease targets after merging and de-duplication was performed with the help of the Venn package in R software 3.6.0 [Based on the Genecards Human Genetic Database  , “homo sapiens” was specified as the present setting, and the confidence level was set to 0.4. The output was saved as a TSV file and loaded into Cytoscape 3.8.0 to create a PPI network.To obtain the relationship on protein interactions, the cluster of targeted genes obtained from “1.3” was imported to the String database  after the GO function had been annotated. The required Biocmanager and other program packages were installed at the Bioconductor (https://www.bioconductor.org/) website. Bar plots and bubble charts were plotted.Enrichment analysis of GO and KEGG pathway analysis was performed. The GO enrichment analysis includes cell component (CC), molecular function (MF), and biological process (BP). The target proteins obtained after weight loss at the predicted target sites were imported into the GO database. A 2.7https://pubchem.ncbi.nlm.nih.gov/compound/) and exported to a ligand file in PDBQT format. Preparation of protein receptor file: based on the previous network pharmacology screening findings, the target protein’s 3D structure was obtained from the protein database (PDB) (http://www.rcsb.org/). Molecule ligands and protein receptors were saved in PDBQT format with AutoDock. To specify the molecular docking range, the PDBQT structures of the receptor and ligand were imported into AutoDock. Finally, molecular docking was performed using AutoDock and R software to select the binding mode with the lowest free energy. Binding energy less than 0 indicates that the ligand and receptor molecules can bind spontaneously. The more negative the energy the better the ligand.Preparation of molecule ligands file: according to the core protein obtained from “1.5” by “1.4” chemical composition–target network diagram to match the corresponding ligand, 3D crystal structure of drug molecule structures were obtained from PubChem databases  website. The compound drug targets and disease targets obtained in “1.1” and “1.2” were imported into the R software, and the target genes were obtained and Venn diagrams were drawn. A total of 85 drug–disease target genes were obtained  with a greater degree of significance, and they were identified as potentially relevant genes involved in the processes of HLJDD therapy of COVID-19-associated AKI .3.6p-value of each item and the number of genes enriched on them. The enrichment conditions were p-value <0.05 and Q-value <0.05, and the rest was set by default. The GO analysis had shown 1,714 entries on BP, 59 entries on CC, and 160 entries on MF with p < 0.05 . The top 20 pathways were selected, and the bubble plots were plotted according to the p-value of each pathway and the number of genes enriched on it  screened in “2.5” were molecularly docked with the core components screened in “2.4.” It is generally believed that the drug molecules with binding energy less than −5.00 kJ/mol have better binding activity with the proteins . As show4COVID-19 pneumonia is still in the outbreak phase worldwide. In addition to causing respiratory symptoms, COVID-19-associated AKI is also a widespread concern. One study from the Journal of the American Society of Nephrology (2021) found that patients with COVID-19 pneumonia who were not hospitalized for treatment had a 23% increased risk of AKI within 6 months after comparing the physical status of 89,216 people who recovered from COVID-19 pneumonia to 1,637,467 people who were not infected . PatientThe National Health Commission and other health organizations in numerous locations have sequentially released a variety of diagnoses and treatment regimens since the COVID-19 pneumonia outbreak ,31. The The exact mechanism of COVID-19-associated AKI has not been fully understood –6. ThereAccording to “Novel Coronavirus Pneumonia Diagnosis and Treatment Plan ,” HLJDD is one of the prescriptions recommended for COVID-19 pneumonia that is in its most severe phases . The stuOur study suggests that HLJDD can treat disease through the effective active components acting on the genes of JUN, RELA, and AKT1 together. C-Jun can activate NLRP3 inflammatory vesicles, promote phosphorylation, and mediate inflammatory responses . RELA, aThe GO analysis suggested that cytokine and receptor binding and receptor ligand activity were the main biological processes. The KEGG pathway enrichment analysis showed that these overlapping genes were mainly enriched in pathways, such as lipid and atherosclerosis signaling pathway, IL-17 signaling pathway, and AGE-RACE signaling pathway. Previous studies have shown that atherosclerosis can cause luminal narrowing and a chronic inflammatory response in the vascular wall. The pathogenesis of COVID-19-associated AKI was based on damage to the pulmonary and renal capillaries, and abnormalities in lipid and atherosclerotic signaling pathways lead to acute inflammation of the pulmonary and renal capillaries and thus become the basis for pathogenesis. IL-17 can activate the NF-κB and MAPK pathways. Both NF-κB and MAPK are target genes for lL-17 to exert their pro-inflammatory effects, and the IL-17 signaling pathway plays a crucial role in host defense against microbes and the development of inflammatory diseases ,60. AGE-Quercetin and kaempferol were the main components in HLJDD, while JUN, RELA, and AKT1 were the core proteins in the PPI network. The molecular docking validation showed that the lowest binding energies of molecules to proteins were all less than −5.00 kJ/mol, indicating that both active ingredients had good binding activities with JUN, RELA, and AKT1, which was the same as the results of the previous study –65.5This study combined the network pharmacology and bioinformatics to systematically analyze and predict the compound–target pathway mechanism of the action of HLJDD for the treatment of COVID-19-associated AKI. The potential mechanisms of HLJDD in treating COVID-19-associated AKI may be related to anti-inflammation, lipid lowering, counteracting oxidative stress, promoting apoptosis of senescent cells, and repairing damage. These findings may provide potential references and theoretical aspects for the clinical application of HLJDD. However, the network pharmacology has limitations and the pathogenic mechanism of COVID-19-associated AKI is not fully understood at present. Further validations through experiments and clinical practice are needed. The challenge ahead is to translate these potential preclinical discoveries and develop effective drugs for the treatment of COVID-19 and its complications.ACE2angiotensin-converting enzyme IIAKIacute kidney injuryARFacute renal failureCOVID-19Corona virus disease 2019GOgene ontologyHLJDDHuanglian Jiedu DecoctionKEGGKyoto Encyclopedia of genes and genomesPPIprotein–protein interactionTCMtraditional Chinese medicine


Alzheimer’s disease (AD) is a common neurodegenerative disease having complex pathogenesis, approved drugs can only alleviate symptoms of AD for a period of time. Traditional Chinese medicine (TCM) contains multiple active ingredients that can act on multiple targets simultaneously. In this paper, a novel algorithm based on entropy and random walk with the restart of heterogeneous network (RWRHE) is proposed for predicting active ingredients for AD and screening out the effective TCMs for AD. First, Six TCM compounds containing 20 herbs from the AD drug reviews in the CNKI  are collected, their active ingredients and targets are retrieved from different databases. Then, comprehensive similarity networks of active ingredients and targets are constructed based on different aspects and entropy weight, respectively. A comprehensive heterogeneous network is constructed by integrating the known active ingredient-target association information and two comprehensive similarity networks. Subsequently, bi-random walks are applied on the heterogeneous network to predict active ingredient-target associations. AD related targets are selected as the seed nodes, a random walk is carried out on the target similarity network to predict the AD-target associations, and the associations of AD-active ingredients are inferred and scored. The effective herbs and compounds for AD are screened out based on their active ingredients’ scores. The results measured by machine learning and bioinformatics show that the RWRHE algorithm achieves better prediction accuracy, the top 15 active ingredients may act as multi-target agents in the prevention and treatment of AD, Danshen, Gouteng and Chaihu are recommended as effective TCMs for AD, Yiqitongyutang is recommended as effective compound for AD. Alzheimer’s disease (AD) is a persistent and irreversible neurodegenerative disease, whose main clinical features are progressive memory loss, cognitive declination, functional independence loss and so on . The patTraditional Chinese medicine (TCM) contains multiple active ingredients that can act on multi-targets simultaneously, some active pharmacological compounds of herbs have been proven to be applied to the treatment of many diseases . TCM hasWith the development of high-throughput biomedical data, network-based propagation methods are often used to predict associations among biological components. Random Walk is one of the typical methods based on network propagation. The traditional restart random walk (RWR) algorithm only carries out random walks in a single network , many scIn this paper, we develop a novel algorithm based on entropy and random walk with the restart of heterogeneous network (RWRHE) for predicting active ingredients associated with AD and screening out the effective TCMs for AD. Firstly, the similarity measures of active ingredients are calculated from two aspects, including the chemical structure and the Gaussian interaction profile kernel (GIP kernel) similarity . The simSix TCM compounds from the AD drug reviews in the CNKI  are selected, including Dangguishaoyaosan, Yigansan, Yiqitongyutang, Gubenjiannaoye, BKHJ TCM and Yizhi –20. A toThe datasets used in this study include active ingredients, targets and known active ingredient-target associations, which are collected from two databases: TCMSP and HERB (release 2021–01) . Targetsnr active ingredients and nt targets. The matrix Art represents known active ingredient-target associations, and its dimension is nr * nt. The value of Art is 1 if active ingredient i and target j have a known association, otherwise is 0.Let ri and rj is calculated by the Tanimoto score of the binary fingerprint vector [ri.The first similarity measure is calculated based on the chemical structure. The SMILES (Simplified Molecular Input Line Entry Specification) describes the chemical structure of active ingredients , which ct vector , the forc and d are the parameters. According to the previous research [d as log(9999), which represents L(0) = 0.0001. We set L(0.3) < 0.01, which determines c as -15. The improved similarity value Based on the finding that weak similarity between active ingredient pairs provides less information for prediction, the logistic function is used to convert those small similarity values into values close to zero and expand those large similarity values simultaneously . The impresearch , 30, we IP(ri) of active ingredient ri is a binary vector representing the known associations between the active ingredient ri and targets. The GIP kernel similarity between active ingredient ri and active ingredient rj is computed as follows:IP(ri) denotes the i-th row of the matrix Art. Based on the assumption that similar active ingredients tend to associate with similar targets, GIP kernel similarity is used to calculate the similarity between active ingredients. The interaction profile research . The simThe first target similarity measure is calculated based on the protein sequences, which can be retrieved from UniProt database. The Smith-Waterman local alignment algorithm  is used The second target similarity measure is based on the interaction confidence scores of the String database. The gene symbols of targets are entered into the String database, and the organisms are selected as "Homo sapiens". Then the interaction scores of target pairs are as elements of the second similarity matrix ti) is the set of neighbors of target ti, z are the common neighbors of targets ti and tj, k(z) is the degree of z. If two targets have more common neighbors and the degree of common neighbors is small, the similarity between them will be greater than 1, it will be replaced by 0.99.The third target similarity measure is calculated based on the common neighbors of targets. To filter out the target interaction relationships with low confidence, target pairs with interaction scores greater than 0.4 in the String database are regarded as neighbors. The contribution of the common neighbor targets with small degree is greater than that of the common neighbor targets with large degree, each common neighbor target is assigned a weight based on its degree value. The target similarity measure based on the common neighbors is defined as:IP(ti) of target ti is a binary vector representing the known associations between target ti and active ingredients. The GIP kernel similarity between targets ti and tj is computed as follows:IP(ti) denotes the i-th column of the matrix Art. Based on the assumption that similar targets tend to associate with similar active ingredients, the fourth target similarity measure based on GIP kernel is defined. The interaction profile research . The simDifferent similarity measures contain different similarity information and play different roles in measuring the similarity of node pairs. The information entropy is used to select similarity measures in the previous research . In thissij represents the similarity value between active ingredients i and j. k indicates the number of active ingredients. We average the entropy of all rows as the final average entropy value. The average entropy of the m-th similarity matrix is calculated as follows:For the m-th similarity matrix for active ingredients, the entropy ωrm represents the weight of the m-th similarity measure of active ingredients. In the same way, the average entropy and weight of each target similarity measure can be obtained. The average entropy and corresponding weights of the similarity matrix of two active ingredients and four targets are shown in The smaller the average entropy of the similarity matrix is, the less random information is delivered by the similarity measure. The similarity measure with small average entropy occupies a significant proportion of the comprehensive similarity measure. The average entropy of each similarity matrix is normalized after taking the reciprocal, then the weight of the m-th similarity measure is defined as follows:Sr, and the four similarity measures of targets are integrated into a comprehensive target similarity measure St, which are calculated as follows:ωr1 and ωr2 are the weights of active ingredient similarity matrix ωt1, ωt2, ωt3, ωt4 are the weights of target similarity matrix Finally, the two similarity measures of active ingredients are integrated into a comprehensive active ingredient similarity measure nr active ingredients. The comprehensive similarity Sr is the weight between active ingredients ri and rj. nt targets. The comprehensive similarity St is the weight between targets ti and tj.The similarity networks of active ingredients and targets are constructed based on the comprehensive similarity measures of active ingredients and targets. G is constructed based on the known active ingredient-target associations. V = {R, T} is the node set containing active ingredient nodes and target nodes. E = {Art} is the edge set. If active ingredient i and target j have a known association, the weight of edge between them is 1, otherwise is 0.In addition, the active ingredient-target bipartite graph The active ingredient-target heterogeneous network can be constructed by integrating the active ingredient similarity network, target similarity network, and the known active ingredient-target association network. The heterogeneous network is illustrated in Sr, the Laplacian normalization is divided into two steps:D is a diagonal matrix, and the elements of the main diagonal are the sum of corresponding rows of the matrix Sr, i.e Sr after Laplacian normalization is denoted as St after Laplacian normalization is denoted as Art of the active ingredient-target associations is normalized as follows:On the heterogeneous network, a bi-random walk algorithm is used to predict the active ingredient-target associations score matrix. As the previous research , the Lapα = 0.3 is the restart probability, which controls the probability for the walker staying at the starting node. leftRTt is the predicted association matrix between active ingredients and targets in the t-step iteration, leftRTt+1 − leftRTt| < 10−6, the resulted matrix is denoted leftRT.A random walk begins from an active ingredient, then traverses to other target nodes based on its associated targets. The probabilistic associations between the active ingredient and all targets are obtained. The left random walk is performed on the target similarity network to simulate this process:α = 0.3 is the restart probability, the right random walk stops until |rightRTt+1 − rightRTt| < 10−6, the resulted matrix is denoted rightRT. The final result is the average of the left and right random walk results, and the final predicted active ingredient-target association matrix is denoted as RT as follows:Likewise, a random walk starts from a target node, then traverses other active ingredient nodes based on its known associated active ingredients. Then another active ingredient-target association matrix is obtained. The right random walk is conducted on the active ingredient similarity network to mimic this process:RT is shown in The final matrix p0 of targets is constructed such that equal probability is assigned to each target seed, and probability 0 is assigned to other targets, with the sum of the probabilities equal to 1. Starting from the target seed node, according to the topological property of the target similarity network, random walks are carried out to traverse other targets. Then, the AD-targets association probabilities vector could be predicted as follows:α = 0.3 is the restart probability. Pt is the probability vector of the t-step iteration, and its value represents the association probabilities between targets and AD. The iteration stops until |Pt+1 − Pt| < 10−6, the resulted probability vector is denoted as P, and its values are as shown in In this study, the targets from the Alzheimer’s disease pathway (hsa05010) in the KEGG and the targets contained in the AD mini metabolic network  are seleRT and the AD-targets association vector P, the association scores of active ingredients and AD are predicted as follows:RT represents the final predicted association score of active ingredient ri and target tj, P(tj) represents the score of the target tj associated with AD. Pr(ri) represents the score of the active ingredient ri associated with AD, as shown in Combined with the final predicted active ingredient-target association matrix Different similarity measures in a network can lead to differences in the topology and structure characteristics of the network. Therefore, the iterative process of random walk and the accuracy of prediction results are affected by different measures. Ablation analysis is performed on the comprehensive similarity measure to study the influence of different similarity measures on the performance of the RWRHE algorithm. We implement two simplified variants of RWRHE.RWRHE_GIP: By removing GIP kernel similarity, the active ingredient similarity RWRHE_E: By removing entropy, each similarity measure is weighted equally.The Receiver Operating Characteristic Curve (ROC) reflects the relationship between True Positive Rate (TPR) and False Positive Rate (FPR) at different thresholds. The area under the ROC curve (AUC) is used as an evaluation metric to measure the accuracy of prediction results. “Alzheimer’s Disease” is used as the keyword, 63 active ingredients associated with AD are retrieved from the HERB database as the positive control group. At the same time, the active ingredients related to leukemia, mammary carcinoma, fever, and acute pharyngitis are retrieved, and the active ingredients related to AD are excluded, 66 active ingredients are obtained as the negative control group. The positive and negative control groups are shown in To further evaluate the performance of RWRHE algorithm, other five network-based algorithms RWR , RWRHN , HGBI 1, LRWHLDAr ≤ 100 are reported as shown in In addition, the ranks of prediction results also play an important role in evaluating the performance of all algorithms. The cumulative distribution function (CDF) of the ranks is employed to compare the performance of different algorithms. CDF refers to the proportion of active ingredients whose ranking exceeds the top-r threshold in the positive control group. 60 ≤ k in treating AD is calculated based on ranks as follows:rank(i) represents the rank of the active ingredient i associated with AD. Finally, 20 herbs are scored and ranked according to the predicted effect scores of herbs for AD, shown in The RWRHE algorithm predicts the association scores and ranks between active ingredients and AD. According to the active ingredients contained in herbs and ranks of active ingredients associated with AD, the effect score of herb The top 100 targets associated with AD are regarded as potential targets of AD. To learn more information about TCMs and AD, bioinformatics analysis is used to find relevant information about potential targets of AD. GO enrichment analysis and KEGG pathway analysis are performed to clarify relevant biological information about the potential targets of AD. GO enrichment analysis includes biological process (BP), cell component (CC), and molecular function (MF). To screen out the most significantly enriched biological annotations, the top 11 entries with the lowest P value are selected respectively, as shown in The involvement of pathways based on potential targets by KEGG pathway enrichment analysis is plentiful. The top 10 pathways with the smallest P value are drawn as bubble graphs in To further illustrate the relationship between topological properties and relevant biological functions, GO enrichment analysis and KEGG pathway analysis are performed on top 100 targets predicted by the RWRHE_GIP and RWRHE_E algorithm, respectively, shown in To further explain the mechanism of multi-pathway and multi-target in the treatment of AD with TCMs, a target-pathway network is constructed based on the top 10 pathways of KEGG enrichment analysis. The relationships between the targets and the pathways are intuitively visualized, as shown in u by Coach-D is regarded as the final docking energy to evaluate the binding ability between active ingredient and target.The components of TCM are complex and the targets are diverse. Molecular docking can indicate the binding ability between active ingredients and related targets. The Coach-D server is presented to predict the protein-ligand binding sites and ligand-binding poses by molecular docking . The minThe panel of ligands is composed of the top 15 active ingredients by RWRHE and several approved acetylcholinesterase (ACHE) inhibitors for AD. Several approved ACHE inhibitors for AD clinically are Galantamine, Donepezil, Rivastigmine, Tacrine and Huperzine A. The first four are approved by FDA, and Huperzine A is approved in China.76 key enzymes and receptors from AD metabolic network  are sortu for these possible complexes formed by ligands and proteins in the panels are calculated by Coach-D. The smaller the minimum value of docking energy is, the stronger the stability of active ingredient-protein site binding is. The docking energies of these five ACHE inhibitors and 12 categories of proteins are computed and the box plots are shown in The minimum values of EnergyAD is a complex neurodegenerative disease with few approved drugs. TCM has a long history and has a strong clinical basis for more than two thousand years. TCM has the advantage of more active ingredients, multi-target cooperative regulation and lower toxicity. Therefore, the study of new active ingredients from herbs is of great significance for the development of anti-AD drugs.In this study, a novel random walk algorithm called RWRHE is devised to predict active ingredients and effective TCMs associated with AD based on entropy and random walk with the restart of a heterogeneous network. The comprehensive heterogeneous network is constructed by integrating the known active ingredient-target association network, active ingredient similarity network and target similarity networks based on entropy. The active ingredients and effective TCMs for AD are inferred based on random walks. The results measured by machine learning and bioinformatics show that the RWRHE algorithm achieves better prediction accuracy. Particularly, the docking energies of the five approved ACHE inhibitors and their common target ACHE are used as the standard for active ingredients, the results show that the top 15 active ingredients may improve the level of Ach, reduce the generation of Aβ and restrain the phosphorylation of tau, which are involved in the main histopathological features of AD. 20 herbs are ranked according to the active ingredients contained in herbs and ranks of active ingredients associated with AD, Danshen, Gouteng and Chaihu are recommended as effective TCMs for AD. Yiqitongyutang is recommended as effective compound for AD in the same way.This study may provide new directions for building more effective prediction models to identify novel AD drugs. But there are several potential limitations in the current study that could be improved. In future, more validated association data would be incorporated to improve the quality of the heterogeneous network, and more useful and detailed studies would be integrated to improve the prediction ability. Only some predicted active ingredients have been validated for acting on key enzymes of AD in different published papers, we will take experimental validation into account and try to design wet experiments to verify the effectiveness of the predicted active ingredients in future.S1 Table(XLSX)Click here for additional data file.S2 Table(XLSX)Click here for additional data file.S3 Table(XLSX)Click here for additional data file.S4 Table(XLSX)Click here for additional data file.S5 Table(XLSX)Click here for additional data file.S6 Table(XLSX)Click here for additional data file.S7 Table(XLSX)Click here for additional data file.S8 Table(XLSX)Click here for additional data file.S9 Table(XLSX)Click here for additional data file.S10 Table(XLSX)Click here for additional data file.S11 Table(XLSX)Click here for additional data file.S12 Table(XLSX)Click here for additional data file.


Allium sativum L.) is an aromatic herb known for its culinary and medicinal uses for centuries. Both unprocessed (white) and processed (black) garlic are known to protect against the pathobiology of neurological disorders such as Alzheimer’s disease (AD) and Parkinson’s disease (PD), which has been attributed to their anti-inflammatory and antioxidant properties. The information on the effects of processed and unprocessed garlic on neuronal process outgrowth, maturation, and synaptic development is limited. This study aimed at investigating and comparing the effects of the ethanol extracts of unprocessed  and processed  garlic on the maturation of primary hippocampal neurons. Neurite outgrowth was stimulated in a dose-dependent manner by both WGE and BGE and the most effective doses were 15 μg/mL and 60 μg/mL, respectively, without showing cytotoxicity. At this optimal concentration, both extracts promoted axonal and dendritic growth and maturation. Furthermore, both extracts substantially increased the formation of functional synapses. However, the effect of WGE was more robust at every developmental stage of neurons. In addition, the gas chromatography and mass spectrometry (GC-MS) analysis revealed a chemical profile of various bioactives in both BGE and WGE. Linalool, a compound that was found in both extracts, has shown neurite outgrowth-promoting activity in neuronal cultures, suggesting that the neurotrophic activity of garlic extracts is attributed, at least in part, to this compound. By using network pharmacology, linalool’s role in neuronal development can also be observed through its modulatory effect on the signaling molecules of neurotrophic signaling pathways such as glycogen synthase kinase 3 (GSK3β), extracellular signal-regulated protein kinase (Erk1/2), which was further verified by immunocytochemistry. Overall, these findings provide information on the molecular mechanism of processed and unprocessed garlic for neuronal growth, survival, and memory function which may have the potential for the prevention of several neurological disorders.Garlic ( A. sativum L.Neurodegenerative diseases (NDDs) are characterized by the dysregulation of neurogenesis, which in turn impacts neuronal differentiation, global or regional loss of neurons, and compromised neural network accompanying cognitive deficits ,2. NeuroAllium sativum L.), which belongs to the genus Allium and family Liliaceae, has been used as a spice or seasoning for thousands of years throughout South Asia, Central Asia, and northeastern Iran  and protease inhibitor cocktail  were used to lyse the cell pellets following a quick centrifugation step. After incubation for 20 min on ice, the cell lysates were centrifuged in a benchtop microfuge at a speed of 13,000 rpm for 15 min at a temperature of 4 °C. The supernatant was collected. The amount of protein was measured using Advanced Protein Assay Reagent , with a standard of bovine serum albumin (BSA). Each sample’s cell lysate was boiled for 5 min to dissolve 40 μg of protein in 5× sample buffer . The prepared sample was then loaded and run onto an SDS-polyacrylamide gel, and the gel was subsequently blotted onto PVDF (polyvinylidene difluoride) membrane. By incubating the membranes in TBS-T (with 0.05% Tween-20) containing 5% skim milk for one hour at RT, the membranes’ non-specific binding was blocked. The membranes were subjected to the incubation process at 4 °C overnight with primary antibodies specific for postsynaptic density-95 , NR2A, and NR2B , and tubulin α-subunit  as a loading control. Blots were incubated with secondary antibodies conjugated with horseradish peroxidase  after being washed with TTBS for 2 h at RT. Enhanced Chemiluminescence Detection Kit, West Save Gold  was used to identify immunoreactive bands. Protein band intensities were quantified using Image J (ver. 1.49) software.After being scraped and washed with ice-cold 1× D-PBS, the neuronal cells were isolated from six-well cell culture plates. Protein extraction buffer [50 mM Tris–HCl (pH 8.0), NP-40, 0.5%  microscope software and processed with Adobe Photoshop 7.0 software. ImageJ (version 1.49) software was used to perform the morphometric analysis  while the neurite tracer  Sholl plug-in  was used to assess the levels of arborization that were present in both axonal and dendritic trees. We defined axonal and dendritic intersections as the sites at which an axon, a dendrite, or their branch intersected at a specific set of concentric circles, and the branching points were identified between two successive concentric circles using a radial distance of 10 μm between each pair of circles. The Olympus BX53 microscope from Olympus in Tokyo, Japan, with DP74 1/1.2-inch Color CMOS camera and Cellsens software Version: 1.18) was used to acquire the images for the synaptic puncta study.The cells were observed and imaged with a fluorescence microscope  with a Sonyhttps://pubchem.ncbi.nlm.nih.gov/, accessed on 9 August 2023) and then prepared with the Ligprep2.5 module of Schrodinger Suite (2017-1). After that, QikProp  was used to determine the pharmacokinetic parameters including absorption, distribution, metabolism, and excretion/transport (ADME/T).The two-dimensional structure of linalool was obtained from the PubChem database , SwissTarget , TargetNet , and SEA  were utilized. The Target-Net dataset was screened using a cutoff score of greater than 0.5, while no cut-off was applied for other databases. Neuronal development (ND)-related targets were collected from The GeneCards database . In order to find the common targets, a Venn diagram was generated by crossovers between the compound and ND targets.To predict the targets of linalool, the comparative toxicogenomic database (CTD) , which applied linalool-ND common targets that had a total score of greater than 0.4. A cytoscape network analyzer tool was applied in order to find out the topological characteristics of the generated network.A PPI network analysis was carried out using Cytoscape v3.9.0 and STRING 11.0 . The results were shown by a lollipop chart and “Homo sapiens” were selected as the only species. In the enrichment analysis, the KEGG pathway was utilized to depict the consequence of enriched GO terms for biological processes (BP), cellular components (CC), and molecular functions (MF). After that, KEGG mapper was obtained for the neurotrophin-signaling pathway identifying major genes that might be modulated by the bioactive component of the BGE and WGE.GO and KEGG studies were carried out using Shiny GO 0.77 , followed by Duncan’s multiple comparisons tests . p values < 0.05 were considered statistically significant. All of the results from the experiment were summarized using the standard error of the mean (S.E.M.) of three separate replicates of each experiment.To compare data, we used either the Student’s The present findings revealed that both fresh and black garlic promoted neurite outgrowth, stimulated axonal maturation and dendritic arborization, and modulated synaptic formation in hippocampal neurons by recruiting both presynaptic and postsynaptic proteins, suggesting that garlic extracts could play an important role in the development of neuronal morphology. The GC-MS-based phytochemical analysis offered several metabolites, including linalool in particular, that potentiated neuritogenesis. Network pharmacology study also showed linalool, BGE, and WGE-mediated neuritogenic effects by the downstream signaling pathways, including Erk1/2 phosphorylation and GSK3β phosphorylation. These findings suggest that garlic and its bioactive metabolite linalool could be further exploited for the development of potential therapeutic agents against neurodegenerative disorders.


Contrast-induced nephropathy (CIN) is increasingly seen in patients receiving contrast medium. Abelmoschus manihot (L.) Medik.  and its preparations are widely used and effective in the treatment of various chronic kidney diseases and CIN in China. It is supposed to be an important adjuvant therapy for CIN.A. manihot L. The Swiss target prediction platform, OMIM, GeneCards, DisGeNET, and DrugBank databases were mined for information relevant to the prediction of targets that A. manihot L. in the treatment of CIN. Subsequently, STRING database was applied for the construction of the PPI protein interaction network, meanwhile, the core targets were screened. DAVID database was used to perform the GO function and Kegg signal pathway enrichment analysis. AutoDockTools and PYMOAL were used for molecular docking. Vitro experiments were used to verify the effect of TFA, the main active component of A. manihot L., in the intervention of iopromide-induced cells injury.PubMed and CNKI were searched for the main compounds of A. manihot L. were obtained. The top 15 proteins with higher degree value were selected from the PPI network model, AKT1, PIK3R1, EGFR, SRC,AR, APP, TNF, GAPDH, MMP9, and PTPN1, etc. may be core targets. The enrichment analysis indicated that A. manihot L. was involved in the regulation of PI3K/AKT signaling pathway, FoxO signaling pathway, VEGF signaling pathway, HIF-1, TNF signaling pathway, melanoma, hepatitis B, and other signaling pathways which were mainly associated with the regulation of transcription and apoptosis, protein phosphorylation, inflammatory response, aging, and cell proliferation. Molecular docking indicated that the key components and core targets had a good binding ability. The vitro experiments illustrated that TFA reduces iopromide induced renal tubular cell injury and apoptosis, which may be related to regulating the phosphorylation of AKT.A total of 17 chemical components and 133 potential targets in A. manihot L. on CIN, which provide theoretical reference and basis for the study of the pharmacological mechanism of A. manihot L. in the treatment of CIN.The study preliminarily revealed the multi-component, multi-target, and multi-pathway synergistic effects of  Contrast-induced nephropathy (CIN) is a sudden deterioration of renal function caused by intravascular injection of contrast medium (CM) for diagnosis and interventional operation. It is generally defined as renal function damage within 3 days after intravascular injection of CM without other causes, and serum creatinine (Scr) increases by more than 25% or 44μmol/L compared with baseline levels . With thA. manihot L. and its preparations are widely used and effective in the treatment of various chronic kidney diseases and CIN, but the pharmacological mechanism of its treatment of CIN has not been fully elucidated. Therefore, this study undertook an in-depth analysis and prediction of the active component-target-pathway of Abelmoschus manihot L. in the treatment of CIN by the method of network pharmacology and explores the mechanism of A. manihot L. in the treatment of CIN.Traditional Chinese Medicine (TCM) is the most common form of alternative medicine and supplementary medicine in Asia, and has been widely used in the treatment of CKD. Abelmoschus manihot (L.) Medik.  is a herb used in Traditional Chinese Medicine to treat some kidney diseases. Modern pharmacology has confirmed that the flavonoids of Abelmoschus manihot L. have the effects of anti-inflammation, antipyretic, analgesia, relieving cardio-cerebral myocardial ischemia, and hypoxia, protecting renal tubules and glomeruli . A. maniA. manihot L. were searched in CNKI and PubMed. The chemical constituents with pharmacological activities were selected and input into the PubChem database (https://pubchem.ncbi.nlm.nih.gov/) to query the serial number of CAS and download the corresponding sdf molecular structures of the active components. The main active components of A. manihot L. were determined.All the chemical constituents of A. manihot L.The sdf molecular structures of active components were imported into the Swiss Target Prediction platform . The spehttps://www.omim.org/), Gene cards database (https://www.genecards.org/), DrugBank database (https://www.drugbank.ca/), DisGeNET database (https://www.disgenet.org/) with the keywords “Contrast Induced Nephropathy” and “CIN”. Duplicates were deleted and the main genes related to CIN were integrated for further research.A search for target genes related to CIN was performed in the OMIM database (http://bioinfogp.cnb.csic.es/tools/venny/index.html) software was used to obtain the intersection between the predicted targets of A. manihot L. and the main targets of CIN. A Venn diagram was drawn. These intersection genes may be the related targets of A. manihot L. in the treatment of CIN.Venny 2.1.0 (https://string-db.org/) (The intersection of the predicted targets in the network model were imported into the String11.0 database (db.org/) , the spehttps://david.ncifcrf.gov/)   for enriThe main active components, the corresponding CIN related targets, and CIN treatment related signaling pathways were imported into Cytoscape 3.6.1 software. The main active component-target-signaling pathways network model was drawn. The topological analysis was used to analyze the network model and calculate the degree. The larger the value of the degree, the more important the node is in the network. The node size, color, and edge thickness are adjusted according to the degree, and visualization was carried out.A. manihot L. and the core targets obtained from the PPI network. The receptor protein encoded by the gene was identified by the UniProt database (https://www.uniprot.org/), and its three-dimensional structure was obtained from the RCSB PDB database (https://www.rcsb.org/). The PubChem database (https://pubchem.ncbi.nlm.nih.gov/) was used to download the two-dimensional structure of the molecular ligand. Then the structures were imported into the PyMOL2.4.0 software for receptor protein dehydration. The hydrogenation and charge calculation of the protein was carried out by AutoDockTool for structure optimization. The parameters of the receptor protein docking site were set to include active pocket sites that bind to small molecules of ligands. Finally, the AutoDockTools-1.5.6 software was used to dock the key active components with the corresponding targets, optimize the binding affinity between receptors and ligands and obtain the three-dimensional structures.Molecular docking was carried out between the key compounds of A. manihot L. (TFA) were extracted from the dry corolla of A. manihot L. TFA was extracted by the Department of Drug Preparation of the Affiliated Hospital of Nanjing University of Chinese Medicine. Briefly, 0.5 kg of raw A. manihot L. flowers was immersed in 8000mL 75% ethanol for 1 h and then heated to 90°C for 1 h to achieve alcohol extraction as ambrette fluid extract. After filtration, the extract was evaporated to produce a dry extract powder under vacuum at 60°C. The dried residue was dissolved in water for use in the experiments  with iopromide. Iopromide can induce apoptosis of HK2 cells. Therefore, we used iopromide to establish an in vitro model of renal injury induced by a contrast medium. Then TFA was used to interfere with the model to evaluate whether it can regulate AKT phosphorylation and reduce apoptosis.The data of network pharmacology show that the flavonoids are the key components of Bcl-2 (lot no. AF6139) and Bax (lot no. AF0120) were purchased from Affinity Biosciences Co., LLC. . Caspase3 (lot no. 9662) and Cleaved Caspase-3 (Asp175) Antibody (lot no. 9661) were purchased from Cell Signaling Technology Co., Ltd. . Antibodies to AKT (lot no. 60203-2-Ig), antibody to phospho-AKT (Ser473)(lot no. 66444-1-Ig), antibody to GAPDH (lot no. 60004-1-Ig), horseradish peroxidase (HRP)-conjugated Affinipure goat anti-mouse IgG (H+L)(lot no. SA00001-1) and HRP-conjugated Affinipure goat anti-rabbit IgG (H+L) (lot no. SA00001-2) were purchased from Proteintech Biotechnology Co., Ltd. . Iopromide was purchased from Bayer Co., LLC. . Fetal bovine serum (FBS), 0.25% trypsin-EDTA, phosphate buffered saline (PBS), and Dulbecco’s modified Eagle medium/F-12 (DMEM/F-12) were purchased from Thermo Fisher Scientific . Cell Counting Kit-8 (CCK-8) was obtained from APExBIO . One Step TdT-Mediated dUTP Nick End Labeling (TUNEL) Apoptosis Assay Kit was purchased from Vazyme Biotechnology Co., Ltd. . 4,6-diamidino-2-phenylindole (DAPI) staining solution and BCA protein assay kit were purchased from Beyotime Biotechnology .2. The cells at approximately 80% to 90% confluency were digested and passaged.Normal renal tubular epithelial cells (HK2) were obtained from Saiku Biotechnology Co., Ltd. The cells were maintained in DMEM/F-12 supplemented with 10% FBS and incubated at 37°C in 5% CO4 cells per well. After 24 h of incubation, the cells were cultured in a serum-free medium and stimulated with iopromide (111mg I/mL) in the absence and presence of TFA (0.6 mg/mL). After 12 h of treatment, the medium was discarded, and 100μL serum-free medium containing 10% CCK-8 was added to each well. After incubating for another 1 h, the absorbance was measured using a model ELx800 spectrometer  at a wavelength of 450 nm. Cell viability is expressed as a percentage of the control group.HK2 cells were digested and seeded in 96-well plates at 1×10TUNEL staining kit and DAPI staining solution were used to assess the presence of apoptotic cells. Briefly, the cells were incubated and treated in accordance with the experimental protocol, washed three times with PBS, and fixed with 4% paraformaldehyde for 30 min. Then, they were permeabilized with 0.3% Triton X-100 in PBS for another 30 min and washed three times with PBS. 50μL of TUNEL staining solution was added to each well and incubated in the dark for 1 h at 37°C. Then, cells were washed three times with PBS, and 100μL of DAPI staining solution was added to each well, followed by incubation in the dark for 10 min and washing with PBS. Finally, cell fluorescent images were captured using a fluorescence microscope  equipped with standard red and ultraviolet fluorescence cubes. Fluorescent images were analyzed quantitatively using ImageJ software.After incubation and intervention, the cells were lysed in RIPA buffer supplemented with a 2% protease and phosphatase inhibitor cocktail for 20 min on ice. After sonication, the samples were centrifuged at 4°C for 20 min, and each supernatant was collected for subsequent experiments. Total protein concentration was determined using a BCA protein assay kit. Equal amounts of protein (20µg) were separated by 10% or 12% sodium-dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE). The resolved proteins were electrophoretically transferred to polyvinylidene difluoride 9 (PVDF) membranes . The membranes were blocked with 5% non-fat dried milk in PBS containing Tween (PBST) buffer for 1 h at room temperature, washed three times with PBST, and incubated with primary antibodies overnight at 4°C. The membranes were washed again and incubated with HRP-conjugated anti-rabbit or anti-mouse IgG for 1 h at room temperature. The bands were flushed with chemiluminescent HRP substrate and imaged using a chemiluminescence system . Western blot images were analyzed quantitatively by Image Lab software. GAPDH was assayed to confirm equal loading of proteins.p<0.05.Statistical analyses were performed using SPSS software. All values were expressed as mean ± standard deviation (SD). Comparisons of two populations were made using an unpaired 2-tailed Student’s t-test. For multiple groups, statistical significance was determined using a one-way analysis of variance (ANOVA) followed by Dunnett’s test. Statistical significance was set at A. manihot L. were obtained as the main active components of study. The specific information is shown in Through the collation of related papers , 18–23, A. manihot L. were obtained after deleting the integration of repeated invalid targets.The sdf molecular structures of all the main active components in Search OMIM database, Genecards database, DrugBank database, and DisGeNET database with “Contrast Induced Nephropathy” and “CIN” as keywords. A total of 2832 CIN related targets were screened.A. manihot L. were mapped to the main targets related to CIN using Venny 2.1.0 software. A Venn diagram was drawn , 40 cellular composition (CC), 36 molecular functions (MF), and 73 KEGG signaling pathways were obtained. GO and KEGG enrichment analyses were carried out to explore the potential function and mechanism of target genes related to the treatment of CIN in A. manihot L.The 75 related core targets were uploaded to the DAVID database for GO enrichment analysis and KEGG pathway enrichment analysis. A p<0.01 as the screening criterion, the first 10 GO biological functions of these targets were shown in the form of a histogram  selected from the 17 key components were docked with the corresponding two targets  of the 15 core targets. The molecular docking showed that all these active compounds could easily enter and bind to the active pockets of pathway-related proteins. The binding affinity < 0 indicates that the receptor and the ligand have the ability of spontaneous binding. The lower the binding affinity score is, the more stable the crystal structure of the protein corresponding to the formation of the compound and the target is. The results of molecular docking show that the binding affinity between the key components and corresponding core targets of  ability Table 2.in vitro model of CIN induced by iopromide. TFA was used to intervene iopromide-induced HK2 cells injury, and its effect on cellular viability was evaluated by the CCK8 method. Compared with the iopromide group (111mgI/mL), the TFA group (0.6mg/mL) significantly increased cell viability  and Xia Guicheng Innovation and Development Fund of Jiangsu Provincial Hospital of Traditional Chinese Medicine (No. Y19019).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Hiccups may appear to be a common normal phenomenon that does not warrant treatment in the general population. However, severe and persistent hiccups can become annoying and distressing and may decrease the quality of life, especially in cancer patients. The management of hiccups remains challenging. Despite trying many pharmacological and non-pharmacological methods, there is no clear evidence to support the management guidelines. In our case, we successfully treated persistent hiccups of more than four days duration in a patient with acute myeloblastic leukemia with gabapentin. In this case report, we present the case of a 70-year-old male diagnosed with acute myeloblastic leukemia (AML) who developed persistent hiccups of more than four days in duration.Hiccups are a physiological phenomenon that results from the activation of the hiccups reflex arc involving the respiratory muscles of the chest and diaphragm modulated by peripheral phrenic, vagal, and central mid-brain ,2. HiccuHere, we report the successful treatment of persisting hiccups with gabapentin with no adverse effects.Our patient is a 70-year-old diabetic male with depression. He was diagnosed with AML in 2018 and received induction and consolidation therapy with complete remission. The disease relapsed in July 2020, following which he was started on azacytidine/Veneto lax and achieved remission with minimal residual disease. The patient was admitted in august 2021 as a case of febrile neutropenia and sepsis. Intravenous (IV) antibiotics, vancomycin, and meropenem were administered to resolve sepsis. Two weeks following sepsis, he developed nausea and vomiting, four to five times daily of a moderate amount, green in color, mainly after oral intake, and not associated with constipation, diarrhea, or abdominal distension. There were no relieving factors. The patient was on omeprazole, escitalopram, allopurinol, and insulin aspart. On examination, he was vitally and clinically stable, and his abdominal examination was unremarkable. Lab results revealed pancytopenia and acute kidney injury . On the following day, his hiccups improved significantly, as they were reduced by 70% for one day but returned. Thus, the dose was increased to 200 mg TID, and his hiccups resolved almost completely. A follow-up appointment after two weeks showed a sustained effect with hiccups and complete resolution.Hiccups are a temporary benign phenomenon found frequently in the population. Brief episodes of hiccups lasting less than 48 hours are usually not concerning, self-limiting, transient, and non-pathological. However, a systemic review of patients with advanced cancer showed that 1-9% of the patients had persistent or intractable hiccups [The pathophysiology of hiccups remains unknown, with no identifiable anatomical brain centers responsible for hiccups ,11. HoweHiccups may become distressful, causing fatigue, dehydration, malnutrition, insomnia, and mental stress. Studies have shown that hiccups can cause sleep deprivation, as they can persist during both non-rapid eye movement and rapid eye movement sleep . Hiccups2+ channels at the presynaptic terminal of respiratory muscles.In this case, the goal of the palliative care team was to relieve the patient’s symptoms quickly and reduce the number of investigations and side effects of medications to enhance his quality of life. Gabapentin is an anti-epileptic medication. It is typically used in the palliative care of cancer patients to control neuropathic pain. It may effectively treat hiccups by inhibiting the inspiratory muscles mediated by endogenous GABA or  by reducFurthermore, it can supply the nucleus raphe magnus with high serotonin levels, which is the most likely cause of the GABAergic inhibitory impulse to hiccup reflex arc. In our case, we used gabapentin to resolve hiccups completely. The patient was satisfied with the management. He had no side effects and no recurrence of hiccups after two weeks of follow-up.Gabapentin is safe, economical, and available in different formulations. Moreover, it has minimal side effects. Therefore, gabapentin can be considered in the treatment of persistent, intractable, and unmanageable hiccups in palliative care ,19.This case report supports the previous observational studies on the successful treatment of persistent hiccups with gabapentin in an elderly population with terminal diseases as it is safe, widely available, and has no adverse reactions. Additional research is essential to guide the management of persistent and intractable hiccups to improve the quality of life in palliative care patients.


Molecular docking, molecular dynamics simulation, and in vivo study were used for further investigation. MAT could interact with the cavity of the PARP1 protein with the binding energy at −8.5 kcal/mol. Results from molecular dynamics simulations showed that the PARP1-MAT complex was more stable than PARP1 alone and that the binding-free energy of the PARP1-MAT complex was −15.962 kcal/mol. The in vivo study showed that MAT could significantly increase the NAD+ level of the liver of d-gal-induced aging mice. Therefore, MAT could interfere with aging through the PARP1/NAD+-mediated cellular senescence signaling pathway.The aim of this study is to explore the potential targets and molecular mechanism of matrine (MAT) against aging. Bioinformatic-based network pharmacology was used to investigate the aging-related targets and MAT-treated targets. A total of 193 potential genes of MAT against aging were obtained and then the top 10 key genes , histone-lysine N-methyltransferase, albumin, mammalian target of rapamycin, histone deacetylase 2, and matrix metalloproteinase 9) were filtered by the molecular complex detection, maximal clique centrality (MMC) algorithm, and degree. The Metascape tool was used for analyzing biological processes and pathways of the top 10 key genes. The main biological processes were response to an inorganic substance and cellular response to chemical stress (including cellular response to oxidative stress). The major pathways were involved in cellular senescence and the cell cycle. After an analysis of major biological processes and pathways, it appears that PARP1/nicotinamide adenine dinucleotide (NAD The number of over 60-year-olds increased rapidly globally, and it is estimated that the population of over 60-year-olds will reach 2 billion in 2050; meanwhile, the number of over 80-year-olds increased significantly. Moreover, notably, the rate of the aging population increased fastest in low- and-middle-income countries around the world . With agSophora flavescens is a famous medicine in traditional Chinese medicine (TCM), with its root having been used to treat fever, hematochezia, vulvar swelling, and inflammatory diseases [Sophora flavescens. It has been reported that MAT has numerous pharmacological activities, such as anti-depression, colitis-protective activity, antibacterial and antiviral activity, hepatorenal protective activity, antidiabetic, antitumor, and so on [d-gal by inhibiting cellular senescence [diseases . Martinend so on ,7,8,9,10nd so on  but coulnd so on . In the nescence . HoweverNetwork pharmacology analysis is one of the most common methods which uses computers, and high-throughput omics data analysis to study the pharmacology of TCM. We can analyze the links among compounds from TCM, potential targets, and diseases, and obtain information on the system-level mechanism of active compounds . MoleculThe two-dimensional and three-dimensional structures were shown in A total of 3672 targets were discovered using GeneCards (GIFtS > 40), 227 targets were identified using OMIM, and 1261 targets were obtained from the NCBI GENE database. The three different databases were combined and duplicates were removed. Analysis of data in the three different databases identified 4317 targets associated with cellular aging. Next, the MAT targets were intersected with the aging genes to obtain the overlapped genes and a Venn diagram was plotted B.A total of 193 potential genes were obtained after the intersection and then uploaded to the String database to conduct the Protein–Protein Interaction (PPI) network. The PPI network was imported into Cytoscape software (version 3.7.1), and the topological parameters of the network were analyzed. The results showed that the network had 191 nodes and 1115 edges total . The topp < 0.01). Based on the GO enrichment analysis, BP, cellular components (CC), and molecular functions (MF) were significantly enriched  and signaling pathways through Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis , toot mean square fluctuation (RMSF), and radius of gyration (Rg). The RMSD was used to evaluate the different positions of the protein main chain at different simulation times by comparing it with the initial structure of the protein conformation. The smaller fluctuation amplitude indicates a more stable system. The RMSF fluctuation value was calculated to reflect the protein flexibility of each residue. The average values of each RMSF were 0.1783 nm and 0.1528 nm. Compared with the PARP1 system, it was noteworthy that in these areas , the PARP1-MAT system showed obviously reduced fluctuations. Since these areas were mainly located in the affinity pocket, it demonstrated that the PARP1-MAT complex showed lower flexibility and contributed to stabilizing the protein conformation compared with PARP1 alone C. Rg repThe molecular mechanics/Poisson–Boltzmann surface area (MM/PBSA) algorithm was used to investigate whether the MAT affected the stability and interaction of the PARP1-MAT complex; meanwhile, the binding-free energies of the complex were calculated. The g_mmpbsa was used to calculate the binding-free energy of the simulation trajectories of the 60–65 ns intervals. As shown in +), the MD showed that MAT may inhibit PARP1 activity. We investigated the NAD+ changes in the liver after being treated with MAT at different doses in the d-gal-induced aging mice. The results showed that compared with the blank control group, NAD+ decreased significantly in d-gal-induced aging mice. Additionally, relative to the model group, the NAD+ level increased in the liver of MAT at the dose of 2 mg/kg  [Drosophila through AMPKα PARylation [C. elegans, fruit flies, yeast, and mice. mTORC1 inhibition could delay aging by stimulating autophagy, improving mitochondrial functions, clearance of senescent cells, and inhibiting senescence-associated secretory phenotype [The top 10 key genes are PARP1, mTOR, CCND1, CDK1, CCNA2, AR, EZH2, ALB, HDAC2 and MMP9. PARP1 is overactivated and rapidly depletes the intracellular NAD damaged . The depelegans) , and troelegans) . MoreoveRylation . The inhhenotype . CCND1, henotype .d-gal-induced aging model [According to KEGG enrichment analysis, the key genes were involved in the cell cycle and cellular senescence. Cellular senescence is one of the key drivers of aging. Cellular senescence entails the arrest in the G1 phase of the cell cycle in response to damaging stimuli . Decreasng model .+, which is famous for its role in redox reactions, participated in the regulation of biological processes widely. Fluctuations of NAD+ can affect redox reactions, mitochondrial function, inflammation, and so on. The studies showed that NAD+ levels stably declined as aging, and restoration of NAD+ levels could extend health span and ameliorate aging-related diseases [+ supplementation improved mitochondrial function and increased the lifespan in mice [+. The overactivation of PARP1 could consume excessive NAD+ and the decreasing NAD+ level could inhibit the SIRT1 activity and produce the ROS, which finally results in mitochondria dysfunction and cellular senescence [+ could suppress oxidative stress [+ level decreases significantly [+ level through inhibition of PARP1 or through the supplementation of NAD+ (or NAD+ precursor) also could improve lifespan [+-mediating cellular senescence signaling pathway.The key genes were mainly rich in the cellular response to the oxidative stress process in BP, and five genes, PARP1, CDK1, EZH2, HDAC2, and MMP9, were involved in the cellular response to the oxidative stress process. Oxidative stress plays an important role in aging and aging-related diseases. A suitable amount of ROS is beneficial for health; however, excess ROS can damage proteins, lipids, and DNA. As the oxidation reaction center, the mitochondrial area is one of the most important places where ROS is produced. The decrease in mitochondrial efficiency resulting in increased ROS production leads to cellular senescence and aging . NAD+, wdiseases . Previou in mice . PARP1 inescence ,34,35,36e stress . With agficantly ,38. The ficantly ,39,40, alifespan . Therefod-gal-induced decreased NAD+ levels. Therefore, the MAT could delay aging through the inhibition of PARP1 and elevation of NAD+ levels.In this study, the result showed that the binding affinity was −8.5 kcal/mol between PARP1 and MAT according to docking, and MD studies further proved that the binding between PARP1 and MAT was stable. Some previous studies reported that the important binding position of PARP1 inhibitors were Tyr907, Lys903, Ala898, and GLU988 ,43. More+ level increased in the d-gal-induced aging model after being treated with MAT. Therefore, we proposed that the PARP1/NAD+-mediated cellular senescence signaling pathway may be a possible molecular mechanism for MAT in the treatment of aging.Summarily, MAT has been proven to be potentially beneficial against aging based on network pharmacology in the present study. The top 10 key genes were enriched in cellular senescence based on KEGG analysis and the cellular response to oxidative stress according to the BP. Moreover, PARP1 may be one of the most important targets for MAT in treating aging. Meanwhile, it was proven that MAT could bind to the cavity of PARP1 proteins based on molecular docking and dynamics simulation. In addition, the NADhttps://tcmspw.com/tcmsp.php (accessed on 8 June 2022)) [http://www.lilab-ecust.cn/pharmmapper/ (accessed on 20 July 2022)), SwissTargetPrediction ), and SuperPred [https://prediction.charite.de/ (accessed on 21 July 2022)) were used to predicate the candidate target of MAT. Finally, the UniProt database (https://www.uniprot.org/ (accessed on 24 July 2022)) was used to convert all targets into gene symbols in a standard fashion. The chemical structure of MAT was drawn using chemical draw  according to the previous report , and thee 2022)) . The pree 2022)) . TPSA lee 2022)) ,49 (httpuperPred  (https:/https://www.genecards.org/ (accessed on 15 October 2021)), OMIM (https://www.omim.org/ (accessed on 15 October 2021)), and the NCBI GENE database (https://www.ncbi.nlm.nih.gov/gene/ (accessed on 15 October 2021)) were utilized to screen the senescence-related genes. The keywords “cellular senescence” and “aging” were used to screen the targets. Next, the targets from GeneCards were selected if the GIFtS value was greater than 40 [https://bioinfogp.cnb.csic.es/tools/venny/ (accessed on 30 July 2022)) intersection.GeneCards ( than 40 . Subsequhttps://string-db.org/ (accessed on 15 August 2022)) was used to create the PPI Network. The homo species and the minimum confidence level at 0.4 were chosen for analysis. Next, the result was edited by using Cytoscape software (version 3.7.1). The MMC, MCODE algorithms [The STRING database ), which is the functional annotation clustering tool, was used to analyze the KEGG pathway enrichment and the GO function of the top 10 key genes of MAT acting on aging. Meanwhile, the signal pathways, the BP, CC, and MF associated with the key genes of MAT against aging were described. To explain signal pathways and the function of MAT, Metascape (https://www.rcsb.org/ (accessed on 5 October 2022)). Next, the protein was modified as followed: removing water molecules and ligands, adding hydrogen and charge; setting up the docking grid box at the center of the molecule. The MAT was modified as follows: minimizing the energy by ChemDraw 3.0 , adding the charge, and then setting the rotatable key. Finally, the binding conformation with the lowest energy was chosen and the binding diagram was observed by using Discovery Studio .To verify the affinity between MAT and the PARP1, the molecule docking was performed by using Auto Dock software (version 1.5.6). The protein (4zzz) was downloaded from the PDB  to calculate the binding-free energy base on MM/PBSA calculations . Five hud-gal), MAT low-dose group (2 mg/kg MAT), and PAE high-dose group (10 mg/kg MAT). For the first six weeks, the blank control group was injected once daily with saline, and the other groups were treated with once-daily d-gal (150 mg/kg) by subcutaneous injection. From the fifth week, the MAT groups were subjected to MAT  once daily for five weeks, and the blank control group and model group were administered to solvent  in the same way instead. All experimental procedures were conducted and performed following the policies for animal care and use encompass regulations approved by the Institutional Animal Care and Use Committee of Shanxi Agricultural University .The 8-week-old male Institute of Cancer Research mice (weighing 28–30 g) were purchased from Shanxi Medical University . The mice were fed and provided access to food and water freely and housed in constant temperature (23 ± 1 °C) and relative humidity (55 ± 5%) under 12 h:12 h light/dark cycle. The mice were fed to the adaption to the environment for a week before the experiment. The mice were randomly divided into four groups as follows (six mice in one group): the blank control group, model group  following the manufacturer’s protocols. The protein concentration was determined by the Bradford protein assay kit . The NAD+ content was measured at 570 nm. Before detection, the liver tissues were rapidly homogenized in an acid-extracting solution (10 times the tissue weight) with a homogenizer. The homogenates were boiled for 5 min and then centrifuged at 10,000× + were expressed as mean ± SEM. Data were analyzed by one-way analysis of variance analysis. Statistical significance was set at p < 0.05.The results of the relative NAD