IL6, ALB, TNF, SERPINE1, VWF, VCAM1, TGFB1, and SELE. Cluster analysis revealed immune and hemostasis subnetworks, which were strongly interconnected through the major switches ALB and VWF. Enrichment analysis revealed that the PPI immune subnetwork of death due to IS was highly associated with TLR2/4, TNF, JAK-STAT, NOD, IL10, IL13, IL4, and TGF-β1/SMAD pathways. The top biological and molecular functions and pathways enriched in the hemostasis network of death due to IS were platelet degranulation and activation, the intrinsic pathway of fibrin clot formation, the urokinase-type plasminogen activator pathway, post-translational protein phosphorylation, integrin cell-surface interactions, and the proteoglycan-integrin extracellular matrix complex (ECM). Regulation Explorer analysis of transcriptional factors shows: (a) that NFKB1, RELA and SP1 were the major regulating actors of the PPI network; and (b) hsa-mir-26-5p and hsa-16-5p were the major regulating microRNA actors. In conclusion, prevention of death due to IS should consider that current IS treatments may be improved by targeting VWF, the proteoglycan-integrin-ECM complex, TGF-β1/SMAD, NF-κB/RELA and SP1. This study used established biomarkers of death from ischemic stroke (IS) versus stroke survival to perform network, enrichment, and annotation analyses. Protein-protein interaction (PPI) network analysis revealed that the backbone of the highly connective network of IS death consisted of  Acute ischemic stroke (IS) is one of the most prevalent major health problems worldwide, which frequently causes severe functional disabilities and mortality ,2. IS riApart from being a neurodegenerative and neuroinflammatory disorder, IS also an atherothrombotic disease with activation of hemostasis, coagulation, fibrin clotting, and fibrinolysis cascades coupled with endothelial dysfunctions ,8. Thus,The mean carotid intima media thickness (cIMT), as measured through carotid Doppler ultrasonography, is a marker of severity of atherosclerosis and is associated with the severity and outcome of IS ,13. The IS the second leading cause of mortality, and around one-third of IS patients may die within the first few months after the ischemic event ,21. The Risk factors of increased mortality due to IS comprise increased age, increased cIMT scores, previous stroke and TIA, diabetes mellitus, heart disease, atrial fibrillation, increased baseline NIHSS and mRS scores, fever, and size of lesion ,27,28,29SERPINE1 (serine protein inhibitor E1) or plasminogen activator inhibitor 1 (PAI-1), ITGB3 (integrin beta-3), MPG (DNA-3-methyladenine glycosylase), PROCR gene , HABP2 , COL3A1 (type III collagen), FBG, RETN (resistin), VKORC1 (Vitamin K epoxide reductase complex subunit 1), INADL , and FXIIIA Val34Leu genetic variant [10a rs3809783 A>T (MIR10A) and miR-34b/c rs4938723 T>C genetic variant (MIR34B) and the long noncoding RNA (lncRNA) AL110200 are associated with increased mortality rates due to IS [A review of the existing literature shows that a number of genes may increase risk of mortality due to IS, including  variant ,38,39,40ue to IS ,42. Although risk factor control, antithrombotic treatment, and revascularization have been widely applied in clinical practice, IS still a main cause of death . The staTherefore, in the era of precision medicine, novel strategies for the prevention and treatment of IS are urgently needed . Knowledp-enrichment value of 1.0 × 10−16. The average node degree of this network is 24.8 with an average local clustering coefficient of 0.735. The network diameter = 3 and radius = 2, with a characteristic path length = 1.658, network density = 0.388 and heterogeneity = 0.445. The top six hubs were, in descending order of importance: IL6 (degree = 51), TNF (46), ALB (46), VCAM1 (38), IL10 (36) and VWF (35). The top two non-hub bottlenecks were: SELE (0.0298) and TGFB1 (0.0243). As such, the backbone of the network comprises eight proteins, including IL6, TNF, ALB, VCAM-1, IL-10, VWF, SELE, and TGF-β1.MCL cluster analysis with an inflation parameter of three showed two protein communalities see : (a) a fMCODE analysis using KEGG, WikiPaths, PANTHER, REACTOME, and GO molecular and biological terms performed on the cluster two genes showed a first complex representing formation of fibrin clot, a second reflecting the urokinase-type plasminogen activator (uPA) and uPA receptor (uPAR)-mediated (uPA-uPAR) signaling pathway, elastic fiber formation and response to wounding (MCODE2) and a third representing reverse cholesterol transport, plasma lipoprotein particle organization, and protein-lipid complex subunit organization (MCODE3). 2+ were the most significant paths represented in the cluster two network. −16. The average node degree of this network is 23.8 with an average local clustering coefficient of 0.705. The network diameter = 3 and radius = 2, with a characteristic path length = 1.728, network density = 0.344 and heterogeneity = 0.482. The top six hubs were in descending order of importance: IL6 (degree = 55), ALB (49), TNF (48), SERPINE1 (43), VWF (40) and VCAM1 (39). The same DEPs/genes were the top five bottlenecks, and the first two non-hub bottlenecks were TGFB1 (0.022) and SELE (0.013). Therefore, the backbone of this network comprises eight DEPs/DEGs namely IL6, ALB, TNF, SERPINE1, VWF, VCAM1, TGFB1, and SELE.Using MCL cluster analysis with an inflation parameter of 3.4 we found two communalities as shown in inBio Discover was employed to delineate which DOID diseases are over-represented in the DEPs/gene network. Finally, we built another MultiOmics network  using alThe first major finding of this study is that the PPIs network of DEPs and DEPs/genes of death from IS versus stroke survival show a high connectivity and two interconnected subnetworks, namely, a first which is centered around immune genes and a second which is centered around hemostasis-coagulation genes. All query DEPs/genes participated in these networks except INADL. The backbone of this network consists of DEPs/genes which contribute to both subnetworks, namely IL6, TNF, TGFB1, VCAM, and SELE as authorities in the immune subnetwork, and ALB, SERPINE1, and VWF in the hemostasis subnetwork. Moreover, ALB ad VWF are important switches that connect both subnetworks because these DEPs show many significant interactions with proteins in both communities. Therefore, it appears that death due to IS predicted by an integrated response in a network which comprises strongly interconnected immune and hemostasis subnetworks. This indicates that both the immune response  and the activated hemostasis, thrombosis and coagulation pathways  are intertwined phenomena leading to death from IS. The strong interconnections among the immune and hemostasis subnetworks have probably an evolutionary origin . We willThe second major finding of this study is that the most significant paths and functions enriched in the networks of death due to IS were the inflammatory and NF-κB signaling pathways, NFKB1/RELA transcriptional factors, TNF-α and IL-17 signaling, and the TLR, TGF-β1, nucleotide-binding and oligomerization domain (NOD) and JAK-STAT pathways. These results indicate that altered expression of these pathways, and transcriptional factors, may lead to death due to IS.There is now evidence that in IS, NF-κB may be a key factor for the transcriptional induction of cellular adhesion molecules, including SELE and VCAM-1, proinflammatory cytokines, including IL-6 and TNF-α, CRP, and FTH1, growth factors including TGF-β1, and coagulation factors including F8, PAI1, uPA, tissue factor and fibronectin ,48. TranNFKB1 gene may affect IS susceptibility in the Korean population [In animal models of middle cerebral artery occlusion, activation of astroglial NF-κB is downstream of TLR2 activation and a deficiency in TLR2/4 reduces the neurodeficit and IS size . Moreovepulation . uniprot.org (accessed on 19 September 2021). Moreover, the apoptotic responses caused by NF-κB are dictated by acetylation of RELA in Lys310, and RELA, but not p50, knockouts show a decreased infarct size [Furthermore, there is evidence that in IS the deleterious effects of increased NF-κB are associated with RELA activation , which irct size ,55. TLR4-119A allele) are associated with an increased risk of IS [The TLR2/4 complexes play a key role in IS with increased expression of both receptors being associated with the inflammatory response and progression of infarct volume and ischemic damage . Both TLsk of IS . Moreovesk of IS . TNF-α-308 G > A (rs1800629) genetic variant is associated with increased risk of IS [IL17RC gene is associated with a poorer prognosis of IS [Soon after IS onset, microglia cells and circulating monocytes are activated and consequently, increased release of TNF-α and IL-6 may be detected in the brain, cerebrospinal fluid, and bloodstream . Increassk of IS . IS is ask of IS . It is tsk of IS . Moreoveis of IS . There are now some reports that in experimental models of IS, the JAK2-STAT3 pathway is activated and contributes to neuronal damage ,68,69,70NOD-like receptors are a family of cytoplasmic receptors which sense bacterial motifs and danger signals (including uric acid and ATP) and trigger an innate immune response by activating NF-κB and mitogen-activated protein kinase (MAPK) ,75. In tIL-10 rs1800896 variant [IL4 KO mice show a worsened neurological outcome and increased brain damage following cerebral ischemia [Our REACTOME path classifications revealed that our DEP network was highly significantly associated with IL-10, IL-4, and IL-13 pathways. We discussed before that increased activity of the IL-10 pathway in nonsurvivors may be part of a compensatory immune-regulatory mechanism which counterbalances an overzealous inflammatory response . Elevate variant . IL-4 al variant . IL4 KO ischemia . In IS, ischemia . TGFB1 variants are associated with IS [One of the major bottlenecks in the PPI network is TGF-β. This cytokine is elevated in the brain following IS and may exert anti-inflammatory, antiapoptotic and neuroprotective effects, thereby improving repair mechanisms and favoring nerve regeneration ,83,84. T with IS . Moreove with IS ,87, whic with IS ,89. In a rat model of cerebral ischemia/reperfusion, SMAD3 administration may downregulate inflammatory and proapoptotic genes, suggesting that the TGF-β/SMAD pathway is a possible drug target . All in + central memory T cells—in the healthy brain [VCAM-1, another hotspot of the backbone of the PPI network, plays a critical role in the inflammatory response following IS, for example through adhesion of leukocytes to endothelial cells and transendothelial migration via interactions with integrin subunits ,91. Due hy brain . To promhy brain . In IS, hy brain . Cell-adhy brain . Increashy brain . The second most important subnetwork in death due to IS was centered around hemostasis, thrombosis and coagulation genes, which were enriched especially in fibrin clotting or the clotting cascade, TGF-β binding, the uPA and uPAR-mediated (PID-uPA-uPAR) signaling pathways, ECM proteoglycans, integrin cell-surface interactions and platelet activation, aggregation and degranulation. Although abnormalities in the hemostasis axis are not a common cause of IS ,97,98, pVWF gene (Sma I) may be associated with an increased risk of IS [One major hotspot of the backbone of the PPII network and a major connector of both subnetworks is VWF, which shows many interactions with genes from both the immune and hemostasis networks. Such data agree with the view that VWF mediates the crosstalk between immune cells and hemostasis mechanisms and contributes to inflammation, including vascular inflammation ,101. Thesk of IS . Preclinsk of IS . Additiosk of IS .Fibrinogen (FBG) is one of the hemostasis biomarkers with an essential role in the thrombosis process because it is related to platelet aggregation after injury and inflammation ,111. FBGAnother possible major pathway which was found using enrichment analysis is the uPA-uPAR pathway. This pathway is activated in the periphery of growth cones of the injured neurons in IS brains, where it induces repair mechanisms . The binOur enrichment and annotation analyses revealed that the hemostasis subnetwork genes were significantly associated with ECM proteoglycans and integrin cell surface interactions, which are components of the BBB. These functions are impacted by IS, the consequent vasogenic edema, early angiogenesis, inflammation, and reperfusion injury ,128,129.Our network analyses showed that not only the VWF (as discussed in the previous section) but also ALB is a major controller gene between both subnetworks of death due to IS and, consequently, that ALB may also be an important drug target. ALB has anti-inflammatory, antioxidant and neuroprotective capacities and thus plays a role in the immune subnetwork, although a second clustering analysis performed in our study allocated ALB into the hemostasis network. ALB also shows antiplatelet aggregability and is a carrier for two anticlotting compounds, namely heparin cofactor and SERPINC1 ,134. SubVEGF genes  [In the enlarged giant network, VEGFA is another switch between the immune and hemostasis communalities. This gene is a member of the VEGF family and the protein acts as a mitogen, thereby activating endothelial cells and regulating neuronal cells . VEGFA iariants) .There are many more pathways and functions which link inflammation and coagulation, including TLRs  , NOD2 siF8, PAI1, uPA and F3 are target genes of NF-κB. SP1 or transcription factor SP1 (or specificity protein 1) regulates the expression of housekeeping genes which are involved in immune responses, apoptosis, chromatine remodelling, cell differentiation and cell growth [Moreover, our TTRUST enrichment analysis shows that not only NF-κB and RELA (discussed in the previous sections), but also SP1 is a major regulator of both the immune and hemostasis subnetworks. NF-κB is not only critically involved in modulating inflammatory processes but also in thrombotic responses . Thus, Nl growth . This trl growth . Our REACTOME pathway and PANTHER process enrichment analyses performed on all markers including miRNA levels revealed the strong impact of translation, RNA, metabolism of mRNA, RNA splicing, and nonsense-mediated decay on the interactome of death due to IS, indicating that dysfunctions in postischemic translation regulation are involved. During ischemia and brain cell injury, translation may arrest due to lack of ATP, and changes in translational regulation at the mRNA level and the ribosomic network may develop, which may cause a multitude of aberrations in the downstream PPI and metabolic network modules ,144. Thewww.mirbase.org (accessed on 19 September 2021)). Our study also showed that the expression of the most important miRNA, which is over-represented in death due to IS (hsa-mir-16-5p), was found to be increased in IS and to regulate NF-κB transcription [It is interesting to note that both miRNAs, which are altered in death due to IS, regulate immune functions, with hsa-mir-10a regulating IL-8, IL-6, TNF-α, GATA6 apoptotic pathways, and hsa-mir-34b regulating innate immunity  and MIR34B (miR-34b/c rs4938723). “This study is a secondary data analysis on existing data using open, deidentified and noncoded data sets and, therefore, this is nonhuman subjects research which is not subject to the Institutional Review Board (IRB) approval”. In our previous case-control studies, we identified DEPs and metabolic pathways in IS patients who died three months to one year after the IS event and compared them with IS survivors . We founhttps://www.ebi.ac.uk/intact/ (accessed on 19 September 2021)), a primary database based on peer-reviewed publications, and STRING version 11.0 , a predictive database, were used to construct the networks. We constructed zero-order PPIs (seed proteins only) and first-order PPIs  . The network characteristics  were computed using STRING and the Cytoscape plugin Network Analyzer. The backbone of the network was delineated and consisted of the top hubs (nodes with the highest degree) and the top non-hub bottlenecks . The physical interactions between the proteins/genes/mir were visualized using STRING, Cytoscape (https://cytoscape.org (accessed on 19 September 2021), Gene Ontology (GO) net (https://tools.dice-database.org/Gonet/ (accessed on 19 September 2021), OmicsNet (OmicsNet), inBio Discover (https://inbio-discover.com/ (accessed on 19 September 2021), and Metascape (https://metascape.org (accessed on 19 September 2021). Markov Clustering (MCL) was conducted using STRING to detect communalities of highly interconnected nodes with similar attributes and functions. Separate enrichment/annotation analyses were conducted on the formed communalities and upregulated and downregulated genes as well. We constructed three network types, namely a first based on the DEPs delineated in our clinical studies, a second on the combined DEPs + genes; and a third on all data combined. PPI networks were constructed with network expansion as explained previously . In briehttps://genome.jp/kegg/ (accessed on 19 September 2021) and REACTOME  pathways; (b) Enrichr, a gene list enrichment analysis tool (https:/maayanlab.cloud/Enrichr/ (accessed on 19 September 2021) to establish and visualize (via Appyters) GO molecular functions, KEGG pathways, and InterPro domains (InterPro (ebi.ac.uk (accessed on 19 September 2021 ); (c) OmicsNet  to establish REACTOME and PANTHER  biological processes, and the OmicsNet regulation explorer to establish TTRUST transcriptional regulatory relationships (www.grnpedia.org/trrust (accessed on 19 September 2021); (d) Metascape to establish and visualize the over-represented REACTOME, KEGG, PANTHER, and Wiki (WikiPathways—WikiPathways) pathways; (e) inBio Discover to establish DOID human disease phenotypes (Disease Ontology—Institute for Genome Sciences @ University of Maryland (disease-ontology.org (accessed on 19 September 2021); and (f) R package ClusterProfiler to stablish the cellular components over-represented in the network. In addition, Metascape was combined with Molecular Complex Detection (MCODE) to delineate smaller molecular complexes and to visualize enriched ontology clusters. The latter are based on accumulative hypergeometric p-values which are used for filtering whereby the remaining terms are hierarchically clustered into a tree, which is based on a 0.3 kappa score threshold, and is casted into term clusters. OmicsNet was used to build a composite network  coupled with multiomics enrichment analysis. We use false discovery rate (FDR)-corrected p-values.The different PPI networks were examined for their enrichment scores and annotated terms employing (a) STRING to establish the GO biological processes and molecular functions, and KEGG (This study defined (a) the pathways and molecular processes and ensuing pathoclinical conditions  leading to death due to IS; and (b) the possible drug targets which should be utilized to prevent death due to IS. Foremost, the major switches between the immune and hemostasis subnetworks (ALB and VWF) could be targeted to dampen the coordinated response in both subnetworks. Nevertheless, treatment with intravenous ALB did not improve IS outcome and may even cause pulmonary edema and intracerebral hemorrhage . TargetiFurthermore, the most influential genes of the network are other possible drug targets and comprise  IL6, TNF, VCAM1, IL10, and SELE. However, selectively targeting any of these backbone DEPs may improve only part of the network, although combinatorial treatment with other pathways may be more beneficial. Targeting the anti-inflammatory cytokines IL-10, IL-13, and IL-4, which were shown to reduce infarct size, is another option. In this respect, IL-10 would be a good candidate because this cytokine is part of the backbone of the PPI network, although in some conditions this cytokine may increase risk of infections.Targeting the pathways and molecular complexes that are involved in death due to IS may provide better target options, because these pathways/processes govern many players in the PPI network. Candidates are the JAK-STAT, NOD, and TLR2/4 pathways. However, since all these pathways appear to play a role in death due to IS, a combinatorial treatment targeting some of these pathways appear to be indicated. Recently, Zhu et al.  detectedFinally, this study also observed that hsa-mir-16-5p and hsa-mir-26b-5p may be targeted or used as treatments. For example, hsa-mir-26b-5p is part of a LncRNA-miRNA-mRNA network of IS, and appears to regulate the transcriptome and proteome of death due to IS. Importantly, treatment of death due to IS should also target metabolic risk factors such as glucose, HDL-cholesterol, and 25(OH)D.Additionally, none of the subjects in our DEP studies received tPA, which could have influenced biomarker levels and outcome data. As such, our DEP results are representative of the pathways leading to death in a cohort of IS patients who did not receive tPA treatment. In the second section of our studies, we included gene studies, and only one of these adjusted the outcome data for tPA treatment, while another was performed on stroke subjects who received tPA. Most studies, however, did not offer any information concerning tPA treatment. Therefore, future research should focus on the PPI network associated with death from IS in patients treated with tPA, in order to delineate drug targets that may augment the efficacy of tPA in preventing death from IS.Our findings suggest that current treatments aimed at lowering post-IS mortality should target VWF, the proteoglycan-integrin-ECM complex, TGF-β1/SMAD, NFKB/RELA, and maybe SP1.


The prediction of metabolism and biotransformation pathways of xenobiotics is a highly desired tool in environmental sciences, drug discovery, and (eco)toxicology. Several systems predict single transformation steps or complete pathways as series of parallel and subsequent steps. Their performance is commonly evaluated on the level of a single transformation step. Such an approach cannot account for some specific challenges that are caused by specific properties of biotransformation experiments. That is, missing transformation products in the reference data that occur only in low concentrations, e.g. transient intermediates or higher-generation metabolites. Furthermore, some rule-based prediction systems evaluate the performance only based on the defined set of transformation rules. Therefore, the performance of these models cannot be directly compared. In this paper, we introduce a new evaluation framework that extends the evaluation of biotransformation prediction from single transformations to whole pathways, taking into account multiple generations of metabolites. We introduce a procedure to address transient intermediates and propose a weighted scoring system that acknowledges the uncertainty of higher-generation metabolites. We implemented this framework in enviPath and demonstrate its strict performance metrics on predictions of in vitro biotransformation and degradation of xenobiotics in soil. Our approach is model-agnostic and can be transferred to other prediction systems. It is also capable of revealing knowledge gaps in terms of incompletely defined sets of transformation rules. Data requirements for environmental risk assessments of chemicals are rapidly increasing, for example in regulatory processes at the European (cf. REACH ) and gloBenzyl Sulfide pathway from EAWAG-BBD  [Conceptually, biotransformation pathways represent the chemical changes a given starting compound (referred to as root compound in the remainder of the text) undergoes upon biotransformation. They are constructed from compounds (i.e. molecular structures) connected by reactions. The pathway structure can be represented as nodes and edges in a graph. Figure atabase)  as an exExisting methods for the prediction of biotransformation products and pathways can be categorized as either knowledge-based, machine learning-based, or hybrid. Each of the two former approaches has its strengths and weaknesses. Knowledge-based approaches use expert knowledge on the basis of sets of transformation rules, in general leading to a combinatorial explosion caused by the overly general nature of the rules. Machine learning-based approaches predict pathways solely based on existing data, the performance being limited by the lack of large data sets.Hybrid methods, such as machine learning-based relative reasoning models –6 predicBiotransformation or metabolism studies from laboratory experiments or environmental observations are the basis for both training and evaluating these models and usually report only transformation products that were formed in high quantities. This is because low concentration metabolites are considered less relevant and are more difficult to analyze and identify. Therefore, metabolites formed in low concentrations are less likely to be reported. This becomes more relevant for higher generation metabolites, because pathways typically diverge into multiple branches and transformations occur on different time scales. Both effects lead to decreasing maximum concentrations with increasing depth in the pathway. Thus, uncertainty about the actual formation of unreported metabolites increases for higher generation metabolites.Multi-step reactions are represented as a single step in the experimental data.Intermediate metabolites are not observed or not elucidated.Transformation products are incorrectly assigned to the wrong educt.Concentrations of downstream metabolites become too low to be observed.Rule-based evaluation systems fail to address observed transformations not covered by the transformation rules.In this paper, we address these shortcomings by introducing a new Multi-Generation evaluation approach that addresses some of the problems of the state-of-the-art Single-Generation evaluation approach. Multi-Generation evaluation explicitly includes the compound positions in the graph. Instead of only comparing the reactions for each compound independently, entire predicted pathways are compared to experimentally derived validation pathways. Predictions at higher depth get reduced weights to account for the increased uncertainty due to higher likelihood of minor transformation products not being reported in the experimental reference pathway.The performance of prediction models is typically determined by comparing the predicted transformations for each individual compound against the associated transformation products in the experimental reference pathway. This approach does not take into account the position of the compound or reaction in the pathway. Problems arise when:Single-Generation evaluation approaches tend to incorrectly penalize prediction of these intermediates. However, the new Multi-Generation approach can take them into consideration when the downstream products are known. Their prediction is not penalized during the scoring process, and the depths of other downstream compounds in the pathway are adjusted accordingly.The new evaluation approach further introduces a way to treat intermediate metabolites in the predicted pathway. These metabolites are quickly transformed to downstream products and therefore exist only in very low concentrations. As a consequence, they are often neglected or not analyzed in experimental reference pathways. With our new evaluation approach we evaluate whole pathway predictions more realistically than before, independent of the underlying set of transformation rules, more in line with the expectations of experimentalists, and more comparable across models. Therefore, we propose to consider the pathway holistically upon evaluation of predictive performance.Overall, our main contributions are: (1) A new scoring system that quantifies the agreement between two biotransformation pathways. (2) Consideration of compound position (pathway depth) information in the evaluation of pathway predictions via scoring weights. (3) Consideration of intermediate metabolites in the evaluation of pathway predictions. (4) Use of conditional probabilities for depth considerations in predicted biotransformation pathways. This will enable further improvements of the prediction models in future work. Our methodology is a special case of graph analysis that is particularly useful for (bio)degradation or metabolic pathways and chemical reaction networks.In this section, we will first give an overview of state-of-the-art prediction systems for biodegradation pathways and their methods. Then we will summarize related work to our proposed evaluation of prediction systems for biodegradation pathways.Biochemical Network Integrated Computational Explorer (BNICE)  is a fraAbsolute and Relative Reasoning in their predictions of reactions. The process commences by applying biotransformation rules to the starting compound, and these generate potential metabolites. The absolute reasoning process then assigns a level of belief to each biotransformation [Static Scores and Site of Metabolism Scoring are other prediction options that make use of machine learning techniques. The first utilizes an occurrence ratio—actual occurrences over all possible occurrences. The latter further considers similarity on additional chemical properties—attributes from generated fingerprints and molecular weights. The processes in each of these options are repeated for all surviving biotransformations, until some preset stopping conditions are satisfied, such as reaching the maximum depth.METEOR  providesormation , 11. Bioreaction score to indicate its plausibility. The average of all individual reaction scores in the pathway gives the pathway score.PathPred  executesEAWAG-PPS (formerly UM-PPS)  performsBiotransformer  combinesOASIS TIMES  predictsIntegrated scoring systems that attempt to quantify the quality of predictions are not always found in the systems mentioned above. PathPred  computesA related field is the prediction of graph networks using machine learning techniques. Link prediction is a core component in many of the different approaches, such as analyzing information directly from the graph. This includes common neighbors , using mResearch in Graph Isomorphism addresses the quantification of similarity between graphs. Many techniques focus on properties such as orientation or structural arrangements that share little relevancy with biotransformation pathways. However, common metrics such as Graph Edit Distance  can be uIn summary, the work related to predicting biodegradation pathways so far does not take pathway structures into account. Our work in this paper aims to fill this gap by introducing a new approach that evaluates the predictions accordingly.Multi-Generation evaluation that overcomes the limitations of the current approach.In this section, we will first summarize the prediction and evaluation of models in enviPath , and theThe standard enviPath Relative Reasoning models –6, 15 usNot applicable (− 1), Applicable but not observed (0) and Applicable and observed (1). A machine learning model is trained on this matrix and then later used to predict probabilities for the combination of a new compound and the set of transformation rules.with rules Single-Generation evaluation, the predictions are then compared on the level of single reactions to the ground truth. That is, the known transformations are matched to the predictions for each rule and translated into true positives, true negatives, false positives, or false negatives. These counts then are translated to standard performance measures such as accuracy, recall, or precision. Multi-label approaches are used to aggregate the single transformation rule performance to one measure for the whole model. A detailed overview of the training and evaluation process is given in previous work [In the ous work .Multi-Generation approach does not operate on the transformations of each individual compound, but first predicts a whole pathway (see next section) and then operates on the compounds as nodes in the graph. This leads to a couple of additional aspects that require consideration. Please note, however, that the underlying prediction model is identical in both approaches.In contrast to the procedure outlined above, our As discussed before, compounds in the first generation naturally carry higher confidence in the experimental findings, compared to transformations occurring at higher depth in the pathway. This is due to the amount of test substance being divided into multiple reaction branches and only slow conversion over time. Thus, concentrations of higher-generation products are lower, which makes them more difficult to confirm experimentally and less likely to be reported. We therefore introduce a scoring system within our approach to account for the increasing uncertainty when comparing predicted and observed pathways.This scoring system assigns rewards and penalties with weights according to the generation of the respective compounds. The resulting score for a pathway represents the agreement between the predicted and observed pathways. The collective scores for each of the pathways in the validation set are used to compute conventional metrics such as recall-precision curves. This new approach evaluates the pathway as a whole across multiple generations of compounds. This is in contrast to approaches in previous work where predicted reactions in each single generation are evaluated independently.Single-Generation evaluation process, such scenarios would result in all (if any) observed reactions from the educt being ignored. However, if there are further reactions for the product compound in the data, they would still be evaluated. Alternatively, in the Multi-Generation evaluation approach, the prediction would terminate at the initial educt and no further scores will be rewarded besides false negatives for the observed products.The prediction quality of Relative Reasoning models depends on the compatibility between the transformation rules and the training set, as well as the test set. Rule sets with low compatibility can lead to problematic scenarios, e.g. where no applicable rules can be applied to the target compound structure. In the A. The observed pathway has compound A transformed to B then to C, with the reaction from Single-Generation evaluation approach would only evaluate Figure Multi-Generation evaluation approach assigns penalties to both compounds B and C for not being predicted in the pathway. Although the model assigns a high probability to reaction The We predict pathways in the test or validation set starting from their root compound. Each of the possible reactions is predicted using the supplied transformation rules, which can be represented as a possible branch evolving from the educt. We then calculate conditional probabilities for reactions according to their position in the pathway.A is shown in Fig. This procedure takes into account the relationships between the probabilities of upstream reactions with the current reaction. We adjust the preset threshold value dependent on the depth and use it in the pruning process with the resulting conditional probability. This conditional probability is defined by the product of the probability value assigned to the current reaction, multiplied with values from all of the upstream reactions. An example pathway beginning from compound A with probabilities A into compounds B and C, respectively. A probability threshold x of value C is predicted to be not observed. The algorithm then continues to determine the possible reactions for compound B, transforming to compounds D and E at the second generation of the pathway, with respective probabilities The example shows root compound D is predicted to be not observed. This steers the pathway prediction such that branches with high probabilities will be longer, while less likely branches will be cut earlier. Note that while this will change the predictions of a model, this does not introduce a new prediction approach but rather changes the way we use the prediction in the evaluation and application.These values are multiplied with TP, FP and FN are computed as follows:TP Compounds present in both predicted and observed pathways count as true positives, with weights according to their depth in the observed pathway.FP Compounds that only exist in the predicted pathway but not the observed count as false positives, with weights according to their depth in the predicted (adjusted) pathway.FN Compounds that only exist in the observed pathway count as false negatives, with weights according to their depth in the observed pathway.These definitions are used with the following Weighting System and treatment of intermediate metabolites.We calculate the pathway prediction performance based on standard true/false positive/negative counts, with the notable difference that we apply a weighting system and account for intermediates as described below. The quantities Jaccard Index as metric for pathway similarity. It is defined as:TP and FP are the True and False positives, respectively, and FN represents the False negatives. We propose a mathematical model to compare two pathways with multiple generations. In accordance to the natural decrease in experimental certainty along the pathways, the compounds are assigned decreasing weights as their generation or depth level increases. These weight values start at mentclass2pt{minimIntermediate metabolites are compounds with enhanced reactivity. They are quickly transformed to downstream metabolites, and therefore exist only in very low concentrations. These intermediates are sometimes included in the experimental data and sometimes not. This depends on the choice of the author of the experimental study report or the data package and the underlying experimental evidence. If they are not included, the transformation of the educt is reported to lead directly to the downstream metabolite. While prediction of such an intermediate would be mechanistically correct, they might not be present in the available data. Such a scenario would incorrectly inflate the count of false positives during the Single-Generation evaluation, and would be even more detrimental in the Multi-Generation evaluation procedure. The intermediate metabolite would be penalized, along with all metabolites downstream to it, as they would appear at an incorrect depth in the pathway.In order to correctly accommodate the intermediate metabolites in the evaluation procedure, we have designed a process that adjusts the depth level of the downstream compounds accordingly. The process first determines a list of compounds that are present in both the predicted and observed pathways. Then it checks if any of them are immediately downstream to one another in the observed pathway. The compound pairs which fit this criterion are examined to test if additional compounds are between them in the predicted pathway. These compounds are then added to the list of intermediates. Such intermediate metabolites might still be correctly predicted without the downstream node from the observed pathway. However, the use of a correctly predicted downstream nodes is required to identify them in a reliable manner and treat them properly. In other words, we can correct the evaluation of intermediates if and only if they have downstream products in the reference pathway that were correctly predicted.Breadth-first search. The list of in-between compounds is determined and the depth level of the end compound is then decreased by the number of intermediate compounds that are in this list of in-between compounds. The intermediate compounds are ignored by the Multi-Generation evaluation scoring algorithm.The list of intermediate compounds is used to adjust depth levels in the predicted pathway accordingly. The shortest path between each of the compounds in the pathway and the root compound is determined using a Weighting System and the treatment of intermediate metabolites.Figure Single-Generation evaluation approach.We carried out several experiments to assess the proposed evaluation approach. We used a combination of pathway data selection as well as several experiment designs to evaluate the validity and difference in performance compared to the Several sets of biotransformation pathways were used in this work:EAWAG-BBD The set of biodegradation pathways contained in the EAWAG Biocatalysis/Biodegradation Database package  containsEAWAG-SOIL The set of biodegradation pathways in the EAWAG-SOIL package  containsEAWAG-SOIL package we selected diverse subsets of pathways as training and test sets that evenly cover the chemical space. This is done to obtain a representative set without over-representation of certain compound clusters. The selection is based on the Tanimoto similarities from Morgan2 fingerprints [a and b, and TRAIN-SOIL package for model training purposes. The remaining 20TEST-SOIL package which is to be used as a test set. We excluded pathways that are not representative for typical organic chemistry, i.e. when their root compounds are inorganic salts, much larger than the rest, or contain heavy metal elements.From the erprints :\documenlgorithm  to increWe use the set of validated biotransformation rules from the EAWAG-BBD package to build relative reasoning models with compound structures from pathways inside specified training packages. We have set the probability threshold for reactions to a low value of 0.1 for all experiments, in order to efficiently capture differences between the two evaluation approaches. The following are the experiment designs used for examination, and data sets for both evaluation approaches:Full pathway Evaluate each pathway against itself. The result is expected to be 1.Empty pathway Evaluate each pathway against only its starting compound. As the comparison of the pathway starting compound is ignored in the scoring system, the result is expected to be 0.Half full pathways A random process is performed to remove all but the starting compound in approximately 50Evaluation with Test Sets A procedure where the entire chosen list of compounds is used to train a relative reasoning model once. Then we carry out the evaluation on the nominated test set TEST-SOIL. This procedure is performed on these pathway set combinations: TRAIN-SOIL, EAWAG-BBD + TRAIN-SOIL.Validation Test A procedure to strictly validate the accuracy of the proposed mathematical approach that compares biotransformation pathways. Three sub-procedures are performed:Single-Generation evaluation approach, and the list of pathways is used for the Multi-Generation approach. The process is repeated 100 times, and the results of each individual run are averaged. This approach additionally allows an opportunity to also repeatedly examine the model’s prediction ability on data that is new to the training set. This procedure is performed on these pathway set combinations: EAWAG-BBD, EAWAG-SOIL, TRAIN-SOIL, EAWAG-BBD + EAWAG-SOIL, EAWAG-BBD + TRAIN-SOIL.Evaluation with Holdout This procedure uses a random process to select approximately 66Multi-Generation evaluation approach, we summarize the results from the procedures detailed in the Experiments section in the following. We calculated Accuracy, Precision, Recall and Area under the Precision-Recall Curve (AUPRC). Due to the nature of the Multi-Generation evaluation approach, where pathways potentially have an infinite number of true negatives, the false positive rate cannot be computed. In the Single-Generation evaluation approach, the number of true negatives can be calculated from the applicable transformation rules, which are neither predicted (i.e. below the threshold) nor observed experimentally. The Area under the Receiver Operating Characteristic curve (AUROC) is hence only available for the Single-Generation approach and is provided as an indicator.To assess the effectiveness and validity of our 1,1,1-Trichloroethane pathway from the EAWAG-BBD package  is formed indirectly from chloroethane (4) via intermediates. Indeed, there is no transformation rule in EAWAG-BBD for this transformation. Therefore, the model predicts acetaldehyde only via ethanol (5) or 2-chloroethanol (not shown) as intermediate steps.We demonstrate the main differences between the two evaluation approaches on an illustrative example using the age Fig. . For simSingle-Generation evaluation approach will return three true positives: two reactions from the root node leading to products 2 and 3, and one subsequent reaction from 3 to 4. The predicted reaction from chloroethane (4) to ethanol (5) is counted as false positive. The reported transformation from chloroethane (4) to acetaldehyde (6) is not evaluated at all, because there is no corresponding transformation rule in the underlying set of rules. This example demonstrates that the Single-Generation evaluation does not adequately address the likely intermediate and the final product acetaldehyde. The resulting accuracy for this example is 0.75, although the predicted pathway can be considered correct.In this scenario, the Multi-Generation evaluation approach will return two true positives with weight 2 and 3 at depth = 1), one TP with weight 4 at depth = 2) and one TP with weight 6 at depth adjusted from 4 to 3). The intermediate metabolite ethanol (5) gets zero weight and does not influence the final score. The resulting Multi-Generation accuracy for this example is 1. The comparison of the evaluation metrics from both approaches is summarized in Table Multi-Generation accuracy would be lower  than the SG accuracy, because it penalizes also the false positives predicted downstream of primary false positives as discussed below.The mentclass2pt{minimAccuracy, Precision and Recall, as there are only true positives and no false positives or negatives. The expected values for evaluated empty pathways from both packages are also 0 for all three metrics, as there are only false positives without any true positives. The “Half Full” pathways from both packages achieve 1.0 for Precision, and a value that is proportional to the amount of empty pathways , but from a more holistic evaluation, taking into account additional aspects on the pathway level.Figure Single-Generation approach is analogous to evaluating each predicted word written by a columnist individually. The Multi-Generation approach on the other hand, is analogous to extending this to sentences and paragraphs. That is, correct predictions in the former may be penalized in the latter for being in the wrong place. Such a relationship between the two approaches indicate that it is natural to expect this difference in resulting numerical values between the two approaches.In analogy to language processing, the Multi-Generation approach for evaluating relative reasoning prediction models, that are used to predict biodegradation pathways. It includes methodology as well as performance in specifically designed experiments. The new approach evaluates predicted pathways with multiple generations of compounds holistically, in contrast to considering each reaction independently. Our approach additionally takes into consideration the increased uncertainty of observing compounds at higher depths in the pathways. We also propose an algorithm to account for intermediate metabolites, which would otherwise be incorrectly penalized during evaluation.In this paper, we present a new Multi-Generation evaluation metrics are much harder criteria. They provide a more realistic view on the prediction quality of whole pathways and reveal the incompleteness of the underlying transformation rules. With our new approach we can now start to compare the predictivity of different models on an objective basis in a model-agnostic way, i.e. independent from the model architecture and the set of transformation rules. Furthermore, we have demonstrated that our approach is more suitable to address two important characteristics of biotransformation pathway data: missing minor products in the reference data and intermediate metabolites. Single-Generation evaluation on the other hand might still be useful for determining the predictivity for individual (defined) transformation rules or for computationally demanding steps like hyper-parameter optimization.Our experiments show that the Multi-Generation approach we improved our toolbox for the evaluation and comparison of pathway prediction models, which will facilitate the development of better models. In future work, we will use this approach to improve the compatibility of the biotransformation rules, for example by generating and testing new sets of rules. Additionally, we will integrate the new knowledge about likely intermediates into model training.Overall, our experiments demonstrate that it is still a long way until biotransformation prediction models can achieve top accuracy for complete pathways. However, with the


Tripterygium wilfordii Hook F (TwHF) has been used in traditional Chinese medicine (TCM) for treating cardiovascular disease (CVD). However, the underlying pharmacological mechanisms of the effects of TwHF on CVD remain elusive. This study revealed the pharmacological mechanisms of TwHF acting on CVD based on a pharmacology approach.  The active compounds were selected from the Traditional Chinese Medicine Systems Pharmacology (TCMSP) database according to the absorption, distribution, metabolism, and excretion (ADME). The potential targets of TwHF were obtained from the SwissTargetPrediction database. The CVD-related therapeutic targets were collected from the DrugBank, the GeneCards database, and the OMIM database. Protein–protein interaction (PPI) network was generated by the STITCH database. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed by R package. The network of drug-targets-diseases-pathways was constructed by the Cytoscape software. P value ≤0.01. Finally, we constructed the network of TwHF-targets-CVD-GO-KEGG.  The 41 effective ingredients of TwHF and the 178 common targets of TwHF and CVD-related were collected. Furthermore, AKT1, amyloid precursor protein (APP), mitogen-activated protein kinase 1 (MAPK), phosphatidylinositol 3-kinase catalytic subunit alpha (PIK3CA), and cellular tumor antigen p53 (TP53) were identified as the core targets involved in the mechanism of TwHF on CVD. Top ten GO  and KEGG pathways were screened with a  These findings demonstrate that the main active compound of TwHF, the core targets, and pathways maybe provide new insights into the development of a natural therapy for the prevention and treatment of CVD. Cardiovascular disease (CVD) is a collective term for cardiovascular and cerebrovascular diseases, which is the first cause of death in the world . The burTripterygium wilfordii Hook F (TwHF), also known as Leigongteng and Thunder God Vine, has possessed many pharmacological activities such as anticancer, anti-inflammation, antifibrosis, antiatherosclerosis, and antiautoimmune disorders [κB pathway and reducing the expression of NLRP3 inflammasome [isorders –9. Recenisorders . TwHF exammasome –13. In aammasome , 15. HowNetwork pharmacology is an innovative way to analyze the complicated relationship between drugs and disease at the system level, which can provide clues for discovering new drugs . This aphttps://tcmspw.com/tcmsp.php) is an efficient pharmacology resource, which can be used to assess the pharmacokinetics of TCMs or related compounds [http://www.megabionet.org/tcmid/) [https://pubchem.ncbi.nlm.nih.gov/) database [Traditional Chinese Medicine Systems Pharmacology database   and the database .http://www.swisstargetprediction.ch), which is a free public resource used to accurately predict targets for bioactive molecules [P < 0.05) were collected after duplicate contents were removed.The targets of active components in TwHF were obtained from the SwissTargetPrediction (olecules . The thehttp://www.drugbank.ca) [https://omim.org) [https://www.genecards.org) [The CVD-related therapeutic targets were collected from the DrugBank (bank.ca) , the OMImim.org) , and therds.org) . DrugBanhttp://stitch.embl.de/) [The identified targets were uploaded to the STITCH database v5.0 (mbl.de/)  to buildP < 0.05, the top ten GO enrichments and KEGG pathways were screened.GO analysis can supply gene product biological function information and divide candidate targets into various functional modules, including cellular components (CCs), biological pathways (BPs), and molecular functions (MFs) . KEGG anCytoscape is a free application software, which can transform biomolecular interaction networks into a versatile and interactive visualization framework . The corhttp://www.pdb.org/) and decorated by removing the ligands and water motifs, adding hydrogen, and optimizing the mutation sites by the PyMOL (version 2.3). The 3D chemical structural formulas of key ingredients were collected from PubChem and energy minimized by using ChemBioDraw 3D (version 14). The sites, binding ability, and interactions between compounds and targets were analyzed by PyMOL, AutoDockTools (version 1.5.6), and Discovery Studio 2020 Clients [The crystal structures of target proteins were collected from the RCSB Protein Data Bank  for subsequent analysis , mitogen-activated protein kinase 1 (MAPK), phosphatidylinositol 3-kinase catalytic subunit alpha (PIK3CA), and cellular tumor antigen p53 (TP53) were identified based on the number of interactions . These fThe 178 candidate targets were selected for GO and KEGG pathway enrichment analyses. The top ten GO analyses of biological process (BP), cellular component (CC), and molecular function (MF) categories were screened . As the The network visualization of TwHF-targets-CVD-GO-KEGG was generated by using the Cytoscape software .The crystal structures of potential targets, including AKT1 , APP , MAPK1 , PIK3CA , and TP53  were collected . Other dThere is an urgent need to promote new drugs for CVD treatment because of the heavy burden of CVD and the poor efficacy and side effects of the currently used medicines. Network pharmacology was applied to reveal the interaction between medicines and targets of diseases, and it can comprehensively describe the complexity between drugs and diseases , 34. TheIn this study, 41 active compounds of TwHF were determined based on ADME. Pharmacological analysis suggested that these active components may have protective effects against CVD. Nobiletin has been reported to attenuate hypoxia/reoxygenation-induced injury by the inhibition of oxidative stress and apoptosis in H9c2 cardiomyocytes, as well as myocardial ischemia and reperfusion injury in vivo , 39. Triα of PI3K is encoded by the gene PIK3CA, which regulates doxorubicin-induced cardiotoxicity [α inhibitor use in breast cancer is particularly relevant given the prevalence of p110α gain-of-function mutations [Subsequently, the targets of TwHF and CVD were also identified. 178 common candidate targets between TwHF and CVD were selected. Finally, we screened 5 core candidate genes for further analysis. The interactive values and interaction indicate that these targets are closely contacted with other targets in “CVD-target PPI network” and are responsible for TwHF acting on CVD and the pathogenesis of CVD. As was well known, Akt signaling plays an important role in many processes of CVD pathology such as atherosclerosis, vascular remodeling, and cardiac hypertrophy. Several Akt inhibitors have been proven to be potential novel therapeutics for the CVD . PIK3R1,toxicity . Indeed,utations . p38 mitutations . The inautations –55. APP utations , 57. It utations .Top ten GO of each category  and KEGG pathways associated with TwHF acting on CVD were classified. These categories composed of the most key targets are considered specific and meaningful enrichment. The data showed that the major components were mainly related with multiple BPs, such as ERBB signaling pathway, regulation of generation of precursor metabolites and energy, peptidyl-serine phosphorylation, aging, peptidyl-serine modification, regulation of developmental growth, neuron death, regulation of DNA metabolic process, cellular response to peptide, and response to oxidative stress. It is reported that ERBB signaling can regulate cardiovascular development and multiple cardiac cell biology , 60. RegAccording to the screening criteria of high OB, celaxanthin, hypodiolide A, triptofordin B2, and celallocinnine were chosen for the compound-ligand interaction analysis by molecular docking to validate the effects of TwHF acting on CVD. The results of molecular docking reflected that these active compounds possess suitable anti-CVD activity. However, to verify the active properties of TwHF and the molecular target genes of anti-CVD, further experimental studies need to be performed.In summary, the network pharmacology method was performed to unveil the chemical basis and investigate the action mechanism of TwHF on CVD. Firstly, 41 active compounds of TwHF and 5 core target genes  of TwHF against CVD were identified. Then, based on the analysis of GO and KEGG, the cancer pathway was found to be closely associated with the protective effect of TwHF on CVD. It provides a theoretical basis and a clue for the pharmacological mechanism study of TwHF on CVD in this study.There are some limitations to this study. Because of the collection of targets from databases, the predicted targets might be inaccurate and specific. Other values of compounds might be also inaccurate, such as the OB value of triptofordin B2, which is greater than 100%. These values also were predicted by chemometric method. Therefore, further experimental studies of these prediction results are needed to validate the potential applications.


N groups” to excavate the empirical prescriptions, the basic prescriptions for treating CHD were summarized. We named the intersection herbs of the basic prescriptions and the high frequency herbs as the core herbal prescription. To explore the possible mechanisms underlying the anti-CHD effect of the core herbal prescription, the bioactive components of core herbal prescription and their targets were screened out by using network pharmacology. Molecular docking was performed between the bioactive components and core targets. A total of 28 potential active ingredients and 5 core targets were identified for the treatment of CHD with core herbal prescription. The enrichment analysis results indicated that the mechanism of action mainly involved neuroactive ligand-receptor interaction and calcium signaling pathway. The commonly used herbal pairs for CHD with qi deficiency and blood stasis syndrome were Huang-qi and Dang-gui. The mechanism of action of common herbal pairs was also studied by network pharmacology. This study summarized the prescription rule of TCM in the treatment of CHD and may provide a new idea for the treatment of CHD.Though widely used in the treatment of coronary heart disease (CHD), the mechanism of traditional Chinese medicine (TCM) is still unclear because of its complex prescription rules. This study prospectively collected 715 prescriptions of TCM for the treatment of CHD. The characteristics of TCM in prescriptions were described and analyzed, and the rules of prescriptions were analyzed by using association rules. Frequency statistics showed that the high-frequency herbs with a frequency of more than 60% were Gan-cao, Huang-qi, Dang-gui, Chuan-xiong, Yan-hu-suo, and San-qi. The high-frequency herb combinations were summarized by using association rules. By using the method of the “Top  The ach year . As the TCM has a history of treating CHD and related diseases for thousands of years. It has unique advantages and remarkable clinical efficacy . Due to With modern computer technology shedding light on new ways for the innovation of TCM, quantitative research methods have attracted more and more attention. The adopted tools mainly consist of text mining, knowledge discovery, TCM database technology, data mining, and drug discovery through TCMSP . The estIn recent years, with the rapid development of bioinformatics, network pharmacology has become a new and powerful means to systematically reveal the principles and functions of complex biological systems , 13, andNetwork pharmacology can form a complex interaction network based on target molecules, biological functions, and active compounds, which is in line with the natural characteristics of TCM prescriptions and can systematically clarify the mechanism of action of TCM prescriptions at the molecular level. It is expected to be a promising overall strategy for the research of TCM prescriptions –17.This study aims to summarize the prescriptions and medication rules of TCM in the treatment of CHD and analyze its action pathway. Based on the website of prominent TCM doctors, this study conducted data mining on the proven records of the treatment of CHD by prominent TCM doctors. Our study may provide a reference for the clinical use of TCM in the treatment of CHD and the analysis of its potential mechanism of action.https://121.42.193.121/) and setting the subject words as “coronary heart disease” and “western herb diagnosis.” The time was set to be unlimited and the remaining conditions were set to default options. Finally, a total of 715 related cases were retrieved.The cases were searched by using the website of prominent TCM doctors , the chemical components of core herbal prescription were obtained. By using the compounds with bioavailability (OB) ≥30% and drug-like properties (DL) ≥ 0.18 as candidate active ingredients, the targets corresponding to the active ingredients were obtained by using the UniProt database (https://www.Unitprot.org/).With the aid of the Chinese medicine system pharmacology analysis platform , and target protein–protein interaction (PPI) analysis was conducted. The whole node relationship information was exported, a target–target PPI network was constructed by using Cytoscape 3.7.2, and the core targets were screened out according to Degree.The common targets of core herbal prescription and CHD were imported into the String protein interaction database (https://david.ncifcrf.gov/) was used to perform GO enrichment analysis on the core target protein obtained after network merging. Statistical hypergeometric distribution was used to quantitatively evaluate protein populations. P value was used to reflect the significance of biological function of protein. Biological process, molecular function, and cellular component were selected. DAVID software was also used to carry out KEGG gene and signal pathway enrichment analysis on core protein target genes of drug component-disease intersection. Through the enrichment analysis of functional items, the main action pathways of core herbal prescription in the prevention and treatment of CHD were obtained.In this study, DAVID software (https://www.rcsb.org), separately. Then, target protein files were imported into Chimera 1.14 software. Each protein molecule was minimally optimized and hydrodehydrated, and its primary ligand was removed. At the same time, the core active ingredients were introduced, and the treatment method was the same as above except for removing the mixture. Finally, after the performing of molecular docking by AutoDock-Vina 1.1.2 software, the obtained results were visualized.Firstly, taking the mean parameters of “Degree” nodes in the component-target disease network model as reference conditions, the core active ingredients greater than the mean value were screened out. The MOL2 file of the relevant core active ingredients and 3D structure file of the key target protein (PDB format) were downloaded from TCMSP database and RSB database . The shape, size, and color of the diseases, herbs, targets, and ingredients were adjusted, and the target interaction relationship was visualized .Homo sapiens as the limited condition, protein interactions were obtained by using the multiple protein tool. The result involved a total of 128 nodes, 1321 edges, and an average node degree of 20.6, and the average local clustering coefficient was 0.552. As shown in The common target of core herbal prescription and CHD were imported into the String database. With Enrichment analysis of 128 common targets of core herbal prescription and CHD was performed by DAVID software. 132 GO biological processes, 19 GO cell components, and 36 GO molecular functions were obtained. The biological processes included response to drug, adrenergic receptor signaling pathway, response to ethanol, response to hypoxia, and positive regulation of nitric oxide biosynthetic process. The cell components included extracellular space, plasma membrane, caveola, axon terminus, and integral component of plasma membrane. The molecular functions included enzyme binding, drug binding, steroid hormone receptor activity, protein homodimerization activity, RNA polymerase II transcription factor activity, and ligand-activated sequence-specific DNA binding. The top 15 of them were selected by Excel to carry out the three-in-one histogram visualization processing .https://www.bioinformatics.com.cn/) online mapping website , and calcium signaling pathway .CHD is thought to be a result of “congenital deficiency and postnatal malnutrition.” Deficiency in origin includes deficiency in qi, blood, yin, and yang; excess in superficiality includes qi stagnation, blood stasis, phlegm, and cold coagulation . The preN groups” empirical prescription mining method to summarize the basic prescriptions of TCM for the treatment of CHD with the aid of Apriori algorithm. We discovered that the basic prescriptions of TCM for the treatment of CHD were “Gan-cao, Huang-qi, Mai-dong, San-qi, Yan-hu-suo, Wu-wei-zi, and Dang-gui.” We named the intersection herbs of the basic prescriptions and the high-frequency herbs as the core herbal prescription. We found that Gan-cao, Huang-qi, Yan-hu-suo, San-qi, and Dang-gui were core herbal prescription. They can alleviate CHD from multiple angles. Gan-cao has the effects of tonifying the middle body and supplementing Qi [This study collected 715 prescriptions including 182 Chinese herbs. The characteristics of TCM in prescriptions were described and analyzed, and the rules of prescriptions were analyzed by using association rules. Frequency statistics showed that the high frequency herbs with a frequency of more than 60% were Gan-cao, Huang-qi, Dang-gui, Chuan-xiong, Yan-hu-suo, and San-qi. At the same time, we also innovatively put forward the “Top nting Qi . Gan-caonting Qi . The extnting Qi . Huang-qnting Qi . Dang-gunting Qi , 28. Phanting Qi . The appnting Qi . Taking Based on network pharmacology, this study screened the effective active ingredients and targets of core herbal prescription of TCM in the treatment of CHD. The intervention effect and mechanism of core herbal prescription on CHD were systematically studied. Through PPI network analysis, we found that the degree of value in the top five targets were IL6, PTGS2, VEGFA, TNF, and MAPK1. These five key targets may play an important role in the treatment of CHD with core herbal prescription.IL-6 is independently associated with the risk of major adverse cardiovascular events, cardiovascular and all-cause mortality, myocardial infarction, heart failure, and cancer mortality in patients with stable CHD .In the pathological process of CVD, cardiovascular disease stimulates the expression of inflammatory cytokines, leading to the upregulation of inflammatory mediators such as PTGS-2 , 32, whiVEGF is a homodimeric protein, including VEGFA, VEGFB, VEGFC, VEGFD, and placental growth factor (PLGF). VEGFA can induce acute, persistent and chronic leakage , 36, invα/TNFR1 complex is involved in exogenous apoptosis of cardiomyocytes [α/TNFR1, TNF-associated apoptosis-inducing ligand (TRAIL)/DR4 and TRAIL/DR5, and their corresponding death receptors can transmit death signals from cell surface to intracellular pathways through the death domain [As a member of the TNF receptor family, the transmembrane death receptor induces apoptosis through an exogenous pathway . TNF-α/Tmyocytes , 44. Sevh domain .Atherosclerosis is a systemic degenerative inflammatory vascular disease and the primary underlying cause of CHD . Based o132 GO biological processes, 19 GO cell components and 36 GO molecular functions were obtained by enrichment analysis. Biological processes included response to drug, adenylate cyclase-activating receptor signaling pathway, response to ethanol, response to hypoxia, and positive regulation of nitric oxide biosynthetic process. Cell components included extracellular space, plasma membrane, caveola, axon terminus, and integral component of plasma membrane. Molecular functions included enzyme binding, drug binding, steroid hormone receptor activity, protein homodimerization activity, RNA polymerase II transcription factor activity, and ligand-activated sequence-specific DNA binding.A total of 104 pathways were obtained by KEGG pathway analysis, including neuroactive ligand-receptor interaction, calcium signaling pathway, pathways in cancer, cGMP-PKG signaling pathway, and TNF signaling pathway. Among these pathways, neuroactive receptor-ligand interaction has an influence on many organs and systems, including the nervous system, and may cause long-term dysfunction . The calThe results of molecular docking showed that the potential active ingredients of core herbal prescription have good binding properties with the intersection target protein of CHD. Based on this, we speculated that these components may play an important role in regulating the expression of CHD target proteins or interacting with these proteins.Based on data mining, this research summarized the basic prescriptions and core herbal prescription of TCM in treating CHD. The network pharmacological research results found that core herbal prescription can achieve the anti-CHD effects through multicomponent, multitarget, and multisignal pathways. The results of molecular docking showed that the potential active ingredients of core herbal prescription had good binding properties with the intersection target protein of CHD, which confirmed that TCM treated CHD through multiple targets and pathways.Although we have systematically studied the TCM treatment of CHD, there are several limitations of the study. Firstly, due to the limitation of data volume, the results of this study may be biased to some extent. Secondly, there are many methods of data mining for clinical prescriptions, involving association rule analysis, cluster analysis, artificial neural network, Bayesian algorithm, complex network, database method, and so on , 60. HowA new prescription of CHD composed of Gan-cao, Huang-qi, San-qi, Yan-hu-suo, and Dang-gui has been extracted. The commonly used drug pairs for CHD with qi deficiency and blood stasis syndrome were Huang-qi and Dang-gui. The core herbal prescription mainly intervenes in neuroactive ligand-receptor interaction, calcium signaling pathway, pathways in cancer, cGMP-PKG signaling pathway, and TNF signaling pathway. Our results showed that the method and software used in this study can effectively analyze the mechanism and law of TCM prescription.


Keyhole porosity is a key concern in laser powder-bed fusion (LPBF), potentially impacting component fatigue life. However, some keyhole porosity formation mechanisms, e.g., keyhole fluctuation, collapse and bubble growth and shrinkage, remain unclear. Using synchrotron X-ray imaging we reveal keyhole and bubble behaviour, quantifying their formation dynamics. The findings support the hypotheses that: (i) keyhole porosity can initiate not only in unstable, but also in the transition keyhole regimes created by high laser power-velocity conditions, causing fast radial keyhole fluctuations (2.5–10 kHz); (ii) transition regime collapse tends to occur part way up the rear-wall; and (iii) immediately after keyhole collapse, bubbles undergo rapid growth due to pressure equilibration, then shrink due to metal-vapour condensation. Concurrent with condensation, hydrogen diffusion into the bubble slows the shrinkage and stabilises the bubble size. The keyhole fluctuation and bubble evolution mechanisms revealed here may guide the development of control systems for minimising porosity. Understanding the keyhole porosity formation is important in laser powder bed fusion. Here the authors reveal the dynamics of keyhole fluctuation, and collapse that induces bubble formation with three main stages of evolution; growth, shrinkage, and being captured by the solidification front. During LPBF, mid-power (~ 100–1000 W) but tightly focused (spot sizes ~ 20–100 µm) lasers are scanned across successive layers of fine metal powder at high speed (~0.05–4 m s−1), selectively melting and consolidating the powder to build a fully dense part. The typical processing-structure–property linkage for LPBF is: steep thermal gradients and high cooling rates3 (~104–106 K s−1) favouring fine, columnar grains oriented along the build direction, producing as-printed LPBF parts that typically exhibit increased strength, reduced ductility, and increased microstructural and mechanical property anisotropy4, depending on the alloy systems.Laser powder-bed fusion (LPBF) additive manufacturing is being widely explored in both industry and academia5. With increasing laser fluence, the recoil pressure is large enough to open a deep, high aspect ratio vapour depression, referred to as a keyhole6. This is commonly used in laser welding to achieve thin and deep joints7. LPBF often operates in keyhole mode melting6 to ensure complete fusion between successive layers. Additionally, laser absorptivity increases dramatically in keyhole melting due to multiple reflections of the laser beam along the keyhole8, opening the door for fabrication of highly reflective materials  by LPBF, or enable a more economical laser heat source  to be used in LPBF without sacrificing build efficiency10. However, the keyhole is subjected to axial fluctuations and radial perturbations11 that are governed by the balance of energy and pressure14, posing a significant risk for keyhole instability16 and in some cases, collapse. Keyhole collapse often results in the formation of a bubble in the melt pool, which may get trapped by the solidification front to form a pore. Keyhole pores remaining in the final part may act as stress concentrators and sites for crack initiation and growth, making them potentially detrimental to fatigue life17 and other final component mechanical properties19.The laser fluence during LPBF is sufficient to vaporise the metal, generating a recoil pressure that pushes molten metal away from the laser–matter interaction zone20 explained the physics of keyhole pore formation during laser welding and LPBF, revealing the interactive effects of recoil pressure, surface tension, and Marangoni convection on the keyhole, and the competing influence of gravity, drag, buoyancy and thermocapillary forces on bubble motion. Recently, in situ synchrotron X-ray imaging24 has been applied to LPBF, capturing some dynamics of the keyhole and keyhole pore in the sub-surface of melt pool, including: keyhole morphology evolution25; pore formation at turn-around points during raster scanning26; pore elimination by thermocapillary forces27; pore migration under Marangoni-driven flow and pore coalescence29; pores being pushed away from the keyhole tip by acoustic waves emanating from a keyhole collapse16, and pore evolution during multi-layer LPBF31. However, the dynamics of keyhole pore formation are still not fully understood. The role of keyhole fluctuations in keyhole collapse and the evolutions of bubbles  before being captured by the solidification front, are largely unexplored. For the latter, previous studies33 explored the influence of evaporation and condensation on the dynamics of water–vapour bubbles in a superheated liquid, and effect of dissolved gas diffusion on bubble growth in casting35, but it remains unclear how evaporation, vapour condensation, and dissolved gas diffusion affect bubble evolution in LPBF.Several process models19, and also a high laser reflectivity36 in the near-infra-red, presenting challenges for laser processing. We discover a transition regime (II) between the stable (I) and unstable (III) keyhole regimes in LPBF, where the keyhole morphology changes from wide and shallow in II to narrow and deep in III. Pores are also observed to form in II, mostly present at the rear keyhole wall (RKW), while keyhole porosity is more prevalent in III with pores typically forming at the keyhole bottom. Although some prior work has suggested keyhole fluctuation is largely random, we observe regular oscillations in keyhole width and depth with significant trends in fluctuation frequency across the three keyhole regimes. We find these regimes are well defined by the front keyhole wall (FKW) angle, which collapses to a single function of the normalised enthalpy product37 for different materials. By comparing our bubble model with experimental data, we find that the bubble dynamics are defined by fast initial growth induced by pressure equalisation, followed by shrinkage due to metal-vapour condensation. Concurrent with condensation, hydrogen may diffuse into the bubble, slowing bubble shrinkage and stabilising the bubble size. Lastly, we investigate the rapid distortion of bubbles as they interact with the advancing solidification front.Here, we perform in situ synchrotron X-ray imaging during LPBF of a commercial aluminium alloy Al7A77 , which has critical applications in aerospace, biomedical and automotive industries38, Pl laser power, −2 in the keyhole melting regime6. We observed that the keyholes change in morphology from wide and shallow to narrow and deep . We also noticed that the FKW remained relatively smooth at an approximately constant inclination angle, whereas the RKW presented random wrinkles and perturbations. With increasing AED, the keyhole penetration depth increases and the inclination of the FKW become steeper (higher FKW angle 39), which is attributed to higher drilling velocity40  and By supplementing our results with previous studiesh*37 Fig., where \ents Fig., suggest25 reported a nonlinear relationship between the FKW angle and the power density .Within the keyhole-melting regime, recent studies have reported a sharp transition between stable and unstable keyhole melting, typically defined by the onset of keyhole porosityA77 Figs. and 2d, PV). Pores begin to form in this transition regime, and initiate at the RKW rather than at the bottom of the keyhole , which was also observed during laser welding of aluminium alloys15 and low carbon steel46, as well as LPBF of Ti–6Al–4V42. For similar AED, we found this transition regime becomes sharper with decreasing laser power and scan velocity (16. We speculate that the (II) transition regime is induced by the high-PV combination under large AED, which enlarges the melt pool and vapour depression zone, leading to a relatively wider transition from the stable (I) to the unstable (III) keyhole regime. For the laser spot size and alloy used in this study, a high-PV with large AED is defined as −2 based on reference14. At high-PV processing schemes for achieving large build rate in LPBF, we speculate that these schemes are most likely fall into this transition regime (II).In addition, we find that there can be an extended transition regime (II) between the stable (I) and unstable (III) keyhole regimes under high-power-velocity , a narrow, deep keyhole forms, and humps on the FKW predominantly direct metal vapour and reflected laser beams to the bottom of the keyhole. Intense evaporation and recoil pressure at the keyhole bottom can be further amplified by the rapid formation of a vapour cavity  . Note that the keyhole width is extracted as the median width along the whole keyhole depth.To quantify the keyhole and bubble dynamics, we built an image processing pipeline (see the “Methods” section) to extract the keyhole depth and width from in situ X-ray radiographs Fig.. This wa50. We also found significant trends in the keyhole width , we traced the evolutions of the keyhole-induced bubbles and extracted their centroids and equivalent diameters over their lifetime, starting after a bubble is pinched off from the keyhole and ending when the bubble is fully captured by the solidification front . We observed that bubbles evolve through three main stages, with and without the presence of a powder layer:bubbles rapidly grow immediately after being pinched off from the keyhole Fig. , thoughtthe bubbles shrink while migrating towards the rear side of the melt pool Fig. , hypothethey are captured by the advancing solidification front Fig..Using our image processing pipeline , which is generally larger than the ambient pressure 52 (53.In stage (1), as the bubble was pinched off from the keyhole, the bubble inner pressure 34. The presence of hydrogen in keyhole pores was observed by Matsunawa et al. 15, who measured ~3–12% hydrogen content in pores formed during laser welding of aluminium alloy using mass spectrometry. Hydrogen is expected to be present in both the virgin substrate and powder particles. During LPBF, the melt at the advancing solidification front can then become supersaturated with hydrogen, driving hydrogen diffusion from the melt into the bubble32 and it is several orders faster than the diffusion of other atoms54.In stage (2) bubbles shrink while migrating towards the rear side of melt pool, we observed that the bubble shrinkage undergoes a marked slowdown at the later stage of condensation  as bubbles interact with the solidification front, we observed that they experience sudden bursts of growth and shrinkage  will diffuse in, slowing the bubble shrinkage. These stages are highlighted by the tracked bubble colours in Fig. 57, and is therefore expected to be the major content left in the frozen pore.Based on the above findings, from the initiation of a bubble until it gets frozen as a pore, its composition will initially be a combination of metal vapour and shielding gas argon (Ar), which is driven into the keyhole via the Bernoulli effect58, bubble condensation model from Florschuetz and Chao59, and the ideal gas law52 to build a united bubble model (see the “Methods” section) while considering pressure-driven growth, vapour condensation and hydrogen diffusion. We compared the modelled results with experimental measurements under different keyhole melting regimes , shown in Fig. −2) and II (AED = 8 MJ m−2).To verify our hypothesis in the above discussion, we combined the Rayleigh–Plesset equationAs seen from Fig. lts Fig.  show thalts Fig. .20; and (ii) the melt flow velocity and flow pattern are location dependent across the melt pool48, and the induced drag force is local flow velocity dependent. The flow velocity is higher in the middle of the pool, hence when a bubble is formed half way along the RKW, it quickly flows backwards to the rear of melt pool. At the bottom of the pool near the solid–liquid interface, the flow velocity is low, hence bubbles detaching from the very bottom of keyhole remain nearly stationary until captured by the solidification front and turn to pores. The bubble in Fig. 60. In regime II at lower AED, we assume that a strong viscous drag force is responsible for the higher initial speed of bubbles initiated at the RKW.The bubbles migrated a larger distance in II with a tendency of going backward and upward, when initiated at the RKW, than in III Fig. , when in61 using dual-laser LPBF machines62 or hybrid LPBF63, and pore suppression via real-time control of keyhole dynamics  in a broad range of high-energy-beam processing techniques .In summary, this manuscript reveals the lifetime dynamics of keyhole pore , introducing a threshold, the normalised enthalpy product, to reveal and elucidate different keyhole pore generation mechanisms and their corresponding keyhole melting regimes under stable, transition and unstable conditions in LPBF. Our findings on keyhole fluctuation and bubble dynamics provide critical guidance  to achieve in situ pore elimination by remeltingPV combinations with large AED . We found a transition regime (II) between the stable (I) and unstable (III) keyhole regimes. This transition regime (II) is most pronounced for high-entclass1pt{minimaIn addition, we clarified the keyhole pore formation process, including the lifetime dynamics of vapour bubbles in the melt pool, which is characterised by three stages Fig. : (1) fasX–Y galvanometer scanner , an environmental chamber and a sample holder positioned at the centre of the chamber. During the experiment, the chamber was filled with argon gas at a pressure of +10 kPa to reduce oxidation. The keyhole and pores were imaged at high spatial (1.96 µm) and temporal (frame rate 50 kHz) resolutions with a FASTCAM SA-Z 2100K  camera by converting the attenuated X-ray beam to optically visible light using a 100 μm-thick LuAg:Ce scintillator. The resultant field of view was 512 pixels (1 mm) in width by 680 pixels (1.33 mm) in height. The commercially Al7A77 powder  to probe keyhole and bubble dynamics during LPBF using ISOPR , in the form of Eq. /(Flatavg−Darkavg), where FFC, I0, Flatavg and Darkavg represent the flat field corrected image, raw image, average of 100 flat filed images and average of 100 dark field images, respectively.Flat field correction, subtracting the offset background, followed by a 2D Gaussian filtering .Initial image segmentation  over the whole radiograph stack. Examples are shown in Supplementary Fig. v.51 was developed based on the segmented keyhole bubble in step (iv). The minimum number of frames that a bubble is identified was set as 6 for being considered as an effective track. Bubbles observed for less than 6 frames were mainly recaptured by the keyhole.Kalman filter tracking algorithmThe major steps of our image processing pipeline  fails in very limited cases due to background fluctuations . Here we also developed a supervised machine learning (random decision forests) model to segment keyhole and bubbles , the minimum peak distance was set to 5 (100 µs), acting as a simple low pass filter with cut-off frequency of 10 kHz. The minimum peak prominence was set to 1 standard deviation of the keyhole width/depth data set.Matlab’s built-in findpeaks function58,In the LPBF process, a rigorous modelling of the bubble dynamics while coupling the mesoscale and nanosecond multi-physics of the process remains a challenge. Here, we assume that the contents, temperature and pressure within the bubble are homogeneous, the bubble remains spherical and at rest relative to the incompressible melt pool flow. We take the bubble equivalent radius that the bubble is first identified as the bubble initial radius 40, which can be omitted in the above Rayleigh–Plesset equation, the bubble growth rate can be approximated as58Since the surface tension and the viscosity terms  with maximum pressure difference, the bubble grows with a function of time 52 (By combining the above Eqs. –6) and  and 6) aous Eqs. –(6) and 59 built the following relation between the bubble instantaneous radius In the condensation dominated stage (2), Florschuetz and Chao35 as, 70. Accordingly, the bubble radius in stage (2) that is controlled by vapour condensation and hydrogen diffusion may be described as In stage (2), the dissolved hydrogen in melt pool may diffuse into the bubble driven by the concentration difference. The bubble size 32. Here, we used the average All the parameters used in the model were listed in Supplementary Table Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Movie 1Supplementary Movie 2Supplementary Movie 3Supplementary Movie 4Supplementary Movie 5Supplementary Movie 6Supplementary Movie 7Supplementary Movie 8Supplementary Movie 9Supplementary Movie 10Supplementary Movie 11Supplementary Movie 12