Thiothrix is based on comparative whole genome analysis because of the high homology of the 16S ribosomal RNA gene sequences within the genus. We analyzed the possibility of using various conservative genes as phylogenetic markers for the genus Thiothrix. We found that the levels of similarity of the nucleotide sequences of the tRNA(Ile)-lysidine synthase (tilS) and the β subunit of RNA polymerase (rpoB) genes are in good agreement with the average nucleotide identity (ANI) values between the genomes of various representatives of the genus Thiothrix. The genomes of Thiothrix strains MK1, WS, DNT52, DNT53, and H33 were sequenced. Taxonomic analysis using both whole genomes and the tilS gene consistently showed that MK1 and WS belong to Thiothrix lacustris, while DNT52, DNT53, and H33 belong to Thiothrix subterranea. The tilS gene fragments were subjected to high-throughput sequencing to profile the Thiothrix mat of a sulfidic spring, which revealed the presence of known species of Thiothrix and new species-level phylotypes. Thus, the use of tilS and rpoB as phylogenetic markers will allow for rapid analyses of pure cultures and natural communities for the purpose of phylogenetic identification of representatives of the genus Thiothrix.Currently, the phylogeny of the genus  Thiothrix contains lithotrophs capable of oxidizing a range of reduced sulfur compounds. Bacteria lead an attached lifestyle and form abundant fouling both in natural biotopes with a constant supply of hydrogen sulfide and in anthropogenic wastewater treatment plant systems and phosphorus removal bioreactors .Thiothrix sp. CT3 has not been assigned to any Thiothrix species for 30 years due to the high sequence identity of the 16S rRNA gene sequence (98.8–99.4%) and the phenotypically and phylogenetically similar species T. fructosivorans QT, T. caldifontis G1T and T. lacustris BLT . The homology of tilS OTU1 with T. subterranea DNT52, DNT53, and H33 is 92.36%, and with T. subterranea Ku-5T, it is 97.01%. On the phylogenetic tree, this phylotype is separate from the other T. subterranea strains. Thus, the species of T. subterranea in the obtained sample is represented by OTU10 and OTU6 and, probably, by OTU1.The ied well . Three oT. lacustris and T. litoralis species. Unfortunately, the tilS gene is not applicable for their separation. However, on the basis of tilS homology, it can be inferred that both of these species are present in the studied bacterial mat. The OTU8 has 99.67% homology with T. litoralis AS and 98.01% homology with T. lacustris BLT. In contrast, OTE3 has 98.01% homology with T. lacustris BLT and 97.01% homology with T. litoralis AST. Even closer to OTE3 is the T. lacustris strain WS (99.34% identity). The main difference between T. lacustris WS and T. lacustris BLT is the presence of the nif gene cluster in the genome of the WS strain.On the phylogenetic tree, OTU8 and OTU3 are clustered with Thiothrix. Its closest relative is T. fructosivorans QT, but the homology of the tilS sequences is only 91.03%.OTU2 probably belongs to a new species of Recently, prokaryote phylogenomics based on the use of full genome sequences has found answers to many questions. Databases have been expanded with the genomes of uncultivated and difficult-to-cultivate bacteria. However, obtaining full genome sequences, especially when analyzing the metagenomes of microbial communities, is difficult in some cases. Although the 16S rRNA gene is a universal phylogenetic marker, it does not always allow for the determination of the taxonomic position and the identification of closely related species. The search for new genes that can substitute the use of 16S rRNA without losing reliability and informativeness is one of the developing fields in microbiology.Thiothrix depend on the poor performance of the 16S rRNA gene with respect to the determination of the taxonomic relationships between closely related species.The problems of the phylogeny of the genus Thiothrix. However, the need for the sequencing of a large number of isolates with unclear taxonomic identity or the inability to assemble MAG’s from natural environments hindered the study of phylogenetic diversity of the genus Thiothrix. Therefore, the identification of genes that can clarify the systematics of the genus will facilitate phylogenetic studies of the genus Thiothrix. As a result of the evaluation of the bacterial core gene set, the genes tilS and rpoB were selected as potential phylogenetic markers to determine the taxonomic affiliations of species within the genus Thiothrix. The revealed homology thresholds for the tilS and rpoB sequences for the separation of the species of the genus Thiothrix are 91.85% and 95.59%, respectively. In contrast to the 16S rRNA gene, the tilS and rpoB genes make it possible to determine the species affiliations of isolates and to estimate the species composition of Thiothrix in natural ecosystems without the need for full genome sequencing and the assembly of MAGs.Recently, based on whole genome comparisons, the ANI, AAI, and dDDH indices have been used to establish the exact taxonomic position of new isolates of the genus Thiothrix is likely to help control its abundance where its abundant fouling is undesirable.Simple and reliable tools for phylogenetic analyses are required both to construct a systematic of the genus and to study its ecology in natural and anthropogenic systems. Understanding the species composition of


Tnf expression acting post-transcriptionally through the TNF 3’ UTR in a CRISPR screen for novel regulators of TNF. GPATCH2 is a proposed cancer-testis antigen with roles reported in proliferation in cell lines. However, its role in vivo has not been established. We have generated Gpatch2−/− mice on a C57BL/6 background to assess the potential of GPATCH2 as a regulator of Tnf expression. Here we provide the first insights into Gpatch2−/− animals and show that loss of GPATCH2 affects neither basal Tnf expression in mice, nor Tnf expression in intraperitoneal LPS and subcutaneous SMAC-mimetic injection models of inflammation. We detected GPATCH2 protein in mouse testis and at lower levels in several other tissues, however, the morphology of the testis and these other tissues appears normal in Gpatch2−/− animals. Gpatch2−/− mice are viable, appear grossly normal, and we did not detect notable aberrations in lymphoid tissues or blood cell composition. Collectively, our results suggest no discernible role of GPATCH2 in Tnf expression, and the absence of an overt phenotype in Gpatch2−/− mice warrants further investigation of the role of GPATCH2.The cytokine TNF has essential roles in immune defence against diverse pathogens and, when its expression is deregulated, it can drive severe inflammatory disease. The control of TNF levels is therefore critical for normal functioning of the immune system and health. We have identified GPATCH2 as a putative repressor of  TNF expression are associated with several human pathologies, including rheumatoid arthritis and inflammatory bowel disease [TNF is essential for maintaining appropriate TNF levels in tissues and this relies upon discrete regulatory sequences within the TNF 3’ untranslated region (UTR) which in interactions with RNA-binding proteins (RBPs) regulate TNF mRNA stability, translation and splicing [TNF 3’ UTR regulatory elements include the AU-rich element (ARE) and constitutive decay element (CDE) which mediate TNF mRNA decay upon binding by RBPs [TNF New Regulatory Element (NRE) [TNF 3’ UTR in maintaining appropriately low levels of TNF in vivo, we sought to identify novel regulators of TNF mRNA that act through the TNF 3’ UTR. GPATCH2 was identified as a putative repressor of Tnf expression in a CRISPR Tnf reporter screen. This protein belongs to the G-patch domain-containing family of proteins. Members of the G-patch family are varied and diverse in function. However, the G-patch domain, characterised by six highly conserved glycine residues, is present in a number of proteins associated with RNA processing and splicing and is as such a predicted RNA-binding domain [Tumour necrosis factor (TNF) is a major pro-inflammatory cytokine with diverse cellular functions in immune defence against pathogens. Defects in the control of  disease . Post-trsplicing , 3. TNF  by RBPs , 5. We hnt (NRE) , and shont (NRE) . We alsog domain .Gpatch2 mRNA is high in rat and human testis tissue [GPATCH2 mRNA has been noted in human breast cancer samples [Gpatch2−/− mice to investigate the role of GPATCH2 in Tnf expression in vivo. Here we characterise Gpatch2−/− animals and show that loss of GPATCH2 does not affect basal TNF levels in mice and also does not alter Tnf expression in lipopolysaccharide or SMAC-mimetic injection models of inflammation.GPATCH2 is a proposed cancer-testis antigen, as expression of s tissue , 10 whil samples , 10, howTNF 3’UTR contains sequences that participate in the post-transcriptional regulation of this gene. Using a panel of reporter constructs missing one or two of these regulatory elements  and Gpatch2−/− (n > 200) mice developed and aged normally and mice of the three expected genotypes were obtained from inter-crosses of Gpatch2+/− mice at the predicted Mendelian ratios . Therefore, we generated a GPATCH2 specific monoclonal antibody by immunising rats with a N-terminally truncated recombinant mouse GPATCH2 protein comprised of exons 4–10. Lysates from diverse organs from wild-type and Gpatch2−/− (negative control) mice were screened with hybridoma supernatants for GPATCH2 expression, and several were successful in detecting endogenous GPATCH2 protein in the testis (data not shown). With such antibodies we then confirmed successful loss of GPATCH2 expression in Gpatch2−/− mice . To screen for alterations to the immune system, lymphoid organ cell numbers and cell subset composition were examined by flow cytometry in male and female Gpatch2−/− mice at 6–14 weeks. No clear differences were observed in mutants compared to wild-type controls, with the exception of a statistically significant albeit small reduction in the frequency of B cells in the blood of Gpatch2−/− male mice relative to age-matched control males  from wild-type and ent Fig. . TNF levLPS Fig. . GPATCH2DMs Fig. . DespiteTnf expression, we next examined the impact of loss of GPATCH2 in the context of TNF-dependent skin inflammation. SMAC-mimetic compounds (SMs) antagonise inhibitor of apoptosis proteins (IAPs), predisposing cells to cell death. Subcutaneous injection of the SMAC-mimetic Compound A (CompA) [Tnfr1−/− mice do not develop such a reaction following CompA injection [To further evaluate a potential role for GPATCH2 in the control of  (CompA)  depletes (CompA) . This innjection .Gpatch2−/− animals  caspase-3 (CC3) revealed no difference in the quantity or pattern of apoptotic cell death at both 1 day or 3 days post SM-injection , suggesting a role for this highly conserved protein beyond testis structure and function. GPATCH2 mRNA was shown to be elevated in ~40% of samples in a gene expression analysis of 42 clinical breast cancer specimens [Gpatch2−/− mice. While we were unable to determine a role for GPATCH2 in the control of Tnf expression, it would be interesting to assess the impact of the loss of GPATCH2 in the context of breast cancer and NF-κB activation, as this may uncover a role for GPATCH2 in vivo.Of all tissues assayed, GPATCH2 protein expression was highest in the testis. However, pecimens . There ipecimens . The funOrgans were mechanically lysed with a TissueLyser II  in RIPA buffer supplemented with cOmplete Protease Inhibitor Cocktail (Roche). BMDMs were lysed in RIPA buffer supplemented with cOmplete Protease Inhibitor Cocktail. Samples containing 40 µg total protein were reduced and denatured after addition of Laemmli buffer at 100 °C for 5 min, and separated on 4–12% Bis-Tris gels (Invitrogen) or 4–15% Criterion TGX stain free gels (BioRad) before blotting onto an 0.45 µm PVDF membrane (Merck). HRP-conjugated goat anti-rat or anti-mouse IgG secondary antibodies conjugated to horseradish peroxidase (HRP) (Southern Biotech), enhanced chemoluminescence (ECL) reagent (Immobilon) and a Chemidoc XRS +  were used for detection and imaging of proteins. GPATCH2 specific antibodies from Boster Bio (A12468), Proteintech (24366-1-AP) and Biorbyt (orb157265) were tested. AC-74 monoclonal antibody  was used for detection of β-actin. Uncropped immunoblots are shown in Supplementary Fig. Gpatch2 were cloned into T7 pET-15b plasmid (Novagen), and His-tagged recombinant GPATCH2 protein was affinity purified from BL21 E. coli (NEB) under native conditions using Ni-NTA resin (Invitrogen) and imidazole elution. Recombinant truncated GPATCH2 protein was used for immunisation of rats at the WEHI Antibody Facility, and B cell hybridoma supernatants were used at a dilution of 1/50 for detection of endogenous GPATCH2 in Fig. Exons 4–10 (amino acids 258–527) of mouse 2 for 7 days. HEK293T cells were maintained at 37 °C, 10% CO2 in DMEM supplemented with 10% foetal calf serum. BMDMs were treated with 100 ng/mL E. coli 0111. B4 LPS (Sigma). L929-conditioned medium was prepared from L929 cells cultured in DMEM (Gibco) +10% foetal calf serum.To generate BMDMs, bone marrow was flushed from the mouse tibiae and femora and cultured in DMEM supplemented with 10% foetal calf serum (Sigma) and 20% L929-conditioned medium (a source of M-CSF) at 37 °C, 5% COGFP Tnf 3’ UTR deletion reporter constructs  prepared in Dulbecco’s PBS and filter sterilised. Mouse behaviour and rectal temperature were observed every hour post-injection and mice needed to be sacrificed for ethical reasons when either i) their temperature fell below 33 °C or ii) behavioural scoring indicated clear discomfort. The personnel injecting, temperature monitoring and scoring LPS-injected mice were blinded to genotype. For SM-injection, mice were injected subcutaneously into their flanks with 100 µL 1 mg/mL Compound A  prepared in 12% Captisol . Automated blood analysis was performed using the Advia 2120 Blood Analyser .chnology  using thchnology . Exon 3 RNA was isolated from cells and tissues with TRIzol (Invitrogen) according to the manufacturer’s instructions and cDNA was synthesised with oligo(dT) primer and the Tetro cDNA Synthesis Kit (Bioline). The cDNA was amplified using SensiFAST SYBR Hi-ROX (Bioline) and analysed with a 384-well QuantStudio 12K Flex Real-Time PCR System . Oligonucleotide primers used for PCR are shown in Supplementary Table g for 2 min to separate serum. Skin of mice was mechanically lysed in protein extraction buffer  supplemented with cOmplete Protease Inhibitor Cocktail (Roche). Mouse TNF, IL-6 and MCP-1 were quantified with uncoated detection kits (Invitrogen) and read using the CLARIOstar microplate reader .Blood obtained from cardiac or retro-orbital bleeds of mice was incubated at 37 °C for 10 min, chilled at 4 °C for 20 min, and centrifuged at 5000 × Cell sorting was performed using a BD FACSAria III , and cells were analysed on a LSR II flow cytometer (BD). Fluorochrome conjugated antibodies against TCRβ, CD4, and CD8 were used to stain various T cell subsets. Fluorochrome conjugated antibodies against B220, IgM, IgD were used to identify different B cell subsets and fluorochrome conjugated antibodies against CD11b and GR-1 used to identify myeloid cell subsets. All antibodies used for flow cytometry were produced and conjugated to fluorochromes in-house and are listed in Supplementary Table P values from log-rank tests are shown, and a significant difference was defined as p ≤ 0.05. P values were otherwise calculated using the Mann–Whitney test, and only significant p values (p ≤ 0.05) are shown in the figures and supplementary figures.Significance in Kaplan–Meier survival curves were calculated using the log-rank test. Supplementary Materials TextSupplementary Table 1Supplementary Table 2Supplementary Table 3Supplementary Table 4Supplementary Figure 1Supplementary Figure 2Supplementary Figure 3Supplementary Figure 4Supplementary Figure 5ASupplementary Figure 5BSupplementary Figure 5CSupplementary Figure 5C continuedReproducibility Checklist


We investigate the degree to which impact in science and technology is associated with surprising breakthroughs, and how those breakthroughs arise. Identifying breakthroughs across science and technology requires models that distinguish surprising from expected advances at scale. Drawing on tens of millions of research papers and patents across the life sciences, physical sciences and patented inventions, and using a hypergraph model that predicts realized combinations of research contents (article keywords) and contexts , here we show that surprise in terms of unexpected combinations of contents and contexts predicts outsized impact (within the top 10% of citations). These surprising advances emerge across, rather than within researchers or teams—most commonly when scientists from one field publish problem-solving results to an audience from a distant field. Our approach characterizes the frontier of science and technology as a complex hypergraph drawn from high-dimensional embeddings of research contents and contexts, and offers a measure of path-breaking surprise in science and technology. Here, using hypergraph modeling the authors show that surprising research (in terms of unexpected combinations of research contents and contexts) is associated with impact and arises from scientific outsiders solving problems in distant disciplines. Surprise stimulates scientists to forge new claims that make the surprising unsurprising. Here we empirically demonstrate across the life sciences, physical sciences, and patented invention that, following Peirce, surprising discoveries and inventions are predictors of outsized impact. But it is unclear where new hypotheses come from. One account is serendipity or making the most of surprising encounters3, encapsulated in Pasteur’s oft-quoted maxim “chance favors only the prepared mind”4, but this poses a paradox. The successful scientific mind must simultaneously know enough within a scientific or technological context to be surprised at anomalies, but enough outside that context to imagine why they should not be surprising. Here we show how surprising successes systematically emerge across, rather than within researchers; most commonly when those in one field surprisingly publish problem-solving results to audiences in a distant other.19th Century philosopher and scientist Charles Sanders Peirce argued that neither the logics of deduction nor induction alone could characterize the reasoning behind path-breaking new hypotheses in science, but rather their collision in a process he termed abduction. Abduction begins as expectations born of theory, experience or tradition become disrupted by unexpected or surprising findings5. This demands that we predict the composition of future research with sufficient accuracy that what cannot be forecast will surprise the community of scientists and inventors who also compete to anticipate the future7. Our generative model deviates from previous work that simply scores a paper or patent’s novelty by comparing its components to those of an average or random one9. It also deviates from work that focuses on the institutional structures that influence discovery in science15. This conceptual shift to directly model research outputs leads to better assessments of research novelty and its reception as surprise by researchers within scientific and technical communities. Because of this complex interplay, we use unexpected, novel, surprising, and their derivations interchangeably in this paper.We frame surprise in science and technology as the violation of expectations held by those within a scientific field about future advance19 has achieved success in modeling discoveries and inventions with a combinatorial process. Our approach builds on this, drawing inspiration from recent demonstrations that beyond simple combinations, higher-order structure is critical for understanding complex networks, from transportation and neural networks20 to foodwebs22. We model advancing science and technology with an embedding of scientific contents and contexts from biomedical and physical science articles and technology patents. Contents refer to the substance of papers and patents such as concepts and methods, while contexts refer to scientific or technological disciplines from which concepts and methods are drawn.Prior literature23. A new combination of contents may surprise because it has never succeeded before, even though it may have been considered and attempted previously in a shared context27. A new discovery or invention that cuts across divergent contexts may surprise because it has neither been attempted nor imagined before—a combination of ideas inaccessible within a single disciplinary conversation. The separate consideration of contents and contexts also allows us to contrast scientific discovery with technological search: Fields and their boundaries are clear and ever-present for scientists at all phases of scientific production, publishing and promotion, but largely invisible for technological invention and its certification in legally protected patents and marketed products.Distinguishing contents from contexts allows us to characterize the nature of a discovery or invention’s novelty and associated surprise more precisely than before31. Our model articulates a simple cognitive process: Scientists and inventors combine things together that are (1) scientifically or technologically close and (2) cognitively salient. While formally simple, this model is more effective than those from the literature and even advanced deep learning models in capturing surprise and impact (see SI for detailed comparisons). Following from this design, we model the likelihood that contents or contexts become combined in the future as a function of (1) their proximity in a latent embedding space derived from the complex network structure of prior relationship among contents and contexts and (2) their salience to scientists through prior usage frequency.To predict new innovations in science and technology, we develop a model that generates normal discoveries as combinations of prior knowledge32 into high-dimensions, characterizing complete combinations of contents and contexts .Specifically, we develop a generative hypergraph model that extends the mixed-membership stochastic block modelxts Fig. . The modIn this study, we apply our methodology to three major corpora of scientific knowledge and technological advance: 19,916,562 biomedical articles published between 1865 and 2009 from the MEDLINE database; 541,448 articles published between 1893 and 2013 in the physical sciences from journals published by the American Physical Society (APS), and 6,488,262 patents granted between 1979 and 2017 from the US Patent database (see “Methods” for details.) We operationalize research contents as keywords distilled within community-curated ontologies—Medical Subject Heading (MeSH) terms for MEDLINE papers, Physics and Astronomy Classification Scheme (PACS) codes for APS papers, and United States Patent Classification (USPC) codes for patents. We operationalize contexts as disciplinary journals and conferences referenced within a paper (or technology classes cited by a patent). For each dataset in each year we build a hypergraph of contents where each node corresponds to a content keyword and each hyperedge (a “link” between ≥2 nodes) to a research paper or patent that combines all such keywords. Meanwhile, for each dataset in each year we separately build a hypergraph of contexts where each node corresponds to a journal or conference  and each hyperedge to a paper or patent that references these disciplinary contexts as sources of inspiration and influence.37 and gives us further confidence in the model.Across biomedical sciences, physical sciences, and inventions, our model correctly distinguishes between a content combination that turned into a publication and a random combination more than 95% of the time for a given year when trained on data from previous years . New context combinations are also predictable . See “Methods” for detailed model evaluations. The successful prediction of future combinations suggests that our model inscribes a space of latent knowledge. Researchers tend to wander locally across this space in generating new papers and patents. This aligns with previous findings regarding inertia and conservative search in scienceh as its improbability or surprisal38. Isofar as we model contents and contexts separately—a paper is simultaneously a combination of contents  and a combination of contexts —we also measure the content and context novelty of a paper separately corresponding to the suprisal of its content and context combinations, respectively.With a measure of what science and technology is expected, we can assess the novelty of a combination 39 defines a novel family of chemical compounds including Fura-2, discovered to be highly fluorescent and bind to free calcium. This was a surprising discovery—pulling diverse properties together that crossed traditional field boundaries with content and context novelties in the 99th percentile and receiving more than 16,000 citations to date. Research by Yanagisawa and colleagues40 isolated endothelin, one of the most potent vasoconstrictors at the time (1988), and employed several distinct, community spanning methods to examine its properties and mechanisms, discovering its potential as a modulator of ion channels. This discovery is in the 95th percentile of content and context novelty, receiving more than 14,000 citations since publication. By contrast, content and context novelties diverge in work by Altschul and others41, which lies in the 97th percentile of context novelty, but only the 15th percentile of content novelty. The work used a computer system to search protein and DNA databases and did not itself represent a surprising biomedical discovery, but produced a widely used tool sourced from across the computational and bio-sciences fields, making it one of the 15 most cited articles of all time42.The research our model identifies as surprising is perceived and labeled as groundbreaking by established scientists who review it for the Faculty Opinions platform—formerly Faculty of 1000 (facultyopinions.com), as detailed below  papers into citation deciles and normalizing novelty scores, transforming them into percentiles. The decile-averaged content and context novelties increase significantly with citation decile, as shown in Fig. The probability of being a hit paper—in the top 10% of most cited papers published in the same year—increases monotonically with novelty percentile. For biomedical MEDLINE) papers, those with the most surprising combinations of context are on average four times more likely to be a hit paper than random, surprising content combinations are two times more likely Fig. , and theNE papersr(5259751)<0.001, two-tailed p = 0.94), physical science (APS: Pearson-r(136274) = 0.03, two-tailed p < 0.001), and invention (USPatent: Pearson-r(2019493)<0.001, two-tailed p = 0.29). These findings suggest that our separation of content and context is necessary as they capture distinctive aspects of a scientific or technical advance. The experienced distinction between content and context novelty is reinforced by expert classifications of biomedical papers from the Faculty Opinions platform, in which selected publishing scientists post favorite papers (74% of which are hits) and annotate them with predefined tags including “New finding”, “New drug target”, “Technical advance”, “Interesting hypothesis”, and “Controversial”. When we consider papers selected and tagged by experts between 1990 and 2000, comparing against each other and all published papers from that period, we find that content novelty is most strongly and distinctively associated with New findings and Drug targets, while context novelty is most distinctively linked to Controversial, Interesting hypothesis and Technical advance  career novelty for a scientist or engineer as the improbability or surprisal of the combination of contexts in which she has ever published, (2) team novelty by the surprisal of contexts brought together across team members’ publication histories, and (3) expedition novelty by the average distance between team members’ publishing backgrounds and the publication venue of their focal paper or patent . Figure 47, as well as literature on individual and team impacts52. This pattern is different for patents  from scanned technical journals poses a continuing challenge. Moreover, our modeling task distinguishes realized papers from random ones, rather than registering accuracy at predicting all published papers. Finally, our representation of each paper as a hyperedge—an unstructured bag of keywords—ignores the structural recipe at the heart of its scientific contribution. Despite these limitations, our analysis provides a model and measure of surprise that predicts some part of how scientists evaluate research articles and use them in future science.We note several limitations associated with our study. In our analysis, we only evaluate our surprise measure on papers that pass the filter of peer review. We would like to test our model and measures on papers that did not make it through that process—presumably some containing speculative associations culled in review. Unpublished historical papers in most fields remain inaccessible, and we defer their investigation for future research, but we expect that the impact we find associated with surprise hinges on the collective skepticism novel work endures through peer review17. Here we operationalize this with an generative model based on hypergraph embedding and empirically demonstrate the power of higher-order structures in predicting future publications and patents that exceeds prior prediction efforts and even advanced deep learning architectures in capturing novelty and impact. By showing the generative sufficiency of the combinatorial approach for identifying next years’ keyword combinations underlying published discoveries and patented inventions, we demonstrate that it is not simply one among many ways to conceptualize and model novelty, but the preferred model from which to anticipate advance. By revealing that the contents and contexts of research manifest a negligible correlation (<0.1), and that they map onto different scientific judgments from experts, this study disentangles and calls attention to two conceptually distinct dimensions of a novel contribution that had formerly been understood and measured as one. Moreover, while several have demonstrated a bias against novelty for receiving grants or achieving publication57, we show how this is generally replaced by an impact bias favoring novelty among papers selected for successful publication.Combinatorial frameworks have been a promising approach to conceptualizing novelty59. Similarly, our findings reveal that scientists amplify the familiarity of their work to colleagues, editors, and reviewers, increasing their references to familiar sources by nearly 500%, likely in order to gain favor and appear relevant to reviewers and readers—to build on the shoulders of their audience64. This reinforces the internal focus of dense fields, which collectively learn more about less. Inventors, by contrast, cite and search widely to know less about more65, providing new evidence for complementarities between search in science and technology and justifying why fundamental insights emerge not only from fundamental but also practical investigations68.Our findings suggest how models that predict expected and surprising advance represent tools for evaluating the degree to which scientific and technical institutions facilitate progress. For example, our results indicate that granting scientific awards for breakthrough progress, from Nobel Prizes to the plaques and certificates sponsored by nearly every scientific society, tend to reward conservative surprises. Scientific societies convene conferences, publish journals, and honor surprising combinations of scientific contents already familiar to their members, even though surprising context combinations are more predictive of outsized citations and apparent scientific influence. This suggests that awards may represent a conservative influence on scientific advance70. Moreover, our findings call for analysis and experiments to explore the causal relationship between cross-disciplinary expeditions and punctuated advance. If expeditions do prove to systematically cause such advance, trans-disciplinary ventures could be systematically measured72, catalyzed73, incentivized74 and expanded. The suggestive importance of expeditions for impactful surprise contrasts with research that focuses on inter- and multi-disciplinarity as primary sources of advance77, suggesting that they are also consequences of it. Furthermore, a causal relationship between expeditions and advance would hold implications for graduate education, which might be redesigned to target scientific and technological breakthroughs by sponsoring the trans-disciplinary search for problems, framing every student, team and expedition as an experiment, whose complex combination of backgrounds could condition novel hypotheses with the potential not only to succeed or fail, but radically alter the knowledge they conceive and the technology they create.Finally, in exploring collective abduction, our work formalizes the concept of a ‘knowledge expedition’, where scientists from one intellectual region travel to a distant other and address a problem familiar to the new audience, but with a surprising approach. These expeditions are distinct from interdisciplinary scientists or multidisciplinary teams, and if successful, they increase the likelihood that their work will disrupt the frontier and refocus scientific attention. This insight lays out a research agenda to understand the nature and influence of trans-disciplinary research expeditions. This becomes important in the wake of recent research demonstrating that scientific subfields may defend their internal approaches and understanding against invasion from outsidersThere are no human or animal subjects involved in this study. According to the University of Chicago Social and Behavioral Sciences Institutional Review Board, the study “does not meet the definition of regulated human subjects research requiring review per 45 CFR 46.102(e)(1).”This work investigates three major corpora of scientific and technological knowledge: 19,916,562 papers published between 1865 and 2009 in biomedical sciences from the MEDLINE database, 541,448 papers published between 1893 and 2013 in physical sciences from all journals published by the American Physical Society, and 6,488,262 patents granted between 1979 and 2017 from the US Patent database (USPTO). The building blocks of content for those articles and patents are identified using community-curated ontologies—Medical Subject Heading (MeSH) terms for MEDLINE, the Physics and Astronomy Classification Scheme (PACS) codes for APS, and United States Patent Classification (USPC) codes for patents. Then we build hypergraphs of content where each node represents a code from the ontologies and each hyperedge corresponds to a paper or patent that realizes a combination of the nodes.We acknowledge the potential conservative influence from using established keyword ontologies rather than all of the words from titles, abstracts or full-text of articles and patents. Nevertheless, we note that the ontologies we examine do evolve over time, with active additions following the concentration of research in a given area. Moreover, these ontologies allow us to use the community of authors (APS), annotators (MEDLINE), and examiners (USPTO) to crowdsource the disambiguation of scientific and technological terms. Future work may explore how words differ from keywords, especially in the emergence of new fields.MEDLINE is the U.S. National Library of Medicine’s (NLM) bibliographic database. It contains abstracts, citations, and other metadata for more than 25 million journal articles in biomedicine and health, broadly defined to encompass those areas of the life sciences, behavioral sciences, chemical sciences, and bioengineering. The version of data used in this study contains 19,916,562 papers published between 1865 and 2009. Because coverage for papers prior to 1966 is limited, our main analysis focuses on papers published in and after 1966, but with pre-1966 papers as background information when predicting new content and context combinations. To allow the published papers to accumulate enough citations for assessing their impact, our novelty analysis focuses on papers published in and before 2000. In sum, all papers are used to estimate the hypergraph embedding model and 10,057,935 papers are used in the novelty analysis.Medical Subject Headings (MeSH) is the National Library of Medicine’s controlled terminology used for indexing articles in MEDLINE. It is designed to facilitate the determination of subject content in the biomedical literature. MeSH terms are organized hierarchically as a tree with the top-level terms  corresponding to major branches such as “Diseases” and “Chemicals and Drugs”, with multiple levels under each branch. Terms in the bottom level are the most fine-grained, detailed concepts associated with distinct biological phenomena, chemicals, and methods. We use the bottom-level terms from the three branches that are central to the biomedical field— “Diseases”, “Chemicals and Drugs”, and “Analytical, Diagnostic and Therapeutic Techniques and Equipment” (or methods for short)—as nodes in the hypergraphs of content of MEDLINE papers. Terms from the Diseases branch include conditions such as “lathyrism” and “endometriosis”; examples from the Chemicals and Drugs branch include “elastin”, “tropoelastin”, “aminocaproates”, “aminocaproic acids”, “amino acids”, “aminoacetonitrile”, and “amyloid beta-protein”; and examples from the Analytical, Diagnostic and Therapeutic Techniques and Equipment branch include “polyacrylamide gel electrophoresis”, “ion exchange chromatography”, and “ultracentrifugation”. NLM curators manually affix MeSH codes to papers as they are ingested into MEDLINE and made available through the popular PubMed platform.Physical Review, Physical Review A, B, C, D, E, I and X, Physical Review Special Topics - Acceler and Physics, Physical Review Letters, and Reviews of Modern Physics.The APS dataset is released by the American Physical Society (APS). It contains 541,448 papers published between 1893 and 2013 in 12 physics journals: The dataset contains basic metadata for each paper including title, publication year, abstract, etc. It also contains the Physics and Astronomy Classification Scheme (PACS) codes associated with each paper. We use the PACS codes as nodes in hypergraphs of content to characterize APS papers. Similar to MeSH, PACS is also a hierarchical partition of the whole spectrum of subject matter in physics, astronomy, and related sciences. Example PACS codes include “Mathematical methods in physics”, which range from “Quantum Monte Carlo Methods” to “Fourier analysis”; “Instruments…” such as “Electron and ion spectrometers” and “X-ray microscopes”; “Specific theories…” like “Quark-gluon plasma” and “Chiral Langrangians”; and “...specific particles” ranging from “Baryons” to “Quarks”. Unlike MeSH codes, which are added by curators, PACS codes are affixed by authors to their own papers through the publishing process. The PACS codes were developed by the American Institute of Physics in 1970 and have been used by APS since 1975. Since PACS codes are not available for papers published before 1975 and the coverage of them in papers prior to 1980 is limited, our main analysis is restricted to APS papers published after 1980, but with the pre-1980 papers as background information when predicting new content and context combinations. To allow the published papers to accumulate enough citations for assessing their impact, our novelty analysis focuses on papers published in and before 2000. In sum, all papers are used to estimate the hypergraph embedding model and 148,786 papers are used in the novelty analysis.The dataset only contains citations between the APS papers. In order to obtain external citations we query the Web of Science (WOS) database to collect all the journals cited by the APS papers. Particularly, in the WOS database we find all the papers published by the 12 APS journals, and then all the journals cited by those papers. The journals are then used as nodes in hypergraphs of context for the APS papers. In addition, we query the WOS database to collect the number of citations a paper receives for more accurate assessment of the paper’s impact.The US Patent dataset is released by the US Patent & Trademark Office (USPTO). It contains 6,488,262 patents published between 1976 and 2015. The dataset contains basic metadata for each patent such as title, publication year, USPC (United States Patent Classification) codes, etc. The USPC is a classification system used by USPTO to organize all U.S. patent documents and other technical documents into specific technology groupings based on common subject matter. The USPC is a two-layer classification system. The top layer consists of terms called classes, and each class contains subcomponents called subclasses. According to USPTO, a class generally delineates one technology from another and every patent is assigned a main class. As such, we use the class codes as nodes in the hypergraphs of context for patents. Subclasses delineate processes, structural features, and functional features of the subject matter encompassed within the scope of a class, and thus we use subclass codes as content nodes for the patents. In total, there are 158,073 subclass codes (content nodes) and 496 class codes (context nodes).To allow the granted patents to accumulate enough citations for assessing their impact, our novelty analysis focuses on patents granted in and before 2000. In sum, all patents are used to estimate the hypergraph embedding model and 2,436,257 patents between 1976 and 2000 are used in the novelty analysis.78, which contains publication histories of nearly all Nobel prize winners from the past century. However, the focus of that dataset is on the Nobel laureates, but our study focuses on award-winning papers. While it is relatively easy to find out the person who won a prize, it is hard to pinpoint the papers that contribute to the winning of the prize. Li et al. take a generous approach by including papers cited by Nobel lectures and papers published in the same period of one’s prize-winning work . This results in noise for our analysis as not every paper in their dataset is a prize-winning paper. As a conservative solution, for every Nobel laureate we take their most cited paper in the dataset and use only those papers as award-winning papers in our analysis. We acknowledge that a few Nobel prizes are attributed to an opus of work and this filtering process might miss a few relevant papers, but the most important (in terms of impact) paper for every prize is kept, with every paper representing an award-winning paper.The Nobel prize-winning papers are derived from the Nobel laureates dataset by Li et al.25. They define award-winning papers as those authored by a scientist who won an international award or prize. They first created a large list of prizes by drawing from the category pages for biology awards, medicine awards, and chemistry awards in Wikipedia and then validated them with several biomedical scientists. Then they identified the winners of 137 different prizes and awards from biology, medicine, and chemistry. For each winner, if at all possible, they retrieved all papers written from 0 year to 30 years before the award was granted.In Fig. False positives could be introduced due to mis-assignments of papers to awards. However, as these false-positive papers would be typical rather than award-winning, they likely behave like the majority of published papers and dilute the effect we report. False negatives are also possible if we missed papers published by certain award winners. For instance, papers written by authors with non-English characters in their names are underrepresented. These false negatives similarly dilute our estimate of the distinctiveness of award-winning papers. Our non-Nobel prize findings should therefore be viewed as a conservative estimate of the difference between prize winning papers and the pool of all papers. Still, we are able to identify that prize winning papers are significantly higher in content novelty than the average paper within a given citation decile, but that they are not systematically higher in context novelty, likely because awards are typically conferred by a single context—a journal, association, or field.79 and resting on mature analysis tools for simple graphs, which define direct links between entities. Here we model science and technology as a complex hypergraph drawn from an embedding of research contents and contexts, where individual discoveries or inventions are rendered as complete sets of each. For a given hypergraph, whether composed of content or context nodes, the propensity of any combination of nodes to form a hyperedge is modeled as a product of two factors: proximity between nodes in the combination and their cognitive salience. Combinations with higher propensity will be more likely to turn into papers and patents, agreeing with the intuition that people tend to search locally and pursue trending topics.We model discoveries and inventions as hypergraphs of contents and contexts with a combinatorial process that assembles previous concepts and technologies from prior papers or patents. Past research examining combinatorial discovery and invention has modeled only partial combinations, deconstructing new products into component pairsi is associated with a latent vector θi that embeds the node in a latent space constructed to optimize the likelihood of observed papers and patents. Each entry θid of the latent vector denotes the probability that node i belongs to a latent dimension d, and thus h is modeled as the probability that those nodes belong to the same dimension, ∑d∏i∈hθid . This hypergraph embedding model can be understood as an extension of the mixed-membership stochastic block model32, which was designed for networks with only pairwise interactions.To formalize this idea, each node mentclass2pt{minim80. Accordingly, we associate each node i with a latent scalar ri to account for its salience, presumably tied with its overall connectivity in the network.We also account for each node’s cognitive availability because most empirical networks display great heterogeneity in node connectivity, with a few popular contents and contexts attracting the most attention—intensively drawn upon by many papers and patents. Previous work has shown that by integrating heterogeneity of node connectivity, the performance of community detection in real-world networks improvesλh) of combination h—our expectation of its appearance in actual papers and patents—as the product of proximity between nodes in h and their salience or visibility:h is modeled as a Poisson random variable with λh, the propensity of that combination, as its mean:G is the product of probabilities for observing all possible combinations:h and H is the set of all possible combinations.  denotes all unknown parameters:Assembling these components, we model the propensity . We employ a widely used approach in machine learning, negative sampling, to address this imbalance issue. Specifically, in each iteration of the training (optimization) process, we randomly sample as many non-hyperedge combinations as hyperedges to construct balanced hyperedge and non-hyperedge sets. Negative sampling effectively changes the objective function we optimize. The original objective function can be rewritten as E is the set of hyperedges and h is randomly drawn from X then becomes h is a draw from 81, question answering82, and many other applications, outperforming the comparable but unbiased contrastive learning approach by co-minimizing bias and variance81. We also found this approach worked best in our model as seen in high predictive power despite bias. Nonetheless, we acknowledge that estimation could be further improved by considering more advanced sampling schemes83, which we defer to future work.Second, because the real hypergraphs are sparse, the sets of hyperedges and non-hyperedge combinations are exceedingly unbalanced with the number of hyperedges to be on the order of Lastly, to facilitate the stochastic gradient descent, we take a mini-batch of hyperedges and non-hyperedges to compute the gradient of the objective function at each step of the training process.t covered by the dataset, we fit the model to the hypergraph of contents and separately to the hypergraph of contexts up to year t. In total, we estimate T is the number of years covered by our study from the corresponding dataset. Then, we evaluate the fitness of each model by its predictive performance of (out-of-sample) future combinations.As a brief summary, we investigate three datasets: MEDLINE, APS, and US Patent; each dataset contains hypergraph data over several decades; and we model content and context hypergraphs separately. Consequently, for each dataset, for each year h of MeSH terms in year 2009, following Equation (1) For example, given hypergraphs of MeSH terms up to and including year 2008, we estimate the hypergraph embedding model, and use the estimated model to predict hyperedges in 2009. Specifically, using the estimates of the parameters A hit paper (or patent) is defined as one among the top 10% most cited papers (or patents) published in the same year. For example, for all the papers published in 1990, we count all the citations they received in the time period covered by our dataset, and the top 10% most cited papers are hit papers.To study the effect of novelty on the probability of being a hit paper, for all the papers published in each year, we first transform the raw novelty scores of the papers into percentiles, and then divide them into 30 equally sized bins in ascending order of the novelty scores. Then we assess the probability of hit papers for each bin as the fraction of hit papers in that bin. Finally, the probability of hit papers for each bin is plotted against the center percentile value in that bin in Figs. Similarly, to study the joint correlation of different novelties with the probability of being a hit paper, we divide the papers into multiple bins according to content and context novelty simultaneously. For the joint effect of content and context novelty, we divide the paper into 30-by-30 bins in terms of both novelties, then calculate the fraction of hit papers in each bin. Next we regress the probability of being a hit to content and context novelties. Fitted values of hit probability are then visualized as a 2D heat map across the full range of content and context novelty scores.For the joint effect of career, team, and expedition novelty, we divide the papers into 20-by-20-by-20 bins in terms of their source novelty scores, and then calculate the fraction of hit papers in each bin. Again, we fit a polynomial function between hit probability and the three novelties to obtain the average relationship:The resulting 3D heat cube comprises 6 2D heatmaps where color denotes the fitted probability of hit for any given values of .A detailed comparison with other models in predicting hit papers or patents is presented in the Supplementary Information.i published in journal X, we calculate the similarity between the journal X and every journal cited by paper i. Similarity is quantified by the cosine similarity between two vectors representing the content of the two journals, conditioned on the content of paper i. Specifically, each journal is represented by a vector and each entry in the vector corresponds to a content node ; the value of an entry is the number of papers containing the corresponding content node and ever published by the journal, appropriately normalized so that the sum of the vector is 1. In other words, the vector consists of the loadings of the journal on different contents. When calculating the similarity between two journals, we do not directly compute the cosine similarity between their vectors, as those vectors contain substantial information irrelevant to the paper currently under consideration. Instead, we use only the entries corresponding to content nodes in paper i to calculate the cosine similarity between the two journals.To assess the intensity with which scientists and inventors cite contexts  familiar to their audience, we compute the similarity between every pair of context nodes where one cites the other. For example, for a paper As we sweep through all papers (or patents), we obtain a distribution of the similarity between citing-cited context pairs: the number of times for which context nodes at a given similarity with the audience context  are cited. To normalize this distribution, we also compute the potential space of citation similarity—the number of times at which context nodes at a given similarity would be cited at random. We achieve this by the following procedure: for each paper, sample as many context nodes uniformly at random from all context nodes as those originally cited, treat the sampled context nodes as if they were cited by the paper, and carry out the same similarity calculations as above. Finally, we have two distributions of similarity between citing-cited context pairs—one observed and one simulated by random sampling—and we take the ratio of the two as the likelihood of citing a context at a given similarity with the audience’s context.i , we define career, team, and expedition novelty as follows.Assuming C. Then we calculate the surprisal across all contexts in her career using Eq. (2):For any scientist, we collect all contexts  in which she has ever published papers into list T. Next, we use Eq. (2) again to calculate the surprisal of this list: For any paper, we first pool all the contexts  where authors of the paper have published, without duplication. We denote this list of contexts by T. Denoting the context where this paper is published by For any paper, we again collect all contexts where its authors have published into a list 84, which include prominent measures that approach interdisciplinarity from the perspective of journal keyword combinations using the Web of Science86 and diversity or inequality indices  assessed over these keywords88. Our measures differ from those above by being more granular and embedding the keywords into a continuous, high-dimensional space. Other work in the literature accounts for the similarity or difference between research fields combined90. Our measure does this automatically by accounting for the combinations of keywords required to characterize current literature and predict future research. Still others are built atop measures of centrality in networks of publication. By measuring the inner product of term vectors, our measure also approaches a continuous measure of centrality within the embedding space of contents and contexts. In short, our measure captures desiderata from each of the major categories of novelty measurement—diversity of combination, accounting for differences between components, and which represents centrality in the continuous embedded manifold.Our career, team and expedition novelty measures also contribute to the literature on interdisciplinarity measuresFurther information on research design is available in the Supplementary InformationPeer Review FileReporting Summary


Over the last two decades, the changing paradigm of heart failure with preserved ejection fraction (HFpEF) has transformed our understanding not only of the pathophysiology of this clinical entity but also the diagnostic and therapeutic approaches aimed at treating this complex patient population. No longer HFpEF should be seen as simply left ventricular diastolic dysfunction but as a group of that in addition of having small and thick left ventricles with abnormal diastolic filling patterns as their main pathophysiologic abnormality; they also have whole host of different abnormalities. In fact, this heterogeneous clinical entity embodies numerous mechanisms and is linked to multiorgan dysfunction, with hypertension and obesity playing a major role. Although we have gained an enormous amount of understanding not only on the causes but also the downstream effects of HFpEF, there is still much to be learned before we can fully comprehend this complex clinical entity. It is the main intention of this review to synthesize the most recent attributes, mechanism, diagnostic tools, and most useful therapeutic alternatives to be considered when evaluating patients either complaining of dyspnea on exertion as well as exercise intolerance or those recently admitted with HF symptoms but with normal LVEF in the absence of any other valvular abnormalities Over the last 3 decades, heart failure (HF) has become an increasingly important public health problem. As reported on the recent 2022 American Heart Association, the lifetime risk of developing HF remains high, with variation across racial and ethnic groups ranging from 20% to 45% after the age of 45 , 2.While definitions used to differentiate HF with preserved ejection fraction (HFpEF) from HF with reduced (HFrEF) have been somewhat fluid over time and there is a wide data heterogeneity, recent estimates project that HFpEF will become the dominant form of HF while HFrEF will show a decreasing prevalence trend .While adoption of the new universal HF definition eliminated the previously used ambiguous approach to characterize and treat HF, standardization of terms and the use of a more focused approach not only has narrowed the communication gap between clinicians and researchers but also empowered healthcare providers and institutions to better serve patients by delivering more effective health care services –14.As our understanding of HFpEF continues to improve, it has become apparent that fundamental differences exist between HFrEF and HFpEF that could not be simply explained based on difference in EF values. In fact, current data seem to point out that numerous abnormalities are particularly relevant to HFpEF patients. The latter might not only explain differences in terms of clinical presentation and overall outcomes but also diagnostic considerations as well as therapeutic management.This review will revisit our current pathophysiologic understanding of HFpEF and how the different phenotypic expression of this clinical entity can be characterized and diagnosed while providing an updated version of the latest therapeutic options that are now clinically available .First order of business is to recognize the distinction that exists in terms of LV diastolic dysfunction (LVDD) and HFpEF. Specifically, even when LV LVDD plays a critical role in the pathophysiology of HFpEF , not allTo better understand the importance of LV diastolic function in terms of cardiac performance, it is critically important to recognize the specific mechanical elements responsible to determine LV diastolic function including LV relaxation, chamber stiffness, and early diastolic recoil, all of which ultimately determine LV filling pressure .Equally relevant is to keep in mind that LV diastolic function is not simply dependent on LV of these mechanical properties; but it is also modulated by a series of other mechanisms that include right ventricular–LV interaction, left atrial function, pericardial influence on LV filling, LV systolic properties, LV systolic and diastolic dyssynchrony, coronary blood flow, and tissue perfusion .In its most simplistic form, diastole can be divided into 4 components that include (1) isovolumic relaxation, (2) early filling, (3) diastasis, and (4) atrial systole for those in normal sinus rhythm . FurtherNow, these 4 components must be seen in their proper perspective, and to accomplish this goal, it has been proposed to envision the heart as a muscle-powered oscillator. When seen in isolation, the LV is a single woven muscle group that has no opposing muscle group; however, the oscillatory function is dependent on the systolic (contraction) and diastolic relation function of the LV. Consequently, the LV contracts in systole and expands in early diastole in accord to the well-known extracellular and intracellular elastic elements that include the extracellular matrix primarily made of collagen and elastin, the intracellular giant protein titin, and the visceral pericardium, which acts like a “shrink-wrap” and stores elastic energy in systole, contributing to the elastic recoil in diastole –24.The almost perfect harmony existing between LV systole and diastole must be mechanically coupled so that the functions of both cycles are preserved. Explicitly, elastic energy is stored during LV contraction, which powers the chamber wall to recoil or to continue with unopposed relaxation as sarcomere cross-bridge uncoupling proceeds so that both chamber properties of stiffness and relaxation simultaneously determine global diastolic function . ConsequFurthermore, it is critically important to recognize changes in left atrial (LA) size although quite complex and multifactorial ; these cIn so far, it is quite apparent that aside from intrinsic abnormalities within the LV myocardium that directly control chamber stiffness and relaxation, the volumetric load or preload as well as effective atrial contraction is all needed to accomplish normal LV filling.Another important element, closely related to changes in LA size has been described by Kovacs' group as a piston function to characterize the relationship between the LV and LA during the cardiac cycle . This inAfter this brief pathophysiologic introduction describing the most important relevant elements describing LV diastole, it is time to describe ways to diagnose LVDD. Even though invasive LV hemodynamics via cardiac catheterization studies have been traditionally considered the gold standard approach allowing quantification of the rate of myocardial relaxation, echocardiography (echo) has now surpassed invasive methods as the preferred noninvasive imaging tool currently used for routine diagnosis and characterization of LV diastolic dysfunction , 21, 27.τ (tau) [However, it is critically important to recognize that echo is not perfect, and certain limitations need to be reviewed. First, traditional echo-Doppler variables such as the E/A ratio, isovolumic relaxation time, deceleration time, and pulmonary vein Doppler do not allow direct measurement of LV relaxation, stiffness, or filling pressure –31. Secoτ (tau) .Despite the relative simplicity of the 2016 publication by the American Society of Echocardiography and the European Association of Cardiovascular regarding their recommendations pertaining LV diastolic function assessment , not onlDespite these limitations, a complete echo examination to assess LV diastole should be basic information regarding age, gender, body surface area, heart rate and rhythm, and blood pressure.Once this basic demographic data is obtained, quality of all echo and Doppler signals must be determined as this will ultimately ascertain the feasibility if each study in validating or eliminating the possibility that this imaging tool would not be able to establish a definitive diagnosis . The 201Once we have made these determinations, critically important variables that need to be acquired in each comprehensive study need to include measures of LA and LV volumes that are critically important as well as LV wall thickness, LVEF, presence of any valvular abnormalities, and severity of tricuspid regurgitation with an estimate of pulmonary artery pressures .As in any other clinical case, the presence of a single measurement that falls within the normal range does not necessarily indicate normal LV diastolic function. That is, we should strive for consistency between two or more of the indices, and echo indices of LV diastolic function should always be interpreted in a wider context that includes clinical status and the other 2D and other Doppler parameters . The latEven when on this writing, we would not comment on all potential echo Doppler measurements recommended by the American Society of Echocardiography for LV diastolic function assessment ; we woulEven though introduction of this tool has certainly advanced our understanding and provided a more accurate as well as reliable characterization of LV diastolic function, there is a well-described difference between medial and lateral MA diastolic velocities , 33, 37.Thus, in this review we will continue referring to the lateral MA in our discussion of LV diastole, specifically, to the early MA diastolic (e') TDI velocities. Not only this e' velocities have been identified as important prognosticator but also the ratio that is obtained using the transmitral to this e' TDI velocity (E/e') . These mMore importantly, of all available echo LV diastolic parameters that can be used for assessing LV diastolic function, the peak MA TDI e' velocity has the strongest impact on cardiac mortality among other TDI variables . FurtherEven though this review will not discuss all echo Doppler variables, Before discussing HFpEF further, it is important to mention a clinical entity where it has variously been labeled as diastolic heart failure or heart failure with preserved LV function to a more preferred term now of heart failure with normal ejection fraction (HFNEF) as almost half of patients with symptoms of heart failure are found to have a normal LV ejection fraction, and the systolic function is not entirely normal . The chaAs previously mentioned, the causality behind HFpEF extends well beyond the simplistic identification of LVDD. As already established, although LVDD plays a fundamental, overarching role in the pathophysiology of HFpEF , 38, 39,Explicitly, a complex interplay of numerous cardiometabolic diseases such as diabetes, obesity, and hypertension as well as contributing factors within the heart, systemic vasculature, and peripheral tissues has been described .Before we discuss the complex interaction of metabolic factors identified in the clinical expression, we will first succinctly discuss some individual cardiac and peripheral vascular known to affect LV diastole. Understanding of the latter elements would certainly be beneficial when describing the different clinical HFpEF phenotypes.Despite the connotation of “preserved” EF, data has been clear in describing abnormalities in terms of systolic performance such as midwall shortening, torsion and twist, and circumferential and longitudinal shortening using tissue Doppler or strain imaging –47.In addition, these subtle impairments in LV systolic function noted at rest are further worsened during exercise and likely explain the development of dyspnea on exertion and reduced exercise capacity in patients with HFpEF .The importance of LA remodeling has gained enormous momentum as significant pathophysiological, structural, and mechanical functional changes have been identified not only in response to heart failure, hypertension, cardiac valvular disease, diabetes mellitus, and obesity, but also, these changes often become maladaptive and responsible for worse clinical outcomes .The occurrence of LA remodeling differs significantly between the two main HF phenotypes, namely, HFpEF and HFrEF. Specifically, patients with HFrEF exhibit significantly greater LA dilation and mechanical systolic dysfunction . In contBased on these results, any intervention aimed at restoring the LA mechanical function is expected to provide favorable effects on pulmonary vasculature and right heart hemodynamics . ConversFor example, the resultant LA wall scarring and reduction in LA volumes seen after repeated radiofrequency AF ablations have been linked with development of pulmonary hypertension , while iSince the occurrence of AF is known to affect exercise capacity and adversely affect outcomes in HFpEF , 54, intEven though, individual drug treatment interventions will be addressed later; we believe it is important to introduce a relatively new concept that suggests implementation of a more holistic approach when managing AF in HFpEF. This was introduced in the ABC  pathway . In esseTherefore, based on currently available data, it appears as even though HFpEF and AF coexist in many patients and this number is only expected to increase given the continued aging of the population, more studies are needed that will close the gap regarding the close interaction existing between the LV and LA as well as their relationship with the pulmonary artery to right ventricular functional unit. Certainly, much more understanding is needed to better decipher the mechanisms that lead to worse clinical outcomes as well as which therapeutic interventions would be best suited to curtail these adverse events. Certainly, we patiently wait the results of future trials.The importance of the RV-pulmonary artery unit and coupling of this system is critically important in determining RV systolic performance . TherefoTherefore, it would be clinically relevant to objectively assess RV systolic dysfunction when pulmonary pressures are elevated; however, RV dysfunction among HFpEF patients could also occur without RV pressure overload . The relKnowing the well-described cascade of RV anatomical and adaptive changes that occur in response to elevation in pulmonary pressures –65, therTricuspid annular plane systolic excursion (TAPSE)Tricuspid annular systolic tissue velocity (s')RV fractional area changeRV index of myocardial performanceRV free-wall strainMeasures of atrial strainThe recently described TAPSE/PASP ratio that has been found useful in assessing RV-PA coupling and particularly useful in identifying HFpEF patients at greater risk of adverse clinical outcomes.Therefore, it is time to introduce the following echo Doppler variables that should be included in defining LVDD, particularly if pulmonary hypertension is noted –75:TricUnder normal circumstances, the ascending aorta recoils during each cardiac cycle to facilitate early diastolic left ventricular filling . In sharPatients with HFpEF demonstrate increased arterial stiffening that is well beyond what expected for either normal aging or the presence of accompanying hypertension . These vμm and <200 μm in diameter, respectively) as well as capillaries. Even though, these minute vessels cannot be currently imaged despite advances in our current technology; functional assessment of the microcirculation can be attained either by invasive measures of coronary flow reserve or fractional flow reserve or noninvasively using positron emission tomography [The coronary microcirculation encompasses prearterioles and arterioles  . Under nCurrent data suggests that the coronary microvascular is critically related to the development of HFpEF. Metabolic comorbidities highly prevalent in HFpEF can induce a proinflammatory state. A systemic inflammatory state can provoke microvascular endothelial dysfunction and a related reduction in nitric oxide bioavailability leading to negative cardiac remodeling and myocardial dysfunction .Even when patients with HFpEF are known to have elevated in left heart pressures, it remains unclear how this affects pulmonary gas transfer or its determinants at rest and during exercise. This was addressed by Hoeper et al. that showed that patients with HFpEF that exhibit exercise intolerance have altered pulmonary function and gas exchange both at rest and especially during exercise .In a study by Obokata et al., these investigators reported reduction in DLCO greater than 45% and conferred a threefold increased risk of mortality in patients with pulmonary hypertension and HFpEF when compared to patients with a DLCO > 45% . These aReduced capillary densityIncreased intramuscular fat contentMicrovascular endothelial dysfunction that blunts exercise induced peripheral vasodilatation and thus reducing perfusionDecreased ability to peripherally extract oxygenAbnormal pulsatile aortic loading during exerciseReduced venous capacityBaroreflex-mediated venoconstrictionRedistribution of venous blood, mostly because of active vasoconstriction causing decreased splanchnic venous capacityReduced venous returnExcess venous blood volume.The following peripheral abnormalities have been reported among patients with HFpEF , 89–91:Furthermore, anemia has been commonly seen among patients with HFpEF, particularly, in the setting of chronic kidney disease . Even thThe increases in chamber sizes, wall thickness, and increased epicardial fat deposition, especially in obese patients with HFpEF, have been identified as potential contributors of pericardial restraint . Since tFollowing this mechanistic preamble, healthcare providers would certainly appreciate if better categorization of the rather large population of HFpEF could be grouped together according to their clinical presentations. Although such approach might be extremely simplistic to treat a rather complex clinical entity, Bianco et al. proposed a hemodynamic-based classification . These iRosalia et al. similarly categorized patients with HFpEF into pathophysiological categories to help practitioners deliver more individualized therapies . They diWe will discuss these phenotypes in more detail and elucidate the role of noninvasive imaging in phenotyping. Given the lack of universal effective pharmacological and device-based solutions for HFpEF, these classifications can help carefully select appropriate unique therapy on a case-by-case basis, especially with emerging new device therapies .Aside from what we have already established regarding HFpEF and LV diastole, up to 33% patients in HFpEF echocardiographic substudies have normal diastolic function, even in those patients with elevated natriuretic peptides –99.In general, hypertension (HTN) represents the most common comorbidity in HFpEF patients and is implicated not only in its pathogenesis but also in prognosis , 101. FuAlthough considerable morphological heterogeneity exists between increased LV wall thickness and LA dilation, both considered hallmarks of HFpEF ; up to 5However, it is important to be reminded that although concentric remodeling and concentric hypertrophy are common forms of LV remodeling in HFpEF, both eccentric hypertrophy and concentric hypertrophy not only display similar elevations in cardiac filling pressures but also share worse clinical outcomes .In terms of LA size, from one-third to one-half of patients with HFpEF that have normal LA size , 104, a Another important comorbidity related to HFpEF is aging. Increasing evidence not only points out to significant differences that exist between healthy and unhealthy aging but also associated systemic changes that can occur because of these differences –109. HowAll these age-related abnormalities have been shown to compromise the interaction between the heart and vasculature, as this ventricular-vascular unit becomes decoupled, particularly during exertion . The latObesity would represent the next significant comorbidity associated with HFpEF. Multiple studies have demonstrated the close association existing between body mass index (BMI) and HF incidence –116.2 (TOPCAT study) and over 35 kg/m2  [Specifically, more than 80% of patients with HFpEF are either overweight or obese with median/mean BMI of 31 kg/mX trial) , 118. InX trial) .This relationship between BMI and HFpEF must be better clarified. It has become more apparent that measurements of waist circumference and waist-hip ratio should be preferred over BMI when evaluating patients with HFpEF and increased body weight , 121.However, it was quite interesting to highlight the results from the Dallas Heart Study which showed that central adiposity was linked to concentric LVH while lower body obesity with eccentric LVH .When taken together, African American women are the most common ethnic group, independent from body composition to a higher prevalence of HFpEF .Postmenopausal women have a higher incidence of LV diastolic dysfunction, and HFpEF is more prevalent in women than that in men . It is pIn addition to obesity, physical inactivity and low fitness levels have also been identified as risk factors for developing subclinical cardiac structural abnormalities that herald development of HFpEF . ObservaThe next major phenotype associated with HFpEF is coronary artery disease (CAD). Even when obstructive CAD has been one of the most important causes of HFrEF, the relative importance of CAD has gained attention, and it is now being recognized in up to 53% of HFpEF registries , 130. InThe search for pharmacologic therapies that improve major CV outcomes in HFpEF has been generally discouraging. Most positive outcome trials evaluating pharmacologic therapies for HF enrolled patients using a reduced EF as key entry criteria. ACE inhibitors, angiotensin receptor blockers (ARBs), beta-blockers (BB), mineralocorticoid receptor antagonists (MRAs), and SGLT-2 (sodium-glucose cotransport-2) inhibitors have demonstrated robust reductions in mortality and morbidity in HFrEF . The effThe efficacy of BB therapy in HFpEF remains undetermined, and BB use in HFpEF is controversial. Yet, despite the lack of high-quality evidence for BB benefit in HFpEF, observational data from contemporary HFpEF cohorts reveal that BB are nevertheless used at an extremely high rate in this patient population . The ratA recent meta-analysis investigating the efficacy of BB at different EF ranges found that the lower the EF, the higher the benefit of BB . There wHeart rate has emerged as a risk factor in HFpEF. However, the complex heterogeneity of the HFpEF disease state is reflected in the way differing heart rate irregularities can impact HFpEF patients and alter the effect of BB therapy. Data show a high resting heart rate (≥70 beats per minute) that is a risk factor for adverse outcomes in patients with HFpEF , 145. InCompensatory activation of the renin-angiotensin-aldosterone system (RAAS) is a pathophysiologic characteristic shared across the HF spectrum , 140. Ann > 1,000) clinical trials investigating ACEI in HFpEF. A meta-analysis of eight ACEI trials comprising just over 2,000 HFpEF patients found little or no effect on mortality, hospitalization, or quality of life [That have been no large  sacubitril/valsartan, the PARAMOUNT (Prospective Comparison of Angiotensin Receptor Neprilysin Inhibitor with Angiotensin Receptor Blocker on Management of Heart Failure with Preserved Ejection Fraction) trial demonstrated that this ARNI resulted in greater reduction in N-terminal pro–B-type natriuretic peptide and reduction in LA size compared with valsartan .Furthermore, the PARAGON-HF (Efficacy and Safety of the angiotensin receptor neprilysin inhibitor (ARNI) sacubitril/valsartan compared to Valsartan, on Morbidity and Mortality in Heart Failure Patients With Preserved Ejection Fraction) study assessed the efficacy of these interventions in patients with history of AF or documented AF at time of enrollment. Unfortunately, the presence of this arrhythmia is noted to be associated with a higher risk of total HF hospitalizations and CVD death . HoweverP = 0.06) [Further data analysis later published by the PARAGON-HF investigators compared ARNI against ARB in over 10,000 patients with HF and an EF > 45% to determine the benefit regarding CV death and HF hospitalizations . In this = 0.06) . Analyse = 0.06) .P = 0.040) [Further analysis of the PARAGON-HF expanded the composite endpoint by including worsening HF events urgently treated in the ambulatory setting without hospitalization . These u= 0.040) .P = 0.04). Several secondary analyses have further assessed the efficacy of MRA in HFpEF. A post hoc analysis found a regional interaction where patients from the Americas, opposed to Russia/Georgia, randomized to spironolactone, had a significant reduction in the primary composite outcome of death and HF hospitalizations  [Spironolactone, in the TOPCAT trial, failed to reduce all-cause mortality and HF hospitalizations in HFpEF (EF > 45%) patients . However69–0.98) .The relationship between EF (ranging from 44 to 85%) and the efficacy of spironolactone found that the potential benefit was strongest at the lower end of the EF spectrum . A machiP < 0.001) [The most promising pharmacotherapy for HFpEF, to date, is the sodium-glucose cotransport-2 inhibitors (SGLT2i). Having been shown to reduce the development and progression of HF in patients with type 2 diabetes and in those with HFrEF, the EMPEROR-PRESERVED trial sought to investigate the efficacy of the SGLT2i empagliflozin in HFpEF (EF > 40%) patients –161. Tho< 0.001) . The ben< 0.001) .The CHIEF-HF trial demonstrated that canagliflozin significantly improves symptom burden in HF within 12 weeks, regardless of EF . FurtherTwo trials have investigated SGLT2i use in patients recently hospitalized for HF irrespective of EF. In the SOLOIST-WHF trial, the nonselective SGLT1/2 inhibitor sotagliflozin significantly reduced the rate of CV death and HF hospitalizations in patients with diabetes. This effect was consistent across the prespecified subgroup stratified by EF < 50 or ≥50% . The EMPThe mechanism by which the SGLT2i provide protection in HF has not been fully elucidated.Over the last two decades, the changing paradigm of HFpEF has become quite evident. In the late 1990s, HFpEF was not even a clinical term, and patients afflicted with similar symptoms were generally referred to as patients suffering from “diastolic heart failure.” This term used to encompass a group of patients mainly viewed as having small and thick left ventricles with abnormal diastolic filling patterns as their main pathophysiologic abnormality. However, emerging data has shown us that HFpEF is a more complex and dynamic clinical entity that embodies numerous mechanisms and is linked to multiorgan dysfunction, with hypertension and obesity playing a major role.It should now be obvious why prior clinical trials on HFpEF have been for the most part frustrating when compared to HFrEF as HFpEF involves multiple pathophysiological mechanisms, which result in the heterogeneous phenotypes that have potentially confounded previous HFpEF trial results.Even though we have gained an enormous amount of understanding not only on the causes but also downstream effects of HFpEF, there is still much to be learned before we can fully comprehend this complex clinical entity.We hope that this review has synthesized what we know and which direction we should take.


The aim of this study was to systematically review the efficacy and safety of parecoxib and flurbiprofen axetil for perioperative analgesia in children through Bayesian network meta-analysis.We systematically searched PubMed, Embase, Cochrane Library, Web of Science, Sinomed, CNKI, VIP, and Wanfang Data databases on 18 July 2022 to obtain randomized controlled trials comparing perioperative parecoxib or flurbiprofen with placebo or standard treatment for pediatric analgesia. The outcomes were the postoperative pain score and the incidence of adverse events. The Gemtc package of R-4.0.3 and Stata 17.0 were used for Bayesian network meta-analysis.We retrieved 942 articles and 49 randomized controlled trials involving 3,657 patients who met the inclusion criteria. Compared with children who received placebo treatment, those who received flurbiprofen axetil had lower pain sores at each time point within 24 h postoperatively, and those who received parecoxib had lower pain sores at each time point within 12 h postoperatively. Compared with children who received tramadol treatment, both the children who received flurbiprofen axetil or parecoxib had lower pain scores at 8 h postoperatively. The ranking results demonstrated that flurbiprofen axetil had significant superiority in reducing pain scores at 2, 4, and 12 h postoperatively, and parecoxib had significant superiority in reducing pain scores at 0, 0.5, 1, 6, 8, and 24 h postoperatively. In terms of safety, compared with children who received placebo, those who received flurbiprofen axetil or parecoxib had a lower incidence of total adverse events and postoperative agitation. Compared with tramadol, flurbiprofen axetil and parecoxib both significantly reduced the incidence of total adverse events and postoperative nausea and vomiting. Compared with flurbiprofen axetil or fentanyl, parecoxib significantly reduced the incidence of postoperative nausea and vomiting. The ranking results showed that parecoxib was advantageous in decreasing the incidence of total adverse events and postoperative nausea and vomiting.Flurbiprofen axetil was most effective at reducing pain scores at 2, 4, and 12 h postoperatively. Parecoxib had an advantage in terms of reducing pain scores at 0, 0.5, 1, 6, 8, and 24 h postoperatively, as well as the incidence of total adverse events and postoperative nausea and vomiting.https://www.crd.york.ac.uk/PROSPERO/display_record.php?RecordID=348886, PROSPERO (CRD42022348886). Nearly half of the children still report moderate to severe pain during postoperative hospitalization . The resThere are limited options for analgesics for children. Ethical constraints in pediatric clinical trials have led to inadequate clinical evidence for many analgesics in children compared with adults, and the labels of most analgesics clearly state that they are not recommended for children . Opioidsin vivo  inhibitor. Owing to the novel drug carrier system of lipid microspheres, flurbiprofen axetil can be targeted to inflammatory sites and surgical incisions and control the release of the encapsulated drug for a longer-lasting effect . Flurbipin vivo . Parecoxin vivo .Clinical evidence for the use of parecoxib and flurbiprofen ester for perioperative analgesia in children has emerged as multimodal analgesia regimens and is increasingly advocated. However, only one meta-analysis published in 2015 systematically evaluated the effect of perioperative parecoxib on postoperative pain in children, demonstrating that, compared with placebo, parecoxib reduced pain scores and the incidence of adverse events . The effThis network meta-analysis was registered at PROSPERO (CRD42022348886), and the work was conducted following the guidelines of the Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) Statement. We systematically searched PubMed, Embase, Cochrane Library, Web of Science, Sinomed, CNKI, VIP, and Wanfang Data databases on 18 July 2022, using “parecoxib,” “flurbiprofen axetil,” “child, preschool,” “adolescent,” “postoperative period,” “perioperative period,” “intraoperative period,” “preoperative period,” “surgical procedures,” “operative,” and “general surgery” as keywords and MeSH terms combined with Boolean operators. The search equations can be scrutinized in The included studies were limited to those published in English or Chinese. The participants were children or adolescents (younger than 18 years) who underwent elective surgery with general anesthesia. The study groups received parecoxib or flurbiprofen axetil, and the control groups received placebo or standard treatment (fentanyl or tramadol) for perioperative analgesia. The outcomes evaluated were postoperative pain at 0, 0.5, 1, 2, 4, 6, 8, 12, and 24 h and the incidence of total adverse events, nausea and vomiting, and agitation. We excluded guidelines, reviews, comments, conference abstracts without full text and nonrandomized controlled studies.Two reviewers extracted the data independently. Disagreements were resolved by consensus through discussion and consultation with a third reviewer. The name of the first author, year of publication, sample size, patient age, surgery type, study design, and outcome measures were extracted from each study. In multi-arm trials, we only extracted the data from the arm related to our research.The quality of the included articles was evaluated by two reviewers using the Cochrane risk-of-bias tool for randomized controlled trials (RCTs) in the following domains: random sequence generation, concealment of allocation sequence, blinding of patients or outcome assessors, incomplete outcome data, selective reporting, and other types of bias. Disagreements were resolved through discussion or by the third reviewer. The risk of bias in each RCT was graded as high, low, or unclear.I2 and of >50% indicated significant heterogeneity. We used the node-splitting analysis to evaluate the inconsistency between the direct and indirect comparisons. If the P-value was >0.05, it indicated that direct and indirect comparisons tended to be consistent. We used random-effect consistency models within a Bayesian framework. The potential scale reduction factor proposed by Brooks et al. was used to diagnose the convergence degree of the model, and if the value tended to 1, the convergence was good  was used as the treatment effect estimator for continuous variables, while for dichotomous variables, it was the odds ratio. An MD of <0 indicated that the experimental group had a smaller value. The 95% CIs that did not include 0 were considered to show a significant difference between the two groups, which were compared. The odds ratio of <1 indicated that the incidence in the experimental group was lower. The 95% CIs that did not include 1 were considered to show a significant difference between the two groups. Global heterogeneity was assessed by was good . We plotWe retrieved 942 articles, and after the duplicates were removed, 437 articles were screened through titles and abstracts, and the full texts of 167 articles were retrieved to assess eligibility. A total of 49 RCTs involving 3,657 patients were included in the final analysis –67. The The characteristics of the included studies are shown in The quality assessment results of the included RCTs are summarized in A total of 9 RCTs reported pain scores at postoperative 0 and 0.5 h and 8 direct comparisons were established in the network model; 27 trials reported pain scores at postoperative 1 h and 10 direct comparisons were established in the network model; 30 trials reported pain scores at postoperative 2 h and 10 direct comparisons were established in the network model; 31 trials reported pain scores at postoperative 4 h and 10 direct comparisons were established in the network model; 17 trials reported pain scores at postoperative 6 h and 10 direct comparisons were established in the network model; 28 trials reported pain scores at postoperative 8 h and 9 direct comparisons were established in the network model; 28 trials reported pain scores at postoperative 12 h and 8 direct comparisons were established in the network model; and 26 trials reported pain scores at postoperative 24 h and 9 direct comparisons were established in the network model .At postoperative 0 h, flurbiprofen axetil and tramadol reduced the pain scores compared with placebo . At postA total of 40 RCTs reported the incidence of total adverse events and 10 direct comparisons were established in the network model; 34 trials reported the incidence of postoperative nausea and vomiting and 10 direct comparisons were established in the network model; and 13 trials reported the incidence of postoperative agitation and 7 direct comparisons were established in the network model .Compared with the placebo group, flurbiprofen axetil and parecoxib reduced the incidence of total adverse events and postoperative agitation . CompareAcross all outcomes, the assessment of inconsistency always supported the assumption of consistency between direct and indirect evidence. The results of the node splitting models are shown in Our analysis included 49 RCTs with 3,657 children aged 6 months to 14 years, and the aim of this study was to systematically evaluate the efficacy and safety of parecoxib and flurbiprofen axteil in perioperative analgesia in children using a Bayesian network meta-analysis. The results demonstrated that the pain scores of the flurbiprofen axetil group were significantly lower than those in the placebo group at all time points in the 24-h postoperative period, and the pain scores at all time points during the 12-h postoperative period of the parecoxib group were significantly lower than those in the placebo group. From the potential rankings, flurbiprofen axetil was superior to other drugs in reducing pain scores at 2, 4, and 12 h postoperatively, while parecoxib had some advantages in reducing pain scores at 0, 0.5, 1, 6, 8, and 24 h postoperatively.50 for COX-2 for at least 12 h, and the area under the valdecoxib concentration-time curve was similar to that in adults (40 mg)  . A popul (40 mg) . In chil (40 mg) . TherefoIn a previous network meta-analysis, parecoxib for analgesia after tonsillectomy in children was found to be better than fentanyl, and flurbiprofen axetil was superior to tramadol , but it In terms of safety, compared with children who received placebo, those who received flurbiprofen axetil or parecoxib had a lower incidence of total adverse events and postoperative agitation. Compared with tramadol, flurbiprofen axetil and parecoxib both significantly reduced the incidence of total adverse events and postoperative nausea and vomiting. Compared with flurbiprofen axetil or fentanyl, parecoxib significantly reduced the incidence of postoperative nausea and vomiting. The ranking results showed that parecoxib was more advantageous in decreasing the incidence of total adverse events and postoperative nausea and vomiting.Because few adverse events were reported in the included trials, such as respiratory depression, pruritus, headache, dizziness, and urinary retention, only total adverse events, postoperative nausea and vomiting, and agitation were analyzed in this study because of insufficient data. A multicenter retrospective study involving 3,542 patients found that postoperative administration of parecoxib resulted in a 0.2% incidence of adverse events, primarily hypotension, nausea, vomiting, pruritus, and rash . AnotherThere were a few limitations to this study. First, the included trials were associated with high risks of bias because most of them only mentioned “random” without specifying the method of generating the random sequence or describing the allocation concealment in detail. Second, most studies only recorded pain scores within the first 24 h after surgery, paying little attention to 48 or 72 h. Furthermore, we did not analyze other outcomes, such as opioid consumption or proportions of patients requiring rescue analgesia, which may have more clinical implications. Third, data on direct head-to-head comparisons of flurbiprofen axteil and parecoxib within the network were insufficient. Finally, there were some differences between studies in the type of surgery, perioperative care protocol, study drug dose, and timing of administration, which could explain the heterogeneity in our results.Based on the available clinical evidence, our network meta-analysis reached the following conclusions. Perioperative administration of flurbiprofen axetil and parecoxib were both effective in reducing postoperative pain scores and total adverse events compared with placebo. Flurbiprofen axetil and parecoxib also caused fewer total adverse events and postoperative nausea and vomiting compared with tramadol. Flurbiprofen axetil was most effective at reducing pain scores at 2, 4, and 12 h postoperatively. Parecoxib had an advantage in terms of reducing pain scores at 0, 0.5, 1, 6, 8, and 24 h postoperatively, as well as reducing the incidence of total adverse events and postoperative nausea and vomiting.The original contributions presented in the study are included in the article/XiC helped with study design, the selection of included articles, data extraction, quality assessment, data analysis and interpretation, and writing the manuscript. PC and QH helped with the study design, selection of included articles, data extraction, quality assessment, data analysis and interpretation, and the final review. XiaC, MH, and KT helped with the study design and the final review. All authors contributed to the article and approved the submitted version.