Gas Formation in Pyogenic Liver Abscess Is Independently Correlated with Diabetes Mellitus but not with Pathogenic Bacteria

Gas Formation in Pyogenic Liver Abscess Is Independently Correlated with Diabetes Mellitus but not with Pathogenic Bacteria | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Gas Formation in Pyogenic Liver Abscess Is Independently Correlated with Diabetes Mellitus but not with Pathogenic Bacteria Yawen Guo, Hongguang Wang, Zibo Gong, Lulu Chen, Hairui Wang, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3990440/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Gas formation is a typical imaging feature of pyogenic liver abscess (PLA). However, the underlying mechanism of gas formation and its impact on the clinical characteristics of PLA remains unclear. The current study investigated the clinical characteristics of gas-forming PLA (GFPLA) and explored risk factors for GFPLA from both the host and pathogenic bacterium perspectives. Results The GFPLA group exhibited greater disease severity and a higher in-hospital mortality rate. The proportions of patients with diabetes mellitus (DM, P < 0.001) and biliary abnormality ( P = 0.001) were higher in the GFPLA group. DM was an independent risk factor for GFPLA, whereas the time from symptom onset to CT and positivity for Klebsiella pneumoniae and Escherichia coli were not independent risk factors for GFPLA. 16S rDNA sequencing revealed no significant differences in bacterial community richness, diversity, evenness, and composition between the groups( P all > 0.05). In vitro fermentation experiments illustrated that high glucose levels was associated with greater gas production by both Klebsiella pneumoniae and Escherichia coli than low glucose levels (both P < 0.01). Conclusion GFPLA was associated with more severe disease and greater in-hospital mortality than non-GFPLA. Gas formation in PLA might be related to high blood glucose levels, but not to pathogenic bacteria composition. Clinical characteristic Gas formation Pyogenic liver abscess 16S rDNA sequencing Risk factor Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Pyogenic liver abscess (PLA) is a common abdominal infectious disease with an increasing occurrence, currently accounting for approximately 80% of all liver abscesses [ 1 – 4 ]. Gas formation is a typical imaging feature of PLA with a reported incidence of 7–24%[ 5 ]. Some studies demonstrated that patients with gas-forming PLA (GFPLA) tend to have more severe symptoms, a longer hospital stay, and higher mortality rates than patients with non-GFPLA [ 6 – 8 ]. However, Chan et al. reported contradictory results[ 5 ], and thus, there is no consensus on the clinical characteristics of GFPLA. Underlying diseases, such as diabetes mellitus (DM), and previous hepatobiliary surgery, especially biliary enteric anastomosis considered to be closely related to gas formation in PLA 3,[8] . Patients with DM often have high blood glucose levels, providing a favorable environment for bacterial growth and fermentation. Normal bile duct structures are disrupted by biliary enteric anastomosis, allowing gas to escape from the gastrointestinal tract into the abscess cavity. Pathogenic bacteria might also play a key role in GFPLA. Although Klebsiella pneumoniae is the most common pathogen in GFPLA, its relationship with GFLPA remains controversial [ 6 , 9 – 13 ], as several rare cases of GFPLA caused by gas-forming bacteria such as Clostridium perfringens and Salmonella enteritidis have been reported [ 7 , 14 , 15 ]. Despite its common use in microbial identification, conventional culture has several limitations, such as a long culture time, selective restriction in culture medium, and difficulty in culturing certain pathogens. 16S rDNA sequencing can overcome these limitations, and this technique has been gradually applied in microbial research[ 16 – 18 ]. In addition, the time from symptom onset to computed tomography (CT) is also considered to be associated with gas formation in PLA. However, no definitive research has confirmed the aforementioned factors as risk factors for GFPLA or reported the underlying mechanisms. In addition, few studies have utilized 16S rDNA sequencing to evaluate the association between microbial communities and gas formation in PLA. In the present study, the clinical and imaging data of patients with PLA were retrospectively collected to explore the clinical characteristics of GFPLA and identify factors associated with gas formation in PLA. 16S rDNA sequencing was used to further compare bacterial communities between GFPLA and non-GFPLA. Methods Study population This study retrospectively and continuously collected data from patients with PLA from January 2018 to December 2022. The diagnostic criteria for PLA were previously described[ 12 , 19 , 20 ]. Meanwhile, patients lacking preoperative CT data and those without conventional culture results were excluded. The medical records of eligible patients were thoroughly reviewed. Eligible patients were assigned to the GFPLA or non-GFPLA group based on the presence or absence of gas on CT (Fig. 1 ). Additionally, pus samples were prospectively collected for 16S rDNA sequencing from patients with PLA who underwent percutaneous abscess drainage between January 2022 and December 2022. The Ethics Committee of Shengjing Hospital of China Medical University approved this retrospective study (approval number: 2022PS1067K). Clinical variables The following information was recorded from the patients’ clinical records: demographic data; presence of DM and biliary abnormality; time from symptom onset to CT; laboratory data; hospital stay; in-hospital mortality; and conventional culture results. Diabetes was defined according to the 2021 criteria for Diabetes Care[ 21 ]. Biliary abnormality included a history of cholecystoenterostomy, sphincteroplasty of the ampulla of Vater, and biliary stent placement. The time from symptom onset to CT was measured from the first appearance of PLA symptoms ( e.g. , fever, chills, right upper abdominal pain, abdominal discomfort, nausea, vomiting, loss of appetite) to the first CT after hospital admission. 16S rDNA sequencing The full details of the microbiome methodology are provided in the Supporting Information . Length heterogeneity PCR fingerprinting was routinely applied to rapidly survey our samples and standardize the community amplification. The microbial taxa associated with the pus microbiome were characterized by multi-tag sequencing. Statistical analysis was performed using the obtained feature table and feature sequence, as described in the Supporting Information . Alpha and beta diversity were calculated using QIIME2, and all graphs were constructed using the R package (R Foundation for Statistical Computing, Vienna, Austria). The alpha diversity indices included the Chao1 index (indicating bacterial community richness), the Shannon and Simpson indices (indicating bacterial community diversity), and the Pielou-e index (indicating bacterial community evenness). The beta diversity included principal component analysis principal coordinates analysis and so on. In vitro fermentation experiment Because K. pneumoniae and Escherichia coli are the most common pathogens in GFPLA and capable of fermentation, they were selected for this experiment. The samples were divided into the low and high glucose groups (n = 3/group). The glucose concentrations in the culture medium were 5.5 and 25 mmol/L in the low and high glucose groups, respectively. Based on preliminary exploratory experiments, 59 mL of culture medium were added to a graduated conical fermentation tube (graduation, 0.1 mL; capacity, 15 mL). Then, 1 mL of a bacterial suspension [6 × 10 8 colony-forming units (CFU)/mL] prepared using a turbidimeter was mixed into the liquid culture medium, generating a mixed culture fluid with a bacterial concentration of 1 × 10 7 CFU/mL. The conical fermentation tube was sealed to ensure that no residual gas remained in the tip of the fermentation tube, as this could affect the measurement of gas production, and placed in a constant temperature incubator for cultivation. The gas production volume was observed for 0–24 h in both groups. Gas production volumes smaller than 0.1 mL were recorded as 0.1 mL, and those exceeding the tube capacity (15 mL) were recorded as 15 mL. Statistical analyses Continuous variables are presented as the mean ± standard deviation or median (interquartile range), whereas categorical variables are presented as n (%). Differences between continuous data were compared using Student’s t -test or the Mann–Whitney U test, as appropriate. Differences between categorical data were compared using the chi-squared test or Fisher’s exact test, as appropriate. All statistical analyses were performed using SPSS version 25.0 (IBM, Armonk, NY, USA). Two-tailed P < 0.05 indicated statistical significance. Risk factors for gas production in PLA were identified via logistic regression analysis. Considering that in patients with GFPLA and a history of biliary abnormality, the normal structure of the bile ducts is disrupted, gas in the pus cavity could be air migrating from the gastrointestinal tract, we performed logistic regression analysis in two steps(before and after excluding patients with biliary abnormality). GraphPad Prism 8.0 was utilized for pictures creation. Results Patients with GFPLA had more severe clinical characteristics (Table 1 ) Table 1 Clinical characteristics of 194 patients with PLA Characteristics GFPLA (n = 50) Non-GFPLA (n = 144) P Age, years 60 (52.75–66) 61 (48–69) 0.992 Male, n (%) 30 (60.00) 79 (54.86) 0.528 DM, n (%) 38 (76.00) 66 (45.83) < 0.001 Biliary abnormality, n (%) 11 (22.00) 7 (4.86) 0.001 Time from symptom onset to CT, days 8 (4–15) 8 (5–14) 0.621 Leukocytes, × 10 9 /L 10.895 (7.92–15.7) 10.8 (8.1–13.8) 0.832 NEUT, × 10 9 /L 8.45 (6.175–13.1) 8.9 (5.925–11.3075) 0.694 LY, × 10 9 /L 0.95 (0.58–1.5425) 1.2 (0.8–1.6) 0.190 PLT, × 10 9 /L 176.5 (97.75–320) 245.5 (139.5–362.5) 0.034 ALB, g/L 27.5 (24.3–30.65) 29.55 (26.625–33.4) 0.005 TBIL, µmol/L 12.8 (9.35–25.8) 12.9 (8.9–19.7) 0.443 ALT, U/L 46 (22.5–121) 31 (20–59) 0.014 AST, U/L 41 (22–107) 31 (20–59) 0.114 ALP, U/L 194 (121–290.15) 155 (106.9–231.55) 0.025 GGT, U/L 144.5 (70.75–258.25) 143.5 (87.25–244) 0.788 PT, s 14.2 (12.6–15.625) 13.8 (12.8–14.975) 0.450 FIB, g/L 4.8 (3.8–5.3) 5.2 (4.4–5.7) 0.019 DD, µg/L 993 (590.25–3166) 1123 (587–2109) 0.699 FBG, mmol/L 10.69 (6.5025–14.9) 6.89 (5.625–10.88) 0.005 CRP, mg/L 147.5 (103.5–294.575) 127.15 (71.825–193.75) 0.024 In-hospital mortality, n (%) 6 (12.00) 1 (0.69) 0.001 Hospital stay, days 10.5 (7–19.5) 10 (7–14) 0.172 Data are presented as median (interquartile range) unless otherwise indicated. Abbreviations: DM, diabetes mellitus; CT, computed tomography; NEUT, neutrophils; LY, lymphocytes; PLT, platelets; ALB, albumin; TBIL, total bilirubin; ALT, alanine aminotransferase; AST, aspartate transaminase; ALP, alkaline phosphatase; GGT, gamma-glutamyl transferase; PT, prothrombin time; FIB, fibrinogen; DD, d-dimer; FBG, fasting blood glucose; CRP, C-reactive protein The workflow of this study is presented in Fig. 2 . A total of 269 patients were identified over the study period. After excluding patients based on the eligibility criteria, 194 patients were enrolled in the present study (GFPLA, n = 50; non-GFPLA, n = 144). The proportions of patients with DM ( P < 0.001) and biliary abnormality ( P = 0.001) were higher in the GFPLA group. Meanwhile, albumin ( P = 0.005) and fibrinogen levels ( P = 0.019) were lower in the GFPLA group, whereas aspartate aminotransferase ( P = 0.014), alkaline phosphatase ( P = 0.025), and C-reactive protein levels ( P = 0.024) were higher, suggesting that patients with GFPLA had poorer liver function and more severe inflammatory responses. The mortality rate ( P = 0.001) was higher in the GFPLA group, whereas the time from symptom onset to CT ( P = 0.621) and the length of hospital stay ( P = 0.172) did not differ between the groups. DM is an independent risk factor for gas formation in patients with GFPLA without biliary abnormality Logistic regression analysis ( Steps 1 and 2 ) was used to identify factors associated with gas formation in PLA. Step 1 analysis (Table 2 ) suggested that DM and biliary abnormality are independent risk factors for GFPLA, whereas positivity for Klebsiella pneumoniae , Escherichia coli and Enterococcus faecalis and the time from symptom onset to CT were not identified as risk factors. Considering that in patients with GFPLA and a history of biliary abnormality, gas in the pus cavity could be air migrating from the gastrointestinal tract, we conducted Step 2 analysis after excluding 18 patients with a history of biliary abnormality to eliminate its influence. Because of the small sample size (n = 1) after excluding these patients, Enterococcus faecalis was not included as a variable in this analysis. As presented in Table 3 , DM remained an independent risk factor for GFPLA, whereas positivity for Klebsiella pneumoniae and Escherichia coli and the time from symptom onset to CT were not risk factors. Table 2 Logistic regression analysis before excluding patients with a history of biliary abnormality ( Step 1 ) Univariate analysis Multivariate analysis P OR P OR DM < 0.001 3.742 < 0.001 6.119 Biliary abnormality 0.001 5.52 0.031 4.862 Klebsiella pneumoniae 0.005 0.36 0.248 0.529 Escherichia coli 0.024 3.744 0.468 1.839 Enterococcus faecalis 0.026 12.435 0.212 5.015 Time from symptom onset to CT 0.361 0.736 0.446 0.746 Abbreviations: OR, odds ratio/Exp(β); DM, diabetes mellitus; CT, computed tomography Table 3 Logistic regression analysis after excluding patients with a history of biliary abnormality ( Step 2 ) Univariate analysis Multivariate analysis P OR P OR DM < 0.001 7.756 < 0.001 9.815 Klebsiella pneumoniae 0.26 0.603 0.322 0.541 Escherichia coli 0.038 5.105 0.089 6.612 Time from symptom onset to CT 0.134 0.575 0.193 0.582 Abbreviations: OR, odds ratio/Exp(β); DM. diabetes mellitus; CT, computed tomography 16S rDNA sequencing revealed that alpha diversity and bacterial community composition were similar between the groups Logistic regression analysis indicated that pathogenic bacteria compositon was not an independent risk factor for GFPLA. Then, 16S rDNA sequencing was performed using 55 pus samples from patients with PLA (GFPLA: n = 8; non-GFPLA: n = 47) to further explore differences in bacterial community between the GFPLA and non-GFPLA groups. Alpha diversity indices (Chao1, Shannon, Simpson, and Pielou-e) did not differ between the groups, suggesting that bacterial community richness, diversity, and evenness were similar between the groups (Fig. 3 a-d). Bacterial community composition analysis at the specie level (Fig. 4 a) illustrated the top 30 pathogenic bacteria by relative abundance. The results revealed that Klebsiella pneumoniae was the most common pathogen in both groups and Enterococcus faecalis was one of the top 30 species by relative abundance, while Escherichia coli was not. The GFPLA and non-GFPLA groups could not be adequately separated according to the principal component analysis plot (Fig. 4 b), indicating that the bacterial composition of the two groups was similar. The relative abundance of Klebsiella pneumoniae and Enterococcus faecalis did not differ between the two groups(Fig. 5 a,b,P both > 0.05). High glucose levels promoted gas production by Klebsiella pneumoniae and Escherichia coli DM was identified as an independent risk factor for GFPLA, indicating that blood glucose levels are associated with gas formation in PLA. After comprehensively considering the results of logistic regression analysis and 16S rDNA sequencing, we speculated that gas formation is associated with high blood glucose levels rather than pathogenic bacteria composition in patients with PLA. To validate this hypothesis, we conducted an in vitro fermentation experiment. After 24 h of fermentation, the results (Fig. 6 a-f) indicated that the gas production volume significantly differed between the low and high glucose groups for both Klebsiella pneumoniae and Escherichia coli (both P < 0.01). Discussion The results demonstrated that patients with GFPLA had worse liver function, more severe inflammatory responses, and a higher in-hospital mortality rate than patients with non-GFPLA, in line with previous studies indicating that GFPLA has more severe clinical characteristics [ 6 , 8 , 22 – 24 ]. Investigations of risk factors associated with GFPLA can help clarify the underlying pathological mechanisms of the greater disease severity in patients with GFPLA. However, there are few reports on this aspect. The Step 1 analysis identified DM and biliary abnormality as independent risk factors for GFPLA. However, in patients with GFPLA and a history of biliary abnormality, the normal structure of the bile ducts is disrupted, allowing gas to escape from the gastrointestinal tract into the abscess cavity, suggesting that partial gas in pus cavities is more likely to be air migrating from the gastrointestinal cavity more than a product of bacterial metabolism. Meanwhile, Lee et al. reported that the gas composition in the pus cavity is the same as the partial gas composition in the human gastrointestinal cavity [ 25 – 28 ]. To eliminate the influence of biliary abnormality, we conducted the Step 2 analysis after excluding patients with a history of biliary abnormality, and DM remained an independent risk factor for GFPLA, in line with previous studies reporting the close relationship between GFPLA and DM [ 6 , 22 , 24 , 29 ]. Lee et al. hypothesized that gas formation in PLA is linked to increased gas production, impaired gas transportation, and an impaired equilibrium between gas in local tissues and that in abscesses [ 28 ], and all of these conditions can be effectively created by DM. The immune system of patients with diabetes might be less robust as that of healthy individuals, making them more susceptible to bacterial infections and reducing their ability to eliminate bacteria, which leads to abscess formation in the liver [ 30 – 32 ]. The high glucose levels in the tissues of patients with DM provide a favorable microenvironment for vigorous microbe metabolism and growth[ 33 , 34 ]. We hypothesized that gas production increases with increasing blood glucose levels. This was confirmed by the results of in vitro fermentation experiments, in which gas production was higher in the high glucose group. Furthermore, patients with DM often have microvascular complications[ 35 , 36 ], leading to insufficient blood supply to tissues and impaired clearance of metabolic waste, resulting in the decreased transport and accumulation of gas produced within the abscess cavity. The time from symptom onset to CT considered to be linked to gas formation in PLA, but the study results did not identify the time from symptom onset to CT as an independent risk factor for GFPLA. Positivity for Klebsiella pneumoniae and Escherichia coli was not identified as an independent risk factor for GFPLA, contradicting traditional views. Therefore, 16S rDNA sequencing was performed to further investigate the correlation between the bacterial community within the abscess cavity and GFPLA. The results indicated that alpha diversity and the bacterial composition were similar between the two groups. Several rare GFPLA cases caused by gas-forming bacteria such as Clostridium perfringens and Salmonella enteritidis have been reported[ 7 , 37 , 38 ]. However, these bacteria were not detected in any of the 55 pus samples. Meanwhile, the in vitro fermentation results revealed that gas production significantly differed between the low and high glucose groups for both Klebsiella pneumoniae and Escherichia coli , suggesting that high glucose levels can promote gas production by pathogens regardless of the specific type of pathogen. In conclusion, pathogenic bacteria might not be associated with gas formation in PLA. This study had some limitations. First, this was a single-center study, and multicenter studies are needed to increase the sample size in the future. Second, this study did not quantitatively detect or analyze the composition of the gas within the abscess cavity. Finally, the fermentation experiment was an in vitro experiment, and there could be some differences between the in vitro environment and the microenvironment within the abscess cavity. Conclusion GFPLA was associated with more severe clinical characteristics and a higher in-hospital mortality rate than non-GFPLA. After excluding patients with a history of biliary abnormality, DM was identified as an independent risk factor for GFPLA, whereas the time from symptom onset to CT and the presence of Klebsiella pneumoniae and Escherichia coli were not independent risk factors. Based on clinical data, 16S rDNA sequencing and in vitro fermentation experiment results, gas formation could be related to high blood glucose levels but not to pathogenic bacteria composition in PLA. Declarations Authors’ contributions ZC and YG designed the study. LC and ZG collected data. YG and HGW analyzed and interpreted the data. YG, HGW, HRW, and ZC wrote the manuscript. All authors approved the final manuscript and agreed to be accountable for all aspects of the work. Availability of data and materials The data generated and/or analyzed during the current study are available in the Sequence Read Archive of the National Center for Biotechnology Information (http://www.ncbi.nlm.nih.gov/bioproject/917077, accession number PRJNA917077). Funding The present study was supported by the National Natural Science Foundation of China (Grant No. 82272097) and the 345 Talent Project in Shengjing Hospital of China Medical University. The funders had no role in the study design, data collection, analysis and interpretation, decision to publish, and preparation of the manuscript. Acknowledgements We thank Medjaden Inc. for scientific editing of this manuscript. Ethics approval and consent to participate The Ethics Committee of Shengjing Hospital of China Medical University approved this retrospective study(2022PS1067K). Written informed consent was obtained from the individual(s) for the publication of any potentially identifiable images or data included in this article. All methods performed in this study involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. Consent for publication Not applicable. Competing interests The authors declare that they have no competing interests. References Yin D, et al. Clinical characteristics and management of 1572 patients with pyogenic liver abscess: A 12-year retrospective study. Liver Int. 2021;41(4):810–8. Wang WJ, et al. Etiology and clinical manifestations of bacterial liver abscess: A study of 102 cases. Med (Baltim). 2018;97(38):e12326. Jepsen P, et al. A nationwide study of the incidence and 30-day mortality rate of pyogenic liver abscess in Denmark, 1977–2002. 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University","correspondingAuthor":false,"prefix":"","firstName":"Hongguang","middleName":"","lastName":"Wang","suffix":""},{"id":275541583,"identity":"61aadc56-5946-4d07-b319-d4af5d62067e","order_by":2,"name":"Zibo Gong","email":"","orcid":"","institution":"Shengjing Hospital of China Medical University","correspondingAuthor":false,"prefix":"","firstName":"Zibo","middleName":"","lastName":"Gong","suffix":""},{"id":275541584,"identity":"af954238-a563-4960-896e-267a35c02dc6","order_by":3,"name":"Lulu Chen","email":"","orcid":"","institution":"Shengjing Hospital of China Medical University","correspondingAuthor":false,"prefix":"","firstName":"Lulu","middleName":"","lastName":"Chen","suffix":""},{"id":275541585,"identity":"3e88a643-af9d-4e9d-93a6-38204eea4de7","order_by":4,"name":"Hairui Wang","email":"","orcid":"","institution":"Shengjing Hospital of China Medical 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08:29:52","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3990440/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3990440/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":51974255,"identity":"f6617407-bc98-4730-a4ad-2185d80fa2a6","added_by":"auto","created_at":"2024-03-04 19:06:36","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":6777353,"visible":true,"origin":"","legend":"\u003cp\u003eComputed tomography (CT) images of representative patients with gas-forming pyogenic liver abscess (GFPLA) and non- GFPLA. \u003cstrong\u003ea \u003c/strong\u003eCT image of a 49-year-old man with GFPLA with diabetes mellitus, gas in the pus cavity (white arrow), and a filled defect in the branches of the middle hepatic vein (black arrow). \u003cstrong\u003eb\u003c/strong\u003e CT image of a 59-year-old man with PLA without diabetes mellitus, gas in the pus cavity, or a filled defect in the branches of the middle hepatic vein.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-3990440/v1/8b3c3dac8a36fb1b160ec1e9.png"},{"id":51974258,"identity":"15090962-8513-4589-a833-34ce4dec0579","added_by":"auto","created_at":"2024-03-04 19:06:36","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":381361,"visible":true,"origin":"","legend":"\u003cp\u003eWorkflow of the present study.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-3990440/v1/8cf29e525f7d9b94778c19b7.png"},{"id":51974257,"identity":"9895ed42-6be4-405f-841c-e46ecb9e9fe5","added_by":"auto","created_at":"2024-03-04 19:06:36","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":859921,"visible":true,"origin":"","legend":"\u003cp\u003eAlpha diversity did not differ between the gas-forming pyogenic liver abscess (GFPLA) and non-GFPLA groups. \u003cstrong\u003ea \u003c/strong\u003eThe Chao1 index revealed similar bacterial community richness between the groups. \u003cstrong\u003eb,c \u003c/strong\u003eThe Shannon and Simpson indices revealed similar bacterial community diversity between the groups. \u003cstrong\u003ed \u003c/strong\u003eThe\u003cstrong\u003e \u003c/strong\u003ePielou-e index revealed similar bacterial community evenness between the groups.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-3990440/v1/5a99bfc07887194f8cab3451.png"},{"id":51974254,"identity":"c25f70a6-1f95-4779-98cf-cd97072a8dd0","added_by":"auto","created_at":"2024-03-04 19:06:36","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":734012,"visible":true,"origin":"","legend":"\u003cp\u003eBacterial community composition analysis and principal component analysis (PCA) of the gas-forming pyogenic liver abscess (GFPLA) and non-GFPLA groups. \u003cstrong\u003ea \u003c/strong\u003eBacterial community composition analysis at the species level illustrated the top 30 pathogenic bacteria by relative abundance. The results revealed that \u003cem\u003eKlebsiella pneumoniae\u003c/em\u003e was the most common pathogen in both groups and \u003cem\u003eEnterococcus faecalis \u003c/em\u003ewas one of the top 30 species by relative abundance, while \u003cem\u003eEscherichia coli \u003c/em\u003ewas not.\u003cstrong\u003e b \u003c/strong\u003eThe PCA plot revealed that the bacterial composition of the two groups was similar.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-3990440/v1/d5598f00edd6c1d1fbe01c56.png"},{"id":51974261,"identity":"1098bacd-fb6d-45ea-8772-cc4f485d3480","added_by":"auto","created_at":"2024-03-04 19:06:37","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":516283,"visible":true,"origin":"","legend":"\u003cp\u003eThe relative abundance of \u003cem\u003eKlebsiella pneumoniae \u003c/em\u003e(\u003cstrong\u003ea\u003c/strong\u003e) and \u003cem\u003eEnterococcus faecalis \u003c/em\u003e(\u003cstrong\u003eb\u003c/strong\u003e)\u003cem\u003e \u003c/em\u003edid not differ between the two groups. ns, \u003cem\u003eP\u003c/em\u003e\u0026gt;0.05;*, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05; **, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-3990440/v1/b40a9e4f3cf161f859f3383c.png"},{"id":51974260,"identity":"872ec946-2a75-4c3f-97bb-c4c1c4c9de4f","added_by":"auto","created_at":"2024-03-04 19:06:36","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":7277835,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eIn vitro\u003c/em\u003e\u003cstrong\u003e \u003c/strong\u003efermentation experiment of \u003cem\u003eKlebsiella pneumoniae \u003c/em\u003eand \u003cem\u003eEscherichia coli\u003c/em\u003e. The 24-h fermentation experiment for both \u003cem\u003eK. pneumoniae\u003c/em\u003e \u003cstrong\u003e(a-c) \u003c/strong\u003eand \u003cem\u003eE. coli\u003c/em\u003e \u003cstrong\u003e(d-f)\u003c/strong\u003e demonstrated that the gas production volume was significantly larger in the high glucose group than in the low glucose group. ns, \u003cem\u003eP\u003c/em\u003e\u0026gt;0.05;*, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05; **, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01.\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-3990440/v1/d839d61457b67a9d132f830f.png"},{"id":59558698,"identity":"2d368d66-7a5a-46d2-baae-c4fa555171a5","added_by":"auto","created_at":"2024-07-03 07:54:40","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":29464260,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3990440/v1/b6729045-c321-4668-854a-4d12c3ff313c.pdf"},{"id":51974623,"identity":"71c666f4-6844-4ba9-a4bd-84cd03714c3f","added_by":"auto","created_at":"2024-03-04 19:14:36","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":31358,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryinformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-3990440/v1/b8424eefeff43b1837d89fb4.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Gas Formation in Pyogenic Liver Abscess Is Independently Correlated with Diabetes Mellitus but not with Pathogenic Bacteria","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePyogenic liver abscess (PLA) is a common abdominal infectious disease with an increasing occurrence, currently accounting for approximately 80% of all liver abscesses [\u003cspan additionalcitationids=\"CR2 CR3\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Gas formation is a typical imaging feature of PLA with a reported incidence of 7\u0026ndash;24%[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Some studies demonstrated that patients with gas-forming PLA (GFPLA) tend to have more severe symptoms, a longer hospital stay, and higher mortality rates than patients with non-GFPLA [\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. However, Chan \u003cem\u003eet al.\u003c/em\u003e reported contradictory results[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], and thus, there is no consensus on the clinical characteristics of GFPLA.\u003c/p\u003e \u003cp\u003eUnderlying diseases, such as diabetes mellitus (DM), and previous hepatobiliary surgery, especially biliary enteric anastomosis considered to be closely related to gas formation in PLA \u003csup\u003e3,[8]\u003c/sup\u003e. Patients with DM often have high blood glucose levels, providing a favorable environment for bacterial growth and fermentation. Normal bile duct structures are disrupted by biliary enteric anastomosis, allowing gas to escape from the gastrointestinal tract into the abscess cavity. Pathogenic bacteria might also play a key role in GFPLA. Although \u003cem\u003eKlebsiella pneumoniae\u003c/em\u003e is the most common pathogen in GFPLA, its relationship with GFLPA remains controversial [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan additionalcitationids=\"CR10 CR11 CR12\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], as several rare cases of GFPLA caused by gas-forming bacteria such as \u003cem\u003eClostridium perfringens\u003c/em\u003e and \u003cem\u003eSalmonella enteritidis\u003c/em\u003e have been reported [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Despite its common use in microbial identification, conventional culture has several limitations, such as a long culture time, selective restriction in culture medium, and difficulty in culturing certain pathogens. 16S rDNA sequencing can overcome these limitations, and this technique has been gradually applied in microbial research[\u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. In addition, the time from symptom onset to computed tomography (CT) is also considered to be associated with gas formation in PLA. However, no definitive research has confirmed the aforementioned factors as risk factors for GFPLA or reported the underlying mechanisms. In addition, few studies have utilized 16S rDNA sequencing to evaluate the association between microbial communities and gas formation in PLA.\u003c/p\u003e \u003cp\u003eIn the present study, the clinical and imaging data of patients with PLA were retrospectively collected to explore the clinical characteristics of GFPLA and identify factors associated with gas formation in PLA. 16S rDNA sequencing was used to further compare bacterial communities between GFPLA and non-GFPLA.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy population\u003c/h2\u003e \u003cp\u003eThis study retrospectively and continuously collected data from patients with PLA from January 2018 to December 2022. The diagnostic criteria for PLA were previously described[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Meanwhile, patients lacking preoperative CT data and those without conventional culture results were excluded. The medical records of eligible patients were thoroughly reviewed. Eligible patients were assigned to the GFPLA or non-GFPLA group based on the presence or absence of gas on CT (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Additionally, pus samples were prospectively collected for 16S rDNA sequencing from patients with PLA who underwent percutaneous abscess drainage between January 2022 and December 2022. The Ethics Committee of Shengjing Hospital of China Medical University approved this retrospective study (approval number: 2022PS1067K).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eClinical variables\u003c/h2\u003e \u003cp\u003eThe following information was recorded from the patients\u0026rsquo; clinical records: demographic data; presence of DM and biliary abnormality; time from symptom onset to CT; laboratory data; hospital stay; in-hospital mortality; and conventional culture results. Diabetes was defined according to the 2021 criteria for Diabetes Care[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Biliary abnormality included a history of cholecystoenterostomy, sphincteroplasty of the ampulla of Vater, and biliary stent placement. The time from symptom onset to CT was measured from the first appearance of PLA symptoms (\u003cem\u003ee.g.\u003c/em\u003e, fever, chills, right upper abdominal pain, abdominal discomfort, nausea, vomiting, loss of appetite) to the first CT after hospital admission.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e16S rDNA sequencing\u003c/h2\u003e \u003cp\u003eThe full details of the microbiome methodology are provided in the \u003cb\u003eSupporting Information\u003c/b\u003e. Length heterogeneity PCR fingerprinting was routinely applied to rapidly survey our samples and standardize the community amplification. The microbial taxa associated with the pus microbiome were characterized by multi-tag sequencing. Statistical analysis was performed using the obtained feature table and feature sequence, as described in the \u003cb\u003eSupporting Information\u003c/b\u003e. Alpha and beta diversity were calculated using QIIME2, and all graphs were constructed using the R package (R Foundation for Statistical Computing, Vienna, Austria). The alpha diversity indices included the Chao1 index (indicating bacterial community richness), the Shannon and Simpson indices (indicating bacterial community diversity), and the Pielou-e index (indicating bacterial community evenness). The beta diversity included principal component analysis principal coordinates analysis and so on.\u003c/p\u003e \u003cp\u003e \u003cb\u003eIn vitro\u003c/b\u003e \u003cb\u003efermentation experiment\u003c/b\u003e\u003c/p\u003e \u003cp\u003eBecause \u003cem\u003eK. pneumoniae\u003c/em\u003e and \u003cem\u003eEscherichia coli\u003c/em\u003e are the most common pathogens in GFPLA and capable of fermentation, they were selected for this experiment. The samples were divided into the low and high glucose groups (n\u0026thinsp;=\u0026thinsp;3/group). The glucose concentrations in the culture medium were 5.5 and 25 mmol/L in the low and high glucose groups, respectively. Based on preliminary exploratory experiments, 59 mL of culture medium were added to a graduated conical fermentation tube (graduation, 0.1 mL; capacity, 15 mL). Then, 1 mL of a bacterial suspension [6 \u0026times; 10\u003csup\u003e8\u003c/sup\u003e colony-forming units (CFU)/mL] prepared using a turbidimeter was mixed into the liquid culture medium, generating a mixed culture fluid with a bacterial concentration of 1 \u0026times; 10\u003csup\u003e7\u003c/sup\u003e CFU/mL. The conical fermentation tube was sealed to ensure that no residual gas remained in the tip of the fermentation tube, as this could affect the measurement of gas production, and placed in a constant temperature incubator for cultivation. The gas production volume was observed for 0\u0026ndash;24 h in both groups. Gas production volumes smaller than 0.1 mL were recorded as 0.1 mL, and those exceeding the tube capacity (15 mL) were recorded as 15 mL.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analyses\u003c/h2\u003e \u003cp\u003eContinuous variables are presented as the mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation or median (interquartile range), whereas categorical variables are presented as n (%). Differences between continuous data were compared using Student\u0026rsquo;s \u003cem\u003et\u003c/em\u003e-test or the Mann\u0026ndash;Whitney U test, as appropriate. Differences between categorical data were compared using the chi-squared test or Fisher\u0026rsquo;s exact test, as appropriate. All statistical analyses were performed using SPSS version 25.0 (IBM, Armonk, NY, USA). Two-tailed \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 indicated statistical significance. Risk factors for gas production in PLA were identified \u003cem\u003evia\u003c/em\u003e logistic regression analysis. Considering that in patients with GFPLA and a history of biliary abnormality, the normal structure of the bile ducts is disrupted, gas in the pus cavity could be air migrating from the gastrointestinal tract, we performed logistic regression analysis in two steps(before and after excluding patients with biliary abnormality). GraphPad Prism 8.0 was utilized for pictures creation.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003ePatients with GFPLA had more severe clinical characteristics (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e)\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eClinical characteristics of 194 patients with PLA\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGFPLA\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;50)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNon-GFPLA\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;144)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e60 (52.75\u0026ndash;66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e61 (48\u0026ndash;69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.992\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30 (60.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e79 (54.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.528\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDM, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38 (76.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e66 (45.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBiliary abnormality, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11 (22.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (4.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTime from symptom onset to CT, days\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8 (4\u0026ndash;15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 (5\u0026ndash;14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.621\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeukocytes, \u0026times; 10\u003csup\u003e9\u003c/sup\u003e/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.895 (7.92\u0026ndash;15.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.8 (8.1\u0026ndash;13.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.832\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNEUT, \u0026times; 10\u003csup\u003e9\u003c/sup\u003e/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.45 (6.175\u0026ndash;13.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.9 (5.925\u0026ndash;11.3075)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.694\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLY, \u0026times; 10\u003csup\u003e9\u003c/sup\u003e/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.95 (0.58\u0026ndash;1.5425)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.2 (0.8\u0026ndash;1.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.190\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePLT, \u0026times; 10\u003csup\u003e9\u003c/sup\u003e/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e176.5 (97.75\u0026ndash;320)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e245.5 (139.5\u0026ndash;362.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.034\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALB, g/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27.5 (24.3\u0026ndash;30.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29.55 (26.625\u0026ndash;33.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.005\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTBIL, \u0026micro;mol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.8 (9.35\u0026ndash;25.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.9 (8.9\u0026ndash;19.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.443\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALT, U/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46 (22.5\u0026ndash;121)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31 (20\u0026ndash;59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.014\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAST, U/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e41 (22\u0026ndash;107)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31 (20\u0026ndash;59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.114\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALP, U/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e194 (121\u0026ndash;290.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e155 (106.9\u0026ndash;231.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.025\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGGT, U/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e144.5 (70.75\u0026ndash;258.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e143.5 (87.25\u0026ndash;244)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.788\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePT, s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14.2 (12.6\u0026ndash;15.625)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.8 (12.8\u0026ndash;14.975)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.450\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFIB, g/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.8 (3.8\u0026ndash;5.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.2 (4.4\u0026ndash;5.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.019\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDD, \u0026micro;g/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e993 (590.25\u0026ndash;3166)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1123 (587\u0026ndash;2109)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.699\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFBG, mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.69 (6.5025\u0026ndash;14.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.89 (5.625\u0026ndash;10.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.005\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRP, mg/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e147.5 (103.5\u0026ndash;294.575)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e127.15 (71.825\u0026ndash;193.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.024\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIn-hospital mortality, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 (12.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (0.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHospital stay, days\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.5 (7\u0026ndash;19.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 (7\u0026ndash;14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.172\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eData are presented as median (interquartile range) unless otherwise indicated.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAbbreviations:\u003c/strong\u003e DM, diabetes mellitus; CT, computed tomography; NEUT, neutrophils; LY, lymphocytes; PLT, platelets; ALB, albumin; TBIL, total bilirubin; ALT, alanine aminotransferase; AST, aspartate transaminase; ALP, alkaline phosphatase; GGT, gamma-glutamyl transferase; PT, prothrombin time; FIB, fibrinogen; DD, d-dimer; FBG, fasting blood glucose; CRP, C-reactive protein\u003c/p\u003e\n \u003cp\u003eThe workflow of this study is presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. A total of 269 patients were identified over the study period. After excluding patients based on the eligibility criteria, 194 patients were enrolled in the present study (GFPLA, n\u0026thinsp;=\u0026thinsp;50; non-GFPLA, n\u0026thinsp;=\u0026thinsp;144). The proportions of patients with DM (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and biliary abnormality (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001) were higher in the GFPLA group. Meanwhile, albumin (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.005) and fibrinogen levels (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.019) were lower in the GFPLA group, whereas aspartate aminotransferase (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.014), alkaline phosphatase (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.025), and C-reactive protein levels (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.024) were higher, suggesting that patients with GFPLA had poorer liver function and more severe inflammatory responses. The mortality rate (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001) was higher in the GFPLA group, whereas the time from symptom onset to CT (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.621) and the length of hospital stay (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.172) did not differ between the groups.\u003c/p\u003e \u003cp\u003e \u003cb\u003eDM is an independent risk factor for gas formation in patients with GFPLA without biliary abnormality\u003c/b\u003e \u003c/p\u003e \u003cp\u003eLogistic regression analysis (\u003cb\u003eSteps 1 and 2\u003c/b\u003e) was used to identify factors associated with gas formation in PLA. \u003cb\u003eStep 1\u003c/b\u003e analysis (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) suggested that DM and biliary abnormality are independent risk factors for GFPLA, whereas positivity for \u003cem\u003eKlebsiella pneumoniae\u003c/em\u003e, \u003cem\u003eEscherichia coli\u003c/em\u003e and \u003cem\u003eEnterococcus faecalis\u003c/em\u003e and the time from symptom onset to CT were not identified as risk factors. Considering that in patients with GFPLA and a history of biliary abnormality, gas in the pus cavity could be air migrating from the gastrointestinal tract, we conducted \u003cb\u003eStep 2\u003c/b\u003e analysis after excluding 18 patients with a history of biliary abnormality to eliminate its influence. Because of the small sample size (n\u0026thinsp;=\u0026thinsp;1) after excluding these patients, \u003cem\u003eEnterococcus faecalis\u003c/em\u003e was not included as a variable in this analysis. As presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, DM remained an independent risk factor for GFPLA, whereas positivity for \u003cem\u003eKlebsiella pneumoniae\u003c/em\u003e and \u003cem\u003eEscherichia coli\u003c/em\u003e and the time from symptom onset to CT were not risk factors.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLogistic regression analysis before excluding patients with a history of biliary abnormality (\u003cb\u003eStep 1\u003c/b\u003e)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eUnivariate analysis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eMultivariate analysis\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.742\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.119\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBiliary abnormality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.862\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eKlebsiella pneumoniae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.529\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eEscherichia coli\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.744\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.468\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.839\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eEnterococcus faecalis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.435\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.212\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.015\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTime from symptom onset to CT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.361\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.736\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.446\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.746\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eAbbreviations: OR, odds ratio/Exp(β); DM, diabetes mellitus; CT, computed tomography\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLogistic regression analysis after excluding patients with a history of biliary abnormality (\u003cb\u003eStep 2\u003c/b\u003e)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eUnivariate analysis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eMultivariate analysis\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.756\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9.815\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eKlebsiella pneumoniae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.603\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.322\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.541\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eEscherichia coli\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.089\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.612\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTime from symptom onset to CT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.134\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.575\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.193\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.582\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eAbbreviations: OR, odds ratio/Exp(β); DM. diabetes mellitus; CT, computed tomography\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003e16S rDNA sequencing revealed that alpha diversity and bacterial community composition were similar between the groups\u003c/b\u003e \u003c/p\u003e \u003cp\u003eLogistic regression analysis indicated that pathogenic bacteria compositon was not an independent risk factor for GFPLA. Then, 16S rDNA sequencing was performed using 55 pus samples from patients with PLA (GFPLA: n\u0026thinsp;=\u0026thinsp;8; non-GFPLA: n\u0026thinsp;=\u0026thinsp;47) to further explore differences in bacterial community between the GFPLA and non-GFPLA groups.\u003c/p\u003e \u003cp\u003eAlpha diversity indices (Chao1, Shannon, Simpson, and Pielou-e) did not differ between the groups, suggesting that bacterial community richness, diversity, and evenness were similar between the groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea-d). Bacterial community composition analysis at the specie level (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea) illustrated the top 30 pathogenic bacteria by relative abundance. The results revealed that \u003cem\u003eKlebsiella pneumoniae\u003c/em\u003e was the most common pathogen in both groups and \u003cem\u003eEnterococcus faecalis\u003c/em\u003e was one of the top 30 species by relative abundance, while \u003cem\u003eEscherichia coli\u003c/em\u003e was not. The GFPLA and non-GFPLA groups could not be adequately separated according to the principal component analysis plot (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb), indicating that the bacterial composition of the two groups was similar. The relative abundance of \u003cem\u003eKlebsiella pneumoniae\u003c/em\u003e and \u003cem\u003eEnterococcus faecalis\u003c/em\u003e did not differ between the two groups(Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea,b,P both \u0026gt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003e \u003cb\u003eHigh glucose levels promoted gas production by\u003c/b\u003e \u003cb\u003eKlebsiella pneumoniae\u003c/b\u003e \u003cb\u003eand\u003c/b\u003e \u003cb\u003eEscherichia coli\u003c/b\u003e\u003c/p\u003e \u003cp\u003eDM was identified as an independent risk factor for GFPLA, indicating that blood glucose levels are associated with gas formation in PLA. After comprehensively considering the results of logistic regression analysis and 16S rDNA sequencing, we speculated that gas formation is associated with high blood glucose levels rather than pathogenic bacteria composition in patients with PLA. To validate this hypothesis, we conducted an \u003cem\u003ein vitro\u003c/em\u003e fermentation experiment. After 24 h of fermentation, the results (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea-f) indicated that the gas production volume significantly differed between the low and high glucose groups for both \u003cem\u003eKlebsiella pneumoniae and Escherichia coli\u003c/em\u003e (both \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01).\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe results demonstrated that patients with GFPLA had worse liver function, more severe inflammatory responses, and a higher in-hospital mortality rate than patients with non-GFPLA, in line with previous studies indicating that GFPLA has more severe clinical characteristics [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan additionalcitationids=\"CR23\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Investigations of risk factors associated with GFPLA can help clarify the underlying pathological mechanisms of the greater disease severity in patients with GFPLA. However, there are few reports on this aspect.\u003c/p\u003e \u003cp\u003eThe \u003cb\u003eStep 1\u003c/b\u003e analysis identified DM and biliary abnormality as independent risk factors for GFPLA. However, in patients with GFPLA and a history of biliary abnormality, the normal structure of the bile ducts is disrupted, allowing gas to escape from the gastrointestinal tract into the abscess cavity, suggesting that partial gas in pus cavities is more likely to be air migrating from the gastrointestinal cavity more than a product of bacterial metabolism. Meanwhile, Lee \u003cem\u003eet al.\u003c/em\u003e reported that the gas composition in the pus cavity is the same as the partial gas composition in the human gastrointestinal cavity [\u003cspan additionalcitationids=\"CR26 CR27\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. To eliminate the influence of biliary abnormality, we conducted the \u003cb\u003eStep 2\u003c/b\u003e analysis after excluding patients with a history of biliary abnormality, and DM remained an independent risk factor for GFPLA, in line with previous studies reporting the close relationship between GFPLA and DM [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Lee \u003cem\u003eet al.\u003c/em\u003e hypothesized that gas formation in PLA is linked to increased gas production, impaired gas transportation, and an impaired equilibrium between gas in local tissues and that in abscesses [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], and all of these conditions can be effectively created by DM. The immune system of patients with diabetes might be less robust as that of healthy individuals, making them more susceptible to bacterial infections and reducing their ability to eliminate bacteria, which leads to abscess formation in the liver [\u003cspan additionalcitationids=\"CR31\" citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. The high glucose levels in the tissues of patients with DM provide a favorable microenvironment for vigorous microbe metabolism and growth[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. We hypothesized that gas production increases with increasing blood glucose levels. This was confirmed by the results of \u003cem\u003ein vitro\u003c/em\u003e fermentation experiments, in which gas production was higher in the high glucose group. Furthermore, patients with DM often have microvascular complications[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e], leading to insufficient blood supply to tissues and impaired clearance of metabolic waste, resulting in the decreased transport and accumulation of gas produced within the abscess cavity. The time from symptom onset to CT considered to be linked to gas formation in PLA, but the study results did not identify the time from symptom onset to CT as an independent risk factor for GFPLA.\u003c/p\u003e \u003cp\u003ePositivity for \u003cem\u003eKlebsiella pneumoniae\u003c/em\u003e and \u003cem\u003eEscherichia coli\u003c/em\u003e was not identified as an independent risk factor for GFPLA, contradicting traditional views. Therefore, 16S rDNA sequencing was performed to further investigate the correlation between the bacterial community within the abscess cavity and GFPLA. The results indicated that alpha diversity and the bacterial composition were similar between the two groups. Several rare GFPLA cases caused by gas-forming bacteria such as \u003cem\u003eClostridium perfringens\u003c/em\u003e and \u003cem\u003eSalmonella enteritidis\u003c/em\u003e have been reported[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. However, these bacteria were not detected in any of the 55 pus samples. Meanwhile, the \u003cem\u003ein vitro\u003c/em\u003e fermentation results revealed that gas production significantly differed between the low and high glucose groups for both \u003cem\u003eKlebsiella pneumoniae\u003c/em\u003e and \u003cem\u003eEscherichia coli\u003c/em\u003e, suggesting that high glucose levels can promote gas production by pathogens regardless of the specific type of pathogen. In conclusion, pathogenic bacteria might not be associated with gas formation in PLA.\u003c/p\u003e \u003cp\u003eThis study had some limitations. First, this was a single-center study, and multicenter studies are needed to increase the sample size in the future. Second, this study did not quantitatively detect or analyze the composition of the gas within the abscess cavity. Finally, the fermentation experiment was an \u003cem\u003ein vitro\u003c/em\u003e experiment, and there could be some differences between the \u003cem\u003ein vitro\u003c/em\u003e environment and the microenvironment within the abscess cavity.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eGFPLA was associated with more severe clinical characteristics and a higher in-hospital mortality rate than non-GFPLA. After excluding patients with a history of biliary abnormality, DM was identified as an independent risk factor for GFPLA, whereas the time from symptom onset to CT and the presence of \u003cem\u003eKlebsiella pneumoniae\u003c/em\u003e and \u003cem\u003eEscherichia coli\u003c/em\u003e were not independent risk factors. Based on clinical data, 16S rDNA sequencing and \u003cem\u003ein vitro\u003c/em\u003e fermentation experiment results, gas formation could be related to high blood glucose levels but not to pathogenic bacteria composition in PLA.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eZC and YG designed the study. LC and ZG collected data. YG and HGW analyzed and interpreted the data. YG, HGW, HRW, and ZC wrote the manuscript. All authors approved the final manuscript and agreed to be accountable for all aspects of the work.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data generated and/or analyzed during the current study are available in the Sequence Read Archive of the National Center for Biotechnology Information (http://www.ncbi.nlm.nih.gov/bioproject/917077, accession number PRJNA917077).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe present study was supported by the National Natural Science Foundation of China (Grant No. 82272097) and the 345 Talent Project in Shengjing Hospital of China Medical University. The funders had no role in the study design, data collection, analysis and interpretation, decision to publish, and preparation of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank \u003cem\u003eMedjaden\u003c/em\u003e Inc. for scientific editing of this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Ethics Committee of Shengjing Hospital of China Medical University approved this retrospective study(2022PS1067K). Written informed consent was obtained from the individual(s) for the publication of any potentially identifiable images or data included in this article. All methods performed in this study involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eYin D, et al. Clinical characteristics and management of 1572 patients with pyogenic liver abscess: A 12-year retrospective study. Liver Int. 2021;41(4):810\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang WJ, et al. Etiology and clinical manifestations of bacterial liver abscess: A study of 102 cases. 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The vascular complications of diabetes: a review of their management, pathogenesis, and prevention. Expert Rev Endocrinol Metab. 2024;19(1):11\u0026ndash;20.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTohmatsu Y, et al. Liver abscess caused by Clostridium perfringens after left hepatic trisectionectomy for perihilar cholangiocarcinoma: a case report. Surg Case Rep. 2023;9(1):111.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHoshi Y, et al. Survival in a Case of Emphysematous Cholecystitis With Sepsis Caused by Clostridium perfringens. Cureus. 2023;15(11):e49705.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Clinical characteristic, Gas formation, Pyogenic liver abscess, 16S rDNA sequencing, Risk factor","lastPublishedDoi":"10.21203/rs.3.rs-3990440/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3990440/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eGas formation is a typical imaging feature of pyogenic liver abscess (PLA). However, the underlying mechanism of gas formation and its impact on the clinical characteristics of PLA remains unclear. The current study investigated the clinical characteristics of gas-forming PLA (GFPLA) and explored risk factors for GFPLA from both the host and pathogenic bacterium perspectives.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe GFPLA group exhibited greater disease severity and a higher in-hospital mortality rate. The proportions of patients with diabetes mellitus (DM, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and biliary abnormality (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001) were higher in the GFPLA group. DM was an independent risk factor for GFPLA, whereas the time from symptom onset to CT and positivity for \u003cem\u003eKlebsiella pneumoniae\u003c/em\u003e and \u003cem\u003eEscherichia coli\u003c/em\u003e were not independent risk factors for GFPLA. 16S rDNA sequencing revealed no significant differences in bacterial community richness, diversity, evenness, and composition between the groups(\u003cem\u003eP\u003c/em\u003e all \u0026gt;\u0026thinsp;0.05). \u003cem\u003eIn vitro\u003c/em\u003e fermentation experiments illustrated that high glucose levels was associated with greater gas production by both \u003cem\u003eKlebsiella pneumoniae\u003c/em\u003e and \u003cem\u003eEscherichia coli\u003c/em\u003e than low glucose levels (both \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eGFPLA was associated with more severe disease and greater in-hospital mortality than non-GFPLA. Gas formation in PLA might be related to high blood glucose levels, but not to pathogenic bacteria composition.\u003c/p\u003e","manuscriptTitle":"Gas Formation in Pyogenic Liver Abscess Is Independently Correlated with Diabetes Mellitus but not with Pathogenic Bacteria","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-04 19:06:31","doi":"10.21203/rs.3.rs-3990440/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b1049f2d-8d2a-4af1-85bd-344933370c78","owner":[],"postedDate":"March 4th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-07-03T07:46:07+00:00","versionOfRecord":[],"versionCreatedAt":"2024-03-04 19:06:31","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3990440","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3990440","identity":"rs-3990440","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}