Polysaccharides are the most abundant biomolecules in nature, but are the least understood in terms of their chemical structures and biological functions. Polysaccharides cannot be simply sequenced because they are often highly branched and lack a uniform structure. Furthermore, large polymeric structures cannot be directly analyzed by mass spectrometry techniques, a problem that has been solved for polynucleotides and proteins. While restriction enzymes have advanced genomic analysis, and trypsin has advanced proteomic analysis, there has been no equivalent enzyme for universal polysaccharide digestion. We describe the development and application of a chemical method for producing oligosaccharides from polysaccharides. The released oligosaccharides are characterized by advanced liquid chromatography–mass spectrometry (LC–MS) methods with high sensitivity, accuracy and throughput. The technique is first used to identify polysaccharides by oligosaccharide fingerprinting. Next, the polysaccharide compositions of food and feces are determined, further illustrating the utility of technique in food and clinical studies. While mass spectrometry-based proteomics largely relies on digesting proteins into peptides, there is no equivalent strategy for polysaccharide analysis. Here, the authors develop a chemical approach to break down poly- into oligosaccharides and present a workflow to identify polysaccharides by oligosaccharide fingerprinting. But investigations of the detailed structural basis of those functions is ostensibly nonexistent. Understanding the structure/function relationships of polysaccharides requires that their structures be known. They are not. The development of analytical tools for characterizing polysaccharides has lagged far behind than those of DNA and proteins5. Polysaccharide analysis remains slow and tedious, as current strategies still employ methods developed long before the development of high throughput structural tools commonly used in genomic and proteomic analysis8. Unfortunately, these limitations in determining polysaccharide structures hinder our ability to understand their fundamental biological roles in fields from fuel efficiency to human health. The deficiency in structural methods for polysaccharides is becoming particularly acute in foods where the polysaccharide compositions represent a major gap in knowledge and are rarely, if ever, identified or quantified9.The fundamental importance of carbohydrates to biological organisms remains discouragingly elusive. The plants that nourish all life forms and stabilize the planet’s atmosphere, are mostly carbohydrate. Yet scientists struggle to provide even the most crude predictions of how the complexity of polysaccharide structures drives their endogenous functions much less their roles in fueling the entire food web. Epidemiologic and observational studies have tentatively documented that polysaccharides (as fiber) are a vitally important dimension of nutrition and health14. In proteomic analysis, the ability to generate oligopeptides was a necessary first-step toward high throughput analysis. For proteomic analysis, proteases such as trypsin yielded highly reproducible cleavages and produced peptides of sufficient length to identify the parent protein. The methods involved the matching of peptide masses to databases for peptide fingerprinting17, and the use of tandem mass spectrometry to obtain sequence-specific fragments for protein identification18. However, the analogy between protein and polysaccharide sequencing must end here. Unlike proteins and DNA, which have discrete and linear primary structures, polysaccharide structures may be composed of homo or copolymers with stochastically distributed monomers or pendant polymers each with distinct monosaccharide compositions with various glycosidic linkages. To complicate the primary structures further, branching may occur with no distinct length of polymer. Due to the large number of variable structural attributes, polysaccharides do not have precise structures and must be thought of as probabilistic distributions of their innate structural features. The lack of a unified structure further means that there is no enzymatic process, or a trypsin analog, to produce oligosaccharides from most polysaccharides. Instead, there are enzymes such as amylase that produce monosaccharides and oligosaccharides from only specific polysaccharides (amylose and amylopectin). Similarly, animal polysaccharide such as glycosaminoglycans (GAGs) are routinely digested to disaccharides via enzymatic approaches for further analysis by liquid chromatography–mass spectrometry (LC–MS)23. Due to established and robust methods, GAGs were not explored in this report. The lack of a single unified polymeric structure further obviates the use of strategies that aim to reassemble parent polymer sequence from aligning overlapping oligomer sequences. Rather, polysaccharide sequence should be used to understand the frequency that individual structural features  are found in a distribution of similar but, often, structurally unique molecules.The dissociation of polysaccharides into constitutive and measurable oligosaccharides has been the missing critical component for characterizing polysaccharides. Robust digestion methods are the key to polysaccharide analysis, which opens polysaccharides to the most powerful modern chromatographic, mass spectrometry, computational, and robotic techniques25. Acid-based techniques have been used to produce oligosaccharides from polysaccharides with varying levels of success, but generally require precise reaction conditions for each polysaccharide, and is therefore not well suited for the analysis of complicated mixtures or heterogeneous structures28.The structural heterogeneity of polysaccharides is due to the large diversity in monosaccharide and linkage compositions. Thus, there is no universal digestive enzyme for producing oligosaccharides from polysaccharides. The repertoire of natural glycosidase enzymes is conspicuous by their high specificity for specific glycosidic bonds. Therefore, only a limited number of polysaccharide linkages can be digested with a specific enzyme30 and are the largest driver of the gut microbial population35. While there has been rapid progress in characterizing the microbial composition of the gut microbiome37, similar advances have not been made in characterizing the carbohydrates that modulate those populations9. Thus, the impact that dietary carbohydrates have on the gut microbiome is, generally, indirectly measured by genomic and transcriptomic techniques rather than through direct carbohydrate analysis, which continues to hinder our mechanistic understanding of this complicated process40. Therefore, there is an immediate and pressing need to expand the current toolbox of polysaccharide characterization if we are to better understand diet–microbe interactions46.The combination of complexity of polysaccharide structures and specificity of glycosidase catalysis is central to biology itself. Fuel and carbon flows through all ecosystems largely as carbohydrate. Managing these flows for any purpose from energy efficiency and sustainability to human metabolism and health requires that the structures of all biopolymers that contain carbohydrate to be known. The need for more robust polysaccharide characterization techniques is especially crucial for characterizing food polysaccharides. Carbohydrates make up the largest part of most human diets, which are now associated with various metabolic diseasesIn this report, we describe a chemical method for the dissociation of diverse polysaccharides into oligosaccharides. Fenton’s initiation toward defined oligosaccharide groups (FITDOG) employs oxidative chemistry to disassemble structurally diverse polysaccharides into oligosaccharides that are of sufficient length to be structurally probed by LC–MS.3+, and an oxidizing agent, hydrogen peroxide, to produce reactive radical species that cleave glycosidic bonds. The radicals induce oxidative cleavage of the polysaccharide backbone to produce oligosaccharides that are representative of the parent polysaccharide structure  glucose backbone with frequent α(1 → 6) xylose branches that are often terminated with a single β1 → 2) galactose residue → 4 gluc galactos3Pnt2, m/z 771.27) were observed at 16.0 (compound I), 18.9 (II), 21.0 (III), and 31.2 (IV) min, respectively  were obtained from the accurate masses, while structural information was obtained from tandem MS. Isomers, compounds with the same DP but different structures, were present and were structurally elucidated. For example, four isomers with the compositions three hexoses and two pentoses  with reaction time. The shortest reaction time, 0.5 h, yielded products with DP 3–14, while at 2 h only those corresponding to DP 3–8 were observed, and at 4 h only those corresponding to DP 3–6 were observed   Fig. . A more 3 (RT 12.56 min), Hex4 (19.28 min), and Hex5 (26.44 min) . For example, curdlan, a linear β(1 → 3) linked glucose polymer yielded oligosaccharides with compositions corresponding to Hexin) Fig. . Other hin) Fig. , amylose6 was observed to generate eight isomers from lichenan and four from β-glucan , Hex4 , Hex5 , and Hex6 (30.90 min) were identical and shared by the two polysaccharides  and β(1 → 4) linked glucose residues, but in differing ratios. Thus, the composition corresponding to Hexcan Fig. . Moreovedes Fig. . Glucomades Fig. .50.Branched polysaccharides that contain multiple linkage types and multiple monosaccharides yielded the most isomers per DP. Galactomannan is a branched heteropolysaccharides containing a β(1 → 4) linked mannose backbone and α(1–6) linked terminal galactose branches. With FITDOG, the oligosaccharides resembled those from mannooligosaccharides, derived from the mannose backbone, and galactomannooligosaccharides derived from the branching regions Fig. . Arabina3 GlcAOMe (4 isomers), Pent4 GlcAOMe (e isomers), Pent5 GlcAOMe (4 isomers), Pent6 GlcAOMe (3 isomers), and Pent7 GlcAOMe (2 isomers). The presence of these oligosaccharides indicated that O-methylation on the terminal glucuronic acid was preserved during the FITDOG treatment -linked xylose polysaccharide with terminal 4-O-methyl glucuronic acid (GlcAOMe) residues. Under FITDOG conditions, several oligosaccharides were generated with unique compositions including Pntent Fig. . Other mWith the unique oligosaccharide compositions representing each parent polysaccharide, we determined whether the oligosaccharides could be used as diagnostic markers for polysaccharide identification in complicated mixtures such as those found in carbohydrate mixtures. This approach is analogous in proteomics to peptide fingerprinting, which is a robust method for identifying proteins based upon the presence of diagnostic peptides. The oligosaccharide products of polysaccharides were compiled as unique identifiers for the respective parent, which created an oligosaccharide reference library with nearly 400 unique oligosaccharides Fig. .Fig. 5Ol51, mixed linkage β-glucans52, and cellulose53. Oat and wheat bran each produced over 50 distinct oligosaccharides with most matching entries in the reference library , cellulose (75%), β-glucan (34%), and lichenan (24%). Oat bran was composed of amylose/amylopectin (100% coverage), cellulose (75%), β-glucan (38%), and lichenan (24%). Wheat bran also contained arabinoxylan (20%) and xylan (32%). Interestingly, oat bran contained several unmatched pentose oligomers and mixed hexose and pentose containing oligomers demonstrating the presence of a polysaccharide that is yet to be identified by our reference library, which is binned for future identification. Overall, the results matched the general expected polysaccharide composition of both bransThe ability to identify polysaccharides in complicated matrices will yield the biological functions of polysaccharides. In particular, the role of fiber in food is not well understood due to the lack of analytical tools to characterize them. As an illustration of the utility of the method, we probed the fate of complimentary food polysaccharides in an infant gut by monitoring the changes in compositions between the food product and infant feces after consumption. The infants were part of a cohort comprised of exclusively breastfed infants who were fed complementary food in a crossover feeding trial. An infant was fed pear as their first solid food along with continued breastfeeding. Upon completion of the 7-day feeding period, a fecal sample was taken and analyzed for comparison with the native food. This study would provide the fate of dietary carbohydrates as they interact with the infant gut microbiome.Polysaccharides that made up the pear product were examined and found to be composed of, in decreasing order, amylopectin/amylose, glucomannan, arabinan, arabinoxylan, galactomannan, mannan, and lichenan Fig. . Analysi57.Oligosaccharides produced by FITDOG were further structurally elucidated to provide, de novo, the parent polysaccharide. The workflow is shown in Fig. 57. The linkage information was similarly obtained using a separate recently reported workflow for comprehensive linkage analysis56. The monosaccharide analysis employed acid hydrolysis to produce monosaccharides that were subsequently labeled with 3-methyl-1-phenyl-2-pyrazoline-5-one (PMP). UHPLC-QqQ MS analysis required 10 min of run time and measured the absolute abundances of 14 monosaccharides. The glycosidic linkages were analyzed with permethylation followed by acid hydrolysis and PMP labeling. The run time was 15 min and was capable of identifying over 100 distinct glycosidic linkages. The monosaccharide and linkage analysis methods were chosen for their speed, which allowed 96-well plates to be quickly analyzed, and their sensitivity, which allowed multiple analyses to be performed on single HPLC fractions. When the three platforms were integrated, they yielded structural information that could describe oligosaccharides with monosaccharide compositions and glycosidic linkage information. To obtain the final structural feature, the anomeric character of the linkages, the most abundant components were selected for NMR analysis using 1H, 13C NMR and a combination of techniques including COSY, HSQC, HMBC, and H2BC , linkage, or branching Fig. . Fractio2BC Fig. .Fig. 8De59; protection is provided by complex carbohydrates61 and importantly, fuel and carbon flows through ecosystems primarily as complex carbohydrates63. The importance of glycan structures to interkingdom biology was vividly revealed by research on human milk that demonstrated that human mothers produce complicated, structure specific, oligosaccharides not to feed their infants but to feed specific bacteria within their infants64. While the prebiotic effects of oligosaccharides are more widely understood, their antimicrobial and antibiofilm activities are currently under intense investigations67. The challenge of understanding and managing the flows of carbohydrates across all of biology is solving the complexity of carbohydrate structures also across biology. Yet this complexity is far greater than the other biopolymer classes nucleotides and proteins combined. Elucidating carbohydrate structures has been daunting, but a major impediment—the structure agnostic production of oligosaccharides—has now been solved.The life sciences have built a remarkable understanding of the functions of cells and organisms driven by innovations in mapping the structures of life’s biopolymers. As the most abundant biopolymer in nature, the lack of structural methods for polysaccharides has been glaring. Carbohydrate polymers are central to many biological processes. Cell-cell recognition is based on complicated glycan structures3+ catalyzes the formation of the hydroperoxyl radical species, the site of chelation guides oxidation toward nearby hydroxyl groups that render nearby glycosidic bonds more prone to base-induced beta-elimination68. However, this mechanism is not entirely consistent with our observations under the specified conditions, as we do not observe the change in mass associated with the dehydration and formation of the double bond. Determining the true mechanism of this reaction is a topic of ongoing research.The features of FITDOG make it suitable to measure polysaccharides with accuracy, sensitivity, and throughput. Central to the FITDOG analytical strategy is the ability to produce distinct oligosaccharides that are unique to their respective parent polysaccharides. The lack of mono- and disaccharides demonstrates that the catalysis is equivalent to an endo-, rather than an exoglycosidase enzyme. The results further indicate that the cleavages are not random thereby producing distinct oligosaccharides that are consistent with the cleavages of specific glycosidic bonds. The chemical basis underlying the glycosidic specificity of the FITDOG process, in effect favoring the cleavage of certain bonds, is not yet known. The regioselectivity of iron coordination within the polysaccharide chains likely guides the reaction. It has been suggested that the Fe3+ and hydrogen peroxide. For this report, the conditions were optimized, and those presented here yielded the largest number of oligosaccharides while minimizing over oxidation for each polysaccharide tested. Factors that can lead to overoxidation included higher reaction temperatures and high concentrations of hydrogen peroxide. Changing the reaction time can increase or decrease the DP of the oligosaccharide products. Increasing the DP yields greater information regarding the placement of pendant chains from the main polymer chain.There are side reactions—some that can be suppressed and others that also have further utility. Excessive oxidation can occur including the formation of carboxylic acids in the nascent oligosaccharides. A singular feature of polysaccharide-based Fenton chemistry is that these side reactions can be minimized through properly selected experimental conditions that include the reaction times and the relative concentrations of FeThe oligosaccharides produced by FITDOG can be used to identify known polysaccharides structures, which is analogous to peptide fingerprinting in proteomic analysis. This capability will simplify the analysis of complicated polysaccharide mixtures such as those found in food. As different dietary polysaccharides are known to affect the microbiota distinctly, this capability could significantly advance prebiotic/probiotic research.The availability of a universal chemical approach for polysaccharide dissociation allows modern separation and mass spectrometry methods to be applied to the analysis of complex polysaccharides. We show that oligosaccharide fingerprinting is suitable for identifying known polysaccharides, and the reaction is readily scalable and can be expanded to more polysaccharides as new structures become available. Furthermore, the polysaccharide examples that were illustrated belong to several kingdoms including plants, bacteria, fungi, and algae. The method can also be used to elucidate the structures of new and uncharacterized polysaccharides. We showed that oligosaccharides produced by this method are characterizable by tandem MS methods where a more in-depth multi-platform strategy can systematically provide exact oligomer structure. While we have compared the current strategy to peptide fingerprinting, the analogy ends here. Structural analyses of oligosaccharides are significantly more difficult than that of peptides. Therefore, sequencing by tandem MS, a common tool for peptides, yields highly limited information with oligosaccharides, especially of those with limited monomeric mass diversity. However, we show that de novo structural elucidation of oligosaccharides, which previously involved resource extensive processes, can now be performed rapidly. With the structures elucidated, the oligosaccharides can be reconstructed to determine the overall structure of the parent polysaccharide Fig. . This meFinally, this method may find additional utility beyond analysis. When scaled, this method provides the creation of new and previously unattainable oligosaccharides that can be further probed for bioactivity. We look forward to these results as we predict that the method will substantially advance carbohydrate research.2O2), sodium hydroxide (NaOH), sodium borohydride (NaBH4), iron(III) sulfate pentahydrate (Fe2(SO4)3), and glacial acetic acid were purchased from Sigma-Aldrich . Amylopectin was obtained from Carbosynth . Amylose, xyloglucan, arabinoxylan, xylan, glucomannan, galactomannan, mannan, curdlan, galactan, β-glucan, arabinan, and lichenan were purchased from Megazyme . Microcrystalline cellulose was purchased from ACROS Organics. Formic Acid (FA) was purchased from Fisher Scientific . Acetonitrile (HPLC grade) was purchased from Honeywell . Nano-pure water was used for all experiments.Sodium acetate, hydrogen peroxide (HA solution was prepared containing 95% (v/v) 40 mM sodium acetate buffer adjusted to pH 5 with glacial acetic acid, 5% (v/v) hydrogen peroxide (30% v/v), and 65 nM iron(III) sulfate. This mixture was vortexed and added to dry polysaccharide standards to make a final solution of 1 mg/ml. The reaction was incubated at 100 °C for 1 h. The reaction was quenched by adding half of the reaction volume of cold 2 M NaOH. Glacial acetic acid was added for neutralization.4 for 1 h at 65 °C. Oligosaccharides were isolated using nonporous graphitized carbon cartridges. Cartridges were washed with 80% acetonitrile and 0.1% (v/v) TFA in water. The oligosaccharides were loaded and washed with five column volumes of water. The oligosaccharides were eluted with 40% acetonitrile with 0.05% (v/v) TFA. Samples were completely dried by evaporative centrifugation and stored at −20 °C until analysis.Oligosaccharides were reduced by incubation with 1 M NaBHg for 20 min to pellet the polysaccharide fraction and partition it from the endogenous oligosaccharides in the supernatant. The pelleted fraction underwent FITDOG treatment to generate representative oligosaccharides. Oligosaccharides were then reduced and purified using the protocol described in the section above.To separate the endogenous oligosaccharides from the polysaccharides, pear and feces samples underwent 80% ethanol precipitation overnight at −80 °C. Samples were centrifuged at 845 × For analysis by MALDI-MS, 1 µl was plated directly onto a stainless steel MALDI plate. To this, 0.3 µl of 0.01 M NaCl and 0.7 µl of 25 mg/ml 2, 5-dihydroxybenzoic acid was added and mixed within the pipet tip. The samples were dried under vacuum and analyzed on a Bruker UltraFlextreme MALDI-tandem time-of-flight (MALDI-TOF/TOF) instrument. The instrument was operated in positive mode and 95% of max laser power.Samples were reconstituted in nano-pure water before analysis by nano-HPLC-chip/Q-TOF MS. The system included two pumps, a capillary pump for sample loading and a nano pump for analytical separation. In this system, an Agilent 1200 series HPLC is coupled to an Agilent 6520 Q-TOF mass spectrometer through a chip cube interface. The chip contained a 40 nl enrichment column, and a 75 µm × 43 mm analytical column; both columns had PGC as the stationary phase. Sample loading was done with 3% (v/v) acetonitrile/water + 0.1% formic acid at a flow rate of 4 µl/min. Chromatographic separation was performed with a binary gradient of solvent A: (3% (v/v) acetonitrile/water + 0.1% formic acid) and solvent B: (90% acetonitrile/water + 0.1% formic acid) with a flow rate of 0.4 µl/min. The gradient was run for 60 min, 1–5% B, 0–2 min; 5–30%, 2–33 min; 30–99%, 33–38 min; 99–99%, 38–48 min; 99–1%, 48–50 min; 1–1%, 50–60 min.m/z 118.086 to 2721.895. Drying gas was set to 325 °C and with a flow rate of 5 l/min. The fragment, skimmer, and Octapole 1 RF voltages were set to 175, 60, and 750 V, respectively. Fragmentation was performed at a rate of 0.63 spectra/s. The collision energy was based upon the compound mass and expressed by the linear function (collision energy = 1.8 × (m/z) − 2.4).Data were collected in the positive mode and calibrated with internal calibrant ions ranging from m/z 121.051 to 2421.914. Drying gas temperature and flow rate were set to 150 °C and 11 l/min, respectively. Operation voltages for the fragment, skimmer, and octupole 1 RF were 175, 60, and 750 V, respectively. The acquisition rate was set to 0.63 spectra/s. When fragmentation was used, the linear function, collision energy = 1.45 × (m/z) − 3.5, was employed.Samples were reconstituted in nano-pure water before HPLC Q-TOF MS analysis. The analytical separation was carried out using an Agilent 1260 Infinity II HPLC coupled to an Agilent 6530 Accurate-Mass Q-TOF MS. Chromatographic separation was performed on a 150 mm × 1 mm Hypercarb column from Thermo Scientific with a 5 µm particle size. A binary gradient was employed which consisted of solvent A: (3% (v/v) acetonitrile/water + 0.1% formic acid) and solvent B: (90% acetonitrile/water + 0.1% formic acid). A 45 min gradient with a flow rate of 0.150 mL/min was used for chromatographic separation: 3–25% B, 0–15 min; 25–25% B, 15–18 min; 25–99% B, 18–30 min; 99–99% B, 30–32 min; 99–3% B, 32–34 min; 3–3% B, 34–45 min. Samples were run in the positive mode. Internal calibrant ions ranged from The fecal samples were collected from a healthy, term 6-month-old infant who completed the UC Davis Infant Microbiome Nutrition and Development (Infant MiND) Study. The infant was exclusively breastfed at enrollment. The infant was assigned to consume pear (Earth’s Best Stage 1) concurrently with breast milk for 7 days. After 7 days, parents were instructed to scrape the soiled diaper with sterile utensils, to place the fecal samples into sterile tubes, and to seal and store the samples in their kitchen freezers. The fecal samples were transported back to University of California Davis campus on dry ice and stored in −80 °C before being analyzed. The University of California Davis Institutional Review Board approved all aspects of this study and written informed consent was obtained from the participants (Protocol ID: 919505). This study was registered on clinicaltrials.gov (NCT01817127).m/z 118.086–2721.895. Drying gas was set to 150 °C and with a flow rate of 11 l/min. The fragment, skimmer, and Octupole 1 RF voltages were set to 75, 60, and 750 V, respectively. Fragmentation was performed at a rate of 1 spectra/s. The collision energy was based upon the compound mass and expressed by the linear function (collision energy = 1.3 × (m/z) − 3.5). Fractions were collected on 96-well plates at a rate of 30 s per fraction. Collected fractions were dried to completion under vacuum centrifugation and reconstituted in 100 μl of nano-pure water. A 10 μl aliquot was transferred to a separate 96-well plate for monosaccharide composition analysis, while the remaining 90 µl underwent glycosidic linkage analysis.Fractionation and detection of oligosaccharides was performed on an Agilent 1260 Infinity II series HPLC coupled to an Agilent 6530 Q-TOF mass spectrometer and a Teledyne Isco Foxy 200 fraction collector. Oligosaccharides were first separated on a 150 × 4.6 mm Hypercarb column from Thermo Scientific with a 5 µm particle size. A binary gradient was employed and consisted of solvent A: (3% (v/v) acetonitrile/water + 0.1% formic acid) and solvent B: (90% acetonitrile/water + 0.1% formic acid). A 90 min gradient with a flow rate of 1 ml/min was used for chromatographic separation: 5–12% B, 0–90 min; 12–99% B, 90–90.01 min; 99–99% B, 90.01–110 min; 99–5% B, 110–110.01 min; 5–5% B, 110.01–120 min. Post column, a 90:10 flow splitter partitioned the larger stream to the fraction collector and the smaller to the Q-TOF mass spectrometer. Data from the QTOF was collected in the positive mode and calibrated with internal calibrant ions ranging from 54. Briefly, fractionated oligosaccharides underwent acid hydrolysis with 4 M TFA for 2 h at 100 °C. The samples were dried to completion by vacuum centrifugation. Samples and monosaccharide standards (0.001–100 μg/ml) underwent derivatization with 0.2 M PMP in methanol and 28% NH4OH at 70 °C for 30 min. Derivatized products were dried to completion under vacuum centrifugation and reconstituted in nano-pure water. Excess PMP was removed with chloroform extraction. The aqueous layer was analyzed by an Agilent 1290 infinity II UHPLC coupled to an Agilent 6495A QqQ MS employing dynamic multiple reaction monitoring (MRM) mode. An external standard curve was used for absolute quantitation of each monosaccharide in the fractions.Monosaccharide analysis was adapted by Amicucci et al. with the following modifications56. Briefly, fractionated oligosaccharides and a pool of oligosaccharide standards were reacted with saturated NaOH and iodomethane in DMSO. Residual NaOH and DMSO were removed by extraction with DCM and water. The DCM layer was dried to completion under vacuum centrifugation. Samples were hydrolyzed and derivatized in the same manner as the monosaccharide analysis. Samples did not undergo chloroform extraction and were reconstituted in 70% (v/v) methanol/water. Fractions were analyzed on an Agilent 1290 infinity II UHPLC coupled to an Agilent 6495A QqQ MS ran in MRM mode. A pool of oligosaccharide standards was used to assign the glycosidic linkages present.Linkage analysis was adapted from Galermo et al. with the following modifications2O, and measured using 1D 1H (relaxation delay (D1) 2 s; number of scans (NS) 128), 13C NMR , and 2D 1H–1H COSY , 1H–13C HSQC , HMBC , and H2BC . The spectra were then processed with Bruker TopSpin 3.2 and analyzed with MestReNova. The experimental chemical shifts, along with the required monosaccharide and linkage data, were calculated using the CASPER program69, where the oligosaccharide structures, including anomeric characters of linkages, were predicted with ranking scores.NMR spectra were recorded at 303 K on a Bruker AVANCE III 800 MHz spectrometer equipped with a 5 mm Bruker CPTCI cryoprobe. Samples were obtained by combining ten collections of fractionated oligosaccharides of the same components verified with HPLC-QTOF MS and MS/MS data. Based on the monosaccharide and linkage information, the most abundant oligosaccharides were selected for NMR analysis. Each of these selected pooled fractions were dried with vacuum centrifugation before being reconstituted in 0.4 mL of DFurther information on research design is available in the Peer Review FileDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Reporting Summary


Dogs are often adminstered >1 immunosuppressive medication when treating immune‐mediated diseases, and determining whether these different medications affect IL‐2 expression would be useful when performing pharmacodynamic monitoring during cyclosporine therapy.To determine the effects of 5 medications  on activated T‐cell expression of the cytokines IL‐2 and interferon‐gamma (IFN‐γ).Eight healthy dogs.Randomized, cross‐over study comparing values before and after treatment, and comparing values after treatment among drugs. Dogs were administered each drug at standard oral doses for 1 week, with a washout of at least 21 days. Activated T‐cell expression of IL‐2 and IFN‐γ mRNA was measured by quantitative reverse transcription polymerase chain reaction. Blood drug concentrations were measured for cyclosporine, mycophenolate, and leflunomide metabolites.P < .001) than values after treatment  with cyclosporine. Similarly, least squares means before treatment for IL‐2 (1.55 [1.07‐2.02] ΔCt) and IFN‐γ (2.62 [2.32‐2.92] ΔCt) values were significantly lower (both P < .001) than values after treatment  with prednisone. Comparing delta cycle threshold values after treatment among drugs, cyclosporine was significantly different than prednisone (IL‐2 and IFN‐γ both P < .001), with cyclosporine more suppressive than prednisone.Least squares means  before treatment for IL‐2 (2.91 [2.32‐3.50] ΔCt) and IFN‐γ (2.33 [1.66‐3.00 ΔCt]) values were significantly lower (both Prednisone and cyclosporine both affected expression of IL‐2 and IFN‐γ, suggesting that both have the ability to influence results when utilizing pharmacodynamic monitoring of cyclosporine treatment. ΔCtdelta cycle thresholdHPLChigh‐performance liquid chromatographyIFN‐γinterferon‐gammaIL‐2interleukin‐2LOQlimit of quantificationRT‐qPCRquantitative reverse transcription polymerase chain reactionUVultraviolet1Immune‐mediated disorders of dogs are commonly treated with 1 or more immunosuppressive agents, often a glucocorticoid in addition to a second immunosuppressive agent such as cyclosporine, azathioprine, mycophenolate mofetil, or leflunomide.In dogs receiving immunosuppressive treatment, therapeutic drug monitoring through the measurement of blood concentrations, the use of pharmacodynamic assays, or both can be used to ensure appropriate drug doses, particularly when there is individual dog‐to‐dog variability in drug metabolism and a narrow therapeutic window between ineffective and toxic doses. Currently, there are no commercially available assays for measuring blood concentrations of prednisone, mycophenolate mofetil, or azathioprine in dogs, although concentrations of mycophenolic acid (the active metabolite of mycophenolate mofetil) have been measured in an experimental setting. In North America, there are currently commercially available assays for measuring cyclosporine and teriflunomide (the active metabolite of leflunomide) blood concentrations. There is also a commercially available pharmacodynamic assay to evaluate the immunosuppressive effects of cyclosporine in dog via measurement of activated T‐cell production of IL‐2 and IFN‐γ. This assay was developed and validated specifically for cyclosporineThe goal of this study was to identify the effects of the oral administration, at immunosuppressive doses, of 5 common immunosuppressive medications  on the expression of the T‐cell cytokines IL‐2 and IFN‐γ in dogs. Our hypotheses were that cyclosporine and prednisone would suppress cytokine gene expression because they are known to affect transcription factors, with cyclosporine having the greatest effect, and that the proliferation inhibitors azathioprine, mycophenolate mofetil, and leflunomide would not have a significant effect.22.1Eight healthy Walker hound dogs were used in this study. Four dogs were intact females and 4 were neutered males, with a median age of 2 years . Typically, 6‐8 dogs are used for standard pharmacokinetic and pharmacodynamic studies, as described by Riviere,2.2± 0.1 mg/kg, mean ± SD, PO, q24h), azathioprine  , cyclosporine  , mycophenolate mofetil  , or leflunomide  . With each medication, drugs were administered for 7 days, followed by a minimum recovery period of 21 days between dosing with another medication. In previous studies of cyclosporine, maximal changes in cytokine expression were seen within 1 week of commencing the drug, and cytokine expression had returned to values before treatment within 2 weeks of discontinuing the medications.In a 5‐way, randomized, cross‐over study, the dogs were administered either prednisone   analysis for the assessment of IL‐2 and INF‐γ. At the end of each treatment period, blood was again collected in a heparinized tube from each dog, 2 hours after the morning drug dose was administered, for repeat RT‐qPCR analysis. At the same time point , blood was collected to measure peak blood cyclosporine concentrations. Additionally, within minutes before administration of the final morning drug dose, blood was collected to measure trough blood concentrations of the active metabolites of mycophenolate mofetil and leflunomide .2.32.All whole blood samples were activated before RNA extraction with 12.5 ng/mL of phorbol myristate acetate  and 0.8 μM of ionomycin (Sigma), as previously described.RNA was extracted by using a previously published protocol.2.4GOI ‐ Ctnorm, in which GOI is the gene of interest and norm is the reference gene. The 2–ΔΔCt method was used to determine the relative change in expression by using GAPDH as a reference gene where ΔΔCt = (CtGOI – Ctnorm)treated – (CtGOI – Ctnorm)pretreatment.–ΔΔCt) * 100%.  anticoagulant) collected 2 hours after the final morning drug dose were submitted to the Auburn University Clinical Pharmacology Laboratory for measurement of peak blood cyclosporine concentrations. Plasma derived from whole blood (EDTA anticoagulant) collected within minutes before the final morning dose was also submitted to the same laboratory for measurement of trough mycophenolic acid and teriflunomide concentrations.r2) was at least .999 and the predicted concentrations were within 10% of the actual concentrations. Briefly, 500 μL of acetonitrile were added to tubes containing 250 μL of plasma sample.g).2. The linear correlation coefficient for teriflunomide was .9995. The limit of detection and the limit of quantification (LOQ) for teriflunomide were 1.0 and 2.5 μg/mL, respectively. The accuracy (% recovery) for teriflunomide plasma controls samples with concentrations of 2.5, 5, 10, 50, 100, and 200 μg/mL was 92.8, 102.2, 106.6, 103.3, 94.3, and 98.8%, respectively. The precision (CV%) for teriflunomide plasma control samples with concentrations of 2.5, 5, 10, 50, 100, and 200 μg/mL was 3.3, 1.5, 1.9, 3.4, 2.5, and 4.5% respectively.Canine plasma was analyzed for the active leflunomide metabolite, teriflunomide (A77‐1726), by high‐performance liquid chromatography (HPLC) with ultraviolet (UV) detection. The HPLC system consisted of a Waters 717 plus autosampler, Waters 1525 Binary pump and a 2487 UV‐visible detector .d‐galactopyranoside to yield chlorophenol red. Because the product of this hydrolysis reaction has a photometric absorbance peak at 577 nm, the cyclosporine concentration of the sample was quantified by measuring the bichromatic (577 and 700 nm) change in absorbance. The LOQ for cyclosporine was 25 ng/mL. The accuracy (% recovery) for cyclosporine plasma control samples with concentrations of 80, 350, 600, and 1800 ng/mL was 99.6, 96.4, 101.5, and 100.6%, respectively. The precision (CV%) for cyclosporine plasma controls samples with concentrations of 80, 350, 600, and 1800 ng/mL was 10.9, 4.5, 5.4, and 5.1%, respectively.Whole blood concentrations of cyclosporine A were measured by using an automated immunoassay on a Siemens Dimension Xpand Plus integrated chemistry system . The operator loaded samples and reagents onto the analyzer and assigned tests for each sample; all subsequent actions were automatically performed by the Dimension system. The assay lysed a sample of blood and the cyclosporine molecules contained in that sample bind to an antibody‐enzyme  conjugate. Chromium dioxide particles coated with cyclosporine were added to bind the excess antibody‐enzyme conjugate, and the mixture was separated magnetically. The fraction of antibody‐enzyme conjugate bound to intrinsic cyclosporine was mixed with chlorophenol red‐β‐O‐mycophenolic acid [MPA]‐β‐glucuronide) in spiked samples containing mycophenolic acid concentrations of 0‐5 μg/mL.Plasma mycophenolic acid levels were measured by using a homogeneous particle‐enhanced turbidimetric inhibition immunoassay, automated assay, on the Siemens Dimension Xpand Plus system . The assay used a synthetic particle‐mycophenolic acid conjugate and monoclonal antibody specific to mycophenolic acid. Competition for antibody between the particle‐conjugated mycophenolic acid and the mycophenolic acid in the plasma sample determined the rate of aggregation; the rate of aggregation was measured with bichromatic (340 and 700 nm) turbidimetric readings and was inversely proportional to the plasma mycophenolic acid concentration. The LOQ was 0.2 μg/mL. The accuracy (% recovery) for mycophenolic acid plasma control samples with concentrations of 2.5, 7.5, and 25 μg/mL was 109.3, 108.4, and 102.7%, respectively. The precision (CV %) for mycophenolic acid plasma controls samples of 2.5, 7.5, and 25 μg/mL was 1.7, 0.6, and 5.3%, respectively. Manufacturer documentation indicates 61% cross‐reactivity with the parent drug (mycophenolate mofetil) and 49% cross‐reactivity with the primary metabolite . Separate models were fit for each drug with IL‐2 ΔCt or IFN‐γ ΔCt as the response variable. In each model, sample time, cycle, and their interaction were included as fixed effects. Dog identity was included as a random effect. If the sample time‐cycle interaction term was not significant, it was removed and the model refits. The effect of the different drugs on the Day 7 cytokine levels was also assessed by using mixed model linear regression. Separate models were fit for IL‐2 ΔCt and IFN‐γ ΔCt. In each model, drug, cycle, and their interaction were included as fixed effects with the cytokine level in pretreatment samples included as a covariate. Dog identity was included as a random effect. If the drug‐cycle interaction was not significant, it was removed and the model refits. Differences in least squares means of cytokine levels were determined for significant fixed effects. The simulation option was used for adjustment of 33.1P < .001], IFN‐γ [P < .001]) and prednisone  administration. When comparing pretreatment baseline‐activated samples to the samples collected on Day 7 of azathioprine, mycophenolate mofetil, and leflunomide administration, there was no significant differences in cytokine expression . When comparing the ΔCt values on Day 7, there were significant differences between cyclosporine and prednisone , azathioprine , mycophenolate mofetil , and leflunomide . There were significant differences between prednisone and azathioprine , mycophenolate mofetil , and leflunomide . There were no significant differences among azathioprine, mycophenolate mofetil, and leflunomide groups for IL‐2 (P > .5004) and IFN‐γ (P > .4055). Significant differences in baseline levels before treatment were not detected among cycles for either IL‐2 ΔCt (P = .3334) or IFN‐γ ΔCt (P = .1806).Quantitative reverse transcription polymerase chain reaction results are presented in Table Cytokine expression, presented as a percentage of baseline‐activated samples before treatment, is represented in Figure 3.2The median peak cyclosporine whole blood concentration was 2364 ng/mL . For mycophenolic acid trough plasma concentrations, the median value was 2.2 μg/mL . For teriflunomide trough plasma concentrations, the median value was 99.9 μg/mL .4In this study, we utilized a RT‐qPCR assay, validated in dogs, to explore the effects of the administration of 5 common immunosuppressive medications on T‐cell cytokine production. This study demonstrated that the oral administration of cyclosporine and prednisone significantly decreased activated T‐cell expression of IL‐2 and IFN‐γ in healthy dogs, when assessing ΔCt values, but mycophenolate mofetil, azathioprine, and leflunomide did not significantly affect cytokine gene expression. Additionally, there was a greater suppression of IL‐2 and IFN‐γ during cyclosporine administration when compared to prednisone administration.Immune‐mediated and inflammatory disorders of dogs are commonly treated with ≥1 immunosuppressive agents. In dogs, this often includes a glucocorticoid in addition to a second immunosuppressive agent, not only to target the immune system with 2 medications with differing mechanisms of action, but also to enable earlier tapering of the glucocorticoid to minimize steroid‐related adverse effects.In dogs with immune‐mediated disorders, there is limited evidence that prednisone provides superior immunosuppression when compared to other immunosuppressive drugs, as randomized prospective clinical trials are lacking. Other immunosuppressive medications are often used in combination with prednisone to suppress the immune system via >1 mechanism of action, as well as to allow for tapering of the dose of prednisone whereas ≥1 other immunosuppressive drugs continue to inhibit the immune system and promote ongoing disease remission.When utilizing ΔCt values, mycophenolate mofetil, azathioprine and leflunomide did not have a detectable effect on expression of IL‐2 and IFN‐γ. Evaluation of the effects of oral administration of mycophenolate mofetil, azathioprine, and leflunomide on IL‐2 and IFN‐γ expression has not previously been performed in dogs. Our results suggest that suppression of T‐cell cytokine expression is not an important component of the mechanism of action of mycophenolate mofetil, azathioprine and leflunomide. Furthermore, our results suggest that the T‐cell cytokine pharmacodynamic assay would most likely be of minimal benefit for evaluating level of immunosuppression in dogs receiving any 1 of these 3 drugs and that, as has been reported in human medicine, concurrent administration of these medications would be unlikely to have a clinically relevant effect in interpretation of assay results in dogs receiving cyclosporine.Prednisone and cyclosporine did detectably suppress the expression of IL‐2 and IFN‐γ, with cyclosporine markedly decreasing cytokine expression, and prednisone having a more moderate effect. When evaluating percent suppression by using the ΔΔCt method, the median percent of baseline activated samples for prednisone for IL‐2 and IFN‐γ, when comparing treatment values to baseline activated samples before treatment, was 23 and 16%, respectively . The median percent of baseline‐activated samples for cyclosporine for IL‐2 and IFN‐γ, when comparing treatment values to baseline‐activated samples before treatment, was 0.3% for both (consistent with 99.7% suppression from baseline function). The pharmacodynamic assay commercially available through the Mississippi State University Pharmacodynamic Laboratory currently utilizes activated T‐cell cytokine expression to titrate cyclosporine doses in dogs. In this current study, prednisone induced sufficient suppression of cytokine expression to be categorized in the “moderate suppression” category (50%‐80% immune suppression), a category that, for cyclosporine, would typically be insufficient to control severe systemic immune‐mediated disease. Cyclosporine, in contrast, induced profound suppression of cytokine expression, sufficient to be categorized in the “marked” suppression category (95%‐100% immune suppression), a level that is clinically associated with an increased risk of infection. Proposed mechanisms of action for glucocorticoids such as prednisone include inhibition of production and release of cytokines, impaired macrophage activity, impaired complement function, decreased antibody binding, inhibition of antibody production, decreased numbers of lymphocytes, and decreased migration of inflammatory cells into tissues.Blood drug concentrations were measured in all dogs receiving cyclosporine, mycophenolate mofetil, or leflunomide. There were no readily available assays for measuring blood concentrations of prednisone or azathioprine at the time of this study. Samples were collected at the appropriate standard time points  as recommended by the Auburn University Clinical Pharmacology Laboratory for clinical assays of drug concentrations, and processed and shipped according to the laboratory standard protocols. Although all dogs were administered similar drug dosages for these 3 medications, there was a wide variation in blood concentrations dog‐to‐dog for all 3 drugs. This observation emphasizes the fact that a single standard dose is not metabolized similarly in all patients, even in healthy dogs of a similar breed. For cyclosporine, the Auburn University Clinical Pharmacology Laboratory recommends a target peak blood concentration range from 1600 to 2400 ng/mL when treating immune‐mediated diseases. In this study, the highest peak blood concentration (3052 ng/mL) was almost double the lowest peak concentration (1672 ng/mL), despite both dogs having very similar inhibition of IL‐2  and IFN‐γ . Mycophenolate mofetil similarly demonstrated marked variation in trough plasma mycophenolic acid concentrations between dogs administered similar dosages, with the highest trough concentration (5.8 μg/mL) being almost 20 times higher than the lowest trough concentration (0.3 μg/mL). For IL‐2, at both the highest and lowest observed mycophenolic acid concentrations, minimal effects on cytokine expression were observed, with the dog with the highest concentration demonstrating 123% of baseline IL‐2 values, and the dog with the lowest concentration demonstrating 91% of baseline values. Target plasma mycophenolic acid concentrations have not yet been established for dogs. The highest trough plasma concentration of teriflunomide observed, 123.2 μg/mL, was almost double that of the lowest (77.3 μg/mL), despite comparable oral doses of leflunomide. As with mycophenolic acid, for IL‐2, at both the highest and lowest observed teriflunomide concentrations, minimal effects on cytokine expression were observed, with the dog with the highest concentration demonstrating 66% of baseline IL‐2 values, and the dog with the lowest concentration demonstrating 138% of baseline values. Target plasma teriflunomide concentrations have not yet been established for dogs but, based on human data and clinical experience with canine samples submitted to the Auburn University Clinical Pharmacology Laboratory, a concentration greater than 16 μg/mL is considered to be a reasonable starting point for therapeutic response, and subjective clinical therapeutic responses have been observed in dogs with concentrations ranging from 5 to 45 μg/mL. Based on recommended target ranges, all of the dogs in this study exceeded target plasma teriflunomide concentrations, without a significant effect on cytokine expression. Our results therefore suggest that the mechanism of leflunomide immunosuppression, at least during the first week of drug administration, is independent of suppression of T‐cell cytokine expression.We utilized GAPDH as a reference gene for all treatments because this is the gene that has been used in our cyclosporine assay. However, we understand that in gene expression studies, there is no single reference (housekeeping) gene that is suitable for all experimental conditions and each reference must be empirically validated for each treatment, even when monitoring expression of the same gene being evaluated, in the same cell type, of the same species. In this current study, while we did not perform a formal validation for each treatment, GAPDH expression was assessed across treatment groups and found to be comparable both before and after treatment, supporting GAPDH as an appropriate reference gene for this particular study. [Correction added on March 16, 2020, after first online publication: See This study used dogs of a single breed, and utilized healthy dogs rather than dogs with inflammatory or immune‐mediated diseases. Different results might have been attained in studies by using different breeds, or in diseased dogs. Although the effects of cyclosporine on IL‐2 and IFN‐γ expression are known to have reached maximal effect before 7 days of drug administration, the other drugs and their effects on cytokine expression over time have not been evaluated. It is possible that with a longer duration of administration, a detectable effect might have been seen with azathioprine, mycophenolate mofetil, and leflunomide, and a more pronounced effect might have been seen with prednisone. Although there was sufficient statistical power to detect differences in both IL‐2 and IFN‐γ expression before and after treatment with cyclosporine or prednisone, as well as detecting differences in expression between cyclosporine and prednisone treated dogs, it is possible that the sample size of 8 dogs did not provide sufficient power to detect an effect by the other drugs resulting in Type II errors.In conclusion, cyclosporine and prednisone had a significant effect on expression of IL‐2 and IFN‐γ, with cyclosporine having a more pronounced effect than prednisone. Azathioprine, mycophenolate mofetil, and leflunomide did not have a significant effect on these 2 cytokines. At a cyclosporine dosage of 10 mg/kg PO every 12 hours, a significant and profound effect was seen in every dog. Prednisone had less pronounced and more variable effect on these cytokines. Blood drug concentrations were highly variable when dosing cyclosporine, mycophenolate mofetil, and leflunomide.Drs Archer, Mackin, and Thomason oversee the Pharmacodynamic Laboratory at the Mississippi State University College of Veterinary Medicine, which accepts samples from practitioners submitted for pharmacodynamic monitoring of cyclosporine treatment. Dr Boothe oversees the Clinical Pharmacology Laboratory at the Auburn University College of Veterinary Medicine, which accepts samples from practitioners submitted for therapeutic drug monitoring in dogs receiving cyclosporine or leflunomide.The authors declare no off‐label use of antimicrobials.Experimental and animal care protocols were approved by Mississippi State University IACUC. Mississippi State University is accredited by the Association for Assessment and Accreditation of Laboratory Animal Care.Authors declare human ethics approval was not needed for this study.Appendix S1. Description of corrections.Click here for additional data file.


Pelvic fracture with high energy trauma has a high mortality rate, especially in men. In addition, severe multiple trauma, major hemorrhage, and administration of red blood cells predict mortality in elderly patients with pelvic fracture. We herein report a rare case in which multiple arterial embolization occurred after pelvic fracture.An 83-year-old male cyclist was transported to our hospital after being struck by a car. On arrival, he was diagnosed with multiple trauma, including rib fractures with hemothorax, lumbar fractures of the transverse process, and injuries in the right acetabulum, left adrenal gland, and liver. He underwent massive transfusion and transcatheter arterial embolization due to extravasation from the right superior gluteal artery and left adrenal gland. On the second day, owing to right lower leg ischemia, serum creatinine kinase and myoglobin levels were markedly elevated from the reference value; hence, a right above-knee amputation was performed 12 h after the accident. However, both protein levels remained high after amputation, resulting in acute renal injury, which was treated via hemodiafiltration on hospital day 3. In addition, sustained low efficiency hemodialysis and plasma exchange were performed on hospital day 4. Despite these treatments, the patient’s hemodynamics did not improve, and he died on hospital day 8. The autopsy revealed necropsy of the iliopsoas muscles and the digestive tract.The causes of the patient’s death were considered to be persistent rhabdomyolysis and severe hypotension due to iliopsoas necrosis and peritonitis due to digestive tract necrosis. Multiple arterial embolization caused by consumption coagulopathy associated with multiple trauma may account for severe outcomes in this case. Pelvic fracture with high energy trauma has a high mortality rate, especially in men . It can We report a case in which multiple arterial embolization occurred after pelvic fracture.An 83-year-old male cyclist was transported to our hospital 3 h after being struck by a car. He had a medical history of acute coronary syndrome, for which he had received aspirin. His vital signs were as follows: respiratory rate, 22 breaths per minute; oxygen saturation on room air, 96%; systolic blood pressure, 125/67 mmHg; pulse rate, 88 beats per minute; and body temperature, 36.1 °C. On arrival, the patient was diagnosed with multiple trauma including multiple rib fractures with hemothorax, lumbar fractures of the transverse process, and injuries to the right acetabulum, left adrenal gland, left ureter, and liver. His injury severity score was 22. We also found extravasation of the right superior gluteal artery and left adrenal gland. He was treated with a transfusion of red blood cells, plasma, and platelets, and the ratio was 12:26:30. He was administered 1 g tranexamic acid when he presented in this very acute phase. He also required intubation and transcatheter arterial embolization. There was no evidence of stray embolic materials in the external iliac arteries.3/μL (3.3–8.6 × 103/μL); serum hemoglobin, 11.7 mg/dL (13.7–16.8 mg/dL); platelets, 136 × 103/μL (158–348 × 103/μL); serum creatinine kinase, 554 U/L (59–248 U/L); partial thromboplastin time, 37 s (25–38 s); international normalized ratio, 1.31; prothrombin time, 15.8 s (9.8–12.1 s); serum fibrinogen, 107 mg/dL (200–400 mg/dL); serum D-dimer, 155.7 μg/mL (< 1.0 μg/mL); and serum fibrinogen degradation products (FDPs), 305.5 μg/mL (< 5.0 μg/mL). Arterial blood gas analysis revealed mixed acidosis with an elevated serum lactate level .Laboratory test findings  on hospital day 1 were as follows: white blood cells, 22.08 × 10On hospital day 2, his serum creatine kinase was markedly elevated to 22,001 U/L. At this point, his right lower leg showed signs of ischemia including lack of a pulse in the right dorsalis pedis artery and cold leg. These findings suggested arterial embolization in the right thigh, and 12 h after the injury, a right above-knee amputation was performed. However, the serum creatine kinase, 44,229 U/L and myoglobin levels, 67,000 ng/mL (< 110 ng/mL), were markedly high, even after the amputation, resulting in acute renal injury, which was treated via hemodiafiltration on hospital day 3. In addition, sustained low efficiency hemodialysis and plasma exchange were performed on hospital day 4. The patient’s creatine kinase and myoglobin levels are plotted against his time course of treatment in Fig. We performed an autopsy 50 h after his death. A general postmortem examination revealed pulmonary effusion and alveolar hemorrhage. Liver congestion and fatty liver were observed, as were tubular necrosis and nephrosclerosis in the kidney. There were also signs of arteriosclerosis in the aorta, common iliac arteries, and coronary artery. Necrosis was more severe in the left iliopsoas muscle than in the right. Although there were no signs of initial gastrointestinal tract injuries, irreversible ischemic colitis from the descending colon to the rectum was revealed. In addition, thrombus becoming fibrinized was detected in multiple organs in the autopsy specimen. Fig. . This maThe causes of the patient’s death were considered to be persistent rhabdomyolysis due to iliopsoas necrosis and severe hypotension due to digestive tract necrosis and consequent septic peritonitis. Multiple arterial embolization caused by the consumption coagulopathy arising from multiple trauma may be responsible for the acute embolization in multiple organs Fig. . This waAcute traumatic coagulopathy develops in approximately 25% of severely injured individuals. It increases the risk of multisystem organ failure substantially and the risk of death by 4-fold . “Traumap = 0.022) [The most common coagulation disorder after injury is hypercoagulability. Hypofibrinolysis or fibrinolysis shutdown is an integral component of postinjury hypercoagulopathy  and is a= 0.022) . In repo= 0.022) , 6. Rece= 0.022) .Although we did not perform TEG in this case, we presume that fibrinolysis shutdown was the key mechanism underlying the multiple embolisms in our case. Initial D-dimer and FDP levels were significantly higher than normal, which suggests that the hypercoagulopathy in the very acute phase.It is very rare that multiple arterial embolization occurs after multiple trauma, while acute traumatic coagulopathy is often caused by severe trauma. This might explain why severely injured patients could die from hypercoagulation during the acute phase of trauma and may account for the paucity of pelvic fractures associated with multiple arterial emboli in the literature.In this case, the aspirin the patient had received for acute coronary syndrome may have affected the hypercoagulation status, allowing him to survive for several days after his injury. Several studies have investigated the effect of antiplatelet therapy in critically ill patients –11. In tIn addition, this patient’s serum lactate was classified as mixed acidosis. It was thought that the mechanism of this acidosis was mainly from a diabetic origin such as microangiopathy.In conclusion, severe inflammation caused by severe trauma might cause multiple emboli by inducing hypercoagulation and can sometimes leads to death. Our study highlights the confirmation of hypercoagulation in autopsy. In such situations, anticoagulation therapy may prolong survival.


Research shows that the COVID Stress Scales have a robust multifactorial structure, representing five correlated facets of COVID‐19‐related distress: (a) Fear of the dangerousness of COVID‐19, which includes fear of coming into contact with fomites potentially contaminated with SARSCoV2, (b) worry about socioeconomic costs of COVID‐19 , (c) xenophobic fears that foreigners are spreading SARSCoV2, (d) traumatic stress symptoms associated with direct or vicarious traumatic exposure to COVID‐19 , and (e) COVID‐19‐related compulsive checking and reassurance seeking. These factors cohere to form a COVID stress syndrome, which we sought to further delineate in the present study.A population‐representative sample of 6,854 American and Canadian adults completed a self‐report survey comprising questions about current mental health and COVID‐19‐related experiences, distress, and coping.Network analysis revealed that worry about the dangerousness of COVID‐19 is the central feature of the syndrome. Latent class analysis indicated that the syndrome is quasi‐dimensional, comprising five classes differing in syndrome severity. Sixteen percent of participants were in the most severe class and possibly needing mental health services. Syndrome severity was correlated with preexisting psychopathology and with excessive COVID‐19‐related avoidance, panic buying, and coping difficulties during self‐isolation.The findings provide new information about the structure and correlates of COVID stress syndrome. Further research is needed to determine whether the syndrome will abate once the pandemic has passed or whether, for some individuals, it becomes a chronic condition. Data from China, for example, suggests that 25% of the general population have experienced moderate to severe levels of stress‐ or anxiety‐related symptoms in response to COVID‐19 . Almost half (47%) were female and most (57%) were employed . Most (79%) had completed full or partial college, 18% had only completed high school or equivalent, and 3% did not graduate from high school. Most (68%) were Caucasian, with the remainder being Asian (12%), African American/Black (9%), Latino/Hispanic (6%), Native American/Indigenous (1%), or other (3%).Data were collected from Canada and the United States using an Internet‐based self‐report survey delivered in English by Qualtrics, a commercial survey sampling, and administration company, between March 21 and April 1, 2020. As described in more detail elsewhere , current anxiety and depression, various trait characteristics associated with psychopathology, experiences with COVID‐19 , and COVID‐19‐related distress and coping.Current anxiety and depression were assessed using the Patient Health Questionnaire‐4  to 4 (extremely). Coping strategies were assessed on a 5‐point scale ranging from 0 (did not use this coping resource) to 4 (tried it and found it extremely helpful). The rating scale for coping strategies was devised so that it was possible to assess whether or not a coping strategy was used and if it was used, its perceived efficacy in helping the respondent cope with self‐isolation. Individual items of stress and coping were examined rather than scale scores, to gain a better understanding of the nature of stressors, aversive emotions, and coping behaviors associated with self‐isolation.Self‐isolation stress and coping were assessed by measures of (a) stressors associated with self‐isolation , (b) aversive emotions associated with self‐isolation , and (c) coping strategies that might be used to make self‐isolation more tolerable . Only participants who were currently in self‐isolation completed this battery. A list of items assessing aversive emotions appears in Table 2.32.3.1α level was set at .01 instead of .05. This corrects for inflated Type I error without unduly inflating Type II error with a more stringent correction such as a Bonferroni correction. Given the large sample size, substantively trivial correlations would be statistically significant . Accordingly, to facilitate the interpretation of correlations, we used Cohen's  fit than a model consisting of N − 1 classes, and is not significantly different from a model consisting of N + 1 classes.Latent class analyses were new to the present study and were not reported in Taylor et al. . To dete2.4.1The rationale for network analysis is that it provides important information about relationships among elements in a network . Network analysis assumes that nodes  cluster together as they are causally linked to one another. This linking does not assume that nodes are influenced by some underlying factor; rather, network analysis assumes that nodes may directly influence one another  in the network. Three indices of interconnectedness were measured  links to other nodes in the network. Strength was used as the primary indicator of centrality, given that it has the most support as a stable and reliable indicator of centrality  were computed using the R 33.1α) for the multi‐item scales are presented in the Supporting Information.Only 2% of the sample reported that they had been diagnosed with COVID‐19, and only 6% personally knew someone who had been infected with the coronavirus. Only 4% were healthcare workers who might come into contact with patients infected with COVID‐19. On the basis of the cutoffs for the Patient Health Questionnaire‐4  and, therefore, formed a coherent syndrome. Accordingly, the total sum of these scales  was used as the input variable in the latent class analysis. Fit indices for the latent class analysis (see Supporting Information) indicated that a five‐class model was the best‐fitting solution. The number (and %) of participants in each class was as follows: Class 1, n = 170 (3%); Class 2, n = 767 (11%); Class 3, n = 2,161 (32%); Class 4, n = 2,632 (38%); Class 5, n = 1,124 (16%). The classes ranged monotonically from low (Class 1) to high (Class 5) in terms of the scores on the total score on the COVID Stress Scales.The COVID Stress Scales had medium‐to‐large correlations with one another , using the cutoffs described by Kroenke et al. . On the If the latent class cutoffs were to be used for diagnostic purposes, the results suggest that 16% of the sample (class 5) had severe COVID stress syndrome. The five classes differed quantitatively rather than qualitatively in the severity of scores on each of the COVID Stress Scales (see Supporting Information). Accordingly, in the following analyses, the COVID stress syndrome is treated as a quasi‐dimensional construct, assessed by total score.3.3p < .01) links  between nodes in the network defined by the COVID Stress Scales. The Correlation of Stability coefficients were 0.75 for both the nodes and their links (pathways). These values exceed the cutoff of 0.50 , except for the strengths of XEN and CHE, which were not significantly different from one another. Thus, the central node in the network was DAN, followed by SEC. The most peripheral nodes in the network were XEN and CHE. Although DAN was the central node, it was more strongly linked to some nodes than others. DAN had significantly stronger links to SEC, XEN, and TSS, compared with its link to CHE (p < .01).Figure Network analysis also revealed a strong link between TSS and CHE; that is, COVID‐19‐related reexperiencing symptoms  were strongly linked to COVID‐19‐related checking of news media and reassurance seeking about one's health from friends, loved ones, or health professionals. This finding may reflect a dose–effect whereby a greater degree of exposure to COVID‐19‐related news or social media (via checking) leads to a greater frequency of unwanted, intrusive thoughts, images, or nightmares about COVID‐19.3.43.4.1t = 2.36, df = 6,852, p > .01). Contrary to the stereotype that younger people are relatively unconcerned about COVID‐19, results indicated that age and the total score on the COVID Stress Scales had a small‐to‐medium negative correlation . Income level had a small but statistically significant negative correlation with the total score . Women tended to have higher total scores than men , as did people who were unemployed  and people who were less educated . Means and SDs for these analyses and those presented below appear in the Supporting Information materials.The samples from the United States and Canada did not differ in total scores on the COVID Stress Scales  = 60.86, p < .001. Post hoc Student–Neuman–Keuls tests (α = .01) indicated that Caucasians tended to have the lowest scores, people of Black/African American ancestry had intermediate scores, and people of Asian and Hispanic ancestry had highest scores (see Supporting Information for means and SDs).In terms of ethnicity, four main groups were compared: Asian, Black/African American, Hispanic, and Caucasian. The groups significantly differed on the total score; t = 16.13, df = 6,852, p < .001). As noted above, only 2% of the sample had been diagnosed with COVID‐19 and the results reported in this article did not change when those 2% were removed from the analyses. Regarding occupational status, people who were healthcare workers did not differ from other individuals in their total scores . Similarly, people in occupations that put them at increased risk of contracting COVID‐19  did not differ from other individuals on the total score .People who reported that they had been diagnosed with COVID‐19 had significantly higher total scores on the COVID Stress Scales than people who had not been diagnosed . However, people with a preexisting (past year) mental health disorder had significantly higher total scores . People with high scores on the total score were also more likely (p < .001) to have higher and generally large correlations with premorbid health anxiety (r = .49), anxiety sensitivity (r = .59), intolerance of uncertainty (r = .54), disgust propensity (r = .44), disgust sensitivity (r = .52), perceived infectability (r = .35), germ aversion (r = .37), and obsessive‐compulsive contamination concerns (r = .53) and checking rituals (r = .50).The presence (vs. absence) of preexisting general medical conditions  were not associated with heightened total scores (3.4.3p < .001) to be concurrently anxious (r = .54) and depressed (r = .49), and to believe in COVID‐19 conspiracy theories (r = .37), perform hygiene behaviors (r = .33), stockpile food and others supplies (r = .37), and avoid public transport (r = .30) and grocery stores (r = .45) for fear of getting COVID‐19. The correlations were medium to large in size. People with high total scores were also most likely to wear facemasks .People with high scores on the total score on the COVID Stress Scales were more likely (3.4.4SD = 8 days). Note that US states and Canadian provinces and territories differed in their recommendations for self‐isolation at the time of the study; some recommended self‐isolation, whereas others did not. Accordingly, only a portion of our participants were in voluntary self‐isolation at the time of the study.A total of 3,304 participants were in voluntary self‐isolation at the time of assessment, for a mean of 10 days (p < .001) to report problems with a range of stressors during self‐isolation, including running low on prescription medicines, fights or arguments, difficulty taking care of family members or pets, financial problems, lack of personal space, and crowded living conditions (see Supporting Information for details). People who lived alone, compared to those who lived with one or more other people, tended to have lower total scores .Table 3.4.5ps < .001), including adaptive coping strategies  and maladaptive coping strategies . Thus, people with COVID stress syndrome were not passive. They were actively trying to find ways of making self‐isolation more tolerable. Table Of the 28 coping strategies assessed, people with high scores on the total score on the COVID Stress Scales were more likely to have tried all the coping strategies  and an additional 16% were highly distressed (Class 5) and likely in need of mental health services. Worry about infection and its consequences can contribute to behaviors such as panic buying, which can further exacerbate emotional distress and social disruption Taylor, . DespiteRecent conceptualizations of COVID‐19‐related distress tend to be narrow and unidimensional, focusing largely on fear of infection  would lead to changes in all other variables in the network. This suggests, for example, that societally disruptive phenomena such as COVID‐19‐related xenophobia  could be reduced by reducing worries about becoming infected with COVID‐19. Such findings have potentially important implications for planning mental health services .Supporting informationClick here for additional data file.


Passiflora incarnata (PI) to steadily obtain positive effects on sleep and brain microenvironment. However, as mentioned earlier, sleep-inducing efficacy can only be used safely if the behavioral and metabolic abnormalities do not appear.Although the number of prescriptions and dependence on sleeping pills are increasing, the associations with unexpected abnormal behaviors and metabolic diseases caused by the overuse of sleeping pills are not well understood. In particular, such as abnormal eating-behavior and the occurrence of metabolic disorders caused by psychological unstable states are reported. For this reason, herbal medicine, which has not had such side effects in recent years, is attracting attention as an alternative medicine/food for sleeping inducer. We have used ethanol extracts from Thus, in this study, we used Phenomaster equipment to continuously monitor the movement, feeding, water consumption, gas changes, etc. in C57BL/6 mice at a dose of 500 mg/kg/day for 5 consecutive days with PI extract group compared with the control group. Before sacrifice, differences in body composition of mice were also compared. Monitoring of 24 h/5 days through the equipment showed no change in PI-treated group in anything except for significant decrease in blood melatonin levels and activity after PI administration. Taken together, the statistically insignificance of any behavioral and metabolic phenomenon produced by repeated treatment of PI are not only expected to have an accurate sleep effect, but are also free of side effects of the prescribed sleeping pills. This study has given us greater confidence in the safety of the PI extracts we use for sleep-inducer. Passiflora incarnata L. (PI), commonly known as passion flower, that function as sleep aids. A previous study confirmed that the extract induced sleep by single or/and repeated administration to animals. In the current study, we observed the behavioral and metabolic changes caused by repeated oral PI extract administration for 5 days in mice. The safety issues identified in this animal model will be very important factors in the approval by the Korean Food and Drug Affairs (KFDA) during commercialization of this plant-derived sleep inducer. In the present study, we investigated the safety of PI extract as a substitute for sleep medications, by evaluating metabolic and behavioral changes resulting from repeated oral administration.Over the past few decades, the use of sleeping pills has increased dramatically as the number of people who have not experienced proper sleep has increased . Sleep dPassiflora incarnata in Korea was selected and supplied by Natural F&P, and the identification number (n°: N9105701) was assigned for further standard use. In addition, extraction was attempted with the extraction multiple of the material and solvents at 2. Passion flower extract was obtained from the leaves and fruit of PI. Extractions were done with 60% aqueous ethanol for 4 h. The aqueous extract was dried with vacuum-evaporation. After vacuum drying, the extract was standardized, using vitexin 0.3% as a reference compound.Passionflower extract powder (1 g) was dissolved in 50 ml of 50% ethanol for 10 min with sonication. The sample was filtered with a 0.45 um syringe filter. HPLC was performed with an Agilent 1200 system equipped with a model G1312A binary LC pump, an auto sampler, and a diode-array detector. A C-18  column (250 mm × 4.6 mm id and 5 μm particle size) was used. Standards were purchased from Sigma Chemicals.− 1, the oven temperature 35 °C, and the detection wavelength 360 nm.Chromatographic separations were performed with a mobile phase consisting of 0.1% phosphoric acid prepared in nanopure water (87%) and 100% acetonitrile (13%) for 60 min. The injection volume was 20 μl, the mobile phase flow rate 1 ml minPassiflora incarnata treated) group, and monitored with Phenomaster®, an automated combined indirect calorimetry system . The animals were administered vehicle  or PI 500 (PI 500 mg/kg/day) orally with sonde for 5 days at 17:00, 2 h before lights off. Before the experiment, mice were acclimated for 2 days in a metabolic chamber with food and water, and subsequent oxygen consumption (VO2), carbon dioxide production (VCO2) and food consumption were measured for 7 days. The respiratory exchange rate  was calculated using standard in-house software. Energy expenditure (EE) was demonstrated by the equation as EE = 3.815 × 10− 3 × VO2 + 1.232 × 10− 3 × VCO2. Body composition  was measured following animal phenotyping with Mini-spec LF50 . This work was technically supported by the Korea Mouse Phenotyping center, Republic of Korea Project (2013M3A9D5072550) of the Ministry of Science, ICT and Future Planning through the National Research Foundation, Republic of Korea.C57BL/6 mice were used purchased from Charles River Japan to investigate whether chronic PI extract administration adversely effected feeding, activity or caused metabolic changes. Total molecular biological, behavioral and metabolic changes were assessed. The mice were separated into control group (vehicle-treated) and PI (Mice were housed at room temperature (22 ± 2 °C) with 60% humidity under a 12-h light: dark cycle (light cycle: dark cycle from 07:00 to 19:00). The animals were provided free access to normal chow diet  and water. The handling and care of the animals conformed to guidelines of current international laws and policies . The Soonchunhyang University Institutional Animal Care and Use Committee (IACUC) approved all experiments and procedures .The animals were anesthetized with 1 g/kg urethane (Sigma-Aldrich) and perfused transcardially with 0.1 M phosphate Buffer (pH 7.4) to remove as much blood from the body as possible. Before perfusion, blood was collected from the abdominal vein for measurements of serum serotonin and melatonin. After flushing the blood vessels completely, the brains were removed, and the hypothalamus was isolated. The brain tissues were stored at − 80 °C for later processing.The isolated hippocampus and hypothalamic chunks were homogenized in lysis buffer (iNtRon Biotechnology). Protein concentrations were determined with a BCA kit (iNtRon Biotechnology). Total proteins (20 μg per sample) were loaded into each lane of 12% SDS-PAGE, electrophoresed, and transferred to PVDF membranes (Bio-Rad Laboratories). Following transfer, membranes were blocked with TBST , containing 10% skim milk (BD Biosciences). These membranes were incubated with the following primary antibodies: rabbit anti-calretinin  and rabbit anti-GAPDH  at 4 °C overnight. After further washing, membranes were incubated with horseradish peroxidase (HRP)-conjugated anti-rabbit secondary antibodies (Vector). Immunoreactive signals were detected through enhanced chemiluminescence (Abclon) and recorded with the MicroChemi 4.2 system.Calretinin expression in the hippocampus and hypothalamus of PI extract (PI 500) administered mice was significantly higher than that of vehicle (Veh)-treated mice Fig. a. Any boFood intake and water consumption were recorded by Phenomaster metabolic cages for 5 days. All data obtained from the Phenomaster metabolic cages were transformed to provide real-time average consumption for the 24-h light/dark cycles. Food intake and water consumption volumes were not statistically different, except 2 and 3 h before lights-on . The obtained fat, body fluid, and lean mass measurements generally are known to account for about 95% of the total body weight. The body composition data were obtained according to the equipment manufacturer’s protocol, and no index value was statistically different between the Veh group and the PI 500 group Fig. a and b. Passiflora incarnata, has long been known to provide stability and induce sleep [Many studies have shown that the relationship between sleep quality and metabolic rate is very closely associated to their systemic regulatory mechanisms, and data on metabolic rates has been used to predict subjects’ sleep state , 34, 35.ce sleep . It is cce sleep , 14, 20.ce sleep , 27, 37.ce sleep , and parce sleep , and is ce sleep . Howeverce sleep .2/VO2) value remained high for about 9 h after the lights off, and the graph crossed about 2~3 h before the lights on . The activity increased gradually from 2 h (in the red dot-circle) before the lights were turned on, and activity after lights-on was similar to that of the control group. The total activity (AUC of activity) was also significant .There was no difference in the volumes of feed intake or amount of water consumption observed in mice repeatedly administered Veh or PI for 5 days Fig. b, howeveThe alterations of body composition between Veh- and PI-treated groups did not show any significant differences Fig. a and b. These results indicate that PI treatment was effective in increasing GABAergic neuron activity and blood melatonin levels, evidenced by a significant decrease of EE observed at the time when mice are generally active in the dark cycle. In other words, no increase in appetite or increase in body weight was observed, and any body compositions were not changed; only sleeping was changed. Since there have been reports of behavioral abnormalities and metabolic changes that may be caused by the repeated use of diverse prescribing sleeping pills, we tried to find out whether the repeated administration of PI extract may cause such problems in the animal models. Taken together, we did not find any side effects of abnormal metabolic phenotypes or behaviors, such as hyperphagia or unexpected metabolic changes by repeated administration of PI extract to mice for 5 days (Additional file Additional file 1. HPLC chromatograms obtained from extract of passion flower. Peak 1, isoorientin; peak 2, orientin; peak 3, vitexin; peak 4, isovitexinAdditional file 2. Conceptual diagram of graphic research results. No abnormalities were found in the PI extract for the various side effects of sleep-inducing substances


The staging system of remnant gastric cancer (RGC) has not yet been established, with the current staging being based on the guidelines for primary gastric cancer. Often, surgeries for RGC fail to achieve the > 15 lymph nodes needed for TNM staging. Compared with the pN staging system, lymph node ratio (NR) may be more accurate for RGC staging and prognosis prediction. We retrospectively analyzed the data of 208 patients who underwent R0 gastrectomy with curative intent and who have ≤ 15 retrieved lymph nodes (RLNs) for RGC between 2000 and 2014. The patients were divided into four groups on the basis of the NR cutoffs: rN0: 0; rN1: > 0 and ≤ 1/6; rN2: > 1/6 and ≤ 1/2; and rN3: > 1/2. The 5-year overall survival (OS) rates for rN0, rN1, rN2, and rN3 were 84.3%, 64.7%, 31.5%, and 12.7%, respectively. Multivariable analyses revealed that tumor size (p = 0.005), lymphovascular invasion (p = 0.023), and NR (p < 0.001), but not pN stage (p = 0.682), were independent factors for OS. When the RLN count is ≤ 15, the NR is superior to pN as an important and independent prognostic index of RGC, thus predicting the prognosis of RGC patients more accurately. RGC is rare, accounting for only approximately 1–8% of all GC cases2, but its incidence has been rising owing to the increase in early GC incidence, which, in turn, is due to improvements in medical examination and the prolonged survival of patients with primary GC4. Despite its difference from other GC types, RGC staging is still based on the TNM staging system of GC. However, a retrieved lymph node (RLN) count of ≤ 15 may lead to false staging and affect treatment plans and prognosis. Furthermore, most RGC patients are in the advanced stage because of the lack of specific symptoms during the early stage, which leads to a lower radical resection rate and poor prognosis5. The metastatic lymph node (LN) ratio (NR) is reported to be more suitable for primary GC staging compared to a positive LN count, especially when the RLN is low6. This study aimed to develop a more accurate staging system for RGC. We hypothesize that NR would be more exact than the UICC/AJCC pN stage (8th edition) for RGC staging, which we validated through a retrospective analysis.Remnant gastric cancer (RGC) is an unusual but important type of gastric cancer (GC) comprising malignant tumors in the remnant stomach after subtotal distal gastrectomy, regardless of whether the initial disease is benign or malignant as well as the duration between the initial operation and diagnosisWritten informed consent was obtained from all the patients. The study was approved by the Research Ethics Committee of the Affiliated Hospital of China Medical University and all experiments were performed in accordance with relevant guidelines and regulations. We established a retrospectively maintained database of all patients with RGC who underwent surgery at the Department of Oncology, First Affiliated Hospital of China Medical University between January 2000 and December 2014. Of them, we evaluated 243 patients who underwent radical (R0) surgery (with D2 lymphadenectomy) for RGC. However, only 7 of these patients had lymph node metastases > 15, and considering that this number of cases is too small to be representative, we finally excluded them. Surgery had been performed in all patients using an open approach, with the second surgery being total gastrectomy with Roux-en-Y anastomosis. None of the patients received neoadjuvant chemotherapy. All of the study participants were followed up through telephone or written communications until death or end of the study period . We declared that the informed consent from patients has been obtained prior to our study. As some patients invovled in our study died during the follow up, we obtained the informed consent from their guardians.The median and mean follow-up periods were 60 and 83.15 months (range 1–346 months), respectively. In total, 11 patients were lost to follow-up, and 17 died during the postoperative period. Finally, 208 patients were included in the analysis. Postoperative chemotherapy regimens included 5-fluorouracil (5-FU) plus cisplatin, 5-FU plus mitomycin and epirubicin, 5-FU plus leucovorin and cisplatin, 5-FU plus cisplatin and epirubicin, and cisplatin plus oxaliplatin.Data of the following variables were collected: (1) gender ; (2) age (≤ 60 or > 60 years); (3) tumor size (≤ 4 cm or > 4 cm); (4) macroscopic serosal classification ; (5) Borrmann type ; (6) depth of tumor invasion according to the 8th UICC/AJCC ; (7) Lauren’s classification ; (8) histology (differentiated or undifferentiated); (9) lymphovascular invasion; (10) number of positive LNs according to the 8th UICC/AJCC ; (11) NR ; and (12) postoperative adjuvant therapy.The best cut-off approach was used to determine LN ratio categories, with patient survival (log-rank statistic) as the dependent variable. Accordingly, 1/6 and 1/2 were identified as the best cutoff for discriminating differential prognoses in RGC patients. Thus, patients were divided into four groups: rN0, NR = 0; rN1, > 0 and ≤ 1/6; rN2, > 1/6 and ≤ 1/2; and rN3, > 1/2. Cases in which the retrieved LN was 0 were considered as N0 and NR = 0.All statistical analyses were performed using SPSS software version 20.0. We used the Kaplan–Meier method to determine overall survival (OS) and the log-rank test for comparison. Cox’s proportional hazard model (forward-stepwise method) was used for the multivariate analysis to determine the independent factors for prognosis prediction. We divided the multivariate analysis into two steps. In step 1, we analyzed all significantly important prognostic factors using multivariate analysis, with the exception of the revised NR stage. In step 2, we performed multivariate analysis with consideration of all significantly important prognostic factors, including the revised NR stage. Two-tailed p-values and differences were considered significant at p < 0.05.The clinicopathological characteristics of the 208 patients are summarized in Table To determine which is more accurate between pN stage and NR stage as a prognostic index for the OS of RGC patients, we conducted a two-step multivariate analysis of prognosis. In step 1, we performed a multivariate analysis of all the important prognostic factors , except for NR staging. We found that tumor size (p = 0.007), lymphovascular invasion p = 0.002), and pN stage (p < 0.001) were independent factors predicting prognosis  for reconstruction during the initial surgery of RGC patients17. With respect to the type of surgical anastomosis, it was reported that compared with Billroth I, Billroth II surgery increased the risk of RGC by four times18. Xiang et al. found that RGC mostly occurred at the anastomotic sites, and that LN metastasis of the jejunal mesentery and mesenteric root was universal, particularly in patients who underwent Billroth II anastomoses16. In addition, our results demonstrate that T stage was not an independent prognostic factor for patients with RGC. We speculated that RCG is characterized by alterations in the normal anatomic lymphatic flow of the stomach and its association with the surrounding tissue. Adhesion after primary operation may influence the progression of cancer and the infiltration of surrounding organs, especially for RGC at the anastomosis site. In our study, we enrolled 208 RGC cases, there were mainly cases with the depth of invasion at T2 , T3  and T4 . Only 8 cases were T1 stage (3.9%). Alberto et al.19 reported that RGC with pT1 stage had lower rate of lymph node metastasis. Once the depth of invasion was T2 or above, the rate of LN metastasis increased significantly. The lymph node dissection for RGC should be extended beyond the initial operation. A high incidence of LN metastasis in the No. 10–14, 20, 110, 111 was found in the patients who had previously undergone gastric reconstruction. All the regional lymph nodes around the stomach that were not cleared in initial operation should be dissected. The LN metastasis rate was 42.9%, 34.2%, 15.9%, 29.2% and 37.2% in No. 10, 11, 12, 14 and 20 in our study. Therefore, it is not at all surprised that N categories replaced T categories being an independent risk factor for prognosis of RGC. In addition, the small sample size may contribute this result. We also observed LN metastasis of the jejunal mesentery during RGC surgeries (n = 2). However, this needs to be further validated and analyzed in future research.Lu et al. reported that the tumor size may be more hierarchical than the pN (7th UICC) stage system in RGC4. The contrasting findings may be attributed to the different NR cut-off points and the eligibility criteria between the current study and that by Nakagawa et al. In Nakagawa’s study, the NR cut-offs were 0; > 0, ≤ 0.1; > 0.1, ≤ 0.4; and < 0.4. When Marchet analyzed the clinical data of 1853 patients with gastric cancer, NR was divided into 0%, 1–9%, 10–25%, and > 25%20. Satio et al. divided NR stages into 0%, > 5%, 5–10%, 10–20%, 20–30%, and > 30%, and found that NR was an independent prognostic factor in patients with gastric cancer; however, the number of positive LNs was not an independent factor21. Bando et al.22 studied 650 GC patients who had > 15 LN dissections after operation. Their NR staging was divided into 0%, < 10%, 10–25%, and > 25%, and they found that NR staging was more effective than the JGCA LN staging. Moreover, Sun et al. identified the following best-fit cut-off values: rN0: 0%; rN1: 1–20%; rN2: 21–50% and rN3: > 50%, proving the advantage of NR stage in minimizing stage migration for GC patients with insufficient number or level of retrieved LNs23. Furthermore, few scholars have elaborated on the method of dividing NR staging nodes. There is no international consensus; hence, further research is needed to determine the appropriate staging nodes. In our study, we identified the NR stages as rN0: 0; rN1: > 0 and ≤ 1/6; rN2: > 1/6 and ≤ 1/2; and rN3: > 1/2.In contrast to our findings, some studies reported that only pT and pN are the prognostic factors of RGC. Nakagawa et al. performed a similar study but obtained a contrasting conclusion, stating that NR was not better to pN (7th UICC)24, supporting our findings that NR stage can predict the prognosis of RGC patients more accurately than pN stage.Moreover, a significant portion of patients included in our study underwent Billroth I and Roux-Y during the initial surgical reconstruction . However, we only focused on those who underwent Billroth II. In addition, we included cases with RLN ≤ 15, and excluded the very few cases with RLN > 16. By contrast, Nakagawa et al. included both cases of RLN ≤ 15 and > 15. One study showed that if the number of RLN after radical gastrectomy was > 15, then the prognostic value of NR for patients with GC was worse than that of N stageOur study has some limitations. First, it spanned a relatively long period of 15 years. Continuous improvements in surgical instrumentation and chemotherapy regimens may have affected the outcomes. We also evaluated a specific sample population, and thus, our findings need to be further validated in other types of RGC patients and cohorts, particularly in the Western population. Because the number of cases with RLN > 15 is too small to be representative, our study included only samples with ≤ 15 RLN. This may limit the possibility of the NR staging system being developed as a standard staging system in clinical practice, and our findings should hence be verified by further big data studies. We suggest that for RGC with RLN > 15, the original pN stage (8th edition) can be used.In conclusion, the metastatic LN ratio is an independent prognostic index for patients with RGC. When the amount of RLN is ≤ 15, the NR stage can more accurately predict the prognosis of patients with RGC than the pN stage, facilitating the selection of better treatment options.