MetaboLightsapplication/xmlftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS14567/m_MTBLS14567_LC-MS_positive_hilic_v2_maf.tsvftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS14567/a_MTBLS14567_LC-MS_positive_hilic.txtftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS14567/s_MTBLS14567.txtftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS14567/i_Investigation.txtprimaryOK200ftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS14567<p>Metabolites were identified by monitoring specific parent-to-daughter ion transitions using MRM mode.</p>MetaboLightsPublicLiquid Chromatography MS - positive - hilic<p>In liquid chromatography, samples were separated with Poroshell 120 HILIC Column (2.1x100 mm, 2.7 µm) with mobile phase A set as 20 mM ammonium formate acetonitrile solution (water:acetonitrile = 1:9, v/v), and mobile phase B set as 20 mM ammonium formate aqueous solution. Gradient elution was performed with a linear gradient of A 100% 0 min to 70% 11.5 min to 70% 12 min to 100% 15 min at a flow rate of 0.3 mL/min, with the column temperature set at 25 °C and an injection volume of 5 µL.&nbsp;</p>An explainable prognostic prediction panel for sepsis based on serum amino acid profiles.Hanzhe wuJiangsu Ocean Universityblood serummass spectrometry assay<p>Fresh blood samples were centrifuged at 3000 rpm for 15 min, and serum samples were transferred to Eppendorf tubes, which were numbered and stored at −80 °C freezer. For the HC group, 2 mL of venous blood was collected from the blood drawn during physical examination. Blood samples were processed in the same manner as those obtained from patients with sepsis.&nbsp;</p>Homo sapienshttps://www.ebi.ac.uk/metabolights/MTBLS14567Shuangshuang Gu. Department of Emergency, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School. guss2926@njglyy.com.<p>In Agilent MassHunter software, a method is established using standard substance data and applied to all samples.</p>Group13851289296@163.com<p>This retrospective study included patients diagnosed with sepsis and septic shock at the Emergency Intensive Care Center of Huai’an Second People’s Hospital and Nanjing Drum Tower Hospital between December 1, 2018, and May 1, 2023, at The Affiliated lianshui County People's Hospital of Kangda College of Nanjing Medical University between March 1, 2022 and July 1, 2025. The sepsis diagnosis was consistent with the criteria outlined in Sepsis 3.0, wherein patients with a SOFA score of 2 or higher due to infection were included. Septic shock was defined as persistent hypotension, requiring vasoactive drugs to maintain a mean arterial pressure ≥65 mmHg (1 mmHg=0.133 kPa) and serum lactate level greater than 2 mmol/L (&gt;18 mg/dL) in the absence of hypovolemia . The exclusion criteria were as follows: (1) pregnancy status; (2) age &lt;18 years or &gt;80 years; (3) receiving antibiotic treatment within 3 months before inclusion; (4) combined with immunosuppressive diseases (malignant tumors, immunosuppressive treatment, organic disease or drug-induced reduction of immune cells); (5) combined with endocrine metabolic diseases (such as hyperthyroidism, hypothyroidism, pheochromocytoma, primary aldosteronism, etc.); (6) rejection or patients who did not cooperate with treatment; and (7) missing clinical data. Healthy volunteers matched at the physical examination center were also included in the healthy control (HC) group.&nbsp;</p>Metabolomicsultra-performance liquid chromatography-mass spectrometryMetabolomicsAgilent 1290 Infinity UHPLC system/Agilent 6460 Triple Quadrupole LC/MStargeted analysisblood serumAgilent 1290 Infinity II UHPLCHomo sapiensSepsisexperimental blanktargeted metabolite profilingAmino Acidsexperimental sampleultra-performance liquid chromatography-mass spectrometryMetabolomicsAgilent 1290 Infinity UHPLC system/Agilent 6460 Triple Quadrupole LC/MStargeted analysisblood serumAgilent 1290 Infinity II UHPLCHomo sapiensSepsisexperimental blanktargeted metabolite profilingAmino Acidsexperimental sample<p>In mass spectrometry analysis, multiple scan segments were used, each set to a different CE value, to obtain product ions under different CE values. Multiple reaction monitoring (MRM) analysis of a single precursor ion and its multiple product ions was performed to detect and quantify the amino acids in the sample. The mass spectrometry parameters were as follows: drying gas temperature at 330°C, gas flow rate at 13.0 L/min, nebulizer pressure at 35 psi, sheath gas temperature at 390°C, sheath gas flow at 12.0 L/min, and electrospray capillary voltage at 1,500 V.&nbsp;</p>falseAn explainable prognostic prediction panel for sepsis based on serum amino acid profiles<p>Sepsis is a life-threatening syndrome requiring aggressive management, and novel noninvasive biomarkers to enable risk stratification of sepsis with high confidence and to predict the sepsis-related outcomes are urgently needed.&nbsp;</p><p>A mass spectrometry–based quantitative method was used to analyze the abundance of serum amino acids between patients with sepsis and healthy controls (HC), in order to characterize alterations in amino acid profiles associated with sepsis. In addition, multiple machine learning methods were applied to construct a prognostic prediction model for patients with sepsis. The predictive performance was assessed and the feature contributions were screened, followed by the development of an explainable prognostic prediction panel for sepsis.</p><p>Sixty participants in the HC group and 172 patients in the sepsis group (82 patients with septic shock) were enrolled in this study. A discernible segregation trend in amino acid profiles between the HC and sepsis groups was disclosed, meanwhile the abundance of amino acids differed significantly among the HC, septic shock, and non-septic shock groups, indicating that amino acids could differentiate patients with sepsis from the HC group with good diagnostic performance. Then, 172 patients in the sepsis group were assigned to training and validation sets, and 130 patient patients with sepsis in external test set were collected. Five machine learning models (deephit, piecewise constant hazard, probability mass function, resource selection function, and extreme gradient boosting) were afterwards used and the deephit model was selected according to greater area under the curve and clinical benefits in the training, validation, and test sets. In the process of reducing features based on feature importance ranking, the Deephit model based on five features had the best ability to predict survival probability and an optimized Deephit model with screening five features including glutamine, glycine, lysine, pyroglutamic acid and proline was successfully developed to predict the prognostic risk probability for patients with sepsis.</p>2026-05-242026-05-23MTBLS14567