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Notice bibliographique
Résumé
Abstract Background A practical strategy to discover sepsis specific proteins may be to compare the plasma peptides and proteins from patients in the intensive care unit with and without sepsis. The aim was to discover proteins and/or peptides that show greater observation frequency and/or precursor intensity in sepsis. The endogenous tryptic peptides of ICU-Sepsis were compared to ICU Control, ovarian cancer, breast cancer, female normal, sepsis, heart attack, Alzheimer’s and multiple sclerosis along with their institution-matched controls, female normals and normal samples collected directly onto ice. Methods Endogenous tryptic peptides were extracted from individual sepsis and control EDTA plasma samples in a step gradient of acetonitrile for random and independent sampling by LC–ESI–MS/MS with a set of robust and sensitive linear quadrupole ion traps. The MS/MS spectra were fit to fully tryptic peptides within proteins using the X!TANDEM algorithm. The protein observation frequency was counted using the SEQUEST algorithm after selecting the single best charge state and peptide sequence for each MS/MS spectra. The protein observation frequency of ICU-sepsis versus ICU Control was subsequently tested by Chi square analysis. The average protein or peptide log 10 precursor intensity was compared across disease and control treatments by ANOVA in the R statistical system. Results Peptides and/or phosphopeptides of common plasma proteins such as ITIH3, SAA2, SAA1, and FN1 showed increased observation frequency by Chi square (χ 2 > 9, p < 0.003) and/or precursor intensity in sepsis. Cellular gene symbols with large Chi square values from tryptic peptides included POTEB, CTNNA1, U2SURP, KIF24, NLGN2, KSR1, GTF2H1, KIT, RPS6KL1, VAV2, HSPA7, SMC2, TCEB3B, ZNF300, SUPV3L1, ADAMTS20, LAMB4, MCCC1, SUPT6H, SCN9A, SBNO1, EPHA1, ABLIM2, cB5E3.2, EPHA10, GRIN2B, HIVEP2, CCL16, TKT, LRP2 and TMF1 amongst others showed increased observation frequency. Similarly, increased frequency of tryptic phosphopeptides were observed from POM121C, SCN8A, TMED8, NSUN7, SLX4, MADD, DNLZ, PDE3B, UTY, DEPDC7, MTX1, MYO1E, RXRB, SYDE1, FN1, PUS7L, FYCO1, USP26, ACAP2, AHI1, KSR2, LMAN1, ZNF280D and SLC8A2 amongst others. Increases in mean precursor intensity in peptides from common plasma proteins such as ITIH3, SAA2, SAA1, and FN1 as well as cellular proteins such as COL24A1, POTEB, KANK1, SDCBP2, DNAH11, ADAMTS7, MLLT1, TTC21A, TSHR, SLX4, MTCH1, and PUS7L among others were associated with sepsis. The processing of SAA1 included the cleavage of the terminal peptide D/PNHFRPAGLPEKY from the most hydrophilic point of SAA1 on the COOH side of the cystatin C binding that was most apparent in ICU-Sepsis patients compared to all other diseases and controls. Additional cleavage of SAA1 on the NH2 terminus side of the cystatin binding site were observed in ICU-Sepsis. Thus there was disease associated variation in the processing of SAA1 in ICU-Sepsis versus ICU controls or other diseases and controls. Conclusion Specific proteins and peptides that vary between diseases might be discovered by the random and independent sampling of multiple disease and control plasma from different hospital and clinics by LC–ESI–MS/MS for storage in a relational SQL Server database and analysis with the R statistical system that will be a powerful tool for clinical research. The processing of SAA1 may play an unappreciated role in the inflammatory response to Sepsis.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle