MARCATORI SPECIFICI NELLE IBD E PERSONALIZZAZIONE DELLE STRATEGIE TERAPEUTICHE ATTRAVERSO L�APPROCCIO PROTEOMICO
Bibliographic record
Abstract
Inflammatory bowel diseases (IBD) are chronic and relapsing inflammatory conditions of the gastrointestinal tract including Crohn?s disease (CD) and ulcerative colitis (UC). Pathogenic mechanisms of IBDs, etiology and behavior, are not fully understood. They are characterized by a great extent of heterogeneity, in terms of phenotypic presentation and response to different therapies. These aspects lead to a great variability of the efficacy of different therapeutic strategies inducing patient to suffer and imply enormous costs for healthcare systems. In severe IBD and in corticosteroid-dependent or ?resistant cases, the use of biological drugs, targeted towards TNF (infliximab, adalimumab) or ?4?7-mediated lymphocyte adhesion (vedolizumab) is indicated. However, 20-40% of patients do not respond to biological agents, leading to an increase of direct and indirect costs and unnecessary exposure of patients to possible adverse events. Nowadays, the diagnostic and prognostic tools for IBD and the outcome of therapy are largely based on evaluation of clinical symptoms in combination with endoscopy, histology, radiology and non-specific biomarkers from serum or stools. There are no reliable clinical or molecular predictors of response to anti-TNF or anti-leukocyte adhesion drugs. The aim of the project is to promote personalized medicine in IBD, using serum proteomic profiling, to identify potential molecular markers that may predict the behavior of the disease and the response vs. failure of anti-TNF or anti-leukocyte adhesion treatment strategies in IBD patients. After obtaining written informed consent, we prospectively enrolled all the consecutive IBD patients afferent to Gastroenterology and Digestive Endoscopy Unit of IRCCS Policlinico San Donato. All diagnoses must have been previously confirmed by clinical, endoscopic and histologic criteria. Age and sex matched healthy controls were also be enrolled. Clinical data, such as, disease, medication and family medical history were collected; disease location, extension and behavior were classified according to Montreal classification, whereas clinical activity was evaluated using clinical scores, i.e. Harvey-Bradshaw Index (HBI) as appropriate. Patients underwent blood collection for serum. Successively, we obtained Protein Matrix Assisted Laser Desorption Ionization (MALDI) profiling from the collected sera. A total of 40 sera from healthy control and 32 sera from male adult patients affected by CD were analyzed. Before MALDI analysis, the samples underwent immunodeplection in order to eliminate the high abundant protein fractions from the serum. From MALDI analysis, we obtain best separating peaks between different conditions, which represent characteristic serum profiles. The best separating peaks were compared along different groups. Healthy controls versus responder and non responder were compared first, to identify the best peaks able to define control samples and disease samples. To identify the best peaks able to define differences between responders and non responders, these two groups were compared at I infusion and at II infusion time. Finally, total MALDI spectra from controls, responders and non responders were compared together at I infusion and at II second infusion time. This comparison showed one particular peak (corresponding to 3155, 98 m/z) that was changed in all samples and normalized at control level after treatment. This finding could indicate that this peak is typical of the disease. In conclusion specific protein profiles appear to be associated with the absence of response to anti-TNF in CD patients and one single peak is differentially expressed in controls, CD responder to anti-TNF and non-responder; thus, further investigations are required in order to identify the protein that the peak corresponds to.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".