[Dynamic changes of serum proteomic spectra in patients with non-Hodgkin's lymphoma (NHL) before and after chemotherapy and screening of candidate biomarkers for NHL].
Bibliographic record
Abstract
BACKGROUND & OBJECTIVE: Although the complete response rate of non-Hodgkin's lymphoma (NHL) is 70%-80% using modern comprehensive treatments, its relapse rate is about 40%-50%. The minimal residual disease (MRD) may be the reason of recurrence. This study was to detect dynamic changes of serum proteomic spectra in NHL patients before and after chemotherapy, thus to screen candidate markers for NHL. METHODS: The proteomic spectra from serum of 44 NHL patients before chemotherapy, 44 NHL patients who achieved complete remission (CR) after chemotherapy, and 51 healthy individuals were analyzed by surface-enhanced laser desorption/ ionization time of flight mass spectrometry (SELDI-TOF-MS) and Ciphergen ProteinChip 3.1 software. RESULTS: Compared with the normal group, one protein peak (M11710) was up-regulated in untreated NHL group, while was close to the normal level in CR group (P < 0.05); nine other protein peaks (M3322, M4355, M6445, M6646, M8581, M8708, M8918, M13959, M15149) were down-regulated in untreated NHL group, while were close to normal levels in CR group(P < 0.05). Five candidate biomarkers for NHL were screened using the decision tree model. CONCLUSIONS: Expressions of serum proteomic spectra are different before and after chemotherapy in NHL patients. Protein signatures of NHL may be screened using SELDI mass spectrometry combined with ProteinChip software. Those signatures may be helpful in screening MRD, detecting early recurrence and predicting the response to treatments.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".