The Salivary Gland Epithelial Cell in Sjögren’s Syndrome: What Are the Steps Involved in Wounding or Killing Their Secretory Function?
Bibliographic record
Abstract
In their article in this issue of The Journal , Ping, et al used in vitro cultures of epithelial cells from salivary gland tissues of patients with Sjögren’s syndrome (SS), and from control patients lacking inflammatory infiltrates1. The basic process of salivary gland cell death (apoptosis) was similar both in SS salivary epithelial cells and in normal salivary epithelial cells. Thus, the key difference in the salivary gland dysfunction of the patient with SS in vivo appears to be the presence of focal T cell infiltrate in the SS gland that serves as a source of CD40 ligand (CD40L). Although their article is a nice exercise in molecular biology, with the expected molecular gymnastics (electrophoretic mobility shifts, Western blots, and TUNEL assays), the ultimate questions that arise are: What do these results mean for the patient with SS with dry mouth? How do these results influence the future for therapeutic selection of options? The current study by Ping, et al is an extension of their report from 2005 on the role of CD40 and Fas in SS salivary gland cells2. Subtle points in the present study include the observation that inhibition of p38 and nuclear factor-κB (NF-κB) activation does not fully prevent CD95-mediated apoptosis (see their Figure 4). So there must be other pathways that CD40 is activating. Particularly in the regulation of p38 and NF-κB, many different factors influence their canonical and noncanonical activation. Thus, additional factors in situ determine the … Address correspondence to Dr. Fox. E-mail: robertfoxmd{at}mac.com
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".