Distribution of 15 Human Kallikreins in Tissues and Biological Fluids
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: Kallikreins (KLKs) are a group of 15 secreted serine proteases. Some KLKs are established or candidate cancer biomarkers, but for most the physiological function is unknown. We characterized the protein and mRNA abundance patterns of all 15 KLKs in multiple panels of human tissues and biological fluids. METHODS: We used sensitive and specific sandwich-type ELISAs for each KLK. Reverse transcription PCR was used for transcript amplification. Multiple panels of human tissue extracts (adult and fetal) were tested, along with various biological fluids. RESULTS: Quantitative protein expression data on 7 sets of adult and 3 sets of fetal tissues were collected for all 15 KLKs. KLKs were also quantified in the following biological fluids: seminal plasma, breast milk, follicular fluid, breast cyst fluid, breast cancer cytosol, amniotic fluid, ovarian cancer ascites, cerebrospinal fluid, cervicovaginal fluid, and urine. The data were used to generate heat maps of KLK concentrations in tissues and fluids and categorize KLK abundance as highly restricted (KLK2 and KLK3 in prostate), restricted (KLK5 in skin, salivary gland, breast, and esophagus; KLK6 in brain and central nervous system; KLK7 in esophagus, heart, liver, and skin; KLK8 in breast, esophagus, skin, and tonsil; KLK13 in esophagus and tonsil), or wide (KLKs 1, 4, 9, 10, 11, 12, 14, and 15). CONCLUSIONS: Quantitative KLK concentrations in tissues and fluids aid in the elucidation of KLK function, and coexpression patterns provide clues for KLK participation in proteolytic cascades.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| 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) | 0.000 | 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 it