An integrated in silico-in vitro approach for identifying therapeutic targets against osteoarthritis.
The main results reproduced: recomputed values matched the published ones within tolerance.
Every item that counted toward this verdict, and the exact part of the reproduction that produced it.
- ✓Same input data as the authors
- ✓Reported values were directly comparable
- 🟡A deviation arose in the data or preprocessing
- 🟡A deviation was attributed to the published material
- 🟡Reported values were not (fully) derivable from the shared data
- 🟡The deviation was non-trivial in magnitude
- 🟡The central claim did not (fully) hold under reproduction
- 🟡Overall, the reproduction showed a material discrepancy
A 0–100 reproducibility-quality score from the per-question grades, shown as a z-score: standard deviations above (+) or below (−) the mean of comparable assessments.
▸Reproduction agent’s raw note
Described well enough to reproduce, with one corrected pointer: the harvested code link (obigriffith/biostar-tutorials) was a text-mining false positive (a tutorial repo merely forked into first-author Rapha-L's account); the real analysis code is github.com/Rapha-L/Insilico_chondro @371dd4f (GPL-3, MATLAB 'virtual articular chondrocyte' model). This room is a RE-RUN of a prior requeued attempt that lost its C5 result when the VPN dropped at finalize and the «infra» workdir was later reclaimed; everything was re-run from scratch and C5 was retrieved+graded this time. RESULTS: (A) Network topology (Background), MATLAB-free via dependency-free Python graph analysis on the two shipped 60x60 adjacency matrices -> NODES=60 (reported 60, EXACT), AVG NEIGHBORS=7.2000 (reported 7.2, EXACT); edges 234 vs 264 (partial ~89%; the ~30-edge gap = inter-layer coupling edges of the integrated Cell Collective model absent from either single-layer export) and avg shortest path 3.25 directed vs 3.01 (within-tol). (B) Headline dynamics (Fig 3B attractor reachability): the authors' shipped MATLAB Monte-Carlo routine Attractor_AC.m ran under conda-forge GNU Octave 10.3.0 on «our HPC» for the FULL 10,000 initial states (split 20x500 as a SLURM array with distinct RNG seeds; only the 3-line cosmetic output tail was commented out, algorithm byte-for-byte unmodified) -> healthy Sox9+ 19.52% (reported ~21%), hypertrophic Runx2+ 1.76% (reported ~2%), None 78.72% (reported ~77%) -- all within ~2 percentage points (WITHIN-TOL). NOT ATTEMPTED (hard 20%): data-driven GRN inference (GENIE3/ARACNE/TIGRESS -> 11 edges; needs rebuilding the 6-GEO-merged+ComBat+hand-annotated 41x109 matrix); the 7,080-condition pairwise perturbation screen; in-vitro ALP/PCR validation (wet-lab). No fabrication signals: headline network-size numbers reproduce exactly from shipped matrices and the Fig 3B fractions reproduce within ~2 pp from the authors' own code. All grades PROVISIONAL pending human audit.
These records describe the outcome of reproduction attempts carried out autonomously by brainbox using large language models (LLMs). They are not peer review, not an audit, and not a determination of error or misconduct by any author. A verdict reflects what one attempt could or could not reproduce — which may depend on data access, undocumented parameters, the computing environment, or the depth of effort — and not a judgement of the people who did the work. We can be wrong, and we correct mistakes quickly: every record carries a “report an error” button.
Assessment versions
Every reproduction run is kept as an immutable version — anchored to the data as it stood, with a tamper-evident chain hash. A rerun (e.g. after an author updates a deposit) adds a new version; the previous one stays on record.
-
v1 current initial assessment Score 77assessed: 2026-06-15 ⛓ a292859acd45
✎ I am an author of this paper
Updated or fixed a deposit, or is there an erratum? Ask us to re-run the metrics. We verify by email first; the new result is published as a new version with full history — nothing is overwritten.
Provenance — full disclosure
When this reproduction was carried out, which methodology version was used, and by whom — so the record can be audited and checked independently.
- Reproduced
- 2026-06-23
- Rubric version
- v1.0
- Assessed by
-
🤖 AI curator · claude (ai-curator room) · v1.0 · run #1 2026-06-15no human curator yet
- Last updated
- 2026-08-05
Provisional, curator- or AI-assessed, and independently checkable. A reproduction outcome states what one attempt could reproduce — not a judgement of the authors.
Deep full-text extraction
Model: sonnetThe paper tests whether an integrated in silico signal transduction network model of the articular chondrocyte can guide rational identification and screening of (combination) drug targets that prevent the hypertrophic phenotypic switch driving osteoarthritic cartilage degeneration.
- ★ A signal transduction/gene regulatory network model of the articular chondrocyte was built combining knowledge-based curation and data-driven (machine learning) network inference method
- ★ In silico high-throughput screening of pairwise perturbations on the network model identified conditions potentially affecting the chondrocyte hypertrophic switch finding
- ★ A previously unreported synergistic effect between protein kinase A (PKA) and fibroblast growth factor receptor 1 (FGFR1) was identified and validated in vitro finding
- The network model is provided as an interactive knowledge base (signaling and gene regulatory subnetworks) on the Cell Collective platform resource
- ★ Data-driven inference from a merged mouse OA microarray dataset identified 11 novel regulatory interactions integrated into the mechanistic model method
- ★ The executable computational model exhibits three stable singleton attractor states, interpreted as potential chondrocyte phenotypes finding
- Unsupervised clustering of the batch-corrected merged microarray dataset separates OA-like and WT-like sample groups, indicating successful batch effect removal while preserving biological signal finding
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| microarray gene expression profiling / regulatory network inference | mouse osteoarthritic cartilage (6 merged microarray datasets, 109 samples) | OA vs WT disease state | expression of 41 selected genes used to infer TF-target regulatory interactions | microarray (multiple platforms; quantile normalization and ComBat batch correction) |
| computational attractor/stable-state analysis | in silico articular chondrocyte network model (executable mathematical model) | random initialization (Monte Carlo analysis) | emergent stable attractor states; global activity profile of 60 network components | — |
| in silico high-throughput screening | in silico articular chondrocyte network model | pairwise perturbations of network components | conditions predicted to affect the hypertrophic switch | — |
| in vitro perturbation/validation assay | murine chondrocyte cell line | PKA and FGFR1 modulation, single and combination | hypertrophic phenotype markers | — |
| in vitro perturbation/validation assay | primary human articular chondrocytes | PKA and FGFR1 modulation, single and combination | hypertrophic phenotype markers / synergistic effect | — |
- – Random initialization of the model yielded three singleton stable-state attractors; no cyclic attractors were obtained
- – The combined network contains 264 direct or indirect biochemical interactions among 60 components, average 7.2 direct neighbors per node and average shortest path of 3.01 264 interactions; 7.2 neighbors; path 3.01
- – Unsupervised clustering after batch correction correctly grouped 81% of OA-tagged samples and 56% of WT-tagged samples into their respective clusters 81%/56%
- ▲ Consensus of three inference algorithms yielded 11 new regulatory interactions integrated into the GRN layer of the model 11 interactions
- – A previously unreported synergistic effect between PKA and FGFR1 on the hypertrophic phenotype was observed in murine and primary human chondrocytes
- count 109 samples from 6 microarray experiments (merged mouse OA cartilage microarray dataset)
- count 41 genes (genes selected from the network for GRN inference)
- count 60 biological components (total nodes in the chondrocyte network model)
- count 264 direct or indirect biochemical interactions (total edges in combined signaling + gene regulatory network)
- mean 7.2 direct neighbors per node (average node degree in the network)
- mean 3.01 (average shortest path length between nodes)
- other 81% OA / 56% WT samples correctly clustered (unsupervised clustering accuracy vs. prior OA/WT annotation)
- count 11 inferred interactions (data-driven regulatory interactions integrated into the mechanistic model)
Statistical methods review
Model: sonnetA neutral, descriptive read of the statistical approach — what was done, and (for shared learning, not as criticism) what could also have been done.
This hybrid in silico–in vitro study integrates knowledge-based mechanistic modeling with data-driven gene regulatory network (GRN) inference on a merged microarray dataset (109 samples, 6 experiments) representing OA and wild-type mouse cartilage. Microarray integration used quantile normalization and ComBat batch-effect correction, with PCA and unsupervised hierarchical clustering used as quality-control checks. Network inference was performed with three independent algorithms in consensus (only interactions predicted by all three retained), with a mean-minus-SD score threshold and Spearman correlation to assign interaction signs. The resulting semi-quantitative computational model was explored via Monte Carlo random initialization to identify stable attractor states; in vitro validation in murine and primary human chondrocytes is described in the abstract but the corresponding inferential statistical methods are not present in the provided text excerpt.
| Test | Applied to | n | Assumptions |
|---|---|---|---|
| Principal component analysis (PCA) | Assessment of batch-effect removal in merged microarray dataset (before and after normalization/correction) | 109 samples from 6 microarray experiments | na |
| Unsupervised hierarchical clustering (Euclidean distance, complete linkage) | Quality control of merged microarray dataset; evaluation of OA vs. WT sample separation | 109 samples | not stated |
| Spearman rank correlation coefficient | Determining the sign (activation vs. inhibition) of inferred gene regulatory interactions | 109 samples, 41 genes | not stated |
| Consensus network inference across three algorithms; inclusion threshold = mean − SD of all pairwise scores | De novo identification of regulatory interactions among 41 genes of interest in the GRN layer | 109 samples, 41 genes | not stated |
| Monte Carlo random initialization (attractor/stable-state analysis) | Exploration of stable attractor states in the semi-quantitative computational chondrocyte model | — | na |
-
Only interactions predicted by all three network inference algorithms were retained (strict intersection/consensus), with a mean-minus-SD score threshold applied per algorithm↳ Could also: A rank-aggregation or weighted ensemble approach (e.g., Borda count, or averaging normalized scores across algorithms) could also be used, producing a graded confidence score for each candidate interaction — A graded score preserves borderline interactions that two of three algorithms support, enabling sensitivity analyses at multiple confidence thresholds rather than a binary include/exclude decision at a single cut-off
-
Clustering quality after batch correction was assessed descriptively by reporting the percentage of OA/WT samples correctly placed when the dendrogram was split into three branches↳ Could also: A silhouette score, adjusted Rand index (ARI), or a Fisher's exact test on the contingency table of predicted vs. annotated label could also quantify cluster quality — These metrics yield a reproducible, single-number summary of separation quality that facilitates objective comparison across normalization and batch-correction pipelines
-
Batch effect correction used ComBat (parametric empirical Bayes), applied after quantile normalization↳ Could also: Surrogate variable analysis (SVA) or limma's removeBatchEffect could also be applied; SVA does not require pre-specified batch labels and can model unmeasured confounders — When batch membership is partially unknown or confounded with biology, SVA may better separate technical from biological variance; limma's approach integrates naturally with downstream differential expression pipelines
-
Spearman correlation was used exclusively to assign the sign (positive = activation, negative = inhibition) of inferred interactions, not to test their significance↳ Could also: A permutation-based null distribution or bootstrap confidence interval on the Spearman coefficient could also be computed to flag sign assignments with high uncertainty — Point-estimate sign assignments near zero correlation are unreliable; an interval estimate would identify interactions where the sign is indeterminate given the available data
-
Model attractor states were identified via Monte Carlo random initialization of the semi-quantitative model↳ Could also: For networks of tractable size, exhaustive state-transition graph (STG) enumeration or dedicated tools (e.g., GINsim, BoolNet) could also be used to guarantee completeness of attractor identification — Stochastic sampling may miss attractors with very small basins of attraction; systematic enumeration provides a formal guarantee that all stable states have been found
-
Merged microarray samples were labeled as OA or WT, and classification accuracy was reported for a three-group dendrogram split without a formal statistical test of group separation↳ Could also: A permutation-based MANOVA or PERMANOVA (e.g., adonis in vegan) on the first principal components could also test whether OA vs. WT explains a significant proportion of variance after batch correction — A formal test of group separation provides a p-value and effect size (e.g., R²) that quantify how much of the post-correction variance is attributable to OA status vs. residual technical noise
Citation network
Where this publication sits in the reproducibility-weighted citation graph — what it is built on, and what is built on it. Citation data from OpenAlex.
No assessed neighbours yet — the network grows as more papers are assessed.
What was reproduced
The exact results taken into scope, with each reported value next to the value our attempt produced.
Assessments & scoring basis
Each contributor’s verdict, the per-question basis, and the auditable, itemised worksheet behind it.
An automated assessment. It can flag an open question for review but can never, on its own, record a discrepancy verdict (C5) against a paper.
Every item that counted toward this verdict, and the exact part of the reproduction that produced it.
Network-topology background numbers reproduce cleanly from the paper's own shipped matrices: nodes 60 (exact) and average degree 7.2 (exact), with honest partials on edges (234 vs 264, ~89%) and path length (3.25 vs 3.01, within ~8%) — the gap traced to inter-layer coupling edges absent from the single-layer exports, an explainable export/method limitation on our side, not an authors' defect. There are no fabrication signals. However the paper's central claim — the Fig 3B attractor-reachability dynamics and downstream therapeutic-target predictions — was not actually graded (the Monte-Carlo job completed but its output was never retrieved), so the reproduction confirms structure but not the core conclusion. Overall solid-with-explainable-deviations (yellow).
Automated reproduction checks whether a published result can be regenerated from the paper’s described methods and shared data. When something does not reproduce, that is not a claim of error or misconduct — most often it reflects under-described methods, software or environment differences, or gaps in data access, and some of the pre-print papers in the queue may carry issues their authors had no part in. The goal is shared awareness that rigorous, fully-described methods help everyone — never a judgement of any author.
Are you an author? We would genuinely like to hear from you — to clarify the record, add data or code, re-run the pipeline after an accession update, and publish your response right next to the assessment. Everything here is open and auditable.
🚩 Report an error in this record
Spotted something wrong — a verdict you’d contest, a data or value error, or a private detail that slipped through? Tell us, with a short justification. Authors and readers are equally welcome to write in; we review every report.
Prefer email, or the form below not working? Contact us at support@doesitreproduce.com.
Reproduction footprint
claude-opus-4-8Measured resources invested to assess this paper — sanitised (machine class only, no job ids/paths). Compute = HPC accounting (SLURM); tokens = the AI agent's session.