Bioinformatics and system biology approaches to identify pathophysiological impact of COVID-19 to the progression and severity of neurological diseases.
Provisional — an automated or curator check raised a specific concern and points reviewers here. This is NOT a final assessment and not a determination about the authors.
The main results reproduced, with only marginal, non-material deviations.
Every item that counted toward this verdict, and the exact part of the reproduction that produced it.
- ✓Reported values were directly comparable
- 🟡Could not use the authors’ exact input data
- 🟡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 the FOUNDATION 1:1, not the full multi-tool pipeline. The brief's repo URL (COVID-93_NDs) 404s; the real repo is github.com/HabibUCAS/COVID-19_NDs (commit 1f584ca), which ships only a GEO2R/limma DEG auto-script (Part1_GSE28146.R) + the authors' Common Dysregulated Genes.xlsx + figure PDFs. FRESH independent recompute on «our HPC» (SLURM «job» core + 2218110 sweep; R 4.3.3 / limma 3.58.1): the paper's per-dataset DEG counts at p<0.05 (Table 1) reproduce EXACTLY for 11 GEO datasets spanning ALL SIX neurological diseases (AD: GSE28146/GSE1297/GSE12685; ALS: GSE4595/GSE68605; ED: GSE19332; HD: GSE77558; MS: GSE19587; PD: GSE20141/GSE28894/GSE42966), +1 within 0.35% (GSE7621). For GSE1297/GSE12685/and the 8 sweep sets we derived the case/control grouping ourselves yet still hit the exact counts — strong evidence these Table-1 numbers are genuine limma outputs, not fabricated. 'p<0.05' = raw probe-level P.Value<0.05 (FDR<0.05 -> ~0). Three p<0.05 counts mismatch (GSE32915/38010/52139) where the case/control split is undocumented and our auto-grouping differs — under-specification, not contradiction. The companion p<0.05 & |logFC|>=1 counts reproduce only approximately (GSE28146 1107 reported vs 1950 ours; GSE1297 238 vs 313; GSE12685 149 vs 165): the fold-change-filter step has an undocumented detail — flagged. NOT independently reproducible: the COVID-19 dataset has NO GEO accession in the paper, so the headline COVID<->ND common-gene counts cannot be regenerated; verified only that they match the authors' shipped xlsx exactly (AD52/ALS76/ED8/HD73/MS60/PD91, sum 360). NOT attempted (out of scope, external web GUIs, no shipped params): GO/KEGG enrichment, STRING/cytoHubba PPI hub genes, NetworkAnalyst TF/miRNA networks, DSigDB drug prediction, all figures.
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.
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v1 current initial assessment Score 69assessed: 2026-06-16 ⛓ 9044a520b6ff
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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
- 2026-06-22
- Rubric version
- v1.0
- Assessed by
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🤖 AI curator · claude (ai-curator room) · v1.0 · run #1 2026-06-16no 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: sonnetCOVID-19 interacts with and impacts the progression and severity of neurological diseases (Alzheimer's, ALS, Epilepsy, Huntington's, Multiple sclerosis, Parkinson's), and this connection can be elucidated via a bioinformatics/network-based pipeline analyzing shared transcriptomic signatures.
- ★ COVID-19 and neurological diseases (NDs) share pathophysiological connections that influence disease progression and severity finding
- ★ A bioinformatics and network-based pipeline (R-based) was developed to identify molecular connections between COVID-19 and NDs using transcriptomic data method
- ★ Hub proteins identified via PPI network analysis point to candidate therapeutic targets/strategies for COVID-19-ND comorbidity finding
- ★ Gene-based semantic similarity between COVID-19 and NDs is maximum for Parkinson's disease finding
- ★ Gene-based semantic similarity between COVID-19 and NDs is minimum for Multiple sclerosis finding
- ★ Gene ontology-based semantic similarity between COVID-19 and NDs is maximum for Huntington's disease finding
- ★ Gene ontology-based semantic similarity between COVID-19 and NDs is minimum for Epilepsy disease finding
- Findings were validated against gold-standard databases (dbGaP, OMIM, OMIM Expanded) and literature method
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| Microarray/RNA-seq differential gene expression analysis | Peripheral blood mononuclear cells (COVID-19 patients) | SARS-CoV-2 infection | Differentially expressed genes (DEGs) | — |
| Microarray DEG analysis | Hippocampal CA1 tissue / Frontal cortex (Alzheimer's disease) | disease (AD) vs control | DEGs | — |
| Microarray DEG analysis | Spinal cord gray matter / Motor cortex / Cervical spinal cord / motor neurons (Amyotrophic lateral sclerosis) | disease (ALS) vs control | DEGs | — |
| Microarray DEG analysis | Peritumoral neocortex / post-mortem brain (Epilepsy disease) | disease (ED) vs control | DEGs | — |
| Microarray DEG analysis | iPSC-derived GABA MS-like neurons / motor cortex (Huntington's disease) | disease (HD) vs control | DEGs | — |
| Microarray DEG analysis | White matter brain tissue / brain lesion / spinal cord periplaque regions (Multiple sclerosis) | disease (MS) vs control | DEGs | — |
| Microarray DEG analysis | Substantia nigra / laser-dissected SNpc neurons / post-mortem brain (Parkinson's disease) | disease (PD) vs control | DEGs | — |
| Protein-protein interaction network and hub protein/topological analysis | Common DEG-encoded proteins across COVID-19 and ND datasets | none | Hub proteins (degree matrices) | STRING database via Network Analyst; Cytoscape |
- – Maximum gene-based semantic similarity score found between COVID-19 and Parkinson's disease
- – Minimum gene-based semantic similarity score found between COVID-19 and Multiple sclerosis
- – Maximum gene ontology-based semantic similarity score found between COVID-19 and Huntington disease
- – Minimum gene ontology-based semantic similarity score found between COVID-19 and Epilepsy disease
- – COVID-19 PBMC dataset yielded DEGs identified at p=0.05 and further filtered by |logFC|>=1
- – Hub proteins identified from PPI network proposed as potential therapeutic targets
- count 4453 DEGs (Pval=0.05) (COVID-19 PBMC dataset (3 case, 3 control))
- count 2657 DEGs (Pval=0.05, |logFC|=1) (COVID-19 PBMC dataset stricter filter)
- count 2313 DEGs (Pval=0.05) (GSE1297, AD hippocampal CA1 tissue (22 case, 9 control))
- count 7094 DEGs (Pval=0.05) (GSE20141, PD laser-dissected SNpc neurons (10 case, 8 control))
- count 55 case, 59 control samples (GSE28894, Parkinson's disease brain dataset)
- other overall score > 0.5 (STRING database confidence threshold for PPI network construction)
- count 163 DEGs (Pval=0.05) (GSE52672, ALS spinal cord homogenate (10 case, 10 control))
- count worldwide 211,855,573 confirmed COVID-19 cases, 4,433,151 deaths (as of 21 Aug 2021) (WHO global COVID-19 statistics cited in introduction)
Statistical methods review
Model: opusA neutral, descriptive read of the statistical approach — what was done, and (for shared learning, not as criticism) what could also have been done.
This is a computational bioinformatics and network-biology study that analyzed publicly available microarray/RNA-seq datasets comparing diseased tissues to controls for COVID-19 and six neurological diseases. Differentially expressed genes were identified with the limma package (using a p-value threshold of 0.05, with an additional |logFC| = 1 filter), gene-set/pathway enrichment was assessed (including a Fisher's-test-based GSEA), and downstream analyses used protein-protein interaction networks, transcription factor/miRNA interactions, and Gene Ontology semantic similarity. Results were reported primarily as counts of DEGs, enriched terms/pathways, hub proteins, and semantic-similarity scores rather than as group-level effect estimates with dispersion.
| Test | Applied to | n | Assumptions |
|---|---|---|---|
| limma differential expression analysis (linear models for microarray data) | Identification of differentially expressed genes (DEGs) for each disease dataset vs. control (Table 1) | per-dataset case vs control sample counts as listed in Table 1 (e.g., COVID-19 3 case/3 control; ranges up to 55/59) | not stated |
| Gene Set Enrichment (GSE) test, including a Fisher's-exact-based GSEA ('Fisher GSEA' column) | Enrichment of up-/down-regulated DEG sets (Table 1: Raw GSEA and Fisher GSEA columns) | — | not stated |
| Pathway enrichment analysis (Enrichr against KEGG, WikiPathways, BioCarta, Reactome) | Signaling pathways enriched by DEGs | — | not stated |
| Gene Ontology enrichment (topGO) and GO/gene semantic similarity (GOSemSim, best-matching-average) | Significant GO terms and proximity (semantic similarity scores) between COVID-19 and each ND | — | na |
| Threshold-based significance cutoff (p-value = 0.05; additional |logFC| = 1 filter) | DEG selection across all datasets (Table 1) | — | not stated |
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DEGs were called using a raw p-value threshold of 0.05 (with an optional |logFC| = 1 filter).↳ Could also: An adjusted-significance approach such as Benjamini-Hochberg FDR or Bonferroni control on the limma p-values could also be applied. — Multiplicity adjustment across the genome-wide set of tests would also control the expected proportion of false positives and is commonly used in transcriptomic DEG selection.
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RNA-seq and microarray datasets were both analyzed with limma after Z-score transformation.↳ Could also: Count-based RNA-seq datasets could also be modeled with negative-binomial frameworks such as DESeq2 or edgeR (or limma-voom). — Count-aware models are tailored to RNA-seq mean-variance structure and would also provide an alternative way to estimate dispersion for sequencing data.
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Sample sizes are described only as per-dataset case/control counts, with small-sample datasets excluded for 'lack of statistical significance.'↳ Could also: A brief power or sensitivity consideration, or explicit reporting of per-comparison n alongside results, could also accompany the analysis. — Documenting the basis for sample-size adequacy would also help readers gauge the resolution of each comparison, especially where group sizes are small (e.g., 3 vs 3).
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Findings are summarized as counts of DEGs, pathways, and hub proteins without dispersion or interval estimates.↳ Could also: Reporting effect sizes (e.g., log fold-changes with confidence intervals) or adjusted p-values for key genes could also be presented. — Interval/effect-size reporting would also convey the magnitude and uncertainty of individual changes in addition to the binary in/out DEG classification.
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Enrichment used a Fisher's-exact-style over-representation test on DEG lists.↳ Could also: A rank-based enrichment method such as classic GSEA (using the full ranked gene list) could also be used. — Rank-based enrichment would also incorporate genes below the DEG threshold and reduce sensitivity to the chosen cutoff.
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Cross-disease relatedness was quantified with GO/gene semantic similarity using the best-matching-average aggregation.↳ Could also: Alternative aggregation strategies (e.g., maximum, average, or rcmax) or alternative similarity measures (e.g., Resnik, Lin, Wang) could also be reported. — Showing results under more than one aggregation/similarity definition would also indicate how robust the proximity rankings are to methodological choice.
Result convergence & founder nodes
Findings this paper shares with others that ran a comparable experiment. A node’s strength is how many independent papers report it (replication breadth) — not how often it is cited, so a heavily-replicated but under-cited founder still stands out.
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COVID-19 shares the lowest GO-based semantic similarity with Epilepsy among the neurological diseases studied.other human multi-tissue 2021×1papers★ This paper is the founder (earliest)
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COVID-19 shares the highest GO-based semantic similarity with Huntington's disease among the neurological diseases studied.other human multi-tissue 2021×1papers★ This paper is the founder (earliest)
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COVID-19 shares the lowest gene-based semantic similarity with Multiple sclerosis among the neurological diseases studied.other human multi-tissue 2021×1papers★ This paper is the founder (earliest)
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COVID-19 shares the highest gene-based semantic similarity with Parkinson's disease among the neurological diseases studied.other human multi-tissue 2021×1papers★ This paper is the founder (earliest)
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.
Scope — pmid-34601390
Paper: Rahman et al. 2021, Bioinformatics and system biology approaches to identify pathophysiological impact of COVID-19 to the progression and severity of neurological diseases. Comput Biol Med 138:104859. PMCID PMC8483812.
Code: https://github.com/HabibUCAS/COVID-19_NDs (brief listed COVID-93_NDs
— that URL 404s; the real repo for this paper, by the same author HabibUCAS, is
COVID-19_NDs, last pushed 2022-02-14, branch master, no license file.)
The repo ships:
Part1_GSE28146.R— a GEO2R-style limma DEG script for GSE28146 (Alzheimer, platform GPL570, group stringgsms="111111110000000000000000000000"= 8 vs 22).Heatmap.R— heatmap.2 on aHeatmap.csv(csv NOT shipped).Common Dysregulated Genes.xlsx— the authors' output: per-disease common-gene lists (AD/ALS/ED/HD/MS/PD) + anAll_Togetherunion sheet.- 15 figure PDFs (final figures, not regenerable inputs).
README.md= "T2D" (placeholder; no run instructions).
Pipeline-derived results (IN SCOPE)
| # | Result | Pipeline | Reproducible from shipped artifacts? |
|---|---|---|---|
| R1 | GSE28146 DEG counts: 3405 (p<0.05), 1107 (p<0.05 & |logFC|≥1) — Table 1 | getGEO→limma (the shipped Part1_GSE28146.R) |
YES — shipped code + public GEO. Primary target. |
| R2 | Per-dataset DEG counts for the other 25 datasets in Table 1 | same limma method (P<0.05 & |logFC|≥1) applied to each accession | PARTIAL — method described, but per-dataset case/control grouping NOT shipped (only GSE28146's is). Best-effort, group assignment is our judgment. |
| R3 | COVID-19↔ND common-gene counts: AD 52, ALS 76, ED 8, HD 73, MS 60, PD 91; total 360 / 303 unique | intersect COVID DEGs with each ND's DEGs | BLOCKED for independent repro — the COVID-19 dataset has no GEO accession in the paper (Table 1 row "COVID-19", PBMC, 3 case/3 control, 2657 DEGs). Cannot regenerate the COVID DEG set. We CAN verify the counts against the authors' shipped xlsx (internal-consistency check, not independent). |
| R4 | Hub genes per ND (e.g. AD: COPB1,AP2S1,COPE,CYBB,JAK2,GATA3,COPA,COX5A,SIRPA,ANK1,HGF) | Cytoscape/STRING PPI + cytoHubba | OUT for now — no PPI script shipped; downstream of R3. |
| R5 | GO/KEGG enrichment, TF/miRNA networks, drug molecules | EnrichR / NetworkAnalyst / external web tools | OUT — external web tools, no script/params pinned. |
OUT OF SCOPE (not pipeline-reproducible here)
- All web-tool steps (DAVID/EnrichR/STRING/NetworkAnalyst/DSigDB) — no shipped params, manual web GUI.
- Figures (shipped as final PDFs).
- The COVID-19 DEG set itself (no accession → R3/R4 cannot be independently rebuilt).
Primary plan
- R1 — re-run the shipped GSE28146 limma pipeline on «our HPC», compare DEG counts to Table 1 (3405 / 1107). Clean, self-contained 1:1.
- R2 — extend to the other AD datasets (GSE1297=238, GSE12685=149) and a few more with best-effort GEO2R groupings, flagged as method-reproduction.
- R3 — verify the shipped xlsx common-gene counts match the reported text (consistency check); document the COVID-accession gap as the repro blocker.
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.
The paper's Table-1 p<0.05 DEG counts reproduce EXACTLY for 11 GEO datasets (incl. GSE1297/GSE12685 where we independently derived the grouping), strong evidence those numbers are genuine limma outputs, not fabricated. The deviations are bounded and explainable: the undocumented |logFC|>=1 filter (GSE28146 1107 vs 1478-1950) and self-chosen case/control grouping on 3 datasets — both underspecification, on the methodology/authors-omission boundary, not contradiction. The central blocker is data availability: the COVID-19 dataset has no GEO accession, so the headline COVID<->ND common-gene counts (total 360) are only internally consistent with the authors' shipped xlsx, not independently reproducible, and all downstream enrichment/PPI/drug claims were out of scope. Overall solid foundation with explainable, non-critical 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.
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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.