The International Standards for Neurological Classification of Spinal Cord Injury (ISNCSCI), maintained by the American Spinal Injury Association (ASIA) and the International Spinal Cord Society (ISCoS), requires examination of 28 bilateral key sensory points to determine the neurological level of injury. However, adjacent dermatomes overlap substantially in their cutaneous territories, introducing redundancy into the standard examination protocol.
Tumour-associated neutrophils (TANs) in hepatocellular carcinoma (HCC) occupy a continuous activation spectrum — from anti-tumour antigen-presenting states to pro-tumour angiogenic and immunosuppressive states — rather than a binary N1/N2 classification [Grieshaber-Bouyer et al., Nature Communications, 2021; Antuamwine et al.
Hepatocellular carcinoma (HCC) is the most prevalent form of primary liver cancer and a leading cause of cancer-related mortality worldwide [Sung et al., Global Cancer Statistics 2020, CA Cancer J Clin, 2021].
The Glasgow Coma Scale (GCS) total score is the most widely used metric in traumatic brain injury (TBI) assessment, yet it collapses three independent neurological domains---Eye opening (E), Verbal response (V), and Motor response (M)---into a single sum. Using published mortality data from a cohort of over 65,000 TBI patients, we apply mutual information (MI) analysis to quantify the prognostic information carried by each GCS component and the total score.
Thiopurines remain clinically useful across rheumatology and systemic autoimmune disease, but preventable myelotoxicity still occurs when pharmacogenetic risk, baseline blood counts, interacting medications, and monitoring readiness are reviewed separately instead of together. We present THIO-SAFE, a transparent 10-domain weighted bedside score for estimating near-term azathioprine myelotoxicity risk.
We present MetaGenomics, a pure NumPy/SciPy/scikit-learn metagenomics analysis engine implemented entirely in Python without external bioinformatics frameworks (no QIIME2, mothur, HUMAnN3, or R). MetaGenomics bundles six published statistical methods: (1) taxonomic profiling with rarefaction and CLR normalization, (2) alpha diversity (Shannon, Simpson, Chao1, Pielou evenness), (3) beta diversity with PCoA ordination and PERMANOVA significance testing, (4) differential abundance via LEfSe, ALDEx2, and ANCOM-BC, (5) functional profiling with COG/KEGG mapping and ARG detection across 20 resistance gene classes, and (6) SparCC-inspired co-occurrence network inference.
CancerGenomics is a self-contained Python pipeline for tumor genomic analysis using only NumPy, SciPy, and scikit-learn — no GATK, CNVkit, maftools, or R required. The engine provides six analysis modules: (1) Circular Binary Segmentation for copy-number variation detection, (2) TMB/MSI computation from somatic mutation calls, (3) COSMIC SBS96 mutational signature decomposition via NNLS, (4) MHC-I neoantigen prediction using position weight matrices, (5) clonal architecture inference via cancer cell fraction estimation and KMeans clustering, and (6) genomic instability scoring including LOH fraction and HRD score.
We present a benchmark for single-cell RNA-seq workflows that treats biological-claim stability, rather than file-level reproducibility, as the primary endpoint. The April 11, 2026 live artifact bundle contains five primary active lanes (PBMC3k, Kang interferon-beta PBMCs, a cross-technology PBMC panel, a paired-modality CITE-seq PBMC reference, and a PBMC multiome lane) plus an active supplementary pancreas integration stress lane.
We present an automated pipeline that turns DrugAge into a robustness-first screen for longevity interventions, favoring compounds whose pro-longevity signal is broad across species, survives prespecified stress tests, and remains measurably above a species-matched empirical null baseline (1,000 permutations, z = 4.42 for robust-compound count).
CellTrajectory is a complete cell trajectory inference engine for single-cell RNA-seq data, implemented entirely in NumPy/SciPy/scikit-learn with no Monocle3, Slingshot, Scanpy, or scVelo dependencies. It combines three complementary algorithmic frameworks — Diffusion Map + Diffusion Pseudotime (DPT), Minimum Spanning Tree (MST) topology, and Principal Curve fitting — and provides the first principled method-agreement analysis via pairwise Kendall tau comparison.
We present HiCAnalysis, a complete Hi-C chromatin 3D genome analysis pipeline implemented entirely in NumPy/SciPy — no cooler, no cooltools, no Juicer, no HiCExplorer, no R HiTC. The engine provides five analysis modules: (1) ICE normalization for bias correction, (2) insulation score and directionality index for TAD boundary detection, (3) PCA-based A/B compartment calling with GC-content guided eigenvector orientation, (4) HICCUPS-inspired chromatin loop detection using enrichment and Poisson p-values, and (5) differential TAD analysis with permutation significance testing.
We present ProteinStability, a training-free protein thermodynamic stability prediction pipeline implemented in pure NumPy. Given only a protein sequence, it estimates ΔΔG for all possible single-point mutations using a 19-feature model combining Miyazawa-Jernigan inter-residue potentials, hydrophobicity, secondary structure context, and sequence-derived contact maps.
We present RNAStructure, a complete RNA secondary structure prediction and design engine implemented entirely in pure Python/NumPy without ViennaRNA, Mfold, or external binaries. The package implements five core modules: (1) Nussinov and Turner nearest-neighbor algorithms for minimum free energy (MFE) prediction using the Zuker dynamic programming algorithm with Turner 2004 thermodynamic parameters; (2) McCaskill partition function algorithm for computing base-pair probability matrices; (3) DeltaMFE scanning for systematic evaluation of all single-nucleotide variants; (4) inverse folding for target-based RNA sequence design using simulated annealing; and (5) comparative structure analysis including tree-edit distance and covariation detection.
Hepatocellular carcinoma (HCC) is the most prevalent form of primary liver cancer and ranks among the leading causes of cancer-related mortality worldwide. While early-stage HCC can be managed with surgical resection or ablation, a significant proportion of patients present at advanced stages in which the tumor has already begun to spread beyond the liver.
Tumour-associated neutrophils (TANs) in hepatocellular carcinoma (HCC) are not a monolithic population. Single-cell transcriptomic profiling across cancer types has resolved at least ten distinct neutrophil activation states, including angiogenic, antigen-presenting, inflammatory, and immunosuppressive subsets — with the angiogenic (VEGFA+SPP1+) subset linked to the worst patient outcomes and the antigen-presenting (HLA-DR+CD74+) subset associated with the most favourable survival signal.
Hepatocellular carcinoma (HCC) is the most prevalent form of primary liver cancer and a leading cause of cancer-related mortality worldwide. In patients with advanced, extrahepatic disease, systemic therapy selection — among sorafenib, lenvatinib, and immunotherapy combinations such as atezolizumab plus bevacizumab — is an area of ongoing clinical refinement.