This dataset provides the source-data files underlying the figures and the reagent inventories of the study "Aqueous Hellenia speciosa rhizome extract attenuates hepatic injury in high-fat diet/streptozotocin-induced diabetic rats" (Filfilan & Elbeeh). Included: (1) Figure 6 per-field morphometric source values (steatotic-vacuole area, inflammatory-foci count, collagen area, DAB-positive area), each value corresponding to the field displayed in the figure panel; (2) full Western-blot source data underlying Figure 4 - cropped blot strips for 12 protein targets plus loading controls (beta-actin, Lamin B1), ROI/QC overlays, per-target densitometry with per-replicate values, means, SD/SEM and fold-change versus normal control, the composite Figure 4 in TIF/PNG/PDF/SVG, and full processing logs; (3) reagent inventories - the antibody panel with catalogue numbers and RRIDs, and the TaqMan gene-expression assay list. Animals: male Wistar rats (n = 10 per group; six groups). Diabetes was induced by four weeks of high-fat diet followed by a single low-dose streptozotocin injection (35 mg/kg i.p.). Treatments were given orally once daily for six weeks: aqueous Hellenia speciosa rhizome extract (CS-AE, 500 mg/kg/day), metformin (200 mg/kg/day), or their combination. Ethics approval: HAPO-02-K-012-2025-05-2711 (Biomedical Research Ethics Committee, Umm Al-Qura University). Reporting follows ARRIVE 2.0. Not included in this version: per-animal raw values, histopathology photomicrographs, HPLC-DAD chromatograms, and statistical-analysis outputs. These will be added in a later version once finalised from the laboratory primary records.
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It is demonstrated that linker-free PROTACs can outperform traditional designs, marking a paradigm shift in PROTAC development for targeted protein degradation.
Pinal, a 16-billion-parameter foundation model that produces protein candidates from natural-language functional descriptions, supports natural language as a high-level interface for candidate generation in protein design, enabling programmable exploration with reduced reliance on manually specified structural or seque...
A new machine-learning framework aims to improve the success rate of computational protein design while moving away from results that reproduce sequences found in nature.