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Modular Dearomative 1,4-Addition to Simple Arenes with Dual Nucleophiles: Overriding the Inherent 1,2-Selectivity via η <sup>6</sup> -Coordination
Enhanced MobileNet with multi-scale feature fusion for automated breast cancer histopathology classification
Abstract Accurate and efficient diagnosis of breast cancer from histopathological images remains a major challenge in clinical practice due to subjective interpretation, inter-observer variability, and labor-intensive manual examination. To address these limitations, this work introduces a transfer learning–based framework for automated breast cancer classification using the Breast Cancer Histology Images (BACH) dataset. Several pre-trained deep architectures—including MobileNet, ResNet variants, EfficientNet, and Vision Transformers—were evaluated and extended with a Multi-Scale Feature Fusion (MSFF) module to capture morphological heterogeneity across spatial resolutions. Among these, the Enhanced MobileNet (E‑MobileNet) with MSFF outperforming recent state‑of‑the‑art models and achieving a classification accuracy of 95%, precision of 95%, recall of 94%, and F1‑score of 96%. The framework was further validated on the BreaKHis dataset across multiple magnifications, achieving an average accuracy of 90.6%. These results confirm the robustness and generalization capability of the proposed model for practical clinical deployment in digital pathology.
Chiral-Ligand-Modulated Nickel-Catalyzed Stereoselective Radical Migratory C2-Arylation of Carbohydrates
Environmental performance and cereal production in Ethiopia: evidence from an ARDL bounds testing approach
Unlocking Azulene Functionalization via Strain-Induced Azulyne Intermediates
Memory B cells in mature follicle-like tertiary lymphoid structures predict BCG response in non-muscle-invasive bladder cancer
Decoding Galectin–Glycan Recognition with <sup>19</sup> F-Tagged Lectins: from Simple Glycans to the Cellular Glycocalyx
OmiXAI: An ensemble XAI pipeline for interpretable deep learning in omics data
Time-Scale Renormalization of Correlation Decay Governs Li-Ion Transport in Garnet Solid Electrolytes
Effect of digital smile simulation on patients’ decision to accept combined orthodontic restorative treatment planning for smile makeover
Structurally Defined Low-Coordination Single-Atom Strategy for CO <sub>2</sub> Photoconversion to Formic Acid
Comparative evaluation of manual and automated ACMG/AMP variant classification: implications for clinical genetic practice
Abstract Automated implementations of the ACMG/AMP variant classification guidelines are increasingly used to support clinical genomics, yet systematic comparisons with expert human curation remain limited. In this study, we benchmarked several widely used automated and AI‑assisted tools, including Franklin, VarSome, MobiDetails, GeneBe, InterVar, and VarChat, against dual independent curator assessments across diverse variant types. We quantified criterion‑level agreement, evidence weighting behavior, and classification concordance, with a particular focus on calibration around key ACMG criteria. Our analyses revealed that discrepancies between tools and curators concentrated around evidence‑strength calibration and near‑boundary categories (LP↔P, LP↔VUS). Loss‑of‑function variants showed the highest concordance, reflecting the maturity of PVS1‑based decision trees, whereas missense and splicing variants exhibited wider variability driven by differences in PM1 hotspot definitions, PP3/BP4 predictor thresholds, and access to case‑level and segregation evidence. Tool‑specific patterns were evident: Franklin and VarSome demonstrated high concordance but a slight pathogenic-leaning bias; VarChat showed near‑neutral calibration; GeneBe yielded higher and more variable evidence totals; and InterVar applied more conservative, lower‑weight scoring. Overall, our findings indicate that automated tools perform reliably for structured, data‑rich evidence but benefit from expert adjudication for context‑dependent criteria. This is especially relevant as laboratories prepare for the forthcoming ACMG v4 framework while still operating under established ACMG and ACGS recommendations, creating a transitional period in which robust and transparent workflows remain essential.