SCIENCE CHINA Materials•2026•DOI: 10.1007/s40843-025-3503-9
This correction addresses an image misuse in the original publication (Sci China Mater, 2025, 68(6): 2095, DOI: 10.1007/s40843-025-3311-6). Specifically, a fluorescent image in Fig. 4d, depicting live/dead cells after treatment with MPDA@TMZ without laser irradiation, was erroneously presented. The corrected Fig. 4 is provided, and the authors confirm that the results and conclusions of the original paper remain unaffected. The correction ensures the integrity of the reported data, particularly the cell viability and apoptosis assays. The study focuses on mesoporous bowl-shaped polydopamine (MPDA) nanoparticles co-loaded with temozolomide (TMZ) and indocyanine green (ICG) for synergistic glioblastoma therapy. The corrected figure includes CLSM images of G422 cells after incubation with various formulations (ICG, sPDA@ICG, mPDA@ICG, MPDA@ICG), cell viability curves, quantitative fluorescence intensity, live/dead staining, and apoptosis quantification. Statistical significance is denoted as ****p < 0.01. The correction maintains the scientific validity of the findings, which demonstrate the potential of MPDA-based nanoplatforms for combined chemo-photothermal therapy.
SCIENCE CHINA Materials•2026•DOI: 10.1007/s40843-025-3786-8
Conventional cancer diagnostic techniques, such as tissue sampling and microscopy, are invasive and prone to misdiagnosis, driving the need for non-invasive, precise alternatives. Chiral biophotonics, exploiting circularly polarized light (CPL), offers unique polarization-selective interactions with biological tissues, enabling higher imaging contrast and molecular-level discrimination. However, current CPL detection technologies are passive and single-mode, lacking dynamic tunability and parallel processing capabilities. Meanwhile, AI-assisted diagnostics rely on separated sensing and computing units, suffering from poor integration and transmission inefficiency. Here, we report a near-infrared (NIR) chiral organic synaptic photodiode with electrically tunable dual-mode operation, enabling simultaneous CPL detection and neuromorphic processing. Under negative bias, the device operates as a highly sensitive CPL detector for chiroptical signal acquisition. Under positive bias, it exhibits history-dependent synaptic behavior with photocurrent dissymmetry factor (g_ph) dynamically tunable up to -0.06. By integrating this device into an optical convolutional neural network (OCNN), we achieved intelligent cancer detection with CPL-based imaging. Experimental results demonstrate that CPL detection accuracy reaches 83%, approaching the theoretical 87%, significantly outperforming natural light detection at 65%. The device enhances image contrast and feature extraction, laying a foundation for intelligent, adaptive diagnostic systems.
SCIENCE CHINA Materials•2025•DOI: 10.1007/s40843-025-3524-y
The 12-lead electrocardiogram (ECG) is indispensable for the initial diagnosis of cardiac conditions, yet existing neuromorphic hardware for multi-lead ECG monitoring requires multiple array circuits and two operational processes, imposing severe constraints on device consistency and diagnostic accuracy. This study introduces a neuromorphic parallel computing hardware architecture based on quantum dot synaptic transistors that leverages trap and surface electric field effects to enable 12-lead ECG monitoring within a single array circuit, eliminating the need for twelve separate circuits. The system concurrently processes multiple ECG signals and produces final outputs without external computing or control circuits. A 12-transistor array, termed STAC, directly processes one-dimensional ECG data without additional conversion circuits, integrating a feature extraction layer at the pixel level and a feature fusion layer at the circuit level. Classification of ECG signals from the MIT-BIH Arrhythmia Database and the Chinese Twelve-Lead ECG Challenge Database yields a training accuracy exceeding 98%. A five-class ECG signal classification task achieves 96.2% recognition accuracy, with a 5×5 confusion matrix confirming high classification precision across normal (N) and four abnormal categories (A, V, L, R). The architecture accurately detects myocardial infarction by fine-tuning internal weights, demonstrating proficiency in monitoring abnormal ECG signals. This advancement offers a compact, low-cost solution for wearable and portable 12-lead ECG monitoring devices, enabling real-time cardiac assessment with reduced hardware complexity and enhanced diagnostic reliability.