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This paper proposes a highly efficient antibacterial system based on a synergistic combination of photodynamic therapy, photothermal therapy, and chemotherapy. Chitosan oligosaccharide functionalized graphene quantum dots (GQDs-COS) with short-term exposure to 450 nm visible light are used to promote rapid healing in bacteria-infected wounds. The GQDs undergo strong photochemical transformation to rapidly produce radical oxygen species and heat under light illumination, while the COS has an innate antimicrobial ability. Moreover, the positively charged GQDs-COS can easily capture bacteria via electrostatic interactions and kill Gram-positive and Gram-negative bacteria by multivalent interactions and synergistic effects. The antibacterial action of this nanocomposite causes irreversible damage to outer and inner bacterial membranes, resulting in cytoplasm leakage and death. The system has good hemocompatibility and low cytotoxicity and can improve the healing of infected wounds, as demonstrated by the examination of pathological tissue sections and inflammatory markers. These results suggest that GQDs anchored with bioactive molecules are a potential photo-activated antimicrobial strategy for anti-infective therapy.

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Summary This paper investigates a Savitzky‐Golay filter based bidirectional long short‐term memory network (SG‐BiLSTM) by using the Adam algorithm for the state of charge (SOC) estimation of lithium batteries. In this hybrid method, a BiLSTM network is constructed to estimate SOC by using the discharge current and the terminal voltage as inputs, the Adam algorithm is adopted to update the weights and biases of the BiLSTM, and the SG filter is introduced to process the estimated SOCs. In the experimental part, the urban dynamometer driving schedule (UDDS) profile is performed on a battery test platform for data acquisition. In the simulation part, the root mean squared error (RMSE) and the coefficient of determination (R2) is used to evaluate the model performance under different cases. The estimation results indicate that: the SG‐BiLSTM has faster convergence speed and higher estimation accuracy when compared with other methods; the SG‐BiLSTM shows strong robustness when applied to the data set with random noises added; appropriately increasing the hidden neurons helps to improve the model performance, but excessive increase will lead to overfitting.

The solubility and photosensitive activity of phthalocyanine are crucial to photodynamic antibacterial performance. However, highly conjugated phthalocyanine with high singlet oxygen generation efficiency tends to aggregate in aqueous environments, leading to poor solubility and photodynamic antibacterial activity. Herein, we propose a novel photodynamic antibacterial therapeutic platform by a phthalocyanine-based polymeric photosensitizer for the efficient healing of a bacteria-infected wound. A prepared phthalocyanine-based chain-transfer agent and a tertiary amino group-containing monomer are applied in the reversible addition–fragmentation chain-transfer polymerization for the preparation of the polymeric photosensitizer, which is subsequently quaternized to obtain a positively charged surface. This water-soluble phthalocyanine-based polymer can strongly concentrate on bacterial membranes via electrostatic interaction. The formed singlet oxygen by the phthalocyanine-based polymer after 680 nm light irradiation plays an essential role in killing the Gram-positive and Gram-negative bacteria. The study of antibacterial action indicates that this nanocomposite can cause irreversible damage to the bacterial membranes, which can cause cytoplasm leakage and bacterial death. Moreover, this therapeutic platform has excellent biocompatibility and the capacity to heal the wounds of bacterial infections. Experimental results indicate that the design strategy of this phthalocyanine-based polymer can extend the application of the hydrophobic photosensitizer in the biomedical field.

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Prognostic analysis for early-stage (stage I/II) melanomas is of paramount importance for customized surveillance and treatment plans. Since immune checkpoint inhibitors have recently been approved for stage IIB and IIC melanomas, prognostic tools to identify patients at high risk of recurrence have become even more critical. This study aims to assess the effectiveness of machine-learning algorithms in predicting melanoma recurrence using clinical and histopathologic features from Electronic Health Records (EHRs). We collected 1720 early-stage melanomas: 1172 from the Mass General Brigham healthcare system (MGB) and 548 from the Dana-Farber Cancer Institute (DFCI). We extracted 36 clinicopathologic features and used them to predict the recurrence risk with supervised machine-learning algorithms. Models were evaluated internally and externally: (1) five-fold cross-validation of the MGB cohort; (2) the MGB cohort for training and the DFCI cohort for testing independently. In the internal and external validations, respectively, we achieved a recurrence classification performance of AUC: 0.845 and 0.812, and a time-to-event prediction performance of time-dependent AUC: 0.853 and 0.820. Breslow tumor thickness and mitotic rate were identified as the most predictive features. Our results suggest that machine-learning algorithms can extract predictive signals from clinicopathologic features for early-stage melanoma recurrence prediction, which will enable the identification of patients that may benefit from adjuvant immunotherapy.