RESUMO
Background: According to traditional Chinese medicine theory, a Qi-deficiency constitution is characterized by a lower voice frequency, shortness of breath, reluctance to speak, an introverted personality, emotional instability, and timidity. People with Qi-deficiency constitution are prone to repeated colds and have a higher probability of chronic diseases and depression. However, a person with a Balanced constitution is relatively healthy in all physical and psychological aspects. At present, the determination of whether one has a Qi-deficiency constitution or a Balanced constitution are mostly based on a scale, which is easily affected by subjective factors. As an objective method of diagnosis, the human voice is worthy of research. Therefore, the purpose of this study is to improve the objectivity of determining Qi-deficiency constitution and Balanced constitution through one's voice and to explore the feasibility of deep learning in TCM constitution recognition. Methods: The voices of 48 subjects were collected, and the constitution classification results were obtained from the classification and determination of TCM constitutions. Then, the constitution was classified according to the ResNet residual neural network model. Results: A total of 720 voice data points were collected from 48 subjects. The classification accuracy rate of the Qi-deficiency constitution and Balanced constitution was 81.5% according to ResNet. The loss values of the model training and test sets gradually decreased to 0, while the ACC values of the training and test sets tended to increase, and the ACC values of the training set approached 1. The ROC curve shows an AUC value of 0.85. Conclusion: The Qi-deficiency constitution and Balanced constitution determination method based on the ResNet residual neural network model proposed in this study can improve the efficiency of constitution recognition and provide decision support for clinical practice.
RESUMO
As lactoferrin (LF) plays an essential role in physiological processes, the detection of LF has attracted increasing attention in the field of disease diagnosis. However, most current methods require expensive equipment, laborious pretreatment, and long processing time. In this work, carboxyl-rich carbon dots (COOH-CDs) were facilely prepared through a one-step, low-cost hydrothermal process with tartaric acid as the precursor. The COOH-CDs had abundant carboxyl on the surface and showed strong blue emission. Moreover, COOH-CDs were used as a fluorescent sensor toward Fe3+ and showed high selectivity for Fe3+ with the limit of detection (LoD) of 3.18 nM. Density functional theory (DFT) calculations were performed to reveal the mechanism of excellent performance for Fe3+ detection. Meanwhile, COOH-CDs showed no obvious effect on lactobacillus plantarum growth, which means that COOH-CDs have good biocompatibility. Due to the nontoxicity and excellent detection performance for Fe3+, COOH-CDs were employed as a fluorescent sensor toward LF and showed satisfying performance with an LoD of 0.776 µg/mL, which was better than those of the other methods.
