Evaluation Study on the Use of Non-Contact Prevention and Protection Products in the Context of COVID-19: A Comprehensive Evaluation Method from AHP and Entropy Weight Method

Quantitative ternary network-oriented discovery of Q-markers from traditional Chinese medicine prescriptions: Bu-Zhong-Yi-Qi-Tang as a case study

BACKGROUND

The proposal of Q-markers for traditional Chinese medicine (TCM) represents a novel avenue of research pertaining to the quality control of TCM prescriptions. However, prior exploratory studies on Q-markers with multiple properties consistently neglected the consideration of weights, hampering our ability to accurately gauge the significance of each property and potentially leading to a flawed comprehension of Q-markers.

PURPOSE

In this study, a quantitative ternary network strategy was firstly proposed to visually discover the Q-markers from TCM prescriptions, and it has been successfully applied into the quality control study of Bu-Zhong-Yi-Qi-Tang (BZYQT), a classical TCM prescription.

METHODS

Firstly, the contents of 34 components in BZYQT, along with the kinetic features of 17 candidate Q-markers in biosamples (plasma and small intestinal contents), were characterized by UPLC-QqQ-MS/MS, and their immunomodulatory activities in macrophages and splenic lymphocytes were also assessed. Next, the obtained data were integrated into three properties: testability, bioavailability, effectiveness, and their weights were calculated using the entropy weight method to further establish a ternary network for quantitatively screening Q-markers. Subsequently, the identified Q-markers of BZYQT were utilized for the holistic quality evaluation of 36 batches of the commercial BZYQT preparation, Bu-Zhong-Yi-Qi-Pill (BZYQP) produced by three manufacturers, through similarity evaluation of the Q-marker-based fingerprint.

RESULTS

Nine compounds (hesperidin, astragaloside IV, ononin, 18β-glycyrrhizic acid, narirutin, calycosin, cimigenoside, astragaloside II, and liquiritin) showing three core properties, including testability, bioavailability, and effectiveness, were screened out as Q-markers of BZYQT based on their rankings in terms of regression area of the ternary network. Employing Q-markers as common peaks, the similarity values of 36 batches BZYQP ranged 0.914-0.998 under HPLC-UVD mode, and 0.631-1.000 under HPLC-ELSD mode, which were less than the similarity values evaluated by the conventional common peaks (HPLC-UVD mode: 0.946-0.990; HPLC-ELSD mode: 0.957-0.997). This observation suggests that the identified Q-markers are more representative as common peaks in chromatographic fingerprints for the holistic quality evaluation of TCM-related products from different manufacturers.

CONCLUSION

The quantitative discovery of Q-markers from BZYQT laid an important foundation for holistic quality assessment of its related commercially available products, and our work offering a new strategy for ensuring the consistency and efficacy of TCM prescriptions.

A deep learning model adjusting for infant gender, age, height, and weight to determine whether the individual infant suit ultrasound examination of developmental dysplasia of the hip (DDH)

Objective

To examine the correlation between specific indicators and the quality of hip joint ultrasound images in infants and determine whether the individual infant suit ultrasound examination for developmental dysplasia of the hip (DDH).

Method

We retrospectively selected infants aged 0-6 months, undergone ultrasound imaging of the left hip joint between September 2021 and March 2022 at Shenzhen Children's Hospital. Using the entropy weighting method, weights were assigned to anatomical structures. Moreover, prospective data was collected from infants aged 5-11 months. The left hip joint was imaged, scored and weighted as before. The correlation between the weighted image quality scores and individual indicators were studied, with the last weighted image quality score used as the dependent variable and the individual indicators used as independent variables. A Long-short term memory (LSTM) model was used to fit the data and evaluate its effectiveness. Finally, The randomly selected images were manually measured and compared to measurements made using artificial intelligence (AI).

Results

According to the entropy weight method, the weights of each anatomical structure as follows: bony rim point 0.29, lower iliac limb point 0.41, and glenoid labrum 0.30. The final weighted score for ultrasound image quality is calculated by multiplying each score by its respective weight. Infant gender, age, height, and weight were found to be significantly correlated with the final weighted score of image quality (P < 0.05). The LSTM fitting model had a coefficient of determination (R2) of 0.95. The intra-class correlation coefficient (ICC) for the α and β angles between manual measurement and AI measurement was 0.98 and 0.93, respectively.

Conclusion

The quality of ultrasound images for infants can be influenced by the individual indicators (gender, age, height, and weight). The LSTM model showed good fitting efficiency and can help clinicians select whether the individual infant suit ultrasound examination of DDH.