Under the current upsurge of cross-disciplinary research between artificial intelligence （AI） and traditional Chinese medicine （TCM）， the inheritance of academic thought in TCM is confronted with the deep-seated predicament of cognitive paradigm dislocation. The existing AI model is based on the classical reasoning mode of formal logic and statistical probability， which is difficult to adapt to the nonlinear and holistic cognitive logic of TCM with image-based thinking and syndrome-differentiated treatment as the core， resulting in systematic misreading and loss of the core essence in digital inheritance. Based on this， this paper proposes to construct a new cognitive model oriented towards the context of TCM. Its essence is not to have AI imitate the thinking of TCM， but to establish an auxiliary understanding framework based on semantic mapping and relational reasoning. It is advocated that the role of AI should be reconstructed from a knowledge substitute to a clinical interactor， and its application boundary in the inheritance of TCM wisdom should be clarified. By establishing the theoretical framework and practical path of human-computer collaboration， this paper aims to provide a scientific basis for realizing the deep integration of AI technology and the academic thought in TCM.

Under the current upsurge of cross-disciplinary research between artificial intelligence （AI） and traditional Chinese medicine （TCM）， the inheritance of academic thought in TCM is confronted with the deep-seated predicament of cognitive paradigm dislocation. The existing AI model is based on the classical reasoning mode of formal logic and statistical probability， which is difficult to adapt to the nonlinear and holistic cognitive logic of TCM with image-based thinking and syndrome-differentiated treatment as the core， resulting in systematic misreading and loss of the core essence in digital inheritance. Based on this， this paper proposes to construct a new cognitive model oriented towards the context of TCM. Its essence is not to have AI imitate the thinking of TCM， but to establish an auxiliary understanding framework based on semantic mapping and relational reasoning. It is advocated that the role of AI should be reconstructed from a knowledge substitute to a clinical interactor， and its application boundary in the inheritance of TCM wisdom should be clarified. By establishing the theoretical framework and practical path of human-computer collaboration， this paper aims to provide a scientific basis for realizing the deep integration of AI technology and the academic thought in TCM.

By systematically analyzing the bottlenecks in current traditional Chinese medicine （TCM） efficacy evaluation， this study addresses the limitations of TCM efficacy assessment methods in terms of individualization， holism， and complexity representation. Drawing on the concepts and methodologies of evidence-based medicine and systems biology， this paper proposes a new evaluation path by integrating multidisciplinary technologies such as big data， artificial intelligence （AI）， omics techniques， and bioinformatics， and encompassing from foundational data， individualized evaluation， and mechanistic analysis to evidence translation. The path first integrates real-world data， multi-omics profiling， and dynamic monitoring via smart devices to construct a high-dimensional dynamic data foundation. Subsequently， leveraging this foundation， it combines digital twins， dynamic symptom networks， and reinforcement learning to develop a dynamic efficacy evaluation method based on individual self-reference， enabling a shift from group mean comparisons to individual potential outcome comparisons. Furthermore， adopting the systems biology paradigm， it employs dynamic omics monitoring and deep learning models to dissect the synergistic mechanisms of TCM compound prescriptions from network regulation， emergent effects， and dynamic evolution dimensions. Finally， it enhances real-world evidence strength through target trial emulation and causal inference， achieving model iteration via incremental learning. This path aims to advance efficacy evaluation from "point validation" to "systematic characterization" and from "outcome assessment" to "process mechanism elucidation，" providing methodological support for the modernization of TCM.

Objective:To explore the clinical characteristics of patients with metastatic gastric tumor and to differentiate them from those with primary malignant gastric tumors.Methods:This study retrospectively analyzed the clinical data of 21 patients with metastatic gastric tumor confirmed by gastroscopy biopsy and pathology.The clinical manifestations,gastroscopy characteristics,pathological results,and the time interval between the diagnosis of primary malignant tumor and the confirmation of gastric metastasis were summarized.Results:The patient primarily exhibited symptoms related to gastric involvement;gastroscopy findings resembled those of primary malignant tumors of the stomach;pathological results were consistent with primary malignant tumors;the time interval between the diagnosis of primary cancer and the confirmation of metastatic gastric tumor varied significantly,ranging from up to 18 years to simultaneous detection.Conclusion:Metastatic gastric tumors are similar to primary malignant tumors of the stomach in terms of clinical manifestations and endoscopic features,making clinical differentiation challenging.The main approach for distinguishing them is based on pathological examination.

Taking Xiaochaihu Decoction as an example,the application differences of classical prescriptions in modern medical context and Chinese medicine practice are compared and analyzed from the aspects of clinical application scope,understanding of pre-scription connotation,dosage specification,dosage form and decoction method.Strategies to solve the differences in efficacy are pro-posed:integrating the wisdom of classical prescriptions and reshaping the framework of Chinese medicine diagnosis and treatment;transforming the results of modern pharmacology and exploring the principles of classical prescriptions;controlling drug quality stand-ards and exploring new uses and dosages of classical prescriptions;keeping pace with the times in Chinese medicine decoction and strengthening management and control to ensure efficacy.It is believed that combining the essence of Chinese medicine with modern technology can make the application of classical prescriptions maintain traditional characteristics while meeting modern clinical require-ments.This can not only improve the adaptability of classical prescriptions to modern complex diseases,but also provide a reference for the modernization of traditional medicine.

Objective:Middle ear cholesteatoma is a common otolaryngological disease, and traditional diagnostic methods have certain limitations. This study aims to construct a computer-aided diagnosis model for middle ear cholesteatoma based on integrated convolutional neural networks (CNNs) to improve diagnostic accuracy and efficiency.Methods:Firstly, Data were collected from patients who visited the Department of Otorhinolaryngology Head and Neck Surgery at the First People′s Hospital of Yinchuan between January 2020 and December 2021. 8 000 temporal bone CT images were collected, including 5 000 images diagnosed pathologically as middle ear cholesteatoma and 3 000 normal images. A five-fold cross-validation method was used to divide the dataset into training and testing sets. Next, a transfer learning approach was used to initialize model parameters, and the AlexNet, GoogleNet, and ResNet networks were pre-trained to extract deep features from the images. Then, the Softmax classification algorithm was applied to classify the features, resulting in three independent classifiers. These classifiers were combined using an ensemble learning method with a weighted voting approach to obtain the final diagnostic results. Finally, the model was evaluated by comparing the ensemble classifier with individual classifiers to assess its accuracy, precision, sensitivity, specificity, and diagnostic time, and a comparison with low-mid-and high-experience physician groups was conducted to comprehensively evaluate the model′s diagnostic performance.Results:The experimental results showed that the model achieved an accuracy of 88.8%(178/200), precision of 92.9%,(112/120) sensitivity of 89.8%(108/120), and specificity of 88.1%(70/80). The average diagnostic time for individual patient temporal bone CT images was reduced to 2-3 seconds. Compared to the diagnostic results from low-mid-and high-experience physician groups, the model demonstrated significant advantages and effectively assisted clinicians in making rapid and accurate middle ear cholesteatoma diagnoses.Conclusion:The proposed middle ear cholesteatoma diagnostic model based on integrated convolutional neural networks exhibits high recognition accuracy and rapid diagnostic speed, significantly improving clinical diagnostic efficiency, especially in early screening and auxiliary diagnosis, making it of considerable value in clinical practice.

OBJECTIVE: To explore the implementation effect of hour-1 bundle for sepsis patients based on crisis resource management (CRM) system. METHODS: A historical control study was conducted. The hour-1 bundle for sepsis based on CRM was used to train 24 nurses in the emergency department from October 2022 to March 2023. Clinical data of sepsis patients admitted to the emergency department of the First People's Hospital of Zunyi from April 2022 to September 2023 were collected. The patients were divided into three groups based on different stages of CRM system construction: control group (before construction, from April to September in 2022), improvement group (during construction, from October 2022 to March 2023) and observation group (after construction, from April to September in 2023). The baseline data, implementation rate of hour-1 bundle [including blood culture, antibiotic usage, blood lactic acid (Lac) detection, fluid resuscitation, hypertensors usage], identification and diagnosis time, and prognosis parameters [including correction rate of hypoxemia, intensive care unit (ICU) occupancy rate, and 28-day survival rate]. Sepsis cognition survey and non-technical skill (NTS) evaluation of nurses in emergency department were conducted before and after training. RESULTS: Finally 43 cases were enrolled in the control group, improvement group and observation group, respectively. There was no statistically significant difference in baseline data including the gender, age, primary site, heart rate, systolic blood pressure, acute physiology and chronic health evaluation II (APACHE II) score, sequential organ failure assessment (SOFA) score, mechanical ventilation ratio among the three groups with comparability. With the gradual improvement of the CRM system, the implementation rate of 1-hour bundle was gradually increased, and the implementation rate in the control group, improvement group and observation group were 65.12% (28/43), 74.42% (32/43) and 88.37% (38/43), respectively, with statistically significant difference (P < 0.05). It was mainly reflected in the completion rate of blood culture, antibiotic usage rate, Lac detection rate and hypertensors usage rate within 1 hour, which were significantly higher in the observation group than those in the control group [completion rate of blood culture: 90.70% (39/43) vs. 62.79% (27/43), antibiotic usage rate: 88.37% (38/43) vs. 60.47% (26/43), Lac detection rate: 93.02% (40/43) vs. 72.09% (31/43), hypertensors usage rate: 88.37% (38/43) vs. 60.47% (26/43), all P < 0.05]. The fluid resuscitation rates within 1 hour in the three groups were all over 90%, with no statistically significant difference among the three groups. The recognition and diagnosis time in the observation group was significantly shorter than that in the control group and the improvement group (hours: 0.41±0.15 vs. 0.61±0.21, 0.51±0.18, both P < 0.05), the correction rate of hypoxemia and 28-day survival rate were significantly higher than those in the control group [correction rate of hypoxemia: 95.35% (41/43) vs. 74.42% (32/43), 28-day survival rate: 83.72% (36/43) vs. 60.47% (26/43), both P < 0.05], and ICU occupancy rate was significantly lower than that in the control group [72.09% (31/43) vs. 93.02% (40/43), P < 0.05]. After training in the CRM system, the score of the sepsis awareness survey questionnaire for emergency department nurses was significantly increased as compared with before training (60.42±5.29 vs. 44.17±9.21, P < 0.01), and NTS also showed significant improvement. CONCLUSION CRM plays a significant role in promoting the implementation of sepsis hour-1 bundle, which can improve the implementation rate of hour-1 bundle and NTS of medical staff, effectively improve patients' hypoxemia, reduce patients' ICU occupancy rate and 28-day risk of death.
Humans ; Sepsis/therapy* ; Emergency Service, Hospital ; Patient Care Bundles ; Intensive Care Units ; Female ; Male ; Middle Aged

Objective:To explore the clinical characteristics of patients with metastatic gastric tumor and to differentiate them from those with primary malignant gastric tumors.Methods:This study retrospectively analyzed the clinical data of 21 patients with metastatic gastric tumor confirmed by gastroscopy biopsy and pathology.The clinical manifestations,gastroscopy characteristics,pathological results,and the time interval between the diagnosis of primary malignant tumor and the confirmation of gastric metastasis were summarized.Results:The patient primarily exhibited symptoms related to gastric involvement;gastroscopy findings resembled those of primary malignant tumors of the stomach;pathological results were consistent with primary malignant tumors;the time interval between the diagnosis of primary cancer and the confirmation of metastatic gastric tumor varied significantly,ranging from up to 18 years to simultaneous detection.Conclusion:Metastatic gastric tumors are similar to primary malignant tumors of the stomach in terms of clinical manifestations and endoscopic features,making clinical differentiation challenging.The main approach for distinguishing them is based on pathological examination.

Taking Xiaochaihu Decoction as an example,the application differences of classical prescriptions in modern medical context and Chinese medicine practice are compared and analyzed from the aspects of clinical application scope,understanding of pre-scription connotation,dosage specification,dosage form and decoction method.Strategies to solve the differences in efficacy are pro-posed:integrating the wisdom of classical prescriptions and reshaping the framework of Chinese medicine diagnosis and treatment;transforming the results of modern pharmacology and exploring the principles of classical prescriptions;controlling drug quality stand-ards and exploring new uses and dosages of classical prescriptions;keeping pace with the times in Chinese medicine decoction and strengthening management and control to ensure efficacy.It is believed that combining the essence of Chinese medicine with modern technology can make the application of classical prescriptions maintain traditional characteristics while meeting modern clinical require-ments.This can not only improve the adaptability of classical prescriptions to modern complex diseases,but also provide a reference for the modernization of traditional medicine.

Objective:Middle ear cholesteatoma is a common otolaryngological disease, and traditional diagnostic methods have certain limitations. This study aims to construct a computer-aided diagnosis model for middle ear cholesteatoma based on integrated convolutional neural networks (CNNs) to improve diagnostic accuracy and efficiency.Methods:Firstly, Data were collected from patients who visited the Department of Otorhinolaryngology Head and Neck Surgery at the First People′s Hospital of Yinchuan between January 2020 and December 2021. 8 000 temporal bone CT images were collected, including 5 000 images diagnosed pathologically as middle ear cholesteatoma and 3 000 normal images. A five-fold cross-validation method was used to divide the dataset into training and testing sets. Next, a transfer learning approach was used to initialize model parameters, and the AlexNet, GoogleNet, and ResNet networks were pre-trained to extract deep features from the images. Then, the Softmax classification algorithm was applied to classify the features, resulting in three independent classifiers. These classifiers were combined using an ensemble learning method with a weighted voting approach to obtain the final diagnostic results. Finally, the model was evaluated by comparing the ensemble classifier with individual classifiers to assess its accuracy, precision, sensitivity, specificity, and diagnostic time, and a comparison with low-mid-and high-experience physician groups was conducted to comprehensively evaluate the model′s diagnostic performance.Results:The experimental results showed that the model achieved an accuracy of 88.8%(178/200), precision of 92.9%,(112/120) sensitivity of 89.8%(108/120), and specificity of 88.1%(70/80). The average diagnostic time for individual patient temporal bone CT images was reduced to 2-3 seconds. Compared to the diagnostic results from low-mid-and high-experience physician groups, the model demonstrated significant advantages and effectively assisted clinicians in making rapid and accurate middle ear cholesteatoma diagnoses.Conclusion:The proposed middle ear cholesteatoma diagnostic model based on integrated convolutional neural networks exhibits high recognition accuracy and rapid diagnostic speed, significantly improving clinical diagnostic efficiency, especially in early screening and auxiliary diagnosis, making it of considerable value in clinical practice.