Background Ozone (O₃) is a major air pollutant. The existing monitoring system has uneven distribution of sites, insufficient coverage in underdeveloped areas, and low temporal resolution, making it difficult to obtain hourly data. This limits the dynamic identification of pollution and the formulation of prevention and control strategies. Objective To construct an hourly O₃ concentration estimation model based on ensemble machine learning, aiming to improve the accuracy of pollution exposure assessment and explore O₃ health impacts. Methods This study integrated land use regression modeling with modern machine learning techniques, employing random forest and XGBoost algorithms to construct base models, and stacking integration using non-negative least squares. The ensemble model was trained and validated across China using high-resolution, multi-source geographic data (e.g., meteorologicaldata, population density, land cover types, and aerosol optical thickness). It was tested in Taiyuan City, combined with a distributed lag non-linear model to analyze the association between O₃ and emergency admissions. Results The constructed ensemble model performed well in predicting O₃ concentration, with a higher coefficient of determination (R²) and a lower root-mean-square deviation (RMSE) compared to the single models. The R² improved from 0.90 to 0.92, and the RMSE decreased from 11.41 to 10.62, enhancing both prediction accuracy and generalization ability. In the application to Taiyuan City, the model successfully imputed the hourly-level data for the entire year. The distributed lag non-linear model analysis revealed that the relative risk (RR) values for the 6th to 8th days following O₃ exposure were 1.14 (95%CI: 1.01, 1.29), 1.16 (95%CI: 1.02, 1.31), and 1.14 (95%CI: 1.01, 1.29), respectively, which were significantly higher than 1, indicating a significant lagged association (lagged 6-8 d) between O₃ and the number of emergency room visits. Conclusion A high-precision, hourly-level O₃ concentration estimation model is successfully constructed by combining the land use regression model with an ensemble machine learning approach to provide a scientific basis for environmental policy formulation and public health intervention. The application of the model verifies its generalization ability and practical application value, which can provide a new technical framework for subsequent environmental health research.

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.

ObjectiveTo observe the effect of Yulin Hukun decoction on autophagy mediated by phosphatidylinositol 3-kinase （PI3K）/protein kinase B （Akt）/mammalian target of rapamycin （mTOR） signaling pathway in the mouse model of cyclophosphamide-induced diminished ovarian reserve and explore the follicular development-improving mechanism of this decoction. MethodsSixty female ICR mice with normal estrous cycle were assigned into a blank group （n=10） and a modeling group （n=50）. The model was established by intraperitoneal injection of cyclophosphamide （60 mg·kg^-1） for 5 days. The successfully modeled mice were randomly grouped as follows： model， estradiol （0.26 mg·kg^-1）， and high-， medium-， and low-dose （56.42， 28.21， 14.105 g·kg^-1， respectively） Yulin Hukun decoction， with 10 mice in each group. The blank group and the model group received normal saline （10 mL·kg^-1）. The intervention was performed once a day for 21 days. The general conditions， estrous cycle， body weight， and ovary index were observed and recorded for each group. Serum levels of follicle-stimulating hormone （FSH）， luteinizing hormone （LH）， estradiol （E₂）， and anti-Müllerian hormone （AMH） were measured by enzyme-linked immunosorbent assay. Histopathological changes in the ovarian tissue were observed by hematoxylin-eosin staining. Western blot was employed to determine the protein levels of PI3K， Akt， mTOR， autophagy-related protein 7 （Atg7）， beclin1， microtubule-associated protein 1 light chain 3Ⅱ （LC3Ⅱ）， ubiquitin-binding adaptor protein （p62）， forkhead box protein O1 （FoxO1）， and acetylated forkhead box protein O1 （Ac-FoxO1） in mouse ovaries. Real-time PCR was adopted to determine the mRNA levels of PI3K， Akt， mTOR， Atg7， beclin1， and LC3Ⅱ in the mouse ovarian tissue. ResultsCompared with the blank group， the model group had disturbed estrous cycle， decreased body weight （P<0.05）， loose ovarian structure with increased atretic follicles， increased serum FSH level （P<0.05）， and decreased AMH and estradiol levels （P<0.05）. Compared with the model group， the treatment groups showed recovered estrous cycles and body weight. The estradiol group and high- and medium-dose Yulin Hukun decoction groups showed declined FSH level （P<0.05） and elevated AMH levels （P<0.05）. In addition， the treatment groups showed downregulated protein levels of Atg7， LC3Ⅱ， beclin1， FoxO1， and Ac-FoxO1 （P<0.01）， upregulated protein levels of PI3K， Akt， mTOR， and p62 （P<0.01） in the ovarian tissue， gradual repair of the ovarian structure， with more intact and numerous follicles of various stages. ConclusionYulin Hukun decoction can inhibit autophagy in ovarian granulosa cells by activating the PI3K/Akt/mTOR signaling pathway and inhibiting the expression of autophagy-related proteins and transcription factors， thereby improving follicular development and ovarian reserve.

It attempts to determine the theoretical connotation and clinical application of the twelve-meridian based syndrome/pattern differentiation of TCM through the systematic analysis and elaboration, so as to promote the completion of meridian differentiation system. The exploration is conducted on the main body of traditional meridian-syndrome differentiation, meaning the meridian differentiation in terms of location of illness and that in terms of symptoms. The existing problems and causes are analyzed, and the specific methods of meridian differentiation put forward in line with the characteristics of meridian distribution and symptoms. In reference with Huangdi Neijing (Yellow Emperors' Canon of Medicine) and other ancient literature, the theoretical evidences of meridian differentiation are deeply analyzed in view of physiological/pathological characteristics that has been neglected in the past, such as qi and blood of meridians, opening, closing and pivoting, and time. Additionally, the category issues related to twelve-meridian differentiation and their relationship with six-meridian differentiation are expounded. The summary on the multiple connotations of twelve-meridian differentiation is of great significance on re-understanding meridians, perfecting meridian-collateral differentiation system and improving the accuracy on meridian-based treatment. Besides, the reconstruction of meridian differentiation and its framework is considered profoundly.
Meridians ; Humans ; Medicine, Chinese Traditional ; Acupuncture Therapy ; Diagnosis, Differential ; History, Ancient ; Acupuncture Points ; Medicine in Literature

BACKGROUND: The role of inflammation in the development of gestational diabetes mellitus (GDM) has recently become a focus of research. The systemic immune-inflammation index (SII) and systemic inflammation response index (SIRI), novel indices, reflect the body's chronic immune-inflammatory state. This study aimed to investigate the associations between the SII or SIRI and GDM. METHODS: A prospective birth cohort study was conducted at Beijing Obstetrics and Gynecology Hospital from February 2018 to December 2020, recruiting participants in their first trimester of pregnancy. Baseline SII and SIRI values were derived from routine clinical blood results, calculated as follows: SII = neutrophil (Neut) count × platelet (PLT) count/lymphocyte (Lymph) count, SIRI = Neut count × monocyte (Mono) count/Lymph count, with participants being grouped by quartiles of their SII or SIRI values. Participants were followed up for GDM with a 75-g, 2-h oral glucose tolerance test (OGTT) at 24-28 weeks of gestation using the glucose thresholds of the International Association of Diabetes and Pregnancy Study Groups (IADPSG). Logistic regression was used to analyze the odds ratios (ORs) (95% confidence intervals [CIs]) for the the associations between SII, SIRI, and the risk of GDM. RESULTS: Among the 28,124 women included in the study, the average age was 31.8 ± 3.8 years, and 15.76% (4432/28,124) developed GDM. Higher SII and SIRI quartiles were correlated with increased GDM rates, with rates ranging from 12.26% (862/7031) in the lowest quartile to 20.10% (1413/7031) in the highest quartile for the SII ( Ptrend <0.001) and 11.92-19.31% for the SIRI ( Ptrend <0.001). The ORs (95% CIs) of the second, third, and fourth SII quartiles were 1.09 (0.98-1.21), 1.21 (1.09-1.34), and 1.39 (1.26-1.54), respectively. The SIRI findings paralleled the SII outcomes. For the second through fourth quartiles, the ORs (95% CIs) were 1.24 (1.12-1.38), 1.41 (1.27-1.57), and 1.64 (1.48-1.82), respectively. These associations were maintained in subgroup and sensitivity analyses. CONCLUSION The SII and SIRI are potential independent risk factors contributing to the onset of GDM.
Humans ; Female ; Pregnancy ; Diabetes, Gestational/immunology* ; Prospective Studies ; Adult ; Inflammation/immunology* ; Glucose Tolerance Test ; Birth Cohort

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.