Viral pneumonia is an infectious disease caused by virus invading the lung parenchyma and interstitial tissue and causing lung inflammation， with the incidence rising year by year. Traditional Chinese medicine （TCM） can treat viral pneumonia in a multi-component， multi-target， and holistic manner by targeting the core pathogenesis of pneumonia caused by different respiratory viruses， demonstrating minimal side effects and significant advantages. According to the theory of healthy Qi and pathogenic Qi in TCM， the struggle between healthy Qi and pathogenic Qi and the imbalance between immunity and inflammation run through the entire process of viral pneumonia， and the immunity-inflammation status at different stages of the disease reflects different relationships between healthy Qi and pathogenic Qi. Immune dysfunction leads to the deficiency of healthy Qi， causing viral infections. The struggle between healthy Qi and pathogenic Qi causes immunity-inflammation imbalance， leading to the onset of viral pneumonia. Inflammatory damage causes persistent accumulation of phlegm and stasis， leading to the progression of viral pneumonia. The cytokine storm causes immunodepletion， leading to the excess of pathogenic Qi and diminution of healthy Qi and the deterioration of viral pneumonia. After the recovery from viral pneumonia， there is a long-term imbalance between immunity and micro-inflammation， which results in healthy Qi deficiency and pathogenic Qi lingering. Healthy Qi deficiency and pathogenic Qi excess act as common core causes of pneumonia caused by different respiratory viruses. Clinical treatment should emphasize both replenishing healthy Qi and eliminating pathogenic Qi， helping to restore the balance between healthy Qi and pathogenic Qi as well as between immunity and inflammation， thus promoting the recovery of patients from viral pneumonia. According to the TCM theory of healthy Qi and pathogenic Qi， this article summarizes the immunity-inflammation mechanisms at different stages of viral pneumonia， and explores the application of the method of replenishing healthy Qi and eliminating pathogenic Qi in viral pneumonia. The aim is to probe into the scientific connotation of the TCM theory of healthy Qi and pathogenic Qi in viral pneumonia and provide ideas for the clinical application of the method of replenishing healthy Qi and eliminating pathogenic Qi to assist in the treatment of viral pneumonia.

The principle of treating different diseases with the same therapy is the essence of syndrome differentiation and treatment in traditional Chinese medicine （TCM）. It means that when the same pathogenic changes or the same symptoms appear in the development of different diseases， the same principles or methods can be used for treatment. Due to the complexity and high variability of viral pathogenicity， the precise and effective treatment of different types of viral pneumonia （VP） has always been a research focus and difficulty in modern medicine. VP belongs to the category of external-contraction febrile disease， warm disease， and epidemic in TCM. Haoqin Qingdantang （HQQDD） is a representative formula for clearing heat and dispelling dampness in warm diseases， and its intervention in VP caused by various viral infections has significant effects. This study， guided by the theory of treating different diseases with the same therapy， links the related studies on using HQQDD to treat different types of VP and finds that influenza virus pneumonia （IVP）， severe acute respiratory syndrome （SARS）， and COVID-19 all have a common pathogenic mechanism of dampness-heat at different stages of respective diseases. When these diseases are dominated by damp-heat factors， the use of HQQDD yields remarkable therapeutic effects. Modern pharmacological studies have confirmed that HQQDD can inhibit virus replication， reduce fever reactions， inhibit the expression of inflammatory mediators， and regulate immune balance. Moreover， the sovereign medicine in this formula has excellent antiviral activity， and the formula reflects rich scientific connotations of treating VP. According to the theory of treating different diseases with the same therapy and based on the effective treatment practice and modern pharmacological research of HQQDD for different types of VP， this paper mines the underlying TCM theory of treatment with the same therapy， explores the syndrome differentiation and treatment strategy and effect mechanism of this formula for different types of VP， and analyzes the treatment mechanism and characteristics， with the aim of providing evidence and reference for the clinical application and modern research of HQQDD.

ObjectiveTo establish the quality standards for Sennae Folium（Cassia angustifolia） dispensing granules based on standard decoctions. MethodsHigh performance liquid chromatography（HPLC） specific chromatograms were established for 15 batches of Sennae Folium（C. angustifolia） standard decoctions and 10 of Sennae Folium（C. angustifolia） dispensing granules from different manufacturers， and the similarity evaluation， hierarchical cluster analysis（HCA） and principal component analysis（PCA） were performed. Linear calibration with two reference substances（LCTRS） and quantitative analysis of multi-components by single-marker（QAMS） were established for the common peaks in the specific chromatograms to determine the contents of main components in the decoction pieces， standard decoctions and dispensing granules， and to calculate their transfer rates from decoction pieces to standard decoctions and dispensing granules. ResultsThe similarities of specific chromatograms of 15 batches of Sennae Folium（C. angustifolia） standard decoctions and 10 batches of Sennae Folium（C. angustifolia） dispensing granules were all greater than 0.95， and a total of 8 characteristic peaks were calibrated， and five of them were identified， including kaempferol-3，7-O-diglucoside， apigenin-6，8-di-C-glucoside， quercetin-3-O-gentianoside， sennoside B and sennoside A. HCA and PCA results showed that there were certain differences in the composition of different batches of standard decoctions， but no clustering was observed in the production area. As the standard decoctions， the extract rate of 15 batches of samples was 26.54%-45.38%， the contents of kaempferol-3，7-O-diglucoside， apigenin-6，8-di-C-glucoside， quercetin-3-O-gentianoside， sennoside B and sennoside A were 12.16-19.26， 2.57-4.94， 3.27-5.11， 6.75-11.39， 4.69-7.79 mg·g^-1， and their transfer rates from decoction pieces to standard decoctions were 45.41%-79.02%， 29.12%-55.07%， 40.52%-67.90%， 24.72%-49.12%， 27.54%-49.34%， respectively. The extract rates of Sennae Folium（C. angustifolia） dispensing granules（C8-C10） were 38.10%-39.50%， the transfer rates of the above five components from decoction pieces to dispensing granules were 72.85%-73.58%， 53.43%-53.94%， 40.19%-40.74%， 24.62%-25.00%， 28.65%-29.11%， respectively， which were generally consistent with the transfer rates from decoction pieces to standard decoctions. ConclusionCompared with the relative retention time method， LCTRS has higher prediction accuracy and is more suitable for chromatographic columns. The established quality control standard of Sennae Folium（C. angustifolia） dispensing granules based on standard decoction is reasonable and reliable， and all indicators of samples from different manufacturers are within the range specified based on the standard decoction， which can provide reference for the quality control and process research of this dispensing granules.

Animal experiments are widely used in biomedical research for safety assessment, toxicological analysis, efficacy evaluation, and mechanism exploration. In recent years, the ethical review system has become more stringent, and awareness of animal welfare has continuously increased. To promote more efficient and cost-effective drug research and development, the United States passed the Food and Drug Administration (FDA) Modernization Act 2.0 in September 2022, which removed the federal mandate requiring animal testing in preclinical drug research. In April 2025, the FDA further proposed to adopt a series of "new alternative methods" in the research and development of drugs such as monoclonal antibodies, which included artificial intelligence computing models, organoid toxicity tests, and 3D micro-physiological systems, thereby gradually phasing out traditional animal experiment models. Among these cutting-edge technologies, 3D bioprinting models are a significant alternative and complement to animal models, owing to their high biomimetic properties, reproducibility, and scalability. This review provides a comprehensive overview of advancements and applications of 3D bioprinting technology in the fields of biomedical and pharmaceutical research. It starts by detailing the essential elements of 3D bioprinting, including the selection and functional design of biomaterials, along with an explanation of the principles and characteristics of various printing strategies, highlighting the advantages in constructing complex multicellular spatial structures, regulating microenvironments, and guiding cell fate. It then discusses the typical applications of 3D bioprinting in drug research and development,including high-throughput screening of drug efficacy by constructing disease models such as tumors, infectious diseases, and rare diseases, as well as conducting drug toxicology research by building organ-specific models such as those of liver and heart. Additionally,the review examines the role of 3D bioprinting in tissue engineering, discussing its contributions to the construction of functional tissues such as bone, cartilage, skin, and blood vessels, as well as the latest progress in regeneration and replacement. Furthermore, this review analyzes the complementary advantages of 3D bioprinting models and animal models in the research of disease progression, drug mechanisms, precision medicine, drug development, and tissue regeneration, and discusses the potential and challenges of their integration in improving model accuracy and physiological relevance. In conclusion, as a cutting-edge in vitro modeling and manufacturing technology, 3D bioprinting is gradually establishing a comprehensive application system covering disease modeling, drug screening, toxicity prediction, and tissue regeneration.

Animal experiments are widely used in biomedical research for safety assessment, toxicological analysis, efficacy evaluation, and mechanism exploration. In recent years, the ethical review system has become more stringent, and awareness of animal welfare has continuously increased. To promote more efficient and cost-effective drug research and development, the United States passed the Food and Drug Administration (FDA) Modernization Act 2.0 in September 2022, which removed the federal mandate requiring animal testing in preclinical drug research. In April 2025, the FDA further proposed to adopt a series of "new alternative methods" in the research and development of drugs such as monoclonal antibodies, which included artificial intelligence computing models, organoid toxicity tests, and 3D micro-physiological systems, thereby gradually phasing out traditional animal experiment models. Among these cutting-edge technologies, 3D bioprinting models are a significant alternative and complement to animal models, owing to their high biomimetic properties, reproducibility, and scalability. This review provides a comprehensive overview of advancements and applications of 3D bioprinting technology in the fields of biomedical and pharmaceutical research. It starts by detailing the essential elements of 3D bioprinting, including the selection and functional design of biomaterials, along with an explanation of the principles and characteristics of various printing strategies, highlighting the advantages in constructing complex multicellular spatial structures, regulating microenvironments, and guiding cell fate. It then discusses the typical applications of 3D bioprinting in drug research and development,including high-throughput screening of drug efficacy by constructing disease models such as tumors, infectious diseases, and rare diseases, as well as conducting drug toxicology research by building organ-specific models such as those of liver and heart. Additionally,the review examines the role of 3D bioprinting in tissue engineering, discussing its contributions to the construction of functional tissues such as bone, cartilage, skin, and blood vessels, as well as the latest progress in regeneration and replacement. Furthermore, this review analyzes the complementary advantages of 3D bioprinting models and animal models in the research of disease progression, drug mechanisms, precision medicine, drug development, and tissue regeneration, and discusses the potential and challenges of their integration in improving model accuracy and physiological relevance. In conclusion, as a cutting-edge in vitro modeling and manufacturing technology, 3D bioprinting is gradually establishing a comprehensive application system covering disease modeling, drug screening, toxicity prediction, and tissue regeneration.

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

BACKGROUND: Over 20 million colonoscopies are performed in China annually. An automatic clinical decision support system (CDSS) with accurate semantic recognition of colonoscopy reports and guideline-based is helpful to relieve the increasing medical burden and standardize the healthcare. In this study, the CDSS was built under a hierarchical-label interpretable classification framework, trained by a state-of-the-art transformer-based model, and validated in a multi-center style. METHODS: We conducted stratified sampling on a previously established dataset containing 302,965 electronic colonoscopy reports with pathology, identified 2041 patients' records representative of overall features, and randomly divided into the training and testing sets (7:3). A total of five main labels and 22 sublabels were applied to annotate each record on a network platform, and the data were trained respectively by three pre-training models on Chinese corpus website, including bidirectional encoder representations from transformers (BERT)-base-Chinese (BC), the BERT-wwm-ext-Chinese (BWEC), and ernie-3.0-base-zh (E3BZ). The performance of trained models was subsequently compared with a randomly initialized model, and the preferred model was selected. Model fine-tuning was applied to further enhance the capacity. The system was validated in five other hospitals with 3177 consecutive colonoscopy cases. RESULTS: The E3BZ pre-trained model exhibited the best performance, with a 90.18% accuracy and a 69.14% Macro-F1 score overall. The model achieved 100% accuracy in identifying cancer cases and 99.16% for normal cases. In external validation, the model exhibited favorable consistency and good performance among five hospitals. CONCLUSIONS The novel CDSS possesses high-level semantic recognition of colonoscopy reports, provides appropriate recommendations, and holds the potential to be a powerful tool for physicians and patients. The hierarchical multi-label strategy and pre-training method should be amendable to manage more medical text in the future.
Humans ; Colonoscopy/methods* ; Deep Learning ; Decision Support Systems, Clinical ; Female ; Male

ObjectiveTo establish the specific chromatogram of Chuanxiong Rhizoma dispensing granules（CRdg）， and to evaluate its quality by chemometrics and two reference substances for determination of multiple components（TRSDMC）. MethodsHigh performance liquid chromatography（HPLC） specific chromatograms were established using 13 batches of CRdg from 7 manufacturers， and preliminary quality evaluation was performed by similarity evaluation and chemometrics analysis. Eight characteristic peaks in the specific chromatogram of CRdg were measured on 22 different types of C₁₈ columns， and the actual retention times were recorded. Taking chlorogenic acid（peak 1） and senkyunolide A（peak 8） as double standard compounds， the retention times of the eight characteristic peaks were predicted by linear calibration using two reference substances（LCTRS）， and the method was validated on three other columns of different brands. Taking chlorogenic acid as reference peak， the relative correction factor method（RCFM） was used to quantify cryptochlorogenic acid， caffeic acid， ferulic acid， senkyunolide I and senkyunolide A， and the results were compared with the external standard method（ESM）. ResultsThe similarities of specific chromatograms of 13 batches of CRdg were all >0.90， and a total of 8 characteristic peaks were calibrated， and six of them were identified， including chlorogenic acid（peak 1）， cryptochlorogenic acid（peak 2）， caffeic acid（peak 3）， ferulic acid（peak 5）， senkyunolide I（peak 6） and senkyunolide A（peak 8）. Through chemometric analysis， it was found that ferulic acid， chlorogenic acid， senkyunolide I and cryptochlorogenic acid were the main components causing quality difference in CRdg， and the accuracy of LCTRS in predicting the retention time of 8 characteristic peaks was superior to that of the relative retention time method（RRT）. Further comparison of the results obtained from RCFM and ESM showed that there was no statistically significant difference between the two methods. ConclusionA quality evaluation method for CRdg based on HPLC specific chromatogram and TRSDMC is established， its qualitative accuracy is better than that of RRT， the quantitative accuracy is similar to that of ESM， and 4 quality-differentiated components among different manufacturers are found. This method is stable and reliable， and has reference value for the quality evaluation of other dispensing granules.

ObjectiveTo establish the specific chromatogram of Chuanxiong Rhizoma dispensing granules（CRdg）， and to evaluate its quality by chemometrics and two reference substances for determination of multiple components（TRSDMC）. MethodsHigh performance liquid chromatography（HPLC） specific chromatograms were established using 13 batches of CRdg from 7 manufacturers， and preliminary quality evaluation was performed by similarity evaluation and chemometrics analysis. Eight characteristic peaks in the specific chromatogram of CRdg were measured on 22 different types of C₁₈ columns， and the actual retention times were recorded. Taking chlorogenic acid（peak 1） and senkyunolide A（peak 8） as double standard compounds， the retention times of the eight characteristic peaks were predicted by linear calibration using two reference substances（LCTRS）， and the method was validated on three other columns of different brands. Taking chlorogenic acid as reference peak， the relative correction factor method（RCFM） was used to quantify cryptochlorogenic acid， caffeic acid， ferulic acid， senkyunolide I and senkyunolide A， and the results were compared with the external standard method（ESM）. ResultsThe similarities of specific chromatograms of 13 batches of CRdg were all >0.90， and a total of 8 characteristic peaks were calibrated， and six of them were identified， including chlorogenic acid（peak 1）， cryptochlorogenic acid（peak 2）， caffeic acid（peak 3）， ferulic acid（peak 5）， senkyunolide I（peak 6） and senkyunolide A（peak 8）. Through chemometric analysis， it was found that ferulic acid， chlorogenic acid， senkyunolide I and cryptochlorogenic acid were the main components causing quality difference in CRdg， and the accuracy of LCTRS in predicting the retention time of 8 characteristic peaks was superior to that of the relative retention time method（RRT）. Further comparison of the results obtained from RCFM and ESM showed that there was no statistically significant difference between the two methods. ConclusionA quality evaluation method for CRdg based on HPLC specific chromatogram and TRSDMC is established， its qualitative accuracy is better than that of RRT， the quantitative accuracy is similar to that of ESM， and 4 quality-differentiated components among different manufacturers are found. This method is stable and reliable， and has reference value for the quality evaluation of other dispensing granules.

Objective: To analyze the institutional and regional differences in influenza vaccination for teachers and students, so as to provide a basis for influenza prevention and control in campus. Methods: Influenza vaccination data for teachers and students in Shandong Province from 2015 to 2024 were collected from Immunization Information Systems. Joinpoint regression models were used to calculate the annual percent change (APC) and average annual percent change (AAPC) in first-dose vaccination rates. Results: The vaccination coverages for the first dose of influenza vaccine among teachers in Shandong Province from 2015 to 2024 were 0.01%, 0.02%, 0.15%, 0.29%, 0.60%, 2.06%, 1.64%, 2.27%, 3.00%, and 2.43%, with a turning point in 2020. For preschools, primary/secondary schools, and higher education institutions, APCs during 2015-2020 were 163.36%, 162.09%, and 174.94%, respectively( P <0.01), declining to 9.53%, 9.92%, and 8.14% during 2020-2024( P >0.05). Corresponding AAPCs were 78.32%, 78.13%, and 81.61%( P <0.01). High, middle, and low GDP regions exhibited APCs of 173.84%, 162.75%, and 136.67% during 2015-2020( P <0.01) and 5.77%, 13.92%, and 11.86% during 2020-2024( P >0.05), with AAPCs of 79.42%, 81.23% , and 69.63%( P <0.01), respectively. Among students, vaccination coverage ranged from 1.59% to 7.20%. Preschool students showed no turning points, with an AAPC of 18.73%( P <0.01). Primary/secondary students had turning points in 2018 and 2022, APCs were -32.59% during 2015-2018( P <0.01), 48.45% during 2018-2022( P <0.01), and -26.25% during 2022-2024( P =0.04), yielding an AAPC of -2.32%( P =0.45). Higher education students had a turning point in 2020, with APCs of 63.27% during 2015-2020( P <0.01) and 4.31% during 2020-2024 ( P =0.77), and the AAPC was 33.79% during 2015-2024 ( P <0.01). High, middle, and low GDP regions for students showed no turning points, with AAPCs of 10.46%( P =0.18), 13.67%( P =0.01), and 10.42%( P = 0.05 ), respectively. Conclusions The influenza vaccination rate among teachers and students in Shandong Province has shown an upward trend from 2015 to 2024, but the overall level is still relatively low. However, continued efforts are needed to enhance health education on influenza and vaccination, to further improve the vaccination rate of teachers and students.