To develop the Pharmacovigilance Guidelines for Clinical Application of Chinese Patent Medicines for Mucosal Administration in response to common problems， including insufficient safety information in package inserts， amplified medication risks in special populations， and non-standard clinical practices， thus establishing a risk management system tailored to the characteristics of Chinese patent medicines for mucosal administration. An approach combining qualitative and quantitative methods was adopted. In accordance with the Drug Administration Law of the People's Republic of China （2019 revision） and the GB/T 1.1—2020 standard， a systematic search was performed in the Chinese Pharmacopoeia （2020 edition）， the Catalog of Medicines Covered by Medical Insurance （2022 edition）， Chinese databases ［China Network of Knowledge Infrastructure （CNKI）， Wanfang Data （Wanfang）， and VIP journal resource integration service platform （VIP）］， and international databases （Cochrane Library， PubMed， and EMbase）. Guideline outlines were developed through questionnaire surveys， expert interviews， and the nominal group technique. The content of each item was formulated with full consideration of traditional Chinese medicine （TCM） incompatibility， as well as the conceptual connotations and extensions of pharmacovigilance. The results included 54 Chinese patent medicines for mucosal administration from the Chinese Pharmacopoeia （2020 edition） and 58 from the Catalog of Medicines Covered by Medical Insurance （2022 edition）. Safety-related items in the corresponding package inserts were collected， and 27 relevant publications were retrieved. Thirty experts from 24 institutions were mobilized for the drafting， and opinions from 61 external experts were solicited. A pharmacovigilance framework was established， covering the full chain of "monitoring， identification， assessment， and control". Based on seven anatomical sites， including nasal， ocular， and oral mucosa， a stratified monitoring system was constructed. The guideline proposed key recommendations on improving package insert sections such as "Adverse Reactions"， "Contraindications"， and "Precautions"， clinical procedure standardization in healthcare institutions， risk control， and dynamic pharmacovigilance. The Guideline provides evidence-based support tailored to the risk profile of Chinese patent medicines for mucosal administration， filling the current gap in international pharmacovigilance standards in this field， while offering technical support for safety management across the full life cycle of medicines for mucosal administration.

To develop the Pharmacovigilance Guidelines for Clinical Application of Chinese Patent Medicines for Mucosal Administration in response to common problems， including insufficient safety information in package inserts， amplified medication risks in special populations， and non-standard clinical practices， thus establishing a risk management system tailored to the characteristics of Chinese patent medicines for mucosal administration. An approach combining qualitative and quantitative methods was adopted. In accordance with the Drug Administration Law of the People's Republic of China （2019 revision） and the GB/T 1.1—2020 standard， a systematic search was performed in the Chinese Pharmacopoeia （2020 edition）， the Catalog of Medicines Covered by Medical Insurance （2022 edition）， Chinese databases ［China Network of Knowledge Infrastructure （CNKI）， Wanfang Data （Wanfang）， and VIP journal resource integration service platform （VIP）］， and international databases （Cochrane Library， PubMed， and EMbase）. Guideline outlines were developed through questionnaire surveys， expert interviews， and the nominal group technique. The content of each item was formulated with full consideration of traditional Chinese medicine （TCM） incompatibility， as well as the conceptual connotations and extensions of pharmacovigilance. The results included 54 Chinese patent medicines for mucosal administration from the Chinese Pharmacopoeia （2020 edition） and 58 from the Catalog of Medicines Covered by Medical Insurance （2022 edition）. Safety-related items in the corresponding package inserts were collected， and 27 relevant publications were retrieved. Thirty experts from 24 institutions were mobilized for the drafting， and opinions from 61 external experts were solicited. A pharmacovigilance framework was established， covering the full chain of "monitoring， identification， assessment， and control". Based on seven anatomical sites， including nasal， ocular， and oral mucosa， a stratified monitoring system was constructed. The guideline proposed key recommendations on improving package insert sections such as "Adverse Reactions"， "Contraindications"， and "Precautions"， clinical procedure standardization in healthcare institutions， risk control， and dynamic pharmacovigilance. The Guideline provides evidence-based support tailored to the risk profile of Chinese patent medicines for mucosal administration， filling the current gap in international pharmacovigilance standards in this field， while offering technical support for safety management across the full life cycle of medicines for mucosal administration.

Objective To investigate the relationship between brain white matter lesions (WML) and triglyceride glucose (TyG) index in non-diabetic elderly individuals based on magnetic resonance imaging. Methods A total of 523 non-diabetic elderly individuals aged ≥ 60 years were selected from Jinan, Shandong Province, China from June 2018 to December 2019. According to the quartiles of TyG index, there were 133 participants in the first quartile (Q1) group, 127 in the second quartile (Q2) group, 132 in the third quartile (Q3) group, and 131 in the fourth quartile (Q4) group. All participants underwent brain magnetic resonance imaging to evaluate paraventricular, deep, and total WML volumes, as well as Fazekas scores. Results Compared with Q1, Q2, and Q3 groups, Q4 group showed significant increase in periventricular, deep, and total WML volumes (P < 0.05). The proportion of participants with a Fazekas score ≥ 2 in the periventricular, deep, and total WML was higher in the Q4 group compared with the Q1 and Q2 groups (P < 0.05). The proportion of participants with a Fazekas score ≥ 2 in deep WML was higher in Q4 group than in Q3 group (P < 0.05). TyG index was significantly positively correlated with periventricular, deep, and total WML volumes (r = 0.401, 0.405, and 0.445, P < 0.001). After adjusting for confounding factors, TyG index was still significantly positively correlated with periventricular, deep, and total WML volumes (P < 0.001). Logistic regression analysis showed that compared with Q1 group, the risk of Fazekas score ≥ 2 in periventricular WML was 1.950-fold (95% confidence interval [CI]: 1.154-3.294, P = 0.013) in Q3 group and 3.411-fold (95% CI: 1.984-5.863, P < 0.001) in Q4 group, the risk of Fazekas score ≥ 2 in total WML was 2.529-fold (95%CI: 1.444-4.430, P = 0.001) in Q3 group and 4.486-fold (95%CI: 2.314-8.696, P < 0.001) in Q4 group. The risk of Fazekas score ≥ 2 in deep WML was 2.953-fold (95%CI: 1.708-5.106, P < 0.001) in Q4 group compared with Q1 group. Conclusion Increased TyG index is an independent risk factor for WML in non-diabetic elderly individuals.

Membrane proteins are integral components of cellular membranes, accounting for approximately 30% of the mammalian proteome and serving as targets for 60% of FDA-approved drugs. They are critical to both physiological functions and disease mechanisms. Their functional protein-protein interactions form the basis for many physiological processes, such as signal transduction, material transport, and cell communication. Membrane protein interactions are characterized by membrane environment dependence, spatial asymmetry, weak interaction strength, high dynamics, and a variety of interaction sites. Therefore, in situ analysis is essential for revealing the structural basis and kinetics of these proteins. This paper introduces currently available in situ analytical techniques for studying membrane protein interactions and evaluates the characteristics of each. These techniques are divided into two categories: label-based techniques (e.g., co-immunoprecipitation, proximity ligation assay, bimolecular fluorescence complementation, resonance energy transfer, and proximity labeling) and label-free techniques (e.g., cryo-electron tomography, in situ cross-linking mass spectrometry, Raman spectroscopy, electron paramagnetic resonance, nuclear magnetic resonance, and structure prediction tools). Each technique is critically assessed in terms of its historical development, strengths, and limitations. Based on the authors’ relevant research, the paper further discusses the key issues and trends in the application of these techniques, providing valuable references for the field of membrane protein research. Label-based techniques rely on molecular tags or antibodies to detect proximity or interactions, offering high specificity and adaptability for dynamic studies. For instance, proximity ligation assay combines the specificity of antibodies with the sensitivity of PCR amplification, while proximity labeling enables spatial mapping of interactomes. Conversely, label-free techniques, such as cryo-electron tomography, provide near-native structural insights, and Raman spectroscopy directly probes molecular interactions without perturbing the membrane environment. Despite advancements, these methods face several universal challenges: (1) indirect detection, relying on proximity or tagged proxies rather than direct interaction measurement; (2) limited capacity for continuous dynamic monitoring in live cells; and (3) potential artificial influences introduced by labeling or sample preparation, which may alter native conformations. Emerging trends emphasize the multimodal integration of complementary techniques to overcome individual limitations. For example, combining in situ cross-linking mass spectrometry with proximity labeling enhances both spatial resolution and interaction coverage, enabling high-throughput subcellular interactome mapping. Similarly, coupling fluorescence resonance energy transfer with nuclear magnetic resonance and artificial intelligence (AI) simulations integrates dynamic structural data, atomic-level details, and predictive modeling for holistic insights. Advances in AI, exemplified by AlphaFold’s ability to predict interaction interfaces, further augment experimental data, accelerating structure-function analyses. Future developments in cryo-electron microscopy, super-resolution imaging, and machine learning are poised to refine spatiotemporal resolution and scalability. In conclusion, in situ analysis of membrane protein interactions remains indispensable for deciphering their roles in health and disease. While current technologies have significantly advanced our understanding, persistent gaps highlight the need for innovative, integrative approaches. By synergizing experimental and computational tools, researchers can achieve multiscale, real-time, and perturbation-free analyses, ultimately unraveling the dynamic complexity of membrane protein networks and driving therapeutic discovery.

Objective To analyze the risk factors associated with the occurrence of rectal neuroendocrine tumors (RNETs) and construct a risk prediction model. Methods Clinical data of patients who underwent electronic colonoscopy were collected. The clinical information on patients with and without RNETs were compared, and potential risk factors for RNETs were identified. Binary logistic regression was performed to analyze the relevant risk factors and construct a risk prediction model. Results Among 164 patients, 66 were diagnosed with RNETs, and 98 who did not have such a condition were randomly selected. Univariate logistic regression analysis revealed that age, fatty liver, anxiety and depression, total cholesterol, triglyceride levels, and carcinoembryonic antigen (CEA) were significant factors influencing the occurrence of RNETs (P<0.05). Multivariate logistic regression analysis identified age (P=0.015), anxiety and depression (P=0.031), cholesterol level (P=0.009), fatty liver (P=0.001), and CEA (P<0.001) as independent risk factors for RNETs. The participants were randomly divided into training and test sets at a 7:3 ratio. The training set was used to construct a nomogram-based risk prediction model, and the testing set was used for internal validation. The area under the curve values for the training and testing sets were 0.843 and 0.772, respectively (P>0.05). These findings indicate a good discriminative performance. The calibration curves for the training and testing sets were in good agreement with the 45° standard line, which suggests that the predicted probabilities were consistent with the actual outcomes. Decision curve analysis showed that the model provided a high net benefit within a threshold range of 0.2 to 0.7 for clinical decision making. Conclusion Young age, fatty liver, high CEA levels, high cholesterol levels, and anxiety and depression are independent risk factors for RNETs. The nomogram model constructed based on these risk factors exhibits a strong capability to predict the occurrence of RNETs, and clinical intervention can be considered based on the predicted probability values.

ObjectiveTo investigate the quality markers of Xiaoqinglong granules（XQLG） for treating bronchial asthma using the analytic hierarchy process（AHP）-entropy weight method（EWM）， network pharmacology and high performance liquid chromatography（HPLC） content determination. MethodsEffectiveness， testability and peculiarity component data of XQLG in treating bronchial asthma were constructed through database retrieval， literature review， and network pharmacology. Subsequently， AHP-EWM was used to quantitatively identify and weight the control layer and element layer， the relevant compounds were selected as candidate quality markers based on comprehensive scores. Further comparison of reference substances and establishment of HPLC content determination method were used to determine the potential quality markers of XQLG， which were verified by molecular docking with disease targets. ResultsA total of 13 components， including glycyrrhizic acid， paeoniflorin， schisandrol A， isoliquiritigenin， 6-gingerol， ephedrine， liquiritin， albiflorin， liquiritigenin， 6-shogaol， pseudoephedrine， cinnamic acid and cinnamaldehyde， were identified as potential quality markers of XQLG by AHP-EWM. Quantitative analysis indicated that all aforementioned quality markers could be detected in 13 batches of XQLG， indicating that it had stable testability as a quality marker. Among these 13 batches of samples， ephedrine and paeoniflorin exhibited good consistency in content， while pseudoephedrine and cinnamaldehyde showed poor consistency. Molecular docking analysis revealed that the 13 compounds exhibited binding energies with the core targets -2.11 kcal·mol^-1， indicating that the 13 compounds could spontaneously bind to the disease targets， which may be the material basis for the treatment of bronchial asthma with XQLG. ConclusionIn this study， 13 compounds were screened by AHP-EWM combined with network pharmacology and HPLC as quality markers for the treatment of bronchial asthma by XQLG， laying the foundation for enhancing the quality standards of this preparation.

In this study, potential quality markers(Q-markers) of Schisandrae Chinensis Fructus for treating bronchial asthma were predicted based on analytic hierarchy process(AHP), entropy weight method(EWM), fingerprint, and network pharmacology. AHPEWM was employed to quantitatively identify the Q-markers of Schisandrae Chinensis Fructus. AHP was used to weight the primary indicators(effectiveness, measurability, and specificity), while EWM was employed to analyze the secondary indicators of each primer indicator. Further, through fingerprint combined with network pharmacology, a ″component-target-pathway″ network was constructed to screen the components of Schisandrae Chinensis Fructus for treating bronchial asthma. It was finally determined that schisandrol A,schisandrin A, and schisandrin B were potential Q-markers of Schisandrae Chinensis Fructus in the treatment of bronchial asthma. This study is the first to comprehensively use AHP-EWM, fingerprint, and network pharmacology to screen the key Q-markers of Schisandrae Chinensis Fructus in the treatment of bronchial asthma. This study provides a scientific basis for improving the quality standard of Schisandrae Chinensis Fructus and lays a foundation for studying its material basis in treating bronchial asthma.
Schisandra/chemistry* ; Asthma/drug therapy* ; Drugs, Chinese Herbal/therapeutic use* ; Network Pharmacology ; Humans ; Entropy ; Lignans/analysis* ; Fruit/chemistry* ; Quality Control ; Cyclooctanes ; Polycyclic Compounds/analysis*

This study aims to explore the impact of host plants on the quality of Taxilli Herba and provide a theoretical basis for the quality control of Taxilli Herba. The components of Taxilli Herba from three different host plants(Morus alba, Salix babylonica, and Cinnamomum cassia) and its 3 hosts(mulberry branch, willow branch, and cinnamon branch) were detected by widely targeted metabolomics based on ultra-high performance liquid chromatography-tandem mass spectrometry(UPLC-MS/MS). Principal component analysis(PCA), orthogonal partial least squares discriminant analysis(OPLS-DA), and Venn diagram were employed for analysis. A total of 717 metabolites were detected in Taxilli Herba from the three host plants and the branches of these host plants by UPLC-MS/MS. The results of PCA and OPLS-DA of Taxilli Herba from the three different host plants showed an obvious separation trend due to the different effects of host plants. The Venn diagram showed that there were 32, 8, and 26 characteristic metabolites in samples of Taxilli Herba from M. alba host, S. babylonica host, and C. cassia host, respectively. It was found by comparing the characteristic metabolites of Taxilli Herba and its hosts that each host transmits its characteristic components to Taxilli Herba, so that the Taxilli Herba contains the characteristic components of the host. The Kyoto Encyclopedia of Genes and Genomes(KEGG) pathway analysis showed that the differential metabolites of Taxilli Herba from the three hosts were mainly enriched in flavonoid biosynthesis, arginine and proline metabolism, and glycolysis/gluconeogenesis pathways. Furthermore, the differential metabolites enriching pathways of Taxilli Herba from the three hosts were different depending on the host. In a word, host plants have a significant impact on the metabolites of Taxilli Herba, and it may be an important factor for the quality of Taxilli Herba.
Metabolomics/methods* ; Drugs, Chinese Herbal/chemistry* ; Chromatography, High Pressure Liquid ; Tandem Mass Spectrometry ; Quality Control ; Salix/chemistry* ; Cinnamomum aromaticum/metabolism* ; Principal Component Analysis

Testicular histology based on testicular biopsy is an important factor for determining appropriate testicular sperm extraction surgery and predicting sperm retrieval outcomes in patients with azoospermia. Therefore, we developed a deep learning (DL) model to establish the associations between testicular grayscale ultrasound images and testicular histology. We retrospectively included two-dimensional testicular grayscale ultrasound from patients with azoospermia (353 men with 4357 images between July 2017 and December 2021 in The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China) to develop a DL model. We obtained testicular histology during conventional testicular sperm extraction. Our DL model was trained based on ultrasound images or fusion data (ultrasound images fused with the corresponding testicular volume) to distinguish spermatozoa presence in pathology (SPP) and spermatozoa absence in pathology (SAP) and to classify maturation arrest (MA) and Sertoli cell-only syndrome (SCOS) in patients with SAP. Areas under the receiver operating characteristic curve (AUCs), accuracy, sensitivity, and specificity were used to analyze model performance. DL based on images achieved an AUC of 0.922 (95% confidence interval [CI]: 0.908-0.935), a sensitivity of 80.9%, a specificity of 84.6%, and an accuracy of 83.5% in predicting SPP (including normal spermatogenesis and hypospermatogenesis) and SAP (including MA and SCOS). In the identification of SCOS and MA, DL on fusion data yielded better diagnostic performance with an AUC of 0.979 (95% CI: 0.969-0.989), a sensitivity of 89.7%, a specificity of 97.1%, and an accuracy of 92.1%. Our study provides a noninvasive method to predict testicular histology for patients with azoospermia, which would avoid unnecessary testicular biopsy.
Humans ; Male ; Azoospermia/diagnostic imaging* ; Deep Learning ; Testis/pathology* ; Retrospective Studies ; Adult ; Ultrasonography/methods* ; Sperm Retrieval ; Sertoli Cell-Only Syndrome/diagnostic imaging*