ObjectiveTo establish an ultra-performance liquid fingerprint and multi-components determination method for Naomaili granules. To evaluate the quality of different batches by chemometrics， and the anti-neuroinflammatory effects of water extract and main components of Naomaili granules were tested in vitro. MethodsThe similarity and common peaks of 27 batches of Naomaili granules were evaluated by using Ultra performance liquid chromatography （UPLC） fingerprint detection. Ultra-performance liquid chromatography-tandem mass spectrometry （UPLC-MS/MS） technology was used to determine the content of the index components in Naomaili granules and to evaluate the quality of different batches of Naomaili granules by chemometrics. LPS-induced BV-2 cell inflammation model was used to investigate the anti-neuroinflammatory effects of the water extract and main components of Naomaili granules. ResultsThe similarity of fingerprints of 27 batches of samples was > 0.90. A total of 32 common peaks were calibrated， and 23 of them were identified and assigned. In 27 batches of Naomaili granules， the mass fractions of 14 components that were stachydrine hydrochloride， leonurine hydrochloride， calycosin-7-O-glucoside， calycosin，tanshinoneⅠ， cryptotanshinone， tanshinoneⅡ_A， ginsenoside Rb₁， notoginsenoside R₁， ginsenoside Rg₁， paeoniflorin， albiflorin， lactiflorin， and salvianolic acid B were found to be 2.902-3.498， 0.233-0.343， 0.111-0.301， 0.07-0.152， 0.136-0.228， 0.195-0.390， 0.324-0.482， 1.056-1.435， 0.271-0.397， 1.318-1.649， 3.038-4.059， 2.263-3.455， 0.152-0.232， 2.931-3.991 mg∙g^-1， respectively. Multivariate statistical analysis showed that paeoniflorin， ginsenoside Rg₁， ginsenoside Rb₁ and staphylline hydrochloride were quality difference markers to control the stability of the preparation. The results of bioactive experiment showed that the water extract of Naomaili granules and the eight main components with high content in the prescription had a dose-dependent inhibitory effect on the release of NO in the cell supernatant. Among them， salvianolic acid B and ginsenoside Rb₁ had strong anti-inflammatory activity， with IC₅₀ values of （36.11±0.15） mg∙L^-1 and （27.24±0.54） mg∙L^-1， respectively. ConclusionThe quality evaluation method of Naomaili granules established in this study was accurate and reproducible. Four quality difference markers were screened out， and eight key pharmacodynamic substances of Naomaili granules against neuroinflammation were screened out by in vitro cell experiments.

Objective: To report the antibody identification, blood management during pregnancy and the monitoring process of fetal hemolytic disease of fetus and newborn (HDFN) in a pregnant woman with a history of blood transfusion and pregnancy who developed anti-Jr . Methods: Saline tube technique and anti-human globulin technique were used for maternal blood typing, unexpected antibody screening and identification, as well as for determining antibody titer and IgG subclasses. PCR-SSP was employed for genotyping of 18 blood group systems. Next-generation sequencing (NGS) was utilized for gene sequencing of 38 blood group systems. Sanger sequencing was applied to verify rare blood group mutations detected by NGS and to investigate the corresponding rare blood group genes in family members. Blood preparation was achieved through anemia management in prenatal clinics and autologous blood collection during pregnancy. The newborn underwent the three primary tests for HDFN and plasma IgG subclass testing. Results: The pregnant woman's blood type was B, RhD positive, with a positive unexpected antibody screen, and the antibody identification pattern was consistent with a high-frequency antigen antibody. Gene sequencing revealed a homozygous ABCG2 c.376C>T mutation in the woman, resulting in the Jr(a-) phenotype, and anti-Jr antibody was present in her plasma. No compatible Jr(a-) blood was found among family members. The maternal anti-Jr IgG titer remained stable at 256 during pregnancy, with no detectable IgG1 or IgG3 subclasses against the Jr antigen. A total of 800 mL of autologous blood was collected in two stages during pregnancy. The newborn was B, RhD positive, Jr(a+), with a positive unexpected antibody screen (anti-Jr ). IgG subclass typing detected no IgG1 or IgG3. The direct antiglobulin test was positive, while the acid elution test was negative. Conclusion: The combination of serology and blood group genetic analysis provides a diagnostic basis for identifying antibodies to high-frequency antigens. Managing perinatal anemia and implementing staged autologous blood storage can secure blood supply for the perioperative period. IgG antibody subclass typing offers a reference for clinical assessment and prevention of HDFN.

ObjectiveTo establish a model that can quickly identify the aroma components in different parts of Nardostachys jatamansi， so as to provide a quality control basis for the market circulation and clinical use of N. jatamansi. MethodsHeadspace solid-phase microextraction-gas chromatography-mass spectrometry（HS-SPME-GC-MS） combined with Smart aroma database and National Institute of Standards and Technology（NIST） database were used to characterize the aroma components in different parts of N. jatamansi， and the aroma components were quantified according to relative response factor（RRF） and three internal standards， and the markers of aroma differences in different parts of N. jatamansi were identified by orthogonal partial least squares-discriminant analysis（OPLS-DA） and cluster thermal analysis based on variable importance in the projection（VIP） value >1 and P<0.01. The odor data of different parts of N. jatamansi were collected by Heracles Ⅱ Neo ultra-fast gas phase electronic nose， and the correlation between compound types of aroma components collected by the ultra-fast gas phase electronic nose and the detection results of HS-SPME-GC-MS was investigated by drawing odor fingerprints and odor response radargrams. Chromatographic peak information with distinguishing ability≥0.700 and peak area≥200 was selected as sensor data， and the rapid identification model of different parts of N. jatamansi was established by principal component analysis（PCA）， discriminant factor alysis（DFA）， soft independent modeling of class analogies（SIMCA） and statistical quality control analysis（SQCA）. ResultsThe HS-SPME-GC-MS results showed that there were 28 common components in the underground and aboveground parts of N. jatamansi， of which 22 could be quantified and 12 significantly different components were screened out. Among these 12 components， the contents of five components（ethyl isovalerate， 2-pentylfuran， benzyl alcohol， nonanal and glacial acetic acid，） in the aboveground part of N. jatamansi were significantly higher than those in the underground part（P<0.01）， the contents of β-ionone， patchouli alcohol， α-caryophyllene， linalyl butyrate， valencene， 1，8-cineole and p-cymene in the underground part of N. jatamansi were significantly higher than those in the aboveground part（P<0.01）. Heracles Ⅱ Neo electronic nose results showed that the PCA discrimination index of the underground and aboveground parts of N. jatamansi was 82， and the contribution rates of the principal component factors were 99.94% and 99.89% when 2 and 3 principal components were extracted， respectively. The contribution rate of the discriminant factor 1 of the DFA model constructed on the basis of PCA was 100%， the validation score of the SIMCA model for discrimination of the two parts was 99， and SQCA could clearly distinguish different parts of N. jatamansi. ConclusionHS-SPME-GC-MS can clarify the differential markers of underground and aboveground parts of N. jatamansi. The four analytical models provided by Heracles Ⅱ Neo electronic nose（PCA， DFA， SIMCA and SQCA） can realize the rapid identification of different parts of N. jatamansi. Combining the two results， it is speculated that terpenes and carboxylic acids may be the main factors contributing to the difference in aroma between the underground and aboveground parts of N. jatamansi.

The Pharmacopeia of the People’s Republic of China 2025 Edition (referred to as the Chinese Pharmacopoeia 2025 Edition, ChP 2025) will be promulgated and implemented. This article introduces the process of development of ChP 2025 Edition (Volume Ⅱ), including the selection, the revision of general notices,the addition and revision of drug monographs, etc., and provides some analysis and examples to illustrate,which can facilitate the readers to understand and implement the ChP 2025 Edition (Volume Ⅱ).

Objective: Negative symptoms in schizophrenia indicate a poor prognosis. However, the mechanisms underlying the development of negative symptoms remain unclear. This study investigated the relationship between negative symptoms in schizophrenia and frontal alpha asymmetry (FAA). Methods: The study used a 32-channel electroencephalography to acquire alpha power in 4 target-paired sites in each patient. Regional alpha asymmetry was calculated based on the alpha power using EEGLAB Frontal Alpha Asymmetry Toolbox. Results: Sixty schizophrenia patients with predominant negative symptoms (PNS), 72 stabilized schizophrenia (SS) patients, and 73 healthy control (HC) participants were enrolled in this study. No significant differences were observed in FAA between the PNS and SS groups, although both groups exhibited reduced P3-P4 alpha asymmetry compared to HCs. A positive correlation was found between F7-F8 alpha asymmetry and illness duration. Additionally, a predictive model based on P3-P4 alpha asymmetry scores was able to differentiate schizophrenia patients from HCs, achieving a sensitivity of 71.2% and a specificity of 72.6%. Conclusion This study highlighted that parietal alpha asymmetry could serve as a valuable diagnostic tool for schizophrenia.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Negative symptoms in schizophrenia indicate a poor prognosis. However, the mechanisms underlying the development of negative symptoms remain unclear. This study investigated the relationship between negative symptoms in schizophrenia and frontal alpha asymmetry (FAA). Methods: The study used a 32-channel electroencephalography to acquire alpha power in 4 target-paired sites in each patient. Regional alpha asymmetry was calculated based on the alpha power using EEGLAB Frontal Alpha Asymmetry Toolbox. Results: Sixty schizophrenia patients with predominant negative symptoms (PNS), 72 stabilized schizophrenia (SS) patients, and 73 healthy control (HC) participants were enrolled in this study. No significant differences were observed in FAA between the PNS and SS groups, although both groups exhibited reduced P3-P4 alpha asymmetry compared to HCs. A positive correlation was found between F7-F8 alpha asymmetry and illness duration. Additionally, a predictive model based on P3-P4 alpha asymmetry scores was able to differentiate schizophrenia patients from HCs, achieving a sensitivity of 71.2% and a specificity of 72.6%. Conclusion This study highlighted that parietal alpha asymmetry could serve as a valuable diagnostic tool for schizophrenia.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

This study aimed to explore the effects and mechanisms of total extracts from Anthriscus sylvestris on pulmonary hypertension in rats. Sixty male SD rats were divided into normal(NC) group, model(M) group, positive drug sildenafil(Y) group, low-dose A. sylvestris(ES-L) group, medium-dose A. sylvestris(ES-M) group, and high-dose A. sylvestris(ES-H) group. On day 1, rats were intraperitoneally injected with monocrotaline(60 mg·kg~(-1)) to induce pulmonary hypertension, and the rat model was established on day 28. From days 15 to 28, intragastric administration of the respective treatments was performed. After modeling and treatment, small animal echocardiography was used to detect the right heart function of the rats. Arterial blood gas was measured using a blood gas analyzer. Hematoxylin and eosin(HE) staining and Masson staining were performed to observe cardiopulmonary pathological damage. Flow cytometry was used to detect apoptosis in the lung and myocardial tissues and reactive oxygen species(ROS) levels. Western blot was applied to detect the expression levels of transforming growth factor-β1(TGF-β1), phosphorylated mothers against decapentaplegic homolog 3(p-Smad3), Smad3, tissue plasminogen activator(t-PA), and plasminogen activator inhibitor-1(PAI-1) in lung tissue. A blood routine analyzer was used to measure inflammatory immune cell levels in the blood. Enzyme-linked immunosorbent assay(ELISA) was used to detect the expression levels of P-selectin and thromboxane A2(TXA2) in plasma. The results showed that, compared with the NC group, right heart hypertrophy index, right ventricular free wall thickness, right heart internal diameter, partial carbon dioxide pressure(PaCO_2), apoptosis in cardiopulmonary tissue, and ROS levels were significantly increased in the M group. In contrast, the ratio of pulmonary blood flow acceleration time(PAT)/ejection time(PET), right cardiac output, change rate of right ventricular systolic area, systolic displacement of the tricuspid ring, oxygen partial pressure(PaO_2), and blood oxygen saturation(SaO_2) were significantly decreased in the M group. After administration of the total extract of A. sylvestris, right heart function and blood gas levels were significantly improved, while apoptosis in cardiopulmonary tissue and ROS levels significantly decreased. Further testing revealed that the total extract of A. sylvestris significantly decreased the levels of interleukin-1β(IL-1β), interleukin-6(IL-6), and PAI-1 proteins in lung tissue, while increasing the expression of t-PA. Additionally, the extract reduced the levels of inflammatory cells such as leukocytes, lymphocytes, granulocytes, and monocytes in the blood, as well as the levels of P-selectin and TXA2 in plasma. Metabolomics results showed that the total extract of A. sylvestris significantly affected metabolic pathways, including arginine biosynthesis, tyrosine metabolism, and taurine and hypotaurine metabolism. In conclusion, the total extract of A. sylvestris may exert an anti-pulmonary hypertension effect by inhibiting the TGF-β1/Smad3 signaling pathway, thereby alleviating immune-inflammatory responses and thrombosis.
Animals ; Male ; Smad3 Protein/metabolism* ; Transforming Growth Factor beta1/metabolism* ; Rats, Sprague-Dawley ; Rats ; Signal Transduction/drug effects* ; Hypertension, Pulmonary/genetics* ; Thrombosis/immunology* ; Drugs, Chinese Herbal/administration & dosage* ; Humans ; Apoptosis/drug effects*

This study aims to systematically characterize the targeted effects of Quanduzhong Capsules on cartilage lesions in knee osteoarthritis by integrating spatial transcriptomics data mining and animal experiments validation, thereby elucidating the related molecular mechanisms. A knee osteoarthritis model was established using Sprague-Dawley(SD) rats, via a modified Hulth method. Hematoxylin and eosin(HE) staining was employed to detect knee osteoarthritis-associated pathological changes in knee cartilage. Candidate targets of Quanduzhong Capsules were collected from the HIT 2.0 database, followed by bioinformatics analysis of spatial transcriptomics datasets(GSE254844) from cartilage tissues in clinical knee osteoarthritis patients to identify spatially specific disease genes. Furthermore, a "formula candidate targets-spatially specific genes in cartilage lesions" interaction network was constructed to explore the effects and major mechanisms of Quanduzhong Capsules in distinct cartilage regions. Experimental validation was conducted through immunohistochemistry using animal-derived biospecimens. The results indicated that Quanduzhong Capsules effectively inhibited the degenerative changes in the cartilage of affected joints in rats, which was associated with the regulation of Quanduzhong Capsules on the thioredoxin-interacting protein(TXNIP)-NOD-like receptor family pyrin domain containing 3(NLRP3)-bone morphogenetic protein receptor type 2(BMPR2)-fibronectin 1(FN1)-matrix metallopeptidase 2(MMP2) signal axis in the articular cartilage surface and superficial zones, subsequently inhibiting cartilage matrix degradation leading to oxidative stress and inflammatory diffusion. In summary, this study clarifies the spatially specific targeted effects and protective mechanisms of Quanduzhong Capsules within pathological cartilage regions in knee osteoarthritis, providing theoretical and experimental support for the clinical application of this drug in the targeted therapy on the inflamed cartilage.
Animals ; Osteoarthritis, Knee/metabolism* ; Drugs, Chinese Herbal/administration & dosage* ; Rats, Sprague-Dawley ; Rats ; Male ; Humans ; Capsules ; Female ; Disease Models, Animal