ObjectiveTo evaluate the therapeutic effects of modified Banxia Xiexintang（MBXT） on polycystic ovary syndrome with insulin resistance（PCOS-IR） rats and reveal its potential mechanisms based on the integrated analysis of transcriptomics and metabolomics. MethodsFemale SD rats were selected， and a PCOS-IR model was established by intragastric administration of letrozole combined with a high-fat diet for 21 days. The modeled rats were randomly divided into the model group， MBXT low-， medium-， and high-dose groups（6.62， 13.23， 26.46 g·kg^-1）， and metformin group（0.158 g·kg^-1）， with a normal group set up separately. After 14 days of administration， the estrous cycle was observed， ovarian morphology was examined by hematoxylin-eosin（HE） staining， and the levels of testosterone（T）， estradiol（E₂）， follicle-stimulating hormone（FSH）， and luteinizing hormone（LH） in serum were detected by enzyme-linked immunosorbent assay（ELISA）. Serum metabolites and ovarian tissue gene expression were detected using ultra-performance liquid chromatography-quadrupole-electrostatic orbitrap mass spectrometry（UPLC-Q-Orbitrap-MS） and RNA-Seq technology， respectively， followed by multi-omics integrated analysis. ResultsPharmacodynamic findings revealed that all MBXT dose groups could reversed abnormal estrous cycles in PCOS-IR rats， improve polycystic ovarian lesions， and normalize dysregulated serum hormone levels（T， LH， E₂， FS， P<0.05， P<0.01）. Metabolomic analysis revealed that compared with the model group， MBXT reversed 278 differential metabolites such as estrone and S-formylglutathione， mainly involving pathways such as steroid hormone biosynthesis， glutathione metabolism， and lipid peroxidation regulation. Transcriptomic analysis identified 434 differentially expressed genes， and enrichment analysis revealed that MBXT significantly regulated lipid peroxidation defense systems， including glutathione metabolism， peroxisome function， and fatty acid metabolism， thereby intervening in ferroptosis processes. It also engaged in inflammation-related pathways such as the chemokine signaling pathway. Integrated analysis revealed that both metabolomics and transcriptomics co-enriched metabolic pathways associated with ferroptosis and fatty acid metabolism. And key Hub genes［such as Ras-related C3 botulinum toxin substrate 2 gene（Rac2） and Fas ligand gene（Faslg）］ showed significant correlations with differential metabolites. ConclusionMBXT can effectively ameliorate reproductive dysfunction and metabolic disorders in PCOS-IR rats. Its mechanism may be related to remodeling the immune-metabolism network， particularly by regulating MHC-mediated immune responses， inhibiting local ovarian ferroptosis， and enhancing steroid hormone synthesis pathways.

ObjectiveTo investigate the mechanism of modified Banxia Xiexintang（MBXT） in improving ovarian dysfunction in polycystic ovary syndrome with insulin resistance（PCOS-IR） rats by inhibiting ferroptosis through the adenosine monophosphate（AMP）-activated protein kinase（AMPK）/fatty acid synthase（FASN）/glutathione peroxidase 4（GPX4） signaling pathway. MethodsSeventy-six female SD rats were randomly divided into a normal group（n=13） and a modeling group（n=63）. The modeling group established a PCOS-IR model by intragastric administration of letrozole combined with a high-fat diet for 21 days. After successful modeling， these rats were randomly divided into the model group， MBXT low-， medium-， and high-dose groups（6.62， 13.23， 26.46 g·kg^-1）， metformin group（0.158 g·kg^-1）， and high-dose of MBXT combined with ferroptosis inducer Erastin group（15 mg·kg^-1）， with 10 rats in each group. After 14 days of intervention， ovarian pathological morphology was observed by hematoxylin-eosin（HE） staining， the mitochondrial ultrastructure of granulosa cells was observed by transmission electron microscopy（TEM）， ovarian reactive oxygen species（ROS） levels were detected by dihydroethidium（DHE） probe， biochemical methods were used to detect Fe²⁺， malondialdehyde（MDA）， glutathione（GSH） and other indicators in ovarian tissues， serum sex hormone and insulin levels were measured by enzyme-linked immunosorbent assay（ELISA）， and the protein expressions of AMPK， FASN， acyl-CoA synthetase long-chain family member 4（ACSL4）， GPX4， and solute carrier family 7 member 11（SLC7A11） in ovarian tissues were detected by Western blot. ResultsCompared with the normal group， the model group showed polycystic changes in the ovaries， with atrophy of mitochondria in granulosa cells and increased membrane density. Serum levels of testosterone（T）， luteinizing hormone（LH）， and insulin were significantly increased（P<0.01）. The levels of ROS， MDA， 4-hydroxynonenal（4-HNE）， and Fe²⁺ in ovarian tissues were significantly elevated（P<0.01）， while adenosine triphosphate（ATP）， GSH， and reduced nicotinamide adenine dinucleotide phosphate （NADPH） levels were significantly decreased（P<0.01）. The phosphorylation levels of AMPK and acetyl-CoA carboxylase （ACC）， as well as the protein expressions of SLC7A11， GPX4， and ferroptosis suppressor protein 1（FSP1） were significantly downregulated（P<0.01）， whereas the expressions of FASN， ACSL4， and nuclear receptor coactivator 4（NCOA4） were significantly upregulated（P<0.01）. Compared with the model group， MBXT intervention at various doses improved the above pathological changes and biochemical indicators in a dose-dependent manner， with the high-dose group showing the most significant effect（P<0.01）. Compared with the MBXT high-dose group， the high-dose of MBXT combined with ferroptosis inducer Erastin group restored ovarian ferroptosis characteristics in rats， with increased ROS and lipid peroxidation products， and altered expressions of key proteins（P<0.05， P<0.01）. ConclusionMBXT can effectively improve ovarian function and metabolic disorders in PCOS-IR rats. Its mechanism may be related to activating the AMPK/ACC signaling pathway， downregulating FASN and ACSL4 to reduce lipid peroxidation substrates， and restoring glucose-6-phosphate dehydrogenase/phosphoglycerate dehydrogenase（G6PD/PHGDH） metabolic flux to enhance the GPX4/FSP1 antioxidant defense system， thereby inhibiting ferroptosis in ovarian granulosa cells.

BACKGROUND:Curcumin is the main active ingredient of turmeric and has significant medicinal value in anti-tumor,anti-inflammatory,antioxidant and other aspects.However,its poor water solubility,unstable chemical properties and easy decomposition lead to difficulty in extracting curcumin and low extraction yield.Therefore,it is particularly important to optimize the curcumin extraction method.OBJECTIVE:To enhance the extraction yield and utilization value of curcumin and optimize the curcumin extraction process and curcumin nanoparticle preparation process.METHODS:Curcumin was extracted from turmeric by ethanol extraction,ultrasonic extraction,ionic liquid extraction,enzyme extraction,and ionic liquid combined with ultrasonic assisted enzyme extraction.The curcumin extraction yield was detected by high performance liquid chromatography;the best extraction method was determined,and subsequent process optimization experiments were carried out.The curcumin extraction yield was the response value with the type of ionic liquid,reaction temperature,ultrasonic time,liquid-to-solid ratio,ionic liquid concentration,and enzyme-drug mass ratio as parameters.The optimal production process of ionic liquid combined with ultrasonic assisted enzyme extraction was determined by single factor combined response surface experiment.The optimal process for preparing curcumin nanoparticles by ionic crosslinking method was determined by single factor combined response surface experiment with acetic acid concentration,chitosan to sodium tripolyphosphate mass ratio,stirring rate,curcumin mass concentration,sodium tripolyphosphate mass concentration,and chitosan mass concentration as parameters,and drug encapsulation efficiency as response value.Curcumin nanoparticles were prepared under the optimal process,and the particle size,polydispersity index,Zata potential value,drug loading,stability,hemolysis rate,and antioxidant capacity in vivo and in vitro of the nanoparticles were detected.RESULTS AND CONCLUSION:(1)Among the five extraction methods,the curcumin yield of ionic liquid combined with ultrasound-assisted enzyme extraction was the highest,and this method was selected as the curcumin extraction method for subsequent experiments.The results of single factor combined response surface experiment showed that the optimal process for curcumin extraction was:ionic liquid selected 1-hexyl-3-methylimidazolium chloride,reaction temperature 55 ℃,liquid-to-solid ratio 40 mL/g,ultrasound time 57 minutes,ionic liquid concentration 57％,enzyme-drug mass ratio 3.5:10,and the obtained turmeric extraction yield was 3.10％.The optimal preparation process of curcumin nanoparticles was:glacial acetic acid concentration 0.5％,chitosan and sodium tripolyphosphate mass ratio 5.0:1,stirring speed 150 r/min,curcumin mass concentration 2.23 mg/mL,sodium tripolyphosphate mass concentration 1.45 mg/mL,chitosan mass concentration 3.63 mg/mL,and the obtained drug encapsulation efficiency was 90.61％.(2)The drug loading of curcumin nanoparticles was(14.49±0.23)％,the average particle size was(76.95±1.65)nm,the polydispersity coefficient was 0.15±0.02,and the Zata potential value was(32.37±1.46)mV.The curcumin nanoparticles had good stability and blood compatibility,did not induce hemolysis,and had stronger antioxidant capacity in vivo and in vitro than free curcumin.(3)The results show that the process optimization not only solves the problems of low extraction yield,poor solubility,and low bioavailability of curcumin,but also enhances its antioxidant activity in vivo and in vitro.

Objective To explore the number of peripheral blood regulatory T cells (Treg) in patients with chronic kidney disease (CKD) and its correlation with vascular calcification. Methods This was a single-center, cross-sectional, and observational study. Non-dialysis patients with CKD treated at Zhongshan Hospital, Fudan University from March 2021 to March 2022 were enrolled. Abdominal aortic calcification (AAC) was assessed using lateral abdominal X-ray. Number of Treg and cytokine levels were measured by flow cytometry. Logistic regression analysis was performed to evaluate the related factors for AAC in CKD patients. Results A total of 83 patients were included, aged 17–86 years, with 57 males (68.7%). The distribution of CKD stages was as follows: stage G1 in 7 patients (8.4%), stage G2 in 17 patients (20.5%), stage G3 in 21 patients (25.3%), stage G4 in 19 patients (22.9%), and stage G5 in 19 patients (22.9%). No AAC was observed in patients with stages G1 and G2, while the prevalence of AAC in patients with stages G3, G4, and G5 was 23.8%, 21.1%, and 26.3%, respectively. Compared with stage G1 patients, those with stages G3–5 showed decreased number of peripheral blood Treg and elevated levels of interleukin (IL)-6 and IL-17F (P＜0.05). The area under the receiver operating characteristic curve for number of peripheral blood Treg in predicting AAC in CKD patients was 0.766 (95%CI 0.652–0.879, P＝0.002). Logistic regression analysis showed that decreased number of Treg was related factor for AAC in CKD patients (OR＝0.957, 95%CI 0.922–0.992, P＝0.018). Conclusion As CKD progresses, number of peripheral blood Treg significantly decreases, which is correlated with AAC in CKD patients.

BACKGROUND:Curcumin is the main active ingredient of turmeric and has significant medicinal value in anti-tumor,anti-inflammatory,antioxidant and other aspects.However,its poor water solubility,unstable chemical properties and easy decomposition lead to difficulty in extracting curcumin and low extraction yield.Therefore,it is particularly important to optimize the curcumin extraction method.OBJECTIVE:To enhance the extraction yield and utilization value of curcumin and optimize the curcumin extraction process and curcumin nanoparticle preparation process.METHODS:Curcumin was extracted from turmeric by ethanol extraction,ultrasonic extraction,ionic liquid extraction,enzyme extraction,and ionic liquid combined with ultrasonic assisted enzyme extraction.The curcumin extraction yield was detected by high performance liquid chromatography;the best extraction method was determined,and subsequent process optimization experiments were carried out.The curcumin extraction yield was the response value with the type of ionic liquid,reaction temperature,ultrasonic time,liquid-to-solid ratio,ionic liquid concentration,and enzyme-drug mass ratio as parameters.The optimal production process of ionic liquid combined with ultrasonic assisted enzyme extraction was determined by single factor combined response surface experiment.The optimal process for preparing curcumin nanoparticles by ionic crosslinking method was determined by single factor combined response surface experiment with acetic acid concentration,chitosan to sodium tripolyphosphate mass ratio,stirring rate,curcumin mass concentration,sodium tripolyphosphate mass concentration,and chitosan mass concentration as parameters,and drug encapsulation efficiency as response value.Curcumin nanoparticles were prepared under the optimal process,and the particle size,polydispersity index,Zata potential value,drug loading,stability,hemolysis rate,and antioxidant capacity in vivo and in vitro of the nanoparticles were detected.RESULTS AND CONCLUSION:(1)Among the five extraction methods,the curcumin yield of ionic liquid combined with ultrasound-assisted enzyme extraction was the highest,and this method was selected as the curcumin extraction method for subsequent experiments.The results of single factor combined response surface experiment showed that the optimal process for curcumin extraction was:ionic liquid selected 1-hexyl-3-methylimidazolium chloride,reaction temperature 55 ℃,liquid-to-solid ratio 40 mL/g,ultrasound time 57 minutes,ionic liquid concentration 57％,enzyme-drug mass ratio 3.5:10,and the obtained turmeric extraction yield was 3.10％.The optimal preparation process of curcumin nanoparticles was:glacial acetic acid concentration 0.5％,chitosan and sodium tripolyphosphate mass ratio 5.0:1,stirring speed 150 r/min,curcumin mass concentration 2.23 mg/mL,sodium tripolyphosphate mass concentration 1.45 mg/mL,chitosan mass concentration 3.63 mg/mL,and the obtained drug encapsulation efficiency was 90.61％.(2)The drug loading of curcumin nanoparticles was(14.49±0.23)％,the average particle size was(76.95±1.65)nm,the polydispersity coefficient was 0.15±0.02,and the Zata potential value was(32.37±1.46)mV.The curcumin nanoparticles had good stability and blood compatibility,did not induce hemolysis,and had stronger antioxidant capacity in vivo and in vitro than free curcumin.(3)The results show that the process optimization not only solves the problems of low extraction yield,poor solubility,and low bioavailability of curcumin,but also enhances its antioxidant activity in vivo and in vitro.

ObjectiveTo systematically characterize the in vitro chemical constituents of Entada phaseoloides and its absorbed prototype compounds and metabolites in a neuropathic pain （NP） model， thereby elucidating the potential pharmacological material basis underlying its analgesic activity. MethodsUltra-high-performance liquid chromatography coupled with quadrupole-Orbitrap high-resolution mass spectrometry （UHPLC-Q-Orbitrap HRMS） was used to analyze the constituents of the 80% methanol extract of E. phaseoloides. Thirty-six mice were divided into a blank group， low-， medium-， and high-dose E. phaseoloides extract groups （25， 50， and 100 mg·kg^-1， respectively）， an aspirin group （400 mg·kg^-1）， and a morphine group （10 mg·kg^-1）， with six mice in each group. The tail-flick threshold was measured. Twenty-four mice were randomly divided into a blank group and low-， medium-， and high-dose E. phaseoloides extract groups （25， 50， and 100 mg·kg^-1， respectively）， with six mice in each group， and motor coordination was evaluated. A spinal nerve ligation （SNL） model was established in mice. Twenty-four mice were randomly divided into a sham-operated （Sham） group， an SNL group， an SNL + E. phaseoloides extract group （750 mg·kg^-1）， and an SNL + pregabalin group （20 mg·kg^-1）， with 6 mice in each group. Mechanical allodynia and thermal hyperalgesia were evaluated. Behavioral assessments were combined to determine the optimal onset time of the analgesic effect， and plasma samples were collected at this time point. Absorbed components and metabolites were systematically identified based on accurate mass， MS/MS fragmentation patterns， retention time， and comparisons with literature data and reference standards. Meanwhile， network pharmacology， target similarity analysis， and molecular docking validation were integrated to evaluate the potential effects of absorbed components on disease intervention. ResultsCompared with the blank group， the high-dose E. phaseoloides extract group and morphine group showed significantly increased pain response thresholds （P<0.01）， without affecting motor coordination in normal mice. Compared with the Sham group， the SNL group exhibited significantly decreased thresholds for mechanical allodynia and thermal hyperalgesia （P<0.01）. Compared with the SNL group， the SNL + E. phaseoloides extract group and SNL + pregabalin group showed significantly increased thresholds for mechanical allodynia and thermal hyperalgesia （P<0.01）. The analgesic effect of E. phaseoloides was most significant at 2 h after administration. A total of 70 constituents were identified in vitro， and 22 prototype compounds and 29 metabolites were detected in plasma. The targets of the absorbed components were mainly enriched in biological processes associated with pain signal perception and transmission and showed a high degree of overlap with disease genes related to neuropathic pain. In addition， multiple components exhibited strong binding energies with core pain-related targets. Based on integrated analysis of in vitro content， plasma exposure levels， and target associations， ketanthamide A-β-D-pyranoside and ketengzide were identified as the most representative pharmacologically active substances responsible for the analgesic effect. ConclusionThis study systematically elucidates the material basis of E. phaseoloides intervention in neuropathic pain from three aspects， including chemical constituent characteristics， in vivo behavioral characteristics， and potential targets， providing important theoretical foundations and experimental support for further studies on its pharmacological mechanism and analgesic drug development.

OBJECTIVE To improve the research capacity and research enthusiasm of dispensing pharmacists in pharmacy intravenous admixture services （PIVAS）， facilitate their career development， and further promote the sustainable development of hospital pharmacy disciplines. METHODS A full-chain pharmacy practice research path covering topic orientation， topic evaluation， and implementation was explored and constructed for PIVAS. Multiple approaches including framework condensation for topic selection， department empowerment and improved incentive mechanisms were adopted to systematically boost the research competence and enthusiasm of PIVAS dispensing pharmacists. Four dimensions of indicators， including PIVAS research output， operational level， career development of staff and external influence， were compared between the pre-implementation period （2020- 2022） and post-implementation period （2023-2025） to evaluate the implementation effect of the path. RESULTS After the application of the PIVAS pharmacy practice research path， the number of published papers and comprehensive quality score of papers increased by 150.00% and 161.90% respectively compared with those before implementation. The number of approved research projects and comprehensive quality score of projects rose by 166.67% and 225.00%， and the number of authorized patents increased by 100.00%. The monthly number of external errors decreased from （0.42±0.18） pieces/month pre-implementation to （0.28±0.23） pieces/month post-implementation （P＞0.05）. Dispensing efficiency was elevated from （121.83±2.48） bags/（hour· person） to （139.00±3.46） bags/（hour·person） （P＜0.05）. The research participation rate of PIVAS dispensing pharmacists grew from 15.00% to 50.00%， and the overall satisfaction rate of staff increased from 55.00% to 85.00% （P＜0.05）. Within the 3 years after path implementation， our hospital PIVAS received a total of 22 trainees from external medical institutions， and the number of external institutional visits increased by 374.07% versus the pre-implementation stage. CONCLUSIONS The constructed pharmacy practice research path for PIVAS can significantly improve PIVAS research output， operational efficiency and external influence， and effectively promote the career growth of dispensing pharmacists working in PIVAS.

ObjectiveDiscriminating atypical hepatocellular carcinoma (HCC) from other malignancies in liver nodules classified as Liver Imaging Reporting and Data System category M (LR-M) remains a significant diagnostic challenge on conventional ultrasound examination. The LR-M category, originally intended to capture non-HCC malignancies, paradoxically contains up to 63% of atypical HCCs that deviate from classic enhancement patterns, leading to potential misdiagnosis and suboptimal treatment planning. While deep learning has shown promise in HCC diagnosis, most existing models rely exclusively on single-modality ultrasound, overlooking the diagnostic benefits of integrating complementary information from multiple imaging sources. To address this gap, we propose a novel attention-weighted tri-modal ultrasound network (TUS-Net) that integrates contrast-enhanced ultrasound (CEUS), B-mode ultrasound (BUS), and time-intensity curves (TICs) to improve diagnostic accuracy for these clinically challenging lesions. MethodsOur framework incorporates a three-dimensional convolutional neural network (C3D) backbone to extract spatiotemporal features from CEUS videos, capturing dynamic vascular patterns critical for lesion characterization. To effectively fuse complementary modalities, we introduce a dual-channel feature fusion module (DCFFM) that adaptively combines features from CEUS and BUS through channel-wise attention mechanisms, allowing the model to dynamically weigh the contribution of each modality based on diagnostic relevance. Additionally, we propose a temporal intensity feature fusion module (TIFFM) that leverages quantitative hemodynamic information from TICs to guide the model’s attention toward diagnostically critical temporal phases, such as arterial wash-in and portal venous washout. The model is further enhanced by automated lesion localization using YOLOX and class activation mapping for interpretability, ensuring that predictions align with clinically meaningful imaging features. ResultsEvaluated on a tri-modal ultrasound dataset comprising 161 patients with pathologically confirmed LR-M nodules (131 atypical HCC and 30 non-HCC malignancies), our model achieved an accuracy of 86.83%, a sensitivity of 92.50%, a specificity of 75.50%, and an AUC of 89.32% in screening atypical HCC. Compared to single-modality baselines, TUS-Net demonstrated superior specificity, a clinically critical metric given the higher risk associated with misclassifying non-HCC malignancies. Ablation studies confirmed the contribution of each module, with the full model outperforming both standard C3D and 3D ResNet backbones integrated with attention mechanisms. A reader study involving junior and senior radiologists further validated the clinical utility of AI assistance, showing consistent improvements in specificity and inter-reader consistency, particularly for less experienced clinicians. ConclusionThese results surpass existing benchmark models and demonstrate the potential of our approach to enhance diagnostic precision in clinically specific cases. By intelligently fusing multi-modal ultrasound data with attention-guided mechanisms, TUS-Net offers a reliable and interpretable tool that holds promise for improving the non-invasive diagnosis of atypical HCC in challenging LR-M liver nodules.

ObjectiveDiscriminating atypical hepatocellular carcinoma (HCC) from other malignancies in liver nodules classified as Liver Imaging Reporting and Data System category M (LR-M) remains a significant diagnostic challenge on conventional ultrasound examination. The LR-M category, originally intended to capture non-HCC malignancies, paradoxically contains up to 63% of atypical HCCs that deviate from classic enhancement patterns, leading to potential misdiagnosis and suboptimal treatment planning. While deep learning has shown promise in HCC diagnosis, most existing models rely exclusively on single-modality ultrasound, overlooking the diagnostic benefits of integrating complementary information from multiple imaging sources. To address this gap, we propose a novel attention-weighted tri-modal ultrasound network (TUS-Net) that integrates contrast-enhanced ultrasound (CEUS), B-mode ultrasound (BUS), and time-intensity curves (TICs) to improve diagnostic accuracy for these clinically challenging lesions. MethodsOur framework incorporates a three-dimensional convolutional neural network (C3D) backbone to extract spatiotemporal features from CEUS videos, capturing dynamic vascular patterns critical for lesion characterization. To effectively fuse complementary modalities, we introduce a dual-channel feature fusion module (DCFFM) that adaptively combines features from CEUS and BUS through channel-wise attention mechanisms, allowing the model to dynamically weigh the contribution of each modality based on diagnostic relevance. Additionally, we propose a temporal intensity feature fusion module (TIFFM) that leverages quantitative hemodynamic information from TICs to guide the model’s attention toward diagnostically critical temporal phases, such as arterial wash-in and portal venous washout. The model is further enhanced by automated lesion localization using YOLOX and class activation mapping for interpretability, ensuring that predictions align with clinically meaningful imaging features. ResultsEvaluated on a tri-modal ultrasound dataset comprising 161 patients with pathologically confirmed LR-M nodules (131 atypical HCC and 30 non-HCC malignancies), our model achieved an accuracy of 86.83%, a sensitivity of 92.50%, a specificity of 75.50%, and an AUC of 89.32% in screening atypical HCC. Compared to single-modality baselines, TUS-Net demonstrated superior specificity, a clinically critical metric given the higher risk associated with misclassifying non-HCC malignancies. Ablation studies confirmed the contribution of each module, with the full model outperforming both standard C3D and 3D ResNet backbones integrated with attention mechanisms. A reader study involving junior and senior radiologists further validated the clinical utility of AI assistance, showing consistent improvements in specificity and inter-reader consistency, particularly for less experienced clinicians. ConclusionThese results surpass existing benchmark models and demonstrate the potential of our approach to enhance diagnostic precision in clinically specific cases. By intelligently fusing multi-modal ultrasound data with attention-guided mechanisms, TUS-Net offers a reliable and interpretable tool that holds promise for improving the non-invasive diagnosis of atypical HCC in challenging LR-M liver nodules.