OBJECTIVE: To explore the application value of artificial intelligence (AI)-assisted chromosomal karyotype analysis in the diagnosis of prenatal chromosomal mosaicism. METHODS: A retrospective analysis was conducted on 172 pregnant women who underwent amniocentesis at the Department of Medical Genetics and Prenatal Diagnosis, the Third Affiliated Hospital of Zhengzhou University between January 2019 and December 2024. All cases whose fetuses were diagnosed with chromosomal mosaicism via karyotype analysis and stratified into two groups based on the analytical software employed: the conventional analysis group (n = 70), which utilized Leica analysis software for karyotype image recognition and cell counting; and the AI-assisted analysis group (n = 102), which utilized AI-assisted software for the same procedures. The clinical performance of AI-assisted karyotype analysis in diagnosing chromosomal mosaicism was comprehensively evaluated by comparing the types of mosaic karyotypes, distribution of mosaic ratios, and verification outcomes of different detection modalities between the two groups. This study was approved by the Medical Ethics Committee of the Third Affiliated Hospital of Zhengzhou University (Ethics No.: 2024-406-01). RESULTS: No statistically significant difference was observed in baseline characteristics (maternal age, gestational week, and indications for prenatal diagnosis) between the two groups. Regarding the detection efficacy for numerical and structural mosaicisms, no significant difference was found in the detection of numerical mosaicism. However, the conventional analysis group exhibited a significantly higher detection rate of autosomal structural mosaicism compared to the AI-assisted group (11.43% vs. 0.98%, P < 0.05). Numerical mosaicism cases were further verified using copy number variation sequencing (CNV-seq) and/or fluorescence in situ hybridization (FISH). The AI-assisted group demonstrated a significantly lower inconsistency rate (5.56% vs. 20.41%, P < 0.05) compared to the conventional group. For low-proportion (< 10%) chromosomal mosaicism, the AI-assisted group had a significantly lower detection rate (13.25% vs. 29.69%, P < 0.05). Subsequent validation of low-proportion mosaicism by CNV-seq and/or FISH showed a higher consistency rate in the AI-assisted group (81.82% vs. 54.55%), though the difference did not reach statistical significance (P = 0.360). CONCLUSION For the karyotyping analysis of prenatal chromosomal mosaicism, AI-assisted karyotype analysis shows high accuracy and consistency in identifying numerical chromosomal mosaicism, particularly in reducing the detection of low-proportion (< 10%) mosaicism while improving verification accuracy. AI-assisted analysis can significantly improve the detection accuracy of numerical mosaicism and mitigate the risk of misclassification for low-proportion (< 10%) mosaicism, thereby providing more precise clinical evidence for the prenatal diagnosis of chromosomal mosaicisms.
Humans ; Female ; Mosaicism ; Pregnancy ; Karyotyping/methods* ; Artificial Intelligence ; Prenatal Diagnosis/methods* ; Adult ; Retrospective Studies ; Chromosome Disorders/genetics* ; Amniocentesis

The Type III Secretion System (T3SS) serves as a pivotal virulence apparatus for numerous Gram-negative bacterial pathogens, enabling them to infect both animal and plant hosts. Functioning as a molecular syringe, the T3SS directly translocates bacterial effector proteins from the bacterial cytoplasm into the interior of eukaryotic host cells. These effectors are central weapons that precisely manipulate a wide spectrum of host cellular physiological processes, ranging from cytoskeletal dynamics to immune signaling, to establish a favorable niche for bacterial survival and proliferation. Among the diverse arsenal of T3SS effectors, the YopJ family constitutes a critical group of virulence factors. Members of this family are characterized by a conserved catalytic triad structure—a hallmark of the CE clan of cysteine proteases that has been evolutionarily repurposed to confer acetyltransferase activity. A defining and intriguing feature of these enzymes is their stringent dependence on a host-derived eukaryotic cofactor, inositol hexakisphosphate (IP₆), for allosteric activation. This requirement acts as a sophisticated molecular safeguard, ensuring enzymatic activity only within the appropriate host environment, thereby preventing detrimental effects on the bacterium itself. While seminal studies on individual members such as Yersinia’s YopJ and Salmonella’s AvrA have provided deep mechanistic insights, a systematic and integrative understanding of the structure-function relationships across the entire family remains fragmented. Key questions persist regarding how a conserved catalytic core has diverged to recognize distinct host substrates in different kingdoms of life. To address this gap, this article provides a systematic review of the YopJ family, focusing on three interconnected aspects: their structural features, their catalytic mechanism, and their divergent immunosuppressive strategies in animal versus plant hosts. By conducting a comparative analysis of the sequences and resolved three-dimensional structures of three representative members (e.g., HopZ1a, PopP2, AvrA), we elucidate regions of significant variation embedded within the conserved core catalytic architecture. These variable regions, often involving surface loops and substrate-binding interfaces, are crucial determinants of target specificity and functional specialization. The functional divergence of this effector family is most apparent when comparing their modes of action in different hosts. In animal hosts, YopJ-family effectors primarily sabotage innate immune signaling pathways. They achieve this by acetylating key serine and threonine residues within the activation loops of critical kinases in the MAPK and NF‑κB pathways. This post-translational modification blocks the phosphorylation and subsequent activation of these kinases, leading to potent suppression of inflammatory cytokine production. Conversely, in plant hosts, the strategy broadens to dismantle the two-tiered plant immune system. YopJ homologs target a more diverse set of substrates, including immune-associated receptor-like cytoplasmic kinases (RLCKs), microtubule networks via tubulin acetylation (which disrupts cellular trafficking and signaling), and transcription factors central to defense gene regulation. This multi-target approach effectively suppresses both Pattern-Triggered Immunity (PTI) and Effector-Triggered Immunity (ETI). In conclusion, this synthesis aims to deepen the mechanistic understanding of YopJ family-mediated pathogenesis by integrating structural biology with cellular function across host kingdoms. Elucidating the precise molecular basis for substrate selection—how conserved platforms achieve target diversity—is a major frontier. Furthermore, this knowledge provides a vital theoretical foundation for developing novel anti-virulence strategies. Targeting the conserved IP₆-binding pocket or the catalytic acetyltransferase activity itself represents a promising avenue for designing broad-spectrum inhibitors that could disarm this critical family of bacterial effectors, potentially offering new therapeutic approaches against a range of pathogenic bacteria.

ObjectiveHeadspace solid-phase microextraction-gas chromatography-mass spectrometry（HS-SPME-GC-MS） and GC-triple quadrupole MS（GC-QqQ-MS） in combination with non-targeted and targeted metabolomics were employed to systematically analyze the chemical composition differences of Xihuangwan prepared with natural musk and artificial musk， and establish an identification system for them. MethodsThe volatile components of 9 batches of Xihuangwan samples from 8 manufacturers were analyzed by HS-SPME-GC-MS non-targeted metabolomics， and identified by comparing their MS data with the National Institute of Standards and Technology（NIST） spectral library. Orthogonal partial least squares-discriminant analysis（OPLS-DA） was used to identify differential volatile components of Xihuangwan prepared with natural musk and artificial musk. Additionally， GC-QqQ-MS targeted metabolomics was applied to quantify the levels of α-pinene， β-elemene， muscone， dehydroepiandrosterone， bornyl acetate， and octyl acetate in 27 batches of samples from 9 manufacturers. Cluster analysis， principal component analysis（PCA）， and partial least squares-discriminant analysis（PLS-DA） were conducted to further explore the differences in volatile components between Xihuangwan samples prepared with natural musk and artificial musk. ResultsNon-targeted metabolomics identified 291 volatile compounds in Xihuangwan， including alkanes， esters， alkanes， alcohols， ketones， naphthalenes and others. OPLS-DA analysis revealed distinct separation between Xihuangwan samples containing artificial musk（A1， C1， D1， E1， F1， G1， I1） and those containing natural musk（H1， H3）. A total of 30 differential metabolites were identified. The relative contents of these 30 differential metabolites were visualized using a radar chart， revealing significant differences in the levels of octanol， borneol acetate and muscone. Cluster analysis and PCA results from targeted metabolomics indicated that Xihuangwan could be classified into two distinct groups：one composed of natural musk（H1， H3） and the other of artificial musk， sample H2. PLS-DA identified muscone， octyl acetate， and dehydroepiandrosterone as key differential volatile components. Although no significant difference was observed in the content of octyl acetate between the two groups， statistically significant differences were found for muscone and dehydroepiandrosterone（P<0.05）. ConclusionMuscone and dehydroepiandrosterone can be used for the differentiation of Xihuangwan samples containing natural musk from those containing artificial musk. This study systematically and comprehensively analyzed the differences in the types and contents of major volatile components in Xihuangwan prepared with natural musk and artificial musk， providing a scientific basis for quality evaluation and control of Xihuangwan.

ObjectiveHeadspace solid-phase microextraction-gas chromatography-mass spectrometry（HS-SPME-GC-MS） and GC-triple quadrupole MS（GC-QqQ-MS） in combination with non-targeted and targeted metabolomics were employed to systematically analyze the chemical composition differences of Xihuangwan prepared with natural musk and artificial musk， and establish an identification system for them. MethodsThe volatile components of 9 batches of Xihuangwan samples from 8 manufacturers were analyzed by HS-SPME-GC-MS non-targeted metabolomics， and identified by comparing their MS data with the National Institute of Standards and Technology（NIST） spectral library. Orthogonal partial least squares-discriminant analysis（OPLS-DA） was used to identify differential volatile components of Xihuangwan prepared with natural musk and artificial musk. Additionally， GC-QqQ-MS targeted metabolomics was applied to quantify the levels of α-pinene， β-elemene， muscone， dehydroepiandrosterone， bornyl acetate， and octyl acetate in 27 batches of samples from 9 manufacturers. Cluster analysis， principal component analysis（PCA）， and partial least squares-discriminant analysis（PLS-DA） were conducted to further explore the differences in volatile components between Xihuangwan samples prepared with natural musk and artificial musk. ResultsNon-targeted metabolomics identified 291 volatile compounds in Xihuangwan， including alkanes， esters， alkanes， alcohols， ketones， naphthalenes and others. OPLS-DA analysis revealed distinct separation between Xihuangwan samples containing artificial musk（A1， C1， D1， E1， F1， G1， I1） and those containing natural musk（H1， H3）. A total of 30 differential metabolites were identified. The relative contents of these 30 differential metabolites were visualized using a radar chart， revealing significant differences in the levels of octanol， borneol acetate and muscone. Cluster analysis and PCA results from targeted metabolomics indicated that Xihuangwan could be classified into two distinct groups：one composed of natural musk（H1， H3） and the other of artificial musk， sample H2. PLS-DA identified muscone， octyl acetate， and dehydroepiandrosterone as key differential volatile components. Although no significant difference was observed in the content of octyl acetate between the two groups， statistically significant differences were found for muscone and dehydroepiandrosterone（P<0.05）. ConclusionMuscone and dehydroepiandrosterone can be used for the differentiation of Xihuangwan samples containing natural musk from those containing artificial musk. This study systematically and comprehensively analyzed the differences in the types and contents of major volatile components in Xihuangwan prepared with natural musk and artificial musk， providing a scientific basis for quality evaluation and control of Xihuangwan.

Objective To evaluate the short-term effects of comprehensive health management interventions for stroke high-risk population screening on the prevention and treatment of ischemic stroke, and to provide reference and basis for improving and exploring health management and prevention strategies for stroke high-risk population. Methods From 2018 to 2022, 13 community health service centers in Jiading District, Shanghai were selected in the present study. Based on information push platform, stroke risk assessment and health intervention follow-up were conducted for community residents through convenience sampling. The residents were divided into a full course intervention group (intervention group) and a routine intervention group (control group) according to different health intervention measures and forms. The incidence of ischemic stroke in the two groups of survey subjects was tracked within 36 months. Results A total of 52144 subjects were included in the study. The total number of patients in the full course intervention group was 14227, with an incidence density of 577.32/100 000 (556.49/100 000-598.12/100 000), which was lower than that of the conventional intervention group (37 917), with an incidence density of 1 485.47/100 000 (1 464.99/100 000-1 505.94/100 000) (χ²=2490.212, P<0.001). The relative risk of the full course intervention group was 0.39, and the relative risk of stroke risk factors in the full course intervention group from low to high was 0.33, 0.43, 0.45, and 0.49, respectively. The incidence density of males in the full course intervention group was 660.76 (627.46/100 000 - 694.05/100 000), with a relative risk of 0.43, and the incidence density of female patients was 509.71/100 000 (483.37/100 000 - 536.05/100 000), with a relative risk of 0.35. The overall incidence density of the population under 62 years old gourp, 62-75 years old group and over 75 years old group was 197.45/100 000 (173.09/100 000 -221.80/100 000), 608.36/100 000 (580.19/100 000-636.54/100 000), and 1 025.06/100 000 (958.51/100 000-1 091.61/100 000), with relative risks of 0.51, 0.44, and 0.38, respectively. Conclusion Comprehensive health management measures can effectively reduce the short-term risk of ischemic stroke, and should be further promoted and improved to enhance the effectiveness of stroke prevention and control.

OBJECTIVE To prepare an intelligent responsive liposome-hydrogel nanopreparation co-loaded with gambogic acid （GA）， and characterize its antitumor activity in vitro . METHODS GA-ICG-Lip-gel was prepared by ethanol injection and cold dissolution， incorporating GA and the photosensitizer indocyanine green （ICG）. The appearance and microscopic morphology of GA-ICG-Lip-gel were observed， its encapsulation efficiency and drug loading capacity were measured， and its photothermal conversion performance， photothermal stability， and infrared imaging properties were investigated， along with the determination of its in vitro release profile. Human breast cancer MCF-7 cells were used as objects to investigate the effects of GA-ICG-Lip-gel （or with near-infrared light irradiation） on cell viability， migration ability， and the cellular uptake capacity of GA-ICG-Lip-gel. RESULTS GA-ICG-Lip-gel existed in a solution state at room temperature and transformed into a gel state at 37 ℃. Its microstructure was dense with small pores， and its encapsulation efficiency and drug loading were （96.07±0.86） % and （6.28±1.16） %， respectively. After exposure to near-infrared light， the temperature of GA-ICG-Lip-gel rose above 42 ℃， with no significant attenuation observed in the heating curve. The heating efficiency was dependent on both the irradiation time and drug concentration. Compared to media without gelatinase， the cumulative release rate of GA-ICG-Lip-gel increased in media containing gelatinase. In vitro studies showed that GA-ICG-Lip-gel could be efficiently taken up by MCF-7 cells； GA-ICG-Lip-gel significantly inhibited the viability and migration ability of MCF-7 cells （ P ＜0.05）， and this inhibitory effect was further enhanced under near-infrared light irradiation. CONCLUSIONS This study successfully prepares GA-ICG-Lip-gel， which exhibits favorable photothermal conversion properties and temperature/enzyme dual-responsive drug release characteristics， and demonstrates significant inhibitory effects on the proliferation and migration of breast cancer cells.

Glycogen synthase 1 (GYS1) is the rate-limiting enzyme responsible for glycogen synthesis in skeletal muscle, heart, brain, and other extrahepatic tissues, playing a central role in systemic energy homeostasis. The human GYS1 gene maps to chromosome 19q13.33, comprises 16 exons, and encodes a 737-amino-acid polypeptide that is highly conserved across mammals. GYS1 activity is subject to multilayered and precisely coordinated regulation. At the transcriptional level, the GYS1 promoter contains a hypoxia response element (HRE) that mediates HIF-1α-dependent induction under low-oxygen conditions, as well as a muscle-specific enhancer harboring MEF2 and MyoD binding sites that confers tissue-restricted expression. At the post-translational level, a hierarchical phosphorylation cascade serves as the primary activity switch: glycogen synthase kinase 3β (GSK3β) sequentially phosphorylates four C-terminal serine residues following casein kinase II priming, while protein kinase A (PKA) and AMP-activated protein kinase (AMPK) provide parallel inhibitory inputs at both N- and C-terminal sites. Dephosphorylation and reactivation are mediated by protein phosphatase 1 (PP1) through tissue-specific glycogen-targeting regulatory subunits such as PPP1R3A and PPP1R3B, which anchor PP1 to glycogen particles and direct its activity toward GYS1. The allosteric activator glucose-6-phosphate (G6P) binds at the dimer interface, simultaneously enhancing catalytic efficiency and promoting dephosphorylation susceptibility, thereby establishing a feed-forward activation loop that couples substrate availability to glycogen synthesis. Beyond phosphorylation, GYS1 is regulated by ubiquitination (mediated by the E3 ligase PJA1), acetylation, O-linked β-N-acetylglucosamine (O-GlcNAc) modification, and SUMOylation, which collectively modulate protein stability, subcellular localization, and protein-protein interactions. Epigenetic mechanisms, including CpG island methylation and histone acetylation dynamics, govern chromatin accessibility at the GYS1 locus, while muscle-specific microRNAs such as miR-1 and miR-206 fine-tune GYS1 expression at the post-transcriptional level. Dysregulation of GYS1 has been identified as a central pathogenic driver in a spectrum of human diseases. In inherited glycogen storage disorders—including Lafora disease, adult polyglucosan body disease (APBD), and Pompe disease—loss of upstream regulatory control leads to GYS1 hyperactivation and the accumulation of structurally abnormal or excessive glycogen, resulting in progressive neurodegeneration, myopathy, and multiorgan dysfunction. In type 2 diabetes mellitus (T2DM), impaired insulin signaling through the PI3K-AKT-GSK3β axis maintains GYS1 in a hyperphosphorylated inactive state in skeletal muscle, compromising postprandial glucose disposal and exacerbating hyperglycemia. In oncology, GYS1 exhibits context-dependent roles across multiple cancer types. In hepatocellular carcinoma, FMO2⁺ cancer-associated fibroblasts stabilize GYS1 by competitively inhibiting PJA1-mediated ubiquitination, and stabilized GYS1 subsequently activates NF‑κB/CCL19 signaling to promote tertiary lymphoid structure formation and enhance anti-PD-1 immunotherapy responsiveness. In clear cell renal cell carcinoma, GYS1 promotes tumor progression through non-canonical NF‑κB pathway activation via the scaffold protein RPS27A. In triple-negative breast cancer, GYS1 has been identified as a trigger of disulfidptosis and an activator of NF-κB signaling through non-enzymatic facilitation of IκBα degradation. In colorectal cancer, mitochondrial fission deficiency drives AMPK-dependent GYS1 upregulation and glycogen accumulation as a compensatory survival mechanism, while in cervical cancer, GYS1-maintained glycogen reserves fuel the pentose phosphate pathway to generate NADPH for ROS clearance, thereby conferring cisplatin resistance in cancer stem cells. Therapeutic strategies targeting GYS1 have gained substantial momentum across these disease contexts. For glycogen storage disorders, antisense oligonucleotides, small interfering RNAs (e.g., ABX1100), and small-molecule inhibitors (e.g., MZ-101) have demonstrated preclinical and early clinical efficacy in reducing pathological glycogen accumulation. For T2DM, pharmacological activation of GYS1 through GSK3β inhibition or enhancement of PP1-mediated dephosphorylation is being explored to restore insulin-stimulated glycogen synthesis. In cancer, GYS1-directed interventions—including targeted silencing to sensitize tumors to chemotherapy and immune microenvironment modulation to enhance immunotherapy—represent emerging precision oncology approaches. This review provides a comprehensive and integrated account of GYS1 gene structure, tissue-specific distribution, regulatory networks, and pathogenic roles in metabolic disorders and malignancies, with the aim of establishing a theoretical framework for the development of GYS1-targeted precision therapies.

ObjectiveTo investigate the effect and mechanism of Shengxian Yixin Granule （升陷益心颗粒， SYG） in treating heart failure with preserved ejection fraction （HFpEF）. MethodsExperiment 1： Forty male C57BL/6 mice were randomly divided into control group， model group， low-dose ［25 mg/（kg·d）］， medium-dose ［50 mg/（kg·d）］， and high-dose ［（75 mg/（kg·d）］ SYG groups， with 8 mice per group. Except for the control group， HFpEF was induced by a high-fat diet combined with Nω-nitro-L-arginine methyl ester in drinking water for 16 weeks. After 10 weeks of modeling， treatment groups received corresponding doses of SYG by gavage， while the control group and model group received equal volume of normal saline， for 6 weeks. After 6 weeks of administration， the body weight， blood pressure including systolic blood pressure （SBP） and diastolic blood pressure （DBP）， and heart weight-to-tibia length ratio （HW/TL） were recorded. Echocardiography was used to evaluate left ventricular ejection fraction （LVEF） and the ratio of early to late diastolic transmitral flow velocity （E/A）. Plasma N-terminal pro-brain natriuretic peptide （NT-proBNP） and C-reactive protein （CRP） levels were measured by ELISA. The expression level of brain natriuretic peptide （BNP） in heart tissue was detected by qRT-PCR. Blood lipid levels， including low-density lipoprotein cholesterol （LDL-C）， triglycerides （TG）， and total cholesterol （TC）， were determined by a biochemical analyzer. Splenic proportions of M1 macrophages， M2 macrophages， T helper 17 （Th17） cells， and regulatory T （Treg） cells were analyzed by flow cytometry. Experiment 2： RAW 264.7 cells were stimulated with lipopolysaccharide （LPS） to establish an M1 macrophage polarization model， and then treated with SYG-containing serum for 24 h. The cells were divided into four groups， including blank serum group， high-dose SYG-containing serum group， LPS group， and LPS plus high-dose SYG-containing serum group. Combined with asignal transducer and activator of transcription 3 （Stat3） inhibitor for 24 h， the cells were divided into a lipopolysaccharide group， a high-dose SYG-containing serum group， an inhibitor group， and high-dose SYG-containing serum plus inhibitor group. The expression levels of inducible nitric oxide synthase （iNOS） and interleukin-1β （IL-1β） in each group were detected by qRT-PCR. The protein levels of iNOS， IL-1β， Stat3， and phosphorylated Stat3 （p-Stat3） were detected by Western Blotting. ResultsExperiment 1： Compared to the control group， the model group showed significantly decreased E/A ratio， and increased cardiac BNP expression， plasma NT-proBNP，HW/TL ratio， body weight， TC， LDL-C， SBP， and DBP， as well as shortened exhaustion distance and exhaustion time （P＜0.05 or P＜0.01）. Compared to the model group， the E/A ratio increased in the low-， medium-， and high-dose SYG groups； cardiac BNP expression decreased in the low- and high-dose groups， and plasma NT-proBNP levels decreased in the high-dose group； the HW/TL ratio and body weight decreased， while exhaustion distance and exhaustion time increased in the medium- and high-dose groups； TG levels decreased in the low- and medium-dose groups， and LDL-C levels decreased in the medium- and high-dose groups； SBP and DBP decreased in the low-， medium-， and high-dose groups （P＜0.05 or P＜0.01）. Compared to the low-dose SYG group， body weight decreased， and both exhaustion distance and exhaustion time increased in the medium- and high-dose groups （P＜0.05 or P＜0.01）. Compared to the medium-dose SYG group， the exhaustion time increased in the high-dose SYG group （P＜0.01）. Experiment 2： In the LPS‑induced M1 macrophage polarization model， compared to the blank serum group， the LPS group showed significantly increased mRNA and protein expression of iNOS and IL‑1β， as well as the p‑Stat3/Stat3 ratio （P＜0.01）. Compared to the LPS group， the high‑dose SYG-containing serum plus LPS group showed significantly decreased mRNA and protein expression of iNOS and IL‑1β， as well as the p‑Stat3/Stat3 ratio （P＜0.05 or P＜0.01）. Compared to the LPS group， the inhibitor group also showed significantly reduced these parameters （P＜0.05 or P＜0.01）. ConclusionSYG can improve cardiac function， ventricular remodeling， exercise capacity， and lipid metabolism in HFpEF mice， while inhibiting inflammatory responses. Its mechanism may be related to the regulation of the Stat3 signaling pathway and inhibition of M1 macrophage polarization.

Curcumin (Cur), a natural polyphenolic compound extracted from the rhizomes of plants belonging to the Zingiberaceae and Araceae families, has attracted considerable interest for its remarkable antioxidant, anti-inflammatory, antibacterial, and anticancer activities. Extensive studies have focused on its potential in treating various diseases, including cancers, neurodegenerative diseases, inflammation, and autoimmune disorders. However, its clinical application has been hampered by inherent physicochemical challenges, such as poor aqueous solubility, short half-life, and low bioavailability. To address these challenges, nanodrug delivery systems have been developed to improve the pharmacokinetic and pharmacodynamic profiles of curcumin, thereby enhancing its therapeutic efficacy. This review summarizes the pharmacological activities and underlying mechanisms of curcumin, encompassing its antioxidant, anti-inflammatory, anticancer, antiviral, and antibacterial effects. Furthermore, it highlights the significant advancements achieved through the development of curcumin-based nanomedicines. By synthesizing current findings, this review aims to provide insights that may inspire innovative strategies for the future design and broader applications of curcumin nanomedicines.

ObjectiveTo investigate the effect and mechanism of Shengxian Yixin Granule （升陷益心颗粒， SYG） in treating heart failure with preserved ejection fraction （HFpEF）. MethodsExperiment 1： Forty male C57BL/6 mice were randomly divided into control group， model group， low-dose ［25 mg/（kg·d）］， medium-dose ［50 mg/（kg·d）］， and high-dose ［（75 mg/（kg·d）］ SYG groups， with 8 mice per group. Except for the control group， HFpEF was induced by a high-fat diet combined with Nω-nitro-L-arginine methyl ester in drinking water for 16 weeks. After 10 weeks of modeling， treatment groups received corresponding doses of SYG by gavage， while the control group and model group received equal volume of normal saline， for 6 weeks. After 6 weeks of administration， the body weight， blood pressure including systolic blood pressure （SBP） and diastolic blood pressure （DBP）， and heart weight-to-tibia length ratio （HW/TL） were recorded. Echocardiography was used to evaluate left ventricular ejection fraction （LVEF） and the ratio of early to late diastolic transmitral flow velocity （E/A）. Plasma N-terminal pro-brain natriuretic peptide （NT-proBNP） and C-reactive protein （CRP） levels were measured by ELISA. The expression level of brain natriuretic peptide （BNP） in heart tissue was detected by qRT-PCR. Blood lipid levels， including low-density lipoprotein cholesterol （LDL-C）， triglycerides （TG）， and total cholesterol （TC）， were determined by a biochemical analyzer. Splenic proportions of M1 macrophages， M2 macrophages， T helper 17 （Th17） cells， and regulatory T （Treg） cells were analyzed by flow cytometry. Experiment 2： RAW 264.7 cells were stimulated with lipopolysaccharide （LPS） to establish an M1 macrophage polarization model， and then treated with SYG-containing serum for 24 h. The cells were divided into four groups， including blank serum group， high-dose SYG-containing serum group， LPS group， and LPS plus high-dose SYG-containing serum group. Combined with asignal transducer and activator of transcription 3 （Stat3） inhibitor for 24 h， the cells were divided into a lipopolysaccharide group， a high-dose SYG-containing serum group， an inhibitor group， and high-dose SYG-containing serum plus inhibitor group. The expression levels of inducible nitric oxide synthase （iNOS） and interleukin-1β （IL-1β） in each group were detected by qRT-PCR. The protein levels of iNOS， IL-1β， Stat3， and phosphorylated Stat3 （p-Stat3） were detected by Western Blotting. ResultsExperiment 1： Compared to the control group， the model group showed significantly decreased E/A ratio， and increased cardiac BNP expression， plasma NT-proBNP，HW/TL ratio， body weight， TC， LDL-C， SBP， and DBP， as well as shortened exhaustion distance and exhaustion time （P＜0.05 or P＜0.01）. Compared to the model group， the E/A ratio increased in the low-， medium-， and high-dose SYG groups； cardiac BNP expression decreased in the low- and high-dose groups， and plasma NT-proBNP levels decreased in the high-dose group； the HW/TL ratio and body weight decreased， while exhaustion distance and exhaustion time increased in the medium- and high-dose groups； TG levels decreased in the low- and medium-dose groups， and LDL-C levels decreased in the medium- and high-dose groups； SBP and DBP decreased in the low-， medium-， and high-dose groups （P＜0.05 or P＜0.01）. Compared to the low-dose SYG group， body weight decreased， and both exhaustion distance and exhaustion time increased in the medium- and high-dose groups （P＜0.05 or P＜0.01）. Compared to the medium-dose SYG group， the exhaustion time increased in the high-dose SYG group （P＜0.01）. Experiment 2： In the LPS‑induced M1 macrophage polarization model， compared to the blank serum group， the LPS group showed significantly increased mRNA and protein expression of iNOS and IL‑1β， as well as the p‑Stat3/Stat3 ratio （P＜0.01）. Compared to the LPS group， the high‑dose SYG-containing serum plus LPS group showed significantly decreased mRNA and protein expression of iNOS and IL‑1β， as well as the p‑Stat3/Stat3 ratio （P＜0.05 or P＜0.01）. Compared to the LPS group， the inhibitor group also showed significantly reduced these parameters （P＜0.05 or P＜0.01）. ConclusionSYG can improve cardiac function， ventricular remodeling， exercise capacity， and lipid metabolism in HFpEF mice， while inhibiting inflammatory responses. Its mechanism may be related to the regulation of the Stat3 signaling pathway and inhibition of M1 macrophage polarization.