Objective: To explore the early effects of birth weight at different gestational ages on blood pressure trajectory among primary school students, so as to provide evidence for incorporating gestational age birth weight into individualized early warning and intervention strategies for childhood hypertension. Methods: From May to November 2023, a purposeful sampling method was used to recruit 1 676 students in grade 1-3 from three primary schools in a certain urban district of Chongqing. Follow up assessments were conducted in May 2024(T1), November 2024(T2), and May 2025(T3). General demographic and birth related information were collected via self administered questionnaires, while height, weight and blood pressure were obtained through physical examinations. Linear mixed effects model was used to analyze the associations between birth weight at different gestational ages and blood pressure trajectories. Results: During the T1-T3 period, the systolic blood pressure of boys were 98.5 (93.0, 104.5 ),98.5 (93.5, 105.0), and 97.5 (92.5, 103.5)mmHg, respectively, while the diastolic blood pressure were 60.5 (56.5, 65.0), 61.5 ( 57.0 , 65.5), and 60.0 (56.0, 64.0)mmHg, respectively. For girls, the systolic blood pressure were 95.5 (90.0, 102.0),95.5 (90.5, 101.5), and 96.0 (90.5, 101.5)mmHg, respectively, and the diastolic blood pressure were 60.5 (56.0, 64.5 ),61.5 (57.5, 65.5), and 59.5 (56.0, 63.0)mmHg, respectively. Through Friedman test within both boys and girls, diostolic blood pressure were statistically significant across three measurements( χ 2=48.85，81.54，both P <0.01). The proportion of normal blood pressure increased , and the proportion of prehypertension and hypertension decreased with time( χ 2=39.72，25.62，both P < 0.01 ). Linear mixed effects model analysis revealed that after adjusting for age, sex, household income monthly, parental education, family history of hypertension and maternal pregnancy complications, large for gestational age had significantly higher trajectories of systolic ( β = 1.50) and diastolic( β =0.94) blood pressure compared to appropriate for gestational age(both P <0.01). Conclusion Large for gestational age is associated with elevated blood pressure trajectories during school age, and it may be considered as an early indicator for individualized screening and intervention for childhood hypertension.

ObjectiveSpinal cord injury (SCI) directly impairs the regulatory function of the autonomic nervous system, induces intestinal dysfunction, and significantly reduces patients’ quality of life. Preclinical studies have shown that electroacupuncture (EA) therapy can regulate the brain-gut axis and is used to treat central nervous system diseases such as major depressive disorder, Alzheimer’s disease and Parkinson’s disease. Recent research has established that fecal microbiota transplantation (FMT) from EA-treated SCI rats restored intestinal motility and colonic morphology. However, it remains unclear whether the regulation of gut microbiota by EA therapy directly contributes to neural repair after SCI. This study aims to explore whether gut microbiota mediates the neuroprotective effect of EA in the treatment of SCI and its possible mechanism. MethodsThe study employed RNA transcriptome analysis of spinal cord tissue to characterize gene expression profiles and to identify key signaling pathways following EA treatment for SCI. Hematoxylin-Eosin (HE) staining and Nissl staining were used to observe the morphological changes in spinal cord tissue. Western blot (WB) and enzyme-linked immunosorbent assay (ELISA) were applied to detect the effects of EA on the expression of proteins related to nucleotide-binding domain leucine-rich repeat and pyrin domain-containing receptor 3 (NLRP3) -dependent pyroptosis. Using 16S rDNA sequencing, the study observed alterations in gut microbiota diversity and community composition in SCI rats. Prior to establishing SCI models, rats were pretreated with an antibiotic cocktail to induce gut dysbiosis, and the effects on intestinal function and spinal cord neural repair were evaluated. FMT was performed to investigate the regulatory effects of post-EA FMT on motor function, general status, liver and spleen indices, and NLRP3-mediated pyroptosis in SCI rats. ResultsEA improved motor function and reduced regulated neuronal cell death in SCI rats. Transcriptomic analysis demonstrated the activation of immune- and inflammation-related pathways post-SCI, including NOD-like receptors, nuclear factor-kappa B(NF-κB), and Toll-like receptor (TLR) pathways. EA primarily influenced intestinal inflammation and autoimmune functions. 16S rDNA sequencing illustrated that EA did not alter the diversity of gut microbiota. However, EA altered the gut microbiota composition in SCI rats, increasing Lactobacillus and Akkermansia genera while rebalancing the Firmicutes/Bacteroidetes ratio. Furthermore, depletion of gut microbiota by antibiotics disrupted the intestinal barrier, reduced the expression of intestinal barrier proteins Zonula Occludens-1 (ZO-1) and Occludin, elevated serum lipopolysaccharide-binding protein (LBP) levels, exacerbated spinal cord tissue damage, and hindered motor function recovery in SCI rats. FMT from donors treated with EA reduced LBP levels in the intestine, blood, and spinal cord of rats, inhibited the TLR4 myeloid differentiation primary response protein 88 (MyD88)-NF‑κB pathway and NLRP3-dependent pyroptosis, and improved motor function. On the other hand, FMT treatment resulted in decreased body weight and food intake, whereas FMT using EA-treated donors effectively alleviated these alterations. ConclusionEA effectively alleviated neuroinflammatory responses in rats with SCI, primarily through regulating the gut microbiota and suppressing the NLRP3-dependent pyroptosis signaling pathway.

The initiation of adaptive immune responses relies on the precise recognition and interpretation of antigenic information. In this process, the specific binding of T cell receptors (TCRs) to peptide-major histocompatibility complex (pMHC) molecules represents one of the key molecular events in the initiation of adaptive immune responses. Accordingly, the structural features of TCR-pMHC complexes provide a fundamental basis for dissecting antigen recognition mechanisms and support rational vaccine design, therapeutic target discovery in TCR-based immunotherapy, and TCR identification and optimization. However, experimental determination of TCR-pMHC structures remains costly, time-consuming, and limited in coverage, making computational approaches essential for rapidly obtaining reliable structural information. Computational methods for predicting the structures of TCR-pMHC complexes have advanced rapidly in recent years, driven by progress in deep learning-based modeling frameworks and the increasing availability of structural and sequence resources. Despite these developments, most existing tools do not adequately distinguish the key structural and biophysical differences between MHC class I (MHC-I) and MHC class II (MHC-II) complexes during model construction. As a consequence, their predictive performance differs substantially between class I and class II complexes. In general, structural predictions for class I complexes outperform those for class II complexes. This discrepancy may be related to several fundamental differences between the two systems, including the architecture of the peptide-binding groove, the distribution of peptide lengths, and the properties of peptide flanking residues (PFRs). Compared with MHC-I molecules, MHC-II molecules usually bind longer antigenic peptides, which typically range from 13 to 25 amino acids in length. PFRs at both termini of these peptides participate in regulating the overall conformation of TCR-pMHC class II complexes and exert a pronounced effect on the geometric and physicochemical characteristics of the TCR-pMHC binding interface. Furthermore, within the TCR recognition interface, the complementarity-determining regions (CDRs) consist of segments that differ markedly in conformational behavior. They commonly include regions that are relatively rigid and structurally stable, together with highly flexible segments exhibiting substantial conformational plasticity. These rigidity-flexibility features constitute an essential structural basis enabling TCRs to recognize diverse peptide-MHC ligands and to accommodate conformational heterogeneity at the interface. However, many current modeling tools, in an effort to enforce global conformational stability or reduce structural noise, tend to over-constrain intrinsically flexible regions. Such oversimplification may lead to inappropriate rigidification of flexible CDR loops, resulting in local structural distortions, compromised interface geometry, or even complete modeling failure for specific complexes. Against this background, the review approaches the field from the perspective of computational differences between MHC-I and MHC-II complexes. We first systematically organize and summarize available resources related to TCRs and pMHCs, including structural datasets, sequence databases, prediction tools, and benchmarking studies. We then focus on five representative tools capable of predicting both class I and class II complexes—AlphaFold2, AlphaFold3, TCRmodel2, tFold-TCR, and TCR-pHLA_ModellerS. After excluding structures present in the training sets of these tools, we constructed a benchmark dataset comprising 25 class I and 10 class II TCR-pMHC complexes in the bound state and conducted a systematic evaluation using this dataset. We first employ widely used general evaluation metrics, including All-Atom Root Mean Square Deviation (All-Atom RMSD), Backbone RMSD, Template Modeling score (TM-score), and DockQ, to assess the global conformational accuracy and interface modeling quality of class I and class II complexes. For class II complexes, we propose for the first time a peptide flanking residue deviation index, including the PFRs-Deviation Index (PFRs-DI), N-PFR-Deviation Index (N-PFR-DI), and C-PFR-Deviation Index (C-PFR-DI), to quantitatively characterize conformational deviations in PFRs. In addition, we propose the CDR conformational consistency index (CCC) designed to qualitatively evaluate the ability of prediction tools to capture TCR CDR conformational flexibility. These metrics collectively assess a tool’s ability to model both overall conformation and critical functional regions, thereby addressing the limitations of existing evaluation criteria that overemphasize global structure while inadequately capturing modeling quality in key functional areas. This establishes a unified analytical framework for MHC-I and MHC-II complexes to guide data resource selection, modeling strategy formulation, and evaluation system development. The framework further advances computational modeling and provides crucial support for multi-scale analysis of TCR-pMHC recognition mechanisms and their biological functions.

ObjectiveSpinal cord injury (SCI) directly impairs the regulatory function of the autonomic nervous system, induces intestinal dysfunction, and significantly reduces patients’ quality of life. Preclinical studies have shown that electroacupuncture (EA) therapy can regulate the brain-gut axis and is used to treat central nervous system diseases such as major depressive disorder, Alzheimer’s disease and Parkinson’s disease. Recent research has established that fecal microbiota transplantation (FMT) from EA-treated SCI rats restored intestinal motility and colonic morphology. However, it remains unclear whether the regulation of gut microbiota by EA therapy directly contributes to neural repair after SCI. This study aims to explore whether gut microbiota mediates the neuroprotective effect of EA in the treatment of SCI and its possible mechanism. MethodsThe study employed RNA transcriptome analysis of spinal cord tissue to characterize gene expression profiles and to identify key signaling pathways following EA treatment for SCI. Hematoxylin-Eosin (HE) staining and Nissl staining were used to observe the morphological changes in spinal cord tissue. Western blot (WB) and enzyme-linked immunosorbent assay (ELISA) were applied to detect the effects of EA on the expression of proteins related to nucleotide-binding domain leucine-rich repeat and pyrin domain-containing receptor 3 (NLRP3) -dependent pyroptosis. Using 16S rDNA sequencing, the study observed alterations in gut microbiota diversity and community composition in SCI rats. Prior to establishing SCI models, rats were pretreated with an antibiotic cocktail to induce gut dysbiosis, and the effects on intestinal function and spinal cord neural repair were evaluated. FMT was performed to investigate the regulatory effects of post-EA FMT on motor function, general status, liver and spleen indices, and NLRP3-mediated pyroptosis in SCI rats. ResultsEA improved motor function and reduced regulated neuronal cell death in SCI rats. Transcriptomic analysis demonstrated the activation of immune- and inflammation-related pathways post-SCI, including NOD-like receptors, nuclear factor-kappa B(NF-κB), and Toll-like receptor (TLR) pathways. EA primarily influenced intestinal inflammation and autoimmune functions. 16S rDNA sequencing illustrated that EA did not alter the diversity of gut microbiota. However, EA altered the gut microbiota composition in SCI rats, increasing Lactobacillus and Akkermansia genera while rebalancing the Firmicutes/Bacteroidetes ratio. Furthermore, depletion of gut microbiota by antibiotics disrupted the intestinal barrier, reduced the expression of intestinal barrier proteins Zonula Occludens-1 (ZO-1) and Occludin, elevated serum lipopolysaccharide-binding protein (LBP) levels, exacerbated spinal cord tissue damage, and hindered motor function recovery in SCI rats. FMT from donors treated with EA reduced LBP levels in the intestine, blood, and spinal cord of rats, inhibited the TLR4 myeloid differentiation primary response protein 88 (MyD88)-NF‑κB pathway and NLRP3-dependent pyroptosis, and improved motor function. On the other hand, FMT treatment resulted in decreased body weight and food intake, whereas FMT using EA-treated donors effectively alleviated these alterations. ConclusionEA effectively alleviated neuroinflammatory responses in rats with SCI, primarily through regulating the gut microbiota and suppressing the NLRP3-dependent pyroptosis signaling pathway.

The initiation of adaptive immune responses relies on the precise recognition and interpretation of antigenic information. In this process, the specific binding of T cell receptors (TCRs) to peptide-major histocompatibility complex (pMHC) molecules represents one of the key molecular events in the initiation of adaptive immune responses. Accordingly, the structural features of TCR-pMHC complexes provide a fundamental basis for dissecting antigen recognition mechanisms and support rational vaccine design, therapeutic target discovery in TCR-based immunotherapy, and TCR identification and optimization. However, experimental determination of TCR-pMHC structures remains costly, time-consuming, and limited in coverage, making computational approaches essential for rapidly obtaining reliable structural information. Computational methods for predicting the structures of TCR-pMHC complexes have advanced rapidly in recent years, driven by progress in deep learning-based modeling frameworks and the increasing availability of structural and sequence resources. Despite these developments, most existing tools do not adequately distinguish the key structural and biophysical differences between MHC class I (MHC-I) and MHC class II (MHC-II) complexes during model construction. As a consequence, their predictive performance differs substantially between class I and class II complexes. In general, structural predictions for class I complexes outperform those for class II complexes. This discrepancy may be related to several fundamental differences between the two systems, including the architecture of the peptide-binding groove, the distribution of peptide lengths, and the properties of peptide flanking residues (PFRs). Compared with MHC-I molecules, MHC-II molecules usually bind longer antigenic peptides, which typically range from 13 to 25 amino acids in length. PFRs at both termini of these peptides participate in regulating the overall conformation of TCR-pMHC class II complexes and exert a pronounced effect on the geometric and physicochemical characteristics of the TCR-pMHC binding interface. Furthermore, within the TCR recognition interface, the complementarity-determining regions (CDRs) consist of segments that differ markedly in conformational behavior. They commonly include regions that are relatively rigid and structurally stable, together with highly flexible segments exhibiting substantial conformational plasticity. These rigidity-flexibility features constitute an essential structural basis enabling TCRs to recognize diverse peptide-MHC ligands and to accommodate conformational heterogeneity at the interface. However, many current modeling tools, in an effort to enforce global conformational stability or reduce structural noise, tend to over-constrain intrinsically flexible regions. Such oversimplification may lead to inappropriate rigidification of flexible CDR loops, resulting in local structural distortions, compromised interface geometry, or even complete modeling failure for specific complexes. Against this background, the review approaches the field from the perspective of computational differences between MHC-I and MHC-II complexes. We first systematically organize and summarize available resources related to TCRs and pMHCs, including structural datasets, sequence databases, prediction tools, and benchmarking studies. We then focus on five representative tools capable of predicting both class I and class II complexes—AlphaFold2, AlphaFold3, TCRmodel2, tFold-TCR, and TCR-pHLA_ModellerS. After excluding structures present in the training sets of these tools, we constructed a benchmark dataset comprising 25 class I and 10 class II TCR-pMHC complexes in the bound state and conducted a systematic evaluation using this dataset. We first employ widely used general evaluation metrics, including All-Atom Root Mean Square Deviation (All-Atom RMSD), Backbone RMSD, Template Modeling score (TM-score), and DockQ, to assess the global conformational accuracy and interface modeling quality of class I and class II complexes. For class II complexes, we propose for the first time a peptide flanking residue deviation index, including the PFRs-Deviation Index (PFRs-DI), N-PFR-Deviation Index (N-PFR-DI), and C-PFR-Deviation Index (C-PFR-DI), to quantitatively characterize conformational deviations in PFRs. In addition, we propose the CDR conformational consistency index (CCC) designed to qualitatively evaluate the ability of prediction tools to capture TCR CDR conformational flexibility. These metrics collectively assess a tool’s ability to model both overall conformation and critical functional regions, thereby addressing the limitations of existing evaluation criteria that overemphasize global structure while inadequately capturing modeling quality in key functional areas. This establishes a unified analytical framework for MHC-I and MHC-II complexes to guide data resource selection, modeling strategy formulation, and evaluation system development. The framework further advances computational modeling and provides crucial support for multi-scale analysis of TCR-pMHC recognition mechanisms and their biological functions.

ObjectiveTo explore the effects and mechanism of the modified Danggui Yinzi on "itch-anxiety" model rats of chronic urticaria （CU）. MethodsThe 36 SPF-grade 6-8-week-old female SD rats were randomly divided into a blank control group，a model group，a positive control group，a low-dose modified Danggui Yinzi group，a medium-dose modified Danggui Yinzi group，and a high-dose modified Danggui Yinzi group. A "itch-anxiety" model was established by intraperitoneal injection of a suspension of sodium chloride and aluminum hydroxide and ovalbumin，combined with chronic unpredictable emotional stress stimulation. After successful modeling，rats in each group were administered drugs by gavage. The positive control group was given intragastric administration of the drug solutions of cetirizine and fluoxetine （2.08 mg·kg^-1·d^-1 fluoxetine， 2 mg·kg^-1·d^-1 cetirizine）， the low-，medium-，and high-dose modified Danggui Yinzi groups were administered traditional Chinese medicine at 1.44，2.88， 5.76 g·kg^-1， respectively，while the blank control group and model group were given an equal volume of normal saline. All interventions lasted for 15 days. Behavioral changes were evaluated by the elevated plus-maze test （detecting the percentage of entries into the open arms （OE%），the percentage of time spent in the open arms （OT%），and the total number of entries into the open and closed arms （TNE）），the open-field test （detecting total activity，average movement speed，and latency to enter the central area），and scratching behavior observation. Pathological changes of skin tissues were observed by hematoxylin-eosin （HE） staining and toluidine blue staining，while those of amygdala tissues were observed by HE staining，Nissl staining，and immunofluorescence detection of ionized calcium-binding adapter molecule-1 （Iba-1）. The content of immunoglobulin E （IgE），interleukin-33 （IL-33），histamine in serum and glutamate in the amygdala was detected by enzyme-linked immunosorbent assay （ELISA）. Western blot was used to detect the protein expression of striatal-enriched protein tyrosine phosphatase （STEP），N-methyl-D-aspartate receptor subunit 2B （NR2B）， calmodulin-dependent protein kinase Ⅱ （CaMKⅡ），phosphorylated CaMKⅡ （p-CaMKⅡ），mitogen-activated protein kinase （MAPK），phosphorylated MAPK （p-MAPK），nuclear factor kappa-light-chain-enhancer of activated B cells （NF-κB），phosphorylated NF-κB （p-NF-κB），and postsynaptic density protein-95 （PSD-95） in the amygdala. ResultsCompared with the blank control group，the model group rats showed obvious anxiety-like behaviors （decreased OE%，OT%，and TNE，reduced total activity，slower average movement speed，and prolonged latency to enter the central area），increased scratching times，obvious skin inflammation and mast cell degranulation，severe amygdala tissue damage，increased glutamate content in the amygdala，and elevated levels of IgE and IL-33 in serum. The expression of STEP，NF-κB，p-NF-κB，NR2B，MAPK，p-MAPK，CaMKⅡ，and p-CaMKⅡ proteins in the amygdala increased，while the expression of PSD-95 protein decreased （P<0.05）. Compared with the model group，the modified Danggui Yinzi group of each dose had increased OE%，OT%，TNE，total activity，and average movement speed，shortened latency to enter the central area， reduced scratching times，alleviated skin inflammation and mast cell degranulation，relieved amygdala tissue damage，decreased glutamate content in the amygdala，and reduced levels of IgE and IL-33 in serum. Moreover，compared with the model group，the low -，medium-，and high-dose modified Danggui Yinzi groups showed decreased expression levels of STEP，NF-κB，p-NF-κB，NR2B，MAPK，p-MAPK，CaMKⅡ，and p-CaMKⅡ proteins in the amygdala，and increased expression of PSD-95 protein. There was a significant dose-effect relationship，with the high-dose group showing the most significant regulatory effect （P<0.05）. ConclusionThe modified Danggui Yinzi has a therapeutic effect on "itch-anxiety" model rats of CU. Its mechanism may be related to regulating glutamate metabolism in the amygdala，modulating the STEP/NR2B/CaMKⅡ/MAPK/NF-κB pathway，and regulating the expression of PSD-95.

The innate immune system can be boosted in response to subsequent triggers by pre-exposure to microbes or microbial products, known as “trained immunity”. Compared to classical immune memory, innate trained immunity has several different features. Firstly, the molecules involved in trained immunity differ from those involved in classical immune memory. Innate trained immunity mainly involves innate immune cells (e.g., myeloid immune cells, natural killer cells, innate lymphoid cells) and their effector molecules (e.g., pattern recognition receptor (PRR), various cytokines), as well as some kinds of non-immune cells (e.g., microglial cells). Secondly, the increased responsiveness to secondary stimuli during innate trained immunity is not specific to a particular pathogen, but influences epigenetic reprogramming in the cell through signaling pathways, leading to the sustained changes in genes transcriptional process, which ultimately affects cellular physiology without permanent genetic changes (e.g., mutations or recombination). Finally, innate trained immunity relies on an altered functional state of innate immune cells that could persist for weeks to months after initial stimulus removal. An appropriate inducer could induce trained immunity in innate lymphocytes, such as exogenous stimulants (including vaccines) and endogenous stimulants, which was firstly discovered in bone marrow derived immune cells. However, mature bone marrow derived immune cells are short-lived cells, that may not be able to transmit memory phenotypes to their offspring and provide long-term protection. Therefore, trained immunity is more likely to be relied on long-lived cells, such as epithelial stem cells, mesenchymal stromal cells and non-immune cells such as fibroblasts. Epigenetic reprogramming is one of the key molecular mechanisms that induces trained immunity, including DNA modifications, non-coding RNAs, histone modifications and chromatin remodeling. In addition to epigenetic reprogramming, different cellular metabolic pathways are involved in the regulation of innate trained immunity, including aerobic glycolysis, glutamine catabolism, cholesterol metabolism and fatty acid synthesis, through a series of intracellular cascade responses triggered by the recognition of PRR specific ligands. In the view of evolutionary, trained immunity is beneficial in enhancing protection against secondary infections with an induction in the evolutionary protective process against infections. Therefore, innate trained immunity plays an important role in therapy against diseases such as tumors and infections, which has signature therapeutic effects in these diseases. In organ transplantation, trained immunity has been associated with acute rejection, which prolongs the survival of allografts. However, trained immunity is not always protective but pathological in some cases, and dysregulated trained immunity contributes to the development of inflammatory and autoimmune diseases. Trained immunity provides a novel form of immune memory, but when inappropriately activated, may lead to an attack on tissues, causing autoinflammation. In autoimmune diseases such as rheumatoid arthritis and atherosclerosis, trained immunity may lead to enhance inflammation and tissue lesion in diseased regions. In Alzheimer’s disease and Parkinson’s disease, trained immunity may lead to over-activation of microglial cells, triggering neuroinflammation even nerve injury. This paper summarizes the basis and mechanisms of innate trained immunity, including the different cell types involved, the impacts on diseases and the effects as a therapeutic strategy to provide novel ideas for different diseases.

Addressing the enduring challenge of evaluating traditional Chinese medicines (TCMs), the integrated evidence chain-based effectiveness evaluation of TCMs (Eff-iEC) has emerged. This paper explored its capacity through a demonstration study that evaluated the effectiveness evidence of six commonly used anti-hepatic fibrosis Chinese patent medicines (CPMs), including Biejiajian Pill (BP), Dahuang Zhechong Pill (DZP), Biejia Ruangan Compound (BRC), Fuzheng Huayu Capsule (FHC), Anluo Huaxian Pill (AHP), and Heluo Shugan Capsule (HSC), using both Eff-iEC and the Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) system. The recognition of these CPMs within the TCM academic community was also assessed through their inclusion in relevant medical documents. Results showed that the evidence of BRC and FHC received higher assessments in both Eff-iEC and GRADE system, while the assessments for others varied. Analysis of community recognition revealed that Eff-iEC more accurately reflects the clinical value of these CPMs, exhibiting superior evaluative capabilities. By breaking through the conventional pattern of TCMs effectiveness evaluation, Eff-iEC offers a novel epistemology that better aligns with the clinical realities and reasoning of TCMs, providing a coherent methodology for clinical decision-making, new drug evaluations, and health policy formulation.

The current study aimed to clarify the roles of apolipoprotein A5 (ApoA5) and milk fat globule-epidermal growth factor 8 (Mfge8) in regulating myocardial lipid deposition and the regulatory relationship between them. The serum levels of ApoA5 and Mfge8 in obese and healthy people were compared, and the obesity mouse model induced by the high-fat diet (HFD) was established. In addition, primary cardiomyocytes were purified and identified from the hearts of suckling mice. The 0.8 mmol/L sodium palmitate treatment was used to establish the lipid deposition cardiomyocyte model in vitro. ApoA5-overexpressing adenovirus was used to observe its effects on cardiac function and lipids. The expressions of the fatty acid uptake-related molecules and Mfge8 on transcription or translation levels were detected. Co-immunoprecipitation was used to verify the interaction between ApoA5 and Mfge8 proteins. Immunofluorescence was used to observe the co-localization of Mfge8 protein with ApoA5 or lysosome-associated membrane protein 2 (LAMP2). Recombinant rMfge8 was added to cardiomyocytes to investigate the regulatory mechanism of ApoA5 on Mfge8. The results showed that participants in the simple obesity group had a significant decrease in serum ApoA5 levels (P < 0.05) and a significant increase in Mfge8 levels (P < 0.05) in comparison with the healthy control group. The adenovirus treatment successfully overexpressed ApoA5 in HFD-fed obese mice and palmitic acid-induced lipid deposition cardiomyocytes, respectively. ApoA5 reduced the weight of HFD-fed obese mice (P < 0.05), shortened left ventricular isovolumic relaxation time (IVRT), increased left ventricular ejection fraction (LVEF), and significantly reduced plasma levels of triglycerides (TG) and cholesterol (CHOL) (P < 0.05). In myocardial tissue and cardiomyocytes, the overexpression of ApoA5 significantly reduced the deposition of TG (P < 0.05), transcription of fatty acid translocase (FAT/CD36) (P < 0.05), fatty acid-binding protein (FABP) (P < 0.05), and fatty acid transport protein (FATP) (P < 0.05), and protein expression of Mfge8 (P < 0.05), while the transcription levels of Mfge8 were not significantly altered (P > 0.05). In vitro, the Mfge8 protein was captured using ApoA5 as bait protein, indicating a direct interaction between them. Overexpression of ApoA5 led to an increase in co-localization of Mfge8 with ApoA5 or LAMP2 in cardiomyocytes under lipid deposition status. On this basis, exogenous added recombinant rMfge8 counteracted the improvement of lipid deposition in cardiomyocytes by ApoA5. The above results indicate that the overexpression of ApoA5 can reduce fatty acid uptake in myocardial cells under lipid deposition status by regulating the content and cellular localization of Mfge8 protein, thereby significantly reducing myocardial lipid deposition and improving cardiac diastolic and systolic function.
Animals ; Humans ; Mice ; Myocytes, Cardiac/metabolism* ; Obesity/physiopathology* ; Male ; Apolipoprotein A-V/blood* ; Lipid Metabolism/physiology* ; Milk Proteins/blood* ; Myocardium/metabolism* ; Diet, High-Fat ; Antigens, Surface/physiology* ; Mice, Inbred C57BL ; Cells, Cultured ; Female

Purpose: The aim of this study was to investigate the feasibility of an intelligent handheld ultrasound (US) device for assisting non-expert general practitioners (GPs) in detecting carotid plaques (CPs) in community populations. Methods: This prospective parallel controlled trial recruited 111 consecutive community residents. All of them underwent examinations by non-expert GPs and specialist doctors using handheld US devices (setting A, setting B, and setting C). The results of setting C with specialist doctors were considered the gold standard. Carotid intima-media thickness (CIMT) and the features of CPs were measured and recorded. The diagnostic performance of GPs in distinguishing CPs was evaluated using a receiver operating characteristic curve. Inter-observer agreement was compared using the intragroup correlation coefficient (ICC). Questionnaires were completed to evaluate clinical benefits. Results: Among the 111 community residents, 80, 96, and 112 CPs were detected in settings A, B, and C, respectively. Setting B exhibited better diagnostic performance than setting A for detecting CPs (area under the curve, 0.856 vs. 0.749; P<0.01). Setting B had better consistency with setting C than setting A in CIMT measurement and the assessment of CPs (ICC, 0.731 to 0.923). Moreover, measurements in setting B required less time than the other two settings (44.59 seconds vs. 108.87 seconds vs. 126.13 seconds, both P<0.01). Conclusion Using an intelligent handheld US device, GPs can perform CP screening and achieve a diagnostic capability comparable to that of specialist doctors.