Objective: To assess the effect of 12-year-old children s pit and fissure sealants on the health of first permanent molars, so as to provide evidence for optimizing caries prevention strategies among children. Methods: In March 2025, a cluster random sampling method was used to conduct oral examinations on 965 students aged 12 from Chengdu s 2021 Comprehensive Intervention Program for Pediatric Oral Diseases. Data from the Comprehensive Intervention System for Children s Oral Diseases were referenced. Participants were divided into a sealed group ( n =755) and an unsealed group ( n =210) based on whether they had received sealants on their first permanent molars. Chi square test or analysis of variance were used to compare indicators such as caries incidence, new caries detection rate, and new caries mean (DMFT increment) between the two groups Results: The sealed group showed significantly lower caries incidence, new caries detection rate, and new caries mean (33.38%, 17.65%, 0.59±1.00) compared to the unsealed group (43.81%, 24.70%, 0.87±1.22)( χ 2/F =7.79, 18.26, 9.55, all P <0.05). However, no significant difference was found in the filled teeth ratio between the two groups (20.38% , 20.16%; χ 2=0.01, P =0.94). In girls, the sealed group exhibited significantly lower caries incidence, new caries detection rate, and new caries mean (36.78%, 20.99%, 0.69± 1.10 ) than the unsealed group (57.55%, 33.52%, 1.15±1.29) ( χ 2/F =14.42, 23.76, 10.92, all P <0.05), whereas no significant differences were observed between boys in the sealed (30.47%, 14.85%, 0.50±0.89) and unsealed groups (29.81%, 16.18%, 0.59± 1.08) ( χ 2/F =0.02, 0.41, 0.74, all P >0.05). Boys had significantly lower new caries detection rates and new caries means than girls in both groups ( χ 2/F =16.20, 6.94; 29.93, 11.84, all P <0.05). In urban areas, the sealed group had lower new caries detection rates and new caries means (19.37%, 0.68±1.04) than the unsealed group (24.66%, 0.90±1.20) ( χ 2/F =6.86, 3.94, both P <0.05). In suburban areas, all indicators for the sealed group (24.71%, 13.77%, 0.42±0.87) were significantly lower than those for the unsealed group (38.81%, 24.77%, 0.82±1.28) ( χ 2/F =5.28, 15.36, 6.00, all P <0.05). Indicators from specialized dental institutions (11.25%, 4.81%, 0.16±0.56) were significantly lower than those from county level or above general hospitals (33.33%, 19.11%, 0.38±1.00) and primary healthcare institutions (37.59%, 19.24%, 0.67±1.05) ( χ 2/F =20.99, 34.31, 21.08 , all P <0.01). Conclusions The 12-year-old children s pit and fissure sealants effectively reduce the caries incidence in first permanent molars, particularly showing significant effectiveness in girls and suburban children. Intervention strategies should be optimized according to gender.

ObjectiveTo evaluate the comprehensive intervention effects of Huangqin Qingre Chubi Capsule （黄芩清热除痹胶囊， HQC） on self-perception of patients （SPP） and immune inflammation in patients with rheumatoid arthritis （RA）， and to explore its potential mechanisms. MethodsClinical data of 452 RA patients were retrospectively collected. Patients were divided into a control group （274 cases）， treated with conventional western medicine， and an observation group （178 cases）， treated with HQC for at least 2 weeks in addition to conventional western medicine. The treatment duration was 2 weeks for both groups. Propensity score matching （PSM） was performed at a ratio of 1∶1 to match patients between groups. SPP including the Chinese version of the short form-36 health survey （SF-36）， self-rating anxiety scale （SAS）， self-rating depression scale （SDS）， visual analog scale （VAS）， and Chinese patient-reported index for rheumatoid arthritis （CPRI-RA）， as well as immune inflammatory indicators， including erythrocyte sedimentation rate （ESR）， high-sensitivity C-reactive protein （hs-CRP）， rheumatoid factor （RF）， anti-cyclic citrullinated peptide antibody （anti-CCP）， interleukin-6 （IL-6）， immunoglobulin A （IgA）， immunoglobulin G （IgG）， immunoglobulin M （IgM）， complement C3， and complement C4， were collected before and after treatment. Spearman correlation analysis was used to assess the relationships between SPP and immune inflammatory indicators. Logistic regression， association rule analysis， and mediation analysis were performed to evaluate the effects and potential pathways of HQC on SPP and immune inflammatory indicators. Network pharmacology was applied to identify the active components and core targets of HQC in the treatment of RA， followed by molecular docking verification. In cell experiments， cells were divided into normal group， model group， 20% medicated serum group， and 80 nmol/L control group. Human synovial fibroblasts （FLS） were cultured with complete medium in the normal group， while human rheumatoid arthritis fibroblast-like synoviocytes （RA-FLS） were cultured in the model group. In the 20% medicated serum group， RA-FLS were cultured with medium containing 20% HQC-medicated serum， and in the 80 nmol/L control group， RA-FLS were cultured with complete medium containing 80 nmol/L methotrexate suspension. After 48 h of culture， cell viability was detected by cell counting kit-8 （CCK-8） assay. Levels of interleukin-1β （IL-1β）， interleukin-6 （IL-6）， interleukin-8 （IL-8）， and interleukin-10 （IL-10） in the cell supernatant were measured by enzyme-linked immunosorbent assay （ELISA）. Protein levels of matrix metalloproteinase 9 （MMP9）， transcription factor AP-1 subunit （JUN）， vascular endothelial growth factor A （VEGFA）， and C-X-C motif chemokine ligand 8 （CXCL8） were detected by Western Blot， and cell migration ability was evaluated using Transwell assay. ResultsAfter PSM， 178 cases were included in each group. After treatment， SF-36 scores increased， while scores of SAS， SDS， VAS and CPRI-RA， levels of ESR， hs-CRP， IL-6， complement C3， and complement C4 levels decreased in both groups； IgG and IgM levels were also reduced in the observation group （P＜0.05）. Physical functioning （correlation coefficient -0.19， P＜0.05） and social functioning （correlation coefficient -0.18， P＜0.05） of SF-36 were negatively correlated with hs-CRP， while VAS score was positively correlated with hs-CRP （correlation coefficient 0.19， P＜0.05）. HQC showed high associations with improvements in multiple indicators of SPP and immune inflammatory， and acted as a protective factor for the improvement of several SPP； hs-CRP and ESR played partial mediating roles in the improvement of SPP induced by HQC （P＜0.05）. Network pharmacology analysis identified baicalein， quercetin， α1-sitosterol， β-sitosterol， stigmasterol， baicalin， and crocetin as the core active components， and JUN， IL-6， VEGFA， MMP9， IL-1β， and CXCL8 as the core targets. Molecular docking results showed strong binding affinities of quercetin with VEGFA， JUN， MMP9， IL-6， and IL-1β， of baicalin with VEGFA and MMP9， and of wogonin with CXCL8. Cell experiments demonstrated that HQC and methotrexate inhibited RA-FLS viability and migration， reduced levels of IL-1β， IL-6， and IL-8， decreased protein levels of MMP9， JUN， VEGFA， and CXCL8， and increased IL-10 levels （P＜0.05）. ConclusionHQC can improve SPP in RA by regulating immune inflammatory responses. Its mechanism may be related to multi-pathway and multi-target inhibition of synovial cell inflammation and migration.

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.

Asthma and cough variant asthma （CVA） are both chronic heterogeneous diseases characterized by airway microenvironment homeostasis disruption as their core pathological basis. In recent years， micro ribonucleic acid （miRNA）， as core post-transcriptional regulators， have been shown to finely modulate multiple critical signaling pathways， including Janus kinase/signal transducer and activator of transcription （JAK/STAT）， nuclear factor-κB （NF-κB）， transforming growth factor-β/Smad （TGF-β/Smad）， and phosphoinositide 3-kinase/protein kinase B （PI3K/Akt）， as well as various pathological processes such as airway epithelial barrier restoration， type 1 helper T cell（Th1）/Th2 immune balance， M1/M2 macrophage polarization， airway smooth muscle cell function， and airway hyperresponsiveness. miRNAs play a pivotal role in maintaining and disrupting airway microenvironment homeostasis. Based on recent Chinese and international literature， a logical framework centered on "airway microenvironment homeostasis disruption， miRNA regulation， and microenvironment restoration" was constructed. From the perspective of the airway microenvironment， the therapeutic roles of miRNA in asthma and CVA were systematically summarized， and the cascade regulatory mechanisms of miRNA throughout the entire disease course were elucidated. The hub miRNA was identified， and research progress on traditional Chinese medicine intervention strategies was explored. Furthermore， current clinical studies on RNA therapeutics and traditional Chinese medicine in achieving multi-target and multi-pathway integrated treatment by modulating miRNA were analyzed. The value of miRNA as biomarkers for diagnosis， phenotyping， and prognosis assessment， as well as the potential and application prospects of miRNA mimics and antagonists in precision therapy， were summarized， with the ultimate goal of advancing precision therapy for asthma and CVA.

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.

OBJECTIVE: To observe the effects of Tiaoshu Anshen (regulating the hinge and calming the mind) acupuncture on sleep quality and serum levels of 5-hydroxytryptamine (5-HT) and dopamine (DA) in patients with chronic insomnia. METHODS: A total of 58 patients with chronic insomnia were randomly divided into an acupuncture group and a medication group, 29 cases in each group. Tiaoshu Anshen acupuncture was applied at Baihui (GV20) and bilateral Shenmen (HT7), Sanyinjiao (SP6), Benshen (GB13) in the acupuncture group, once a day, 1-day interval was taken after 6 consecutive days of treatment. Estazolam tablet was given orally before bed in the medication group, 1 mg each time. The 4-week treatment was required in both groups. Before and after treatment, the sleep quality was assessed by Pittsburgh sleep quality index (PSQI) and polysomnography (PSG), the serum levels of 5-HT and DA were detected by ELISA. RESULTS: After treatment, the item scores and total scores of PSQI were decreased compared with those before treatment in the two groups (P<0.05); in the acupuncture group, the scores of sleep quality, sleep latency, sleep time, sleep efficiency, sleep disorders and total score of PSQI were lower than those in the medication group (P<0.05). After treatment, the total sleep time (TST) was prolonged (P<0.05), the sleep latency (SL) and wake after sleep onset (WASO) were shortened (P<0.05), the sleep efficiency (SE%), percentage of non-rapid eye movement stage 3 (N3%), percentage of rapid eye movement stage (REM%) and serum levels of 5-HT were increased (P<0.05) compared with those before treatment; the percentage of non-rapid eye movement stage 1 (N1%), percentage of non-rapid eye movement stage 2 (N2%) and serum levels of DA were decreased (P<0.05) compared with those before treatment in the two groups. After treatment, in the acupuncture group, TST was longer, while SL and WASO were shorter than those in the medication group (P<0.05), SE%, N3%, REM% and serum level of 5-HT were higher, while N1%, N2% and serum level of DA were lower than those in the medication group (P<0.05). CONCLUSION Tiaoshu Anshen acupuncture may improve the sleep quality by regulating the serum neurotransmitter levels i.e. 5-HT and DA in patients with chronic insomnia.
Humans ; Sleep Initiation and Maintenance Disorders/physiopathology* ; Male ; Acupuncture Therapy ; Female ; Middle Aged ; Adult ; Serotonin/blood* ; Sleep Quality ; Acupuncture Points ; Dopamine/blood* ; Aged ; Neurotransmitter Agents/blood* ; Young Adult

The accumulation of uremic toxins such as urea nitrogen, blood creatinine, and uric acid of patients with renal failure in vivo would lead to aggravated kidney damage. In this study, coated aldehyde oxy-starch (CAO) was used as an adsorbent to investigate its in vitro adsorption performance on renal failure indexes for urea, indoxyl sulfate (INS), monomethylamine (MMA), dimethylamine (DMA), uric acid (UA), and creatinine (Cr). The effects of variables such as pH, temperature, concentration, dosage, and time on the adsorption capacity of CAO were systematically investigated, employing analytical techniques of high-performance liquid chromatography (HPLC) and gas chromatography (GC). The results revealed that CAO exhibited a strong adsorption capacity for urea, INS, and MMA, alongside a moderate adsorption capacity for DMA, UA, and Cr. The adsorption kinetics and thermodynamic studies indicated that the adsorption of urea and UA by CAO were fitted in pseudo-first-order kinetics, and the adsorption isotherm aligned with the Freundlich adsorption model. The enthalpy change ΔH of urea in adsorption was in the range of 40 to 60 kJ·mol^-1, which demonstrated the presence of strong adsorption force due to the interactions of coordinating groups. The ΔH of UA was greater than 80 kJ·mol^-1, indicating the generation of chemical bonds during the adsorption. Both of them, Gibbs free energy ΔG was less than 0, within the range of -20 to 0 kJ·mol^-1, suggested that the adsorption of urea and UA by CAO occurred spontaneously as physical adsorption process. The adsorption entropy ΔS of urea and UA was > 0, which indicated an increase in entropy throughout the adsorption. The infrared spectroscopygram showed the formation of a chemical bond, specifically the imine bond, following the adsorption of urea by CAO, thereby indicating a chemical reaction during the adsorption. This study elucidates the adsorption mechanism of CAO on various indexes of renal failure, providing a scientific basis for its clinical usage.