Atherosclerosis (AS), the primary pathological contributor to cardiovascular diseases (CVDs), has increasingly affected younger populations due to modern dietary habits and sedentary lifestyles. Current diagnostic modalities, including ultrasound, MRI, and CT, primarily identify advanced lesions and inadequately evaluate plaque vulnerability, thereby hindering early detection. Conventional treatments, which involve long-term medications associated with side effects such as hepatic injury and surgical interventions that carry risks of restenosis and hemorrhage, underscore the urgent need for non-invasive, cost-effective early diagnostic methods and targeted therapies. Gut microbiota metabolites are pivotal in AS pathogenesis, with trimethylamine N-oxide (TMAO) and short-chain fatty acids (SCFAs) serving as functionally opposing biomarkers. TMAO is produced when gut bacteria, specifically Firmicutes and Proteobacteria, metabolize dietary choline and carnitine into trimethylamine (TMA), which the liver subsequently converts to TMAO via flavin-containing monooxygenase 3 (FMO3); TMAO is then excreted in urine. Variability in TMAO levels is influenced by marine food consumption and FMO3 modulation, which can be affected by genetics, age, and diet. Mechanistically, TMAO exacerbates AS by disrupting cholesterol metabolism, inducing endothelial dysfunction through the elevation of reactive oxygen species (ROS) and pro-inflammatory cytokines such as IL-6, and reducing nitric oxide levels. Additionally, TMAO activates NF-κB and NLRP3 pathways while enhancing platelet reactivity. Clinically, elevated TMAO levels correlate with early AS and serve as predictors of mortality in patients with stable coronary artery disease (CAD) and acute coronary syndrome (ACS), as well as major adverse cardiovascular events (MACE) in stroke patients. Conversely, SCFAs—namely acetate, propionate, and butyrate—are produced by gut bacteria such as Akkermansia muciniphila and Faecalibacterium prausnitzii through the fermentation of dietary fiber. These metabolites exert anti-AS effects: acetate aids in maintaining metabolic homeostasis; propionate protects endothelial function and reduces plaque area; and butyrate fortifies intestinal barriers while suppressing inflammation. Furthermore, SCFAs cross-regulate bile acid metabolism, thereby influencing TMAO levels, and antagonize the pro-inflammatory and lipid-disrupting effects of TMAO. The use of TMAO and SCFAs as standalone biomarkers is constrained by limitations. TMAO lacks specificity, while SCFA levels fluctuate based on gut microbiota and dietary intake. Traditional AS risk assessment tools, which include clinical indicators, imaging techniques, and single biomarkers such as CRP, LDL-C, and ASCVD scores, overlook gut metabolism and demonstrate inadequate performance in younger populations. This review advocates for an “antagonistic-complementary” combined strategy: utilizing acetate and TMAO for early AS, propionate and TMAO for progressive AS, and butyrate and TMAO for advanced AS, addressing endothelial dysfunction, lipid deposition, and plaque stability/thrombosis risk, respectively. For clinical application, standardization of detection methods is crucial; liquid chromatography-mass spectrometry (LC-MS) is the gold standard, necessitating a unified sample pretreatment protocol, such as extraction with 1% formic acid in methanol. Additionally, dried blood spots (DBS) facilitate non-invasive testing, provided that dietary controls are implemented prior to detection, including a 12-hour fast and avoidance of high-choline and high-fiber foods. Existing challenges encompass the absence of standardized systems, limited large-scale validation, and ambiguous interactions with conditions such as hypertension. The authors’ team has previously established connections between gut metabolites and AS, including the reduction of TMAO as a preventive measure for AS, thereby reinforcing this proposed strategy. Future research should prioritize standardization, the development of machine learning-optimized models, validation of interventions, and the exploration of multi-omics-based “gut microbiota-metabolite-vascular” networks. In conclusion, the combined detection of TMAO and SCFAs offers a novel framework for AS risk assessment, facilitating early diagnosis and targeted interventions while enhancing the integration of gut metabolism into cardiovascular disease management.

Atherosclerosis (AS), the primary pathological contributor to cardiovascular diseases (CVDs), has increasingly affected younger populations due to modern dietary habits and sedentary lifestyles. Current diagnostic modalities, including ultrasound, MRI, and CT, primarily identify advanced lesions and inadequately evaluate plaque vulnerability, thereby hindering early detection. Conventional treatments, which involve long-term medications associated with side effects such as hepatic injury and surgical interventions that carry risks of restenosis and hemorrhage, underscore the urgent need for non-invasive, cost-effective early diagnostic methods and targeted therapies. Gut microbiota metabolites are pivotal in AS pathogenesis, with trimethylamine N-oxide (TMAO) and short-chain fatty acids (SCFAs) serving as functionally opposing biomarkers. TMAO is produced when gut bacteria, specifically Firmicutes and Proteobacteria, metabolize dietary choline and carnitine into trimethylamine (TMA), which the liver subsequently converts to TMAO via flavin-containing monooxygenase 3 (FMO3); TMAO is then excreted in urine. Variability in TMAO levels is influenced by marine food consumption and FMO3 modulation, which can be affected by genetics, age, and diet. Mechanistically, TMAO exacerbates AS by disrupting cholesterol metabolism, inducing endothelial dysfunction through the elevation of reactive oxygen species (ROS) and pro-inflammatory cytokines such as IL-6, and reducing nitric oxide levels. Additionally, TMAO activates NF-κB and NLRP3 pathways while enhancing platelet reactivity. Clinically, elevated TMAO levels correlate with early AS and serve as predictors of mortality in patients with stable coronary artery disease (CAD) and acute coronary syndrome (ACS), as well as major adverse cardiovascular events (MACE) in stroke patients. Conversely, SCFAs—namely acetate, propionate, and butyrate—are produced by gut bacteria such as Akkermansia muciniphila and Faecalibacterium prausnitzii through the fermentation of dietary fiber. These metabolites exert anti-AS effects: acetate aids in maintaining metabolic homeostasis; propionate protects endothelial function and reduces plaque area; and butyrate fortifies intestinal barriers while suppressing inflammation. Furthermore, SCFAs cross-regulate bile acid metabolism, thereby influencing TMAO levels, and antagonize the pro-inflammatory and lipid-disrupting effects of TMAO. The use of TMAO and SCFAs as standalone biomarkers is constrained by limitations. TMAO lacks specificity, while SCFA levels fluctuate based on gut microbiota and dietary intake. Traditional AS risk assessment tools, which include clinical indicators, imaging techniques, and single biomarkers such as CRP, LDL-C, and ASCVD scores, overlook gut metabolism and demonstrate inadequate performance in younger populations. This review advocates for an “antagonistic-complementary” combined strategy: utilizing acetate and TMAO for early AS, propionate and TMAO for progressive AS, and butyrate and TMAO for advanced AS, addressing endothelial dysfunction, lipid deposition, and plaque stability/thrombosis risk, respectively. For clinical application, standardization of detection methods is crucial; liquid chromatography-mass spectrometry (LC-MS) is the gold standard, necessitating a unified sample pretreatment protocol, such as extraction with 1% formic acid in methanol. Additionally, dried blood spots (DBS) facilitate non-invasive testing, provided that dietary controls are implemented prior to detection, including a 12-hour fast and avoidance of high-choline and high-fiber foods. Existing challenges encompass the absence of standardized systems, limited large-scale validation, and ambiguous interactions with conditions such as hypertension. The authors’ team has previously established connections between gut metabolites and AS, including the reduction of TMAO as a preventive measure for AS, thereby reinforcing this proposed strategy. Future research should prioritize standardization, the development of machine learning-optimized models, validation of interventions, and the exploration of multi-omics-based “gut microbiota-metabolite-vascular” networks. In conclusion, the combined detection of TMAO and SCFAs offers a novel framework for AS risk assessment, facilitating early diagnosis and targeted interventions while enhancing the integration of gut metabolism into cardiovascular disease management.

ObjectiveThis paper aims to find the identified and validated clinical biomarker data building upon a clinical study of early-phase phase Ⅱ and investigate the correlation analysis of Huanglian Jiedu Wan on syndrome improvement and clinical biomarkers in the treatment of "excess heat-toxicity" based on a machine learning model. Additionally， the effective prediction of clinical biomarker values for the main symptoms of the "excess heat-toxicity" syndrome was assessed. MethodsA total of 229 patients meeting the inclusion criteria for "excess heat-toxicity" syndrome were randomly divided into the Huanglian Jiedu Wan group and the placebo group. Syndrome score transition matrices were constructed for the Huanglian Jiedu Wan group and the placebo group based on three main symptoms of "excess heat-toxicity" syndrome， such as oral ulcers， sore throat， and gum swelling and pain. Data from the patients with these three syndromes were also integrated for an overall analysis. The corresponding syndrome score transition matrices were further constructed to visualize symptom change trends of the patients in the two groups via heatmaps. Based on the identified and validated clinical biomarkers related to inflammation， oxidative stress， and energy metabolism in the early phase， Spearman correlation analysis was employed to analyze and evaluate the associations between clinical biomarkers and syndrome improvement. Key clinical biomarkers reflecting the effect of Huanglian Jiedu Wan were screened through the comparison of differences between groups. An extreme gradient boosting （XGBoost） algorithm was used to develop a prediction model for main symptom classification， with classification performance evaluated through 10-fold cross-validation. Feature importance analysis was applied to identify variables with the greatest contribution to the prediction result. ResultsThe syndrome transition matrix results indicated that the Huanglian Jiedu Wan group showed a superior effect to the placebo group in improving oral ulcers， sore throat， and overall symptoms， with significant effects observed especially in sore throat and overall symptom analyses （P<0.01）. Spearman correlation analysis revealed that several clinical biomarkers positively correlated with "excess heat-toxicity" syndrome and its main symptom improvement， were also called "heat-related biomarkers"， including succinic acid， α-ketoglutaric acid， glycine， lactic acid， adenosine monophosphate （AMP）， tumor necrosis factor-α （TNF-α）， interferon-γ （IFN-γ）， interleukin-1β （IL-1β）， interleukin-4 （IL-4）， interleukin-6 （IL-6）， interleukin-8 （IL-8）， interleukin-10 （IL-10）， and so on. Conversely， clinical biomarkers negatively correlated with symptom severity， were also called "heat-clearing related biomarkers" after administration of Huanglian Jiedu Wan， including malic acid， fumaric acid， cis-aconitic acid， adrenocorticotropic hormone （ACTH）， IL-1β， IL-4， IL-8， succinic acid， and citric acid. The XGBoost classification model using all 52 biomarkers as variables achieved an average test accuracy of 0.754 and an average F1 score of 0.777. Feature importance analysis identified the scores of glutamic acid in saliva and IL-6 were the highest in all the variables， with importance scores of 0.081 and 0.080， respectively. After screening out 14 key variables and optimizing the parameters， model performance improved to an average accuracy of 0.758 and an F1 score of 0.798. Feature importance analysis further determined that the glutamic acid in saliva and IL-6 showed obvious changes after screening the variables， confirming the good syndrome prediction ability of the model constructed by these key clinical biomarkers. ConclusionThis study systematically elucidates the correlation between syndrome improvement and clinical biomarkers of Huanglian Jiedu Wan in the treatment of "excess heat-toxicity" syndrome. An XGBoost classification model based on key clinical biomarkers is successfully established， achieving effective prediction of the symptoms related to the "excess heat-toxicity" syndrome such as oral ulcers and sore throat and providing a new insight for objective identification of traditional Chinese medicine syndromes.

ObjectiveThis paper aims to find the identified and validated clinical biomarker data building upon a clinical study of early-phase phase Ⅱ and investigate the correlation analysis of Huanglian Jiedu Wan on syndrome improvement and clinical biomarkers in the treatment of "excess heat-toxicity" based on a machine learning model. Additionally， the effective prediction of clinical biomarker values for the main symptoms of the "excess heat-toxicity" syndrome was assessed. MethodsA total of 229 patients meeting the inclusion criteria for "excess heat-toxicity" syndrome were randomly divided into the Huanglian Jiedu Wan group and the placebo group. Syndrome score transition matrices were constructed for the Huanglian Jiedu Wan group and the placebo group based on three main symptoms of "excess heat-toxicity" syndrome， such as oral ulcers， sore throat， and gum swelling and pain. Data from the patients with these three syndromes were also integrated for an overall analysis. The corresponding syndrome score transition matrices were further constructed to visualize symptom change trends of the patients in the two groups via heatmaps. Based on the identified and validated clinical biomarkers related to inflammation， oxidative stress， and energy metabolism in the early phase， Spearman correlation analysis was employed to analyze and evaluate the associations between clinical biomarkers and syndrome improvement. Key clinical biomarkers reflecting the effect of Huanglian Jiedu Wan were screened through the comparison of differences between groups. An extreme gradient boosting （XGBoost） algorithm was used to develop a prediction model for main symptom classification， with classification performance evaluated through 10-fold cross-validation. Feature importance analysis was applied to identify variables with the greatest contribution to the prediction result. ResultsThe syndrome transition matrix results indicated that the Huanglian Jiedu Wan group showed a superior effect to the placebo group in improving oral ulcers， sore throat， and overall symptoms， with significant effects observed especially in sore throat and overall symptom analyses （P<0.01）. Spearman correlation analysis revealed that several clinical biomarkers positively correlated with "excess heat-toxicity" syndrome and its main symptom improvement， were also called "heat-related biomarkers"， including succinic acid， α-ketoglutaric acid， glycine， lactic acid， adenosine monophosphate （AMP）， tumor necrosis factor-α （TNF-α）， interferon-γ （IFN-γ）， interleukin-1β （IL-1β）， interleukin-4 （IL-4）， interleukin-6 （IL-6）， interleukin-8 （IL-8）， interleukin-10 （IL-10）， and so on. Conversely， clinical biomarkers negatively correlated with symptom severity， were also called "heat-clearing related biomarkers" after administration of Huanglian Jiedu Wan， including malic acid， fumaric acid， cis-aconitic acid， adrenocorticotropic hormone （ACTH）， IL-1β， IL-4， IL-8， succinic acid， and citric acid. The XGBoost classification model using all 52 biomarkers as variables achieved an average test accuracy of 0.754 and an average F1 score of 0.777. Feature importance analysis identified the scores of glutamic acid in saliva and IL-6 were the highest in all the variables， with importance scores of 0.081 and 0.080， respectively. After screening out 14 key variables and optimizing the parameters， model performance improved to an average accuracy of 0.758 and an F1 score of 0.798. Feature importance analysis further determined that the glutamic acid in saliva and IL-6 showed obvious changes after screening the variables， confirming the good syndrome prediction ability of the model constructed by these key clinical biomarkers. ConclusionThis study systematically elucidates the correlation between syndrome improvement and clinical biomarkers of Huanglian Jiedu Wan in the treatment of "excess heat-toxicity" syndrome. An XGBoost classification model based on key clinical biomarkers is successfully established， achieving effective prediction of the symptoms related to the "excess heat-toxicity" syndrome such as oral ulcers and sore throat and providing a new insight for objective identification of traditional Chinese medicine syndromes.

ObjectiveTo establish a highly specific and sensitive triplex TaqMan quantitative PCR method for the detection of mouse hepatitis virus (MHV), mouse parvovirus (MPV), and reovirus type 3 (Reo-3) in laboratory mice. MethodsRecombinant plasmid standards were constructed using conserved genomic regions of the three target viruses. Specific primers and probes were designed, and a triplex TaqMan quantitative PCR (qPCR) system was optimized through preliminary experiments. Sensitivity was evaluated using 10-fold serial dilutions (10¹-10⁷ copies/μL). Intra- and inter-assay repeatability were assessed via reproducibility experiments. ResultsA strong linear correlation (R ²>0.99) was observed between copy number and Ct value at template concentrations of 10²-10⁷ copies/μL. Intra-assay and inter-assay coefficients of variation (CV) were both below 5%. No cross-reactivity was observed with mouse cytomegalovirus (MCMV), vesicular stomatitis virus (VSV), Sendai virus (SeV), or pneumonia virus of mice (PVM). ConclusionThe triplex TaqMan quantitative PCR method established in this study has high sensitivity, good repeatability, and strong specificity. It enables the rapid, specific, and sensitive detection of MHV, MPV, and Reo-3, allows the simultaneous detection of multiple pathogens in a single tube, and can be applied to the detection of clinical samples from laboratory animals and to epidemiological investigations, thereby providing effective scientific and technological support for the prevention and control of viral infection and the interruption of transmission.

Plant-derived exosome-like nanovesicles (PELNs), characterized by a natural lipid bilayer membrane, have rapidly emerged as a prominent research frontier in medicine owing to their unique biological properties and robust therapeutic potential. This review comprehensively examines the biological profiles, mechanistic functions, and recent engineering advancements of PELNs. In terms of composition, PELNs are uniquely enriched in plant-specific glycolipids, phosphatidylserine, secondary metabolites, and highly stable 2'-O-methylated miRNAs. This distinct molecular makeup endows them with exceptional biocompatibility, negligible immunogenicity, and the capacity for cross-species molecular communication. Mechanistically, PELNs demonstrate profound anti-inflammatory efficacy by suppressing the NF-κB and NLRP3 inflammasome pathways. They also serve as potent immune modulators, driving macrophage M1/M2 polarization and regulating T cell activity. Additionally, PELNs exhibit promising antitumor capabilities, targeting malignancies via reactive oxygen species (ROS) induction, TRAIL pathway activation, and tumor microenvironment remodeling. Crucially, the plant miRNAs encapsulated within PELNs remain highly stable in the gastrointestinal tract, allowing them to selectively alter gene expression in specific gut microbiota communities. This interaction deeply influences host immunity and metabolism, highlighting the vital role in cross-species regulation. Advancements in bioengineering have further expanded the clinical utility of PELNs. Targeted delivery efficiency can be significantly amplified via surface functionalization (e.g., folate and RGD sequences) and state-of-the-art drug loading technologies such as sonication and electroporation. Consequently, engineered PELNs surpass traditional synthetic nanocarriers in penetrating natural physiological barriers, particularly for oral and transdermal drug administration. Despite these advantages, clinical translation is currently hindered by the lack of standardized isolation protocols, challenges in scalable manufacturing, and the need for robust quality control frameworks. Looking forward, the integration of multi-omics approaches and AI-driven “molecular fingerprinting”—coupled with the design of synthetic biomimetic vesicles—will be instrumental in overcoming these bottlenecks, ultimately establishing PELNs as a next-generation platform for precision medicine and targeted nanotherapeutic delivery.

Objective To understand mosquito density, species composition, and seasonal variations around the Beijing Capital International Airport(BCIA)and provide scientific evidence for mosquito-borne disease prevention and control. Methods Meteorological data were collected from 2016 to 2022, and mosquito density was monitored at seven surveillance sites around the BCIA from May to October each year using CO2-baited mosquito traps to analyze the relationship between mosquito density and meteorological factors. Results In total,68 518 female mosquitoes were captured, with a mosquito density of 10.20 per light · hour. The dominant species was Culex pipiens pallens(73.90%), followed by Aedes albopictus(9.71%)and Ae. vexans(9.29%);the mosquito density was highest in 2016(14.8 per light·hour)and lowest in 2022(4.46 per light·hour). Significant statistical differences were observed in mosquito density among different species(F=18.118, P<0.05). The density of Cx. pipiens pallens and other mosquito species showed a mutually exclusive trend; the peak of mosquito density varied based on years and habitats, with significant statistical differences in mosquito density among different habitats(F=8.504, P<0.05). The highest mosquito density was observed near the Wenyu River(21.50 per light·hour), whereas the lowest was recorded at the BCIA construction site(2.44 per light· hour). The monthly average temperature(r=0.595), monthly average highest temperature(r=0.575), and monthly average lowest temperature(r=0.624)showed moderate positive correlations, whereas the monthly average air pressure(r=-0.484)showed a moderate negative correlation. The monthly average minimum temperature was included in the regression model(F=25.575, P<0.000), and the equation Y=1.029X4-8.181 was used. The monthly average air pressure 2 months prior(b=-2.418, β=-1.619, P<0.05)and the monthly average relative humidity(b=-0.739, β=-1.201, P<0.05)significantly negatively predicted the mosquito density. Notably, the regression equation used was y=2526.170+(-2.418)X9+(-0.739)X13. The exposure-response analysis revealed that the density of mosquitoes was not linearly related to the average monthly air pressure, rainfall, and duration of sunshine. Conclusions The dominant mosquito species around the BCIA was Cx. pipiens pallens, Ae. albopictus, and Ae. vexans. During the control of Cx. pipiens pallens, the impact on other mosquito species must also be considered. Environmental management, breeding sites, and scientific use of pesticides should be prioritized based on the activity periods of mosquitoes. Further studies on mosquitoes and meteorological factors should be conducted to provide references and novel avenues for mosquito control.

ObjectiveCell adhesion is a critical process that regulates cellular physiological functions. Quantitative characterization of adhesion dynamics is essential for elucidating the intrinsic mechanical mechanisms underlying cellular activities. Although atomic force microscopy-based single-cell force spectroscopy is widely used for single-cell adhesion measurements, it requires complex chemical modifications for preparation of live-cell probes, leading to limitations such as cumbersome operation, low throughput, and potential impacts on cell viability. Fluidic force microscopy, which combines atomic force microscopy with microfluidic probes, is a technique allowing the operation of force-controlled nanopipettes in aqueous environments. By applying negative or positive pressure via a pressure controller, a single living cell can be captured onto or released from the cantilever under physiological conditions. This procedure offers a simple workflow and high assay throughput for single-cell adhesion measurements without the need for chemical functionalization. In this study, fluidic force microscopy-based single-cell force spectroscopy was adopted to achieve long-term quantitative characterization of single-cell adhesion dynamics in a simpler and more efficient manner, comparing the dynamic differences in adhesion establishment between two cell lines with different differentiation levels. MethodsHEK 293T and hTERT RPE-1 cells were non-invasively captured on the cantilever of a fluidic force microscope via its integrated microfluidic system during 40 h of adhesion culture. Cell-substrate detachment assays were performed, and force-distance curves were recorded to extract key mechanical adhesion parameters, including adhesion force, adhesion energy, and maximum detachment distance. These measurements were combined with real-time monitoring of cell spreading area to systematically characterize the dynamic evolution of single-cell adhesion. ResultshTERT RPE-1 cells rapidly entered a stable adhesion phase within 1 h after seeding, with both area-normalized adhesion force and area-normalized adhesion energy reaching peak values. In contrast, HEK 293T cells required 4 h to achieve stable adhesion. Subsequently, the adhesion force, adhesion energy and maximum detachment distance of hTERT RPE-1 and HEK 293T cells stabilized at approximately 240 nN vs. 30 nN, 2.2 pJ vs. 0.12 pJ and 6 μm vs. 4 μm, respectively. hTERT RPE-1 cells reached the peak of area-normalized adhesion parameters earlier than HEK 293T cells, with their peak area-normalized adhesion force and area-normalized adhesion energy being substantially elevated relative to HEK 293T cells. HEK 293T cells presented stronger linear correlations among adhesion energy, maximum detachment distance and adhesion force compared with hTERT RPE-1 cells. For both cell lines, cell spreading area exhibited a weak correlation with adhesion force. Whereas the area-normalized adhesion parameters of HEK 293T cells remained relatively constant throughout the adhesion process, hTERT RPE-1 cells exhibited elevated values in the early phase, followed by a gradual decline. These results indicated distinct dynamic adhesion patterns between the two cell types, with hTERT RPE-1 cells exhibiting stronger adhesion strength and higher adhesion efficiency. ConclusionIn this study, fluidic force microscopy-based single-cell force spectroscopy was successfully applied to perform long-term in situ quantitative measurement of the adhesion dynamics in single adherent cells. The approach revealed divergent adhesion patterns between HEK 293T and hTERT RPE-1 cells, suggesting a close association between cell differentiation and adhesion behaviors. These findings provide quantitative mechanical evidence for further understanding the underlying mechanisms of cell adhesion.

OBJECTIVE: To investigate the effect of acupuncture on advanced cancer patients by meta-analysis. METHODS: Nine databases (the Cochrane Central Register of Controlled Trials, MEDLINE, Web of Science, Embase, China National Knowledge Infrastructure, the Cumulative Index to Nursing and Allied Health Literature, Chinese Biomedical Literature Database, China Science and Technology Journal Database, and WanFang Data) were searched for randomized controlled trials (RCTs) on acupuncture in advanced cancer patients published from inception to February 13, 2023 and updated to June 1, 2023. Primary outcomes were quality of life (QOL), while secondary outcomes were pain, fatigue, and adverse events (side effects). Data synthesis was performed using RevMan V.5.3 to calculate pooled effect sizes. RoB-2 was used for the risk of bias, and the quality of evidence was assessed using the Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) tool. RESULTS: Totally 17 RCTs involving 1,178 participants were included, 15 of which were pooled for meta-analysis. Most studies demonstrated some concern for the overall risk of bias. The pooled data indicated that acupuncture was associated with improved QOL [mean difference (MD)=6.67, 95% confidence interval (CI): 5.09 to 8.26], pain (MD=-1.18, 95% CI -2.28 to -0.08), and adverse events (risk ratio=0.30, 95% CI: 0.26 to 0.57) compared with control groups. Fatigue outcome was not included. Heterogeneity was substantial, and GRADE evidence was very low for both QOL and pain. CONCLUSIONS Acupuncture could benefit patients with advanced cancer and is considered safe compared with usual care. However, the evidence regarding QOL and pain outcomes requires further validation. It is crucial to encourage the development of high-quality studies to strengthen this evidence. (Registry No. CRD42023423539).
Humans ; Acupuncture Therapy ; Neoplasms/therapy* ; Quality of Life ; Randomized Controlled Trials as Topic ; Treatment Outcome

OBJECTIVES: To explore the underlying pharmacological mechanisms and its potential effects of Chinese medicine herbal formula Sini Powder (SNP) on hepatocellular carcinoma (HCC). METHODS: The active components of SNP and their in vivo distribution were identified using ultraperformance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry. Construction of component-target-disease networks, protein-protein interaction network, Gene Ontology function and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis, and molecular docking were employed to analyze the active components and anti-HCC mechanisms of SNP. Cell viability assay and wound healing assay were utilized to confirm the effect of SNP-containing serum (2.5%, 5.0%, 10%, 20%, and 40%), isoprenaline or propranolol (both 10, 100, and 1,000 µ mol/L) on proliferation and migration of HepG 2 or Huh7 cells. Meanwhile, the effect of isoprenaline or propranolol on the β 2 adrenergic receptor (ADRB2) mRNA expression on HepG2 cells were measured by real-time quantitative reverse transcription (RT-qPCR). Mice with subcutaneous tumors were either subjected to chronic restraint stress (CRS) followed by SNP administration (364 mg/mL) or directly treated with SNP (364 mg/mL). These two parallel experiments were performed to validate the effects of SNP on stress responses. Stress-related proteins and hormones were quantified using RT-qPCR, enzyme-linked immunosorbent assay, and immunohistochemistry. Metagenomic sequencing was performed to confirm the influence of SNP on the gut microbiota in the tumor-bearing CRS mice. RESULTS: The distribution of the 12 active components of SNP was confirmed in various tissues and feces. Network pharmacology analysis confirmed the anti-HCC effects of the 5 active components. The potential anti-HCC mechanisms of SNP may involve the epidermal growth factor receptor (EGFR), proto-oncogene tyrosine-protein kinase Src (SRC) and signal transducer and activator of transcription 3 (STAT3) pathways. SNP-containing serum inhibited the proliferation of HepG2 and Huh7 cells at concentrations of 2.5% and 5.0%, respectively, after 24 h of treatment. Furthermore, SNP suppressed tumor progression in tumor-bearing mice exposed to CRS. SNP treatment also downregulated the expressions of stress-related proteins and pro-inflammatory cytokines, primarily by modulating the gut microbiota. Specifically, the abundance of Alistipes and Prevotella, which belong to the phylum Bacteroidetes, increased in the SNP-treated group, whereas Lachnospira, in the phylum Firmicutes, decreased. CONCLUSION SNP can combat HCC by alleviating stress responses through the regulation of gut microbiota.
Animals ; Gastrointestinal Microbiome/drug effects* ; Liver Neoplasms/microbiology* ; Carcinoma, Hepatocellular/microbiology* ; Humans ; Drugs, Chinese Herbal/therapeutic use* ; Powders ; Cell Proliferation/drug effects* ; Mice ; Molecular Docking Simulation ; Cell Line, Tumor ; Hep G2 Cells ; Receptors, Adrenergic, beta-2/genetics* ; Stress, Physiological/drug effects* ; Cell Movement/drug effects* ; Male ; Protein Interaction Maps/drug effects* ; Cell Survival/drug effects* ; Proto-Oncogene Mas