Objective To elucidate the changes in the gut microbiota and molecular mechanism of huaier in enhancing the efficacy of oxaliplatin (OXA) chemotherapy in gastric cancer (GC). Methods The effects of huaier on the gut microbiota structure and intestinal barrier function in MKN45-bearing mice were analyzed by using 16S rRNA sequencing, RT-qPCR, and IHC. UPLC-MS and network pharmacology, as well as in vivo experimental approaches, were employed to investigate the key bioactive constituents of huaier systematically and elucidate the underlying mechanisms by which huaier exerts therapeutic effects against GC. The expression of the PI3K/AKT/SREBP pathway was detected in cancer cells and tissues via Western blot analysis. The safety profile of huaier combined with OXA was verified through serum biochemistry and HE-stained tissue section analyses. Results Huaier inhibited the PI3K/AKT/SREBP pathway by increasing the abundance of Akkermansia muciniphila, reduced lipid droplet deposition, and ultimately enhanced the sensitivity to OXA in the treatment of GC. Conclusion This study demonstrates the value of huaier in gastric cancer treatment by enhancing chemosensitivity and provides novel insights into its systemic therapeutic effects through molecular mechanisms and gut microbiota modulation.

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.

With the continuous deepening of the concept of precision diagnosis and treatment for lung cancer, how to achieve higher efficiency and accuracy in the screening, diagnosis, and treatment pathways in clinical practice has become an important issue that urgently needs to be overcome. The current clinical difficulty lies in the fact that despite continuous advancements in imaging and molecular diagnostic technologies, there are still limitations in manual efficiency and subjective experience when it comes to massive data analysis and multi-scale feature extraction. Artificial intelligence (AI), especially algorithm systems based on deep learning, is an innovative technology capable of deeply empowering medical big data. This method utilizes algorithms such as convolutional neural networks, combined with radiomics, pathomics, and multi-modal data fusion analysis, demonstrating immense potential in early precise detection and benign-malignant differentiation of pulmonary nodules, digital pathological subtype recognition and non-invasive prediction of driver genes, precise 3D surgical planning and automatic delineation of radiotherapy target volumes, as well as dynamic risk warning during follow-up. This innovative technology provides a brand-new solution for realizing intelligent and individualized lung cancer diagnosis and treatment models. This consensus, based on the latest evidence from evidence-based medicine and combined with the development trends in the AI field and real-world clinical needs, was ultimately formed by gathering the consensus opinions of multidisciplinary experts in radiology, pathology, thoracic surgery, and other fields. The main content covers the application specifications of AI in the three core scenarios of lung cancer screening, diagnosis, and treatment, the technical standards for data collection and algorithm validation, as well as the ethical and regulatory challenges faced at the current stage. It aims to clarify the applicable boundaries of AI as a clinical auxiliary decision support tool, providing scientific guidance and standardized exploration directions for peers currently engaged in or planning to carry out AI-assisted clinical diagnosis, treatment, and translation of lung cancer.

Atherosclerosis (AS) remains the core pathological basis underlying the high incidence and high rates of mortality and disability associated with cardiovascular disease (CVD) worldwide. Its essence is not merely lipid deposition, but rather an immune-mediated disease of the vascular wall characterized by an interplay of lipid metabolism disorders and chronic inflammation, with damage to vascular endothelial cells serving as the initiating event. As the disease progresses, it involves complex synergistic interactions among various cellular components, including endothelial cells, macrophages, and inflammatory cells, ultimately leading to plaque formation, instability, and even fatal thrombotic events. In recent years, the central driving role of lipid metabolic reprogramming in the progression of AS has garnered increasing attention from the scientific community. Among the vast array of lipid molecules, long-chain fatty acids (LCFAs) have become a primary focus of research due to their exceptional physiological functions. Traditional views have primarily emphasized the basic physiological functions of LCFAs: serving as highly efficient energy substrates through mitochondrial β-oxidation and acting as key structural components of cellular phospholipid membranes. However, emerging evidence clearly indicates that the functions of LCFAs extend far beyond those of mere metabolic fuel. They also act as potent bioactive signaling molecules, playing an indispensable multidimensional role in the pathogenesis of AS. Equally noteworthy and representing a paradigm shift in cardiovascular research is the emerging theory of the “gut-heart axis”. This theoretical framework views the human gut microbiota—comprising trillions of microorganisms—as a critical and metabolically active “bioreactor”. A wealth of clinical and multi-cohort epidemiological studies have conclusively demonstrated that imbalances in the composition and function of the gut microbiota are highly correlated with the clinical risk and severity of AS. Within this axis, the gut microbiota serves as the primary processing hub for dietary lipids. It actively participates in the digestion and biochemical remodeling of LCFAs, thereby altering their saturation and chemical structure and generating a wide variety of gut microbial metabolites. The effects of these gut-derived lipid metabolites extend far beyond the local intestinal microenvironment. Upon entering the bloodstream, these circulating microbiota metabolites act as endocrine signals. Given the extreme complexity of the underlying mechanisms, a comprehensive elucidation of the synergistic and bidirectional interactions between LCFAs and the gut microbiota in vascular pathology is particularly urgent. Therefore, this article aims to provide a systematic review of the multidimensional regulatory mechanisms of LCFAs and their associated gut microbiota metabolites in the onset, progression, and clinical manifestations of AS. By thoroughly exploring the interaction patterns within the “LCFAs-gut microbiota-AS” triad, this review seeks to fundamentally expand our understanding of the pathogenesis of CVDs. More importantly, translating these mechanistic insights into clinical practice holds tremendous promise. We hope to provide a solid theoretical foundation for the future development of novel AS prevention and treatment strategies based on non-traditional approaches. These include precision nutritional interventions (i.e., dietary lipid intake plans tailored to an individual’s unique microbiome profile) and targeted microbiome modulation therapies (such as next-generation probiotics, prebiotics, or specific metabolite supplements). Targeting the gut as a “reactor” to treat vascular wall lesions represents a promising direction for future cardiovascular medicine.

The mental health management of laboratory animals is a critical factor in ensuring the reliability of scientific research data. However, due to the subtle nature of mental state alterations and the limitations of current assessment methods, the mental health of laboratory animals is often overlooked by researchers. Therefore, there is an urgent need to explore a new, objective, simple, and practical method for evaluating mental health. Stress, as a primary factor inducing alterations in the mental state of animals, can influence experimental results across multiple research fields through the neuroendocrine-immune network. This paper first elucidates the necessity of mental health management in laboratory animals from three perspectives: factors contributing to stress, neural mechanisms of stress, and research areas affected by stress. It highlights that excluding animals with abnormal mental states before experiments can enhance the efficiency and reproducibility of biomedical studies. Second, this paper briefly summarizes existing methods for assessing the health of laboratory animals, pointing out that approaches such as behavioral tests and metabolomics have many limitations in evaluating stress responses. Existing methods struggle to meet the demand for simple, objective, and non-invasive assessment methods in animal management. Therefore, this paper focuses on hair, a biological sample with the advantages of cumulative information and non-invasive collection, and systematically describes the theoretical basis and technological advances in using hair characteristics as indicators of stress responses from three perspectives: hair traits, the levels of substances in hair, and artificial intelligence (AI). It proposes and supports an innovative technical pathway that combines hair traits with AI-based image analysis, with the aim of offering an improved solution for non-invasive and objective stress assessment, while providing theoretical support for improving the health management system of laboratory animals.

Objective:To summarize the clinical characteristics of elderly patients with stage Ⅰ diffuse large B-cell lymphoma (DLBCL) and analyze the factors associated with prognosis.Methods:A case series study was conducted by retrospectively collecting clinical data from patients aged over 60 years with newly diagnosed stage Ⅰ DLBCL across 20 medical centers in Jiangsu Province, China, between June 2010 and April 2023. The involved site, classification and treatment plan were summarized. The primary endpoints were progression-free survival (PFS) and overall survival (OS). Statistical analyses were performed using the Kaplan-Meier method, and Cox regression model.Results:The study included 255 patients with a median age of 69 years, of whom 130 (51.0%) were male, 66 (25.9%) were aged ≥75 years and 26 (10.1%) had a high Charlson Comorbidity Index (CCI) score of ≥2. Extranodal involvement was observed in 163 (63.9%) patients, with the stomach (37.4%, 61/163), intestine (19.0%, 31/163), testes (11.0%, 18/163), and breast (7.4%, 12/163) being the most frequently affected sites. The non-germinal center B-cell (non-GCB) subtype was prevalent in 63.7% of patients (142/223), with no significant difference between the nodal and extranodal groups ( P=0.681). Furthermore, 73.9% (184/249) and 11.7% (29/249) of patients received the R-CHOP (rituximab, cyclophosphamide, doxorubicin, vincristine, prednisone) and R-miniCHOP regimen, respectively. The overall 3-year PFS rate was 81.5%, and the 3-year OS rate was 85.6%. Patients aged ≥75 years ( HR=2.910, 95% CI 1.565-5.408, P=0.001) and/or with a CCI score ≥2 ( HR=2.324, 95% CI 1.141-4.732, P=0.020) had a significantly poorer PFS. Incorporating age ≥75 years and CCI score ≥2 into the stage-modified international prognostic index (sm-IPI) can better stratify the prognosis of elderly patients with stage Ⅰ DLBCL. The 3-year PFS rate was 48.7% in the high-risk group versus 85.7% in the low-risk group ( P<0.001). Conclusions:Our findings show that the elderly patients with stage Ⅰ DLBCL were predominantly characterized by extranodal involvement (particularly in the stomach and intestinal tract) and non-GCB subtype. Age ≥75 years and CCI ≥2 were identified as independent prognostic factors. The newly established sm-IPI-75-CCI incorporating these factors demonstrated superior prognostic discrimination compared to conventional risk assessment systems.

AIM To explore the relieving effect of Er Miao Wan on atopic dermatitis in mice.METHODS In vivo experiment:BALB/c mice were randomly divided into normal group,model group,dexamethasone group(2 mg/kg)and high,medium and low dose groups of Er Miao Wan(4.68,2.34 and 1.17 g/kg).The mouse model of atopic dermatitis was established by repeatedly smearing DNCB solution,and the model was given orally for 21 days.The skin lesion condition on the back of mice,ear swelling degree,and the weight difference between ear lobes were observed and recorded.HE staining was used to observe the histopathological changes in the skin lesion tissues of mice.Toluidine blue(TB)staining was used to observe the infiltration of mast cells in skin lesions.The expression of macrophage marker F4/80 in skin lesions was detected by IHC.The serum levels of TSLP,IL-4,IL-5 and total IgE were detected by ELISA.In vitro experiment:RAW264.7 cells in logarithmic growth period were given 400,200 and 100 μg/mL Er Miao Wan for intervention.Cell proliferation was detected by CCK-8 method.NO level in cell supernatant was detected by Griess method.TNF-α,IL-1 β and IL-6 levels in cell supernatant were detected by ELISA method.The expressions of proteins related to the MAPK/NF-κB signaling pathway in cells was detected by Western blot.RESULTS In vivo experiment:Compared with the model group,the scores of back skin lesions,the swelling degree of right ear and the weight difference between left and right ear pieces in the high-dose group of Er Miao Wan decreased(P＜0.05,P＜0.01),the thickness of skin lesions decreased,the infiltration of mast cells and macrophages decreased(P＜0.05,P＜0.01),and the inflammatory factors TSLP,IL-4,IL-5 and total IgE levels in serum decreased(P＜0.05,P＜0.01),and the expression of F4/80 in the skin lesions decreased(P＜0.01).In vitro experiment:Compared with the model group,the levels of NO,TNF-α,IL-1 β and IL-6 in Er Miao Wan 400 and 200 μg/mL groups decreased(P＜0.05,P＜0.01),and the phosphorylation levels of P38,JNK and P65 proteins decreased(P＜0.05,P＜0.01).CONCLUSION Er Miao Wan can alleviate skin lesion inflammation in DNCB-induced atopic dermatitis mice,and its mechanism may be related to inhibiting the activation of MAPK/NF-κB signaling pathway of macrophage,reducing macrophage infiltration and reducing Th2 cytokines.

On the basis of that the fluorescence wavelength of copper nanoclusters(CuNCs)could cover the entire visible region,multicolor fluorescent CuNCs/starch composites were prepared and applied in fingermark development.With L-glutathione as the reducing agent and protective ligand,blue emissive and orange emissive CuNCs solutions were obtained in alkaline solutions at 90℃and 25℃,respectively.With the aggregation-induced emission effect induced by ethanol as a poor solvent,the fluorescence of orange emissive CuNCs with a higher intensity was achieved in an ethanol-water solution.With ascorbic acid as the reducing agent and 3-mercaptopropionic acid as the protective agent,green emissive CuNCs solution was prepared in an acid solution.Particle morphologies,chemical compositions and optical properties of these three CuNCs above were investigated using physical characterization and spectroscopic analysis,indicating that well-dispersed CuNCs had excellent photoluminescent properties.These CuNCs solutions were combined with starch to form composite powders by simply drying.The influences of the type of CuNCs and the ratio of CuNCs to starch on the emission wavelength and fluorescence intensity of the products were studied.The obtained CuNCs/starch composites could emit blue,green and orange fluorescence under 365 nm ultraviolet light,respectively,which were suitable for fingermark development.Minutiae and partial level-3 features of latent fingermarks could be effectively developed.High-quality fluorescence fingermark images would be captured using appropriate optical filters to eliminate background interference of various substrates.

By summarizing the mass spectrometric fragmentation patterns of oxyphenisatin substances,an analytical method was established for screening of illegally added oxyphenisatin compounds in weight-loss health foods using high performance liquid chromatography-in-source-fragmentation-quadrupole time-of-flight mass spectrometry(HPLC-ISF-QTOF-MS),along with a quantitative method for 11 kinds of oxyphenisatin compounds.Based on the developed screening method,an oxyphenisatin derivative was discovered in the reference standards,which was tentatively identified as 4-(3-(4-hydroxyphenyl)-2-oxoindolin-3-yl)phenyl acetate and confirmed by MS/MS analysis.The results showed that all 11 kinds of oxyphenisatin compounds had correlation coefficients greater than 0.9971,with limits of detection(LODs)ranging from 0.12 to 0.68 μg/L and limits of quantification(LOQ)from 0.21 to 2.29 μg/L.The LODs for 11 kinds of characteristic ions ranged from 0.45 to 9.11 μg/kg.At spiking levels of 25,50 and 100 μg/kg,the recoveries ranged from 78.9%to 117.3%.The instrumental precision,intra-day method precision and inter-day method precision were 0.23%?1.70%,0.7%?2.4%,and 1.1%?3.3%,respectively.The developed targeted and non-targeted detection method demonstrated high sensitivity,strong stability,rapid analysis,and an expanded screening range for oxyphenisatin substances,and provided robust technical support for regulatory authorities in combating illegal adulteration.