BACKGROUND:Aging is a complex biological process characterized by progressive functional decline across multiple physiological systems, and biological age provides a more accurate reflection of an individual's aging status than chronological age. Dietary antioxidants represent key modifiable targets for delaying aging; however, existing studies lack systematic screening and precise identification of core antioxidant components, and traditional statistical methods have inherent limitations in handling complex associations within high-dimensional dietary data. This study aimed to develop an interpretable machine learning model for predicting accelerated biological aging, and to identify core dietary antioxidants closely associated with accelerated aging risk. METHODS:This study analyzed data from the U.S. National Health and Nutrition Examination Survey (NHANES) 2007-2010 and 2017-2018 cycles, with a final total of 8125 participants included in the analysis. Accelerated biological aging, the primary outcome, was defined using two gold-standard metrics: the Klemera-Doubal Method (KDM) and Phenotypic Age (PhenoAge). Eight machine learning models were constructed, and their performance was evaluated via 10-fold cross-validation. The SHapley Additive Explanations (SHAP) framework was applied to quantify the predictive contribution of each feature. Multivariable logistic regression, restricted cubic spline models, subgroup analyses, and interaction tests were performed to validate the robustness of the observed associations and characterize their dose-response relationships. RESULTS:The XGBoost model exhibited the optimal predictive performance for accelerated aging defined by both metrics, with an area under the receiver operating characteristic curve (AUC) of 0.931 (KDM) and 0.906 (PhenoAge) in 10-fold cross-validation. SHAP analysis consistently identified daidzein, apigenin, magnesium, zinc, and vitamin E as the core dietary antioxidants with the highest predictive contribution to accelerated aging risk. Dose-response analyses revealed significant inverse linear and nonlinear associations between higher intake of these five antioxidants and accelerated aging risk, and these associations were consistently observed across sex and age subgroups. CONCLUSION:The interpretable machine learning framework based on dietary antioxidant profiles can robustly predict accelerated biological aging. XGBoost demonstrated the best performance in predicting accelerated aging risk. Daidzein, apigenin, magnesium, zinc, and vitamin E are core dietary antioxidants closely associated with a reduced risk of accelerated biological aging. This study provides analytical reference at the large population level for the development of nutritional intervention strategies targeting healthy aging. Given the cross-sectional design of the study, our findings only reflect predictive associations rather than causal relationships, and warrant further validation in prospective cohort studies and randomized controlled trials.

Background:Miscarriage is a common and serious adverse pregnancy outcome. Assessing drug-associated miscarriage risk is essential for medication safety in pregnancy. Using the FDA Adverse Event Reporting System this study systematically mined adverse drug events related to miscarriage and combined machine learning with explainable Artificial Intelligence to evaluate potential high-risk drugs. Methods:We retrieved FAERS reports of miscarriage-associated ADEs from 2005 to 2024. Disproportionality analyses were conducted using the reporting odds ratio (ROR), proportional reporting ratio (PRR), Bayesian Confidence Propagation Neural Network (BCPNN), and Multi-Item Gamma Poisson Shrinker (MGPS), with subgroup analyses by age and body weight. An eXtreme Gradient Boosting (XGBoost) model was developed to predict miscarriage risk, and Shapley Additive exPlanations (SHAP) were used to interpret feature contributions. Finally, Weibull distribution modeling characterized the time-to-onset (TTO) from drug exposure to miscarriage. Results:A total of 36,389 ADEs were included. We identified several potential high-risk classes, notably immunomodulators, psychoactive/neuroactive agents, and antimicrobials. The XGBoost model showed favorable discrimination with a mean area under the curve (AUC) of 0.738. SHAP analysis reveals that immunomodulatory factors, such as adalimumab and infliximab, are significant predictors of miscarriage events in this model. The distribution of their SHAP values suggests a strong association between these drugs and miscarriage reports. time-to-onset analyses suggested that most miscarriages occurred within 2 years after drug exposure, with marked heterogeneity in risk timing across agents; anti-Tumor Necrosis Factor-alpha drugs (TNF-α) exhibited a higher early risk. Conclusion:Machine learning and SHAP interpretability analysis based on the FAERS database effectively identified immunomodulators, antiviral drugs, and psychiatric/neuropsychiatric medications as potential risk signals associated with miscarriage. These findings underscore the need for individualized medication assessment that considers patient age and body weight, providing evidence-based guidance and early alerting for reference for drug risk assessment during pregnancy.

BackgroundIn recent years, immune checkpoint inhibitors (ICIs) have shown significant efficacy in treating various malignancies and have become a key therapeutic approach in cancer treatment. However, while ICIs activate the immune system, they can also induce immune-related adverse events (irAEs). Due to the variability in the frequency and severity of irAEs, clinical management faces a significant challenge in balancing antitumor efficacy with the risk of irAEs. Predicting and preventing irAEs during the early stages of treatment has become a critical research focus in cancer immunotherapy. This study aims to evaluate the predictive value of peripheral blood cell counts for irAEs.MethodsStudies meeting the inclusion criteria were identified through database searches. The standardized mean difference (SMD) was used to compare continuous blood cell counts. For studies that did not provide adjusted odds ratios (ORs) and 95% confidence intervals (CIs), crude ORs for categorized blood cell counts were calculated. The study protocol was registered on PROSPERO (CRD42024592126).ResultsThe meta-analysis included 60 studies involving 16,736 cancer patients treated with ICIs. Compared to patients without irAEs, those experiencing irAEs had significantly higher baseline continuous ALC (SMD = 0.12, 95% CI = 0.01-0.24), while ANC (SMD = -0.18, 95% CI = -0.28 to -0.07) and PLR (SMD = -0.32, 95% CI = -0.60 to -0.04) were significantly lower. Similarly, categorized blood cell counts indicated that higher baseline ALC (OR = 2.46, 95% CI = 1.69-3.57) and AEC (OR = 2.05, 95% CI = 1.09-3.85), along with lower baseline NLR (OR = 0.64, 95% CI = 0.50-0.81) and PLR (OR = 0.63, 95% CI = 0.48-0.82), were associated with an increased risk of irAEs. Subgroup analysis further identified cutoff values for ALC (2×10^9/L), NLR (5 or 3), and PLR (180) as better predictors of irAEs.ConclusionHigher baseline ALC and AEC, along with lower baseline ANC, NLR, and PLR, are associated with an increased risk of irAEs. However, further research is needed to determine the optimal cutoff values and to explore the efficacy of blood cell counts in predicting specific types of irAEs.Systematic review registrationhttps://www.crd.york.ac.uk/PROSPERO/, identifier CRD42024592126.

BackgroundThe four-drug regimen for heart failure with reduced ejection fraction (HFrEF) significantly reduces the risks of hospitalization and mortality. To identify key adverse drug events (ADEs) warranting attention with this regimen, we conducted a real-world pharmacovigilance analysis based on the FDA Adverse Event Reporting System (FAERS) events.Research design and methodsWe collected ADE reports of the four-drug regimen from FAERS that matched this regimen over a 10-year period. Disproportionality analysis and subgroup analysis were performed using four algorithms. Time-to-onset (TTO) analysis was used to assess the temporal risk patterns of ADE occurrence. Lastly, logistic regression was applied to investigate the relationship-value between patient characteristics and ADEs.ResultsA total of 1,237 cases with 6,580 ADE reports were collected. Disproportionality analysis identified the most frequent ADEs as hypotension, acute kidney injury (AKI), and hyperkalemia. TTO analysis revealed a median TTO of 39 days for all important medical events, and the median TTO for AKI was 28 days, both fitting an early failure curve.ConclusionIn the comprehensive management of HFrEF with the four-drug regimen, in addition to routine monitoring of ADEs such as hypotension and hyperalemia, early-onset AKI should be a particular focus.

Malignant reproductive system tumors significantly impact the physical and mental health of adolescent and young adult males (AYAMs, aged 15-49 years), particularly their fertility. In recent years, the incidence of testicular and prostate cancer in this population has risen, while early diagnosis and treatment remain challenging. However, global epidemiological data on AYAMs are limited. This study aims to evaluate global trends in the incidence and burden of testicular and prostate cancers among AYAMs from 1990 to 2021, using data from the Global Burden of Disease (GBD) 2021 study, to address the research gap and inform public health strategies. We extracted data on the incidence and disability-adjusted life years (DALYs) for testicular and prostate cancer in adolescents and young adult males (AYAMs) aged 15-49 years from the 2021 Global Burden of Disease (GBD) database, covering 204 countries and territories from 1990 to 2021. As the GBD 2021 database only includes data for testicular cancer (ICD-10 code C62) and prostate cancer (ICD-10 code C61), other male reproductive system cancers, such as penile and epididymal cancer, were excluded. To analyze temporal trends, we applied the estimated annual percentage change (EAPC) method using linear regression models to calculate age-standardized incidence and DALY rates. Additionally, Spearman's rank correlation was used to assess the relationship between age-standardized rates and the Socio-Demographic Index (SDI) for each country and region in 2021. In 2021, a total of 94,229 new cases of male reproductive system cancers were reported globally among AYAMs. From 1990 to 2021, the global age-standardized incidence rate significantly increased (EAPC = 1.38), while the age-standardized DALY rate exhibited a downward trend (EAPC = -0.26). Both testicular cancer and prostate cancer showed a consistent increase in age-standardized incidence rates, while their age-standardized DALY rates declined. Testicular cancer represented a significantly higher proportion of male reproductive system cancers in AYAMs globally compared to prostate cancer, with regional variations in the cancer burden. Additionally, the age-standardized incidence rates of both testicular and prostate cancers were positively correlated with the SDI, while the age-standardized DALY rate displayed a hill-shaped relationship with SDI. Over the past three decades, the incidence of testicular and prostate cancers among adolescents and young adult males (AYAMs) has steadily increased, accompanied by significant regional disparities in DALYs. To reduce the global burden of these cancers, particularly for AYAMs, targeted prevention strategies are crucial. These should include early screening, timely treatment, and public health education tailored to the unique needs of young males. In low- and middle-income countries, efforts should focus on improving health management, promoting physical activity, fostering healthy dietary habits, and enhancing access to early cancer screening for AYAMs. Early detection and intervention are essential for improving survival rates and minimizing the long-term effects of cancer, such as fertility issues and psychological impacts. Collaboration between governments, public health organizations, and social institutions is critical for advancing cancer prevention and control, with a focus on the specific health needs of AYAMs.

BackgroundThe strategy for eliminating viral hepatitis is at a critical juncture, necessitating an updated assessment of global incidence trends.MethodsData on the incidence of four types of acute viral hepatitis (AVH), namely, acute hepatitis A (AHA), acute hepatitis B (AHB), acute hepatitis C (AHC), and acute hepatitis E (AHE), were sourced from the Global Burden of Disease (GBD) Study 2021. The annual percentage change is utilized to elucidate temporal trends, whereas health inequalities and frontier analysis serve to evaluate global health equity and quantify disparities in burden among countries.ResultsIn 2021, the estimated global incidence for AVH was as follows: AHA (160.86 million), AHB (63.53 million), AHE (19.37 million), and AHC (7.01 million). From 2000 to 2021, the age-standardized incidence rates (ASIR) for four types of AVH demonstrated a declining trend, with AHB showing the most significant decrease. It is anticipated that the incidence rates for AHA, AHB, and AHC will continue to decline over the next 15 years; however, the incidence rate of AHE is projected to increase. In 2021, the incidence of AVH displayed a significant negative correlation with the Socio-Demographic Index (SDI), but health disparities between countries have diminished. Countries with the highest potential for elimination of AHB are primarily situated in low and low-middle SDI regions, whereas those for AHA are concentrated in high and high-middle SDI regions. Furthermore, countries with the largest disparities in AHC and AHE were dispersed.ConclusionAlthough global incidence of AVH is decreasing, it remains a serious public health challenge. Reducing health disparities is crucial for the elimination of viral hepatitis.

Objective:This study aims to develop and validate a machine learning model that integrates dietary antioxidants to predict cardiovascular disease (CVD) risk in diabetic patients. By analyzing the contributions of key antioxidants using SHAP values, the study offers evidence-based insights and dietary recommendations to improve cardiovascular health in diabetic individuals. Methods:This study leveraged data from the U.S. National Health and Nutrition Examination Survey (NHANES) to develop predictive models incorporating antioxidant-related variables-including vitamins, minerals, and polyphenols-alongside demographic, lifestyle, and health status factors. Data preprocessing involved collinearity removal, standardization, and class imbalance correction. Multiple machine learning models were developed and evaluated using the mlr3 framework, with benchmark testing performed to compare predictive performance. Feature importance in the best-performing model was interpreted using SHapley Additive exPlanations (SHAP). Results:This study utilized data from 1,356 individuals with diabetes from NHANES, including 332 with comorbid CVD. After removing collinear variables, 27 dietary antioxidant features and 13 baseline covariates were retained. Among all models, XGBoost demonstrated the best predictive performance, with an accuracy of 87.4%, an error rate of 12.6%, and both AUC and PRC values of 0.949. SHAP analysis highlighted Daidzein, magnesium (Mg), epigallocatechin-3-gallate (EGCG), pelargonidin, vitamin A, and theaflavin 3'-gallate as the most influential predictors. Conclusion:XGBoost exhibited the highest predictive performance for cardiovascular disease risk in diabetic patients. SHAP analysis underscored the prominent contribution of dietary antioxidants, with Daidzein and Mg emerging as the most influential predictors.

The skin microbiome has been linked to the etiology and progression of skin cancer, but the causal relationship remains unclear. This study employs two-sample Mendelian randomization (TSMR) and meta-analysis techniques to elucidate the putative genetic causal relationships between skin microbiota and skin cancer. Genetic variant data for the skin microbiome and skin cancer, drawn from large-scale genome-wide association studies, were extracted from European populations. TSMR analysis, heterogeneity tests, horizontal pleiotropy assessments, sensitivity analysis, and directional tests were conducted, followed by a meta-analysis to enhance the reliability of the findings. The TSMR and meta-analysis results indicate a significant association between the Proteobacteria phylum, including Gammaproteobacteria, and an increased risk of melanoma. Conversely, the Staphylococcus genus is significantly associated with a reduced risk of melanoma. Additionally, the Bacteroidetes phylum exhibits a statistically significant association with an elevated risk of basal cell carcinoma. This study furnishes genetic evidence substantiating the causal nexus between the skin microbiome and skin cancer. Further research is warranted to elucidate the underlying mechanisms and explore skin microbiome-centric prophylactic and therapeutic strategies for skin cancer.

Background: Patients with gastrointestinal cancer often have impaired immune function after surgery. This study aimed to assess the efficacy of Sijunzi decoction (SJZD) on immune function in patients with gastrointestinal cancers after surgery. Methods: The electronic databases, including CNKI, Wanfang, SinoMed, Weipu, PubMed, Web of Science, EMBASE, and Cochrane databases were retrieved (March 1, 2024), and the randomized controlled trials (RCTs) that met the criteria were included. Methodologic quality assessment of RCTs was performed using the Cochrane risk of bias tool. The data of RCTs were acquired and analyzed by meta-analysis by Review Manager 5.3, and the quality of the evidence followed the Grading of Recommendations, Assessment, Development and Evaluations approach. This research was registered in the International Platform of Registered Systematic Review and Meta-analysis, 202440001. Based on the network pharmacology, relationships between key genes of SJZD and tumor-infiltrating immune cells in gastrointestinal cancers were explored. Results: Thirteen articles (RCTs) were included, containing 1010 patients with gastrointestinal cancers after surgery. The results of meta-analysis revealed that SJZD with conventional therapies could improve CD3+ T lymphocyte (mean difference [MD] = 5.73, 95% confidence interval [CI]: 2.07-9.39, P = .002), CD4+ T lymphocyte (MD = 5.86, 95% CI: 3.90-7.82, P < .00001), CD4+/CD8+ (MD = 0.29, 95% CI: 0.15-0.43, P < .0001), and reduce CD8+ T lymphocyte (MD = -2.44, 95% CI: -4.03 to -0.85, P = .003) compared with conventional therapies alone. In addition, the funnel plot showed the included RCTs might have publication bias. The Grading of Recommendations, Assessment, Development and Evaluations classification showed low-quality evidence for CD4+, CD8+, and CD4+/CD8+, and very low-quality evidence for other indicators. The network pharmacology results suggested that SJZD may exert effects by regulating the immune cells in the microenvironment of gastrointestinal cancers. Conclusion: SJZD could enhance the immune function of patients with gastrointestinal cancers after surgery. Due to the low quality of articles, more high-quality RCTs are needed to improve the level of evidence.

Background: The challenge of systemic treatment for hepatocellular carcinoma (HCC) stems from the development of drug resistance, primarily driven by the interplay between cancer stem cells (CSCs) and the tumor microenvironment (TME). However, there is a notable dearth of comprehensive research investigating the crosstalk between CSCs and stromal cells or immune cells within the TME of HCC. Methods: We procured single-cell RNA sequencing (scRNA-Seq) data from 16 patients diagnosed with HCC. Employing meticulous data quality control and cell annotation procedures, we delineated distinct CSCs subtypes and performed multi-omics analyses encompassing metabolic activity, cell communication, and cell trajectory. These analyses shed light on the potential molecular mechanisms governing the interaction between CSCs and the TME, while also identifying CSCs' developmental genes. By combining these developmental genes, we employed machine learning algorithms and RT-qPCR to construct and validate a prognostic risk model for HCC. Results: We successfully identified CSCs subtypes residing within malignant cells. Through meticulous enrichment analysis and assessment of metabolic activity, we discovered anomalous metabolic patterns within the CSCs microenvironment, including hypoxia and glucose deprivation. Moreover, CSCs exhibited aberrant activity in signaling pathways associated with lipid metabolism. Furthermore, our investigations into cell communication unveiled that CSCs possess the capacity to modulate stromal cells and immune cells through the secretion of MIF or MDK, consequently exerting regulatory control over the TME. Finally, through cell trajectory analysis, we found developmental genes of CSCs. Leveraging these genes, we successfully developed and validated a prognostic risk model (APCS, ADH4, FTH1, and HSPB1) with machine learning and RT-qPCR. Conclusions: By means of single-cell multi-omics analysis, this study offers valuable insights into the potential molecular mechanisms governing the interaction between CSCs and the TME, elucidating the pivotal role CSCs play within the TME. Additionally, we have successfully established a comprehensive clinical prognostic model through bulk RNA-Seq data.

Distant metastasis is the leading cause of cancer-related mortality, and achieving survival benefits through advancements in systemic therapy remains challenging. Mast cells play a dual role in shaping the tumor microenvironment (TME) and influencing distant metastasis, underscoring the significant research value of targeting mast cells for systemic therapy in advanced cancer. We investigated variations in mast cell infiltration levels in primary and metastatic malignancies using immunocyte infiltration analysis. Mast cell subsets were identified from pan-cancer distant metastasis single-cell sequencing data through dimensionality reduction clustering and cell type annotation, combined with cell trajectory and communication network analyses. A prognostic model was established using WGCNA and 12 machine learning algorithms to identify potential mast cell targets. Drug sensitivity and Mendelian randomization analyses were conducted to select potential drugs targeting mast cells, and their effects on epithelial-mesenchymal transition (EMT) were validated through in vitro experiments, including wound healing, transwell, and western blot assays. Results revealed that activated mast cells show increased infiltration in metastatic tumors, correlating with poor survival duration. XBP1+ mast cells were identified as key components of the inhibitory TME, potentially involved in EMT activation. Simvastatin was identified as a potential drug, reversing EMT induced by XBP1+ mast cells in pan-cancer. Aberrant activation of MEK/ERK signaling in XBP1+ mast cells can stimulate cancer cell EMT by modulating degranulation, while Simvastatin can inhibit EMT by suppressing degranulation.

Amauroderma rugosum (Blume and T. Nees) Torrend (Ganodermataceae) (A. rugosum) has been found to have anti-inflammatory ability in previous studies. The present study aimed to verify the therapeutic benefits of A. rugosum in the treatment of ulcerative colitis and to investigate its underlying mechanism of action. Acute experimental ulcerative colitis was induced by feeding the mice drinking water supplemented with dextran sodium sulfate (DSS). The findings indicated that the ethanolic extract of domesticated A. rugosum exhibited therapeutic efficacy comparable to Salazosulfapyridine (SASP) in mitigating clinical symptoms and the pathological score of the colon. Furthermore, A. rugosum exhibited the capacity to enhance the expression of tight junction (TJ) proteins, while concurrently decreasing the levels of TNF-ɑ and IL-6. A noteworthy finding is that it exhibited the capability to diminish the nuclear translocation of NF-κB p65. In conclusion, A. rugosum attenuates DSS-induced ulcerative colitis by enhancing intestinal barrier function and inhibiting mucosal inflammation.

Background and objectiveHeavy metals, ubiquitous in the environment, pose a global public health concern. The correlation between these and diabetic kidney disease (DKD) remains unclear. Our objective was to explore the correlation between heavy metal exposures and the incidence of DKD.MethodsWe analyzed data from the NHANES (2005–2020), using machine learning, and cross-sectional survey. Our study also involved a bidirectional two-sample Mendelian randomization (MR) analysis.ResultsMachine learning reveals correlation coefficients of −0.5059 and − 0.6510 for urinary Ba and urinary Tl with DKD, respectively. Multifactorial logistic regression implicates urinary Ba, urinary Pb, blood Cd, and blood Pb as potential associates of DKD. When adjusted for all covariates, the odds ratios and 95% confidence intervals are 0.87 (0.78, 0.98) (p = 0.023), 0.70 (0.53, 0.92) (p = 0.012), 0.53 (0.34, 0.82) (p = 0.005), and 0.76 (0.64, 0.90) (p = 0.002) in order. Furthermore, multiplicative interactions between urinary Ba and urinary Sb, urinary Cd and urinary Co, urinary Cd and urinary Pb, and blood Cd and blood Hg might be present. Among the diabetic population, the OR of urinary Tl with DKD is a mere 0.10, with a 95%CI of (0.01, 0.74), urinary Co 0.73 (0.54, 0.98) in Model 3, and urinary Pb 0.72 (0.55, 0.95) in Model 2. Restricted Cubic Splines (RCS) indicate a linear linkage between blood Cd in the general population and urinary Co, urinary Pb, and urinary Tl with DKD among diabetics. An observable trend effect is present between urinary Pb and urinary Tl with DKD. MR analysis reveals odds ratios and 95% confidence intervals of 1.16 (1.03, 1.32) (p = 0.018) and 1.17 (1.00, 1.36) (p = 0.044) for blood Cd and blood Mn, respectively.ConclusionIn the general population, urinary Ba demonstrates a nonlinear inverse association with DKD, whereas in the diabetic population, urinary Tl displays a linear inverse relationship with DKD.

Objective Autoantibodies against MDA5 (melanoma differentiation-associated protein 5) serve as a biomarker for DM (dermatomyositis) and indicate a risk factor for interstitial lung disease (ILD). MDA5 is a protein responsible for sensing RNA virus infection and activating signalling pathways against it. However, little is known about the antigen epitopes on MDA5 autoantibodies. We aimed to determine the interaction of the MDA5 autoantibody-antigen epitope. Methods Cell-based assays (CBAs), immunoprecipitation-immunoblot assays, and various immunoblotting techniques were used in the study. Results We demonstrated that DM patient autoantibodies recognize MDA5 epitopes in a native conformation-dependent manner. Furthermore, we identified the central helicase domain (3Hel? formed by Hel1, Hel2i, Hel2, and pincer as the major epitopes. As proof of principle, the purified 3Hel efficiently absorbed MDA5 autoantibodies from patient sera through immunoprecipitation-immunoblot assay. Conclusion Our study uncovered the nature of the antigen epitopes on MDA5 and can provide guidance for diagnosis and a targeted therapeutic approach development.

BACKGROUND:Liver metastasis (LM) stands as a primary cause of mortality in metastatic colorectal cancer (mCRC), posing a significant impediment to long-term survival benefits from targeted therapy and immunotherapy. However, there is currently a lack of comprehensive investigation into how senescent and exhausted immune cells contribute to LM. METHODS:We gathered single-cell sequencing data from primary colorectal cancer (pCRC) and their corresponding matched LM tissues from 16 mCRC patients. In this study, we identified senescent and exhausted immune cells, performed enrichment analysis, cell communication, cell trajectory, and cell-based in vitro experiments to validate the results of single-cell multi-omics. This process allowed us to construct a regulatory network explaining the occurrence of LM. Finally, we utilized weighted gene co-expression network analysis (WGCNA) and 12 machine learning algorithms to create prognostic risk model. RESULTS:We identified senescent-like myeloid cells (SMCs) and exhausted T cells (TEXs) as the primary senescent and exhausted immune cells. Our findings indicate that SMCs and TEXs can potentially activate transcription factors downstream via ANGPTL4-SDC1/SDC4, this activation plays a role in regulating the epithelial-mesenchymal transition (EMT) program and facilitates the development of LM, the results of cell-based in vitro experiments have provided confirmation of this conclusion. We also developed and validated a prognostic risk model composed of 12 machine learning algorithms. CONCLUSION:This study elucidates the potential molecular mechanisms underlying the occurrence of LM from various angles through single-cell multi-omics analysis in CRC. It also constructs a network illustrating the role of senescent or exhausted immune cells in regulating EMT.

BackgroundFluorouracil (5-FU) is widely used to treat metastatic colorectal cancer (mCRC), but real-world safety data is limited. Our study aimed to evaluate 5-FU's safety profile in a large mCRC population using the FAERS database.Research design and methodsWe conducted disproportionality analyses to identify adverse drug events associated with 5-FU use in mCRC patients from 2004 to 2023. Subgroup analyses, gender difference analyses, and logistic regression were also performed.ResultsWe identified 1,458 reports with 5-FU as the primary suspected drug, with males accounting for 48.8% of reports. Gastrointestinal disorders were the most common adverse event (864 cases), while pregnancy-related conditions showed the strongest signal intensity (ROR = 2.97). We found 19 preferred terms with positive signals, including ischemic hepatitis (ROR = 59.32), blood iron increased (ROR = 59.32), and stress cardiomyopathy (ROR = 51.94). Males were more susceptible to weight loss and skin toxicity. Most adverse events occurred within the first month of 5-FU administration.ConclusionOur study provides a comprehensive analysis of 5-FU's safety profile in mCRC patients, helping healthcare professionals mitigate risks in clinical practice.

Background: The systemic treatment of advanced hepatocellular carcinoma is currently facing a bottleneck. EGCG, the primary active compound in green tea, exhibits anti-tumor effects through various pathways. However, there is a lack of study on EGCG-induced immunogenic cell death (ICD) in hepatocellular carcinoma. Methods: In a previous study, we successfully synthesized folate-modified thermosensitive nano-materials, encapsulated EGCG within nanoparticles using a hydration method, and established the EGCG nano-drug delivery system. The viability of HepG2 cells post-EGCG treatment was assessed via the MTT and EdU assays. Cell migration and invasion were evaluated through wound healing experiments, Transwell assays, and Annexin VFITC/PI assay for apoptosis detection. Additionally, the expression levels of damage-associated molecular patterns (DAMPs) were determined using immunofluorescence, ATP measurement, RT-qPCR, and Western Blot. Results: The drug sensitivity test revealed an IC50 value of 96.94 mu g/mL for EGCG in HepG2 cells after 48 h. EGCG at a low concentration (50 mu g/mL) significantly impeded the migration and invasion of HepG2 cells, showing a clear dose-dependent response. Moreover, medium to high EGCG concentrations induced cell apoptosis in a dose-dependent manner and upregulated DAMPs expression. Immunofluorescence analysis demonstrated a notable increase in CRT expression following low-concentration EGCG treatment. As EGCG concentration increased, cell viability decreased, leading to CRT exposure on the cell membrane. EGCG also notably elevated ATP levels. RT-qPCR and Western Blot analyses indicated elevated expression levels of HGMB1, HSP70, and HSP90 following EGCG intervention. Conclusion: EGCG not only hinders the proliferation, migration, and invasion of hepatocellular carcinoma cells and induces apoptosis, but also holds significant clinical promise in the treatment of malignant tumors by promoting ICD and DAMPs secretion.

The objective of this study was to explore the causal relationship between the use of proton pump inhibitors (PPIs) and 16 types of digestive system tumors. We utilized a 2-sample Mendelian randomization (MR) approach to investigate this relationship. We obtained exposure and outcome data from the UK Biobank and the Finland Biobank, respectively. The genetic data used in the analysis were derived from genome-wide association studies (GWAS) studies conducted on European populations. We screened single nucleotide polymorphisms significantly associated with the use of omeprazole, a commonly used PPIs, as instrumental variables. We then performed MR analyses using the inverse variance weighting (IVW) method, MR-Egger regression, and the weighted median method to evaluate the causal effect of omeprazole use on the 16 types of digestive system tumors. Our MR analysis revealed a significant causal relationship between the use of omeprazole and pancreatic malignancies, but not with any other types of digestive system tumors. The IVW analysis showed an odds ratio of 4.33E-05 (95%CI: [4.87E-09, 0.38], P = .03) and the MR-Egger analysis showed an odds ratio of 5.81E-11 (95%CI: [2.82E-20, 0.12], P = .04). We found no significant heterogeneity or pleiotropy, and sensitivity analysis confirmed the robustness of our results. Furthermore, statistical power calculations suggested that our findings were reliable. Conclusion The use of PPIs is a protective factor for pancreatic malignancies, but no causal relationship has been found with other digestive system tumors.

目的研究薄荷油口腔溃疡膜的制剂处方,建立其有效成分测定方法并进行质量评价.方法筛选处方制备薄荷油口腔溃疡膜,建立紫外分光光度法测定薄荷油口腔溃疡膜中薄荷油含量.结果薄荷油口腔溃疡膜的最佳制剂处方为:聚乙烯醇与羧甲基纤维素钠分开溶胀,两者比例为2∶1,用量分别为2.0 g和1.0 g,甘油用量为1.0 g,吐温-80为1.3 g,薄荷油为0.5 g.结论制得的薄荷油口腔溃疡膜符合《中华人民共和国药典(2020版)》要求,经方法学验证,建立的紫外分光光度法测定结果稳定、可靠.