Yan Liu

This study aimed to investigate the association between serum cotinine (a tobacco exposure biomarker) and tooth loss risk in adults. This study employed a statistical framework incorporating logistic regression, restricted cubic spline (RCS), machine learning, and web page construction to investigate the association between serum cotinine levels and tooth loss in 12,559 adult National Health and Nutrition Examination Survey participants. Network pharmacology was used to identify key targets, whereas molecular docking explored the specific mechanism of cotinine's impact on the target protein. Logistic regression and RCS demonstrated a positive (odds ratios = 1.106, 95% confidence interval: 1.085 to 1.127, p < 0.0001) and nonlinear ( p for nonlinear < 0.0001) correlation between serum cotinine and tooth loss. After re‐identifying 14 core genes via network pharmacology, molecular docking was employed to verify the specific protein molecular mechanism of cotinine's effect on tooth loss. Six different molecular docking methods were used to explain the specific molecular mechanism. The highest affinity of −7.3 kcal/mol was observed for the GSK3B–cotinine interaction. This study showed high serum cotinine levels strongly tied to greater tooth loss risk in adults, likely via core proteins controlling cell cycle and inflammation. Antioxidants partly reduced this risk, laying groundwork for early prevention and targeted treatments of tobacco‐linked oral issues.

Stroke has a multifactorial etiology, and phthalates, as widely used environmental chemicals, may play an underexplored role in cerebrovascular health. This study aimed to evaluate the association between urinary phthalate metabolites and stroke risk using data from the National Health and Nutrition Examination Survey (NHANES), complemented by network toxicology, molecular docking, and machine learning approaches. We analyzed data from 10,771 adult participants in NHANES (2003–2018) who had complete records of stroke status, urinary phthalates, and clinical covariates. Multivariable logistic regression and restricted cubic spline (RCS) models were used to assess associations. We constructed protein-protein interaction networks and performed molecular docking to identify potential toxicological targets. Five machine learning algorithms were employed to develop predictive models for phthalate-related stroke risk. Higher levels of MECPP, MCPP, MNBP, and MEOHP were significantly associated with increased stroke prevalence. Network toxicology identified SRC as a key target, and molecular docking confirmed strong binding affinity between MBZP and SRC. Among the machine learning methods, Random Forest achieved the highest predictive performance (AUC = 0.833). Notably, moderate coffee consumption (≤ 170 g/day) was significantly associated with a reduced risk of stroke (OR = 0.63, 95%CI: 0.51–0.77), suggesting potential dietary mitigation against phthalate-related stroke risk. Our findings demonstrate that certain phthalates may increase stroke risk via molecular pathways involving SRC. Furthermore, moderate coffee consumption may mitigate the adverse cerebrovascular effects of these environmental pollutants. Graphical Abstract Schematic diagram of the research process

α-Glucosidase has always been one essential target for clinical prevention and treatment of diabetes. To develop effective α-glucosidase inhibitors, twenty-seven thiadiazole based β-carboline derivatives (TC1–TC27) were designed and synthesized by pharmacophore hybridization strategy, and systematically evaluated their inhibitory activity and binding characteristics against α-glucosidase. All synthesized derivatives (TC1–TC27) displayed significant inhibitory activity against α-glucosidase, with TC16 emerging as the most potent compound (IC₅₀ = 2.62 ± 0.21 μM), far surpassing the reference inhibitor acarbose (IC₅₀ = 210.75 ± 9.52 μM). Furthermore, fluorescence spectra and CD spectra results illustrated the binding of TC16 onto α-glucosidase, which caused the enzyme conformation transition to induce activity decrease. Finally, molecular docking elucidated hydrogen bonds and hydrophobic interactions kept the binding of TC16 onto α-glucosidase. In summary, this work provides a class of thiadiazole based β-carboline derivatives as potential α-glucosidase inhibitors.

Sarcopenia is a clinicopathological condition characterized by a decrease in muscle strength and muscle mass, playing a crucial role in the prognosis of cancer. Therefore, this study aims to investigate the association between sarcopenia and both all-cause mortality and cancer-specific mortality among cancer patients. Furthermore, we plan to develop risk prediction models using machine learning algorithms to predict 3-year and 5-year survival rates in cancer patients. This study included 1095 cancer patients from the National Health and Nutrition Examination Survey (NHANES) cohorts spanning 1999–2006 and 2011–2014. Initially, we used the Least Absolute Shrinkage and Selection Operator (LASSO)-Cox regression models for feature selection. Subsequently, we employed multivariable Cox regression models to investigate the association between sarcopenia and all-cause and cancer-specific mortality in cancer patients. We developed five machine learning algorithms, including Support Vector Machine (SVM), Logistic Regression (LR), Random Forest (RF), LightGBM, and XGBoost, to predict 3-year and 5-year survival rates and to perform risk stratification. The multivariable COX regression model showed sarcopenia significantly increases the risk of all-cause mortality (HR = 1.33, 95%CI:1.05, 1.70, P = 0.0194) and cancer-specific mortality (HR = 1.67, 95%CI:1.09, 2.55, P = 0.0176) in cancer patients. Among the five machine learning algorithms developed, the LightGBM model demonstrated strong performance in the 3-year and 5-year survival prediction tasks, making it the optimal model selection. Decision curve analysis and Kaplan–Meier curves further confirmed our model’s ability to identify high-risk individuals effectively. Sarcopenia significantly increases the risk of mortality in cancer patients. We developed a survival prediction model for cancer patients that effectively identifies high-risk individuals, thereby providing a foundation for personalized survival assessment.

Alzheimer’s disease (AD) is a neurodegenerative disease characterized by progressive memory loss and cognitive dysfunction that affects millions of people worldwide, placing a massive burden on families and economies. Exercise training can effectively reduce the prevalence of AD and alleviate its symptoms through the modulation of multiple signaling pathways involved in the pathophysiological process of AD, including the PI3K/Akt, Wnt/β-catenin, AMPK-related, MAPK, NF-κB, PINK1-PARKIN, JAK/STAT, and TREM2 signaling pathways. Different signaling pathways also crosstalk with each other through different targets to inhibit the formation of Amyloid β (Aβ) plaques, reduce the level of hyperphosphorylated tau protein, reduce apoptosis, relieve neuroinflammation, reduce autophagy dysfunction, and ultimately improve cognitive impairment in AD patients. This review summarizes the pathophysiological processes of AD affected by exercise training through different signaling pathways. We further provide a reference for the future development of new effective AD prevention and treatment targets to develop promising personalized, combined intervention strategies.

Introduction: Alzheimer’s disease (AD), the leading cause of dementia, remains without adequate treatment. Current models do not fully replicate human physiology and pathology. The advent of human induced pluripotent stem cell (hiPSC) technology offers a novel approach to studying AD.Methods: Our study conducted a bibliometric analysis to assess the application and development of hiPSC technology in AD research. We retrieved 531 articles on hiPSC models of AD from the Web of Science Core Collection, published between January 2010 and June 2024. CiteSpace and VOSviewer were used to analyze authorship, geographic contributions, journal influence, and citation patterns.Results: Our findings reveal a steady increase in publications over 14 years, with the United States leading in contributions, followed by China. Li-Huei Tsai from the Massachusetts Institute of Technology is a prominent researcher. PLoS One emerges as the most influential journal. Research trends have focused on inflammation, astrocytes, microglia, apolipoprotein E (ApoE), and tau.Discussion: Bibliometric analysis is crucial in identifying research gaps and trends and guiding future studies to address unmet needs in understanding and modeling human physiology and pathology. Leveraging hiPSC models to investigate the molecular mechanisms of familial and sporadic AD is expected to provide a crucial foundation for developing future treatment strategies.Conclusion: In summary, the bibliometric findings from this study provide a comprehensive overview of the current research landscape in hiPSC models for AD. It also highlights emerging trends and research gaps, crucial for guiding future research efforts, particularly in exploring novel therapeutic targets and improving understanding of disease mechanisms.

Purpose: This study sought to determine if the use of tranexamic acid (TXA) in preexisting thromboembolic risk patients undergoing total joint arthroplasty (TJA) was linked to an increased risk of death or postoperative complications. Methods: We conducted a comprehensive search for studies up to May 2023 in PubMed, Web of Science, EMBASE, and the Cochrane Library. We included randomized clinical trials, cohort studies, and case–control studies examining the use of TXA during TJA surgeries on high-risk patients. The Cochrane Risk of Bias instrument was used to gauge the excellence of RCTs, while the MINORS index was implemented to evaluate cohort studies. We used mean difference (MD) and relative risk (RR) as effect size indices for continuous and binary data, respectively, along with 95% CIs. Results: Our comprehensive study, incorporating data from 11 diverse studies involving 812 993 patients, conducted a meta-analysis demonstrating significant positive outcomes associated with TXA administration. The findings revealed substantial reductions in critical parameters, including overall blood loss (MD = −237.33; 95% CI (−425.44, −49.23)), transfusion rates (RR = 0.45; 95% CI (0.34, 0.60)), and 90-day unplanned readmission rates (RR = 0.86; 95% CI (0.76, 0.97)). Moreover, TXA administration exhibited a protective effect against adverse events, showing decreased risks of pulmonary embolism (RR = 0.73; 95% CI (0.61, 0.87)), myocardial infarction (RR = 0.47; 95% CI (0.40–0.56)), and stroke (RR = 0.73; 95% CI (0.59–0.90)). Importantly, no increased risk was observed for mortality (RR = 0.53; 95% CI (0.24, 1.13)), deep vein thrombosis (RR = 0.69; 95% CI (0.44, 1.09)), or any of the evaluated complications associated with TXA use. Conclusion: The results of this study indicate that the use of TXA in TJA patients with preexisting thromboembolic risk does not exacerbate complications, including reducing mortality, deep vein thrombosis, and pulmonary embolism. Existing evidence strongly supports the potential benefits of TXA in TJA patients with thromboembolic risk, including lowering blood loss, transfusion, and readmission rates.

Introduction: Sirtuins (SIRTs) comprise a group of histone deacetylase enzymes crucial for regulating metabolic pathways and contributing significantly to various disease mechanisms. Sirtuin 1 (SIRT1), among the seven known mammalian homologs, is extensively investigated and understood, playing a key role in neurodegenerative disorders and cancer. This study focuses on potential as a therapeutic target for conditions such as Parkinson’s disease (PD), Huntington’s disease (HD), and Alzheimer’s disease (AD). Methods: Utilizing positron emission tomography (PET) as a noninvasive molecular imaging modality, we aimed to expedite the validation of a promising sirtuin 1 inhibitor for clinical trials. However, the absence of a validated sirtuin 1 PET radiotracer impedes clinical translation. We present the development of [¹¹C] 1, and ¹¹C-labeled benzoxazine-based derivative, as a lead imaging probe. The radiosynthesis of [¹¹C] 1 resulted in a radiochemical yield of 31 ± 4%. Results: Baseline studies demonstrated that [¹¹C] 1 exhibited excellent blood-brain barrier (BBB) penetration capability, with uniform accumulation throughout various brain regions. Self-blocking studies revealed that introducing an unlabeled compound 1, effectively blocking sirtuin 1, led to a substantial reduction in whole-brain uptake, emphasizing the in vivo specificity of [¹¹C] 1 for sirtuin 1. Discussion: The development of [¹¹C] 1 provides a valuable tool for noninvasive imaging investigations in rodent models with aberrant sirtuin 1 expression. This novel radiotracer holds promise for advancing our understanding of sirtuin 1’s role in disease mechanisms and may facilitate the validation of sirtuin 1 inhibitors in clinical trials.