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Abstract Using follow-up data from the National Health and Nutrition Examination Survey (NHANES) database, we have collected information on 2572 subjects and used generalized linear model to investigate the association between urinary heavy metal levels and glaucoma risk. In addition, we have developed an individualized risk prediction model using machine learning algorithms and further interpreted the model results through feature importance analysis, local cumulative analysis, and interaction effects. In this study, we found significant association between logarithmically calculated arsenic (As) metabolites, especially arsenochlorine (AC), and glaucoma after adjusting for a series of confounders, including urinary creatinine (β = 1.090, 95% CI: 0.313–1.835). The Shapley Additive Explanations (SHAP) analysis results and clinical risk scores also indicated that As metabolites promoted glaucoma more severely than other variables. This study applied machine learning for the first time to explore the relationship between heavy metals and glaucoma while analyzing the effects of multiple heavy metal exposures on the disease, improving the predictive power compared to conventional models. Our results provided important insights into the potential role of heavy metals in the pathogenesis of glaucoma, facilitated the discovery of new biomarkers for early diagnosis, risk assessment, and timely treatment of glaucoma, and guided public health measures to reduce heavy metal exposure.

Background: Brain glucose hypometabolism is an invariant feature and has significant diagnostic value for Alzheimer's disease. Thiamine diphosphate (TDP) is a critical coenzyme for glucose metabolism and significantly reduced in brain and blood samples of patients with Alzheimer's disease (AD). Aims: To explore the diagnostic value of the measurement of blood thiamine metabolites for AD. Methods: Blood TDP, thiamine monophosphate, and thiamine levels were detected using high performance liquid chromatography (HPLC). The study included the exploration and validation phases. In the exploration phase, the samples of 338 control subjects and 43 AD patients were utilized to establish the models for AD diagnosis assayed by receiver operating characteristic (ROC) curve, including the variable γ that represents the best combination of thiamine metabolites and age to predict the possibility of AD. In the validation phase, the values of models were further tested for AD diagnosis using samples of 861 control subjects, 81 AD patients, 70 vascular dementia patients, and 13 frontotemporal dementia patients. Results: TDP and the γ exhibited significant and consistent values for AD diagnosis in both exploration and validation phases. TDP had 0.843 and 0.837 of the areas under ROC curve (AUCs), 77.4% and 81.5% of sensitivities, and 78.1% and 77.2% of specificities respectively in the exploration and validation phases. The γ had 0.938 and 0.910 of AUCs, 81.4% and 80.2% of sensitivities, and 90.5% and 87.2% of specificities respectively in the exploration and validation phases. TDP and the γ can effectively distinguish AD from vascular dementia (64.3% for TDP, 67.1% for γ) and frontotemporal dementia (84.6% for TDP, 100.0% for γ). Interpretation. The measurement of blood thiamine metabolites by HPLC is an ideal diagnostic test for AD with inexpensive, easy to perform, noninvasive merits.

Abstract Background The application of combination therapy for cancer treatment is limited due to poor tumor-specific drug delivery and the abscopal effect. Methods Here, PD-L1- and CD44-responsive multifunctional nanoparticles were developed using a polymer complex of polyethyleneimine and oleic acid (PEI-OA) and loaded with two chemotherapeutic drugs (paclitaxel and chloroquine), an antigen (ovalbumin), an immunopotentiator (CpG), and an immune checkpoint inhibitor (anti-PD-L1 antibody). Results PEI-OA greatly improved the drug loading capacity and encapsulation efficiency of the nanoplatform, while the anti-PD-L1 antibody significantly increased its cellular uptake compared to other treatment formulations. Pharmacodynamic experiments confirmed that the anti-PD-L1 antibody can strongly inhibit primary breast cancer and increase levels of CD4+ and CD8+ T cell at the tumor site. In addition, chloroquine reversed the “immune-cold” environment and improved the anti-tumor effect of both chemotherapeutics and immune checkpoint inhibitors, while it induced strong immune memory and prevented lung metastasis. Conclusions Our strategy serves as a promising approach to the rational design of nanodelivery systems for simultaneous active targeting, autophagy inhibition, and chemotherapy that can be combined with immune-checkpoint inhibitors for enhanced breast cancer treatment.