Semiconducting polymer dots based l-lactate sensor by enzymatic cascade reaction system.

BACKGROUND: l-lactate detection is important for not only assessing exercise intensity, optimizing training regimens, and identifying the lactate threshold in athletes, but also for diagnosing conditions like L-lactateosis, monitoring tissue hypoxia, and guiding critical care decisions. Moreover, l-lactate has been utilized as a biomarker to represent the state of human health. However, the sensitivity of the present l-lactate detection technique is inadequate. RESULTS: Here, we reported a sensitive ratiometric fluorescent probe for l-lactate detection based on platinum octaethylporphyrin (PtOEP) doped semiconducting polymer dots (Pdots-Pt) with enzymatic cascade reaction. With the help of an enzyme cascade reaction, the l-lactate was continuously oxidized to pyruvic and then reduced back to l-lactate for the next cycle. During this process, oxygen and NADH were continuously consumed, which increased the red fluorescence of Pdots-Pt that responded to the changes of oxygen concentration and decreased the blue fluorescence of NADH at the same time. By comparing the fluorescence intensities at these two different wavelengths, the concentration of l-lactate was accurately measured. With the optimal conditions, the probes showed two linear detection ranges from 0.5 nM to 5.0 µM and 5.0 µM-50.0 µM for l-lactate detection. The limit of detection was calculated to be 0.18 nM by 3σ/slope method. Finally, the method shows good detection performance of l-lactate in both bovine serum and artificial serum samples, indicating its potential usage for the selective analysis of l-lactate for health monitoring and disease diagnosis. SIGNIFICANCE: The successful application of the sensing system in the complex biological sample (bovine serum and artificial serum samples) demonstrated that this method could be used for sensitive l-lactate detection in practical clinical applications. This detection system provided an extremely low detection limit, which was several orders of magnitude lower than methods proposed in other literatures.

Iron-Rich Semiconducting Polymer Dots for the Combination of Ferroptosis-Starvation and Phototherapeutic Cancer Therapy.

Chemodynamic therapy (CDT) has emerged as an outstanding antitumor therapeutic method due to its selectivity and utilization of tumor microenvironment. However, there are still unmet requirements to achieve a high antitumor efficiency, including the tumor accumulation of catalyst and enrichment of reactants of Fenton reaction. Here, an iron-loaded semiconducting polymer dot modified with glucose oxidase (Pdot@Fe@GOx) is reported to deliver iron ions into tumor tissues and in situ generation of hydrogen peroxide in tumors. On one hand, Pdot@Fe@GOx converts glucose to gluconic acid and hydrogen peroxide (H2 O2 ) in tumor, which not only consumes glucose of tumor cells, but also provides the H2 O2 for the following Fenton reaction. On the other hand, the Pdot@Fe@GOx delivers active iron ions in tumor to perform CDT with the combination of the generated H2 O2 . In addition, the Pdot@Fe@GOx has both photothermal and photodynamic effects under the irradiation of near-infrared laser, which can improve and compensate the CDT effect to kill cancer cells. This Pdot@Fe@GOx-based multiple-mode therapeutic strategy has successfully achieved a synergistic anticancer effect with minimal side effects and has the potential to be translated into preclinical setting for tumor therapy.

Stratification of population in NHANES 2009-2014 based on exposure pattern of lead, cadmium, mercury, and arsenic and their association with cardiovascular, renal and respiratory outcomes.

BACKGROUND: Environmental exposure to toxic metals is an important risk factor to human health. Traditional methods have examined associations between a health endpoint and exposure to heavy metals by either univariate or multiple regression. In the setting of ubiquitous heterogeneous environmental exposures, statistical methods that incorporate mixed exposures are increasingly relevant and may provide new insight into the association between metal exposure and important cardiovascular, renal and respiratory outcomes. OBJECTIVE: The objective of this study was to classify the population of National Health and Nutrition Examination Survey (NHANES) into different exposure subgroups using modern unsupervised clustering methods based on lead, cadmium, mercury, and arsenic measured in urine or whole blood, and to assess the association between the identified exposure groups and twelve important health endpoints. METHODS: We analyzed a sub-cohort of 9662 subjects participating in the 6 cycles (2003-2004 to 2013-2014) of NHANES study. The urine levels of 3 heavy metals (total arsenic, lead, cadmium) and blood levels of 3 heavy metals (lead, cadmium and mercury) were analyzed using a two-step approach. In the first step, we stratified the population into subgroups using unsupervised clustering (k-medoids) based on levels of metals either in urine or in blood. Then, we examine the association between 12 health endpoints and identified exposure subgroups while controlling for age, sex, race/ethnicity, education, smoking status, BMI, and urinary creatinine. RESULTS: The k-medoids algorithm clustered NHANES population into 2 groups based on either blood or urinary levels of heavy metals. The concentrations of all the three heavy metals were significantly different between the identified groups in blood (p < 2.2e-16) or in urine (p = 0). The group with higher concentrations was defined as the "high-exposure" group, while the group with lower concentrations was defined as "low-exposure" group. Association analysis with health outcomes suggested that the high-exposure group according to either blood or urinary metal levels had significantly higher total mortality (1.63-1.64 times higher, p < 0.0001), mortality caused by malignant neoplasms (2.05-2.62 times higher, p < 0.0002), Gamma-glutamyl transferase (GGT) (1.03-1.05 times higher, p < 0.0001). In addition, the high-exposure group based on blood levels was also significantly associated with SBP, death related to hypertension, heart disease and chronic lower respiratory disease, while the high-exposure group based on urinary concentrations had higher mortality related to nephritis. CONCLUSIONS: We proposed an unsupervised clustering method to stratify the population into high- and low-exposure groups based on the co-exposure of heavy metals. The high-exposure groups, characterized by higher metal concentrations, had significant higher GGT, SBP, DBP, and mortality rates suggesting the detrimental effects of exposure to these heavy metals. The stratification of the NHANES population based on exposure patterns provides an informative method to study the impact of metal exposures on health outcomes.

High-throughput and efficient multilocus genome-wide association study on longitudinal outcomes.

MOTIVATION: With the emerging of high-dimensional genomic data, genetic analysis such as genome-wide association studies (GWAS) have played an important role in identifying disease-related genetic variants and novel treatments. Complex longitudinal phenotypes are commonly collected in medical studies. However, since limited analytical approaches are available for longitudinal traits, these data are often underutilized. In this article, we develop a high-throughput machine learning approach for multilocus GWAS using longitudinal traits by coupling Empirical Bayesian Estimates from mixed-effects modeling with a novel ℓ0-norm algorithm. RESULTS: Extensive simulations demonstrated that the proposed approach not only provided accurate selection of single nucleotide polymorphisms (SNPs) with comparable or higher power but also robust control of false positives. More importantly, this novel approach is highly scalable and could be approximately >1000 times faster than recently published approaches, making genome-wide multilocus analysis of longitudinal traits possible. In addition, our proposed approach can simultaneously analyze millions of SNPs if the computer memory allows, thereby potentially allowing a true multilocus analysis for high-dimensional genomic data. With application to the data from Alzheimer's Disease Neuroimaging Initiative, we confirmed that our approach can identify well-known SNPs associated with AD and were much faster than recently published approaches (≥6000 times). AVAILABILITY AND IMPLEMENTATION: The source code and the testing datasets are available at https://github.com/Myuan2019/EBE_APML0. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Utilization of Adult Data in Designing Pediatric Pharmacokinetic Studies: How Much Are Historical Adult Data Worth?

In pediatric drug development, large amounts of adult data are often available before the start of a pediatric study. It is believed that borrowing this information will improve the efficiency. However, when adult information is not sufficiently similar to that of pediatrics, incorporating adult data will introduce bias and consequently result in efficiency loss. A Bayesian alternative-namely, commensurate prior approach where the level of information borrowing is based on the concordance of adult and pediatric data-was investigated. Simulation results indicate that the commensurate prior approach, in general, provides a balanced and robust trade-off between bias and efficiency gain. The benefit of this approach was quantified in terms of sample size savings, and recommended sample sizes are provided.