<i>Precision</i> vs. recommendation length of the three algorithms for <i>Del.icio.us</i> and <i>Movielens</i>.

<p>The result is obtained by averaging over 50 independent realizations of random data division , and yellow lines represent the error intervals. The parameter for algorithm (III) is set to 0.001. Results on both datasets show that the gravity-model based algorithm (black) outperforms other two baselines.</p

Comparisons of <i>AUC</i> results of respectively considering the effects of mass (), common interest (), and as well as three algorithms (algorithm I, II and III).

<p>The result is obtained by averaging over 50 independent realizations of random data division, and the three digital numbers behind the signs are the corresponding error intervals. The parameter for algorithm (III) is set to 0.001.</p

DataSheet_1_Phthalate metabolites and sex steroid hormones in relation to obesity in US adults: NHANES 2013-2016.docx

BackgroundObesity and metabolic syndrome pose significant health challenges in the United States (US), with connections to disruptions in sex hormone regulation. The increasing prevalence of obesity and metabolic syndrome might be associated with exposure to phthalates (PAEs). Further exploration of the impact of PAEs on obesity is crucial, particularly from a sex hormone perspective.MethodsA total of 7780 adult participants in the National Health and Nutrition Examination Survey (NHANES) from 2013 to 2016 were included in the study. Principal component analysis (PCA) coupled with multinomial logistic regression was employed to elucidate the association between urinary PAEs metabolite concentrations and the likelihood of obesity. Weighted quartiles sum (WQS) regression was utilized to consolidate the impact of mixed PAEs exposure on sex hormone levels (total testosterone (TT), estradiol and sex hormone-binding globulin (SHBG)). We also delved into machine learning models to accurately discern obesity status and identify the key variables contributing most to these models.ResultsPrincipal Component 1 (PC1), characterized by mono(2-ethyl-5-carboxypentyl) phthalate (MECPP), mono(2-ethyl-5-hydroxyhexyl) phthalate (MEHHP), and mono(2-ethyl-5-oxohexyl) phthalate (MEOHP) as major contributors, exhibited a negative association with obesity. Conversely, PC2, with monocarboxyononyl phthalate (MCNP), monocarboxyoctyl phthalate (MCOP), and mono(3-carboxypropyl) phthalate (MCPP) as major contributors, showed a positive association with obesity. Mixed exposure to PAEs was associated with decreased TT levels and increased estradiol and SHBG. During the exploration of the interrelations among obesity, sex hormones, and PAEs, models based on Random Forest (RF) and eXtreme Gradient Boosting (XGBoost) algorithms demonstrated the best classification efficacy. In both models, sex hormones exhibited the highest variable importance, and certain phthalate metabolites made significant contributions to the model’s performance.ConclusionsIndividuals with obesity exhibit lower levels of TT and SHBG, accompanied by elevated estradiol levels. Exposure to PAEs disrupts sex hormone levels, contributing to an increased risk of obesity in US adults. In the exploration of the interrelationships among these three factors, the RF and XGBoost algorithm models demonstrated superior performance, with sex hormones displaying higher variable importance.</p

, , <i>E</i>, <i>r</i>, <i>D</i> and <i>H</i> as the function of ratio of added links.

<p>The result is obtained by averaging over 50 interdependent network realizations. The dash line highlights the corresponding result of <i>ST</i> network. Results from five representative metrics show that the <i>GR</i> model (blue triangle) is the best one to approach the original <i>ST</i> network.</p

as the function of for the two observed datasets, showing that the common feature, , is positively correlated with the object mass.

<p> as the function of for the two observed datasets, showing that the common feature, , is positively correlated with the object mass.</p

<i>InnerS</i> vs. recommendation length of the three algorithms for <i>Del.icio.us</i> and <i>Movielens</i>.

<p>The result is obtained by averaging over 50 independent realizations of random data division, and yellow lines represent the error intervals. The parameter for algorithm (III) is set to 0.001. Results on both datasets show that the gravity-model based algorithm (black) outperforms other two baselines.</p

Evolutionary results of four corresponding networks.

<p> represents the size of the giant component, denotes the clustering coefficient, and are respectively the assortative coefficient and average distance of network, and denotes the network heterogeneity. In the last three rows, it presents both real value of corresponding metric and the error interval (separated by slash), which is calculated as: , where is the metric value of current model and is the corresponding value of <i>ST</i> network. Each value is obtained by averaging over 50 interdependent network realizations.</p

A Supersensitive CTC Analysis System Based on Triangular Silver Nanoprisms and SPION with Function of Capture, Enrichment, Detection, and Release

Detection of circulating tumor cells (CTCs) may be applied for diagnosis of early tumors like a liquid biopsy. However, the sensitivity remains a challenge because CTCs are extremely rare in peripheral blood. In this study, we developed a supersensitive CTC analysis system based on triangular silver nanoprisms (AgNPR) and superparamagnetic iron oxide nanoparticles (SPION) with function of capture, enrichment, detection, and release. The AgNPR was encoded with MBA (i.e., 4-mercaptobenzoic acid) and modified with rBSA (i.e., reductive bovine serum albumin) and FA (i.e., folic acid) generating organic/inorganic composite nanoparticle MBA-AgNPR-rBSA-FA, which has the function of surface-enhanced Raman scattering (SERS). The optimized SERS nanoparticles (i.e., MBA3-AgNPR-rBSA4-FA2) can be utilized for CTC detection in blood samples with high sensitivity and specificity, and the LOD (i.e., limit of detection) reaches to five cells per milliliter. In addition, the SPION was also modified with rBSA and FA generating magnetic nanoparticle SPION-rBSA-FA. Our supersensitive CTC analysis system is composed of MBA3-AgNPR-rBSA4-FA2 and SPION-rBSA-FA nanoparticles, which were applied for capture (via interaction between FA and FRα), enrichment (via magnet), and detection (via SERS) of cancer cells from blood samples. The results demonstrate that our supersensitive CTC analysis system has a better sensitivity and specificity than the SERS nanoparticles alone, and the LOD is up to 1 cell/mL. The flow cytometry and LSCM (i.e., laser scanning confocal microscope) results indicate the CTCs captured, enriched, and isolated by our supersensitive CTC analysis system can also be further released (via adding excessive free FA) for further cell expansion and phenotype identification