Association of High Body Mass Index in Early Life With the Development of Colorectal Cancer.

SIGNIFICANCE: This study on the relationship between early life high BMI and the development of CRC reveals the role of high BMI during childhood and adolescence in the occurrence and progression of CRC. It suggests the importance of restoring normal weight or reducing weight in individuals with high BMI early in life for the prevention of colorectal cancer.

Establishment and validation of a prediction model for gestational diabetes.

AIM: To develop a visual prediction model for gestational diabetes (GD) in pregnant women and to establish an effective and practical tool for clinical application. METHODS: To establish a prediction model, the modelling set included 1756 women enrolled in the Zunyi birth cohort, the internal validation set included 1234 enrolled women, and pregnant women in the Wuhan cohort were included in the external validation set. We established a demographic-lifestyle factor model (DLFM) and a demographic-lifestyle-environmental pollution factor model (DLEFM) based on whether the women were exposed to environmental pollutants. The least absolute shrinkage and selection lasso-logistic regression analyses were used to identify the independent predictors of GD and construct a nomogram for predicting its occurrence. RESULTS: The DLEFM regression analysis showed that a family history of diabetes (odd ratio [OR] 2.28; 95% confidence interval [CI] 1.05-4.71), a history of GD in pregnant women (OR 4.22; 95% CI 1.89-9.41), being overweight or obese before pregnancy (OR 1.71; 95% CI 1.27-2.29), a history of hypertension (OR 2.61; 95% CI 1.41-4.72), sedentary time (h/day) (OR 1.16; 95% CI 1.08-1.24), monobenzyl phthalate (OR 1.95; 95% CI 1.45-2.67) and Q4 mono-ethyl phthalate concentration (OR 1.85; 95% CI 1.26-2.73) were independent predictors. The area under the receiver operating curves for the internal validation of the DLEFM and the DLFM constructed using these seven factors was 0.827 and 0.783, respectively. The calibration curve of the DLEFM was close to the diagonal line. The DLEFM was thus the more optimal model, and the one which we chose. CONCLUSIONS: A nomogram based on preconception factors was constructed to predict the occurrence of GD in the second and third trimesters. It provided an effective tool for the early prediction and timely management of GD.

First Evidence of the Bioaccumulation and Trophic Transfer of Tire Additives and Their Transformation Products in an Estuarine Food Web.

The discovery of the significant lethal impacts of the tire additive transformation product N-(1,3-dimethylbutyl)-N'-phenyl-p-phenylenediamine quinone (6PPD-Q) on coho salmon has garnered global attention. However, the bioaccumulation and trophic transfer of tire additives and their transformation products (TATPs) within food webs remain obscure. This study first characterized the levels and compositions of 15 TATPs in the Pearl River Estuary, estimated their bioaccumulation and trophic transfer potential in 21 estuarine species, and identified priority contaminants. Our observations indicated that TATPs were prevalent in the estuarine environment. Eight, six, seven, and 10 TATPs were first quantified in the shrimp, sea cucumber, snail, and fish samples, with total mean levels of 45, 56, 64, and 67 ng/g (wet weight), respectively. N,N'-Diphenyl-p-phenylenediamine (DPPD) and N,N'-bis(2-methylphenyl)-1,4-benzenediamine (DTPD) exhibited high bioaccumulation. Significant biodilution was only identified for benzothiazole, while DPPD and DTPD displayed biomagnification trends based on Monte Carlo simulations. The mechanisms of bioaccumulation and trophodynamics of TATPs could be explained by their chemical hydrophobicity, molecular mass, and metabolic rates. Based on a multicriteria scoring technique, DPPD, DTPD, and 6PPD-Q were characterized as priority contaminants. This work emphasizes the importance of biomonitoring, particularly for specific hydrophobic tire additives.

Elucidation of protein-ligand interactions by multiple trajectory analysis methods.

The identification of interaction between protein and ligand including binding positions and strength plays a critical role in drug discovery. Molecular docking and molecular dynamics (MD) techniques have been widely applied to predict binding positions and binding affinity. However, there are few works that describe the systematic exploration of the MD trajectory evolution in this context, potentially leaving out important information. To address the problem, we build a framework, Moira (molecular dynamics trajectory analysis), which enables automating the whole process ranging from docking, MD simulations and various analyses as well as visualizations. We utilized Moira to analyze 400 MD simulations in terms of their geometric features (root mean square deviation and protein-ligand interaction profiler) and energetics (molecular mechanics Poisson-Boltzmann surface area) for these trajectories. Finally, we demonstrate the performance of different analysis techniques in distinguishing native poses among four poses.

Effect of combined exposure to phthalates and polycyclic aromatic hydrocarbons during early pregnancy on gestational age and neonatal size: A prospective cohort study.

Many studies have indicated that individual exposure to phthalates (PAEs) or polycyclic aromatic hydrocarbons (PAHs) affects pregnancy outcomes. However, combined exposure to PAEs and PAHs presents a more realistic situation, and research on the combined effects of PAEs and PAHs on gestational age and newborn size is still limited. This study aimed to assess the effects of combined exposure to PAEs and PAHs on neonatal gestational age and birth size. Levels of 9 PAE and 10 PAH metabolites were measured from the urine samples of 1030 women during early pregnancy from the Zunyi Birth Cohort in China. Various statistical models, including linear regression, restricted cubic spline, Bayesian kernel machine regression, and quantile g-computation, were used to study the individual effects, dose-response relationships, and combined effects, respectively. The results of this prospective study revealed that each ten-fold increase in the concentration of monoethyl phthalate (MEP), 2-hydroxynaphthalene (2-OHNap), 2-hydroxyphenanthrene (2-OHPhe), and 1-hydroxypyrene (1-OHPyr) decreased gestational age by 1.033 days (95 % CI: -1.748, -0.319), 0.647 days (95 % CI: -1.076, -0.219), 0.845 days (95 % CI: -1.430, -0.260), and 0.888 days (95 % CI: -1.398, -0.378), respectively. Moreover, when the concentrations of MEP, 2-OHNap, 2-OHPhe, and 1-OHPyr exceeded 0.528, 0.039, 0.012, and 0.002 µg/g Cr, respectively, gestational age decreased in a dose-response manner. Upon analyzing the selected PAE and PAH metabolites as a mixture, we found that they were significantly negatively associated with gestational age, birth weight, and the ponderal index, with 1-OHPyr being the most important contributor. These findings highlight the adverse effects of single and combined exposure to PAEs and PAHs on gestational age. Therefore, future longitudinal cohort studies with larger sample sizes should be conducted across different geographic regions and ethnic groups to confirm the impact of combined exposure to PAEs and PAHs on birth outcomes.

Mathematically Improved XGBoost Algorithm for Truck Hoisting Detection in Container Unloading.

Truck hoisting detection constitutes a key focus in port security, for which no optimal resolution has been identified. To address the issues of high costs, susceptibility to weather conditions, and low accuracy in conventional methods for truck hoisting detection, a non-intrusive detection approach is proposed in this paper. The proposed approach utilizes a mathematical model and an extreme gradient boosting (XGBoost) model. Electrical signals, including voltage and current, collected by Hall sensors are processed by the mathematical model, which augments their physical information. Subsequently, the dataset filtered by the mathematical model is used to train the XGBoost model, enabling the XGBoost model to effectively identify abnormal hoists. Improvements were observed in the performance of the XGBoost model as utilized in this paper. Finally, experiments were conducted at several stations. The overall false positive rate did not exceed 0.7% and no false negatives occurred in the experiments. The experimental results demonstrated the excellent performance of the proposed approach, which can reduce the costs and improve the accuracy of detection in container hoisting.

Defining the Glycosaminoglycan Interactions of Complement Factor H-Related Protein 5.

Complement activation is an important mediator of kidney injury in glomerulonephritis. Complement factor H (FH) and FH-related protein 5 (FHR-5) influence complement activation in C3 glomerulopathy and IgA nephropathy by differentially regulating glomerular complement. FH is a negative regulator of complement C3 activation. Conversely, FHR-5 in vitro promotes C3 activation either directly or by competing with FH for binding to complement C3b. The FH-C3b interaction is enhanced by surface glycosaminoglycans (GAGs) and the FH-GAG interaction is well-characterized. In contrast, the contributions of carbohydrates to the interaction of FHR-5 and C3b are unknown. Using plate-based and microarray technologies we demonstrate that FHR-5 interacts with sulfated GAGs and that this interaction is influenced by the pattern and degree of GAG sulfation. The FHR-5-GAG interaction that we identified has functional relevance as we could show that the ability of FHR-5 to prevent binding of FH to surface C3b is enhanced by surface kidney heparan sulfate. Our findings are important in understanding the molecular basis of the binding of FHR-5 to glomerular complement and the role of FHR-5 in complement-mediated glomerular disease.

Frustrated amino functional group coupling with electric field makes CO2 activation easier.

New models associated with frustrated geometry and an external electric field (EEF) were designed to qualitatively and quantitatively explore CO2 activation through density functional calculations. We investigated the influence on CO2 of the microenvironments of methylamine (CH3NH2) positioned at different heights above a Cu (111) surface in the presence and absence of an electric field. The results demonstrate that at an approximate distance of 4 ± 1 Å between N and the metal surface, neither lower nor higher, under an EEF over 0.4 V Å-1, there is a remarkable synergistic effect between the chemical interaction and EEF that activates CO2, and also lowers the required EEF strength. This is in contrast to the separate factors or any other combinations of them which do not achieve the synergistic effect. In addition, when H in was replaced by F, the O-C-O angle of CO2 is not affected. This phenomenon further illustrates that the synergistic effect is very sensitive to the nucleophilicity of NH2. Various other chemical groups and substrates were investigated, and PHCH3 also displays a distinctive chemisorption CO2 state. The substrate also plays a significant role, except that Au cannot generate a similar effect. Furthermore, constraining or facilitating CO2 activation strongly depends on the distance between the chemical group and the substrate. Appropriate combinations of the three factors related to the substrate Cu, the chemical group CH3NH2 and the EEF provide new protocols to make CO2 activation easier and controllable.

Squared Energy-Resolved Mass Spectrometry Advances Quantitative Bile Acid Submetabolome Characterization.

The bile acid (BA) submetabolome can partially reflect either physiological or pathological status of vertebrates. The structural diversity, however, extensively hinders BA submetabolome clarification. Here, efforts were primarily devoted to enhance structural annotation confidences of BAs, in particular the conjugated BAs, through fortifying a new technology, namely, squared energy-resolved mass spectrometry (ER2-MS), to traditional liquid chromatography with tandem mass spectrometry (LC-MS/MS). Because of possessing two tandem-in-space collision cells, namely, q2 and linear ion trap (LIT) chambers, Qtrap-MS was employed as the fit-for-purpose tool to conduct ER2-MS measurements. The first ER-MS was undertaken in a q2 cell to gain first-generation breakdown graphs to disclose conjugation sites via applying the multiple-reaction monitoring (MRM) program, and the second ER-MS was accomplished in a LIT chamber through programming MRM cubed to acquire second-generation breakdown graphs of concerned ions for scaffold characterization. An authentic BA library consisting of commercial BAs together with their in vitro metabolites was built to record a reference breakdown graph set. Moreover, the so-called universal metabolome standard sample that was prepared by pooling diverse BA-enriched matrices was applied for structural deciphering potential evaluation and quasi-quantitative analysis of all detected BAs as well, according to applying a well-defined quasi-content concept. High-confidence structural analysis was achieved for as many as 201 BAs, and significant impacts occurred for the BA submetabolome of HepG2 cells after lithocholic acid treatment. Together, ER2-MS provides a promising tool to promote, although not limited to, LC-MS/MS-based BA-targeted metabolomics.

Chikungunya Virus Strains from Each Genetic Clade Bind Sulfated Glycosaminoglycans as Attachment Factors.

Chikungunya virus (CHIKV) is an arthritogenic alphavirus that causes debilitating musculoskeletal disease. CHIKV displays broad cell, tissue, and species tropism, which may correlate with the attachment factors and entry receptors used by the virus. Cell surface glycosaminoglycans (GAGs) have been identified as CHIKV attachment factors. However, the specific types of GAGs and potentially other glycans to which CHIKV binds and whether there are strain-specific differences in GAG binding are not fully understood. To identify the types of glycans bound by CHIKV, we conducted glycan microarray analyses and discovered that CHIKV preferentially binds GAGs. Microarray results also indicate that sulfate groups on GAGs are essential for CHIKV binding and that CHIKV binds most strongly to longer GAG chains of heparin and heparan sulfate. To determine whether GAG binding capacity varies among CHIKV strains, a representative strain from each genetic clade was tested. While all strains directly bound to heparin and chondroitin sulfate in enzyme-linked immunosorbent assays (ELISAs) and depended on heparan sulfate for efficient cell binding and infection, we observed some variation by strain. Enzymatic removal of cell surface GAGs and genetic ablation that diminishes GAG expression reduced CHIKV binding and infectivity of all strains. Collectively, these data demonstrate that GAGs are the preferred glycan bound by CHIKV, enhance our understanding of the specific GAG moieties required for CHIKV binding, define strain differences in GAG engagement, and provide further evidence for a critical function of GAGs in CHIKV cell attachment and infection.IMPORTANCE Alphavirus infections are a global health threat, contributing to outbreaks of disease in many parts of the world. Recent epidemics caused by CHIKV, an arthritogenic alphavirus, resulted in more than 8.5 million cases as the virus has spread into new geographic regions, including the Western Hemisphere. CHIKV causes disease in the majority of people infected, leading to severe and debilitating arthritis. Despite the severity of CHIKV disease, there are no licensed therapeutics. Since attachment factors and receptors are determinants of viral tropism and pathogenesis, understanding these virus-host interactions can enhance our knowledge of CHIKV infection. We analyzed over 670 glycans and identified GAGs as the main glycan bound by CHIKV. We defined specific GAG components required for CHIKV binding and assessed strain-specific differences in GAG binding capacity. These studies provide insight about cell surface molecules that CHIKV binds, which could facilitate the development of antiviral therapeutics targeting the CHIKV attachment step.