Exposure-associated DNA methylation among people exposed to multiple industrial pollutants.

BACKGROUND: Current research on the epigenetic repercussions of exposure to a combination of pollutants is limited. This study aims to discern DNA methylation probes associated with exposure to multiple pollutants, serving as early effect markers, and single-nucleotide polymorphisms (SNPs) as surrogate indicators for population susceptibility. The investigation involved the analysis of urine exposure biomarkers for 11 heavy metals (vanadium, arsenic, mercury, cadmium, chromium, nickel, lead, manganese, copper, strontium, thallium), polycyclic aromatic hydrocarbon (PAHs) (1-hydroxypyrene), genome-wide DNA methylation sequencing, and SNPs array on all study participants. The data were integrated with metabolomics information and analyzed both at a community level based on proximity to home addresses relative to the complex and at an individual level based on exposure biomarker concentrations. RESULTS: On a community level, 67 exposure-related CpG probes were identified, while 70 CpG probes were associated with urine arsenic concentration, 2 with mercury, and 46 with vanadium on an individual level. These probes were annotated to genes implicated in cancers and chronic kidney disease. Weighted quantile sum regression analysis revealed that vanadium, mercury, and 1-hydroxypyrene contributed the most to cg08238319 hypomethylation. cg08238319 is annotated to the aryl hydrocarbon receptor repressor (AHRR) gene, and AHRR hypomethylation was correlated with an elevated risk of lung cancer. AHRR was further linked to deregulations in phenylalanine metabolism, alanine, aspartate, and glutamate metabolism, along with heightened oxidative stress. Additionally, three SNPs (rs11085020, rs199442, and rs10947050) corresponding to exposure-related CpG probes exhibited significant interaction effects with multiple heavy metals and PAHs exposure, and have been implicated in cancer progression and respiratory diseases. CONCLUSION: Our findings underscore the pivotal role of AHRR methylation in gene-environment interactions and highlight SNPs that could potentially serve as indicators of population susceptibility in regions exposed to multiple heavy metals and PAHs.

Sensing Mechanism and Characterization of NO2 Gas Sensors Using Gold-Black NP-Decorated Ga2O3 Nanorod Sensing Membranes.

In this work, a vapor cooling condensation system was utilized to deposit various amounts of p-type gold-black nanoparticles (NPs) onto the surface of n-type gallium oxide (Ga2O3) nanorods forming p-n heterojunction-structured sensing membranes of nitrogen dioxide (NO2) gas sensors. The role and the sensing mechanism of the various gold-black NP-decorated Ga2O3 nanorods in NO2 gas sensors were investigated. The coverage and atomic percentage of the sensing membranes were observed using high-resolution transmission electron microscopy (HRTEM) measurements and energy-dispersive spectroscopy (EDS), respectively. For the NO2 gas sensor using the sensing membrane of 60 s-deposited gold-black NP-decorated Ga2O3 nanorods under a NO2 concentration of 10 ppm, the highest responsivity of 5221.1% was obtained. This result was attributed to the spillover effect and the formation of the p-n heterojunction, which increased more ionized-oxygen adsorption sites and promoted the reaction between NO2 gas and Ga2O3 nanorods. Furthermore, the NO2 gas sensor could detect the low NO2 concentration of 100 ppb and achieved a responsivity of 56.9%. The resulting NO2 gas sensor also exhibited excellent selectivity for detecting NO2 gas, with higher responsivity at a NO2 concentration of 10 ppm compared with that of the C2H5OH and NH3 concentrations of 100 ppm.

Investigation of High-Sensitivity NO2 Gas Sensors with Ga2O3 Nanorod Sensing Membrane Grown by Hydrothermal Synthesis Method.

In this work, Ga2O3 nanorods were converted from GaOOH nanorods grown using the hydrothermal synthesis method as the sensing membranes of NO2 gas sensors. Since a sensing membrane with a high surface-to-volume ratio is a very important issue for gas sensors, the thickness of the seed layer and the concentrations of the hydrothermal precursor gallium nitrate nonahydrate (Ga(NO3)3·9H2O) and hexamethylenetetramine (HMT) were optimized to achieve a high surface-to-volume ratio in the GaOOH nanorods. The results showed that the largest surface-to-volume ratio of the GaOOH nanorods could be obtained using the 50-nm-thick SnO2 seed layer and the Ga(NO3)3·9H2O/HMT concentration of 12 mM/10 mM. In addition, the GaOOH nanorods were converted to Ga2O3 nanorods by thermal annealing in a pure N2 ambient atmosphere for 2 h at various temperatures of 300 °C, 400 °C, and 500 °C, respectively. Compared with the Ga2O3 nanorod sensing membranes annealed at 300 °C and 500 °C, the NO2 gas sensors using the 400 °C-annealed Ga2O3 nanorod sensing membrane exhibited optimal responsivity of 1184.6%, a response time of 63.6 s, and a recovery time of 135.7 s at a NO2 concentration of 10 ppm. The low NO2 concentration of 100 ppb could be detected by the Ga2O3 nanorod-structured NO2 gas sensors and the achieved responsivity was 34.2%.

Automated 3D segmentation of the aorta and pulmonary artery for predicting outcomes after thoracoscopic lobectomy in lung cancer patients.

Background: Preoperative two-dimensional manual measurement of pulmonary artery diameter in a single-cut axial view computed tomography (CT) image is a commonly used non-invasive prediction method for pulmonary hypertension. However, the accuracy may be unreliable. Thus, this study aimed to evaluate the correlation of short-term surgical outcomes and pulmonary artery/aorta (PA/Ao) diameter ratio measured by automated three-dimensional (3D) segmentation in lung cancer patients who underwent thoracoscopic lobectomy. Materials and methods: We included 383 consecutive lung cancer patients with thin-slice CT images who underwent lobectomy at a single institute between January 1, 2011 and December 31, 2019. Automated 3D segmentation models were used for 3D vascular reconstruction and measurement of the average diameters of Ao and PA. Propensity-score matching incorporating age, Charlson comorbidity index, and lobectomy performed by uniportal VATS was used to compare clinical outcomes in patients with PA/Ao ratio ≥1 and those <1. Results: Our segmentation method measured 29 (7.57%) patients with a PA/Ao ratio ≥1. After propensity-score matching, a higher overall postoperative complication classified by the Clavien-Dindo classification (p = 0.016) were noted in patients with 3D PA/Ao diameter ratio ≥1 than those of <1. By multivariate logistic regression, patients with a 3D PA/Ao ratio ≥ 1 (p = 0.013) and tumor diameter > 3 cm (p = 0.002) both significantly predict the incidence of postoperative complications. Conclusions: Pulmonary artery/aorta diameter ratio ≥ 1 measured by automated 3D segmentation may predict postoperative complications in lung cancer patients who underwent lobectomy.

Correction: Wang et al. Automated 3D Segmentation of the Aorta and Pulmonary Artery on Non-Contrast-Enhanced Chest Computed Tomography Images in Lung Cancer Patients. Diagnostics 2022, 12, 967.

Errors occurred in the standard deviation of the Dice similarity coefficient (DSC) described in the original publication [...].

Automated 3D Segmentation of the Aorta and Pulmonary Artery on Non-Contrast-Enhanced Chest Computed Tomography Images in Lung Cancer Patients.

Pulmonary hypertension should be preoperatively evaluated for optimal surgical planning to reduce surgical risk in lung cancer patients. Preoperative measurement of vascular diameter in computed tomography (CT) images is a noninvasive prediction method for pulmonary hypertension. However, the current estimation method, 2D manual arterial diameter measurement, may yield inaccurate results owing to low tissue contrast in non-contrast-enhanced CT (NECT). Furthermore, it provides an incomplete evaluation by measuring only the diameter of the arteries rather than the volume. To provide a more complete and accurate estimation, this study proposed a novel two-stage deep learning (DL) model for 3D aortic and pulmonary artery segmentation in NECT. In the first stage, a DL model was constructed to enhance the contrast of NECT; in the second stage, two DL models then applied the enhanced images for aorta and pulmonary artery segmentation. Overall, 179 patients were divided into contrast enhancement model (n = 59), segmentation model (n = 120), and testing (n = 20) groups. The performance of the proposed model was evaluated using Dice similarity coefficient (DSC). The proposed model could achieve 0.97 ± 0.66 and 0.93 ± 0.16 DSC for aortic and pulmonary artery segmentation, respectively. The proposed model may provide 3D diameter information of the arteries before surgery, facilitating the estimation of pulmonary hypertension and supporting preoperative surgical method selection based on the predicted surgical risks.

Distinct Survival Outcomes in Subgroups of Stage III Pancreatic Cancer Patients: Taiwan Cancer Registry and Surveillance, Epidemiology and End Results registry.

PURPOSE: Pancreatic cancer is one of the most malignant cancers with poor survival. The latest edition of the American Joint Committee on Cancer (AJCC) staging system classifies the majority of operable pancreatic cancer patients as stage-III, while dramatic heterogeneity is observed among these patients. Therefore, subgrouping is required to accurately predict their prognosis and define a treatment plan. This study conducts a cohort study to provide a more precise classification system for stage-III pancreatic cancer patients by utilizing clinical variables. METHODS: We analyzed survival using log-rank tests, univariate Cox-regression models, and Kaplan-Meier survival curves for stage-III pancreatic ductal adenocarcinoma (PDAC) patients from the Taiwan Cancer Registry (TCR). Patients were further divided into subgroups using classification and regression tree (CART) algorithm. All results were validated using the SEER database. RESULTS: Among stage-III PDAC patients, lymph node and tumor grade showed significant association with survival. Patients with N2 stage had higher mortality risks (hazard ratio [HR] = 2.30, 95% confidence interval [CI] 1.71-3.08, p < 0.0001) than N0 patients. Patients with grade 3 also had higher risk of mortality (HR = 3.80, 95% CI 2.25-6.39, p < 0.0001) than grade 1 patients. The CART algorithm stratified stage-III patients into four subgroups with significantly different survival rates. The median survival of the four subgroups was 23.5, 18.4, 14.5, and 9.0 months, respectively (p < 0.0001). Similar results were observed with SEER data. CONCLUSIONS: Lymph node involvement and tumor grade are predictive factors for survival in stage-III PDAC patients. This new precise classification system can be used to guide treatment planning in advanced-stage pancreatic cancer.

Predicting Colon Cancer-Specific Survival for the Asian Population Using National Cancer Registry Data from Taiwan.

PURPOSE: Colon cancer is the third most incident and life-threatening cancer in Taiwan. A comprehensive survival prediction system would greatly benefit clinical practice in this area. This study was designed to develop an accurate prognostic model for colon cancer patients by using clinicopathological variables obtained from the Taiwan Cancer Registry database. METHODS: We analyzed 20,218 colon cancer patients from the Taiwan Cancer Registry database, who were diagnosed between 2007 and 2015, were followed up until December 31, 2017, and had undergone curative surgery. We proposed two prognostic models, with different combinations of predictors. The first model used only traditional clinical features. The second model included several colon cancer site-specific factors (circumferential resection margin, perineural invasion, obstruction, and perforation), in addition to the traditional features. Both prediction models were developed by using a Cox proportional hazards model. Furthermore, we investigated whether race is a significant predictor of survival in colon cancer patients by using Model 1 on the Surveillance, Epidemiology, and End Results (SEER) cancer registry dataset. RESULTS: The proposed models displayed a robust prediction performance (all Harrell's c-index >0.8). For both the calibration and validation steps, the differences between the predicted and observed mortality were mostly less than 5%. CONCLUSIONS: The prediction model (Model 1) is an effective predictor of survival regardless of the ethnic background of patients and can potentially help to provide better prediction of colon cancer-specific survival outcomes, thus allowing physicians to improve treatment plans.

Previous Extrapulmonary Malignancies Impact Outcomes in Patients With Surgically Resected Lung Cancer.

Background: As the overall survival of patients with cancer continues to improve, the incidence of second primary malignancies seems to be increasing. Previous studies have shown controversial results regarding the survival of patients with primary lung cancer with previous extrapulmonary malignancies. This study aimed to determine the clinical picture and outcomes of this particular subgroup of patients. Materials and Methods: We included 2,408 patients who underwent pulmonary resection for primary lung cancer at our institute between January 1, 2011 and December 30, 2017 in this retrospective study. Medical records were extracted and clinicopathological parameters and postoperative prognoses were compared between patients with lung cancer with and without previous extrapulmonary malignancies. Results: There were 200 (8.3%) patients with previous extrapulmonary malignancies. Breast cancer (30.5%), gastrointestinal cancer (17%), and thyroid cancer (9%) were the most common previous extrapulmonary malignancies. Age, sex, a family history of lung cancer, and preoperative carcinoembryonic antigen levels were significantly different between the two groups. Patients with previous breast or thyroid cancer had significantly better overall survival than those without previous malignancies. Conversely, patients with other previous extrapulmonary malignancies had significantly poorer overall survival (p < 0.001). The interval between the two cancer diagnoses did not significantly correlate with clinical outcome. Conclusion: Although overall survival was lower in patients with previous extrapulmonary malignancies, previous breast or thyroid cancer did not increase mortality. Our findings may help surgeons to predict prognosis in this subgroup of patients with primary lung cancer.

40 GHz RF biosensor based on microwave coplanar waveguide transmission line for cancer cells (HepG2) dielectric characterization.

This paper presents a 40-GHz RF biosensor that involves using a microwave coplanar waveguide (CPW) transmission line for the dielectric characterization of cancer cells (Hepatoma G2, HepG2). In the past, conventional resonator-based biosensors were designed to operate at a specific resonant peak; however, the dielectric sensitivity of the cells was restricted to a narrow bandwidth. To provide a very wide bandwidth (1-40 GHz), biosensors were based on a microwave CPW transmission line. The proposed biosensor can rapidly measure two frequency-dependent cell-based dielectric parameters of HepG2 cells, microwave attenuation (α(f)cell) and the dielectric constant (εr(f)cell), while removing the microwave parasitic effects (including the cultured medium and substrate materials). The proposed biosensor can be applied in postoperative cancer diagnosis.

Cleavage-site specificity of prolyl endopeptidase FAP investigated with a full-length protein substrate.

Fibroblast activation protein (FAP) is a prolyl-cleaving endopeptidase proposed as an anti-cancer drug target. It is necessary to define its cleavage-site specificity to facilitate the identification of its in vivo substrates and to understand its biological functions. We found that the previously identified substrate of FAP, α(2)-anti-plasmin, is not a robust substrate in vitro. Instead, an intracellular protein, SPRY2, is cleavable by FAP and more suitable for investigation of its substrate specificity in the context of the full-length globular protein. FAP prefers uncharged residues, including small or bulky hydrophobic amino acids, but not charged amino acids, especially acidic residue at P1', P3 and P4 sites. Molecular modelling analysis shows that the substrate-binding site of FAP is surrounded by multiple tyrosine residues and some negatively charged residues, which may exert least preference for substrates with acidic residues. This provides an explanation why FAP cannot cleave interleukins, which have a glutamate at either P4 or P2', despite their P3-P2-P1 sites being identical to SPRY2 or α-AP. Our study provided new information on FAP cleavage-site specificity, which differs from the data obtained by profiling with a peptide library or with the denatured protein, gelatin, as the substrate. Furthermore, our study suggests that negatively charged residues should be avoided when designing FAP inhibitors.