Occurrence of legacy and alternative per- and polyfluoroalkyl substances in serum from high exposure population and their disrupting effects on serum lipids and thyroid function.

High exposure of per- and polyfluoroalkyl substances (PFAS) has been reported in main chemical production areas in China, while epidemiological study on exposure risk of PFAS is still limited. In this study, legacy and alternative PFAS were measured in serum samples from 161 adults living in Laizhou Bay, a famous chemical production area located in Shandong province, Northern China. Based on the concentrations of serum PFAS, the disrupting effects of PFAS on serum lipids and thyroid function were further explored. The results showed that the serum perfluorooctanoic acid (PFOA) (geometric mean (GM): 60 ng/mL) in this region was even higher than serum PFOA of residents living in PFOA contaminated water districts in United States and Sweden. 100 % of the serum PFOA was higher than the reference dose for increased total cholesterol (TC). Consistently, higher serum PFOA was marginally correlated with increased TC level (p = 0.062) and low-density lipoprotein (p = 0.065). In addition, higher perfluoroisopropyl perfluorooctanesulfonate and 6:2 chlorinated polyfluoroalkyl ether sulfonates (6,2 Cl-PFESA) were significantly correlated with increased high-density lipoprotein (p = 0.040, 0.022). No significant association was observed between individual PFAS and any thyroid function biomarker. However, using the principal component analysis derived factors to represent the co-exposure patterns, co-exposure of legacy long-chain PFAS showed synergistic effects on the free thyroxine, while the mixture of alternative PFAS showed a synergistic influence on the total and free triiodothyronine.

Automatic Aortic Valve Cusps Segmentation from CT Images Based on the Cascading Multiple Deep Neural Networks.

Accurate morphological information on aortic valve cusps is critical in treatment planning. Image segmentation is necessary to acquire this information, but manual segmentation is tedious and time consuming. In this paper, we propose a fully automatic aortic valve cusps segmentation method from CT images by combining two deep neural networks, spatial configuration-Net for detecting anatomical landmarks and U-Net for segmentation of aortic valve components. A total of 258 CT volumes of end systolic and end diastolic phases, which include cases with and without severe calcifications, were collected and manually annotated for each aortic valve component. The collected CT volumes were split 6:2:2 for the training, validation and test steps, and our method was evaluated by five-fold cross validation. The segmentation was successful for all CT volumes with 69.26 s as mean processing time. For the segmentation results of the aortic root, the right-coronary cusp, the left-coronary cusp and the non-coronary cusp, mean Dice Coefficient were 0.95, 0.70, 0.69, and 0.67, respectively. There were strong correlations between measurement values automatically calculated based on the annotations and those based on the segmentation results. The results suggest that our method can be used to automatically obtain measurement values for aortic valve morphology.

CoSinGAN: Learning COVID-19 Infection Segmentation from a Single Radiological Image.

Computed tomography (CT) images are currently being adopted as the visual evidence for COVID-19 diagnosis in clinical practice. Automated detection of COVID-19 infection from CT images based on deep models is important for faster examination. Unfortunately, collecting large-scale training data systematically in the early stage is difficult. To address this problem, we explore the feasibility of learning deep models for lung and COVID-19 infection segmentation from a single radiological image by resorting to synthesizing diverse radiological images. Specifically, we propose a novel conditional generative model, called CoSinGAN, which can be learned from a single radiological image with a given condition, i.e., the annotation mask of the lungs and infected regions. Our CoSinGAN is able to capture the conditional distribution of the single radiological image, and further synthesize high-resolution (512 × 512) and diverse radiological images that match the input conditions precisely. We evaluate the efficacy of CoSinGAN in learning lung and infection segmentation from very few radiological images by performing 5-fold cross validation on COVID-19-CT-Seg dataset (20 CT cases) and an independent testing on the MosMed dataset (50 CT cases). Both 2D U-Net and 3D U-Net, learned from four CT slices by using our CoSinGAN, have achieved notable infection segmentation performance, surpassing the COVID-19-CT-Seg-Benchmark, i.e., the counterparts trained on an average of 704 CT slices, by a large margin. Such results strongly confirm that our method has the potential to learn COVID-19 infection segmentation from few radiological images in the early stage of COVID-19 pandemic.

Drr4covid: Learning Automated COVID-19 Infection Segmentation From Digitally Reconstructed Radiographs.

Automated infection measurement and COVID-19 diagnosis based on Chest X-ray (CXR) imaging is important for faster examination, where infection segmentation is an essential step for assessment and quantification. However, due to the heterogeneity of X-ray imaging and the difficulty of annotating infected regions precisely, learning automated infection segmentation on CXRs remains a challenging task. We propose a novel approach, called DRR4Covid, to learn COVID-19 infection segmentation on CXRs from digitally reconstructed radiographs (DRRs). DRR4Covid consists of an infection-aware DRR generator, a segmentation network, and a domain adaptation module. Given a labeled Computed Tomography scan, the infection-aware DRR generator can produce infection-aware DRRs with pixel-level annotations of infected regions for training the segmentation network. The domain adaptation module is designed to enable the segmentation network trained on DRRs to generalize to CXRs. The statistical analyses made on experiment results have indicated that our infection-aware DRRs are significantly better than standard DRRs in learning COVID-19 infection segmentation (p < 0.05) and the domain adaptation module can improve the infection segmentation performance on CXRs significantly (p < 0.05). Without using any annotations of CXRs, our network has achieved a classification score of (Accuracy: 0.949, AUC: 0.987, F1-score: 0.947) and a segmentation score of (Accuracy: 0.956, AUC: 0.980, F1-score: 0.955) on a test set with 558 normal cases and 558 positive cases. Besides, by adjusting the strength of radiological signs of COVID-19 infection in infection-aware DRRs, we estimate the detection limit of X-ray imaging in detecting COVID-19 infection. The estimated detection limit, measured by the percent volume of the lung that is infected by COVID-19, is 19.43% ± 16.29%, and the estimated lower bound of infected voxel contribution rate for significant radiological signs of COVID-19 infection is 20.0%. Our codes are made publicly available at https://github.com/PengyiZhang/DRR4Covid.

Enzyme-Encapsulated Liposome-Linked Immunosorbent Assay Enabling Sensitive Personal Glucose Meter Readout for Portable Detection of Disease Biomarkers.

There is considerable demand for sensitive, selective, and portable detection of disease-associated proteins, particularly in clinical practice and diagnostic applications. Portable devices are highly desired for detection of disease biomarkers in daily life due to the advantages of being simple, rapid, user-friendly, and low-cost. Herein we report an enzyme-encapsulated liposome-linked immunosorbent assay for sensitive detection of proteins using personal glucose meters (PGM) for portable quantitative readout. Liposomes encapsulating a large amount of amyloglucosidase or invertase are surface-coated with recognition elements such as aptamers or antibodies for target recognition. By translating molecular recognition signal into a large amount of glucose with the encapsulated enzyme, disease biomarkers such as thrombin or C-reactive protein (CRP) can be quantitatively detected by a PGM with a high detection limit of 1.8 or 0.30 nM, respectively. With the advantages of portability, ease of use, and low-cost, the method reported here has potential for portable and quantitative detection of various targets for different POC testing scenarios, such as rapid diagnosis in clinic offices, health monitoring at the bedside, and chemical/biochemical safety control in the field.

Discrimination Between Cervical Cancer Cells and Normal Cervical Cells Based on Longitudinal Elasticity Using Atomic Force Microscopy.

The mechanical properties of cells are considered promising biomarkers for the early diagnosis of cancer. Recently, atomic force microscopy (AFM)-based nanoindentation technology has been utilized for the examination of cell cortex mechanics in order to distinguish malignant cells from normal cells. However, few attempts to evaluate the biomechanical properties of cells have focused on the quantification of the non-homogeneous longitudinal elasticity of cellular structures. In the present study, we applied a variation of the method of Carl and Schillers to investigate the differences between longitudinal elasticity of human cervical squamous carcinoma cells (CaSki) and normal cervical epithelial cells (CRL2614) using AFM. The results reveal a three-layer heterogeneous structure in the probing volume of both cell types studied. CaSki cells exhibited a lower whole-cell stiffness and a softer nuclei zone compared to the normal counterpart cells. Moreover, a better differentiated cytoskeleton was found in the inner cytoplasm/nuclei zone of the normal CRL2614 cells, whereas a deeper cytoskeletal distribution was observed in the probing volume of the cancerous counterparts. The sensitive cortical panel of CaSki cells, with a modulus of 0.35~0.47 kPa, was located at 237~225 nm; in normal cells, the elasticity was 1.20~1.32 kPa at 113~128 nm. The present improved method may be validated using the conventional Hertz-Sneddon method, which is widely reported in the literature. In conclusion, our results enable the quantification of the heterogeneous longitudinal elasticity of cancer cells, in particular the correlation with the corresponding depth. Preliminary results indicate that our method may potentially be applied to improve the detection of cancerous cells and provide insights into the pathophysiology of the disease.

Fluorescence sensing of chromium (VI) and ascorbic acid using graphitic carbon nitride nanosheets as a fluorescent "switch".

Using graphitic carbon nitride (g-C3N4) nanosheets, an effective and facile fluorescence sensing approach for the label-free and selective determination of chromium (VI) (Cr(VI)) was developed. The fluorescence of the solution of g-C3N4 nanosheets was quenched effectively by Cr(VI) via the inner filter effect. Under optimal conditions, a wide detection linear range for Cr(VI) was found to be from 0.6 µM to 300 µM with a limit of detection (LOD) of 0.15 µM. In addition, the fluorescence of the solution of g-C3N4 nanosheets-Cr(VI) could be sensitively turned on in the presence of a reductant such as ascorbic acid (AA) via an "on-off-on" fluorescence response through the oxidation-reduction between Cr(VI) and AA. And a wide detection linear range for AA was found to be from 0.5 µM to 200 µM with an LOD of 0.13 µM. Furthermore, the proposed method has the potential application for detection of Cr(VI) in lake waters and AA in biological fluids.

A label-free fluorescence sensing approach for selective and sensitive detection of 2,4,6-trinitrophenol (TNP) in aqueous solution using graphitic carbon nitride nanosheets.

An effective and facile fluorescence sensing approach for the determination of 2,4,6-trinitrophenol (TNP) using the chemically oxidized and liquid exfoliated graphitic carbon nitride (g-C3N4) nanosheets was developed. The strong inner filter effect and molecular interactions (electrostatic, π-π, and hydrogen bonding interactions) between TNP and the g-C3N4 nanosheets led to the fluorescence quenching of the g-C3N4 nanosheets with efficient selectivity and sensitivity. Under optimal conditions, the limit of detection for TNP was found to be 8.2 nM. The proposed approach has potential application for visual detection of TNP in natural water samples for public safety and security.

A facile synthesis of highly luminescent nitrogen-doped graphene quantum dots for the detection of 2,4,6-trinitrophenol in aqueous solution.

A facile bottom-up method for the synthesis of highly fluorescent nitrogen-doped graphene quantum dots (N-GQDs) has been developed via a one-step pyrolysis of citric acid and tris(hydroxymethyl)aminomethane. The obtained N-GQDs emitted strong blue fluorescence under 365 nm UV light excitation with a high quantum yield of 59.2%. They displayed excitation-independent behavior, high resistance to photobleaching and high ionic strength. In addition to the good linear relationship between the fluorescence intensity of the N-GQDs and pH in the range 2-7, the fluorescence intensity of the N-GQDs could be greatly quenched by the addition of a small amount of 2,4,6-trinitrophenol (TNP). A sensitive approach has been developed for the detection of TNP with a detection limit of 0.30 µM, and a linearity ranging from 1 to 60 µM TNP could be obtained. The approach was highly selective and suitable for TNP analysis in natural water samples.

Resolving fine structures of the electric double layer of electrochemical interfaces in ionic liquids with an AFM tip modification strategy.

We report enhanced force detection selectivity based on Coulombic interactions through AFM tip modification for probing fine structures of the electric double layer (EDL) in ionic liquids. When AFM tips anchored with alkylthiol molecular layers having end groups with different charge states (e.g., -CH3, -COO(-), and -NH3(+)) are employed, Coulombic interactions between the tip and a specified layering structure are intensified or diminished depending on the polarities of the tip and the layering species. Systematic potential-dependent measurements of force curves with careful inspection of layered features and thickness analysis allows the fine structure of the EDL at the Au(111)-OMIPF6 interface to be resolved at the subionic level. The enhanced force detection selectivity provides a basis for thoroughly understanding the EDL in ionic liquids.

Ultra-bright alkylated graphene quantum dots.

Highly efficient and stable photoluminescence (PL) are urgently desired for graphene quantum dots (GQDs) to facilitate their prospective applications as optical materials. Here, we report the facile and straightforward synthesis of alkylated graphene quantum dots (AGQDs) via the solvothermal reaction of propagatively alkylated graphene sheets (PAGenes). In contrast to most GQDs reported so far, the synthesized AGQDs process pH-independent and ultra-bright PL with a relative quantum yield of up to 65%. Structural and chemical composition characterization demonstrated that the synthesized AGQDs are nearly oxygen-defect-free with alkyl groups decorated on edges and basal plane, which may contribute to their greatly improved pH tolerance and high quantum efficiency. The photocatalytic performance of AGQDs-P25 nanocomposites was evaluated by the degradation of Rhodamine B under visible light. The photocatalytic rate is ca. 5.9 times higher than that of pure P25, indicating that AGQDs could harness the visible spectrum of sunlight for energy conversion or environmental therapy.

Probing double layer structures of Au (111)-BMIPF6 ionic liquid interfaces from potential-dependent AFM force curves.

High quality AFM force curves are presented with detailed potential dependent layering behaviors of the ionic liquid molecules, from which charged interior and neutral exterior layers are distinguished. The electric double layer is confined within the interior layers of one to two molecular size within the potential range of up to 1 V negative of the PZC.