GDF15 deficiency hinders human trophoblast invasion to mediate pregnancy loss through downregulating Smad1/5 phosphorylation.

Growth differentiation factor 15 (GDF15) belongs to the Transforming growth factor ß(TGF-ß) superfamily. The decrease of GDF15 in the serum of pregnant women was associated with miscarriage. Both IHC and ELISA assays showed that GDF15 in trophoblast tissue and serum of pregnant women who miscarried was significantly lower than in those who had a live birth. GDF15 deficiency was associated with embryo resorption in GDF15 knockout mice through CRIPSR editing. In addition, the migration and invasion ability of HTR-8/SVneo and JEG-3 cells were promoted by GDF15. Mechanistically, GDF15 increased Smad1/5 phosphorylation, resulting in upregulating SNAI1/2, VIMENTIN and downregulating E-CADHERIN. A dual-luciferase reporter assay confirmed that Smad-binding elements (SBE) and/or GC-rich motifs were activated and target genes such as SNAI1/2, SERPINE1, and TIMP3 were transcriptionally regulated by GDF15/Smad5 signaling. Therefore, our data revealed a crucial role of GDF15 on invasion of trophoblast by upregulating the activity of TGF-ß/Smad1/5 pathway.

Partial carbonization of quercetin boosts the antiviral activity against H1N1 influenza A virus.

Inflenza A viruses (IAVs) are highly transmissible and pathogenic Orthomyxoviruses, which have led to worldwide outbreaks and seasonal pandemics of acute respiratory diseases, causing serious threats to public health. Currently used anti-influenza drugs may cause neurological side effects, and they are increasingly less effective against mutant strains. To help prevent the spread of IAVs, in this work, we have developed quercetin-derived carbonized nanogels (CNGsQur) that display potent viral inhibitory, antioxidative, and anti-inflammatory activities. The antiviral CNGsQur were synthesized by mild carbonization of quercetin (Qur), which successfully preserved their antioxidative and anti-inflammatory properties while also contributed enhanced properties, such as water solubility, viral binding, and biocompatibility. Antiviral assays of co-treatment, pre-treatment, and post-treatment indicate that CNGsQur interacts with the virion, revealing that the major antiviral mechanism resulting in the inhibition of the virus is by their attachment on the cell surface. Among them, the selectivity index (SI) of CNGsQur270 (>857.1) clearly indicated its great potential for clinical application in IAVs inhibition, which was much higher than that of pristine quercetin (63.7) and other clinical drugs (4-81). Compared with quercetin at the same dose, the combined effects of viral inhibition, antioxidative and anti-inflammatory activities impart the superior therapeutic effects of CNGsQur270 aerosol inhalation in the treatment of IAVs infection, as evidenced by a mouse model. These CNGsQur effectively prevent the spread of IAVs and suppress virus-induced inflammation while also exhibiting good in vivo biocompatibility. CNGsQur shows much promise as a clinical therapeutic agent against infection by IVAs.

Using a land use regression model with machine learning to estimate ground level PM2.5.

Ambient fine particulate matter (PM2.5) has been ranked as the sixth leading risk factor globally for death and disability. Modelling methods based on having access to a limited number of monitor stations are required for capturing PM2.5 spatial and temporal continuous variations with a sufficient resolution. This study utilized a land use regression (LUR) model with machine learning to assess the spatial-temporal variability of PM2.5. Daily average PM2.5 data was collected from 73 fixed air quality monitoring stations that belonged to the Taiwan EPA on the main island of Taiwan. Nearly 280,000 observations from 2006 to 2016 were used for the analysis. Several datasets were collected to determine spatial predictor variables, including the EPA environmental resources dataset, a meteorological dataset, a land-use inventory, a landmark dataset, a digital road network map, a digital terrain model, MODIS Normalized Difference Vegetation Index (NDVI) database, and a power plant distribution dataset. First, conventional LUR and Hybrid Kriging-LUR were utilized to identify the important predictor variables. Then, deep neural network, random forest, and XGBoost algorithms were used to fit the prediction model based on the variables selected by the LUR models. Data splitting, 10-fold cross validation, external data verification, and seasonal-based and county-based validation methods were used to verify the robustness of the developed models. The results demonstrated that the proposed conventional LUR and Hybrid Kriging-LUR models captured 58% and 89% of PM2.5 variations, respectively. When XGBoost algorithm was incorporated, the explanatory power of the models increased to 73% and 94%, respectively. The Hybrid Kriging-LUR with XGBoost algorithm outperformed the other integrated methods. This study demonstrates the value of combining Hybrid Kriging-LUR model and an XGBoost algorithm for estimating the spatial-temporal variability of PM2.5 exposures.

Kriging-Based Land-Use Regression Models That Use Machine Learning Algorithms to Estimate the Monthly BTEX Concentration.

This paper uses machine learning to refine a Land-use Regression (LUR) model and to estimate the spatial-temporal variation in BTEX concentrations in Kaohsiung, Taiwan. Using the Taiwanese Environmental Protection Agency (EPA) data of BTEX (benzene, toluene, ethylbenzene, and xylenes) concentrations from 2015 to 2018, which includes local emission sources as a result of Asian cultural characteristics, a new LUR model is developed. The 2019 data was then used as external data to verify the reliability of the model. We used hybrid Kriging-land-use regression (Hybrid Kriging-LUR) models, geographically weighted regression (GWR), and two machine learning algorithms-random forest (RF) and extreme gradient boosting (XGBoost)-for model development. Initially, the proposed Hybrid Kriging-LUR models explained each variation in BTEX from 37% to 52%. Using machine learning algorithms (XGBoost) increased the explanatory power of the models for each BTEX, between 61% and 79%. This study compared each combination of the Hybrid Kriging-LUR model and (i) GWR, (ii) RF, and (iii) XGBoost algorithm to estimate the spatiotemporal variation in BTEX concentration. It is shown that a combination of Hybrid Kriging-LUR and the XGBoost algorithm gives better performance than other integrated methods.

A hybrid kriging/land-use regression model with Asian culture-specific sources to assess NO2 spatial-temporal variations.

Kriging interpolation and land use regression (LUR) have characterized the spatial variability of long-term nitrogen dioxide (NO2), but there has been little research on combining these two methods to capture small-scale spatial variation. Furthermore, studies predicting NO2 exposure are almost exclusively based on traffic-related variables, which may not be transferable to Taiwan, a typical Asian country with diverse local emission sources, where densely distributed temples and restaurants may be important for NO2 levels. To advance the exposure estimates in Taiwan, a hybrid kriging/LUR model incorporates culture-specific sources as potential predictors. Based on 14-year NO2 observations from 73 monitoring stations across Taiwan, a set of interpolated NO2 values were generated through a leave-one-out ordinary kriging algorithm, and this was included as an explanatory variable in the stepwise LUR procedures. Kriging interpolated NO2 and culture-specific predictors were entered in the final models, which captured 90% and 87% of NO2 variation in annual and monthly resolution, respectively. Results from 10-fold cross-validation and external data verification demonstrate robust performance of the developed models. This study demonstrates the value of incorporating the kriging-interpolated estimates and culture-specific emission sources into the traditional LUR model structure for predicting NO2, which can be particularly useful for Asian countries.

REX1 promotes EMT-induced cell metastasis by activating the JAK2/STAT3-signaling pathway by targeting SOCS1 in cervical cancer.

ZFP42 zinc finger protein (REX1), a pluripotency marker in mouse pluripotent stem cells, has been identified as a tumor suppressor in several human cancers. However, the function of REX1 in cervical cancer remains unknown. Both IHC and western blot assays demonstrated that the expression of REX1 protein in cervical cancer tissue was much higher than that in normal cervical tissue. A xenograft assay showed that REX1 overexpression in SiHa and HeLa cells facilitated distant metastasis but did not significantly affect tumor formation in vivo. In addition, in vitro cell migration and invasion capabilities were also promoted by REX1. Mechanistically, REX1 overexpression induced epithelial-to-mesenchymal transition (EMT) by upregulating VIMENTIN and downregulating E-CADHERIN. Furthermore, the JAK2/STAT3-signaling pathway was activated in REX1-overexpressing cells, which also exhibited increased levels of p-STAT3 and p-JAK2, as well as downregulated expression of SOCS1, which is an inhibitor of the JAK2/STAT3-signaling pathway, at both the transcriptional and translational levels. A dual-luciferase reporter assay and qChIP assays confirmed that REX1 trans-suppressed the expression of SOCS1 by binding to two specific regions of the SOCS1 promoter. Therefore, all our data suggest that REX1 overexpression could play a crucial role in the metastasis and invasion of cervical cancer by upregulating the activity of the JAK2/STAT3 pathway by trans-suppressing SOCS1 expression.

A hybrid kriging/land-use regression model to assess PM2.5 spatial-temporal variability.

Proximate pollutant data can provide information for land-use predictors in LUR models, when coupled with spatial interpolation of ambient pollutant measurements, may provide better pollutant predictions. This study applies a hybrid kriging/LUR model to assess the spatial-temporal variability of PM2.5 for Taiwan. Using PM2.5 concentrations at 71 EPA monitoring stations from 2006 to 2011, pollutant gradient surfaces were spatially interpolated using a leave-one-out ordinary kriging method based on "n-1" observations. The predicted concentration level of the targeted site was then extracted from the generated kriging map and adopted as a variable in LUR modelling. Annual and monthly resolutions of LUR models were developed to assess the effects by incorporating kriging-based estimates into pollutant predictions. The R2 obtained from conventional LUR procedures was 0.66 and 0.70 for annual and monthly models, respectively, whereas models using the hybrid approach showed better explanatory power (R2 of annual model: 0.85; R2 of monthly model: 0.88). Moreover, kriging-based PM2.5 estimates were the most important factor in the resultant models according to the dominant partial R2 of 0.82 and 0.7 in monthly and yearly models. Cross-validation and external data verification showed similar results, demonstrating robustness of the proposed approach. Using governmental pollutant observations is usually publicly available for most areas, this method provides an efficient mean to better assess PM2.5 spatial-temporal variations and predicts levels for nonmonitored areas.

Characterization and Preliminary Biological Evaluation of 3D-Printed Porous Scaffolds for Engineering Bone Tissues.

Some basic requirements of bone tissue engineering include cells derived from bone tissues, three-dimensional (3D) scaffold materials, and osteogenic factors. In this framework, the critical architecture of the scaffolds plays a crucial role to support and assist the adhesion of the cells, and the subsequent tissue repairs. However, numerous traditional methods suffer from certain drawbacks, such as multi-step preparation, poor reproducibility, high complexity, difficulty in controlling the porous architectures, the shape of the scaffolds, and the existence of solvent residue, which limits their applicability. In this work, we fabricated innovative poly(lactic-co-glycolic acid) (PLGA) porous scaffolds, using 3D-printing technology, to overcome the shortcomings of traditional approaches. In addition, the printing parameters were critically optimized for obtaining scaffolds with normal morphology, appropriate porous architectures, and sufficient mechanical properties, for the accommodation of the bone cells. Various evaluation studies, including the exploration of mechanical properties (compressive strength and yield stress) for different thicknesses, and change of structure (printing angle) and porosity, were performed. Particularly, the degradation rate of the 3D scaffolds, printed in the optimized conditions, in the presence of hydrolytic, as well as enzymatic conditions were investigated. Their assessments were evaluated using the thermal gravimetric analyzer (TGA), differential scanning calorimetry (DSC), and gel permeation chromatography (GPC). These porous scaffolds, with their biocompatibility, biodegradation ability, and mechanical properties, have enabled the embryonic osteoblast precursor cells (MC3T3-E1), to adhere and proliferate in the porous architectures, with increasing time. The generation of highly porous 3D scaffolds, based on 3D printing technology, and their critical evaluation, through various investigations, may undoubtedly provide a reference for further investigations and guide critical optimization of scaffold fabrication, for tissue regeneration.

Effects of Intrinsic and Extrinsic Factors on the Level of Hope and Psychological Health Status of Patients with Cervical Cancer During Radiotherapy.

BACKGROUND This study aimed to explore the factors affecting the level of hope and psychological health status of patients with cervical cancer (CC) during radiotherapy. MATERIAL AND METHODS A total of 480 CC patients were recruited. Psychological distress scale, Herth hope index, functional assessment cancer therapy-cervix, and Jolowiec coping scale were used to conduct surveys on psychological distress, level of hope, quality of life (QOL), and coping style to analyze the factors affecting the level of hope and psychological health status of CC patients. RESULTS The morbidity of significant psychological distress in 480 CC patients during radiotherapy was 68%, and the main factors causing psychological distress were emotional problems and physical problems. During radiotherapy, most patients had middle and high levels of hope, and the psychological distress index of patients was negatively correlated with the level of hope. The QOL of CC patients during radiotherapy were at middle and high levels, and the QOL was positively correlated with confrontment, optimism, appeasement, and self-reliance, but it was negatively correlated with predestination and emotional expression. CONCLUSIONS For CC patients during radiotherapy, the morbidity of psychological distress was high, but they were at middle and high levels of hope.

Land-use regression with long-term satellite-based greenness index and culture-specific sources to model PM2.5 spatial-temporal variability.

This study utilized a long-term satellite-based vegetation index, and considered culture-specific emission sources (temples and Chinese restaurants) with Land-use Regression (LUR) modelling to estimate the spatial-temporal variability of PM2.5 using data from Taipei metropolis, which exhibits typical Asian city characteristics. Annual average PM2.5 concentrations from 2006 to 2012 of 17 air quality monitoring stations established by Environmental Protection Administration of Taiwan were used for model development. PM2.5 measurements from 2013 were used for external data verification. Monthly Normalized Difference Vegetation Index (NDVI) images coupled with buffer analysis were used to assess the spatial-temporal variations of greenness surrounding the monitoring sites. The distribution of temples and Chinese restaurants were included to represent the emission contributions from incense and joss money burning, and gas cooking, respectively. Spearman correlation coefficient and stepwise regression were used for LUR model development, and 10-fold cross-validation and external data verification were applied to verify the model reliability. The results showed a strongly negative correlation (r: -0.71 to -0.77) between NDVI and PM2.5 while temples (r: 0.52 to 0.66) and Chinese restaurants (r: 0.31 to 0.44) were positively correlated to PM2.5 concentrations. With the adjusted model R2 of 0.89, a cross-validated adj-R2 of 0.90, and external validated R2 of 0.83, the high explanatory power of the resultant model was confirmed. Moreover, the averaged NDVI within a 1750 m circular buffer (p < 0.01), the number of Chinese restaurants within a 1750 m buffer (p < 0.01), and the number of temples within a 750 m buffer (p = 0.06) were selected as important predictors during the stepwise selection procedures. According to the partial R2, NDVI explained 66% of PM2.5 variation and was the dominant variable in the developed model. We suggest future studies consider these three factors when establishing LUR models for estimating PM2.5 in other Asian cities.

Electrochemical synthesis, characterization of Ir-Zn containing coordination polymer, and application in oxygen and glucose sensing.

A simple and sensitive biosensor array based on phosphorescence detection that is able to detect oxygen and glucose in human serum, respectively, has been developed. We demonstrate an electrochemical method as a fast, effective, tunable, and versatile means of growing phosphorescence sensing material. This sensing material, crystalline iridium(III)-Zn(II) coordination polymers, namely Ir-Zn(e), was grown on a stainless steel mesh and then doped in a sol-gel matrix. The emission of Ir-Zn(e) was ascribed to a metal-to-ligand charge transfer transition (MLCT). The noteworthy oxygen-sensing properties of Ir-Zn(e) were also evaluated. The optimal oxygen-sensing conditions of Ir-Zn(e) with a deduced K(SV) value of 3.55 were 5 V and 30 °C for 1 hour. Moreover, the short response time (23 s) and the recovery time (21 s) toward oxygen have been measured. The reversibility experiment was carried out for eleven cycles. The resulting >70% recovery of intensity for Ir-Zn(e) on each cycle demonstrated a high degree of reproducibility during the sensing process. The detection limit could be 0.050% for gaseous oxygen. The sensing substrate was subsequently built up under glucose oxidase encapsulated in hydrogel and then immobilized on an egg membrane by the layer-by-layer method. Once the glucose solution was injected into this array, oxygen content depleted simultaneously with a concomitant increase in the phosphorescence of coordination polymers. The linear dynamic range for the determination of glucose was 0.1-6.0 mM, the correlation coefficient (R(2)) was 0.9940 (y = 0.75 [glucose] + 0.539), and the response time was less than 120 s. The minimum detectable concentration for glucose was calculated to be 0.05 mM from three times signal to noise. The photophysical properties of the sensing material and the effects of buffer concentration, pH, interference, matrix effect, temperature, and the stability of the biosensor array have also been studied in detail. The biosensor array was successfully applied to the determination of glucose in human serum.