Implications of perceived empathy from spouses during pregnancy for health-related quality of life among pregnant women: a cross-sectional study in Anhui, China.

BACKGROUND: Empathy is a critical component of nursing care, impacting both nurses' and patients' outcomes. However, perceived empathy from spouses during pregnancy and its impact on health-related quality of life (HRQoL) are unclear. This study aimed to examine pregnant women's perceived empathy from their spouses and assess the relation of perceived empathy on HRQoL. METHODS: This cross-sectional study, performed in the obstetric clinics or wards of four well-known hospitals in Anhui Province, China, included 349 pregnant women in the second or third trimester; participants were recruited by convenience sampling and enrolled from October to December 2021. A general information questionnaire, the Interpersonal Reactivity Index (IRI), a purpose-designed empathy questionnaire and the Medical Outcomes Study 12-item Short-Form Health Survey (SF-12) were used to evaluate the pregnant women's general information, perceptions of empathy and HRQoL. Data were analysed using SPSS 22 at a threshold of P < 0.05. Descriptive analysis, Pearson correlation analysis, Student's t test, ANOVA, and multiple regression analysis were used for analysis. RESULTS: The pregnant women's total empathy, physical component summary (PCS) and mental component summary (MCS) scores were 41.6 ± 9.0, 41.6 ± 7.6, and 47.7 ± 9.1, respectively. Correlation analysis revealed that the purpose-designed empathy questionnaire items were significantly positively correlated with perspective taking and empathic concern but were not correlated with the personal distress dimension and were only partially correlated with the fantasy dimension. Maternal physical condition during pregnancy, planned pregnancy, and occupational stress were predictors of the PCS score (ß = 0.281, P < 0.01; ß = 0.132, P = 0.02; ß = -0.128, P = 0.02). The behavioural empathy item of our purpose-designed empathy questionnaire and empathic concern were important predictors of the MCS score (ß = 0.127, P = 0.02; ß = 0.158, P < 0.01), as well as other demographic and obstetric information, explaining 22.0% of the variance in MCS scores totally (F = 12.228, P < 0.01). CONCLUSIONS: Pregnant women perceived lower empathy from their spouses and reported lower HRQoL. Perceived empathy, particularly behavioural empathy, may significantly impact pregnant women's MCS scores but has no effect on their PCS scores. Strategies that foster perceived empathy from spouses among pregnant women are essential for facilitating healthy pregnancies and potentially improving maternal and child health.

The crosstalk between lung cancer cells and platelets promotes tumor angiogenesis in vivo and in vitro.

PURPOSE: We previously showed that the crosstalk of H1975 cells and platelets (PLTs) may promote tumor angiogenesis. This study aimed to determine whether other lung cell lines (LC) interacting with PLTs could affect tumor angiogenesis through in vivo and in vitro experiments. METHODS: Cell Counting Kit-8, EdU cell proliferation, wound healing, Transwell invasion, F-actin staining, tube formation, ELISA and western blot assays were performed to investigate the properties and the expression levels of vascular endothelial growth factor (VEGF), VEGF receptor 2 (VEGFR2), p-VEGFR2, PI3K, p-PI3K, Akt, p-Akt and eNOS in supernatants or HUVECs. Then, using mouse models, immunohistochemistry was applied to detect the expression levels of CD31 and VEGF. RESULTS: Compared with single-cultured HUVECs (EC) or incubation with either LC supernatant (EC + LC) or activated PLT supernatant (EC + PLT), incubation with SN_LCP (supernatant derived from LC cocultured with PLT, named the EC + LC + PLT group) improved the viability, proliferation, migration, invasion, and tube formation activities of HUVECs and the expression of F-actin, VEGF, VEGFR2, p-VEGFR2, p-PI3K, p-Akt and eNOS in HUVECs. Higher expression levels of CD31 and VEGF were found in the LLC + PLT (mouse model inoculated with Lewis lung cancer (LLC) cells cocultured with PLTs) group than in the LLC (mouse model inoculated with LLC cells alone) group. However, the increased angiogenic properties of HUVECs were inhibited by apatinib, an inhibitor of VEGFR2. CONCLUSION: Lung carcinoma cells interacting with PLTs may play a key role in lung carcinoma angiogenesis through the VEGF/VEGFR2 signaling pathway.

Gambogenic acid antagonizes the expression and effects of long non-coding RNA NEAT1 and triggers autophagy and ferroptosis in melanoma.

In this study, we investigated the molecular mechanism underlying melanoma proliferation, with the aim to discover effective interventions which may markedly improve clinical prognosis. The results showed that gambogenic acid (GNA) could inhibit the proliferation of melanoma cells in vivo (C57BL/6 mice) and in vitro. Long non-coding RNA sequencing was used to identify the most significant long non-coding RNA, i.e., nuclear enriched abundant transcript 1 (NEAT1). NEAT1 was is up-regulated in melanoma, which was found to closely relate to cell proliferation. Melanoma cell lines either over-expressing NEAT1 or with NEAT1 knockdown was established through cloning experiments. A model of transplanted tumors was established to verify the inhibitory effect of GNA on the proliferation of melanoma cells in vitro and in vivo by downregulating NEAT1. Downregulation of NEAT1-induced ferroptosis and autophagy was demonstrated by detecting the effects of NEAT1 overexpressed and downregulated melanoma cell lines and melanoma transplantation model mice. Mechanistically, downregulation of NEAT1 can weaken the direct binding of Slc7a11, indirectly leading to inhibiting GPX-4 activity and subsequent ferroptosis, while, mediating the AMPK/mTOR signal axis-induced autophagy. The levels of Furthermore, NEAT1 decrease under the treatment of Gambogenic acid (GNA), a promising natural anticancer compound, while NEAT1 overexpression suppresses GNA inhibition on cell vitality and eliminates GNA-induced melanoma cell ferroptosis and autophagy.

A Critical Review for Real-Time Continuous Soil Monitoring: Advantages, Challenges, and Perspectives.

Most soil quality measurements have been limited to laboratory-based methods that suffer from time delay, high cost, intensive labor requirement, discrete data collection, and tedious sample pretreatment. Real-time continuous soil monitoring (RTCSM) possesses a great potential to revolutionize field measurements by providing first-hand information for continuously tracking variations of heterogeneous soil parameters and diverse pollutants in a timely manner and thus enable constant updates essential for system control and decision-making. Through a systematic literature search and comprehensive analysis of state-of-the-art RTCSM technologies, extensive discussion of their vital hurdles, and sharing of our future perspectives, this critical review bridges the knowledge gap of spatiotemporal uninterrupted soil monitoring and soil management execution. First, the barriers for reliable RTCSM data acquisition are elucidated by examining typical soil monitoring techniques (e.g., electrochemical and spectroscopic sensors). Next, the prevailing challenges of the RTCSM sensor network, data transmission, data processing, and personalized data management are comprehensively discussed. Furthermore, this review explores RTCSM data application for updating diverse strategies including high-fidelity soil process models, control methodologies, digital soil mapping, soil degradation, food security, and climate change mitigation. Finally, the significance of RTCSM implementation in agricultural and environmental fields is underscored through illuminating future directions and perspectives in this systematic review.

Electrospraying Zwitterionic Copolymers as an Effective Biofouling Control for Accurate and Continuous Monitoring of Wastewater Dynamics in a Real-Time and Long-Term Manner.

Long-term continuous monitoring (LTCM) of water quality can provide high-fidelity datasets essential for executing swift control and enhancing system efficiency. One roadblock for LTCM using solid-state ion-selective electrode (S-ISE) sensors is biofouling on the sensor surface, which perturbs analyte mass transfer and deteriorates the sensor reading accuracy. This study advanced the anti-biofouling property of S-ISE sensors through precisely coating a self-assembled channel-type zwitterionic copolymer poly(trifluoroethyl methacrylate-random-sulfobetaine methacrylate) (PTFEMA-r-SBMA) on the sensor surface using electrospray. The PTFEMA-r-SBMA membrane exhibits exceptional permeability and selectivity to primary ions in water solutions. NH4+ S-ISE sensors with this anti-fouling zwitterionic layer were examined in real wastewater for 55 days consecutively, exhibiting sensitivity close to the theoretical value (59.18 mV/dec) and long-term stability (error <4 mg/L). Furthermore, a denoising data processing algorithm (DDPA) was developed to further improve the sensor accuracy, reducing the S-ISE sensor error to only 1.2 mg/L after 50 days of real wastewater analysis. Based on the dynamic energy cost function and carbon footprint models, LTCM is expected to save 44.9% NH4+ discharge, 12.8% energy consumption, and 26.7% greenhouse emission under normal operational conditions. This study unveils an innovative LTCM methodology by integrating advanced materials (anti-fouling layer coating) with sensor data processing (DDPA).

Forward-Looking Roadmaps for Long-Term Continuous Water Quality Monitoring: Bottlenecks, Innovations, and Prospects in a Critical Review.

Long-term continuous monitoring (LTCM) of water quality can bring far-reaching influences on water ecosystems by providing spatiotemporal data sets of diverse parameters and enabling operation of water and wastewater treatment processes in an energy-saving and cost-effective manner. However, current water monitoring technologies are deficient for long-term accuracy in data collection and processing capability. Inadequate LTCM data impedes water quality assessment and hinders the stakeholders and decision makers from foreseeing emerging problems and executing efficient control methodologies. To tackle this challenge, this review provides a forward-looking roadmap highlighting vital innovations toward LTCM, and elaborates on the impacts of LTCM through a three-hierarchy perspective: data, parameters, and systems. First, we demonstrate the critical needs and challenges of LTCM in natural resource water, drinking water, and wastewater systems, and differentiate LTCM from existing short-term and discrete monitoring techniques. We then elucidate three steps to achieve LTCM in water systems, consisting of data acquisition (water sensors), data processing (machine learning algorithms), and data application (with modeling and process control as two examples). Finally, we explore future opportunities of LTCM in four key domains, water, energy, sensing, and data, and underscore strategies to transfer scientific discoveries to general end-users.

Enhancing the Understanding of Soil Nitrogen Fate Using a 3D-Electrospray Sensor Roll Casted with a Thin-Layer Hydrogel.

Accurate and continuous monitoring of soil nitrogen is critical for determining its fate and providing early warning for swift soil nutrient management. However, the accuracy of existing electrochemical sensors is hurdled by the immobility of targeted ions, ion adsorption to soil particles, and sensor reading noise and drifting over time. In this study, polyacrylamide hydrogel with a thickness of 0.45 µm was coated on the surface of solid-state ion-selective membrane (S-ISM) sensors to absorb water contained in soil and, consequently, enhance the accuracy (R2 > 0.98) and stability (drifting < 0.3 mV/h) of these sensors monitoring ammonium (NH4+) and nitrate (NO3-) ions in soil. An ion transport model was built to simulate the long-term NH4+ dynamic process (R2 > 0.7) by considering the soil adsorption process and soil complexity. Furthermore, a soil-based denoising data processing algorithm (S-DDPA) was developed based on the unique features of soil sensors including the nonlinear mass transfer and ion diffusion on the heterogeneous sensor-hydrogel-soil interface. The 14 day tests using real-world soil demonstrated the effectiveness of S-DDPA to eliminate false signals and retrieve the actual soil nitrogen information for accurate (error: <2 mg/L) and continuous monitoring.

Association of serum levels of inflammatory markers and adipokines with joint symptoms and structures in participants with knee osteoarthritis.

OBJECTIVE: To investigate the longitudinal associations of serum inflammatory markers and adipokines with joint symptoms and structures in participants with knee OA. METHODS: Two hundred participants (46.5% female, mean age 63.1 years, mean BMI 29.5 kg/m2) from Tasmania, part of the VIDEO (Vitamin D Effect on OA) study, were randomly selected in the current study. Serum levels of 19 biomarkers, scores of WOMAC and MRI-assessed knee structures were evaluated at baseline and month 24. The patterns of biomarkers were derived from principal component analysis and their association with knee symptoms and structures were examined using adjusted generalized estimating equations. RESULTS: Five components explained 78% of the total variance. IL-1ß, -2, -4, -6, -8, -17 A, -17 F, -21, -22 and -23 loaded the highest on the first component, which was associated with increased bone marrow lesions (BMLs) and WOMAC dysfunction score. IL-10, -12 and GM-CSF loaded on the second component, which was associated with increased cartilage volume, and decreased effusion synovitis and WOMAC scores. Leptin, adipsin and CRP loaded on the third component, which was positively associated with WOMAC scores. Resistin loaded on the fourth component, which was associated with increased BMLs and cartilage defects. Apelin-36 and adiponectin loaded on the fifth component, which was associated with increased BMLs. CONCLUSION: Various inflammatory and metabolic components were associated differently with joint symptoms and structural changes in knee OA, suggesting a complex inflammatory and metabolic interrelationship in the pathogenesis of knee OA.

Machine Learning: New Ideas and Tools in Environmental Science and Engineering.

The rapid increase in both the quantity and complexity of data that are being generated daily in the field of environmental science and engineering (ESE) demands accompanied advancement in data analytics. Advanced data analysis approaches, such as machine learning (ML), have become indispensable tools for revealing hidden patterns or deducing correlations for which conventional analytical methods face limitations or challenges. However, ML concepts and practices have not been widely utilized by researchers in ESE. This feature explores the potential of ML to revolutionize data analysis and modeling in the ESE field, and covers the essential knowledge needed for such applications. First, we use five examples to illustrate how ML addresses complex ESE problems. We then summarize four major types of applications of ML in ESE: making predictions; extracting feature importance; detecting anomalies; and discovering new materials or chemicals. Next, we introduce the essential knowledge required and current shortcomings in ML applications in ESE, with a focus on three important but often overlooked components when applying ML: correct model development, proper model interpretation, and sound applicability analysis. Finally, we discuss challenges and future opportunities in the application of ML tools in ESE to highlight the potential of ML in this field.

Crosstalk between H1975 tumor cells and platelets to induce the proliferation, migration and tube formation of vascular endothelial cells.

Activated platelets (PLTs) participate in the regulation of tumor angiogenesis, and tumors can activate PLTs. Whether co-culture of lung carcinoma with PLTs improves the function of human umbilical vein endothelial cells (HUVECs) requires further investigation. The present study aimed to investigate the impact of H1975 cell crosstalk with PLTs on the proliferation, migration and tube formation of HUVECs. Following generation of cell-derived supernatants and construction of the co-culture system, Cell Counting Kit-8, flow cytometry, transmission electron microscopy and a meter for epithelial measurement were performed to detect the proliferative ability of HUVECs. Furthermore, the wound healing and Transwell migration assays were performed to detect the migratory ability of HUVECs. A tube formation assay was performed to assess angiogenesis, ELISA was applied to detect the content of vascular endothelial growth factor (VEGF) and western blotting was carried out to measure the expression levels of VEGF receptor 2 (VEGFR2) in HUVECs. Compared with single-cultured HUVECs (control), co-culture with H1975 cells and PLTs (Exp_HP) improved cell proliferation, increased the proportion of cells in the S-phase, destroyed the cell ultrastructure and decreased transepithelial electrical resistance in HUVECs. In addition, a higher relative migration rate, greater number of migrated cells, stronger tube-forming ability and increased expression of VEGF and VEGFR2 were detected in the Exp_HP group compared with the control group. The properties of HUVECs in Exp_H (co-cultured with H1975 cells) were similar to those in Exp_HP, but significantly weaker. Taken together, the results of the present study suggest that tumor cells interacting with PLTs may play an important role in tumor angiogenesis by affecting or mediating changes in the properties of vascular endothelial cells (VECs).

Multiplexed colorimetric detection of SARS-CoV-2 and other pathogens in wastewater on a 3D printed integrated microfluidic chip.

Severe acute respiratory coronavirus 2 (SARS-CoV-2) pandemic has become a global public health emergency. The detection of SARS-CoV-2 and human enteric pathogens in wastewater can provide an early warning of disease outbreak. Herein, a sensitive, multiplexed, colorimetric detection (termed "SMCD") method was established for pathogen detection in wastewater samples. The SMCD method integrated on-chip nucleic acid extraction, two-stage isothermal amplification, and colorimetric detection on a 3D printed microfluidic chip. The colorimetric signal during nucleic acid amplification was recorded in real-time and analyzed by a programmed smartphone without the need for complicated equipment. By combining two-stage isothermal amplification assay into the integrated microfluidic platform, we detected SARS-CoV-2 and human enteric pathogens with sensitivities of 100 genome equivalent (GE)/mL and 500 colony-forming units (CFU)/mL, respectively, in wastewater within one hour. Additionally, we realized smart, connected, on-site detection with a reporting framework embedded in a portable detection platform, which exhibited potential for rapid spatiotemporal epidemiologic data collection regarding the environmental dynamics, transmission, and persistence of infectious diseases.

An integrated E-Tube cap for sample preparation, isothermal amplification and label-free electrochemical detection of DNA.

A simple, disposable, and integrated electronic-tube cap (E-tube cap) for DNA detection at the point-of-care was designed, fabricated, and tested. The E-tube cap contains a 3D printed electrode substrate for DNA extraction and label-free pH sensing detection. One Flinders Technology Associates (Whatman FTA) membrane was incorporated into the 3D printed electrode substrate for the isolation, concentration, and purification of DNA. The E-tube cap with captured DNA by the membrane was inserted directly into a reaction tube for loop-mediated isothermal amplification (LAMP). The isothermal amplification process was monitored in real-time by a 3D printed electrochemical electrode coated with pH-sensitive material (carbon/iridium oxide layer). The pH sensing electrode showed an excellent linear response within the pH range of 6-9 with a slope of -31.32 ± 0.5 mV/pH at room temperature. The utility of the integrated E-tube cap was demonstrated by detecting the presence of lambda DNA spiked in saliva samples with a sensitivity of 100 copies per mL sample within 30 min. Such a simple, rapid, and affordable diagnostic device is particularly suitable for point-of-care molecular diagnostics of infectious diseases.

Exposure, health effects, sensing, and remediation of the emerging PFAS contaminants - Scientific challenges and potential research directions.

Per- and polyfluoroalkyl substances (PFAS) make up a large group of persistent anthropogenic chemicals which are difficult to degrade and/or destroy. PFAS are an emerging class of contaminants, but little is known about the long-term health effects related to exposure. In addition, technologies to identify levels of contamination in the environment and to remediate contaminated sites are currently inadequate. In this opinion-type discussion paper, a team of researchers from the University of Connecticut and the University at Albany discuss the scientific challenges in their specific but intertwined PFAS research areas, including rapid and low-cost detection, energy-saving remediation, the role of T helper cells in immunotoxicity, and the biochemical and molecular effects of PFAS among community residents with measurable PFAS concentrations. Potential research directions that may be employed to address those challenges and improve the understanding of sensing, remediation, exposure to, and health effects of PFAS are then presented. We hope our account of emerging problems related to PFAS contamination will encourage a broad range of scientific experts to bring these research initiatives addressing PFAS into play on a national scale.

Recent progress in the detection of emerging contaminants PFASs.

As an emerging contaminant, per- and polyfluoroalkyl substances (PFASs) make up a large group of persistent anthropogenic chemicals, which are difficult to degrade in the environment. Notwithstanding their wide range of applications in consumer products and industrial processes, PFASs have been detected in the environment as well as in human body. Due to their potential adverse human health effects, the U.S. Environmental Protection Agency (EPA) set the combined concentration of PFOA and PFOS in drinking water at 70 ng/L or 70 ppt (parts per trillion) as a lifetime health advisory level. Current standard detection methods for PFASs heavily rely on chromatographic techniques coupled with mass spectrometry. Although these methods provide accurate, specific, and sensitive measurements, their applications are greatly limited in advanced analytical laboratories because it necessitates expensive instrumentations, professional operators, complicated sample pretreatment, and considerable analysis time. Therefore, other detection methods beyond chromatographic based techniques, such as optical and electrochemical techniques, have also been extensively explored for simple, accessible, inexpensive, rapid, and sensitive detection of PFASs, particularly PFOA and PFOS. The purpose of this review is to provide recent progress in alternative detection platforms relying on non-MS based techniques for PFASs analysis. Starting with a brief introduction about the importance of monitoring PFASs, recent advances in various PFASs detection methods are grouped and discussed based on the difference of signals, with an emphasis on the working principles of different techniques, the sensing mechanism, and the sensing performance. The review is closed with the conclusion and discussion of future trends.

Real-time in situ auto-correction of K+ interference for continuous and long-term NH4+ monitoring in wastewater using solid-state ion selective membrane (S-ISM) sensor assembly.

Potassium ions (K+) present in wastewater has caused severe interference for NH4+ monitoring, over-estimation of NH4+ concentration and ultimately leads to extra energy consumption. Past effort for enhancing the selectivity of NH4+ over K+ were oftentimes complex, costly, or compromised the selectivity and accuracy of the NH4+ ion selective membrane (ISM) sensors. This study targeted this imminent challenge by developing an integrated NH4+/K+ auto-correction solid-state ISM (S-ISM) sensor assembly combined with a data-driven model to monitor [NH4+] under different [NH4+] and [K+] concentrations. The results showed that the interference of K+ was substantially alleviated for NH4+ measurement. The accuracy was enhanced by over 70% when examined using real wastewater and energy consumption was expected to reduce by 26% for a wastewater treatment plant, especially for wastewater with high [K+]. Furthermore, the uniquely structured S-ISMs were made by embedding the ionophores in a robust polyvinyl chloride (PVC) matrix containing plasticizers and a layer of carbon nanotubes (CNT) as ion-to-electron transducer, which maintained the selectivity and accuracy of the S-ISM sensor for 4 weeks in wastewater. NH4+/K+ sensor assembly integrated with data-driven correction models poses great potential in high-efficiency and energy-saving wastewater treatment and water reuse processes.

Toward Long-Term Accurate and Continuous Monitoring of Nitrate in Wastewater Using Poly(tetrafluoroethylene) (PTFE)-Solid-State Ion-Selective Electrodes (S-ISEs).

Long-term accurate and continuous monitoring of nitrate (NO3-) concentration in wastewater and groundwater is critical for determining treatment efficiency and tracking contaminant transport. Current nitrate monitoring technologies, including colorimetric, chromatographic, biometric, and electrochemical sensors, are not feasible for continuous monitoring. This study addressed this challenge by modifying NO3- solid-state ion-selective electrodes (S-ISEs) with poly(tetrafluoroethylene) (PTFE, (C2F4)n). The PTFE-loaded S-ISE membrane polymer matrix reduces water layer formation between the membrane and electrode/solid contact, while paradoxically, the even more hydrophobic PTFE-loaded S-ISE membrane prevents bacterial attachment despite the opposite approach of hydrophilic modifications in other antifouling sensor designs. Specifically, an optimal ratio of 5% PTFE in the S-ISE polymer matrix was determined by a series of characterization tests in real wastewater. Five percent of PTFE alleviated biofouling to the sensor surface by enhancing the negative charge (-4.5 to -45.8 mV) and lowering surface roughness (Ra: 0.56 ± 0.02 nm). It simultaneously mitigated water layer formation between the membrane and electrode by increasing hydrophobicity (contact angle: 104°) and membrane adhesion and thus minimized the reading (mV) drift in the baseline sensitivity ("data drifting"). Long-term accuracy and durability of 5% PTFE-loaded NO3- S-ISEs were well demonstrated in real wastewater over 20 days, an improvement over commercial sensor longevity.