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.

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.

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.

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.

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).

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.

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.

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.

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.

Correction to: Mechanisms and microbial structure of partial denitrification with high nitrite accumulation.

In the published version of the above article, the affiliation of one of the co-authors (Baikun Li) is incorrect.

Correction to: High-throughput profiling of microbial community structures in an ANAMMOX-UASB reactor treating high-strength wastewater.

The published online version contains mistake in the affiliation ID of the author Baikun Li. The correct presentation is given above.

Real-Time in Situ Monitoring of Nitrogen Dynamics in Wastewater Treatment Processes using Wireless, Solid-State, and Ion-Selective Membrane Sensors.

Real-time, in situ accurate monitoring of nitrogen contaminants in wastewater over a long-term period is critical for swift feedback control, enhanced nitrogen removal efficiency, and reduced energy consumption of wastewater treatment processes. Existing nitrogen sensors suffer from high cost, low stability, and short life times, posing hurdles for their mass deployment to capture a complete picture within heterogeneous systems. Tackling this challenge, this study presents solid-state ion-selective membrane (S-ISM) nitrogen sensors for ammonium (NH4+) and nitrate (NO3-) in wastewater that were coupled to a wireless data transmission gateway for real-time remote data access. Lab-scale test and continuous-flow field tests using real municipal wastewater indicated that the S-ISM nitrogen sensors possessed excellent accuracy and precision, high selectivity, and multiday stability. Importantly, autocorrections of the sensor readings on the cloud minimized temperature influences and assured accurate nitrogen concentration readings in remote-sensing applications. It was estimated that real-time, in situ monitoring using wireless S-ISM nitrogen sensors could save 25% of electric energy under normal operational conditions and reduce 22% of nitrogen discharge under shock conditions.

The development and psychometric validation of a Chinese empathy motivation scale.

AIMS AND OBJECTIVES: To develop and validate an instrument to measure nurses' empathy motivation in China (See Supporting Information Appendix S1). BACKGROUND: Nurses are increasingly expected to empathise with patients in clinical settings. However, research investigating nurses' empathy motivation in China is lacking, and no specific instrument exists worldwide. DESIGN: Two-stage cross-sectional study, which follows the STROBE guidelines. Instrument development and psychometric evaluation were used (See Supporting Information Appendix S1). METHODS: A literature review and qualitative interviews with nurses were conducted to generate the initial items. Convenience samples of 340 (for item analysis) and 640 (for psychometric evaluation) clinical nurses working at four tertiary hospitals in Anhui Province were recruited. The scale was validated by content validity, surface validity and item analysis. A total of 640 participants were randomly divided into two equal groups. Exploratory factor analysis (EFA) was used with varimax rotation, confirmatory factor analysis (CFA) and internal consistency reliability to analyse the psychometric properties of the scale (See Supporting Information Appendix S1). RESULTS: From the initial 90-item pool, 27 items were retained by the item analysis. The EFA (N = 290) showed the following six factors on the scale explained 71.266% of the overall variance: amotivation, external regulation, introjected regulation, identified regulation, integrative regulation and intrinsic motivation. Furthermore, when limited to three factors, that is autonomy motivation, controlled motivation and amotivation, 56.578% of the variance was explained. The findings showed high internal consistency. The six-factor solution and three-factor solution of the scale, including 27 items, were both confirmed by the CFA, for example χ2 /df = 1.744, 2.261; RMSEA = 0.051, 0.066; GFI = 0.882, 0.847; TLI = 0.942, 0.902; and RMR = 0.039, 0.049, respectively. CONCLUSIONS: The nurses' empathy motivation scale presents good psychometric properties and can be used to explore nurses' empathy motivation in China (See Supporting Information Appendix S1). RELEVANCE TO CLINICAL PRACTICE: This study offers insight into nurses' complicated reasons for exhibiting empathy.

Stratification of Extracellular Polymeric Substances (EPS) for Aggregated Anammox Microorganisms.

Sludge aggregation and biofilm formation are the most effective approaches to solve the washout of anammox microorganisms. In this study, the structure and composition of EPS (extracellular polymeric substances) were investigated to elucidate the factors for the anammox aggregation property. Anammox sludge taken from 18 lab-scale and pilot-scale reactors treating different types of wastewater was analyzed using EEM-PARAFAC (excitation-emission matrix and parallel factor analysis), FTIR (Fourier transform infrared spectroscopy) and real-time PCR combined with multivariate statistical analysis. The results showed that slime and TB-EPS (tightly bound EPS) were closely related with water quality and sludge morphology, and could be used as the indicators for anammox microbial survival ability and microbial aggregate morphology. Furthermore, slime secreted from anammox bacterial cells may be exhibited higher viscosity to the sludge surface and easily formed the gel network to aggregate. Large amounts of hydrophobic groups of protein in TB-EPS promoted the microbial aggregation. The mechanisms of anammox aggregation explored in this study enhanced the understanding of anammox stability in wastewater treatment processes.

High-throughput profiling of microbial community structures in an ANAMMOX-UASB reactor treating high-strength wastewater.

In this study, the microbial community structure was assessed in an anaerobic ammonium oxidation-upflow anaerobic sludge blanket (ANAMMOX-UASB) reactor treating high-strength wastewater (approximately 700 mg N L(-1) in total nitrogen) by employing Illumina high-throughput sequencing analysis. The reactor was started up and reached a steady state in 26 days by seeding mature ANAMMOX granules, and a high nitrogen removal rate (NRR) of 2.96 kg N m(-3) day(-1) was obtained at 13.2∼17.6 °C. Results revealed that the abundance of ANAMMOX bacteria increased during the operation, though it occupied a low proportion in the system. The phylum Planctomycetes was only 8.39 % on day 148 and Candidatus Brocadia was identified as the dominant ANAMMOX species with a percentage of 2.70 %. The phylum of Chloroflexi, Bacteroidetes, and Proteobacteria constituted a percentage up to 70 % in the community, of which the Chloroflexi and Bacteroidetes were likely to be related to the sludge granulation. In addition, it was found that heterotrophic denitrifying bacteria of Denitratisoma belonging to Proteobacteria phylum occupied a large proportion (22.1∼23.58 %), which was likely caused by the bacteria lysis and decay with the internal carbon source production. The SEM images also showed that plenty of other microorganisms existed in the ANAMMOX-UASB reactor.

Mechanisms and microbial structure of partial denitrification with high nitrite accumulation.

Nitrite (NO2 (-)-N) accumulation in denitrification can provide the substrate for anammox, an efficient and cost-saving process for nitrogen removal from wastewater. This batch-mode study aimed at achieving high NO2 (-)-N accumulation over long-term operation with the acetate as sole organic carbon source and elucidating the mechanisms of NO2 (-)-N accumulation. The results showed that the specific nitrate (NO3 (-)-N) reduction rate (59.61 mg N VSS(-1) h(-1) at NO3 (-)-N of 20 mg/L) was much higher than specific NO2 (-)-N reduction rate (7.30 mg N VSS(-1) h(-1) at NO3 (-)-N of 20 mg/L), and the NO2 (-)-N accumulation proceeded well at the NO3 (-)-N to NO2 (-)-N transformation ratio (NTR) as high as 90 %. NO2 (-)-N accumulation was barely affected by the ratio of chemical oxygen demand (COD) to NO3 (-)-N concentration (C/N). With the addition of NO3 (-)-N, NO2 (-)-N accumulation occurred and the specific NO2 (-)-N reduction rate declined to a much lower level compared with the value in the absence of NO3 (-)-N. This indicated that the denitrifying bacteria in the system preferred to use NO3 (-)-N as electron acceptor rather than use NO2 (-)-N. In addition, the Illumina high-throughput sequencing analysis revealed that the genus of Thauera bacteria was dominant in the denitrifying community with high NO2 (-)-N accumulation and account for 67.25 % of total microorganism. This bacterium might be functional for high NO2 (-)-N accumulation in the presence of NO3 (-)-N.

Determine the operational boundary of a pilot-scale single-stage partial nitritation/anammox system with granular sludge.

The partial nitritation/anammox (PN/A) process has been applied to ammonium-rich wastewater treatment, but the operational boundary has not been well determined for long-term stability. This pilot-scale study was targeted at a single-stage PN/A process using a sequencing batch reactor (SBR) (volume: 53 m(3)) and granulated activated sludge. The maximum nitrogen removal rate reached 0.83 kg N/(m(3)·d). Microbial analysis suggested that ammonium oxidizing bacteria were mainly present in small sludge flocs while anammox bacteria were prone to grow in large sludge granules. The PN/A performance was enhanced when dissolved oxygen (DO) was increased from 0.25 to 0.76 mg/L, and deteriorated at DO higher than 1.15 mg/L. The PN/A was inhibited at free ammonia (FA) over 77.0 mg/L. High DO or FA concentrations inhibited anammox activity and further induced high and inhibitory nitrite concentrations. Therefore, appropriate DO and FA concentrations should be controlled to achieve single-stage PN/A in SBRs.

The effect of salinity on waste activated sludge alkaline fermentation and kinetic analysis.

The effect of salinity on sludge alkaline fermentation at low temperature (20°C) was investigated, and a kinetic analysis was performed. Different doses of sodium chloride (NaCl, 0-25g/L) were added into the fermentation system. The batch-mode results showed that the soluble chemical oxygen demand (SCOD) increased with salinity. The hydrolysate (soluble protein, polysaccharide) and the acidification products (short chain fatty acids (SCFAs), NH4(+)-N, and PO4(3-)-P) increased with salinity initially, but slightly declined respectively at higher level salinity (20g/L or 20-25g/L). However, the hydrolytic acidification performance increased in the presence of salt compared to that without salt. Furthermore, the results of Haldane inhibition kinetics analysis showed that the salt enhanced the hydrolysis rate of particulate organic matter from sludge particulate and the specific utilization of hydrolysate, and decreased the specific utilization of SCFAs. Pearson correlation coefficient analysis indicated that the importance of polysaccharide on the accumulation of SCFAs was reduced with salt addition, but the importance of protein and NH4(+)-N on SCFA accumulation was increased.