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Mixed methods study of views and experience of non-hospitalised individuals with long COVID of using pacing interventions

Synergistic effects and optimization of cement kiln dust and glass powder incorporation in self compacting mortar using central composite design

Study on the gas flow resistance state in unstable collapse boreholes and the classification of borehole types

Gene editing, metabolomics, network pharmacology strategies to explore terpenoid content and anti-TMV activity in NtSPS1 knockout Nicotiana tabacum

Rinsenoside Rg1 and its involvement in Hippo–YAP signaling pathway alleviating symptoms of depressive-like behavior

Optimization of growth and induction conditions for the production of recombinant whole cell cyclohexanone monooxygenase in Escherichia coli

Stability analysis of grid-connected hydropower plant considering turbine nonlinearity and parameter-varying penstock model

Outside Front Cover: High‐Throughput Computational Screening of Small Molecular Crystals for Sustainable Piezoelectric Materials (Angew. Chem. Int. Ed. 18/2025)

The relationship between professional quality of life and work environment among nurses in neonate care units

Introduction The work environment is a critical determinant of the professional quality of life (ProQoL) of Neonatal Intensive Care Unit (NICU) nurses. While compassion satisfaction enhances job satisfaction, burnout and secondary traumatic stress have adverse effects on the well-being of nurses and the quality of care provided to patients. This study explores the relationship between the work environment and ProQoL among NICU nurses working in the West Bank, an area plagued by resource scarcity, political instability, and staffing shortages. Methods A cross-sectional study was conducted among 233 NICU nurses in West Bank hospitals from 9 January 2025 to 27 January 2025. Data were collected using the ProQOL and the Practice Environment Scale of the Nursing Work Index (PES-NWI) and analyzed using descriptive statistics, Pearson correlation, and Cronbach’s alpha with SPSS version 23. Results Most nurses reported average compassion satisfaction (94.8%), burnout (91.0%), and secondary traumatic stress (84.1%). The practice environment was moderately favorable, mean 2.7 (SD = 0.3), with strong Collegial Nurse-Physician Relations, mean 2.8 (SD = 0.5), and low Staffing and Resource Adequacy, mean 2.6 (SD = 0.4). The Spearman’s correlation analysis revealed that a positive relationship existed between the favorable environment and compassion satisfaction (r = 0.747, p < 0.001), while there was a negative correlation with burnout (r = -0.604, p < 0.001) and secondary traumatic stress (r = -0.151, p = 0.021). Discussion The results suggest improving staffing and resources, nurse-physician collaboration, and emotional support, which are vital in improving ProQoL in a highly demanding environment.

Effectiveness of Anti-SARS-CoV-2 monoclonal antibodies in real-life: RNAemia and clinical outcomes in high-risk COVID-19 patients

Background Anti-SARS-CoV-2 neutralizing monoclonal antibodies (mAbs) have been shown to have clinical benefits in treating high-risk patients with mild-moderate COVID-19. SARS-CoV-2 RNA in serum (RNAemia), is usually associated with severe disease and deaths. This study evaluates real-life data on the effectiveness of mAbs therapies against SARS-CoV-2 infections by different viral variants, particularly in the presence of RNAemia, focusing on clinical outcomes. Methods From March 2021 to May 2022, high-risk patients with PCR-confirmed mild-moderate COVID-19 were enrolled at the Clinic of Infectious Diseases, San Paolo Hospital in Milan. Patients received Bamlanivimab/Bamlanivimab + Etesevimab/Casirivimab + Imdevimab/Sotrovimab based on Agenzia Italiana del Farmaco (AIFA) guidelines and prevalent SARS-CoV-2 variants. Nasopharyngeal swabs (NPS) and plasma samples were collected at infusion (t0) and after 7 days (t1). NPS viral loads and RNAemia were quantified using RT-qPCR, and variant typing was conducted. Clinical outcomes were evaluated, including time to symptom resolution and adverse effects. Results Among 176 enrolled patients, treatment efficacy was observed in 96.6% with a median time to symptom resolution of 12 days (IQR 10–19). Viral load significantly decreased in both NPS and plasma by day 7 post-treatment (p<0.001). At t0, RNAemia was present in 61.9% of patients and NPS viral loads were higher in patients with RNAemia (p=0.002). However, after treatment, no significant differences in viral loads and times to symptom resolution were noted between patients with and without RNAemia. Omicron-infected patients exhibited higher plasma viral loads compared to Alpha and Delta variants (p<0.001) and the presence of RNAemia was significantly associated with Omicron (p<0.001). Vaccinated patients achieved faster recovery regardless of variant type (p=0.001). Conclusion Early administration of anti-SARS-CoV-2 mAbs in high-risk patients significantly reduced viral loads in NPS and plasma and improved clinical outcomes. Despite the presence of RNAemia, these tailored mAb therapies led to favorable recovery times and minimal adverse effects.

Leveraging advanced graph neural networks for the enhanced classification of post anesthesia states to aid surgical procedures

Anesthesia plays a pivotal role in modern surgery by facilitating controlled states of unconsciousness. Precise control is crucial for safe and pain-free surgeries. Monitoring anesthesia depth accurately is essential to guide anesthesiologists, optimize drug usage, and mitigate postoperative complications. This study focuses on enhancing the classification performance of anesthesia-induced transitions between wakefulness and deep sleep into eight classes by leveraging advanced graph neural network (GNN). The research combines seven datasets into a single dataset comprising 290 samples and investigates key brain regions, to develop a robust classification framework. Initially, the dataset is augmented using the Synthetic Minority Over-sampling Technique (SMOTE) to expand the sample size to 1197. A graph-based approach is employed to get the intricate relationships between features, constructing a graph dataset with 1197 nodes and 714,610 edges, where nodes represent data samples and edges are the connections between the nodes. The connection (edge weight) is calculated using Spearman correlation coefficient matrix. An optimized GNN model is developed through an ablation study of eight hyperparameters, achieving an accuracy of 92.8%. The model’s performance is further evaluated against one-dimensional (1D) CNN, and six machine learning models, demonstrating superior classification capabilities for small and imbalanced datasets. Additionally, we evaluated the proposed model on six different anesthesia datasets, observing no decline in performance. This work advances the understanding and classification of anesthesia states, providing a valuable tool for improved anesthesia management.

Sensory effects of COVID-19 in wine professionals

Objective To evaluate the sensory impacts of COVID-19 infection among wine professionals, and consequences on personal and professional well-being. Our goal was to better understand these effects on an occupational cohort who relies heavily on intact sensory function. Design The study employs an explanatory sequential mixed methods design comprising of two distinct phases: 1) a cross-sectional survey, followed by 2) qualitative interviews with a subsample of survey respondents. Setting Wine professionals were recruited at the global level, excluding locations where Instagram is restricted or banned. Participants Wine professionals (n=128) (ages ≥ 19 years) infected with COVID-19 and experienced sensory impacts were included in the study analysis. Eleven participants completed qualitative interviews. Interventions None. Main outcome measures Symptom profiles, details of taste and smell impact on personal and professional well-being. Effects on specific wine tasting attributes were also evaluated. Results Infected participants reported typical COVID-19 symptoms. The most frequent first noticed symptoms were sore throat (21.09%; 27/128), loss of taste or smell (19.53%; 25/128), fever (17.19%; 22/128) and cough (16.41%; 21/128). For those infected and sensory affected, the extent of taste and smell loss was most reported as severe in the majority of cases. The duration of taste and smell loss was resolved within 4 weeks for most participants. A vast proportion of participants reported an impact on their involvement in the wine profession, with the impact severity ranging from significant (20.31%; 26/128), somewhat (57.03%; 73/128), and not at all (22.66%; 29/128). Additionally, participants predominately reported having some impact on their overall quality of life, which was characterized as severe (7.14%; 9/126), moderate (37.30%; 47/126), mild (30.16%; 38/126), and none (25.40%; 32/126). Conclusion Wine professionals infected with COVID-19 and who experienced sensory alterations reported concerns about their professional and personal well-being with worries about a potential changing life narrative from losing vital sensory attributes. Policies to provide further resources, including therapeutics, for these professionals and others who suffer from sensory dysfunction are warranted.

A divide-and-conquer approach based on deep learning for long RNA secondary structure prediction: Focus on pseudoknots identification

The accurate prediction of RNA secondary structure, and pseudoknots in particular, is of great importance in understanding the functions of RNAs since they give insights into their folding in three-dimensional space. However, existing approaches often face computational challenges or lack precision when dealing with long RNA sequences and/or pseudoknots. To address this, we propose a divide-and-conquer method based on deep learning, called DivideFold, for predicting the secondary structures including pseudoknots of long RNAs. Our approach is able to scale to long RNAs by recursively partitioning sequences into smaller fragments until they can be managed by an existing model able to predict RNA secondary structure including pseudoknots. We show that our approach exhibits superior performance compared to state-of-the-art methods for pseudoknot prediction and secondary structure prediction including pseudoknots for long RNAs. The source code of DivideFold, along with all the datasets used in this study, is accessible at https://evryrna.ibisc.univ-evry.fr/evryrna/dividefold/home.

Thinking of norms—or being told what they are: The effect of social information on donation decisions

The effect of social information (descriptive versus injunctive norms) on people’s donation decisions was examined in two studies. In Study 1 (N=376) participants received information about the norm (a high versus a low anchor) for each norm type, while in Study 2 (N=392) participants were instructed to think of the social norm (what one ought to do vs. what most people do) before their donation decision. Results suggest that when actual information was given (Study 1), a high anchor reduced participants’ initial willingness to donate—but among those who did decide to donate, the high anchor resulted in greater donation amounts than the low anchor. This pattern held true for both injunctive and descriptive norms. Merely thinking about the injunctive norm—without any anchor (Study 2)—increased donations, compared with thinking about the descriptive norm, or control conditions. Possible explanations, and the implications for charitable giving are discussed.

Noninvasive prognostication of hepatocellular carcinoma based on cell-free DNA methylation

Background The current noninvasive prognostic evaluation methods for hepatocellular carcinoma (HCC), which are largely reliant on radiographic imaging features and serum biomarkers such as alpha-fetoprotein (AFP), have limited effectiveness in discriminating patient outcomes. Identification of new prognostic biomarkers is a critical unmet need to improve treatment decision-making. Epigenetic changes in cell-free DNA (cfDNA) have shown promise in early cancer diagnosis and prognosis. Thus, we aim to evaluate the potential of cfDNA methylation as a noninvasive predictor for prognostication in patients with active, radiographically viable HCC. Methods Using Illumina HumanMethylation450 array data of 377 HCC tumors and 50 adjacent normal tissues obtained from The Cancer Genome Atlas (TCGA), we identified 158 HCC-related DNA methylation markers associated with overall survival (OS). This signature was further validated in 29 HCC tumor tissue samples. Subsequently, we applied the signature to an independent cohort of 52 patients with plasma cfDNA samples by calculating the cfDNA methylation-based risk score (methRisk) via random survival forest models with 10-fold cross-validation for the prognostication of OS. Results The cfDNA-based methRisk showed strong discriminatory power when evaluated as a single predictor for OS (3-year AUC = 0.81, 95% CI: 0.68–0.94). Integrating the methRisk with existing risk indices like Barcelona clinic liver cancer (BCLC) staging significantly improved the noninvasive prognostic assessments for OS (3-year AUC = 0.91, 95% CI: 0.80–1), and methRisk remained an independent predictor of survival in the multivariate Cox model (P = 0.007). Conclusions Our study serves as a pilot study demonstrating that cfDNA methylation biomarkers assessed from a peripheral blood draw can stratify HCC patients into clinically meaningful risk groups. These findings indicate that cfDNA methylation is a promising noninvasive prognostic biomarker for HCC, providing a proof-of-concept for its potential clinical utility and laying the groundwork for broader applications.

Examining trip-level errors in passively collected mobile device data for data quality assurance

Location-based service (LBS) data passively collected by mobile devices has been widely adopted in multiple fields for its advantages in revealing travel behaviors. Data quality assessments have always been important steps for analyses using the data, but the impact of trip-level errors has not been a focus of these assessments. We examine a newly emerged type of error present at trip-level in LBS datasets that violates the spatio-temporal consistency of such data by including trips on road segments where and when there should be no trips. We designed a distributed-computing workflow to quantify the errors by comparing the number of trips on closed road segments during road closures with time periods before and after. Using two real-world cases from 2023, we examined multiple datasets acquired from major vendors in the US, and several of the datasets contained a significant number of trip-level errors. These findings point to the errors being present in recent datasets that have not otherwise been processed for data quality and can significantly impact analyses by data users. Data users should consider conducting trip-level error data quality checks as part of their preprocessing steps.

Climate change, geography and trade agreements: A perspective of Asian bilateral trade

This study investigates the simultaneous effects of geographic factors, trade agreements, and climate change on bilateral exports in Asian countries. We estimate the correlation with bilateral exports by utilizing a panel data set from 2000 to 2020, employing various econometric techniques, particularly the structural gravity model. Therefore, this study aims to examine the simultaneous or complementary impact of influencing factors on exports and link them with the gross domestic product. Findings demonstrate that geographic factors are crucial for determining bilateral exports in terms of increasing trends. Furthermore, geography plays a crucial role in enhancing the magnitude and probability of bilateral exports between trading partner countries. Moreover, bilateral exports have declined because of the simultaneous impact of geographic factors, climate change, and economic size. Thus, geographic factors and economic size affect marginal exports to varying degrees. This study suggests that the simultaneous increase in economic size, trade agreements, and geographic factors can enhance the bilateral export level and its probability at an above-average rate between trading partner countries.

Solute carrier family 2 member 2 (glucose transporter 2): a common factor of hepatocyte and hepatocellular carcinoma differentiation

GLUT2 (SLC2A2), a vital glucose transporter in liver, pancreas, and kidney tissues, regulates blood glucose levels and energy metabolism. Beyond its metabolic role, SLC2A2 contributes to cell differentiation and metabolic adaptation during embryogenesis and tissue regeneration. Despite its significance, the role of SLC2A2 in liver differentiation and hepatocellular carcinoma (HCC) remains underexplored. This study investigated SLC2A2’s role in liver differentiation using in silico, in vitro, and in vivo approaches. Analysis of GEO datasets (GSE132606, GSE25417, GSE67848) and TCGA HCC data revealed that while SLC2A2 expression decreases with HCC progression, stemness-associated genes, including SOX2 and POU5F1, are upregulated. Zebrafish embryos injected with SLC2A2-targeting morpholino exhibited reduced expression of the liver differentiation marker fabp10a without significantly altering the hepatoblast marker hhex. In HepG2 cells, SLC2A2 knockdown increased stemness and IGF1R pathway markers, indicating a shift toward less differentiated states. These findings suggest that SLC2A2 supports liver differentiation by regulating glucose metabolism and suppressing pathways associated with stemness and malignancy. Targeting SLC2A2 may serve as a promising therapeutic strategy for liver-related diseases, particularly HCC, by addressing its dual role in differentiation and tumor progression. Further mechanistic studies are warranted to fully elucidate these processes.

An analysis of the impact of administrative approval reform on the technological complexity of manufacturing exports

As the global economic landscape evolves, the low technological content and persistent lack of international competitiveness in China’s manufacturing exports have become increasingly apparent, underscoring the urgent need for a transition from “quantity” to “quality” in the sector. Administrative approval reform, a key pillar of institutional innovation in the new era, plays a critical role in enhancing the technological complexity of manufacturing exports and strengthening international competitiveness. Using data from 2001 to 2013, this study investigates the impact of administrative approval reform on the technological complexity of manufacturing exports and explores its underlying mechanisms from both theoretical and empirical perspectives. Theoretical model analysis suggests that administrative approval reform effectively increases technological complexity by reducing the marginal and fixed costs associated with adjusting product complexity. Empirical findings provide robust evidence that administrative approval reform significantly enhances technological complexity, with results holding across various sensitivity tests. At the micro level, the reduction of institutional transaction costs emerges as a key channel through which the reform exerts its impact. Additionally, increasing investment in research and development, fixed assets, and technological innovation are identified as critical pathways influencing technological complexity. The reform’s effects are particularly pronounced for non-state-owned enterprises and firms located in coastal and port cities, as revealed by a heterogeneity analysis. Furthermore, a decomposition of city-level export technological complexity using the DOP method shows that improved inter-firm resource allocation—by shifting market share from firms with lower technological complexity to those with higher technological complexity—serves as the primary mechanism driving the observed improvements at the city level. This study contributes to the literature by providing empirical evidence on the role of administrative approval reform in fostering the technological upgrading of manufacturing exports, highlighting its differentiated impact across firm types and regions. The findings offer valuable insights for policymakers seeking to enhance the technological complexity and international competitiveness of manufacturing exports in China.

Determinants of solid fuel use in Sub-Saharan Africa: A multilevel analysis using DHS data

Background Dawn in human history life excessively depends on different energy sources for various purposes including cooking food and heating. Energy sources determine the economic development of the community, at the same time it is a public health problem due to environmental pollution. Worldwide Health Organization (WHO) data indicate that about 7 million deaths are attributed to indoor air pollution yearly. According to the 2019 WHO report, 90% of African people depend on dirty energy sources for domestic purposes. Understanding the prevalence and factors of solid fuel use enables policymakers to take measures to prevent its effect on public health by concerned bodies. However, research is done covering such a large area, and the sample size is limited. Therefore, the main objective of this research is to determine factors affecting solid fuel use in Sub-Saharan Africa. Methods The data source is the Demographic and Health Surveys (DHS), a regionally representative survey. Households in DHS are selected using a two-stage cluster sampling methodology. A total of 233, 391 weighted samples were included in the study. A multilevel logistic regression modeling approach was applied to estimate the influence of both individual and community-level factors on solid fuel use. Results The prevalence of solid fuel use in sub-Saharan Africa was 82.05%, with 95% CI (81.90, 82.21). Based on multilevel regression of the final model, household heads aged over 60 years (AOR = 1.12, 95% CI; 1.05–1.19), unmarried household heads (AOR = 1.14, 95% CI; 1.09–1.20), household heads no having education (AOR = 4.91, 95% CI; 4.59–5.25), poor wealth index (AOR = 12.46, 95% CI; 11.34–13.70), not watching television (AOR = 2.38, 95% CI; 2.23–2.53), households without access to electricity (AOR = 3.97, 95% CI; 3.74–4.22), Family size between four & seven (AOR = 3.94, 95% CI; 3.68–4.21), lower education levels (AOR = 2.18, 95% CI; 1.84–2.59), low media exposure (AOR = 1.39, 95% CI; 1.30–1.49), low-income levels (AOR = 63.42, 95% CI; 56.15–71.62) and being rural (AOR = 3.88, 95% CI; 3.68–4.09) were significantly associated with solid fuel use in sub-Saharan Africa. Conclusions The study showed that the prevalence of solid fuel use was high in sub-Saharan Africa. Factors such as the age and marital status of the household head, educational status of the household head, wealth index, watching television, access to electricity, family size, community-level education, community-level media exposure, residence, income level, and community-level poverty were significantly associated with solid fuel use. Solid fuel cooking has been related to respiratory and cardiovascular problems such as lung cancer, chronic obstructive pulmonary disease, and heart disease, as well as pneumonia in children, the elderly, and women who spend most of their time at home. Public health policymakers and possible stakeholders should act on poverty reduction, increase access to electricity, and educational messages through media, and increase educational infrastructures, which can minimize solid fuel use. It is also vital to raise awareness of the potential health risks related to solid fuel use in sub-Saharan Africa. Since human beings exist on land, life excessively depends on different forms of energy sources for various purposes including cooking food and heating sources. Energy sources determine the economic development of the community, at the same time it is a public health problem due to environmental pollution [1,2]. Especially using dirty energy sources like cow dung, firewood, crop residue, and charcoal are sources of indoor air pollution leading to health problems for inhabitants [3]. Worldwide Health Organization (WHO) data indicate that about 7 million deaths are attributed to indoor air pollution each year; millions more are at risk of heart attacks, lung ailments, strokes, and other respiratory and cardiovascular conditions [4]. Reports indicate that household air pollution causes an expected 1.6–3.8 million premature deaths annually [5,6].