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The impact of straw and its post-pyrolysis incorporation on functional microbes and mineralization of organic carbon in yellow paddy soil
The impact of straw and biochar on carbon mineralization and the function of carbon cycle genes in paddy soil is important for soil nutrient management and the transformation of carbon pools. This research is based on a five-year field experiment with four treatments: no fertilizer application (CK); chemical fertilizer only (NPK); straw combined with chemical fertilizer (NPKS); and biochar combined with chemical fertilizer (NPKB). By integrating indoor mineralization culture with metagenomic approaches, we analyzed the response of organic carbon mineralization and carbon cycle genes in typical paddy soil from Guizhou Province, China, to different fertilization treatments. The result shows that the various fertilization treatments significantly increased the levels of soil organic carbon, dissolved organic carbon, microbial biomass carbon, and readily oxidizable organic carbon. The NPKS treatment increased the rate of soil organic carbon mineralization, whereas the NPKB treatment decreased it. Overall, the NPK and NPKB treatments increased the relative abundance of carbon fixation genes. The NPKS treatment increased the relative abundance of carbon degradation genes. The NPKS treatment increased the abundance of Proteobacteria, whereas the NPKB treatment decreased the abundance of Actinobacteria. Biochar after straw pyrolysis can reduce carbon loss and enhance sequestration of soil carbon, whereas straw decreases soil organic carbon stability, accelerating the transformation of soil carbon pools. Future research should encompass long-term impact assessments to comprehensively understand the enduring effects of these fertilization treatments on soil carbon mineralization and the function of carbon cycle genes.
Microscopic evidence for nanoparticle-mediated growth of native gold in sulfide deposits at the Higashi–Aogashima Knoll Caldera hydrothermal field
Gold (or electrum) in hydrothermal fluid precipitates directly from gold sulfide complex and/or partly via suspended nanoparticles. The hydrothermal fluid contains “invisible gold” that is atomically dispersed in sulfide minerals or as nanoparticles with a size of less than 10 nm. However, the contribution of these gold nanoparticles to the formation of native gold and its alloy with silver (electrum) remains unclear. The Higashi–Aogashima Knoll Caldera hydrothermal field, south of Tokyo, Japan, is an area of significant seafloor hydrothermal activity that is known for high-grade gold-containing minerals in sulfide-rich rocks. In this study, dry-polished thin sections were created to minimize sample damage and scanning and transmission electron microscopy were used to investigated the cross-sectional and three-dimensional morphologies of native gold grains in a sulfide-rich mound rock from the Central Cone site of the caldera. The surfaces of the gold grains comprised nanoparticles with sizes of 5–50 nm that were also attached to their periphery, which suggests that gold nanoparticles in deep-sea hydrothermal fluid were involved in the mineralization of the gold. In addition, the distribution of silver was uneven within the gold grains, which suggests that the gold precipitation comprised multiple stages at different temperatures that resulted in the post-deposition or secondary remobilization of silver.
LARP3 inhibits the apoptosis of hepatocellular carcinoma via the ROS/PI3K/c-Fos axis
Primary hepatocellular carcinoma (PHC) is the sixth most common cancer and the third leading cause of cancer death worldwide. Hepatocellular carcinoma (HCC) accounts for 75%-85% of PHC. LARP3 is aberrantly expressed in multiple cancers. We found that it is significantly highly expressed in the liver cancer tissues of HCC patients, but the exact role and specific mechanism of this abnormal expression are not yet clear. In this study, through bioinformatics analysis, we concluded that LARP3 expression is associated with a poor prognosis for patients with HCC. Through cellular experiments such as gene editing and phenotypic functions, we found that LARP3 promotes the occurrence and development of HCC and inhibits apoptosis. Finally, through biological means such as RNA sequencing, flow cytometry, western blotting, and the construction of a subcutaneous tumorigenesis model in nude mice, we concluded that inhibition of HCC apoptosis by LARP3 is related to LARP3 negatively regulating ROS level and inhibiting the PI3K/c-Fos/apoptosis axis. This study will provide potential targets for the treatment of HCC.
Classification-augmented survival estimation (CASE): A novel method for individualized long-term survival prediction with application to liver transplantation
Survival analysis is critical in many fields, particularly in healthcare where it can guide medical decisions. Conventional survival analysis methods like Kaplan-Meier and Cox proportional hazards models to generate survival curves indicating probability of survival v. time have limitations, especially for long-term prediction, due to assumptions that all instances follow a general population-level survival curve. Machine learning classification models, even those designed for survival predictions like random survival forest (RSF), also struggle to provide accurate long-term predictions due to class imbalance. We improve upon traditional survival machine learning approaches through a novel framework called classification-augmented survival estimation (CASE), which treats survival as a classification task that ultimately yields survival curves, beginning with dataset augmentation to improve class imbalance for use with any classification model. Unlike other approaches, CASE additionally provides an exact survival time prediction. We demonstrate CASE on a liver transplant case study to predict >20 years survival post-transplant, finding that CASE dataset augmentation improved AUCs from 0.69 to 0.88 and F1 scores from 0.32 to 0.73. Compared to Kaplan-Meier, Cox, and RSF survival models, the CASE framework demonstrated better performance across various existing survival metrics, as well as our novel metric, mean of individual areas under the survival curve (mAUSC). Further, we develop novel temporal feature importance methods to understand how different features may vary in survival importance over time, potentially providing actionable insights in real-world survival problems.
Passive earth pressure analysis considering hydraulic and mechanical hysteresis for unsaturated soil
This paper addresses the mechanical characteristics of a passive earth pressure problem taking into account water retention curve (SWRC) hysteresis. Both hydraulic (drying and wetting cycles) and mechanical hysteresis were considered. Parametric studies were carried out at various air entry values (AEV = 5–30 kPa), different wall frictions (δ = 0, 0.33 ϕ, 0.5 ϕ, 0.67 ϕ and δ = ϕ), and unsaturated conditions (covering the zone from the fully saturated to the transition suction, 0 - ~40 kPa) using an upper bound theorem. The numerical results were compared with a passive earth pressure equation based on the limit equilibrium method. The results indicated significant influences of SWRC hysteresis and wall frictions on the passive thrust (Pp) for the modeled suction profiles. An increase of 1.31-fold in Pp was obtained when the AEV changed from 10 to 20 kPa at the water table (Hw) = -2 m. Additionally, the combined effects of AEV and δ were found to be significant. An increase of 4.14-fold in Pp at Hw = -3 m was obtained when comparing the case of AEV = 30 kPa and δ = ϕ with AEV = 5 kPa and δ = 0. Based on the parametric studies, a series of design charts of the exerted passive thrust were proposed.
Economic impact and clinical benefits of clinical pharmacy interventions: A six-year multi-center study using an innovative medication management tool
Background Increasing healthcare costs, particularly in Low- and Middle-Income Countries (LMICs) like Egypt, highlight the need for rational economic strategies. Clinical pharmacy interventions offer potential benefits by reducing drug therapy problems and associated costs, thereby supporting healthcare system sustainability. Objective This study evaluates the economic impact and clinical benefits of clinical pharmacy interventions in four tertiary hospitals in Egypt by implementing an innovative tool for medication management, focusing on cost avoidance and return on investment (ROI), while accounting for case severity and drug therapy problem (DTP) resolution. Methods Utilizing a digital tool, cost avoidance was assessed by considering the severity of patient cases and the effectiveness of clinical pharmacists’ interventions in resolving identified DTPs. Additionally, ROI was calculated by incorporating both full-time equivalent (FTE) and non-FTE costs to evaluate the overall economic impact of clinical pharmacy services over a six-year period across four tertiary care hospitals in Egypt. Results Over six years, a total of 492,612 patients were reviewed, leading to 19,240 comprehensive clinical pharmacy interventions. These interventions achieved an 88.63% resolution of DTPs, significantly reducing patient risk by preventing adverse DTP consequences, resulting in a total cost avoidance of EGP 265.32 million (USD 8.60 million) and an average ROI of 7.6 (760%). This underscores the substantial economic impact of clinical pharmacy services, particularly in LMICs and countries transitioning to universal health insurance coverage, where cost efficiency and patient safety are critical. Conclusion This study underscores the importance of clinical pharmacy interventions in improving healthcare outcomes and generating significant economic benefits, particularly in low- and middle-income countries. By accounting for case severity and the level of DTP consequences, along with the efficiency of clinical pharmacist-led interventions in resolving DTPs, the economic impact of these services can be more accurately evaluated. These findings are essential for informing policy decisions, highlighting the critical role of clinical pharmacy services in supporting healthcare systems facing economic constraints.
Glaucoma detection and staging from visual field images using machine learning techniques
Purpose In this study, we investigated the performance of deep learning (DL) models to differentiate between normal and glaucomatous visual fields (VFs) and classify glaucoma from early to the advanced stage to observe if the DL model can stage glaucoma as Mills criteria using only the pattern deviation (PD) plots. The DL model results were compared with a machine learning (ML) classifier trained on conventional VF parameters. Methods A total of 265 PD plots and 265 numerical datasets of Humphrey 24–2 VF images were collected from 119 normal and 146 glaucomatous eyes to train the DL models to classify the images into four groups: normal, early glaucoma, moderate glaucoma, and advanced glaucoma. The two popular pre-trained DL models: ResNet18 and VGG16, were used to train the PD images using five-fold cross-validation (CV) and observed the performance using balanced, pre-augmented data (n = 476 images), imbalanced original data (n = 265) and feature extraction. The trained images were further investigated using the Grad-CAM visualization technique. Moreover, four ML models were trained from the global indices: mean deviation (MD), pattern standard deviation (PSD) and visual field index (VFI), using five-fold CV to compare the classification performance with the DL model’s result. Results The DL model, ResNet18 trained from balanced, pre-augmented PD images, achieved high accuracy in classifying the groups with an overall F1-score: 96.8%, precision: 97.0%, recall: 96.9%, and specificity: 99.0%. The highest F1 score was 87.8% for ResNet18 with the original dataset and 88.7% for VGG16 with feature extraction. The DL models successfully localized the affected VF loss in PD plots. Among the ML models, the random forest (RF) classifier performed best with an F1 score of 96%. Conclusion The DL model trained from PD plots was promising in differentiating normal and glaucomatous groups and performed similarly to conventional global indices. Hence, the evidence-based DL model trained from PD images demonstrated that the DL model could stage glaucoma using only PD plots like Mills criteria. This automated DL model will assist clinicians in precision glaucoma detection and progression management during extensive glaucoma screening.
Characteristics of ChatGPT users from Germany: Implications for the digital divide from web tracking data
A major challenge of our time is reducing disparities in access to and effective use of digital technologies, with recent discussions highlighting the role of AI in exacerbating the digital divide. We examine user characteristics that predict usage of the AI-powered conversational agent ChatGPT. We combine behavioral and survey data in a web tracked sample of N = 1376 German citizens to investigate differences in ChatGPT activity (usage, visits, and adoption) during the first 11 months from the launch of the service (November 30, 2022). Guided by a model of technology acceptance (UTAUT-2), we examine the role of socio-demographics commonly associated with the digital divide in ChatGPT activity and explore further socio-political attributes identified via stability selection in Lasso regressions. We confirm that lower age and higher education affect ChatGPT usage, but do not find that gender or income do. We find full-time employment and more children to be barriers to ChatGPT activity. Using a variety of social media was positively associated with ChatGPT activity. In terms of political variables, political knowledge and political self-efficacy as well as some political behaviors such as voting, debating political issues online and offline and political action online were all associated with ChatGPT activity, with online political debating and political self-efficacy negatively so. Finally, need for cognition and communication skills such as writing, attending meetings, or giving presentations, were also associated with ChatGPT engagement, though chairing/organizing meetings was negatively associated. Our research informs efforts to address digital disparities and promote digital literacy among underserved populations by presenting implications, recommendations, and discussions on ethical and social issues of our findings.
Characterizing the within-person variability of food insecurity in everyday life
Food insecurity (FI), the lack of access to adequate food, is linked with negative health and psychological outcomes. FI is typically measured retrospectively over the last year; although this measurement is useful to understand FI prevalence to inform broad policy, it leaves the experience of FI in everyday life poorly understood. Understanding how FI varies across shorter periods of time (days or weeks) can help inform FI prevention and/or intervention. This study characterizes within-person (day-to-day) variance in FI in everyday life. Low-income parents of school-aged children at risk for FI (n = 153) completed daily text message surveys in two-week bursts. Daily FI was measured with 4 yes/no items ranging in severity: worry about food, parent eating less than they should, child eating less than they should, and skipping meals. Items were analyzed as a sum score and individually (to examine FI severity). Among parents who reported FI at least once, FI meaningfully varied day-to-day within individuals (~26% of variation). Different indicators of FI, however, had different proportions of between- and within-person variability: Worry about food, a less severe aspect of FI, had 32% daily variation, whereas the more severe aspect of meal skipping had 45% daily variation. Thus, although substantial between-person differences in FI exist, there is meaningful within-person variability in FI. -person FI variability may be related to the indicator (e.g., severe FI shows greater within-person variability). Considering within-person FI variability, and not just average FI level, may help us understand how FI undermines functioning and how and when best to intervene.
Flexible and modular latent transition analysis—A tutorial using R
Latent transition analysis (LTA) is a useful statistical modelling approach for describe transitions between latent classes over time. LTA may be characterized in terms of prevalence at each time point and through transition probabilities over time. Investigating predictors of these transitions is often of key interest. Currently, LTA can mostly be carried out using commercial and specialized software and only to some limited extent by means of open source statistical software. This tutorial demonstrates a flexible and modular approach for LTA, providing a powerful alternative using R through a combination latent class analysis and multiple logistic regression models. This approach has several advantages from a modelling perspective, as demonstrated through revisiting a previously conducted LTA, published in PLoS ONE recently. In short, results were very similar to the original analysis using commercial software although some additional novel results were also obtained. The proposed alternative approach offers more options in terms of choice of effect measures, model assumptions such as hierarchical structures and covariate adjustment, and differential handling of missing data. R code snippets are provided in the tutorial. A detailed accompanying script is also provided for full reproducibility.
Comparison of mortality in people with type 2 diabetes between different ethnic groups: Systematic review and meta-analysis of longitudinal studies
Aims Type 2 diabetes (T2D) is more common in certain ethnic groups. This systematic review compares mortality risk between people with T2D from different ethnic groups and includes recent larger studies. Methods We searched nine databases using PRISMA guidelines (PROSPERO CRD42022372542). We included community-based prospective studies among adults with T2D from at least two different ethnicities. Two independent reviewers undertook screening, data extraction and quality assessment using the Newcastle-Ottawa Scale. The primary outcome compared all-cause mortality rates between ethnic groups (hazard ratio (HR) with 95% confidence intervals). Results From 30,825 searched records, we included 13 studies (7 meta-analysed), incorporating 573,173 T2D participants; 12 were good quality. Mortality risk was lower amongst people with T2D from South Asian [HR 0.68 (0.65–0.72)], Black [HR 0.82 (0.77–0.87)] and Chinese [HR 0.57 (0.46–0.70)] ethnicity compared to people of White ethnicity. Narrative synthesis corroborated these findings but demonstrated that people of indigenous Māori ethnicity had greater mortality risk compared to European ethnicity. Conclusions People with T2D of South Asian, Black and Chinese ethnicity have lower all-cause mortality risk than White ethnicity, with Māori ethnicity having higher mortality risk. Factors explaining mortality differences require further study, including understanding complication risk by ethnicity, to improve diabetes outcomes.
RETRACTED: The artificial intelligence-based agricultural field irrigation warning system using GA-BP neural network under smart agriculture
This work explores an intelligent field irrigation warning system based on the Enhanced Genetic Algorithm—Backpropagation Neural Network (EGA-BPNN) model in the context of smart agriculture. To achieve this, irrigation flow prediction in agricultural fields is chosen as the research topic. Firstly, the BPNN principles are studied, revealing issues such as sensitivity to initial values, susceptibility to local optima, and sample dependency. To address these problems, a genetic algorithm (GA) is adopted for optimizing the BPNN, and the EGA-BPNN model is used to predict irrigation flow in agricultural fields. Secondly, the EGA-BPNN model can overcome the local optimization and overfitting problems of traditional BPNN through the global search ability of GA. Moreover, it is suitable for the irrigation flow prediction task with complex environmental factors in smart agriculture. Finally, comparative experiments compare the prediction accuracy of BPNN and EGA-BPNN using single and dual water level flow prediction models respectively. The results reveal that as the number of nodes in the hidden layer increases, the model’s Mean Squared Error (MSE) and Relative Error (RE) show a decreasing trend, indicating an improvement in model prediction accuracy. When the number of nodes in the hidden layer increases from 6 to 16, the MSE of the single and dual water level flow prediction models decreases from 4.53×10 −4 to 3.68×10 −4 and 2.38×10 −4 to 1.66×10 −4 , respectively. Under a standalone BPNN, the absolute relative error in flow prediction is 1.09%. In contrast, the EGA-BPNN model achieves a significantly lower mean absolute relative error of 0.41% for single-flow prediction, demonstrating superior prediction performance. Furthermore, compared to the BPNN, the EGA-BPNN model exhibits a 2.11 reduction in MSE, further emphasizing the positive impact of introducing the GA on model performance. The research outcomes contribute to more accurate water resource planning and management, providing a more reliable basis for decision-making.
Emotional distress and affective knowledge representation one year after the COVID-19 outbreak
This study examines whether the detrimental effects of the COVID-19 pandemic on the affectivity of the population extend one year after the outbreak. In an online-mobile session, participants completed surveys (i.e., demographic characteristics, positive-negative affectivity, interoceptive awareness) and a similarity judgment task of triplets of emotional concepts, from which we derived 2D maps of their affective knowledge representation. Compared with pre-pandemic data derived from a comparable population, we report three main findings. First, we observed enhanced negative affectivity during the pandemic, but no changes in positive affectivity levels. Second, increased self-reported interoceptive awareness compared to pre-pandemic data, with greater attention to bodily sensations and adaptive aspects of interoceptive sensitivity. Furthermore, female participants reported higher scores than males on the questionnaire subscales of Emotional Awareness and Attention Regulation. Third, the effect of pandemic-related conditions is also apparent in the mental organization of emotional concepts, especially for female participants (i.e., reduced coherence in the organization of the concepts along the arousal dimension and more misclassification of concepts based on arousal) and participants who did not perform physical activity (a collapse of the arousal dimension). Some of the effects of the pandemic, thus, persist about a year after the outbreak. These results advise providing programs of psychological and emotional assistance throughout the pandemic beyond the outbreak, and that age-dependent gender differences should be accounted for to define tailored interventions. Physical activity might relieve pandemic-related stressors, so it should be promoted during particularly stressful periods for the population.
Strategies to reduce hyperglycemia-related anxiety in elite athletes with type 1 diabetes: A qualitative analysis
Objective Managing blood glucose levels is challenging for elite athletes with type 1 diabetes (T1D) as competition can cause unpredictable fluctuations. While fear of hypoglycemia during physical activity is well documented, research on hyperglycemia-related anxiety (HRA) is limited. HRA refers to the heightened fear that hyperglycemia-related symptoms will impair functioning. This study investigates current strategies employed to mitigate HRA during competition and the development of alternative approaches. Research design and methods Elite athletes with TID, aged >14 who self-reported HRA during competition were recruited. Elite athletes were defined as individuals exercising >10 hours per week whose athletic performance has achieved the highest competition level. 60 to 90-minute virtual semi-structured interviews were analyzed using an Interpretative Phenomenological Analysis. Results Ten elite athletes with T1D (average age 25 ± 3 years; T1D duration 12 ± 8 years; number of competitions per year 27 ± 19; training time per week 12 ± 6 hours) reported the strategies they currently use to mitigate HRA. These strategies include managing insulin and nutrition intake, embracing social support networks, using technology, practicing relaxation techniques, establishing routines, performing pre-competition aerobic exercise, and maintaining adequate sleep hygiene. Several additional approaches that could be implemented were identified including establishing targeted support networks, developing peer-reviewed resources on HRA, ensuring support teams have sufficient tools, and improving existing technology. Conclusions Elite athletes with T1D use physiological and psychological strategies to mitigate HRA during competition. This finding highlights the need for increased support and education for these athletes, and advancements in technology. A multidisciplinary approach involving healthcare professionals, athletic staff, and peer mentors could help integrate personalized anxiety management and diabetes care strategies into training regimens, enhancing both mental resilience and performance outcomes for athletes with T1D.
Impact of low-carbon economic policies on the corporate environmental responsibility model in China
This study investigates the impact of low-carbon economic policies on Corporate Environmental Responsibility (CER) in Chinese A-share listed companies, with a particular focus on the role of financing constraints as a mediating factor. Despite a decrease in environmental pollution incidents in 2022, the economic and social impacts of such incidents remain significant, highlighting the need for stronger environmental governance. Building upon previous research, this study utilizes data from the Shanghai and Shenzhen stock exchanges (2010–2020) and employs a Difference-in-Differences (DID) model to assess the effects of low-carbon economic policies introduced in 2016 on CER. The findings reveal that these policies positively influence CER and that financial constraints act as a mediator. The study finds how low-carbon policies indirectly promote environmental commitments by alleviating financial barriers. The research provides valuable insights for policy formulation, advocating for intensified reforms on the financial supply side to foster a sustainable economic framework. Additionally, it underscores the importance of implementing robust low-carbon policies to elevate corporate environmental responsibility. However, the study also notes limitations related to data scope and potential external factors influencing the results. These findings contribute to the broader discourse on sustainable development, offering a blueprint for harmonizing economic growth with environmental preservation and informing future research in this area.
Perceptions and practices of imaging personnel and physicians regarding the use of brain MRI for dementia diagnosis in Uganda
Introduction Diagnosing dementia remains challenging in low-income settings due to limited diagnostic options and the absence of definitive biomarkers. The use of brain MRI in the diagnosis of dementia is infrequent in Uganda, and even when it is used, subtle findings like mild regional atrophy are often overlooked, despite being crucial for imaging diagnosis. Objective The purpose of this study was to explore the perceptions and practices of imaging personnel and physicians regarding the use of brain MRI as a diagnostic approach for dementia in Uganda. Methods This was an exploratory qualitative study involving radiologists, technologists, senior house officers and psychiatrists. The participants were 25 in total. Data was collected through key informant interviews and focus group discussions and analyzed thematically using an inductive approach. Results The study revealed three key themes: Brain MRI Practices for Diagnosing Dementia, Facilitators of Appropriate MRI Use, and Barriers to Appropriate Use of Brain MRI. Sub-themes under these themes included cost considerations, poor and good MRI practices, MRI as a standard operating procedure, positive attitudes towards brain MRI, and barriers such as structural, financial, operational, technical, and patient-related issues. Participants acknowledged the high accuracy and superiority of brain MRI for diagnosing dementia and recognized it as the standard of care. However, its use in Uganda is limited due to high costs, restricted access, mechanical failures, patient claustrophobia, myths and misconceptions, and interpretation difficulties by radiologists and inappropriate protocols by technologists. Conclusion The study identifies barriers to effective brain MRI use for dementia diagnosis in Uganda, including limited training, high costs, and uneven equipment distribution. Despite this, providers are positive about MRI adoption. Enhancing training, awareness, and phased rollouts can improve outcomes. Future research should focus on similar low-resource settings for validation.
Private, non-profit, and plantation: Oil palm smallholders in management-assistance programs vary in socio-demographics, attitudes, and management practices
Smallholder farmers produce over 40% of global palm oil, the world’s most traded and controversial vegetable oil. Awareness of the effects of palm oil production on ecosystems and human communities has increased drastically in recent years, with ever louder calls for the private and public sector to develop programs to support sustainable cultivation by smallholder farmers. To effectively influence smallholder practices and ensure positive social outcomes, such schemes must consider the variety in perspectives of farmers and align with their priorities. We conducted social surveys on smallholder farmers in Indonesia and Malaysia with varying degrees of participation in programs that offer advice and support with plantation management (“management-assistance programs”) led by an industrial palm oil producer in Indonesia and a conservation-focused NGO in Malaysia. We surveyed farmers on their demographics, attitudes, and management decisions. Our analyses act as case studies to investigate the similarities and differences between smallholder palm oil producers involved in different schemes, allowing us to determine the alignment between the intentions of partnership programs and the current realities of smallholder plantations. The relationship between heterogeneity of social factors and management decisions and degree of program involvement differed across different groups and region: Indonesian smallholders most closely partnered with the private sector were the most varied in socio-demographics and attitudes but showed little variation in management inputs, while Malaysian smallholders most closely partnered with an NGO were the most heterogenous across all survey sections. Specifically, Indonesian farmers partnered with the private sector used less herbicide, more fertilizer, and had higher yield and total household income than farmers completely uninvolved with management assistance programs. In Malaysia, farmers partnered with an NGO also had higher yield and fertilizer application than independent farmers, however they used significantly more herbicide and had lower total household income. Our findings demonstrate the wide variety of smallholder farmers in both regions, directly opposing a ‘one-size-fits-all’ approach to sustainability. The wide variety of existing management practices also provides a potentially valuable natural experiment to identify high-yield, environmentally-friendly management approaches. When taken in context, our findings may inform the interventions of management-assistance programs, ensuring they are approaching the most relevant farmer groups in the most effective way.
Pooled PPIseq: Screening the SARS-CoV-2 and human interface with a scalable multiplexed protein-protein interaction assay platform
Protein-Protein Interactions (PPIs) are a key interface between virus and host, and these interactions are important to both viral reprogramming of the host and to host restriction of viral infection. In particular, viral-host PPI networks can be used to further our understanding of the molecular mechanisms of tissue specificity, host range, and virulence. At higher scales, viral-host PPI screening could also be used to screen for small-molecule antivirals that interfere with essential viral-host interactions, or to explore how the PPI networks between interacting viral and host genomes co-evolve. Current high-throughput PPI assays have screened entire viral-host PPI networks. However, these studies are time consuming, often require specialized equipment, and are difficult to further scale. Here, we develop methods that make larger-scale viral-host PPI screening more accessible. This approach combines the mDHFR split-tag reporter with the iSeq2 interaction-barcoding system to permit massively-multiplexed PPI quantification by simple pooled engineering of barcoded constructs, integration of these constructs into budding yeast, and fitness measurements by pooled cell competitions and barcode-sequencing. We applied this method to screen for PPIs between SARS-CoV-2 proteins and human proteins, screening in triplicate >180,000 ORF-ORF combinations represented by >1,000,000 barcoded lineages. Our results complement previous screens by identifying 74 putative PPIs, including interactions between ORF7A with the taste receptors TAS2R41 and TAS2R7, and between NSP4 with the transmembrane KDELR2 and KDELR3. We show that this PPI screening method is highly scalable, enabling larger studies aimed at generating a broad understanding of how viral effector proteins converge on cellular targets to effect replication.
Reaching Syrian migrants through Dutch municipal registries for hepatitis B and C point-of-care testing
Undetected chronic hepatitis B virus (HBV) and hepatitis C virus (HCV) infections can lead to cirrhosis and liver cancer. Syrian migrants are the largest non-European migrant group in the Netherlands with HBV and HCV prevalence rates above 2%. This study aimed to reach Syrian migrants for HBV and HCV testing using point-of-care tests (POCT). A multifaceted strategy was employed to reach Syrian migrants aged ≥16 years from two Dutch municipalities for free-of-charge HBsAg and anti-HCV POCT using finger prick blood at the regional Public Health Service. All were personally invited by the Public Health Service by postal mail, based on municipal registry data. Respondents’ medical history data were analysed descriptively and data on age, sex, and municipality were compared with non-participating invitees, using Pearson’s Chi-square test. Of the study population (N = 832), 32.3% (n = 269) attended the testing. The mean age of participants was 36 years (range 16–70), 59.1% were men, and 66.5% were unemployed. Non-participation was higher in the younger age groups (<30 years) (p < .001). The POCT using finger prick blood was well received. None tested HBsAg or anti-HCV positive. With approximately one-third of participation, this study demonstrated relatively high reach of Syrian migrants for testing, compared to studies with similar recruitment methods. However, while the reach could be considered successful, testing failed to demonstrate new infection in this key population. Thereby, other methods may be preferred to identify new HBV and HCV infections, such as opportunistic testing within existing care processes.
Experiences and perceptions of respectful maternity care among mothers during childbirth in health facilities of Eastern province of Rwanda: An appreciative inquiry
Introduction The World Health Organization (WHO) has emphasized the importance of ensuring respectful and dignified childbirth experiences. However, many countries, including Rwanda, have documented negative experiences during childbirth. Identifying best practices can help uncover sustainable solutions for resource-limited settings rather than focusing solely on the challenges and negative aspects. This study aimed to explore and describe how mothers in Rwanda’s Eastern Province perceived and valued their childbirth experiences during their most recent labour and delivery. Methods We conducted a qualitative, exploratory, descriptive study. Purposive sampling targeted mothers who reported receiving respectful care during labour and childbirth. We selected 30 mothers from five hospitals in the Eastern Province of Rwanda. Data collection involved in-depth interviews (IDIs) following the first four stages of the Appreciative Inquiry (AI) 5D cycle. We employed the thematic analysis and used NVivo 12 to organize codes and develop a codebook. Results Three main themes, each with subthemes, emerged from the analysis. The first theme, appreciated care, included compassionate care and emotional support, autonomy and self-determination, timely care, privacy and confidentiality, and a supportive environment. The second theme perceived respectful care, addressed the meaning of respectful care and the sources of the participants’ satisfaction. The third theme, strategies for improvement, focused on increasing women’s self-control, sustaining providers’ positive behaviours, and fostering caring leadership. Participants described receiving compassionate, empathetic, and dignified care, with timely attention to their needs. They expressed satisfaction with the services provided, noting that healthcare providers were kind, polite, and dedicated, often going beyond their duties. Mothers recommended to be more involved in decision-making, maintaining an optimal environment for childbirth, and enhancing community trust and professionalism in maternity care. They also acknowledged their responsibility to ensure proper birth spacing. Conclusion The aspects of care the participants appreciated could be consistently maintained and promoted. There is a need to build on the progress made in delivering RMC to strengthen community trust and elevate expectations. Given the sensitive nature of RMC, we recommend conducting further studies using the Appreciative Inquiry approach.