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Quality of basic emergency obstetric and newborn care services from patients’ perspective in selected public health centers in Addis Ababa, Ethiopia 2022: A cross-sectional study
Background The majority of maternal and neonatal deaths occur within the first 24 hours of birth. To minimize maternal as well as neonatal morbidity and mortality, it is important to supply quality Basic Emergency Obstetric and Newborn Care. Basic emergency obstetric and newborn care services prevent immediate obstetric problems. There have been studies in Ethiopia that have looked at the availability of EmONC services. However, from the clients’ perspective and experience, there is insufficient knowledge of quality BEmONC services. Objective To assess the quality of basic emergency obstetric and newborn care (BEmONC) services and associated factors from the perspective of mothers in selected public health centers in Addis Ababa, Ethiopia, 2022. Methods A facility-based cross-sectional study was used among mothers receiving at least one of the signal functions of BEmONC services. A total of 377 mothers were enrolled. Eleven public health centers, one from each of the 11 sub-cities, were selected by simple random sampling. Respondents were chosen by a systematic random sampling method. A structured questionnaire from Open Data Kit version 2022.1.2 was used. Finally, it was exported to SPSS version 26 for analysis. Bivariate analysis at a P-value of 0.25 and multivariable analysis at a P-value of 0.05 were applied. Results The overall quality of BEmONC services from the mothers’ perspective was 56.9%. Mothers who paid for services had lower odds of rating the quality as good compared to those who received services for free (AOR = 0.564; 95% CI: 0.327–0.971). Additionally, mothers aged 20 to 24 years had a lower likelihood of viewing the quality as good compared to those older than 35 years (AOR = 0.362; 95% CI: 0.157–0.837). However, mothers who were accompanied by relatives had significantly higher odds of rating the quality as good than those who were alone (AOR = 18.557; 95% CI: 3.844–89.588). Regarding monthly income, respondents with an average monthly income of less than 1,500 ETB had higher odds of rating the quality as good compared to those earning more than 6,000 ETB (AOR = 2.429; 95% CI: 1.026–5.753). Conclusion and recommendation The total quality of BEmONC services from the perspective of mothers was suboptimal. It was predicted by age, monthly income, presence of a companion, and payment. This study strongly recommends that more should be done to ensure that the services given are more client-centered.
Research on recycling value grading and real-time perception of rock debris from TBM tunneling
Abstract During the construction of TBM tunnels, a substantial quantity of rock debris is generated, leading to significant land occupation and environmental pollution. Recycling rock debris into construction materials and other resources emerges as a viable solution to these problems. To realize the continuous classified storage and disposal of tunnel rock debris, this research explores the four-level processing network, establishes an objective function for evaluating the recycling value of tunnel rock debris during TBM tunneling, and grades the recycling value by calculating the weight and similarity of their performance indicators (uniaxial compressive strength, content of acicular and flattened particles, mud content, and crushing index) through the TOPSIS method. Through correlation and weight analysis, we identify five key characteristics, i.e. cutterhead torque, tool penetration, cutterhead thrust, advancing rate, and support shoe pump pressure, to conduct real-time perception of the recycling value level of rock debris. Leveraging a comprehensive database that encompasses both tunnel rock debris performance indicators and TBM tunneling parameters, perception models are constructed using different machine learning algorithms. After Bayesian hyperparameter optimization, the perception models based on CART, SVM, KNN, and ANN demonstrate accuracies of 67.5%, 80.0%, 82.5%, and 83.8% respectively. Notably, the hyperparameter optimization significantly enhances the accuracy of the ANN perception model. When applying the optimized ANN-based rock debris recycling value grade perception model to TBM tunnel engineering, the tested perception accuracy rate stands at 83.3%, demonstrating its effectiveness and potential for practical applications. This approach provides valuable guidance for the graded storage and efficient recycling of tunnel rock debris and helps to alleviate the pollution problem.
Superpowers want to control critical mineral supplies — local communities need a stronger say
Cooperation in the face of disaster
As calamities and health crises are expected to recur and become more frequent, we rely more on cooperation to prevent similar situations and to cope with their aftermaths. However, it is not clear if, how and why people cooperate in uncertain situations where losses can result from inadequate cooperation. Through theoretical modelling, experiments and simulations, we show the behavioural patterns driving cooperation in a stochastic environment. Specifically, by introducing stochastic shocks to a threshold public goods game where one can randomly incur losses when group contributions are below a specific level, we investigate what happens to cooperation when disasters strike repeatedly. The findings show that compared to a control setting, cooperation is higher and persists when there is a risk for disasters to strike, and that this is sustained by unconditional cooperation. People give more and do not match the contributions of others, contrasting the conditionality observed in deterministic environments. In other words, we observe a contribution divergence in uncertain environments wherein some give unconditionally while others free-ride. We study three different types of uncertainty about the disaster: the probability of a disaster, additionally if it is uncertain how much cooperation is required to avoid them (threshold level), and how much losses will be incurred (impact). The results are similar in countries having different natural disaster risks, the Philippines and Sweden. Simulating for a longer time period suggests the importance of promoting unconditionality to foster sustained cooperation in facing an uncertain world.
Polygenic score analysis identifies distinct genetic risk profiles in Alzheimer’s disease comorbidities
Comprehensive analysis of CMTM family and immune infiltration in esophageal carcinoma
Objective Esophageal carcinoma (ESCA) is one of the most common malignant diseases and contributes to the annual burden of death worldwide. A better understanding of the underlying molecular changes is urgently required to identify early diagnostic biomarkers and effective therapeutics. The chemokine-like factor (CKLF)-like MARVEL transmembrane domain-containing family (CMTMs) is reported to be entangled in many human cancers. However, the role of CMTMs in ESCA remains unclear. Methods The differential expressions of CMTMs between ESCA and normal tissues were analyzed using TCGA database. The relationships between CMTMs and immune infiltration in the tumor microenvironment (TME) were also evaluated to explore their underlying values in the diagnosis and prognosis of ESCA. Results The results showed that ESCA showed significantly higher expressions of CMTM1,3,6,7 and lower expressions of CMTM4,5 than normal tissue (P < 0.05). Meanwhile, CMTM3,4,8 expressions were correlated with the tumor stage of ECSA patients. The analysis on immune infiltrations (CD8 + T, Tregs, NK and macrophages) showed that M2 macrophages was dominant in TME, with significantly higher levels than the other cells (F = 326.93, P < 0.001). The higher abundance of M2 macrophages and Tregs significantly shortened the survival time of patients with ESCA (P = 0.01). Interestingly, the expression levels of CMTM1,3,5,7 were comparable to the abundance of M2 macrophages (CMTM1: r = 0.172168; CMTM3: r = 0.313221; CMTM5: r = 0.130669; CMTM7: r = 0.119922; P < 0.05). CMTM2,4,5,7,8 positively correlated with Tregs (P < 0.05). Moreover, we found positive associations between the expression of CMTMs and the signatures of M2 macrophages (MS4A4A, VSIG4 and CD163). Conclusion There were differential expressions of CMTMs between ESCA and normal tissues. Furthermore, the expression of CMTMs was positively correlated with M2 macrophages, indicating a possibility that CMTMs may become a new immunotherapy target for ESCA.
Evaluation on the interface characteristics, mechanism and performance of the dry modified SBS asphalt mixtures by multiscale methods
Using artificial intelligence tools to automate data extraction for living evidence syntheses
Living evidence synthesis (LES) involves repeatedly updating a systematic review or meta-analysis at regular intervals to incorporate new evidence into the summary results. It requires a considerable amount of human time investment in the article search, collection, and data extraction phases. Tools exist to automate the retrieval of relevant journal articles, but pulling data out of those articles is currently still a manual process. In this article, we present a proof-of-concept Python program that leverages artificial intelligence (AI) tools (specifically, ChatGPT) to parse a batch of journal articles and extract relevant results, greatly reducing the human time investment in this action without compromising on accuracy. Our program is tested on a set of journal articles that estimate the mean incubation period for COVID-19, an epidemiological parameter of importance for mathematical modelling. We also discuss important limitations related to the total amount of information and rate at which that information can be sent to the AI engine. This work contributes to the ongoing discussion about the use of AI and the role such tools can have in scientific research.
Machine learning models to predict osteoporosis in patients with chronic kidney disease stage 3–5 and end-stage kidney disease
Minerals will shape future geopolitical order
Entropy difference-based EEG channel selection technique for automated detection of ADHD
Attention deficit hyperactivity disorder (ADHD) is one of the common neurodevelopmental disorders in children. This paper presents an automated approach for ADHD detection using the proposed entropy difference (EnD)-based encephalogram (EEG) channel selection approach. In the proposed approach, we selected the most significant EEG channels for the accurate identification of ADHD using an EnD-based channel selection approach. Secondly, a set of features is extracted from the selected channels and fed to a classifier. To verify the effectiveness of the channels selected, we explored three sets of features and classifiers. More specifically, we explored discrete wavelet transform (DWT), empirical mode decomposition (EMD) and symmetrically-weighted local binary pattern (SLBP)-based features. To perform automated classification, we have used k-nearest neighbor (k-NN), Ensemble classifier, and support vectors machine (SVM) classifiers. Our proposed approach yielded the highest accuracy of 99.29% using the public database. In addition, the proposed EnD-based channel selection has consistently provided better classification accuracies than the entropy-based channel selection approach. Also, the developed method has outperformed the existing approaches in automated ADHD detection.
Glucagon like peptide-1 modulates urinary sodium excretion in diabetic kidney disease via ENaC activation
Development of a multivariable prognostic prediction model for skin tears in older nursing home residents
Abstract Skin tears are traumatic wounds and are among the most prevalent skin conditions in older adults, particularly those in long-term care facilities. These injuries can lead to complications such as infection, pain, reduced quality of life, and increased healthcare costs. This study aimed to identify risk factors for skin tear development in nursing home residents aged 65 years or older and to develop a predictive prognostic model. A secondary data analysis was performed on long-term care nursing home residents ≥ 65 years who participated in a cluster-randomized controlled clinical trial conducted in Berlin, Germany, from April 2019 to June 2021. A total of 101 residents were included. At week 12, 19 residents (18.8%) developed at least one skin tear. The best-fit predictive model identified lower Body Mass Index, lower Barthel Index scores, presence of xerosis cutis on the legs, and regular corticosteroid use as significant risk factors for skin tear development. The model demonstrated good discriminatory ability (area under the curve: 0.823), with sensitivity and specificity rates of 73.7% and 74.4%, respectively. These risk factors could help identify at-risk individuals, enabling targeted preventive measures. However, the model requires validation in a prospective cohort to confirm its applicability in clinical practice.