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Early detection of intractable postpartum hemorrhage
Abstract Postpartum hemorrhage (PPH) is a leading cause of maternal mortality worldwide, with timely detection complicated by the inability to visualize bleeding within the uterine cavity. This study aimed to develop a method for early detection of intractable PPH, characterized by arterial contrast extravasation on dynamic CT (PRACE), often requiring uterine arterial embolization. The study comprised two components: (1) an ex vivo study, evaluating the PRACE visualization model using a postpartum uterine cavity simulation, and (2) an in vivo study assessing whether the time interval for bleeding to appear at the vagina (TI-V) could serve as an indicator of intractable PPH. The ex vivo simulation of PPH at various flow rates suggested that the overflow time from the uterine cavity may assist in stratifying life-threatening PPH. In the in vivo study, TI-V was measured in cases of uncomplicated vaginal delivery and severe PPH, including those transported for treatment. The results indicated that a TI-V of less than 2 s strongly predicted intractable PPH, with a positive predictive value of 100% and a negative predictive value of 98.2%. This straightforward method could significantly improve maternal health outcomes by enabling early identification and management of severe PPH.
Naringenin impairs mitochondrial function via ROS to induce apoptosis in tamoxifen resistant MCF-7 breast cancer cells
Breast cancer is the second leading cause of cancer deaths among women. While tamoxifen, a commonly used drug therapy in breast cancer patients, is effective, many patients acquire tamoxifen resistance. Therefore, it is essential to identify alternative or combination therapeutics for the treatment of breast cancer. Naringenin, a naturally occurring flavonoid, has been reported to elicit antioxidant, anti-proliferative, and pro-apoptotic effects in cancer cells. The current study aimed to identify the mechanism by which naringenin induces apoptosis in tamoxifen-resistant breast cancer cells. The present study demonstrated that naringenin induced an increase in ROS, resulting in oxidative stress, impaired mitochondrial function, and apoptosis in tamoxifen-resistant breast cancer cells. Our study reports that naringenin specifically increases mitochondrial superoxide anions and hydrogen peroxide production while also causing mitochondrial dysfunction. These studies provide novel evidence for the mechanism by which naringenin induces apoptosis in tamoxifen-resistant breast cancer cells and supports the use of naringenin as a therapeutic on breast cancer cells and drug-resistant cancer cells.
Supplemental effect of dietary nucleotides on hematological profile, hepatic biomarkers, antioxidant capacity, and digestive functions in Sterlet sturgeon, Acipenser ruthenus
Editorial Note: Model of tumor dormancy/recurrence after short-term chemotherapy
Maximum admittance method for cerebrovascular outlet boundary conditions and importance of stenosis severity as a dominant factor on hemodynamics
Positive relationship between education level and risk perception and behavioral response: A machine learning approach
This paper aims to examine the influence mechanism of education level as a key situational factor in the relationship between risk perception and behavioral response, encompassing both behavioral intention and preparatory behavior. Utilizing non-parametric estimation techniques in machine learning, particularly the Random Forest and XGBoost algorithms, this study develops predictive models to analyze the impact of 27 influencing factors on behavioral responses following risk perception. The findings indicate that, while the model’s fit for preparatory behavior is 25.71% and its fit for behavioral intention is below 20%, the model effectively identifies key influencing factors. Further analysis employing SHAP values demonstrates that education level not only exerts a significant influence but also exhibits varying effects across different educational groups. Moreover, statistical testing corroborates the importance of education level in the relationship between risk perception and behavioral response, providing a robust scientific foundation for the development of risk management policies.
Immobilization of CuO onto melamine functionalized magnetic nanocomposite: an efficient catalyst for the Preparation of benzimidazole compounds
The role of health literacy in the relationship between mothers’ knowledge and practices of iron supplementation in children (aged 12 to 24 months): A structural equation model
Introduction Iron deficiency anemia represents the most common form of anemia globally and constitutes a significant public health concern, particularly in developing nations. Therefore, supplementation is one of the best strategies for protecting children from anemia. The objective of the study was to assess the level of knowledge and practices of mothers with children aged 12 to 24 months and to assess the mediating role of health literacy in this relationship. Methods and materials This cross-sectional study was conducted on 435 mothers of children (aged 12 to 24 months) referred to Tehran healthcare centers. Information was collected through socio-demographic and reproductive checklists, knowledge and practice questionnaires, and health literacy questionnaires. The data were analyzed by SPSS26 and AMOS24 software and a significance level less than 0.05 was considered. Results Among the participants, 18.4% had poor knowledge, 47.4% had moderate knowledge, and only 34.2% had good knowledge. The mothers’ practice score regarding iron drop feeding was moderate (8.22 ± 2.27). A total of 37.9%, 54.7%, and 7.4% had good, moderate, and poor performance, respectively. Pearson’s correlation coefficient indicated a significant positive association between mothers’ understanding of iron drop feeding and their corresponding practices, as well as the practices of mothers with children aged 12 to 24 months (P < 0.001, r = 0.421). According to the results, health literacy and mothers’ knowledge can predict 40% of the changes in mothers’ practices, which is partial or moderate. Health literacy plays a mediating role in the relationship between mothers’ knowledge and practices. Conclusion Considering the effect of anemia on children’s health, paying attention to mothers’ health literacy as an important factor to improve their performance is essential.
Experimental study on flexural behavior of bonded steel-concrete composite beams
China has already taken steps to reduce retractions of papers from its hospitals
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.