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Picture fuzzy soft set TAOV approach for material selection for cryogenic storage tank for liquid nitrogen transportation
Publisher Correction: hsa-mir-483-3p modulates delayed breast cancer recurrence
A synergistic approach for enhanced eye blink detection using wavelet analysis, autoencoding and Crow-Search optimized k-NN algorithm
Impact of anti-VZV IgG levels on Parkinson’s disease risk and progression: a Mendelian randomization analysis
Elevated temperature and pressure performance of water based drilling mud with green synthesized zinc oxide nanoparticles and biodegradable polymer
Abstract Water-based mud (WBM) faces challenges in high-temperature, high-pressure (HTHP) conditions due to fluid loss and property degradation. Enhancing eco-friendly drilling fluids with optimal rheology is crucial for sustainable, cost-effective, and environmentally safe drilling operations. This study formulated a WBM using green-synthesized zinc oxide (ZnO) nanoparticles (NPs, ~ 45 nm) and tragacanth gum (TG), a biodegradable natural polymer. The synthesized ZnO NPs were comprehensively characterized using energy-dispersive X-ray spectroscopy (EDS), field-emission scanning electron microscopy (FE-SEM), Raman spectroscopy, X-ray photoelectron spectroscopy (XPS), and thermogravimetric analysis (TGA/DTG) to determine their structural, morphological, and chemical properties. Rheological properties, including flow behavior index (n), consistency index (K), plastic viscosity (PV), and yield point (YP), were analyzed at 25, 50, and 75 °C using the Bingham-plastic and Power-law models. The accuracy of the model was validated using Analysis of Variance (ANOVA), which assessed the significance of the results. Additionally, Design Expert software was utilized to optimize the concentrations of TG and ZnO for elevated temperature applications. Moreover, the response surface methodology (RSM) results were evaluated by reporting the R2 and accuracy metrics, confirming the strong correlation between predicted and actual values, which demonstrates the model’s robustness. Three optimal samples underwent HTHP filtration tests at 120 °C and 500 psi. The ideal formulation of 750 ppm TG and 0.25 wt% ZnO NPs improved PV by 27.84%, YP by 43.16%, reduced fluid loss by 54.16%, and mud cake thickness by 25%. The optimized sample showed superior performance, with a ‘K’ of 56.12 cp and a ‘n’ of 0.2272, ensuring effectiveness under HTHP conditions. This sustainable formulation reduced environmental contamination risks and drilling fluid consumption while enhancing operational efficiency.
Reinforcement learning-driven task migration for effective temperature management in 3D noc systems
Kaiser acoustic emission ground stress testing study on shale oil reservoir in Y block of Ordos basin, China
The influence of environment on adolescents’ physical exercise behavior based on family community and school micro-systems
Systematic development and refinement of a user-centered evidence-based digital toolkit for supporting self-care in gestational diabetes mellitus
A sampling-based winner determination model and algorithm for logistics service procurement auctions under double uncertainty
Spatial–temporal evolution, drivers, and pathways of the synergistic effects of digital transformation on pollution and carbon reduction in heavily polluting enterprises
Abstract Under the “dual carbon” goals, heavily polluting enterprises face dual pressures to reduce both pollution and carbon emissions, necessitating the urgent exploration of effective pathways for coordinated emission reductions. This study investigates the potential of digital transformation in enterprises to achieve synergistic emission reductions. First, the entropy method is employed to measure enterprise digitalization and pollutant levels, and the spatial–temporal evolution characteristics of regional coordinated emission reductions are analyzed. Subsequently, using panel data from heavily polluting enterprises in the Yangtze River Economic Belt, the study examines the impact of digital transformation on pollution and carbon reduction, its underlying mechanisms, and the moderating effects of environmental policies on these relationships. Robustness tests confirm the synergy between carbon and pollution emissions. The findings reveal that digital transformation contributes to the synergistic reduction of carbon and pollutant emissions in enterprises, primarily through two pathways: the coordinated integration of internal innovation resources and the collaborative engagement in external innovation networks. Furthermore, air pollution control policies and low-carbon city initiatives significantly enhance the synergistic emission reduction effects of digitalization. Interestingly, heavily polluting enterprises located in the downstream regions of the Yangtze River, those with smaller operational scales, or those facing strong financing constraints, demonstrate more pronounced synergistic emission reduction effects through digital transformation. Based on these conclusions, we recommend that governments focus on strengthening either “pollution reduction” or “carbon reduction” policies, as either alone can yield dual emission reduction benefits. Additionally, tailoring regional emission reduction policies to local conditions can maximize economic and environmental benefits.
Durability characteristics of geopolymer concrete produced using gold ore tailings along with recycled coarse aggregates
Exploring the causal role of the human gut microbiome in endometrial cancer: a Mendelian randomization approach
Abstract Endometrial cancer presents a major public health issue, particularly in post-menopausal women. Whilst there are known risk factors for the disease, including oestrogen and obesity, these factors do not fully explain risk variability in cancer outcomes. The identification of novel risk factors may aid in better understanding of endometrial cancer development and, given the link with oestrogen metabolism, obesity and the risk of various cancers, the gut microbiome could be one such risk factor. Mendelian randomization (MR), a method that reduces biases of conventional epidemiological studies (namely, confounding and reverse causation) by using genetic variants to proxy exposures, was used to investigate the effect of gut microbial traits on endometrial cancer risk. Whilst our initial analyses showed that the presence of an unclassified group of bacteria in the Erysipelotrichaceae family increased the risk of oestrogen-dependent endometrial cancer (odds ratio (OR) per approximate doubling of the genetic liability to presence vs. absence: 1.13; 95% CI 1.01, 1.26; P = 0.03), subsequent sensitivity analyses, including colocalisation, provided insufficient evidence to support causality. This work highlights the importance of using a robust MR analysis pipeline, including sensitivity analyses to assess the validity of causal effect estimates obtained using MR.
Optimization of zirconia nanopowder precipitation process using Taguchi experimental design methodology
Spatio-temporal modelling and prediction of malaria incidence in Mozambique using climatic indicators from 2001 to 2018
Abstract Accurate malaria predictions are essential for implementing timely interventions, particularly in Mozambique, where climate factors strongly influence transmission. This study aims to develop and evaluate a spatial–temporal prediction model for malaria incidence in Mozambique for potential use in a malaria early warning system (MEWS). We used monthly data on malaria cases from 2001 to 2018 in Mozambique, the model incorporated lagged climate variables selected through Deviance Information Criterion (DIC), including mean temperature and precipitation (1–2 months), relative humidity (5–6 months), and Normalized Different Vegetation Index (NDVI) (3–4 months). Predictive distributions from monthly cross-validations were employed to calculate threshold exceedance probabilities, with district-specific thresholds set at the 75th percentile of historical monthly malaria incidence. The model’s ability to predict high and low malaria seasons was evaluated using receiver operating characteristic (ROC) analysis. Results indicated that malaria incidence in Mozambique peaks from November to April, offering a predictive lead time of up to 4 months. The model demonstrated high predictive power with an area under the curve (AUC) of 0.897 (0.893–0.901), sensitivity of 0.835 (0.827–0.843), and specificity of 0.793 (0.787–0.798), underscoring its suitability for integration into a MEWS. Thus, incorporating climate information within a multisectoral approach is essential for enhancing malaria prevention interventions effectiveness.
A novel ex vivo protocol that mimics length and excitation changes of human muscles during walking induces force losses in EDL but not in soleus of mdx mice
Although eccentric contraction protocols are widely used to study the pathophysiology and potential treatments for Duchenne muscular dystrophy (DMD), they do not reflect the stresses, strains, strain rates, and excitation profiles that DMD muscles experience during human daily functional tasks, like walking. This limitation of eccentric contractions may impede our understanding of disease progression in DMD and proper assessment of treatment efficacy. The goals of this study were to examine the extent of force loss induced by a gait cycling protocol we developed, and compare to that from a typical eccentric contraction protocol in soleus and extensor digitorum longus (EDL) muscles of mdx mice. To achieve this goal, mdx soleus and EDL muscles were subjected to eccentric contractions at three levels of strain (10%, 20% and 30% optimal length Lo) and up to 200 cycles of our gait cycling protocol that mimicked the length changes and excitation patterns of the corresponding muscles during human walking gait. Our results showed that EDL but not soleus muscles had significant losses in isometric tetanic forces after the cycling protocols. Compared to the eccentric contraction protocol, the decrements in contractile performance from the cycling protocol were similar to those from the eccentric contractions at 10% in soleus and 20% Lo in EDL. Together, these results indicated the gait cycling protocol is a valuable experimental approach to better understand disease progression and to screen and evaluate efficacy of novel therapeutics for DMD.
Neuroinvasive and neurovirulent potential of SARS-CoV-2 in the acute and post-acute phase of intranasally inoculated ferrets
Severe acute respiratory syndrome corona virus 2 (SARS-CoV-2) can cause systemic disease, including neurological complications, even after mild respiratory disease. Previous studies have shown that SARS-CoV-2 infection can induce neurovirulence through microglial activation in the brains of patients and experimentally inoculated animals, which are models representative for moderate to severe respiratory disease. Here, we aimed to investigate the neuroinvasive and neurovirulent potential of SARS-CoV-2 in intranasally inoculated ferrets, a model for subclinical to mild respiratory disease. The presence of viral RNA, histological lesions, virus-infected cells, and the number and surface area of microglia and astrocytes were investigated. Viral RNA was detected in various respiratory tissue samples by qPCR at 7 days post inoculation (dpi). Virus antigen was detected in the nasal turbinates of ferrets sacrificed at 7 dpi and was associated with inflammation. Viral RNA was detected in the brains of ferrets sacrificed 7 dpi, but in situ hybridization nor immunohistochemistry did confirm evidence for viral RNA or antigen in the brain. Histopathological analysis of the brains showed no evidence for an influx of inflammatory cells. Despite this, we observed an increased number of Alzheimer type II astrocytes in the hindbrains of SARS-CoV-2 inoculated ferrets. Additionally, we detected increased microglial activation in the olfactory bulb and hippocampus, and a decrease in the astrocytic activation status in the white matter and hippocampus of SARS-CoV-2 inoculated ferrets. In conclusion, although SARS-CoV-2 has limited neuroinvasive potential in this model for subclinical to mild respiratory disease, there is evidence for neurovirulent potential. This study highlights the value of this ferret model to study the neuropathogenecity of SARS-CoV-2 and reveals that a mild SARS-CoV-2 infection can affect both microglia and astrocytes in different parts of the brain.
Vitamin D probiotics fortification improve vitamin D and total antioxidant capacity levels among pregnant women: a single-blinded randomized controlled trial
Adherence to intravenous chemotherapy and associated factors among patients with cancer at Hawassa University Comprehensive Specialized Hospital Cancer Treatment Center, Sidama Region, Southern Ethiopia
Background Medication adherence refers to how closely a patient follows the prescribed timing and dosage of their treatment. Adherence to chemotherapy is particularly complex and multifaceted, and it can have a significant impact on the effectiveness of the therapy. In Ethiopia, non-adherence to chemotherapy is on the rise, but there has been limited research specifically on adherence to intravenous (IV) chemotherapy. Objective This study assesses IV chemotherapy adherence and associated factors among cancer patients in the Sidama Region, southern Ethiopia. Methods A hospital-based cross-sectional study included a purposive sample of 413 cancer patients undergoing IV chemotherapy. The Morisky Medication Adherence Scale was used to measure adherence levels. Data analysis was performed using SPSS version 26, employing descriptive statistics such as frequency distribution, mean, median, and standard deviation to describe the characteristics and magnitude of IV chemotherapy adherence. Bivariable and multivariable logistical regression analysis was conducted to identify factors associated with IV chemotherapy adherence. Result The current study revealed that the overall magnitude of good adherence toward chemotherapy treatment among cancer patients was 176/413(42.6%), with a 95% CI of 38–47.9. The multivariable analysis identified several independent factors associated with IV chemotherapy adherence. These factors included being married (AOR = 3.2, 95% CI:1.3,8), employment as a government employee (AOR = 2.4,95% CI:1.3,4.5), availability of transportation (AOR = 5.5, 95% CI:2.1,14), Good social support (AOR = 2.1,95% CI:1.9,11.3) and having a curative goal of treatment (AOR = 1.7,95% CI:1.1,2.7). Conclusion In the current study, the magnitude of intravenous chemotherapy adherence among patients with cancer was low compared to published national and international findings. Possible contributing factors include marital status, employment, Transportation availability, and social support. Targeted measures are needed to improve adherence and, as a result, maximize therapeutic benefits.