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Quantifying the impact of surface roughness on contact angle dynamics under varying conditions

Platelet distribution width as a cost-effective marker for sepsis-associated acute kidney injury: A retrospective cross-section study

Background Sepsis-associated acute kidney injury (S-AKI) is a critical complication with high morbidity and mortality. The potential predictive role of platelet distribution width (PDW) in S-AKI remains to be elucidated, and its clinical implications in S-AKI are still not well understood. Objective This study aims to determine whether platelet distribution width within 24 hours of admission could serve as a predictor of S-AKI in septic patients. Method A retrospective analysis of platelet indices in patients with sepsis at the Affiliated Tongren Hospital of Capital Medical University, a tertiary medical center, was conducted from 2015 to 2022. Patients with sepsis were divided into two groups: an S-AKI group and a non-AKI group based on the presence of S-AKI during hospital. Clinical characteristics and laboratory parameters at admission were compared between two groups. A Multivariate logistic regression analysis was conducted to identify risk factors for S-AKI in septic patients. Additionally, receiver operating characteristics (ROC) curve was employed to evaluate the predictive value of these indices for S-AKI in septic patients. Result A total of 410 patients with sepsis were included in the study, including 57 in S-AKI group and 353 in non-AKI group. The levels of PDW and average platelet volume were significantly higher in the S-AKI group compared to those in the non-AKI group. Furthermore, PDW exhibited a positive correlation with SOFA score, APACHE II score, and LDH levels (r = 0.273, r = 0.153, r = 0.233), yielding P-values <0.001, 0.008, and < 0.001 respectively. Multivariate logistic regression analysis identified PDW (OR = 1.324, 95% CI: 1.124–1.559, P = 0.001), SOFA scores (OR = 1.264, 95% CI: 1.011–1.579, P = .040) and LDH (OR = 1.005, 95% CI: 1.002–1.008, P = .002) as independent risk factors for S-AKI in sepsis patients. The area under curve (AUC) values for predicting S -AKI using PDW, SOFA, LDH, and combined SOFA-PDW metrics were found to be approximately equal to 0.696 (95% CI: 0.621–0.771, P = .000), 0.771 (95% CI: 0.706–0.837, P = .000) and 0.695 (95% CI: 0.611–0.780, P = .000), 0.799 (95% CI: 0.739–0.858) respectively. Conclusion PDW values on admission may serve as a useful potential indicator of disease severity and a potential parameter for predicting S-AKI.

Bacterial acidic agents-assisted multi-elemental (Ni, Co, and Li) leaching of used lithium-ion batteries at high pulp densities

Differences in COVID-19 testing perceptions among caregivers of children with medical complexity by rurality

Background COVID-19 testing safeguards the health of children with medical complexity (CMC) through several key mechanisms, such as the implementation of clinical action plans and COVID-19-directed therapies. However, testing utility is limited by barriers to access and perceptions surrounding use. This study investigated associations between rurality and COVID-19 testing access, intent, motivators, and concerns for caregivers of CMC. Methods We conducted a cross-sectional survey (April – June 2022) of English- and Spanish-speaking caregivers of children with at least one complex chronic condition between ages 5–17 at an academic medical center in the Midwestern USA. Rurality was dichotomized using Rural-Urban Commuting Area codes. Outcomes represented COVID-19 testing access, intent, motivators, and concerns. Covariates included demographic and clinical characteristics. Unadjusted and adjusted logistic regression analyses examined associations between rurality and each outcome. Results Among 1,432 responses (response rate 49%), 359 (25%) were classified as rural. Respondents had varied education, income, and insurance levels. In the multivariable models, rural and urban caregivers reported similarly high testing access, but rural caregivers had significantly less testing intent (adjusted Odds Ratio [95% CI]: 0.53, [0.40, 0.71]). Notably, rural caregivers were significantly more likely to indicate “It will be difficult to get needed healthcare if my child has it” (2.49 [1.19, 5.18]). Conclusions While rural and urban CMC caregivers reported generally high access and ease of COVID-19 testing, potentially modifiable factors exist to improve testing intention and decrease barriers, including communication regarding testing utility and timing as well as access to effective treatment response upon testing positive.

In silico prediction of GRP78-CRIPTO binding sites to improve therapeutic targeting in glioblastoma

Abstract Glioblastoma multiforme (GBM) is one of the most malignant tumors in central nervous system (CNS) tumors. The glucose-regulated protein 78 (GRP78) and CRIPTO (Cripto-1), a protein that belongs to the EGF-CFC (epidermal growth factor cripto-1 FRL-1 cryptic) family, are overexpressed in GBM. A complex between GRP78 SBDβ (substrate binding domain beta) and CRIPTO CFC domain was reported in previous studies. This complex activates MAPK/AKT signaling, Src/PI3K/AKT, and Smad2/3 pathways which is a reason for tumor proliferation. In this work, we study how the two proteins form the complex figuring out binding sites between GRP78 and CRIPTO utilizing computational biophysics and bioinformatics tools, such as protein–protein docking, molecular dynamics simulation and MMGBSA calculations. Haddock web server results of 4 regions from the CFC domain (region1 (− 70.4), region2 (− 78.7), region3 (− 74.2), region4 (− 86.8)) with selected residues of the SBDβ are then simulated for 100 ns MDS then MMGBSA were calculated for the four complexes. The results reveal the stability of the complexes with binding free energy (complex1 (− 15.07 kcal/mol), complex2 (− 59.78 kcal/mol), complex3 (− 81.92 kcal/mol), complex4 (− 126.26 kcal/mol). All these findings ensure that GRP78 SBDβ associates with the CRIPTO CFC domain, and the binding sites suggested make stable interactions between the proteins.

Association between anthropometric factors and meningioma risk: A systematic review and meta-analysis

Background Data regarding the association between anthropometric factors and meningioma risk are inconsistent. Our aim was to investigate the association of body mass index (BMI), height, waist to hip ratio (WHR), waist circumference, and meningioma risk through a comprehensive meta-analysis. Methods An extensive review of literature was conducted in PubMed and Embase databases. Random-effects models were used to pool the study-specific relative risk estimates (RRs) and 95% confidence intervals (CIs). Moreover, we employed a dose-response meta-analysis with a one-stage robust error meta-regression (REMR) model. Results We included nine prospective studies for four anthropometric factors listed above and meningioma risk. Compared with normal weight, both overweight (RR:1.11, 95% CI: 1.04, 1.19; P = 0.003, I2 = 0.0%) and obesity (RR: 1.38, 95% CI:1.16, 1.64; P < 0.001, I2 = 54.7%) were statistically significantly associated with meningioma risk. Dose-response analysis showed a nonlinear relationship between BMI and meningioma risk (P = 0.038). For height, a positive association was identified for men (RR:1.30, 95% CI:1.08, 1.56; P = 0.005, I2 = 0.0%) but not women (RR:1.13, 95% CI: 0.94,1.36; P = 0.186, I2 = 49.8%). Highest vs. lowest levels analyses also showed a positive association between meningioma risk and waist circumference (RR:1.89, 95% CI:1.34, 2.66; P < 0.001, I2 = 0.0%) and WHR (RR:1.40, 95% CI:1.00, 1.94; P = 0.048, I2 = 0.0%). Conclusion Our meta-analysis indicates greater height (in men) and excess weight and body fat mass were associated with an increased risk of meningioma. Further prospective studies with particular attention to sex disparity and dose-response analysis are warranted to confirm our observation.

Sex difference in the relationship between 24-h sodium–potassium ratio and prevalence of metabolic syndromes: a cross-sectional study

Age at menarche and its association with preschool BMI among girls in Northern Norway

Background A decreasing age of menarche has been reported across the Western world. Early menarche is associated with unfavorable health outcomes. Aim The aims of this study were to describe the age at menarche in a general population sample in Norway and the associations between body mass index (BMI) categories at preschool (approximately 6 years of age) and age at menarche. Methods We used self-reported age at menarche among girls who participated in the population-based study Fit Futures 1 (FF 2010–2011), mostly born in 1994, to calculate age at menarche. The preschool BMI from health records was divided into BMI categories according to validated cutoffs on the basis of age and sex from the International Obesity Task Force (IOTF). We estimated the effect of preschool BMI on age at menarche via a linear regression model adjusted for socioeconomic status (SES). Results Among 500 girls with a mean age of 16.5 years (standard deviation (SD) ± 1.4), 497 (99%) had completed menarche. The mean age at menarche was 13.0 years (SD ± 1.2). According to the fitted linear regression model, preschool obesity was a statistically significant predictor of age at menarche and was associated with menarche 9.5 months earlier than a normal preschool BMI was. R2 estimated that preschool BMI could explain 3% of the variance in age at menarche. Conclusion The mean age at menarche in Northern Norway was 13.0 (SD ± 1.2) years, similar to previous Norwegian studies. Childhood obesity was associated with earlier age at menarche.

Efficacy and safety outcomes of the Paul glaucoma implant compared to the Ahmed glaucoma valve

Abstract This study compares the one-year outcomes of standalone Ahmed glaucoma valve (AGV) implantation and standalone Paul glaucoma implant (PGI) in adult patients with primary and secondary glaucoma. A retrospective, single-center, comparative study was conducted on adult patients who underwent standalone PGI and AGV at the University Medical Center Mainz. The primary outcome measures were the changes of IOP and the number of antiglaucoma eye medication at one year postoperatively. Secondary outcome measures included complete and qualified success rates, failure rates, visual acuity logMAR and the incidence of adverse events. A total of 24 adult patients were included in the AGV group and 28 in the PGI group. The median preoperative intraocular pressure decreased from 29.5mmHg (Interquartile range (IQR) 21–42) to 16.0mmHg (IQR 7–37) in the AGV group, and from 34.0 mmHg (IQR 13–56) to 16.0 (IQR 7–21) mmHg in the PGI group at the one-year follow-up. The median number of classes of intraocular pressure-lowering medications reduced from 3.5 to 0 in the AGV group, and from 3.0 to 0 in the PGI group. There were no statistically significant differences between the groups for any success criteria or failure. The AGV produced more encapsulation than the PGI, and the latter more tube exposures. Both the Ahmed Glaucoma Valve and the Paul Glaucoma Implant effectively reduce IOP and the number of antiglaucoma medications at one year with comparable safety profiles.

Design and analysis of a dual-port multiband RF-to-DC converter for IoT energy harvesting applications

Internet of Things (IoT) demands efficient and sustainable energy sources for autonomous power systems. This article presents the design and analysis of a Dual-Port Multiband RF-to-DC Converter employing a multi-stage Cockcroft Walton Voltage Multiplier (CWVM) topology, which is optimized for IoT energy harvesting applications. By utilizing L-network and π-network impedance matching with distributed elements, the converter effectively harvests RF energy across six frequency bands ranging from 0.87 to 2.5 GHz. The dual-port architecture enhances power output and provides redundancy, improving the reliability of the energy harvesting system. While, the multiband feature improves the versatility of energy acquisition. The converter achieves a peak efficiency of 66% and 62% at 10 kΩ and 18 kΩ, respectively. The performance of the proposed design is comprehensively analyzed and optimized, considering the impedance matching, output voltage, and conversion efficiency. The performance of the proposed design is compared with recent converter designs in the literature, which shows that this research is a valuable contribution to the ongoing development of energy-efficient solutions. This work has significant implications for powering low-power sensors, wireless sensor networks, and other IoT devices in diverse environments, addressing the need for prolonged autonomy and reduced reliance on traditional power sources.

IL-17A regulates airway remodelling in COPD through the PI3K/AKT/mTOR pathway

Research and application of digital measure management and control technology for characteristic low-efficiency gas wells in Gas Field A

Gas Field A has entered the middle and late stages of development, with the number of low-efficiency wells increasing year by year. In the Songliao old area, extreme cold weather (-40°C) has led to 45% of gas wells experiencing freeze-offs. In the Sichuan-Chongqing exploration area, high formation water volume and salinity (35 × 10 4 mg/L) have resulted in 44% of wells suffering from liquid loading. The annual demand for thawing and foam drainage measures reaches 3,000 well interventions. The large workload and high costs of maintenance make it difficult to ensure the timing and frequency of interventions, affecting the gas recovery efficiency. By establishing an integrated analysis and remote monitoring platform that combines “condition diagnosis, measure adjustment, and remote monitoring,” the use of “freeze-off prediction + precise chemical injection” has improved the opening rate of freeze-off wells. The application of “optimized foam drainage injection parameters” ensures the stable production of liquid-loaded wells. The implementation of this technology is expected to generate over 20 million yuan in benefits and enhance the analysis, decision-making, and control capabilities of gas production processes under extreme weather and production conditions.

Study on dynamic responses and impact factors of long span deck type CFST arch bridge under vehicle loads

Brazilian and Mexican propolis and their possible mechanism of action against non-enveloped viruses

Propolis is a resinous substance collected by honeybees and is mostly composed of polyphenols which vary by geographical location. This study investigated the possible mechanism of action of phenolic compounds of Brazilian and Mexican green and red propolis against two non-enveloped viruses. Bacteriophage surrogates, ΦX174 and MS2 were used to assess antiviral properties. Propolis samples were characterized by performing a phenolic profile using ultra-performance liquid chromatography (UPLC), which included 12 phenolic compounds such as phenylpropanoids, flavonoids, phenols, and phenolic. Quercetin, eugenol, kaempferol and naringenin were the most abundant compounds found in propolis. In silico molecular docking was also conducted to determine binding energy and molecular interaction and putative mechanism of propolis phenolic compounds with two viral capsid proteins and two proteins involved in viral replication and infection. The best antiviral effect was in green propolis with a ~ 3,1 and ~ 4.5 log10 reduction in MS2, and ΦX174, respectively. Molecular docking simulations revealed that ΦX174 was also more sensitive to the phenolic compounds and that the combination of quercetin and kaempferol showed the greatest antiviral effect as a possible mechanism, through binding to the viral capsid proteins near the viral genome binding sites.

Development of ZnO-NPs reinforced chitosan nanofiber mats with improved antibacterial and biocompatibility properties

Abstract This paper studied the possibility of fabricating a nano-composite based on chitosan incorporated with ZnO-NPs as a promising textile for wound dressing purposes. The nanofiber mat was obtained from dispersions of ZnO-NPs in chitosan-based solution blended with PVA (Cs/PVA/ZnO-NPs scaffold). The extracted chitosan was characterized using FTIR, FE-SEM, XRD, and TGA analysis. The electrospinning optimization process was successfully done for Cs and PVA mixture and a good combination of polymers, solvent, and the ratios developed through an optimization process (10wt.% PVA and 1wt.% CS in AcAcetic 80%). The nanofibers had an average diameter below 200 nm, while the incorporation of ZnO-NPs decreased their average diameter below 150 nm. FTIR, FE-SEM, XRD analysis were used to evaluate the scaffold structure. The FE-SEM analysis proved the smooth and bead-free morphology of the fibers. Elemental analysis of the mat revealed a good distribution of ZnO-NPs along nanofibers. Cell culture studies with L929 mouse fibroblast cells revealed good viability of the cell on the Cs/PVA/ZnO-NPs scaffold. The nanoparticles improved capability of the mat for growth inhibition rate of bacterial colonies and also its wettability. The results also showed the nontoxicity of CS/PVA/ZnO-NPs composite and its considerable potential for future application in wound dressing.

The histone deacetylase inhibitor CT-101 flips the switch to fetal hemoglobin expression in sickle cell disease mice

The most common hemoglobin disorder worldwide is sickle cell disease (SCD) caused by a point mutation in the adult β-globin gene. As a result, hemoglobin S production occurs leading to clinical symptoms including vaso-occlusive pain, organ damage, and a shortened lifespan. Hydroxyurea is the only FDA-approved fetal hemoglobin (HbF) inducer in the United States that ameliorates the clinical severity of SCD. Due to challenges with hydroxyurea, our study aimed to address the unmet need for the development of non-chemotherapeutic HbF inducers. We investigated the ability of CT-101, a Class 1 histone deacetylase inhibitor, to flip the γ-globin to β-globin switch in a humanized SCD mouse model. Pharmacokinetic parameters were assessed in CD-1 and Townes SCD mice after a single intraperitoneal drug dose. Similar drug uptake and half-life were observed in both animals. Subsequent studies in β-YAC mice expressing human γ-globin and β-globin genes established the optimal dose of CT-101 that induces HbF without peripheral blood toxicity. Subsequent confirmatory studies were conducted in the SCD mouse treated with intraperitoneal CT-101, demonstrating increases in F-cells, HbF, and γ-globin gene mRNA levels. Hydroxyurea combined with CT-101 significantly decreased spleen size and hemorrhagic infarcts and improved splenic extramedullary hematopoiesis. Our novel agent, CT-101, flipped the switch by activating γ-globin gene transcription and HbF protein synthesis in the preclinical SCD mouse model without significant toxicity in the peripheral blood. These findings support the development of an oral CT-101 formulation for clinical testing in SCD.

Improved adaptive CUSUM control chart for industrial process monitoring under measurement error

iProtDNA-SMOTE: Enhancing protein-DNA binding sites prediction through imbalanced graph neural networks

Protein-DNA interactions play a crucial role in cellular biology, essential for maintaining life processes and regulating cellular functions. We propose a method called iProtDNA-SMOTE, which utilizes non-equilibrium graph neural networks along with pre-trained protein language models to predict DNA binding residues. This approach effectively addresses the class imbalance issue in predicting protein-DNA binding sites by leveraging unbalanced graph data, thus enhancing model’s generalization and specificity. We trained the model on two datasets, TR646 and TR573, and conducted a series of experiments to evaluate its performance. The model achieved AUC values of 0.850, 0.896, and 0.858 on the independent test datasets TE46, TE129, and TE181, respectively. These results indicate that iProtDNA-SMOTE outperforms existing methods in terms of accuracy and generalization for predicting DNA binding sites, offering reliable and effective predictions to minimize errors. The model has been thoroughly validated for its ability to predict protein-DNA binding sites with high reliability and precision. For the convenience of the scientific community, the benchmark datasets and codes are publicly available at https://github.com/primrosehry/iProtDNA-SMOTE.

Generative design optimization of tree distribution for enhanced thermal comfort in communal spaces with special reference to hot arid climates

Abstract The quality of the communal outdoor environment is crucial for enhancing the urban quality and the well-being of its residents. These spaces are essential for providing more opportunities for social interaction and leisure. However, in hot arid climates like Egypt, achieving optimal outdoor thermal comfort remains a challenge. Accordingly, more comprehensive methodologies are highly needed to improve the research-based design of landscape parameters and components for developing outdoor thermal comfort performance using an iterative design exploration process that employs AI-driven software. These applications, help designers in solving multi-objective design quandaries through the generation and evaluation of numerous design options. Therefore, this study explores the efficiency of generative design tools in optimizing tree distribution based on mutation evolution to enhance outdoor thermal comfort, providing a dynamic, iterative approach that adapts to diverse urban morphologies. The methodology adopts a simulation-based analysis for framing this study, which is classified into three main phases. Firstly, analyze the current environment for specific outdoor spaces with different settings in Madinaty, New Cairo (fully clustered with buildings neighborhood, semi-clustered neighborhood, fully open neighborhood). Secondly, a generative design tool with a Dynamo evolutionary algorithm is utilized to optimize the tree distribution across the communal areas of these three spaces considering the current built environment. Lastly, testing thermal comfort using Grasshopper and Ladybug simulation to assess the Universal Thermal Climate Index (UTCI) between the base case scenarios and the optimized scenarios to validate the generative design tool. Results indicate tangible improvements across the three different neighborhoods. In the Clustered Neighborhood area, the optimized design with 33 trees resulted in a lower UTCI (with an arithmetic mean of 37.55 °C) compared to the base case with 43 trees (38 °C). In the Semi-Clustered Neighborhood area, the optimized design with 45 trees highly improves the UTCI (38.01 °C), compared with the base case with 27 trees (39.40 °C). Lastly, for the Fully Open Neighborhood area, the optimized design with 25 trees achieved a slightly improved UTCI (39.55 °C) over the base case of 31 trees (39.60 °C).

Accurate total consumer price index forecasting with data augmentation, multivariate features, and sentiment analysis: A case study in Korea

The Consumer Price Index (CPI) is a key economic indicator used by policymakers worldwide to monitor inflation and guide monetary policy decisions. In Korea, the CPI significantly impacts decisions on interest rates, fiscal policy frameworks, and the Bank of Korea’s strategies for economic stability. Given its importance, accurately forecasting the Total CPI is crucial for informed decision-making. Achieving accurate estimation, however, presents several challenges. First, the Korean Total CPI is calculated as a weighted sum of 462 items grouped into 12 categories of goods and services. This heterogeneity makes it difficult to account for all variations in consumer behavior and price dynamics. Second, the monthly frequency of CPI data results in a relatively sparse time series, limiting the performance of the analysis. Furthermore, external factors such as policy changes and pandemics add further volatility to the CPI. To address these challenges, we propose a novel framework consisting of four key components: (1) a hybrid Convolutional Neural Network-Long Short-Term Memory mechanism designed to capture complex patterns in CPI data, enhancing estimation accuracy; (2) multivariate inputs that incorporate CPI component indices alongside auxiliary variables for richer contextual information; (3) data augmentation through linear interpolation to convert monthly data into daily data, optimizing it for highly parametrized deep learning models; and (4) sentiment index derived from Korean CPI-related news articles, providing insights into external factors influencing CPI fluctuations. Experimental results demonstrate that the proposed model outperforms existing approaches in CPI prediction, as evidenced by lower RMSE values. This improved accuracy has the potential to support the development of more timely and effective economic policies.