bluuebunny/crossref_metadata_2025_split huggingface dataset:
The Role Of Serum Prolidase Activity, MMP-1, MMP-7, TGF- β Values In Prediction Of Early Fibrosis In Patients With Moderate And Severe Covid-19
Authors: Zengin Didem Dogu , Ergun Dilek , Yormaz Burcu , Ergun Recai , Guven Halil , Korez Muslu Kazim , Ozer Halil , Unlu Ali , Tulek Baykal , Kanat Fikret 
Published: July 2024

Abstract

Background
This study aimed to identify predictive factors for pulmonary fibrosis development in COVID-19 patients by analysing thorax CT (Computed tomography) findings, serum prolidase activity, MMP-1, MMP-7, TGF-β values, laboratory findings, and demographic characteristics.
Materials and methods
The investigation involved 68 patients, both male and female, aged 18 years and older, who were volunteers and had been diagnosed with confirmed or possible COVID-19. Thorax CT were evaluated by common consensus by the pulmonologist and the radiologist. Patients were evaluated in two categories, group 1 and group 2, according to the status of fibrotic changes, and 3-month fibrosis scores were calculated. These findings in both lungs were calculated and noted for the lobes, considering lobar spread. Correlations between quantitative parameters were assessed with Spearman's rho correlation coefficient. Comparisons between independent samples were carried out with an independent sample t-test or Mann-Whitney U test. The Pearson chi-square test and Fisher's exact test were used to evaluate the relationship between categorical variables.
Results
Serum prolidase activity, MMP-1, MMP-7 and TGF-β biomarkers were not statistically significant among groups. LDH was found to be significantly high in the group with fibrotic changes. Additionally, the group with fibrotic changes also had higher levels of fibrinogen. The percentage of neutrophils, the severity of the disease, muscle-joint pain and fatigue symptoms, and the duration of the hospital stay were correlated with the total scores of fibrosis at the 3rd month. In the group with fibrotic changes, muscle-joint pain and fatigue symptoms and the duration of hospital stay were longer than the other group.
Conclusions
The group with fibrotic changes showed an increase in biomarkers. However, this increase did not reach a statistically significant level, suggesting that the third month may be an early period for these changes. The group with fibrotic changes showed high levels of LDH, one of the most important laboratory parameters of pulmonary fibrosis risk factors, along with fibrinogen, suggesting that these parameters are valuable in predicting pulmonary fibrosis. Patients with fibrotic changes can experience specific symptoms, commonly seen in COVID-19.
Trial registration
Thaiclinicaltrials.org, TCTR20240701005, retrospectively registered on 24 June 2024.
Analysis of the effects of vehicle emission at heavily used intersections along Asian Highway 47 in Indore, India
Authors: Lashkari Ruchir , Kale Rohini , Tripathi Shilpa 
Published: July 2024

Abstract

The winter months of November and December 2022 saw simultaneous measurements of Respirable Suspended Particulate Matter (RSPM - PM 10), Gaseous pollutants such as SO2, NO2, Total Vehicular Pollution load, and Air Quality Index at the busiest traffic divergence corridor on Asian Highway 47 at Indore city, which is IDTL Toll Manglia & Bicholi Mardana. Utilizing a respirable dust sampler, the sampling process was carried out in accordance with CPCB regulations. With the use of a respirable dust sampler, the morning at IDTL Toll Manglia and Bicholi Mardana Square has the lowest RSPM (101.45 µg/m3) and the highest RSPM (152.78 µg/m3). During the month, the lowest recorded RSPM value in the morning was 100.15 µg/m3, while the maximum was 139.73 µg/m3. During nighttime peak hours, Bicholi Mardana recorded the lowest value of 143.68 µg/m3 and the greatest concentration of 173.38 µg/m3. Conversely, IDTL Toll Manglia recorded the lowest concentration of 168.58 µg/m3 and the highest concentration of 190.82 µg/m3. The concentration of gaseous pollutants was found to be higher at IDTL Toll Manglia and lower at Bicholi Mardana & AQI. Both sampling stations showed signs of moderate quantities in the morning, while heavy concentrations were found at both in the evening. With the appropriate sample sites, this data can be used to assess the current state of vehicular pollution and aid in the formulation of future preventive strategies.
Genome-wide local ancestry and the functional consequences of admixture in African and European cattle populations
Authors: MacHugh David , McHugo Gillian , Ward James , Ng’ang’a Said , Frantz Laurent , Salter-Townshend Michael , Hill Emmeline , O'Gorman Grace , Meade Kieran , Hall Thomas 
Published: July 2024

Abstract

Bos taurus (taurine) and Bos indicus (indicine) cattle diverged at least 150,000 years ago and, since that time, substantial genomic differences have evolved between the two lineages. During the last two millennia, genetic exchange in Africa has resulted in a complex tapestry of taurine-indicine ancestry, with most cattle populations exhibiting varying levels of admixture. Similarly, there are several Southern European cattle populations that also show evidence for historical gene flow from indicine cattle, the highest levels of which are found in the Central Italian White breeds. Here we use two different software tools (MOSAIC and ELAI) for local ancestry inference (LAI) with genome-wide high- and low-density SNP array data sets in hybrid African and Italian cattle populations and obtained broadly similar results despite critical differences in the two LAI methodologies used. Our analyses identified genomic regions with elevated levels of retained or introgressed ancestry from the African taurine, European taurine, Asian indicine lineages. Functional enrichment of genes underlying these ancestry peaks highlighted biological processes relating to immunobiology and olfaction, some of which may relate to differing susceptibilities to infectious diseases, including bovine tuberculosis, East Coast fever, and tropical theileriosis. Notably, for retained African taurine ancestry in admixed trypanotolerant cattle we observed enrichment of genes associated with haemoglobin and oxygen transport. This may reflect positive selection of genomic variants that enhance control of severe anaemia, a debilitating feature of trypanosomiasis disease, which severely constrains cattle agriculture across much of sub-Saharan Africa.
Curcumin Mitigates Polycystic Ovary Syndrome in Mice by Suppressing TLR4/MyD88/NF-κB Signaling Pathway Activation and Reducing Intestinal Mucosal Permeability
Authors: Wang zhen , Yang Qin , Wan Qin 
Published: July 2024

Abstract

Polycystic ovary syndrome (PCOS) stands as a prevalent endocrine-metabolic disorder impairing female fertility, often intertwined with alterations in intestinal mucosal barrier integrity and chronic inflammation. Curcumin, renowned for its multifaceted biological activities including anti-inflammatory, anti-tumor, and antioxidant properties, emerges as a potential therapeutic agent for PCOS. In this study, we investigated the effects and underlying mechanisms of curcumin in mitigating PCOS in mice. The PCOS mouse model was induced by administering dehydroepiandrosterone (DHEA) dissolved in soybean oil (6mg/100g BW) for 20 days, followed by a 45-day intervention period with curcumin. Subsequent euthanasia enabled examination of relevant indicators. Evaluation encompassed ovarian and colonic morphology, intestinal mucosal barrier function, and the TLR4/MyD88/NF-κB signaling pathway in each experimental group. Our findings indicate that curcumin holds promise in alleviating PCOS symptoms. By inhibiting the activation of the TLR4/MyD88/NF-κB signaling pathway and reducing intestinal mucosal permeability, curcumin demonstrates potential as a clinical intervention for this disorder. These findings highlight the therapeutic potential of curcumin in managing PCOS and warrant further clinical investigation to validate its efficacy.
Global stability in an SIRS model with zoonotic transmission, nonlinear incidence rate and temporary immunity
Authors: Barua Saumen , Dénes Attila 
Published: July 2024

Abstract

We establish and study an SIRS epidemic model for a zoonotic disease only transmitted from animals to humans but not the opposite way, with a general nonlinear incidence rate assuming that the animal population has already reached an endemic equilibrium. Due to the zoonotic transmission, there is no disease-free equilibrium and no threshold dynamics can be observed. Using a transformation of variables, we derive a Lyapunov function for the global asymptotic stability of the unique endemic equilibrium.
Mathematics Subject Classification: AMS 92D30 , 34A99
AI Content Self-Detection for Transformer-based large Language Models
Authors: Caiado Antonio , Hahsler Michael 
Published: July 2024

Abstract

The usage of generative artificial intelligence (AI)tools based on large language models, including ChatGPT, Bard, and Claude, for text generation, has many exciting applications with the potential for phenomenal productivity gains. One issue is authorship attribution when using AI tools. This is especially important in an academic setting where the inappropriate use regenerative AI tools may hinder student learning or stifle research by, creating a large amount of automatically generated derivative work. Existing plagiarism detection systems can trace the source of submitted text but are not yet equipped with methods to accurately detect AI-generated text. This paper introduces the dea of direct origin detection and evaluates whether generative AI systems can recognize their output and distinguish it from human-written texts. We argue why current transformer-based models may be able to self-detect their own generated text and perform a small empirical study using zero-shot learning to investigate if that is the case. Results reveal varying capabilities of AI systems to identify their generated text. Google’s Bard model exhibits the largest capability of self-detection with an accuracy of 94%, followed by OpenAI’s ChatGPT with 83%.On the other hand, Anthropic’s Claude model seems to be not able to self-detect.
Social Media and Sentimental Analysis: Central Bank of Nigeria Currency Redesign Policy
Authors: Oladapo Kayode Abiodun , Akinbo Racheal Shade 
Published: July 2024

Abstract

The identification and measurement of an online audience through the social media platform capitalise on the tonality of emotions on the social media presence. On October 20, the most populous country and acclaimed Africa’s largest economy, Nigeria announced the plans to redesign 200, 500 and 1000 banknotes in replacement of the existing ones. Nigerian citizens expressed different opinions over social media in support of or understanding of the proposed plan and process. Research has shown that shared sentiments on social media can influence the opinions of others and thus the Central Bank of Nigeria's currency redesign policy. This study, therefore, aimed to identify and analyse general sentiments towards the process of the currency redesign policy with the purpose of determining the citizen's attitude towards the policy, based on social media comments. Firstly, sentiment analysis was performed on naira redesign-related posts from a selected social media using lexicon-based and supervised machine learning techniques with the purpose of determining a summarised polarity percentage (i.e. negative or positive). The post was collected between January and February 2023. In addition, the performance of the lexicon-based classifier and seven machine learning-based classifiers was implemented and compared in order to use the best-performing classifier in determining the sentiment polarity of the post. Also, the thematic analysis on both positive and negative posts to further understand and revealed general views about the currency redesign policy. Finally, the analytical findings and the possibility of changing the currency redesign policy was discussed.
A new combination of ether and ester as FLC with a liquid dispersant for decreasing the transition time and optimization of fluid loss and rheological properties of fresh cement slurry
Authors: Khazaei Mohammad , Dehvedar Mohsen 
Published: July 2024

Abstract

Fluid loss causes the rheological properties and mechanical properties of the slurry to change completely. Therefore, fluid loss proves that the slurry cannot have proper properties. In this study, the effect of several types of FLC (Fluid loss control) and a liquid dispersant on the fresh cement slurries are considered. the change in rheological properties, compressive strength, Fluid loss control, free water, settling, mixing time, and thickening time were investigated. Also, a challenging issue during well cementing in the oil well is fluid migration from the pumped cement into the permeable formation. Excessive fluid loss can create space for gas entry into the cement slurry, hence fluid loss control additives are also utilized to reduce gas migration. The cement slurry weight was 118 pcf used in 13 3/8” casing and 7” liners. An FLC composed of two substances (including ester-ether) was selected after examining materials with amide, ester, ether, alcoholic, and carboxylic acid functional groups. The ultimate recommendation introduced a combination of three substances (FLC1+FLC2+Liquid Dispersant) as proposed materials for controlling the mentioned properties in the slurry of fresh cement at 180℉ and 210℉ for compressive strength and 150 ℉ and 180 ℉ for BHCT slurry characteristics. The designed formulation using these additives leads to a reduction in transition time. The reduction in transition time plays a significant role in decreasing gas migration into the cement slurry.
On the use of Synthetic Data for Machine Learning prediction of Self-Healing Capacity of Concrete
Authors: de Oliveira Franciana Sokoloski , Stefani Ricardo 
Published: July 2024

Abstract

This work investigated the use of synthetic data to overcome the limitations of scarce experimental data in predicting the self-healing capacity of bacteria-driven concrete. We generated a synthetic dataset based on real-world data, significantly expanding the original dataset and then trained and compared machine learning models, including probabilistic and ensemble methods, to predict the concrete self-healing capacity. The results demonstrate that the ensemble methods, particularly the random forest (RF) method (accuracy = 0.863 and F1-score = 0.863), outperformed the probabilistic models and achieved high accuracy in predicting self-healing capacity. The trained models were further applied to real-word data examples, showing high accuracy. This research validates the utility of synthetic data in predicting modelling accuracy and reliability in civil engineering, particularly in areas with limited experimental data. The findings contribute to the growing use of ML and AI in concrete research and demonstrate the transformative potential of synthetic data in addressing challenges in civil engineering.
Multi-Omics Profiles of Chronic Low Back Pain and Fibromyalgia - Study Protocol
Authors: Curatolo Michele , Chiu Abby P. , Chia Catherine , Ward Ava , Johnston Sandra K. , Klein Rebecca M. , Henze Darrell A. , Zhu Wentao , Raftery Daniel 
Published: July 2024

Abstract

Background
Chronic low back pain (CLBP) and fibromyalgia (FM) are leading causes of suffering, disability, and social costs. Current pharmacological treatments do not target molecular mechanisms driving CLBP and FM, and no validated biomarkers are available, hampering the development of effective therapeutics. Omics research has the potential to substantially advance our ability to develop mechanism-specific therapeutics by identifying pathways involved in the pathophysiology of CLBP and FM, and facilitate the development of diagnostic, predictive, and prognostic biomarkers. We will conduct a blood and urine multi-omics study in comprehensively phenotyped and clinically characterized patients with CLBP and FM. Our aims are to identify molecular pathways potentially involved in the pathophysiology of CLBP and FM that would shift the focus of research to the development of target-specific therapeutics, and identify candidate diagnostic, predictive, and prognostic biomarkers.
Methods
We are conducting a prospective cohort study of adults ≥18 years of age with CLBP (n=100) and FM (n=100), and pain-free controls (n=200). Phenotyping measures include demographics, medication use, pain-related clinical characteristics, physical function, neuropathiccomponents (quantitative sensory tests and DN4 questionnaire), pain facilitation (temporal summation), and psychosocial function as moderator. Blood and urine samples are collected to analyze metabolomics, lipidomics and proteomics. We will integrate the overall omics data to identify common mechanisms and pathways, and associate multi-omics profiles to pain-related clinical characteristics, physical function, indicators of neuropathic pain, and pain facilitation, with psychosocial variables as moderators.
Discussion
Our study addresses the need for a better understanding of the molecular mechanisms underlying chronic low back pain and fibromyalgia. Using a multi-omics approach, we hope to identify converging evidence for potential targets of future therapeutic developments, as well as promising candidate biomarkers for further investigation by biomarker validation studies. We believe that accurate patient phenotyping will be essential for the discovery process, as both conditions are characterized by high heterogeneity and complexity, likely rendering molecular mechanisms phenotype specific.
Bricks from Industry Wastes for Sustainable Development
Authors: N Narasimha Murthy K 
Published: July 2024

Abstract

In the past few decades, there has been rapid exploitation of natural materials from the construction industry all over the globe. For the manufacturing of bricks, lake beds are being encroached on and exploited for clay mining. Industrial wastes like Ground Granulated Blast Furnace Slag (GGBS), Red Mud, and Marble powder are not being managed and used efficiently. Making newer products from these wastes will help reduce the environmental hazards and demand for natural resources which encourages sustainable development. Marble has been used in the construction field for a long time as a flooring or cladding material but not as a constituent material of brick. A few research works have been reported to use marble powder as an alternative material in the manufacturing of bricks. In this paper, innovative bricks were prepared using these three industrial wastes (GGBS, Red Mud, and Marble powder) in different proportions. In this study, bricks were manufactured with varied marble powder proportions along with red mud and GGBS. The bricks of size 210mm X 110mm X 70mm were prepared and burnt at 600⁰C and investigated their properties under burnt and unburnt conditions as per IS code provisions. The experimental results reveal that the compressive strength and density of fired and unfired bricks decreases with the increase of marble powder proportion and the optimum percentage of marble powder is 30% can be used to manufacture the bricks of class A or B according to IS codes. It is noticed that the compressive strength varies from 2.84 Mpa to 7.9 MPa and density decreases from 2121.5 kg/m3 to 1578.2 kg/m3 for burnt bricks. For unfired clay bricks, it varied from 1.5 Mpa to 4.11 Mpa. And density 2157.9 kg/m3 to 1635.2 kg/m3.
Pharmacotherapy and drug prescription: Do dental scholars have the proper knowledge?
Authors: de Santana Adan Lucas Pantoja , Petta Thais de Mendonça , de Sousa Karoline Lima , Pinheiro Helder Henrique Costa , D’Almeida-Couto Roberta Souza 
Published: July 2024

Abstract

Background
Despite attending pharmacotherapy courses around half of the program, a large number of dental scholars still have doubts about how to prescribe drugs during supervised final-year practice internships. This cross-sectional and descriptive survey study aimed to evaluate the level of pharmacology knowledge of dental scholars attending different undergraduate terms at two dental schools.
Methods
After ethical approval and informed consent form signing, 128 dental scholars of the sixth (who recently attended the pharmacotherapy or equivalent course) or tenth undergraduate term anonymously answered a previously validated multiple-choice questionnaire composed of 4 questions on drug prescription regulation and 10 questions on pharmacological clinical cases. The level of knowledge of the dental scholars was categorized (poor, fair good, or excellent) and the frequencies of correct answers were compared by using the chi-square tests (p < 0.05).
Results
The percentages of correct answers from academics at different undergraduate terms within the same dental school were not significantly different (p = 0.938). In both dental schools, the majority of academics presented poor and fair knowledge levels. Overall, academics from dental school B provided a significantly greater percentage of correct answers than did those from dental school A (p = 0.025). Questions regarding drug prescription for medical emergencies and adequate concentrations of fluorides presented the lowest average percentages of correct answers (7.8% and 13.3%, respectively).
Conclusions
The pharmacotherapy and drug prescription knowledge level of academics from the two dental schools were predominantly poor and fair.
Enhancing Wind Energy Generation: Designing and Optimizing a Novel FO Fuzzy PD+I Regulator Using the SSO Method
Authors: Dembri Rafik , Rahmani Lazhar , Babes Badreddine , Zaini Hatim G. , Ghoneim Sherif S. M. , Sharaf Ahmed B. Abou 
Published: July 2024

Abstract

In this article, an optimized design methodology is presented for a Fractional-Order Fuzzy Proportional-Derivative with Integral (FO Fuzzy PD + I) regulator using the Social Spider Optimization (SSO) technique. The research focuses on its application in the control of Doubly Fed Induction Generator (DFIG)-based wind turbine systems (WTS). The FO Fuzzy PD + I controller integrates the capabilities of the Fuzzy intelligent regulator and the Fractional-Order Proportional-Integral-Derivative (FO-PID) controller, enhancing DFIG current control while allowing independent control of active and reactive power. The approach is incorporated within the Direct Vector Control (DVC) strategy of the DFIG's rotor-side converter (RSC), replacing the conventional Proportional-Integral (PI) regulator in the internal current loops. Extensive performance evaluations are conducted under various operating conditions, including active power reference changes, parameter uncertainties, and rapid wind speed variations. Comparative analyses with SSO-optimized PID and Fuzzy regulators show the FO Fuzzy PD + I regulator performs better in terms of maximum overshoot, extreme undershoot, settling time, and Total Harmonic Distortion (THD) reduction. These findings underscore the regulator's potential for enhancing the reliability and efficiency of DFIG-based WTS. Furthermore, the adaptability of the FO Fuzzy PD + I regulator positions it as a versatile control solution with implications extending beyond the realm of wind energy, potentially benefiting various industrial sectors that demand precise and dynamic control mechanisms. As renewable energy sources continue to gain prominence in the global energy landscape, this research contributes to the broader mission of realizing a sustainable and eco-friendly energy future.