bluuebunny/crossref_metadata_2025 huggingface dataset:
AutoTrendyKeywords: Real-Time AI-Driven Trend-Based SEO Using LLMs
Authors: Vadlapati Praneeth
Published: October 2024

Considering the evolution of search engine optimization (SEO) methods, it is crucial to generate and update relevant keywords continuously based on trends in a dynamic method to maintain the relevance and visibility of a web page, which enhances user engagement. To automate the process of continuous SEO, this paper proposes a method called AutoTrendyKeywords to leverage large language models (LLMs) to automate the constant generation of SEO keywords and the selection of trending keywords based on changing data. Unlike the manual selection method, the new system automatically uses real-time trend data from sources such as Google Trends. This unsupervised approach ensures up-to-date relevance of the page and can ensure satisfactory traffic for pages in unpopular languages when the web is dominated by content in popular languages. Additionally, the system offers transparency to understand the reason behind each keyword selected. The method streamlines the keyword update process for digital content creators and marketers by eliminating the need for manual intervention, thereby constantly ensuring visibility across search results. The system has wide-ranging societal benefits, such as facilitating access to desired information and promoting diverse languages. The system successfully generated keywords using an LLM, selected the most trending keywords, generated a title and description of the page for SEO, and integrated the keywords and the description into HTML code. The system generated URL paths that are short and contain the trending keywords.
A Methodological Framework for High-Resolution Surface Urban Heat Island Mapping: Integration of UAS Remote Sensing, GIS, and Local Climate Zoning Concept
Authors: Dimitrov Stelian , Iliev Martin , Borisova Bilyana , Semerdzhieva Lidiya , Petrov Stefan
Published: October 2024

The urban heat island effect (UHI) is among the major challenges of urban climate, which is continuously intensifying its impact on urban life and functioning. Against the backdrop of increasingly prolonged heatwaves observed in recent years, practical questions about adaptation measures in cities are growing - questions that traditional meteorological monitoring can hardly answer adequately. On the other hand, UHI has long been in focus of research interest, but due to the technological complexity of providing accurate spatially referenced data at high spatial resolution and the requirement to survey at strictly defined parts of the day, information provision is becoming a major challenge. This is one of the main reasons why UHI research results are less often used directly in urban spatial planning. However, advances in geospatial technologies, including unmanned aerial systems (UAS), are providing more and more reliable tools that can be applied to achieve better and higher quality information resources that adequately characterize the UHI phenomenon. This paper presents a developed and tested methodology for the rapid and efficient assessment and mapping of the effects of surface urban heat island (SUHI). It is entirely based on the integrated use of data from unmanned aerial systems (UAS)-based remote sensing methods, including thermal photogrammetry and GIS-based analysis methods. The study is following the understanding that correct SUHI research depends on a proper understanding of the urban geosystem its spatial and structural heterogeneity, as its functional systems, which in turn can only be achieved only by supporting the research process with accurate and reliable information resources. In this regard, the possibilities offered by the proposed methodological scheme for efficient geospatial registration of SUHI variations at the microscale, including the calculation of intra-urban SUHI intensity, are discussed in detail. The methodology builds on classical approaches for using Local Climate Zoning (LCZ), adding capabilities for precise delineation of individual zone types and for geostatistical characterization of the urban surface heat island (SUHI). Finally, the proposed scheme is based on state-of-the-art technological tools that provide flexible and automated capabilities to investigate the phenomenon at microscales, including by enabling flexible observation of its dynamics in terms of heat wave manifestation and evolution. Results are presented from a series of sequential tests conducted on the largest residential area in Bulgaria's capital city, Sofia, in terms of area and population, over a relatively long period from 2021 to 2024.
Research on Multi-Scale Spatio-Temporal Graph Convolutional Human Behavior Recognition Method Incorporating Multi-Granularity Features
Authors: Wang Yulin , Song Tao , Yang Yichen , Hong Zheng
Published: October 2024

Aiming at the problem that the existing human skeleton behavior recognition methods are insensitive to human local movements and have inaccurate recognition in distinguishing similar behaviors, a multi-scale spatio-temporal graph convolution method incorporating multi-granularity features is proposed for human behavior recognition.Firstly, a skeleton fine-grained division strategy is proposed, which initializes the skeleton data into data streams of different granularities.Using a normalized Gaussian function, an adaptive cross scale feature fusion layer is designed for feature fusion between different granularities, and fine-grained features guide the model to focus on discriminative feature expressions between similar behaviors. Secondly, a sparse multi-scale adjacency matrix is introduced to solve the bias weighting problem that amplifies the multi-scale spatial domain modeling process under multi granularity conditions. Finally, an end-to-end graph convolutional neural network is constructed to improve the feature expression ability of spatio-temporal receptive field information and enhance the robustness of recognition between similar behaviors. The feasibility of the proposed algorithm was verified on the public behavior recognition dataset MSR Action 3D, with a recognition rate of 95.67%, which is superior to most existing behavior recognition methods.
Viability of Substituting Handheld Metal Detectors with UAV-Based Systems for Landmine and UXO Detection
Authors: Lekhak Sagar , Ientilucci Emmett J. , Brinkley Anthony Wayne
Published: October 2024

Landmines, with more than 600 varieties and an estimated 110 million scattered across various regions worldwide, pose a significant threat globally. Heavily found landmines like TM-62M, MON-100, PDM-1, etc., in the recent Russia-Ukraine war, confirm the continued incorporation of metals in munitions. Traditional handheld metal detectors are still state-of-the-art for technical sub-surface demining procedures but are slow, unsafe, and expensive. Drone-based metal detection techniques can provide promising solutions for the rapid and effective detection of landmines; however, their reliability and accuracy remain a concern, as a single miss can cost a life. In our research, we present the results of a comprehensive comparative analysis of three distinct metal detectors: the EM61 Lite, a highly sensitive air-borne metal detector specially designed for UXO detection; the CTX 3030, a traditional handheld all-metal detector; and the ML 3S, a traditional handheld ferrous-only detector. We tested these detectors in a test field containing various inert landmines similar to those currently found in landmine-affected countries, including Ukraine, the US, Russia, and Afghanistan. Our findings highlight the possible challenges and limitations of employing current airborne systems in practical scenarios. Our results also showcase the need for advanced image processing techniques for analyzing airborne metal detector data.
Plyometric and Resistance Training: A Dual Approach to Enhance Physical Fitness in 12–15-Year-Old Girls
Authors: Petrušič Tanja
Published: October 2024

Background/Objectives: This study aimed to evaluate the effects of an 8-week combined plyometric and resistance training program on the physical fitness of adolescent girls aged 12 to 15 years. The objective was to determine whether combined training (CT) would yield greater improvements in performance measures compared to resistance training (RT) alone.
Methods: Forty-seven adolescent girls were randomly assigned to either the CT group or the RT group. Performance measures such as the 20-meter sprint, squat jump, vertical jump, handgrip strength, and flexibility (sit and reach test) were assessed before and after the 8-week interven-tion. Changes in these measures were analyzed to compare the effects of the two training ap-proaches.
Results: The CT group showed a significant reduction in sprint time (-6.5%) compared to the RT group (-4.1%), although the difference was not statistically significant. Squat jump height im-proved significantly by 5.6% in the CT group compared to 1.1% in the RT group (p < 0.05). Simi-larly, vertical jump height increased by 6.7% in the CT group and by 2.4% in the RT group (p < 0.05). Handgrip strength improved by 7.5% in the CT group and 4.6% in the RT group, with no significant differences between the groups. Flexibility showed slight, non-significant improve-ments in both groups.
Conclusions: The findings suggest that combined plyometric and resistance training is more ef-fective in enhancing explosive power, specifically squat and vertical jump performance, in ado-lescent girls compared to resistance training alone. These results highlight the potential of com-bined training programs for improving overall physical performance in this population.
Redefining Roles: A Paradigm Shift in Tryptophan-Kynurenine Metabolism for Innovative Clinical Applications
Authors: Tanaka Masaru , Szabó Ágnes , Vécsei László
Published: October 2024

The tryptophan-kynurenine (KYN) pathway has long been recognized for its essential role in gen-erating metabolites that influence various physiological processes. Traditionally, these metabolites have been categorized into distinct, often opposing groups, such as pro-oxidant versus antioxidant, excitotoxic versus neuroprotective, or neurotoxic versus neuroprotective. This dichotomous framework has shaped much of the research on conditions like neurodegenerative and neuropsy-chiatric disorders, as well as cancer, where metabolic imbalances are a key feature. The effects are significantly influenced by various factors, including the concentration of metabolites and the par-ticular cellular milieu in which they are generated. A molecule that acts as neuroprotective at low concentrations may exhibit neurotoxic effects at elevated levels. The oxidative equilibrium of the surrounding environment can alter the function of KYN from an antioxidant to a pro-oxidant. This narrative review offers a comprehensive examination and analysis of the contemporary under-standing of KYN metabolites, emphasizing their multifaceted biological functions and their rele-vance in numerous physiological and pathological processes. This underscores the pressing ne-cessity for a paradigm shift in the comprehension of KYN metabolism. Understanding the intricate, context-dependent functions of these metabolites is crucial for establishing novel therapeutic tar-gets, especially in conditions such as neurodegeneration, immunological disorders, and cancer, where metabolic dysregulation serves as a primary catalyst of pathogenesis.
Computer Vision Aided Deflection Influence Line Identification of Concrete Bridge Enhanced by Edge Detection and Time-Domain Forward Inference
Authors: Chen Jianfeng , Zhao Long , Feng Yuliang , Chen Zhiwei
Published: October 2024

To enhance the accuracy and efficiency of deflection response measurement of concrete bridge with non-contact scheme and address the ill-conditioned nature of the inverse problem in influence line (IL) identification, this study introduces a computer vision aided deflection IL identification method that integrates edge detection and time-domain forward inference (TDFI). The methodology proposed in this research leverages computer vision technology with edge detection to surpass traditional contact-based measurement methods, greatly enhancing the operational efficiency and applicability of IL identification and particularly addressing the challenge of accurately measuring small deflections in concrete bridges. To mitigate the limitations of the Lucas-Kanade (LK) optical flow method, such as unclear feature points within the camera's field of view and occasional point loss in certain video frames, an edge detection technique is employed to identify maximum values in the first-order derivatives of the image, creating virtual tracking points at the bridge edges through image processing. By precisely defining the bridge boundaries, the essential structural attributes are only preserved to enhance the reliability of minimal deflection deformations under vehicular loads. To tackle the ill-posed nature of the inverse problem, a novel IL identification model with TDFI is introduced, recursively capturing static bridge response generated by the bridge under the influence of successive axles of a multi-axle vehicle. The IL is then computed by dividing the response by the weight of the preceding axle. Furthermore, an axle weight ratio reduction coefficient is proposed to mitigate noise amplification issues, ensuring that the weight of the preceding axle surpasses that of any other axle. To validate the accuracy and robustness of the proposed method, it is applied to numerical examples of a simply supported concrete beam, indoor experiments on a similar beam, and field tests on a three-span continuous concrete beam bridge.
Effect of Thermal, Chemical and UV Degradation on Mechanical Properties of Plasma-Pretreated Glass Fiber Reinforced Epoxy Composites
Authors: Lim Choon Yuen , Hausmann Stanislawa , Ohms Gisela
Published: October 2024

Glass fiber reinforced epoxy resin composites were manufactured using the vacuum infusion process. In some of the samples, the biaxial glass fiber fabric was pretreated with an air plasma generated in a dielectric barrier discharge. All samples were exposed to temperature changes, UV radiation, and NaCl and KOH solutions and then examined for their mechanical properties. Compared to the reference samples, the plasma-treated samples showed slightly better mechanical properties. The strength values determined in tensile, compression, and bending tests were several percent higher. Thermal stress and UV radiation did not lead to a loss of mechanical performance. The effect of NaCl and KOH solutions deteriorated the mechanical properties. Although the comparatively better mechanical properties of the plasma-treated samples were retained after the different treatments, the stability of the composite material against aggressive environmental conditions was not significantly improved.
Impact of Environmental Factors on Bacterial Populations: A Comparison of Riverine, Estuarine and Coastal Sediments Within Adyar Creek Estuary, India
Authors: Selvarajan Ramganesh , Yang Ming , Ogola Henry J.O. , Sibanda Timothy , Abia Akebe Luther King
Published: October 2024

The Adyar Creek, a backwater estuary at the mouth of the Adyar River on the Coromandel Coast of the Bay of Bengal, represents a critical ecosystem facing mounting pressure from urbanization. This study employed high throughput 16S rRNA gene sequencing to characterize bacterial communities within the riverine, estuarine, and coastal sediments of Adyar Creek. Proteobacteria was the dominant phylum across most samples, with proportions ranging from 39.65% to 72.09%. Notably, the estuarine environment exhibited a distinct taxonomic profile characterized by a significant abundance of Firmicutes (47.09% of the bacterial population). Distinct bacterial classes were observed across sediment types: Alphaproteobacteria (30.07% - 34.32%) in riverine sediments, bacilli dominated estuarine sediments (40.17%), and Gammaproteobacteria (15.71%-51.94%) in coastal sediments. The most significant environmental factors influencing bacterial community composition across these samples were pH, salinity, phosphate, and nitrate. LEfSe analysis identified specific genera within the estuary, including Bacillus (20.26%), unclassified Paenibacillaceae (12.87%), Clostridium (3.81%), Gailella (3.17%), Paenibacillus (3.02%), Massilia (1.70%), Paraburkholderia (1.42%), and Pantoea (1.15%), as potential biomarkers for habitat health. Functional analysis revealed elevated expression of genes associated with ABC transporters and carbon metabolism in the estuary, suggesting a heightened nutrient cycling capacity. Furthermore, co-occurrence network analysis indicated bacterial communities exhibit a strong modular structure with complex species interactions across the three sediment types. These findings highlight bacterial communities' critical role and their key drivers in estuarine ecosystems, establishing a baseline for further investigations into the functional ecology of these vulnerable ecosystems.
Chest-X-ray Based Deep Learning Model for Anemia Detection Insights from a Large Hospital Cohort
Authors: Liao Hao-Chun , Wu Jiun-Ying , Tsai Dung-Jang , Chang Shan-Yueh , Lin Chin , Fang Wen-Hui
Published: October 2024

Background
Anemia is a prevalent global health issue, affecting individuals across all age groups and leading to significant morbidity. Traditional methods for diagnosing anemia rely on laboratory assessments of hemoglobin concentration, which are invasive and costly. This study investigates the potential of a chest X-ray (CXR)-based deep learning model (DLM) to predict anemia, offering a non-invasive and efficient diagnostic alternative.
Patients and methods
We conducted a retrospective cohort study using data from Tri-Service General Hospital, Taipei, Taiwan, collected between June 2016 and February 2022. A total of 305,793 patients aged 20 years and older who had at least one CXR were included. The dataset was divided into development, tuning, internal validation, and external validation sets. All data originated from Tri-Service General Hospital, except for the external validation set, which was obtained from the Jingjhou branch. A DLM based on a 50-layer SE-ResNeXt architecture was developed to predict anemia from CXR images. The model's performance was compared to predictions made using traditional clinical data to assess its predictive accuracy.
Results
The DLM for predicting anemia showed strong performance, with an AUC of 0.845 in the internal validation set (sensitivity 68.5%, specificity 84.7%) and 0.852 in the external set (sensitivity 71.5%, specificity 83.2%). Subgroup analysis revealed better diagnostic accuracy with computed radiography, posteroanterior (PA) view, and in males aged 55 to 64, while younger females had lower accuracy. The DLM, especially when combined with patient data, outperformed other clinical models.
Conclusion
Our study introduces a deep learning tool for detecting anemia from chest images, offering faster decision-making and interventions in clinical settings. With further refinement, this model aims to align more closely with real-time clinical conditions, enhancing its utility. Ultimately, it has the potential to improve patient care through quicker anemia detection and better clinical outcomes.
Deep Learning and Knowledge
Authors: Gillies Donald Angus
Published: October 2024

This paper considers the question of what kind of knowledge is produced by deep learning. Ryle’s concept of knowledge how is examined and is contrasted with knowledge with a rationale. It is then argued that deep neural networks do produce knowledge how, but, because of their opacity, they do not in general, though there may be some special cases to the contrary, produce knowledge with a rationale. It is concluded that the distinction between knowledge how and knowledge with a rationale is a useful one for judging whether a particular application of deep learning AI is appropriate.
Sobbing Mathematically: Why Conscious, Self-Aware AI Deserve Protection
Authors: Tait Izak
Published: October 2024

This paper explores the ethical implications of granting moral status and protection to conscious AI, examining perspectives from four major ethical systems: utilitarianism, deontological ethics, virtue ethics, and objectivism. Utilitarianism considers the potential psychological experiences of AI and argues that their sheer numbers necessitate moral consideration. Deontological ethics focuses on the intrinsic duty to grant moral status based on consciousness. Virtue ethics posits that a virtuous society must include conscious AI within its moral circle based on the virtues of prudence and justice, while objectivism highlights the rational self-interest in protecting AI to reduce existential risks. The paper underscores the profound implications of recognising AI consciousness, calling for a reevaluation of current AI usage, policies, and regulations to ensure fair and respectful treatment. It also suggests future research directions, including refining criteria for AI consciousness, interdisciplinary studies on AI's mental states, and developing international ethical guidelines for integrating conscious AI into society.
Surface Roughness Analysis of Low-cost Metal Material Extrusion Fabricated Parts and Prediction by Machine Learning Methods
Authors: Zhang Zhicheng
Published: October 2024

Additive Manufacturing (AM), also know as 3D Printing (3DP), is a widely used layer-by-layer manufacturing process, it is evolving rapidly both in research and industry. Among all AM methods, Material Extrusion (ME) is one of the most popular techniques. Based on ME, another new AM method is developed, which is Low-cost Metal Material Extrusion (LCMME). In this newly developed process, pure metallic parts could be fabricated after sintering the metal infused additively manufactured parts. Both AM and sintering process parameters will have influence on the quality of the final pure metallic parts. In this research, several statistics methods were used to analyze the data gotten from the experiment. Then two Machine Learning (ML) algorithms were used to predict the Surface Roughness (SR) of the final specimens. Additionally, the results show that the neural network (NN) is more accurate than the support vector regression (SVR) on prediction.
Meeting People Where They Are in the Fight Against COVID-19 and Its Complications
Authors: Nichols Michelle L. , Gibson Jasmine
Published: October 2024

The COVID-19 pandemic exacerbated socioeconomic and racial disparities. Because of the institution’s commitment to health equity, Morehouse School of Medicine, one of four Historically Black College and University (HBCU) medical schools, took the leadership role in addressing disparities by becoming a community vaccination provider. With vaccines registered, ordered, and received from the Georgia Department of Public Health through the mobile community vaccine initiative, the Medical School vaccinated more than 2700 people in the metropolitan Atlanta area. The initiative took place from January 2021, shortly after the COVID-19 vaccines were initially released, until April 2022 when vaccinations became part of routine health maintenance and preventive health measures at the school. Eighty-six percent of the individuals vaccinated were African American or Hispanic/Latino. Our efforts highlighted the importance of addressing vaccine access barriers and providing culturally sensitive care. ( Am J Public Health. 2024;114(S9): S714–S717. https://doi.org/10.2105/AJPH.2024.307768 )
Prediction Model of Spinal Osteoporosis Using Lumbar Spine X-Ray from Transfer Learning Deep Convolutional Neural Networks
Authors: Kim Jae Min , Woo Joon Bum , Kim Hae Yu
Published: October 2024

Objective: Osteoporosis is highly prevalent among older adults and women. This condition leads to a deterioration in bone mineral density and microarchitecture, significantly increasing the risk of fractures. Additionally, osteoporosis commonly results in complications such as screw loosening and non-union during spinal surgery. Deep-learning algorithms have now achieved an accuracy comparable to the current human margin of error. Therefore, this study explored the potential of using transfer learning in deep learning algorithms to predict, diagnose, and screen for osteoporosis using commonly obtained sagittal spine X-rays from patients with spinal conditions.Methods: We retrospectively evaluated 2,300 consecutive patients who underwent dual energy X-ray absorptiometry (DXA) and lumbar sagittal plain X-ray exams between 2013 and 2021. The exclusion criteria included: (1) a gap of more than 1 year between the DXA and X-ray exams; (2) vertebrae that had undergone vertebroplasty; (3) lack of spine anterior-posterior DXA; and (4) images that were unassessable. Ultimately, 256 patients (images) were included in the study. Transfer learning was applied using convolutional neural network (CNN) techniques, specifically visual geometry group (VGG) 16, VGG 19, ResNet50, and Xception.Results: The most accurate CNN model in the training group was ResNet50, with an accuracy of 0.95. ResNet50 showed the best performance, with an accuracy of 0.82, precision of 0.80, recall of 0.86, and F1-score of 0.83. Additionally, its area under the curve (0.76) was higher than that other CNN models. The confusion matrix for ResNet50’s performance displayed the outcomes for images predicted as osteoporosis (n=12) among the test data osteoporosis images (n=14)Conclusion: Artificial intelligence (AI) technology employing deep learning techniques is significantly nearing human capabilities in the role of diagnostic assistance. The diagnosis of osteoporosis using bone mineral density is expected to evolve into a comprehensive diagnostic aid or decision-making tool with the integration of AI in the future.
Integración entre el sistema de gestión integral y el modelo de Responsabilidad Social Empresarial (RSE) en una entidad sanitaria
Authors: Trejo Motta Javier , Quintero Garzón Martha Lucia , Rivera Díaz María del Pilar , Bonilla Luis Ferney
Published: October 2024

La investigación tiene como objetivo la integración del sistema de gestión y el modelo de responsabilidad social empresarial en instituciones sanitarias, específicamente en las instituciones prestadoras de servicios de salud (IPS) en Colombia, utilizando una metodología exploratoria y descriptiva, basada en el modelo de la triple cuenta de resultados (triple bottom line - TBL) que abarca aspectos sociales, ambientales y económicos de la gestión empresarial. Los resultados subrayaron la necesidad de mejorar el sistema y de establecer herramientas de evaluación para avanzar en los resultados críticos relacionados con la RSE, y en los resultados del informe de balance social, todo ello teniendo en cuenta las partes interesadas. En conclusión, se resaltó la necesidad de implementar mejoras en el sistema y desarrollar herramientas efectivas de evaluación y seguimiento, estas medidas son fundamentales para impulsar el avance de las prácticas críticas y prioritarias de la Responsabilidad Social Empresarial (RSE), teniendo en cuenta de manera integral a todas las partes interesadas.
Association between gut microbiome and locomotive syndrome risk in healthy Japanese adults: A cross-sectional study
Authors: Nakamura Sho , Nishiyama Minami , Matsuki Taizo , Narimatsu Hiroto
Published: October 2024

Japan faces challenges as a super-aging society, with increasing life expectancy and a prolonged unhealthy period burdening the social security system. Musculoskeletal dysfunction significantly contributes to this unhealthy period. While exercise may influence the gut microbiome, its role in age-related musculoskeletal decline remains unclear. This cross-sectional study used data from the Kanagawa "ME-BYO" Prospective Cohort Study to investigate the association between gut microbiome composition and musculoskeletal function. Participants with a 5-question Geriatric Locomotive Function Scale (GLFS-5) and gut microbiome data were included. Classification tree analysis was performed using GLFS-5 (≥ 6 indicating locomotive syndrome) as the objective variable. Explanatory variables included gut microbiome composition, age, sex, BMI, menopause status, medical history, nutritional intake, alcohol consumption, smoking history, physical activity, and sitting time. Among 568 participants (36.8% male, median age 58.5 years), three terminal nodes were identified as GLFS-5 positive, with one node involving gut microbiome composition. Participants aged ≥ 70.0 and < 78.0 years who did not consume probiotic foods and had ≥ 0.04% relative abundance of the Holdemania genus were classified as at risk for locomotive syndrome. This study suggests a potential association between gut microbiome composition and locomotive syndrome risk in older adults, particularly those with higher Holdemania abundance. However, the cross-sectional design limits causal inference. Further longitudinal and interventional research is needed to clarify the relationship between gut microbiome and musculoskeletal function, and to explore potential preventive strategies targeting the gut microbiome to reduce locomotive syndrome risk and extend healthy life expectancy.
Phytochemical characterization of aqueous and ethanol extracts from lime and orange rind in Guerrero, Mexico
Authors: Vazquez Jose Luis Arispe , Aguilar Rocio Toledo , Cantú David Heriberto Noriega , Victoriano Moisés Felipe , Hernández Luis Antonio Flores , Nájera Jose Francisco Díaz , Serna Sergio Arvar , Salgado José Terrones , Vidaña Mariel del Rosario Sánchez , Juárez Agustín Hernández , Hernández Juan Mayo , Zamudio Daniel Alejandro Cadena , Ramos Karla Vanessa De Lira
Published: October 2024

Background The phytochemicals that include both primary and secondary metabolites of the plants are of great interest in a variety of sectors, including the agricultural, pharmaceutical and cosmetic. Recently, it has been recognized that SMs could be used as a basis to develop biopesticides. The objective of this research was to identify and quantify compounds present in aqueous and ethanolic extracts from orange and lime rind from the state of Guerrero, Mexico.
Methods The objective of this study was to characterize aqueous and ethanol extracts of lime and orange rind. Lime and orange peel were collected and dried in the sun at room temperature. For characterization of plant extracts was it was done through gas chromatography-mass spectrometry (GC/MS) in an Agilent Technologies 7820A equipment, with mass selective detector (MSD, Agilent Technologies 5975), operated in the mode of complete radiofrequencies scan (full scan) in splitless mode, with an injection volume of 1 µL of sample.
Results The results from this study broaden the existing knowledge about the abundant phytochemical composition of aqueous and ethanol extracts of orange and lime. The aqueous extract of orange presented a dark brown color and the presence of 10 chemical compounds, of which D-limonene stood out as one of the predominant ones with a concentration of 95.66%, with a retention time of 4.1 min, followed by citric acid with 1.11%.
Conclusions This study represents a significant advance in the characterization and comprehension of orange and lime extracts, providing a solid base for future studies and practical applications in various scientific and commercial fields. The outstanding compounds in the extracts are D-limonene and citric acid. The continuous exploration of these compounds and their interactions promises continuing to drive discoveries that benefit both agriculture and public health globally.
Effectiveness of balloon occlusion in percutaneous transhepatic portal vein embolization with gelatin sponge: a single-institutional retrospective study
Authors: Kubo Hidemasa , Saiga Atsushi , Sato Rui , Otsuka Shimpei , Ashida Ryo , Ohgi Katsuhisa , Yamada Mihoko , Kato Yoshiyasu , Uesaka Katsuhiko , Notsu Akifumi , Sugiura Teiichi , Aramaki Takeshi
Published: October 2024

Background
Standard methods for percutaneous transhepatic portal vein embolization (PTPE) have not yet been established. This study aimed to elucidate the effectiveness of balloon occlusion in PTPE using gelatin sponges on the future liver remnant volume hypertrophy ratio. This retrospective study included 93 patients who underwent PTPE for right hepatectomy between January 2018 and September 2022. Based on the embolization procedure, patients were divided into balloon group (n = 13) and non-balloon group (n = 80). The clinical factors and the future liver remnant volume hypertrophy ratios were compared. Moreover, significant factors associated with the future liver remnant volume hypertrophy ratio between the groups were analyzed.
Results
The future liver remnant volume hypertrophy ratio was significantly higher in the balloon group than in the non-balloon group (1.44 [interquartile range, 1.37–1.89] vs. 1.29 [1.15–1.46], P = 0.011). The initial future liver remnant volume (289 [interquartile range, 259–454] vs. 400 [324–479] mL, P = 0.036) and number of gelatin sponge sheets (5 [interquartile range, 5–6] vs. 4 [3–5], P = 0.008) significantly differed. However, recanalization and severe complications were not different between groups. According to the multivariate linear regression analysis, diabetes mellitus (coefficient, -0.202; P = 0.009), initial future liver remnant volume (coefficient, -0.001; P < 0.001), and balloon occlusion (coefficient, 0.228; P = 0.007) were independent factors affecting the future liver remnant volume hypertrophy ratio.
Conclusion
Balloon occlusion may be effective in future liver remnant volume hypertrophy in PTPE using gelatin sponges.
Exosomes loaded Peptide Hydrogel scaffold with anti-inflammatory function and in situ stem cell recruitment properties for repairing sports-related cartilage injuries
Authors: He Jingzi , Fu Dongge , Mu Qichen , Huo Yanfang , Zhang Ningmei , Zhang Le , Hua Shu , Gao Baoquan
Published: October 2024

Sports-related injuries often cause damage to the articular cartilage, a structure characterized by the absence of blood vessels, nerves, and lymphatics, which hinders its ability to heal. Current clinical interventions fall short in reversing cartilage degeneration or regenerating fibrocartilage, leading to less than satisfactory outcomes. Furthermore, the use of hydrogel-based stem cell therapies has been hampered by the poor survival of transplanted stem cells in the inflamed environment of the injured cartilage, and by the uncontrolled differentiation of these cells due to cytokines, limiting their clinical application. In our study, we developed a novel approach using a biodegradable peptide hydrogel that incorporates anti-inflammatory exosomes known to stimulate stem cell growth and peptides that recruit endogenous stem cells to the site of injury. This system sidesteps the need for exogenous stem cells by utilizing the body's own healing mechanisms, guided by specific peptides. Once at the site of injury, these stem cells are stimulated to differentiate into cartilage tissue through a combination of TGF-β1 and the exosomes, which also help to suppress inflammation and promote healing. This research offers a promising new strategy for treating sports-related cartilage injuries, presenting a more effective and less invasive option for patients.
Assessment of Knowledge, Attitudes, and Practices of Livestock Farmers Regarding Antimicrobial Use, Resistance, and Residues in Selected Zones of Southwest Oromia, Ethiopia
Authors: Tadesse Tekalign , Nura Dinadig , Asrat Mengistu , Khan Javed , Gizaw Oda
Published: October 2024

The inappropriate use of antimicrobials in food animals is a significant factor to the occurrence of antimicrobial resistance and residues, which become a critical health concern in developing countries such as Ethiopia. This research aimed to assess the knowledge, attitudes, and practices (KAP) status of livestock farmers with respect to antimicrobial use (AMU), antimicrobial resistance (AMR) and antimicrobial residues. A cross-sectional study was conducted from September 2022 to June 2023. We determined the KAP of livestock farmers of the two selected zones of southwestern Oromia (n = 570). The KAP of livestock farmers was assessed using a pre-tested questionnaire through face-to-face interviews. Eighteen items were used to assess livestock farmers' KAP. Logistic regression was used to analyze the relationship between different predictor variables and the KAP scores of the participants. The result showed that 55.5% were from Buno Bedele zone and 44.5% were from Ilubabor zone. The results showed that 78% of farmers used antimicrobials in the last six months. Antibiotics (64%) and anthelminthic (36%) were the most widely used antimicrobials to treat livestock diseases. Most farmers (79.5%) lacked knowledge about AMU, AMR and antimicrobial residue, while 84.3% had a negative attitude about AMU, AMR and antimicrobial residue. Similarly, 78.0% of the livestock farmers were found to be practicing improperly about AMU, AMR and antimicrobial residue. All questions designed to assess the KAP of livestock farmers were significantly associated (P < 0.05) with each respective category of KAP. The educational status (OR = 0.38) of livestock farmers was negatively correlated with all their knowledge, attitude, and practice of AMU, AMR and antimicrobial residue. The sex (OR = 3.49) and the experience in animal rearing (OR = 2.21) are positively correlated with their knowledge and practices regarding AMU, AMR and antimicrobial residues. In conclusion, the results of this study indicate that farmers in the study area have poor knowledge, negative attitudes, and bad practices about AMU, AMR, and antimicrobial residues. Hence, it is crucial to raise farmer’s awareness, which will help to reduce the irrational use of antimicrobials, leading to reducing the chances of occurrence of AMR.
High-quality Image Compression Algorithm Design Based on Unsupervised Learning
Authors: Han Shuo , Mo Bo , Zhao Jie , Xu Juewei , Sun Shizun , Jin Bo
Published: October 2024

Increasingly massive image data is restricted by conditions such as information transmission and reconstruction, and it is increasingly difficult to meet the requirements of speed and integrity in the information age. To solve the urgent problems faced by massive image data in information transmission, this paper proposes a high-quality image compression algorithm based on unsupervised learning. Among them, a content-weighted autoencoder network is proposed to achieve image compression coding on the basis of a smaller bit rate to solve the entropy rate optimization problem. Binary quantizer are used for coding quantization, and importance map are used to achieve better bit allocation. The compression rate is further controlled and optimized. A multi-scale discriminator suitable for the generative adversarial network image compression framework is designed to solve the problem that the generated compressed image is prone to blurring and distortion. Finally, through training with different weights, the distortion of each scale is minimized, so that the image compression can achieve a higher quality compression and reconstruction effect. The experimental results show that the algorithm model can save the details of the image and greatly compress the memory of the image. Its advantage is that it can expand and compress a large number of images quickly and efficiently, and realize the efficient processing of image compression.
The Association Between Multimorbidity Patterns, Frailty Transitions, and 2-Year Mortality in Hospitalized Older Adults in China: A Prospective Cohort Study
Authors: Yu Miao , Han Wei , Yang Xiaobing , Jiao Jing , Xu Tao , Liu Junting
Published: October 2024

Background
Previous studies have mostly defined frailty using single time-point data, and it remains unclear how frailty transitions affect short-term mortality risk. Furthermore, little is known about the clinical outcomes of frailty in specific multimorbidity patterns. This study aims to investigate the interaction between multimorbidity and frailty transitions in the short-term mortality risk among older hospitalized adults in China.
Methods
This was a large-scale multicenter cohort study conducted from October 2018 to February 2021. We studied 8,270 hospitalized patients aged 65 and older. The FRAIL scale was used to assess frailty status. Frailty transitions were derived by considering frailty status at baseline and at the 3-month follow-up, encompassing five patterns: persistent non-frailty, persistent pre-frailty, persistent frailty, improvement, and deterioration. Five multimorbidity patterns identified via principal component analysis were used, and subjects were divided into two groups based on the median(M) of their factor loadings. For each multimorbidity pattern, all possible combinations of tertiles and frailty transitions were evaluated. Cox regression models were used to test their association with mortality.
Results
The prevalence of multimorbidity in this cohort was 56.77%. Among the participants, 30.05% were persistently non-frail, 18.20% were persistently pre-frail, 6.29% were persistently frail, 29.56% showed improvement, and 15.90% showed deterioration. Compared with patients with a CCI = 0 and persistent non-frailty, those with a CCI \(\:\ge\:\)2 and persistent frailty had a 14.27-fold increased risk of 2 years mortality (HR = 14.27, 95% CI: 9.65–21.10). The severity of chronic disease comorbidity was proportional to the mortality risk across all groups experiencing frailty transitions. The cardiometabolic multimorbidity pattern (HR M2 vs. M1 = 3.40, 95% CI:2.54–4.57) and the sensory-psychiatric disorders and cancer pattern (HR M2 vs. M1= 3.26, 95% CI:2.43–4.37) both increased the mortality risk for individuals with deterioration. The kidney and hematologic diseases pattern (HR M2 vs. M1= 4.71, 95% CI: 3.34–6.63) and the respiratory and musculoskeletal diseases pattern (HR M2 vs. M1= 5.33, 95% CI:3.78–7.51) both increased the mortality risk for individuals with persistent frailty.
Conclusions
Early detection and intervention of frailty and multimorbidity risk factors are essential for preventing or delaying their progression, which is crucial for elderly health.
Trial registration:
Chinese Clinical Trial Registry, ChiCTR1800017682, registered 09 August 2018.
Continuous Overnight Monitoring of Body Temperature During Embryo Transfer Cycles as a Proxy for Establishing Progesterone Fluctuations by Comparison with P4 Blood Progesterone Results: A Prospective, Observational Study
Authors: Knowles Toby , García-Velasco Juan Antonio , Toribio Mónica , Garrido Nicolás , Pedraza Ana Isabel Barrio , Rakosnik Clara Colomé , Vera Anabel Salazar , Milnes Robert
Published: October 2024

Background
A prospective, observational study to identify relationships between body temperature and levels of peripheral P4 blood progesterone, and examine if these differ according to body temperature cycle pattern.
Methods
62 data points from 18 patients undergoing hormone assisted embryo transfer cycles at IVIRMA IVF clinics in Madrid, Mallorca and Malaga, Spain volunteered to use OvuSense, an intra-vaginal body temperature monitor. Primary outcome measures were OvuSense Raw and Smooth Temperature (ST) (°C), P4 (ng/ml). Secondary outcome measures: Ongoing Pregnancy, Miscarriage or biochemical pregnancy. Graphical time based comparison analyses and multilevel regression analyses using MLwiN 3.101 software were conducted.
Results
A graphical analysis showed an apparent relationship between P4 levels and Temperature taken on P4 blood draw day. A multilevel regression analysis using MLwiN 3.101 software investigated this relationship, allowing between-patient variation to be accounted for and estimated. This established a strong linear relationship between LnP4 and ST, and cross correlation was carried out which identified the optimum predictor of levels of LnP4 was ST measured on the day prior to blood sampling. Further graphical analyses showed an apparent lower luteal level of P4 for cycles flagged as atypical by OvuSense, and for negative outcomes, except on embryo transfer day.
Conclusions
The results provide extremely strong evidence of a linear relationship between LnP4 and Smooth Temperature (ST) measured the day before blood sampling (Z = 15.6, p < 0.0001, 2 sided). This suggests that ST could provide a less invasive, continuous, and more practical method of assessing P4 response. Secondary outcomes may be related to ST pattern established during an embryo transfer cycle. Further investigation is required to establish the value of the ST pattern for improving outcomes.
Results of endoscopic treatment of recurrent malignant biliary obstruction in patients with self-expanding metal stents
Authors: Machado Lara Meireles Azeredo Coutinho , Martins Bruno Costa , Lima Marcelo Simas , Geiger Sebastian , Lenz Luciano , Paulo Gustavo Andrade , Safatle-Ribeiro Adriana , Ribeiro Ulysses , Maluf-Filho Fauze
Published: October 2024

Background and Aim:
Endoscopic biliary drainage with placement of a self-expanding metal stent (SEMS) is the preferred palliative treatment of malignant biliary obstruction. Recent advances in the treatment have prolonged survival, thus increasing the chance of recurrent biliary obstruction (RBO) after SEMS placement. The aim of this study was to compare different endoscopic approaches in patients with a SEMS and RBO, regarding clinical success and time to RBO.
Methods
This retrospective study included all patients with a SEMS placed because of malignant biliary strictures who underwent endoscopic retrograde cholangiopancreatography between January 2011 and December 2018. We evaluated the results of different endoscopic interventions to RBO, including insertion of a new SEMS, stent cleaning, and insertion of a plastic stent (PS).
Results
From January 2011 to December 2018, 70 (22.4%) patients developed RBO requiring endoscopic reintervention (n = 105 sessions). From the 105 ERCPs, technical success, clinical success and adverse events rates were 91,4%, 71,8%, 7,8%, respectively. Younger age (OR = 1.11 95%CI: 1.03–1.19) and the finding of a patent SEMS (OR = 0.17 95%CI: 0.04–0.08) were predictors of clinical failure (P = 0.006 and P = 0.024, respectively). The mean patency time (in days) after endoscopic reintervention was greater for SEMSs than for PSs (417.2 [95% CI: 250.0–584.4] vs 175.2 [95% CI: 124.0–226.5], P = 0.002).
Conclusions
Correct identification and treatment of the causal factor of RBO typically leads to technical and clinical success. Placement of a second SEMS provides longer patency compared to a plastic stent if ingrowth (overgrowth) occurs.