bluuebunny/crossref_metadata_2025 huggingface dataset:
Specifics of the Molecular Conformations and Physicochemical Properties of Merocyanine Form of Spirooxazine Derivative: Insights from Experimental and Annealing-Molecular Dynamics Studies
Authors: Neacsu Andreea , Chihaia Viorel , Alexiev Valentin , Hadjichristov Georgi B. , Stela Minkovska
Published: January 2025

This research focuses on the merocyanine (MC) form of a spiroindolinonaphthoxazine compound whose IUPAC signature is 4‐[(2E)‐1,1‐dimethyl‐2‐({[(1Z)‐2‐oxo‐1,2‐dihydronaphthalen‐1‐ylidene]amino}methylidene)‐1H,2H,3H‐benzo[e]indol‐3‐yl]butane‐1‐sulfonic acid. The MC molecule has multiple fragments with different degrees of mobility. The conformational changes and the flexibility of MC in presence and in absence of the solvent molecules were studied by Annealing-Molecular Dynamics (MD) cycles, providing insights into how they orient and interact with each other and with solvent molecules. The molecular packing of MC in presence and in ab-sence of solvents with different polarities was thoroughly investigated in order to determine, how the physicochemical interactions with the solvent influence the structure and stability of the MC molecule. Furthermore, the powders of MC obtained from its solutions in water, methanol, ethanol and acetonitrile were experimentally characterized using differential scanning calorimetry, thermogravimetry, Fourier transform infrared spectroscopy, and scanning electron microscopy. Both calculations and experimental results reveal the effect of the solvent polarity on the dissolved MC molecule.
Leveraging Java Programming for Climate Action: Developing a Solution to Address Air Quality and Sustainable Development Goal 13
Authors: Aziz Mohamad Fayyadh bin Abdul , Amin Noor , Kumaran Pavithra A/P Uthaya , Ahmed Ahmed Hossam Mahmoud Mahmoud , Zaman Salman , Kareem Nurul Syaziah binti Abdul , Qin Chia Yi
Published: January 2025

Climate change and air quality have become high-priority global issues, especially with the implementation of SDG 13 to take urgent action to combat climate change and its impacts. Air quality is a pressing public health and environmental challenge, as nine out of ten people in the world breathe polluted air, and millions die as a result every year. The Java programming language is used to identify and implement a solution that works toward achieving SDG 13 targets, with particular emphasis on enhancing air quality monitoring and climate action. Concretely, the designed program would be directed to integrate the policies and procedures of national contributions of countries concerning climate change (Target 13.2), improve climate change awareness, education for climate change mitigation, and resilience against climate-related hazards (Targets 13.3 and 13.1). The program will contribute to the wider outcome of enhanced support to developing countries on climate finance, Targets 13.4 and 13.5. The design includes real-time data analytics tracking air pollution levels and predicting air quality trends that give actionable insights to governments, organizations, and the public. The proposed solution aims to reduce the damage to health, ecosystems, and economies due to pollution by addressing air quality-related issues, thus supporting big climate action. The solution not only looks at reducing immediate air pollution effects but, in fact, helps toward the long-term goals of SDG 13 by facilitating adaptive capacity, education for climate change, and other global financial flows in support of climate action for developing countries. It is in this manner that the project aspires to actively join the sustained effort in sustainable development currently waged at the international level.
What If It’s Not Diabetic Macular Edema?
Authors: Acan Durgul , Girgin Yurdagul , Karahan Eyyup
Published: January 2025

Multiple myeloma is primarily diagnosed through extraocular findings; however, in patients who have not yet received a diagnosis, hyperviscosity retinopathy and/or paraproteinemic maculopathy may present as initial symptoms. In diabetic patients, the clinical diagnosis can become even more complicated due to diabetic ocular findings. Two diabetic patients presented with retinal hemorrhages and macular edema that did not respond to intravitreal anti-vascular endothelial growth factors. The “silent macula” appearance was observed in both fundus fluorescein angiography (FFA). After systemic investigation, these patients were diagnosed with multiple myeloma. Paraproteinemic maculopathy can particularly mimic diabetic macular edema in diabetic patients and should be considered in cases of atypical findings, particularly when serous retinal detachments are present without apparent leakage on FFA.
Exploring the Landscape of Large and Small Language Models: Advancements, Trade-offs, and Future Directions
Authors: Shams Duha , Salama Ikraam , Callixtus Idowu
Published: January 2025

Recent advances in natural language processing (NLP) have led to the development of large language models (LLMs) and small language models (small LMs), which have revolutionized the field. LLMs, such as GPT-3 and PaLM, are capable of performing a wide range of tasks with state-of-the-art accuracy, thanks to their vast number of parameters and extensive training data. However, these models are resource-intensive, requiring significant computational power for both training and deployment. In contrast, small LMs offer a more efficient alternative, with reduced computational requirements and faster inference times, making them well-suited for resource-constrained environments such as mobile devices and real-time applications. This survey explores the key differences between LLMs and small LMs, focusing on aspects such as model size, computational efficiency, performance, and deployment scenarios. We also discuss the trade-offs associated with selecting between the two, and highlight techniques such as knowledge distillation and model pruning that are used to optimize small LMs. Finally, we examine the future directions of language model research, including hybrid approaches that combine the strengths of both LLMs and small LMs, and advancements aimed at improving energy efficiency and sustainability. Our goal is to provide a comprehensive overview of the current landscape of LLMs and small LMs, and to offer insights into the ongoing challenges and opportunities in the field of NLP.
Overview on the Development of a Data Collection Tool Based on GreenCOMP: The Middle Stage of Its Development Process
Authors: Ferreira-Santos João , Marques Margarida , Pombo Lúcia
Published: January 2025

This paper presents the selection process of a GreenComp-based data collection tool designed to evaluate sustainability competences within the EduCITY project. EduCITY aims to create a smart learning city environment based on mobile Augmented Reality games integrating on its game-mechanics topics concerning Sustainability and Sustainable Development. Besides the games, it is required a specific data collection tool aligned to its research question, but wider enough to be used in another research contexts. The data collection tool research process involved several stages, culminating in the production of three academic papers. This paper focuses on the contextual and theoretical framework rather than the methodological details. This paper is the second element of a series of three, with the first paper detailing a systematic literature review and the third paper focusing on the specific methodologies for implementing the questionnaire and analyzing the data collected from the Educity activities. The findings will be used to refine and adapt the data collection tool to different contexts, contributing to a deeper understanding of sustainability competences and their role in educational initiatives.
Isolation and Characterisation of Acid Soluble Collagens and Pepsin Soluble Collagens from Eel (Anguilla japonica Temminck Et Schlegel) Skin and Bone
Authors: Zhang Ningning , Guo Shaoli , Zheng Yuting , Li Weili
Published: January 2025

Eel (Anguilla japonica) is an important and valuable food fish in East Asia and its by-products have been reported to include bioactive and profitable components. This study aimed to extract, characterise and compare the structure and properties of acid-soluble collagens (ASCs) and pepsin-soluble collagens (PSCs) from the skin and bone of eel (Anguilla japonica), providing insights into their composition, structure and properties for various applications. The yields of ASC-S (from skin), PSC-S (from skin), ASC-B (from bone), and PSC-B (from bone) were 12.16%, 15.54%, 0.79%, and 1.34% on dry weight basis respectively. Glycine, the dominant amino acid, accounted for 16.66% to 22.67% of total amino acids in all samples. SDS-PAGE and FTIR analyses showed the typical triple-helical structure of type I collagen with slight variations in molecular order in extract and intermolecular cross-linking between skin and bone collagens. The denaturation temperature (Tmax1) measured by differential scanning calorimetry (DSC) is 81.39 °C and 74.34 °C respectively for ASC-B and ASC-S. The bone collagen has higher thermal resistance than skin collagen. Surface morphology imaged using scanning electron microscope (SEM) showed that the bone collagen had a denser network structure, whilst the skin collagen was more fibrous and porous. The findings suggest that eel-derived collagens from skin and bone, can serve as potential alternatives in food, cosmetic, and healthcare industries.
Impact of the COVID-19 Pandemic on the Utilization of Antibiotics in the University Clinical Dentistry Center of Kosovo
Authors: Hoti Agon , Sutej Ivana , Jakupi Arianit
Published: January 2025

The COVID-19 pandemic led to significant disruptions in healthcare services worldwide, including dental care, resulting in increased reliance on antibiotics as a substitute for in-person dental interventions. This study aimed to examine the utilization of different classes of antibiotics at the University Dental Clinical Center of Kosovo during the COVID-19 pandemic and compare it with prescription trends from the pre-pandemic period. Methods: A retrospective analysis was conducted on antibiotic prescription data from the University Dental Clinical Center of Kosovo, spanning from 2019 to 2022. The study compared prescription trends before and during the pandemic and identified the most prescribed antibiotic classes. Additionally, findings were contextualized by reviewing global literature on antibiotic use in dental practices during the pandemic. Results: The study revealed a significant increase in the prescription of broad-spectrum antibiotics, including amoxicillin, clavulanic acid, clindamycin, and metronidazole, during the pandemic period. These findings mirrored global trends, where restricted access to dental care and increased self-medication led to higher antibiotic consumption. Concerns about antimicrobial resistance (AMR) were raised due to the over-prescription of these antibiotics during the pandemic. Conclusions: The study highlights the impact of the COVID-19 pandemic on dental antibiotic prescription practices in Kosovo, revealing a concerning increase in broad-spectrum antibiotic use. This underscores the need for improved antibiotic stewardship in dental settings, particularly during public health crises, to prevent the exacerbation of antimicrobial resistance. Ensuring continued access to routine dental care and developing robust protocols for antibiotic prescription during emergencies are essential to mitigate the long-term public health impacts of increased antibiotic use.
Identifying Novel Emotions and Wellbeing of Horses From Videos Through Unsupervised Learning
Authors: Bhave Aarya , Kieson Emily , Hafner Alina , Gloor Peter A
Published: January 2025

This research applies unsupervised learning on a large original dataset of horses in the wild to identify previously unidentified horse emotions. We construct a novel, high-quality, diverse dataset of 3,929 images consisting of five wild horse breeds worldwide at different geographical locations. We base our analysis on the seven Panksepp emotions of mammals “Exploring”, “Sadness”, “Playing”, “Rage”, “Fear”, “Affectionate” and “Lust”, along with one additional emotion “Pain” which has been shown to be highly relevant for horses. We apply the contrastive learning framework MoCo (Momentum Contrast for Unsupervised Visual Representation Learning) on our dataset to predict the seven Panksepp emotions and “Pain” using unsupervised learning. We significantly modify the MoCo framework, building a custom downstream classifier network that connects with a frozen CNN encoder which is pretrained using MoCo. Our method allows the encoder network to learn similarities and differences within image groups on its own without labels. The clusters thus formed are indicative of deeper nuances and complexities within a horse’s mood, which can possibly hint towards existence of novel and complex equine emotions.
Anaphoric and Cataphoric Uses of the Definite Article “the” in Essays
Authors: Shokirova Makhbubakhon , Uzakova Odinakhon
Published: January 2025

This study examines the use of the definite article “the” in college application essays written by non-native speakers of English, focusing in particular on its “anaphoric” (backward-looking) and “cataphoric” (forward-looking) functions. The study analyzes ten essays written by Uzbek students and examines how they use “the” to create cohesion and improve the clarity of their narratives. A mixed methods approach was used, combining both quantitative analysis of frequency of article use and qualitative analysis of patterns in anaphoric and cataphoric references. The results show that non-native speakers use “that” strategically to maintain narrative flow, with anaphoric references occurring more frequently. The study highlights the importance of cohesive elements in challenging texts, such as college application essays, and provides practical advice for improving the teaching of English to non-native speakers.
Application of Artificial Intelligence in the Early Diagnosis of Potentially Malignant Skin Lesions: A Cross-Sectional Study in a Population Assisted by the Public Health System in Brazil
Authors: Silva Mariana Toledo , Batista Marília Jesus , Okita Karina Lumy , Oliveira-Ciabati Livia , Carvalho Isabelle , de Melo Alessia Nolyq Oliveira , Ferreira Juliana Arêas de Souza Lima Bentrame , Bulisani Luis Eduardo Pedigoni , Marchi Evaldo
Published: January 2025

Abstract: This study aims to evaluate the impact of an Artificial Intelligence (AI) algorithm developed for diagnosing potentially malignant skin lesions in shortening the time between diagnosis and treatment in a population assisted by the public health system in a Brazilian municipality. This study included 492 patients with dermatological lesions. Images of skin lesions were processed by an AI algorithm and classified as benign or atypical/malignant. Two dermatologists evaluated the same lesions to determine the agreement rate with the AI diagnosis. The time between the primary care consultation and the specialist evaluation, and the time between the diagnosis by the dermatologist and the biopsy of the atypical/malignant lesions were estimated. There was substantial agreement between dermatologists, and reasonable but significant agreement between the AI algorithm and the dermatologists. The AI model predicted malignant lesions with a sensitivity of 89.2%, specificity of 34.4% and accuracy of 78.8%. The mean time between the primary care consultation and the specialist evaluation was 188 ± 96 days, and between the specialist diagnosis and the biopsy of the atypical/malignant lesions was 25 ± 10 days. In conclusion, there was reasonable agreement between the AI algorithm and dermatologists. AI-powered support tools could empower primary care physicians to deliver faster and more precise diagnoses, expedite referrals for suspected malignant skin lesions, and reduce delays in treatment.
GuideNet: Leveraging UNet as Guidance for Anatomical Landmark-Based Breast Segmentation in Thermograms
Authors: Alvarez-Padilla Francisco J , Argudin-Ferran Mayelin V , Flores-Nunez Jorge L , Alvarez-Padilla Juan R
Published: January 2025

Breast cancer remains the most prevalent cancer among women worldwide, underscoring the critical need for effective early detection methods. Thermography presents a non-invasive and radiation-free alternative, offering significant potential in breast cancer screening; however, challenges such as noise, low resolution, and anatomical variability limit its potential. The aim of this project is to propose a method for segmenting the breast in thermograms to analyze thermal variations that might be associated with medical pathologies. To address these issues, we developed a hybrid segmentation approach that combines traditional computer vision techniques with a Dense Multiscale U-Net architecture, leveraging anatomical references extracted directly from thermographic images. Our method integrates the adaptability of deep learning with the domain-specific precision of traditional computer vision techniques, creating a hybrid framework designed to overcome individual limitations and enhance segmentation robustness. Our method demonstrated substantial advancements in segmentation performance, achieving a Dice Similarity Coefficient (DSC) of 0.987 and an Intersection over Union (IoU) of 0.963. These results not only surpassed those of standalone computer vision methods (DSC: 0.983, IoU: 0.934) and neural networks (DSC: 0.972, IoU: 0.922), but also highlighted the robustness, reproducibility, and precision of the hybrid approach in addressing critical challenges. Notably, the method effectively delineates the weak supramammary boundary, a common limitation of other techniques, thereby enhancing its clinical relevance and reliability in diverse imaging scenarios. This approach enhances the precision and robustness of breast segmentation, particularly in challenging regions such as the supramammary boundary. By bridging the gap between traditional and deep learning methods, this hybrid framework represents a significant advancement in thermography-based breast segmentation and lays the foundation for improving diagnostic accuracy in breast cancer screening.
Bridging the Gap: A Quantum-Classical Exploration of Newtonian Mechanics in the Big Bang Theory
Authors: Tiwari Vidhan
Published: January 2025

The Big Bang theory has reshaped cosmology, positing that the universe originated from a singularity—a point of infinite density and temperature. While this model provides a robust narrative of cosmic evolution, the true mechanisms driving the initial expansion remain largely unexplored. This paper ventures into this territory by examining whether Newton’s three laws of motion, traditionally confined to macroscopic bodies, might offer insights into the very birth of the cosmos. By conceptualizing the universe’s expansion through a quantum-classical synthesis, we present Newtonian mechanics not merely as historical relics but as potentially revelatory tools for comprehending cosmic origins. While the classical framework alone cannot encapsulate quantum or relativistic phenomena, its foundational principles of force, inertia, and reaction propose a fresh analytical lens. This exploration ultimately argues for the integration of classical, quantum, and relativistic physics into a singular narrative, one capable of unearthing the forces that ushered the universe into existence.
Green Banking Initiatives in India: Challenges and Opportunities
Authors: Karthikeyan Pranav
Published: January 2025

Green banking has emerged as a crucial strategy to promote environmentalsustainability within the financial sector. This study quantitatively examines the initiatives,challenges, and opportunities of green banking in India. By analysing data from Indian banks,environmental indices, and regulatory frameworks, this paper identifies key drivers, evaluatesthe effectiveness of existing measures, and provides insights into the challenges hinderingwidespread adoption. The findings aim to inform policy interventions and banking practices toenhance sustainability in India's financial sector
Genetic Diversity of Siamese Rosewood Dalbergia cochinchinensis Pierre in Thailand: Insights from Chloroplast and Nuclear DNA Analysis
Authors: Prompen Benjarat , Saijuntha Weerachai , Pilap Warayutt , Thanonkeo Sudarat
Published: January 2025

Siamese rosewood (Dalbergia cochinchinensis Pierre) is a highly valuable economic tree species in Thailand. This study investigated genetic diversity across 72 tree samples from 17 Thai locations using two genetic markers: the chloroplast maturase K (matK) gene and the nuclear internal transcribed spacer (ITS) region, which includes ITS1, 5.8S ribosomal RNA gene, and ITS2. Analysis of 48 matK and 65 ITS sequences revealed 34 (DcM1–DcM34) and 31 (DcI1–DcI31) haplotypes, respectively. The Saraburi province haplotype DcM10 exhibited the highest genetic divergence, differing by 25 mutational steps from other haplotypes. Phylogenetic analysis incorporating GenBank sequences from other Dalbergia species showed that matK sequences separated D. cochinchinensis into two distinct clades: Clade A comprising most Thai samples, and Clade B containing Saraburi samples grouped with sequences from Laos and Cambodia. In contrast, ITS analysis revealed a single monophyletic group. These findings have important applications for conservation strategies, particularly in prioritizing the genetically distinct Saraburi population for protection, and can inform breeding programs to maintain genetic diversity. Additionally, the identified genetic markers could be developed into tools for timber authentication to combat illegal logging, while the revealed population structure can guide reforestation efforts and international conservation collaboration with Laos and Cambodia.
Intensive Care Outcomes in Acute Exacerbations of Idiopathic Pulmonary Fibrosis: A Retrospective Cohort Study
Authors: ARI Maşide , Akinci Ozyurek Berna , Yildiz Murat , Ozdemir Tarkan , Hoşgün Derya , Sahin Ozdemirel Tugce , Ensarioğlu Kerem , Erdogdu Mahmut Hamdi , Eraslan Doganay Guler , Doganci Melek , Menteş Oral , Tüten Ömer Faruk , Celik Deniz
Published: January 2025

Introduction and Objective: Acute exacerbation of idiopathic pulmonary fibrosis (IPF-AE) often results in severe respiratory distress requiring treatment in the intensive care unit and has a high mortality rate. Identifying prognostic markers and assessing disease severity are crucial for clinicians to gain detailed insights. The mean platelet volume-to-platelet count ratio (MPR) is an inflammatory marker commonly used in malignancies. This study aimed to evaluate MPR and other factors affecting mortality in patients with IPF-AE who were monitored in the intensive care unit (ICU). Materials and Methods: This retrospective study was conducted on patients monitored in the ICU for IPF-AE between 2017 and 2023. Demographic characteristics, vital signs, laboratory and imaging findings, and administered treatments were reviewed. MPR was calculated by dividing the mean platelet volume by the platelet count. The primary endpoint was defined as 1-month in-hospital mortality. Results: A total of 59 patients monitored in the ICU for IPF-AE were included in the study. The mean age of the patients was 62.75 years, and 81.4% of the participants were male. During the 30-day follow-up period, 62.7% of the patients died. The need for invasive mechanical ventilation (IMV) was significantly associated with increased mortality (p<0.001). The optimal cutoff value for MPR was determined to be 0.033, with a sensitivity of 83.7% and specificity of 63.64%, indicating its predictive value for mortality (AUC: 0.764; 95% CI: 0.635–0.864; p<0.001). Conclusions: In this study, the need for IMV emerged as a critical parameter in predicting mortality in patients with IPF-AE. Additionally, the use of the MPR as a prognostic biomarker may offer a novel approach in the management of IPF patients. These findings could contribute to the development of strategies aimed at early intervention in IPF patients. Further studies with larger sample sizes are needed to validate these results.
Domain Adaptation and Fine-Tuning of a Deep Learning Segmentation Model of Small Agricultural Burn Area Detection Using Hi-Resolution Sentinel-2 Observations: A Case Study of Punjab, India
Authors: Anand Anamika , Imasu Ryoichi , Dhaka Surendra , Patra Prabir K.
Published: January 2025

High-resolution Sentinel-2 imagery combined with a deep learning (DL) segmentation model offers a promising approach for accurate mapping of small and fragmented agricultural burn areas. Initially, the model was trained using ICNF burn area data from Portugal to capture large fire and burn area delineation, thereby achieving moderate accuracy. Subsequent fine-tuning using annotated data from Punjab improved the model’s ability to detect small burn patches, demonstrating higher accuracy than the baseline Normalized Burn Ratio (NBR) Index method. On-ground validation using buffer zone analysis and crop field images confirmed the effectiveness of DL approach. Challenges such as cloud interference, temporal gaps in satellite data, and limited reference data for training persist, but this study underscores the methodogical advancements and potential of DL models applied for small burn area detection in agricultural settings. The model achieved overall accuracy of 98.7%, a macro-F1 score of 97.6%, IoU 0.54, and a Dice coefficient of 0.64, demonstrating its for detailed burn area delineation capability. The model can capture burn area smaller than 250m2, but the model at present is less efficient at representing the full extent of the fires. Overall, outcomes demonstrate the model’s applicability to generalize to a new domain despite regional differences among research areas.
Immunohistochemical, Ultrastructural, and Histological Analysis of Two Type 1 Collagen Membranes Used for Tissue Regeneration in the Subepithelial Region
Authors: Vieira de Oliveira Victor Hugo , Brum Igor da silva , Elias Carlos Nelson , Frigo Lucio , Do Nascimento Ana Lúcia Rosa , Pereira Mario José dos Santos , Carvalho Marco Antonio , de Carvalho Jorge José
Published: January 2025

Collagen membranes for tissue regeneration have been widely used in medicine and dentistry. Collagen membranes have a high biocompatibility and adequate healing time. The objective of the present work was to develop the combination of the use of type 1 collagen membranes in association with synthetic nanohydroxyapatite grafts. This study used 32 adult male Wistar rats weighing 250 g at 12 weeks of age. Critical defects of 8 mm were made in the temporoparietal region. The rats were divided into 5 groups of 8 rats each: sham group (only bone defects were made without being filled with biomaterial); Bio-Oss Group - the defect was filled with xenogeneic hydroxyapatite (Bio-Oss® - Geistlich); Bio-Oss + Membrane - the defect was filled with xenogeneic hydroxyapatite (Bio-Oss® - Geistlich) associated with collagen membrane (Mucrograft® – Geistlich); Blue-Bone® Group – the defect was filled with synthetic hydroxyapatite (Blue-Bone® – Regener); and Blue-Bone® + Green® Membrane Group – the defect was filled with synthetic hydroxyapatite (Blue-Bone® – Regener) associated with the type 1 collagen membrane (Green® Membrane Perio - Regener). The immunohistomic analysis showed that type 1 collagen membrane did not have contaminants. The ultrastructure analysis showed the type 1 collagen membrane has thin collagen fibers. The histology results showed it was possible through Goldner's Trichrome to measure the amount of collagen in each group analyzed. In conclusion, the type 1 collagen membrane, called Green Membrane Perio, has fibers organically organized, can be indicated for tissue regeneration, and the application is considered safe and biocompatible.
Lyophilized Extract from Larvae of the Blowfly Lucilia sericata as a New Strategy for the Management of Chronic Wounds
Authors: Hoff Norman-Philipp , Gestmann Falk Peer , Jansen Theresa Maria , Janssen Sarah , Petersdorf Sabine , Homey Bernhard , Gerber Peter Arne , Mehlhorn Heinz
Published: January 2025

Background: Chronic wounds represent a growing challenge in the aging population, significantly impairing quality of life, increasing the frequency of medical consultations, and imposing substantial healthcare costs. Chronic wounds are prone to complications, including local and systemic infections, and in severe cases, amputations. The therapeutic use of live larvae from the blowfly Lucilia sericata (biological debridement) has regained attention for its ability to debride necrotic tissue and stimulate granulation. Despite its benefits, this therapy is constrained by logistical challenges in producing and delivering live larvae and by patient adherence issues. Objectives: This study aimed to develop a lyophilized extract of Lucilia sericata larvae and evaluate its efficacy in treating chronic wounds. Methods: A lyophilized extract (Larveel®, Alpha-Biocare GmbH, Neuss, Germany) of the larvae of Lucilia sericata was produced under GMP conditions. In a total of ten patients with chronic refractory wounds the extract was used in individual therapeutic trials and its effect on bacterial colonization and wound healing was investigated. Results: Of ten patients, three discontinued treatment due to P. aeruginosa colonization. In seven patients, significant fibrin reduction, granulation, and wound healing occurred, with two achieving complete closure and four showing advanced epithelialization. Conclusions: In 7 of 10 patients, the application of the extract led to a marked reduction in wound slough, improved granulation, and progression of wound healing. These effects are likely attributed to the extract’s ability to disrupt bacterial biofilm formation. The findings suggest that this novel therapeutic approach may provide a practical and effective alternative to live larval therapy for managing chronic wounds.
COVID-19 Readmission Is Highest Among Refugees in Denmark
Authors: Moussa Amar Ali , Mohammad Marwa , Halgreen Eiset Andreas , Freja Storgaard Signe , Wejse Christian
Published: January 2025

In several countries, a higher incidence of COVID-19 is seen in vulnerable groups including certain immigrant groups compared to host population. This study compared the severity of COVID-19 at the onset of admission and the odds ratio (OR) of COVID-19 readmission within 30 days among Refugees, Others, and native Danes. This cross-sectional study was based on clinical data from 159 COVID-19 positive patients admitted at hospitals in Region Midtjylland in 2020.
The medians with interquartile ranges revealed no major differences between the groups in status at admission based on certain clinical parameters or biomarkers.
Refugees (51.8%) and Others (41.7%) had less comorbidities than native Danes (61.2%) in the Charlson Comorbidity Index (CCI). Native Danes constituted the largest percentage of the highest CCI scores.
17.5% of all 159 patients were readmitted. The readmission prevalence among Refugees was 23.1%, 8.3% in Others, and 17.0% in native Danes. When adjusting for age, sex, and CCI, Refugees had an OR of 1.88 (95% CI, 0.61-5.74) and Others had an OR of 0.61 (95% CI, 0.07-5.41) of readmission compared to native Danes.
Refugees had the highest prevalence and adjusted OR for COVID-19 readmission. However, the results of the logistic regression were not statistically significant.
Magnetic Noise Mitigation Strategies for the Einstein Telescope Infrastructure
Authors: Garaventa Barbara , Armato Federico , Chincarini Andrea , Fiori Irene
Published: January 2025

The Einstein Telescope (ET) will be a third-generation Gravitational Wave (GW) detector that will tackle cutting-edge technological challenges. ET will be constructed at a depth of 200-300 meters to isolate it from vibrations caused by seismic waves and human activities, which are sources of noise for GW detection. To meet ET’s objectives, it will be necessary to improve low-frequency sensitivity by about two orders of magnitude compared to current interferometers (LIGO, Virgo). Magnetic noise is a limiting noise in the frequency range from a few Hz up to around 100 Hz in future GW detectors. This article will discuss the magnetic noise mitigation strategies under development, based on experiences from Virgo.
Rhizobiome and Its Architecture in the Crop Improvement and Resistance Towards Pathogens
Authors: Naziya Banu
Published: January 2025

Rhizobiome is a network of beneficial microbes (fungi, bacteria, nematodes, actino-mycetes, mycorrhizae) and to study their interaction is fascinating in producing higher crop yield and increase resistance against phytopathogens. The interactions could be competitive, neutral, commensal and mutualistic. Beneficial interactions chiefly colo-nize roots and maximize plant growth parameters like plant height, leaves number & fruit/pod/seed number. The positive microinoculants fix nitrogen, solubilize P and Fe, produce phytohormones, improve plant defense chemicals and enzyme machinery in root tissues upon pathogen invasion. The rhizomicroinoculants also increase photo-synthetic and nutritional proficiency by maximizing total chlorophyll, carotenoid, carbohydrates, amino acids and proteins in plants. They play a crucial role in pre-venting disease outbreak under stressful situations like drought, metal stress, salinity, high temperature, soil erosion, change in rainfall and humidity. Understanding rhi-zomicrobiome interaction and preparing novel rhizospheric microbial formulations could be good strategy to improve plant growth parameters, fruit yield and defense ability towards pathogen.
Advanced DFE and MLD Techniques for Enhanced 5G mm-Wave A-RoF Performance at 60 GHz
Authors: Farooq Umar , Miliou Amalia
Published: January 2025

This article presents the Decision Feedback Equalizer (DFE) and the Maximum Likelihood detection (MLD) algorithms designed in MATLAB to equalize the received signal in a dispersive optical link up to 120 Km. The algorithms are tested using a converged 5G mm-wave analog radio-over-fiber (A-RoF) system at 60 GHz. The algorithm’s performance is measured regarding error vector magnitude (EVM) values before and after equalization, for different optical fiber lengths and modulation formats (QPSK, 16-QAM, 64-QAM, and 128-QAM) and shows a clear performance improvement of the output signal. Moreover, the performance of the proposed algorithms is compared to three commonly used algorithms: the simple least mean square (LMS) algorithm, the constant modulus algorithm (CMA), and the adaptive median filtering (AMF) demonstrating superior results in both QPSK and 16-QAM and extending the transmission distance up to 120 km. DFE has a significant advantage over LMS and AMF in reducing the inter-symbol interference (ISI) in a dispersive channel by using previous decision feedback, resulting in quicker convergence and more precise equalization. MLD on the other hand is highly effective in improving detection accuracy by taking into account the probability of various symbol sequences achieving lower error rates and enhancing performance in advanced modulation schemes. Furthermore, DFE and MLD are particularly suitable for higher-order modulation formats like 64-QAM and 128-QAM, where accurate equalization and error detection are of utmost importance. The enhanced functionalities of DFE and MLD in managing greater modulation orders and expanding transmission range highlight their efficacy in improving the performance and dependability of our system.
Optimal Portfolio and Trading Strategy Using Machine Learning
Authors: Ouazzane Karim , Yung Tang Kai Hung Po , Ghanem Mohamed Chahine
Published: January 2025

This research presents machine learning models for forecasting the future returns of a portfolio from NASDAQ semiconductors assets by financial analysis, optimization, and technical analysis to form a trading strategy. The performance of the portfolio is evaluated by back-testing. Data were collected from 2011 to 2019 for the sector of semiconductor companies listed on Nasdaq. The project consists of 4 sub-tasks. The first sub-task is to use the annual financial ratios of each company under the sector of semiconductors from 2011 to 2018 to project the company returns in 2019 using machine learning algorithms. Then, the top 5 highest-return assets would be selected to form a portfolio. After the optimization of the portfolio by Monte Carlo simulation, the classifiers adopt the technical indicators of the portfolio assets from 2011 to 2018 to predict the trading signals (buy or sell) in 2019. The trading actions in 2019 are simulated by back-testing. The result shows that the optimal portfolio using the simulated trading strategy can have a profit of 50%. The profit is worse than the buy-and-hold strategy but better than the portfolio without optimization.
Exploring the Utility of Renal Resistive Index in Critical Care: Insights into ARDS and Cardiac Failure
Authors: Cuttone Giuseppe , Geraci Giulio , La via Luigi , Sorbello Massimiliano , Pappalardo Federico , Carollo Caterina
Published: January 2025

The renal resistive index (RRI), a doppler ultrasound-derived parameter measuring renal vascular resistance, has emerged as a promising non-invasive tool to evaluate renal haemodynamics in critically ill patients, particularly those with Acute Respiratory Distress Syndrome (ARDS) and heart failure (HF). This review examines the current evidence for RRI measurement in these conditions, exploring its physiological bases, methodology, clinical applications, and limitations. In ARDS, RRI reflects the complex interactions between positive pressure ventilation, hypoxemia, and systemic inflammation, showing a role in predicting acute kidney injury and monitoring response to interventions. In HF, RRI is able to assess venous congestion and cardiorenal interactions, also serving as a prognostic indicator. Many studies have shown RRI’s superiority or complementarity to traditional biomarkers in predicting renal dysfunction, though its interpretation requires consideration of multiple patient-related factors. Key challenges include operator dependency, lack of standardization, and complex interpretation in multi-organ dysfunction. Future research should focus on measurement standardization, development of automated techniques, investigation of novel applications like Intra-Parenchymal Renal Resistive Index Variation, and validation of RRI-guided management strategies. Despite its limitations, RRI represents a valuable tool offering bedside and real-time insights into renal hemodynamics and potential guidance for therapeutical interventions. Further research is needed to fully clarify its clinical potential and address current limitations, particularly in critical care setting involving multiple organ dysfunction.
N-alkylated 5,5-diphenylhydantoin Derivatives: Synthesis, X-ray, Spectroscopic Characterization, Hirshfeld Surface Analysis, DFT, Molecular Docking, Molecular Dynamics Simulations, and Cholesterol Oxidase Inhibitory Estimation
Authors: Lamssane Houda , Haoudi Amal , Thiruvalluvar Aravazhi Amalan , Hökelek Tuncer , Varadharajan Venkatramanan , Chakroune Said , Anouar El Hassane , Rodi Youssef Kandri , Mazzah Ahmed , Mague Joel T. , Alsaif Nawaf A. , M Alanazi Mohammed , El Monfalouti Hanae , Sebbar Nada Kheira
Published: January 2025

Nine N-alkylated phenytoin derivatives (5-13) have been synthesized and characterized using spectroscopic tools with the structures of 4-6 confirmed by X-ray crystallography. The starting material, phenytoin 2, was obtained by a base-catalyzed condensation from benzyl and urea. Its alkylation with benzyl chloride yields 3 and 4. Subsequently, the alkylation of 3 with various alkyl halides under solid-liquid phase transfer catalysis (PTC) led to the formation of 5-13. The intercontacts in 4-6 crystals are identified by analyzing their corresponding Hirshfeld surfaces and fingerprint plots, and the HS analysis reveals that the most intercontacts involved H…H interactions. DFT calculations relatively well reproduce X-ray geometrical parameters in IEF-PCM at the B3LYP/6-3111++G(d,p) level of theory. The calculated electronic and molecular properties demonstrate that the N-alkylation of 5-6 may have slight effects on the calculated, and that reactivity is relatively similar. The inhibitory potency of 4-6 towards cholesterol oxidase (ChOx) is estimated by investigating their binding affinity into the binding site of binding cholesterol oxidase. The compound 4 with N-benzyl moiety displays higher binding affinity than 5 and 6 with N-methyl and N-ethyl moieties. Additionally, the stability of 4 in the binding site ChOx was evaluated and an 80 ns MD simulation was conducted. MD analysis shows that the 4-ChOx complex remained stable throughout the simulation, showing minimal conformational changes, which confirms the stability of 4 in the ChOx binding site.
Cardiometabolic Risks: Cellular and Molecular Mechanisms
Authors: Tate Ashok , Rao Gundu H.R.
Published: January 2025

Cardiometabolic diseases, including ischemic heart disease, hypertensive heart disease, and associated vascular conditions, have experienced a sharp global increase in prevalence and mortality over recent decades. According to the World Heart Federation, the global death toll from cardiovascular diseases grew from 12 million in 1990 to 20.5 million in 2021. The development of cardiometabolic diseases such as hypertension, type 2 diabetes, obesity, and vascular disorders is driven by a complex interplay of risk factors. These include oxidative stress, chronic inflammation, imbalances in blood glucose and lipid levels, formation of lipid peroxides, increased intima-media thickness (IMT), subclinical atherosclerosis, coronary artery calcification, arterial narrowing, the formation of vulnerable plaques, and activation of platelet and coagulation pathways. These risk factors contribute to metabolic disorders by promoting insulin resistance, endothelial dysfunction, and the progression of atherosclerosis. Despite this understanding, routine annual checkups often fail to prioritize early risk detection or the development of preventive and management strategies. Instead, these checkups typically focus on diagnosing existing metabolic conditions and managing them to prevent acute vascular events. However, the South Asian Society on Atherosclerosis and Thrombosis has been actively promoting global education and the development of preventive strategies. Recent research advances underscore the importance of addressing oxidative stress, inflammation, and lipid oxidation to slow disease progression. Innovative therapeutic approaches, including statins, small-interfering RNA (siRNA), non-coding RNA (ncRNA) therapies, and gene-editing tools like CRISPR/Cas9, have shown considerable promise in lowering cholesterol and lipoprotein(a) levels, lowering risks associated with type-2 diabetes, stabilizing arterial plaques, and reducing overall cardiovascular risk. Additionally, breakthroughs in mRNA vaccines and computational genomics emphasize the potential of precision medicine in tackling chronic diseases. This overview highlights key findings on the roles of oxidative stress, inflammation, atherosclerosis, platelet-vessel wall interactions, and acute vascular occlusion in the development and progress of cardiometabolic diseases. Major clinical trials indicate that addressing a few modifiable risk factors can significantly reduce premature deaths from cardiovascular diseases. Nevertheless, managing cardiometabolic diseases on a population scale remains a formidable challenge. Enhanced preventive measures, better diagnostic tools, and widespread education are critical. Collaborative efforts involving governments, healthcare systems, professional organizations, and communities are essential to alleviating the societal burden of these conditions and improving health outcomes globally. This, in turn, can improve health outcomes, particularly in low-and middle-income countries where healthcare infrastructure and resources are often limited.
Harvesting Salinity Gradient Energy by Diffusion of Ions, Liquid Water and Water Vapor
Authors: Veerman Joost
Published: January 2025

In this paper we briefly discuss the main points of salinity gradient energy (SGE). First we discuss the sources of SGE and the methods to harvest it. Then we calculate, using the laws of physical chemistry, the amount of energy that can be harvested with three selected methods based on the diffusion of ions, of liquid water and of water vapor respectively. Then we give an overview of the applications, highlighting a number of new developments such as assisted reverse electrodialysis (ARED) and energy storage. It turns out that reverse electrodialysis offers unexpected possibilities such as energy storage, utilizing waste heat and the administration of transdermal drug delivery; a technique that has been launched very recently.
A Qualitative Analysis on Negotiation Tactics and Supplier Relationship Management in Multinational Supply Chains
Authors: Cooper Mason
Published: January 2025

This study explores the intricate dynamics of negotiation tactics and supplier relationship management within multinational supply chains, emphasizing their critical role in ensuring operational efficiency and strategic alignment. By examining key elements such as trust, cultural sensitivity, collaboration, risk management, digitalization, power dynamics, ethics, and interpersonal skills, the research highlights how these factors shape the success and sustainability of supply chain partnerships. Trust emerged as a cornerstone, facilitating transparency and collaboration, while cultural sensitivity enabled smoother interactions in diverse, multicultural settings. Collaborative practices and proactive risk management strategies further strengthened the resilience and adaptability of supply chains, particularly in volatile global markets. The transformative impact of digital technologies on negotiation and supplier management was evident, offering enhanced decision-making capabilities, real-time communication, and performance monitoring, although these advancements were accompanied by challenges such as data security concerns and infrastructure costs. The study also underscores the importance of balancing power dynamics and fostering equitable negotiations to sustain long-term partnerships. Ethical considerations, including sustainability and corporate responsibility, were integral to contemporary supply chain practices, reflecting growing stakeholder expectations. Interpersonal skills, such as effective communication and empathy, were identified as vital for building trust and resolving conflicts during negotiations. This research provides a comprehensive understanding of the multifaceted nature of negotiation and supplier relationship management, offering valuable insights for organizations seeking to navigate the complexities of global supply chains effectively. It emphasizes the need for holistic and adaptive strategies that prioritize collaboration, ethics, and innovation to achieve sustained success in an increasingly interconnected world.