bluuebunny/crossref_metadata_2025 huggingface dataset:
Exploring the Neuropsychiatric Impact of Corticosteroids: A Review of Mental Health Outcomes
Authors: Nasereddin Lara , Alnajjar Omar , Bashar Homam , Abuarab Sara Feras , Al-Adwan Rahma , Chellappan Dinesh Kumar , Barakat Muna
Published: October 2024

Corticosteroids are extensively used in medicine for their powerful anti-inflammatory and immunosuppressive effects. However, their psychiatric side effects—such as mood disturbances, anxiety, and psychosis—are significant yet often underappreciated. This review provides a comprehensive exploration of corticosteroid-induced psychiatric disorders, with a focus on their underlying mechanisms and clinical implications. We examine how corticosteroids influence the Hypothalamic-Pituitary-Adrenal (HPA) axis, leading to dysregulation of stress responses and alterations in neurotransmitter levels, particularly dopamine, serotonin, and glutamate. These changes are linked to structural abnormalities in key brain areas such as the hippocampus and amygdala, which are implicated in mood and anxiety disorders, psychosis, and conditions like post-traumatic stress disorder (PTSD) and eating disorders. This review highlights the need for healthcare providers to be vigilant in recognizing and managing corticosteroid-induced psychiatric symptoms, especially in vulnerable populations with pre-existing mental health conditions. The complex relationship between corticosteroid type, dose, duration, and mental health outcomes is explored, emphasizing the importance of personalized treatment approaches to mitigate psychiatric risks. Given the widespread use of corticosteroids, there is an urgent need for more focused research on their psychiatric side effects. This review underscores the importance of patient education and careful monitoring to ensure optimal therapeutic outcomes while minimizing mental health risks associated with corticosteroid therapy.
First insights into the microbiota within the oral cavity of the pygmy sperm whale (Kogia breviceps)
Authors: Mesquita Cristina Sousa , Soares-Castro Pedro , Ferreira Marisa , Sequeira Marina , Eira Catarina , Santos Pedro Miguel
Published: October 2024

The number of environmental surveillance reports of the ocean ecosystem through systematic studies on marine mammal health and disease is increasing. Understanding host-microbe interactions in cetaceans may contribute to the identification of compromised populations, microbial markers of disease, and ultimately to scientifically base population management decisions. This study is the first assessment of the oral microbiome of three pygmy sperm whales (Kogia breviceps), found stranded in Atlantic Iberian waters, by 16S rDNA-amplicon sequencing. A constrained canonical analysis approach showed that the oral microbiomes were shaped by the host species when compared to the oral microbiomes of three Odontoceti species obtained in a previous study (Delphinus delphis, Stenella coeruleoalba, and Phocoena phocoena). A total of 9 ASVs, belonging to the genera Campylobacter (2 ASVs), Phocoenobacter (4 ASVs), Psychrobacter (1 ASV), Tenacibaculum (1 ASV), and Terasakiispira (1ASV), contributed exclusively for the pigmy sperm whales clustering. Evaluating these potential microbial signatures from the K. breviceps specimens, we were able to highlight features evidencing high homology with known marine pathogens, such as Actinobacillus delphinicola, Pasteurella skyensis, Tenacibaculum gallaicum, and Te-nacibaculum soleae, thus suggesting a disease association. These should be further explored as mi-crobiota fingerprints towards cetacean population control and biomonitoring.
Peripheral Facial Paralysis: Reanimation Techniques. An Overview
Authors: Saraniti Carmelo , Verro Barbara
Published: October 2024

Peripheral facial paralysis represents a disabling condition with serious psychological and social impact. Patients with peripheral facial paralysis have a disfigurement of the face with loss of harmony and symmetry, and difficulties in everyday facial functions such as speaking, drinking, laughing, and closing their eyes, with impairment of their quality of life. This paralysis leads to impairment of facial expression that represents one of the first means of communication, an important aspect of human interaction. This review aims to explore the reanimation techniques for managing peripheral facial paralysis. An analysis of static and dynamic techniques for facial reanimation is provided, including muscle flaps, nerve grafting techniques, and bioengineering solutions. Dynamic techniques, when feasible, enable better outcomes in terms of facial motility, although the long-term rehabilitation and the long learning curve. Static techniques, while less invasive, provide limited functional recovery and should be combined with dynamic strategies for good results. Each technique showed its benefits and drawbacks; despite several options for facial reanimation, no technique has been detected as the gold standard. Therefore, each patient must be evaluated on an individual basis, considering his medical history, age, expectations, and treatment goals to find the best and most fitting treatment for him.
Hard Outcomes in Critically Ill Patients with Acute Respiratory Distress Syndrome Caused by SARS-CoV-2 Infection: A Survival in a Retrospective Cohort Study of 3 Years of Pandemic
Authors: Mora Martínez Gabriela Maria , Turrubiates Hernández Theno Alexandro , Visoso Palacios Porfirio , Esparza Correa Josue Gerardo , Ramírez Gutiérrez Álvaro Eduardo
Published: October 2024

Introduction. Mexico confirmed its first COVID-19 case in February 2020. Since then, it has been important to describe the presentation and data of critically ill patients with ARDS caused by SARS-CoV-2.
Objectives. To determine the survival of critically ill patients with ARDS due to SARS-CoV-2. Secondary objectives are to identify factors associated with mortality in these patients and to evaluate 90-day survival based on these risk factors.
Materials and Methods. This observational, retrospective study was conducted in an ICU at a tertiary hospital in Mexico City (March 2020 - March 2023). It included patients over 18 years old with COVID-19 confirmed by PCR, excluding those with negative PCR, those who died within the first 24 hours, or those without invasive mechanical ventilation. Demographic, clinical, and treatment data were collected. Descriptive statistical analyses, logistic regression, and Kaplan-Meier analyses were performed. The study was approved by ethical committees and did not require informed consent.
Results. A total of 157 patients with COVID-19 were studied, with a mean age of 62.8 years and a male predominance (74.5%). ARDS was severe in 68.2%, and acute kidney injury (AKI) developed in 70.1%. The mortality rate was 58.6%, with a 90-day survival of 33.1%. Factors such as AKI (OR = 3.4), hemodynamic failure (OR = 6.5), and elevated lactate (OR = 0.201) were independently associated with mortality, with their impact on survival confirmed by the log-rank test.
Conclusion. In critically ill patients with ARDS due to COVID-19, the 90-day survival rate was 33.1%. AKI, hemodynamic failure, and elevated lactate were associated with reduced survival, especially when AKI was combined with hemodynamic failure (26.7%).
Model to Improve Classrooms’ Visual Comfort Using Waste-Based Shading and Its Validation in Mediterranean Schools
Authors: Mo Xinmiao , Valladares Oriol Pons , Ortega Donoso Sara Isabel
Published: October 2024

In Europe, non-residential building stock built before building energy codes consumes more energy and resources than new buildings. Existing educational buildings comprise 17% of this outdated stock. These buildings can be retrofitted to create a conducive learning environment that can improve students’ comfort. Facade renovation has become the first choice for elevating the building performance of the education stock because it is less expensive than other alternatives. This study develops a methodology to optimize facade renovation solutions including: 1) preparation, 2) simulations of the simplified model using local shading, and 3) modelling realistic optimized facade design. This study evaluates visual comfort by considering multiple-dimensional metrics such as useful daylight illuminance (UDI), annual sunlight exposure (ASE), illuminance uniformity and the daylighting factor. The methodology was first applied to improve the facade proposal “Roof to Façade” in the project Waste-based Intelligent Solar Control Devices for Envelope Refurbishment (WiSeR) and simulations with the software DesignBuilder to design tile system facades. The results illustrate that implementing these solutions could efficiently improve indoor visual comfort by considering multiple daylight metrics. Moreover, for a constant gaps surface, the facade distribution with staggered gaps performs better in terms of the daylighting factor, solar gains and uniformity than more continuously connected gaps.
Detection of Patterns of Victimization and Risk of Gender Violence Through Machine Learning Algorithms
Authors: Bernal-Monroy Edna Rocio , Castañeda-Monroy Erika Dajanna , Renteria-Ramos Rafael Ricardo , Campaña-Bastidas Sixto Enrique , Barrera Jessica , Palacios-Yampuezan Tania Maribel , González Gustin Olga Lucia , Tobar-Torres Carlos Fernando , Ceballos-Villada Zeneida Rocio
Published: October 2024

This paper explores the application of machine learning techniques and statistical analysis to identify patterns of victimization and the risk of gender-based violence in San Andrés de Tumaco, Nariño, Colombia. Models were developed to classify women according to their vulnerability and risk of suffering various forms of violence, which were integrated into a decision-making tool for local authorities. The algorithms employed include K-means for clustering, artificial neural networks, random forest, decision trees, and multiclass classification algorithms combined with fuzzy classification techniques to handle incomplete data. Implemented in Python and R, the models were statistically validated to ensure their reliability. Analyses based on health data revealed key patterns of victimization and risks associated with gender-based violence in the region. This study presents a data science model that uses a social determinants approach to assess the characteristics and patterns of violence against women in the Pacific region of Nariño. The research was conducted within the framework of the Orquídeas Program of the Colombian Ministry of Science, Technology, and Innovation.
Bicistronic Vector Expression of Recombinant Jararhagin-C and Its Effects on Endothelial Cells
Authors: Ferraz Karla Fernanda , Caetano de Luca Lhiri Hanna , Orefice Daniele Pereira , Calabria Paula Andreia Lucas , Della-Casa Maisa Splendore , Freitas-de-Sousa Luciana Aparecida , Beraldo-Neto Emidio , Sanabani Sabri Saeed , Magalhaes Geraldo Santana , Clissa Patricia Bianca
Published: October 2024

Jararhagin-C (JarC) is a protein from the venom of Bothrops jararaca consisting of disintegrin-like and cysteine-rich domains. JarC shows a modulating effect on angiogenesis and remodeling of extracellular matrix constituents improving wound healing in mice experimental model. JarC is purified from crude venom and the yield is less than 1 %. The aim of this work was to obtain the recombinant form of JarC and to test its biological activity. For this purpose, the bicistronic vector pSUMOUlp1 was used. This vector allowed the expression of the recombinant toxin JarC (rJarC) in fusion with the small ubiquitin-related modifier (SUMO) as well as the SUMO protease Ulp1. After expression, this protease was able to efficiently remove SUMO from rJarC inside the bacteria. rJarC free from SUMO was purified at the expected molecular mass, and recognized by polyclonal anti-Jararhagin antibodies. In terms of biological activity, both the native and recombinant forms showed no toxicity to the HUVEC cell line CRL1730 and were effective in modulating cell migration activity in experimental in vitro model. These results demonstrate the successful production of rJarC and the preservation of its biological activity, which may facilitate further investigations into the therapeutic potential of this snake venom-derived protein.
Securing Wireless Networks Against Emerging Threats: An Overview of Protocols and Solutions
Authors: Lakhani Rishit , Sachan Ram Chandra
Published: October 2024

As wireless networks have become an integral part of modern communication infrastructure, ensuring their security against a rapidly evolving threat landscape is a critical concern. This research article provides a comprehensive overview of the emerging threats targeting wireless networks, including advanced persistent threats, man-in-the-middle (MitM) attacks, and AI-driven adaptive malware. With the advent of new technologies such as 5G, the Internet of Things (IoT), and artificial intelligence (AI), the attack surface for wireless networks has significantly expanded, demanding more robust and adaptive security protocols.The paper analyzes the efficacy of current wireless security protocols, such as WPA3 and the 802.11i standard, in addressing these emerging vulnerabilities. While these protocols have introduced significant improvements, they are not without limitations. The article further explores innovative solutions such as blockchain-based security frameworks, AI-powered threat detection systems, and the future potential of quantum cryptography in safeguarding wireless communications.Through a critical review of recent case studies and empirical data, the article highlights the key challenges that organizations face in securing wireless networks, particularly in IoT environments where security standards lag behind technological advancements. The research concludes that while existing protocols provide foundational security, they must be continuously updated and augmented with cutting-edge technologies to counter the growing sophistication of cyberattacks.This article aims to provide insights into the state of wireless network security and offer practical recommendations for enhancing security protocols. Future research directions are also discussed, focusing on the integration of AI-driven threat intelligence and the standardization of security protocols across various wireless technologies. The findings underscore the importance of proactive security measures to safeguard wireless networks in an increasingly interconnected world.
Quantitative Approach to Explore Regulatory T Cell Activity in Immuno-Oncology
Authors: Serrano-Alcaide Alejandro , Zalba Sara , Lasarte Juan Jose , Troconiz Iñaki F. , Riva Natalia , Garrido Maria J
Published: October 2024

The failure of immunotherapies in cancer patients is being widely studied due to the complexities present in the tumor microenvironment (TME), where regulatory T cells (Treg) appear to actively participate in providing an immune escape mechanism for tumors. Therefore, therapies to specifically inhibit tumor-infiltrating Treg represent a challenge because Treg are distributed throughout the body and provide physiological immune homeostasis to prevent autoimmune diseases. Characterization of immunological and functional profiles could help to identify the mechanisms that need to be inhibited or activated to ensure Treg modulation in the tumor. To address this, quantitative in-silico approaches based on mechanistic mathematical models integrating multi-scale information from immune and tumor cells and the effect of different therapies have allowed the building of computational frameworks to simulate different hypotheses, some of which have subsequently been experimentally validated. Therefore, this review presents a list of diverse computational mathematical models that examine the role of Treg as a crucial immune resistance mechanism contributing to the failure of immunotherapy. In addition, this review highlights the relevance of certain molecules expressed in Treg that are associated with the TME immunosuppression, which could be incorporated into the mathematical model for a better understanding of the contribution of Treg modulation. Finally, different pre-clinical and clinical combinations of molecules are also included to show the trend of new therapies targeting Treg.
Female Leadership and Green Innovation: Evidence from Asian Firms
Authors: Mansour Marwan Mohammad , Shubita Mohammad Fawzi , Lutfi Abdalwali , Saleh Mohammed W.A. , Saad Mohamed
Published: October 2024

This paper focuses on CEOs, who are considered the strategic leaders of corporations because of their crucial role in making important decisions. This research paper examines how female CEOs influence green innovation (GI) in Asian industrial sector. Its main objective is to answer the following research questions: Are there differences in GI endeavors between Asian industrial firms with female CEOs and those with male CEOs? Does the size of the industrial firms have a positive moderating effect on the relationship between CEO gender and GI? Based on our research questions, the size of firms is likely a determining factor in the GI of female CEOs. This research employs rigorous econometric modeling to analyze a substantial dataset of Asian industrial listed companies spanning from 2013 to 2022. It has been proven that industrial firms led by females CEOs are more inclined to have environmentally friendly practices. Furthermore, the size of the industrial firms amplifies the beneficial influence of female CEOs on the firm's chances of engaging in GI initiatives. Our outcomes stay robust to endogeneity issues using two-step GMM estimators. This study proposes that stakeholders, particularly in Asian countries, should promote the increased representation of females in CEO roles, particularly within large corporations. This is because women-led companies demonstrate superior performance in GI endeavors. Thus, regulators need to implement policies that promote the inclusion of women in CEO positions within large-scale enterprises. These policies may potentially strengthen the private sector’s capacity to foster sustainable innovation.
Quantum Relativity ( Electron Ripple )
Authors: Ismail Ahmed Mohamed , Mohamed Samira Ezzat
Published: October 2024

This research answers the knowledge gap regarding the explanation of the quantum jump of the electron. This scientific paper aims to complete Einstein’s research regarding general relativity and attempt to link general relativity to quantum laws.
Social Media and Influencers; Trends & Effects on Holistic Development of Adolescents
Authors: Patidar Rohit
Published: October 2024

Social Media use among teens specially YouTube, Instagram or Snapchat is on peak today, creators on these platforms are of diverse age groups but dominated by Teenagers and Young adults whom the audience of the same age find easy to relate with. This research seeks to figure out the viable fruits teenagers are able to consume from the help of Social Media and Influencers (SM&I) for the five aspects of their development i.e. 'holistic development”, while briefly touching the type and category of content. In this descriptive research we used a quantitative survey to analyze the perception of teens about influencers and their effects on their development. The research is solely focusing on holistic development with social media uses trends and prevalence. It does not dwell on quality quantity and negative sides of this phenomenon. This could be oveserved after this survey research that adolescents in rural settings too are heavily using social media, impact of social media on teens is huge teens are not only using social media for Identity formation but for formative development as well.
Effects of Titanate on Brake Wear Particle Emission using Brake Material Friction Test Dynamometer
Authors: Daimon Emiko , Ito Yasuhito
Published: October 2024

We investigated the effect of lepidocrocite-type layered titanate, which we have been using to reduce brake-particle emissions in an environmentally friendly manner. The effect of titanate on reduction was evaluated using a small-scale inertia brake-material-friction-test dynamometer. We suggested that brake-particle emissions are related to the microphysical structure of the pad surface, such as the uniformity of friction film and secondary-plateau formation, and that friction materials containing titanate contribute significantly to the reduction in both particle mass (PM) and particle number (PN) emissions of brake particles for both non-asbestos organic (NAO) and low-steel (LS) pads. In our experiments, the PM- and PN-emission levels of brake particles from LS pads with the addition of titanate were comparable to those of NAO pads.
Unique Properties of Naked Mole Rat Hyaluronan— Potential Utilization in Management of Glioblastoma
Authors: Salagean Alex Adrian , Moldovan Cezara Anca-Denisa , Slevin Mark
Published: October 2024

Hyaluronan has been considered in recent years as a potential naturally occurring modifiable scaffold that has the capability to absorb and release drugs over an extended period of time making it suitable as a chemotherapeutic delivery agent. Considering the limited treatment options available in the treatment of glioblastoma, in this review, we discuss the novel utilization of ultra high molecular weight hyaluronan - originally identified as a mechanism for maintaining longevity in the naked mole rat, as both a protective and extracellular matrix optimising scaffold, and a means to deliver therapy in resected brain tumors. The unique properties of this unique form of hyaluronan indicate potential future use in the prevention and treatment of both proliferative based and inflammation driven disease.
Daily Estimates of Global Radiation in the Brazilian Amazon by Simplified Models
Authors: Martim Charles Campoe , Paulista Rhavel Salviano Dias , Castagna Daniela , Borella Daniela Roberta , Almeida Frederico Terra de , Damian João Gabriel Ribeiro , Souza Adilson Pacheco de
Published: October 2024

Solar radiation is an element and a meteorological factor that is present in several processes, such as evapotranspiration, photosynthesis, and energy generation, among others. However, in some regions, there is a limitation in surface data measurements. In this study, 87 empirical models were evaluated to estimate global radiation (Hg) in the Brazilian Amazon Biome; these models were divided into five groups according to the input variables, such as insolation (group I), air temperature (group II), relative humidity (group III), astronomical variables (group IV), and hybrid models (group V) as input variables. The estimates were evaluated by their significance (t-test) and then according to the statistical metrics of the models' performance (R2, MBE, RMSE, and d). The group V model [Hg/Ho = a + b lnΔT + c (S/So)d)] presented the best statistical performance in all evaluated indicators, followed by the group I model [Hg/Ho = a + b (S/So)c ] and then the group II model [Hg/Ho = a + bΔT + c ΔT0.25 + d ΔT0.5 + e (Tmed/Ho)]. The group III models presented low statistical performance, and the group IV models were not significant (NS) in all evaluated meteorological stations. In general, it was found that the simple or hybrid models based on insolation and air temperature were efficient in estimating Hg in the Brazilian Amazon Biome.
Genetic Variants Linked to Opioid Addiction: A Genome Wide Association Study
Authors: Panday Shailesh , Lyman Rachel , Shankar Vijay , Alexov Emil
Published: October 2024

Opioid addiction disorder (OAD) affects millions of people worldwide. While it is known that OAD originates from many factors, including social and environmental factors, the role of genetic variants in developing the disease has also been reported. This study aims to investigate the genetic variants that may be associated with the risk of developing OAD upon exposure. Twenty-three subjects who had previously been given opioid-based painkillers to undergo small surgical treatment were recruited at Prisma Health Upstate clinic and elsewhere. Eleven of them were considered non-persistent opioid users (controls), and 12 of them were persistent opioid users (cases) after an initial surgery and at the time of sample collection. The subjects were asked to provide saliva samples, which were subjected to DNA sequencing at Clemson University Center for Human Genetics, and variant calling was performed. The GWAS for genes known to be associated with OAD resulted in 13 variants (intronic or SNV) with genome-wide significance (raw p-value < 0.01) and two missense variants, rs6265 (Val66Met in BNDF isoform a) and rs1799971 (Asn40Asp) in OPRM1 previously reported in the literature. Furthermore, extending the GWAS to find all the genomic variants and filtering the variants to include only variants found in cases (persistent opioid users) but not in controls (non-persistent opioid users) resulted in 11 new variants (p-value< 0.005). Considering that OAD is a complex disease and the effect may come from different variants in the same genes, we performed a co-occurrence analysis of variants on the genes and identified four genes, LRFN3, ZMIZ1, RYR3, and OR1L6 with three or more variants in the case subjects but not in control individuals.
Building Effective Social Media Strategies for Business: A Systematic Review
Authors: Gumede Thembinkosi T , Chiworeka Joel M , Magoda Asanda S , Thango Bonginkosi
Published: October 2024

This study examines the development of effective social media strategies for businesses, focusing on how small and medium-sized enterprises (SMEs) can leverage these strategies to boost revenue and sustain growth. In the context of a dynamic digital environment, SMEs face challenges in maximizing the benefits of social media platforms due to limited resources and expertise. Using the 2020 Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA), this study analyzed sixty-nine research papers to identify key strategies that contribute to successful social media marketing. The review considered factors such as content quality, engagement techniques, platform selection, and targeted advertising. The findings suggest that tailored strategies that align with business objectives and audience needs, coupled with consistent engagement and content relevance, lead to more significant revenue growth and customer retention. However, the lack of data on long-term impacts and industry-specific insights limits the generalizability of these findings. This study provides a foundation for SMEs to develop strategic social media marketing plans, but further research is needed to explore long-term effects and industry-specific variations.
Automated Cervical Cancer Screening Using Single-Cell Segmentation and Deep Learning: Enhanced Performance with Liquid-Based Cytology
Authors: Rodríguez Mariángel , Córdova Claudio , Benjumeda Isabel , San Martín Sebastián
Published: October 2024

Cervical cancer (CC) remains a significant health issue, especially in low- and middle-income countries (LMICs). While Pap smears are the standard screening method, they have limitations like low sensitivity and subjective interpretation. Liquid-based cytology (LBC) offers improvements but still relies on manual analysis. This study explored the potential of deep learning (DL) for automated cervical cell classification using both Pap smears and LBC samples. A novel image segmentation algorithm was employed to extract single-cell patches for training a ResNet-50 model.The model trained on LBC images achieved remarkably high sensitivity (0.998), specificity (0.997), and accuracy (0.997), outperforming previous CNN models. However, the Pap smear dataset model achieved significantly lower performance (0.694 sensitivity, 0.838 specificity, 0.834 accuracy). This suggests that noisy and poor cell definition in Pap smears pose challenges for automated classification, whereas LBC provides better classifiable cells patches. These findings demonstrate the potential of AI-powered cervical cell classification for improving CC screening, particularly with LBC. The high accuracy and efficiency of DL models combined with effective segmentation can contribute to earlier detection and more timely intervention. Future research should focus on implementing explainable AI models to increase clinician trust and facilitate the adoption of AI-assisted CC screening in LMICs.
Crystallographic and Optical Spectroscopic Study of Metal–Organic 2D Polymeric Crystals of Silver(I)- and Zinc(II)-Squarates
Authors: Ivanova Bojidarka
Published: October 2024

Metal–organic framework materials show feature linear- and nonlinear optical responses such as laser damage threshold, outstanding mechanical properties, thermal stability, and optical transparency as innovative functional materials for nonlinear optical technologies. The non-centrosymmetric crystal structure is precondition for generation of second-order nonlinear optical response, which guarantees technological applications. The ZnII- and AgI-squarate complexes are attractive templates for these purposes due to good crystal growth, optical transparency, high thermal stability, etc. However, debatable is the space group type of catena-((μ2-squarato)-tetra-aqua-zinc(II)) complex ([Zn(C4O4)(H2O)4]) (1) showing centro- and non-centro-symmetric monoclinic C2/c and Cc phases. The same is valid to catena-((μ3-squarato)-(μ2-aqua)-silver(I)) complex (Ag2C4O4) (2) exhibiting, so far, only C2/c phase. This study reports first new crystallographic data on (1) and (2) re-determined at different temperatures (293(2) and 300(2)K) and non-centro-symmetric Cc phase of (2), having different number molecules per unit cell comparing with C2/c phase. There are high-resolution crystallographic measurements of single crystals, experimental electronic absorption and vibrational spectroscopic data together with ultra-high resolution mass spectrometric ones. Experimental results are supported for theoretical optical and nonlinear optical properties obtained via high accuracy quantum chemical static methods and molecular dynamics, using density functional theory as well as chemometrics.
Prognostic Significance and Treatment Response Associations of Genetic Mutations in Chronic Myelomonocytic Leukemia: A Retrospective Cohort Study
Authors: Di Jing , Yenwongfai Leonard N , Arshad Talal , Huang Bin , McDowell Jaclyn K. , Durbin Eric B , Munker Reinhold , Wei Sainan
Published: October 2024

Background: This retrospective cohort study investigates the prognostic significance of genetic mutations in Chronic Myelomonocytic Leukemia (CMML) and their association with treatment responses among patients treated at a single institution, juxtaposed with a statewide dataset from Kentucky. Methods: The study includes 51 patients diagnosed with CMML under the World Health Organization criteria from January 2005 to December 2023. It examines their genomic profiles and subsequent survival outcomes. The analysis also categorizes patients into CMML-1 and CMML-2 subtypes and assesses survival differences between transformed and non-transformed cases. Results: Mutations in TET2, ASXL1, and SRSF2 were found to significantly influence survival, establishing their roles as critical prognostic markers. Additionally, the cohort from the University of Kentucky exhibited distinct survival patterns compared to the broader Kentucky state population, suggesting that demographic and treatment-related factors could underlie these variances. Conclusions: This research underscores the pivotal role of targeted genetic profiling in deciphering the progression of CMML and refining therapeutic strategies. The findings emphasize the necessity for advanced genetic screening in managing CMML to better understand individual prognoses and optimize treatment efficacy, thereby offering insights that could lead to personalized treatment approaches.
Urolithin a Protects Hepatocytes from Palmitic Acid-Induced ER Stress by Regulating Calcium Homeostasis in the MAM
Authors: Ryu Gayoung , Ko Minjeong , Lee Sooyeon , Park Se In , Choi Jin-Woong , Lee Ju Yeon , Kim Jin Young , Kwon Ho Jeong
Published: October 2024

An ellagitannin-derived metabolite Urolithin A (UA) has emerged as a potential therapeutic agent for metabolic disorders due to its antioxidant, anti-inflammatory, and mitochondrial function-improving properties, but its efficacy in protecting against ER stress remains underexplored. The endoplasmic reticulum (ER) is a cellular organelle involved in protein folding, lipid synthesis, and calcium regulation. Perturbations in these functions can lead to ER stress, which contributes to the development and progression of metabolic disorders such as metabolic-associated fatty liver disease (MAFLD). In this study, we identified a novel target protein of UA and elucidated its mechanism for alleviating palmitic acid (PA)-induced ER stress. CETSA-LC-MS/MS analysis revealed that UA binds directly to the sarcoplasmic/endoplasmic reticulum Ca2+-ATPase (SERCA), an important regulator of calcium homeostasis in mitochondria-associated ER membranes (MAMs). As an agonist of SERCA, UA attenuates abnormal calcium fluctuations and ER stress in PA-treated liver cells, thereby contributing to cell survival. In addition, UA inhibits PA-induced mitochondrial ROS and lipid accumulation, further supporting its protective role. The lack of UA activity in SERCA knockdown cells suggests that UA regulates cellular homeostasis through its interaction with SERCA. Collectively, our results demonstrate that UA protects against PA-induced ER stress and enhances cell survival by regulating calcium homeostasis in MAM through SERCA. This study highlights the potential of UA as a therapeutic agent for metabolic disorders associated with ER stress.
An Averaged Halpern Type Algorithm for Solving Fixed Point Problems and Variational Inequality Problems
Authors: Berinde Vasile , Saleh Khairul
Published: October 2024

In this paper we propose and study in the setting of a Hilbert space an averaged Halpern type algorithm for approximating the solution of a common fixed point problem for a couple of nonexpansive and demicontractive mappings with a variational inequality constraint. The strong convergence of the sequence generated by the algorithm is established under feasible assumptions on the parameters involved. In particular, we also obtain the common solution of the fixed point problem for nonexpansive or demicontractive mappings and of a variational inequality problem. Our results extend and generalize various important related results in literature that were established for the pairs (nonexpansive, nonspreading) or (nonexpansive, strongly quasi-nonexpansive) mappings. Numerical tests to illustrate the superiority of our algorithm over the ones existing in literature are also reported.
Transparency of Corporate Governance Through Public Information: Evidence from Spain
Authors: Garayeta Asier , Corral-Lage Javier , Trigo Eduardo , De La Peña J. Iñaki
Published: October 2024

The measurement of corporate governance takes into account not only the profitability and solvency of the companies, but also the degree of information provided: its transparency. Corporate governance has become particularly relevant in recent years and one of the main problems is how to measure it properly. It is usually measured privately, either through audits or private verification companies. However, the public and mandatory information issued by these companies is rarely valued. This is where this research is new. The aim of this article is to develop a CGRI (Corporate Governance Ratio Index) to evaluate the transparency of corporate governance of companies using public and mandatory information. For this purpose, it is applicable to companies listed on the Spanish continuous market. This paper applies the index to the 35 most relevant companies in the Spanish continuous market. Subsequently, the paper asses the interrelation of the index with the main factors of corporate governance such as remuneration, monitoring and governance structure. By using a publicly available index, this study contributes to the transparency and accessibility of CG assessment, providing valuable insights into the effective corporate governance practices and thus the index helps to identify areas for improvement in the company itself and to observe the actions of its competitors.
Non-Palpable Breast Cancer: A Targeting Challenge – Comparison of Radio-Guided vs Wire Wire-Guided Localization Techniques
Authors: Drozgyik András , Kollár Dániel , Dankházi Levente , Harmati István , Szalay Krisztina , Molnár Tamás F
Published: October 2024

The incidence of non-palpable breast cancer is increasing due to widespread screening and neoadjuvant therapies. Among the available tumor localization techniques, radio-guided occult lesion localization (ROLL) has largely replaced wire-guided localization (WGL). This study aimed to compare ROLL and WGL techniques in terms of the effectiveness of isotopic marking of axillary sentinel lymph nodes and to assess patient perspectives along with surgeon and radiologist preferences. A single-center, prospective randomized study enrolled 110 patients with non-palpable breast lesions (56 ROLL, 54 WGL). Breast type, tumor volume, location, histological and radiological features, and localization/surgical duration were evaluated in the context of sentinel lymph node marking using isotope (Technetium-99m labelled human serum albumin) and blue dye. Statistical analysis was performed with significance set at p<0.05 and strong significance at p<0.01. Both techniques were successful in localizing non-palpable lesions (100%). However, patients reported WGL as more painful. A notable difference was observed in axillary lymph node marking, with subareolar isotope injection (used in WGL) showing better results compared to ROLL. While ROLL provided advantages in terms of patient comfort, and logistical simplicity, WGL was superior for axillary sentinel lymph node marking, particularly in inner quadrant tumors, suggesting WGL may be preferred for these cases.
Computational Advances in Ionic Liquid Applications for Green Chemistry: A Critical Review of Lignin Processing and Machine Learning Approaches
Authors: Taylor Brian , Kumar Nikhil , Mishra Dhirendra Kumar , Simmons Blake A , Choudhary Hemant , Sale Kenneth L
Published: October 2024

The valorization and dissolution of lignin using ionic liquids (ILs) is critical for developing sustainable biorefineries and a circular bioeconomy. This review aims to critically assess the current state of computational and machine learning methods for understanding and optimizing IL-based lignin dissolution and valorization processes reported since 2022. The paper examines various computational approaches, from quantum chemistry to machine learning, highlighting their strengths, limitations, and recent advances in predicting and optimizing lignin-IL interactions. Key themes include the challenges in accurately modeling lignin’s complex structure, the development of efficient screening methodologies for ionic liquids to enhance lignin dissolution and valorization processes, and the integration of machine learning with quantum calculations. These computational advances will drive progress in IL-based lignin valorization by providing deeper molecular-level insights and facilitating the rapid screening of novel IL-lignin systems.
Modeling Thunderstorms Using Machine Learning for WA
Authors: Osei Owusu Sekyere Andrew
Published: October 2024

This research developed and evaluated a machine learning model for predicting thunderstorm frequency, in the Wa region of Ghana, with a focus on its impact for agricultural planning and management. Using a range of meteorological variables including Total Column Water(TCW), Total Column Rain Water(TCRW), and Convective Available Potential Energy (CAPE), the research aim to identify important predictors of thunderstorm occurrence and quantify their relative importance. A Random Forest algorithm was employed to create the predictive model, which was trained and tested on historical weather data. The model demonstrated good predictive capabilities, explaining approximate 72.6% of the variance in thunderstorm occurrences, with a mean absolute error of 2.34 storms and an index of agreement of 0.926. Key findings showed the importance of atmospheric moisture content, particularly TCW and TCRW , in predicting thunderstorm frequency. Atmospheric instability measures, such as CAPE, played a secondary but important role. The model showed strength in capturing overall trends in thunderstorm frequencies but exhibited some limitations in predicting extreme events. The research contributes to the field of meteorology by demonstrating the effectiveness of machine learning techniques in capturing complex atmospheric interactions leading to thunderstorm formation. It also provides a framework for linking thunderstorm predictions to potential agricultural impacts, enhancing the practical applicability of weather forecasting in the agricultural sector. The study lays the groundwork for more sophisticated, localized weather predictions systems that can greatly benefit agricultural planning and broader weather dependent activities in the region. Future research dimensions include exploring advanced feature engineering, integrating temporal and spatial analysis, and developing agricultural impacts models to further enhance the practical utility of thunderstorm predictions.
Formulating a Mathematical Model for Living Systems
Authors: Nguyen Toan
Published: October 2024

Prigogine’s 1978 concept of dissipative structures, drawing parallels with living systems, forms the basis for exploring life’s unique traits. However, these identified similarities prove insufficient in capturing the entirety of life. To address this gap, our proposed modeling approach emphasizes the distinctive ability of living organisms to observe other systems—an attribute intricately tied to quantum mechanics’ "measurement" processes, as highlighted by Howard Pattee. This article introduces a comprehensive mathematical model centered on quantum dynamical dissipative systems, portraying living systems as entities defined by their observational capacities within this framework. The exploration extends to the core dynamics of these systems and the intricacies of biological cells, including the impact of membrane potentials on protein states. Within this theoretical structure, the model is expanded to multicellular living systems, revealing how cells observe quantum dynamical systems through protein state changes influenced by membrane potentials. The conclusion acknowledges the current theoretical status of the model, underscoring the crucial need for experimental validation, particularly regarding the superposition state of membrane proteins under the influence of an electric field.
Advancing Bangladesh’s Digital Transition: From Digital Foundations to Technological Leadership
Authors: Rahman Md. Ashiqur , Parvin Rawnaq Ara
Published: October 2024

The transformation of Bangladesh from a Digital Bangladesh to a Digital Bangladesh is an ambitious initiative aimed at leveraging digital technologies to drive economic growth, enhance public service delivery, and improve the quality of life for all citizens. This paper explores the strategic roadmap for achieving this vision, focusing on key priorities such as expanding digital infrastructure, promoting digital literacy and skills, fostering innovation and entrepreneurship, enhancing cybersecurity, and strengthening governance and regulatory frameworks. The transformation journey involves significant challenges and opportunities, requiring robust stakeholder engagement, comprehensive policies, and effective monitoring and evaluation mechanisms. Emerging technologies such as artificial intelligence, blockchain, the Internet of Things, and big data analytics present substantial prospects for innovation and efficiency across various sectors. Promoting digital inclusion, bridging the digital divide, and ensuring accessibility and inclusivity are crucial for ensuring that the benefits of digital transformation are widespread. Moreover, enhancing cybersecurity and data privacy is vital for protecting digital systems and maintaining public trust. The paper also highlights the importance of fostering innovation and entrepreneurship, enhancing public service delivery, and strengthening governance frameworks to support the Digital Bangladesh vision. By addressing these strategic priorities and leveraging the collective efforts of government, private sector, academia, and civil society, Bangladesh aims to create a dynamic and resilient digital economy that drives sustainable growth and development. The successful implementation of the Digital Bangladesh vision will position the nation to thrive in the global digital landscape, ensuring a digitally empowered society that benefits all citizens.
Impact of Soot on Internal Combustion Engine Lubrication – Oil Condition Monitoring, Tribological Properties and Surface Chemistry
Authors: Agocs Adam , Frauscher Marcella , Ristic Andjelka , Doerr Nicole
Published: October 2024

In the study at hand a systemic investigation regarding the tribochemical effects of crankcase soot is presented. Sooted oils were generated via an engine dynamometer test. Both conventional as well as advanced oil condition monitoring methods indicated a mild degradation of additives. The wear volume was greatly increased with the sooted oils in model tribometer tests, despite the high residual zinc dialkyl dithiophosphate (ZDDP) antiwear (AW) levels. Once the soot was removed via ultracentrifugation, the wear volume returned to levels comparable to the fresh oil. Surface investigations revealed that ZDDP tribofilms could not form in the sooted oils, as only a thin sulfide layer was present on the metal surfaces. Meanwhile, typical tribofilms were observable with centrifuged oils. The results indicated that a tribocorrosive mechanism is most likely responsible for the elevated wear in the sooted oils, where only the iron sulfide base layer of ZDDP films is formed, which then is rapidly removed by the soot particles in an abrasive manner.
Selectivity Control in Nitroaldol (Henry) Reaction by Changing the Basic Anion in a Chiral Copper(II) Complex Based on (S)-2-Aminomethylpyrrolidine and 3,5-Di-tert-butylsalicylaldehyde
Authors: Khromova Olga V. , Yashkina Lidiya V. , Stoletova Nadezhda V. , Maleev Victor I. , Belokon Yuri N. , Larionov Vladimir A.
Published: October 2024

This article is a continuation of our previous research on the catalytic capability of chiral copper complex based on commercially available (S)-2-aminomethylpyrrolidine and 3,5-di-tert-butylsalicylaldehyde with various counter-anions in the asymmetric Henry reaction. Our findings indicate that depending on the type of base used, chiral nitroalcohols with yields up to 97% and ee values up to 78%, as well as β-nitrostyrenes with yields up to 82% can be produced. Additionally, it has been found that the outcome of the reaction and the catalytic properties of copper(II) complexes (S)-Cu1 and (S)-Cu2 are influenced by the structure of the aldehyde used.
Carbon-Neutral Forestry Contributes to Global Warming due to Carbon Debt
Authors: Prytz Kjell , van der Spoel David
Published: October 2024

This article presents an evaluation of the environmental impact of forestry based on landscape theory. It has been argued that this type of forestry offers a positive impact on the climate because there is a balance between the amount of greenhouse gas emissions and the absorption of these gases within an entire forested area. However, this analysis will demonstrate that the arrangement and composition of managed forests are linked to a significant carbon debt. This debt represents the disparity between the carbon that would typically be stored in a natural forest and the actual amount of carbon stored in the managed forest. While this excess carbon remains in the atmosphere rather than being sequestered, it contributes to the greenhouse effect. Using the Swedish forestry as an example, the carbon debt is estimated to be comparable in scale to the total accumulated fossil fuel emissions of the country.