bluuebunny/crossref_metadata_2025_split huggingface dataset:
Targeting of Ykl-40 as a Protumor in Personalized Medicine: A New Dimension in Disease Understanding
Authors: Moustafa Hebatallah Ahmed Mohamed , Sohaib Ahmed Umer , Saleem Iqra , Ullah Asmat 
Published: February 2025

Background:
Individualization of the therapeutic plan for cancer patients is the essence
of modern clinical practice. Standard cancer diagnostic and prognostic factors are invasive, and
their value for the stratification of cancer patients with a higher risk of local or distant recurrence
is limited. YKL-40 is a protumor glycoprotein linked to the immunosuppressive tumor in a microenvironment
and an important biomarker of cell activation, proliferation, and migration.

Objective:
The objective is to update the review, and molecular and clinical research should investigate
novel modalities of targeting this glycoprotein for cancer treatment.

Methodology:
Relevant studies published in the English language were identified by searching
PubMed, Google Scholar, and MEDLINE from January 2000 to December 2023. Published
studies that specifically elicited the role of YKL-40 as a biomarker in different types of tumors
were included.

Results:
YKL-40 cancer prognostic effect was reported in various cancer types.

Conclusion:
Since antibodies against YKL-40 can inhibit tumor angiogenesis and cancer progression,
it can be suggested as an attractive candidate for chemical cancer therapy and immunomodulation.
Assessing the Impact of Artificial Intelligence Applications on Diagnostic Accuracy in Saudi Arabian Healthcare: A Systematic Review
Authors: Alqurashi Moutaz Abdulrahman , Alshagrawi Salah 
Published: February 2025

Background
Artificial intelligence (AI) has become a disruptive force with great potential to revolutionize healthcare. The integration of artificial intelligence in healthcare practices and its use in areas, such as the detection and diagnosis of diseases, has led to an increased interest in this topic, which has been key in informing this research study to determine its effect on diagnostic accuracy. However, the impact of AI on healthcare diagnostic accuracy, particularly in the context of Saudi Arabia, remains an underexplored area of research.

Aim
This systematic review sought to address this gap by analyzing the effect of AI applications' diagnostic accuracy in Saudi Arabia's healthcare.

Methods
The study employed a structured and systematic search strategy in compliance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) criteria. Three databases were used to identify the articles, including PubMed, Embase, and CINAHL. The search for relevant articles linked to the impact of AI applications on diagnostic accuracy in the KSA healthcare sector was narrowed down to articles published between 2013 and 2023. This step generated 450 articles, which were further evaluated based on the inclusion criteria of the study to narrow down to 12 articles for analysis.

Results
11 out of 12 studies were conducted between 2020 and 2023, indicating that the last three years have witnessed the largest number of studies on artificial intelligence. The included studies were conducted in KSA and within different hospitals. The studies included 7 cross-sectional studies, 3 observational studies (1 retrospective study), 1 experimental study, and 1 randomized controlled trial (RCT). They all showed that the use of AI has been increasing in healthcare, and its use is enhancing the overall healthcare outcomes and is helpful in a wide variety of diseases and conditions, including chronic diseases.

Conclusion
The models have shown that the use of AI is capable of enhancing diagnostics and treatment quality, which can be essential in planning for preventing care in line with Vision 2030. Hence, the findings of this systematic review can contribute to a better understanding of the role of AI applications in diagnostic accuracy within Saudi Arabia's healthcare and offer insights applicable to regions facing similar challenges.

PROSPERO Registration Number
611347
The Study on Innovative Development of the Elderly Care Industry under the Community-based Elderly Care Model Based on the SERVQUAL Model
Authors: Feng Guanzhou , Li Quanlin 
Published: February 2025

Background
China's rapidly aging population has highlighted the importance of community-based elderly care, particularly in urban areas like Shanghai, as traditional family-based care declines due to urbanization, smaller families, and shifting dynamics. Community care models provide scalable solutions to address the healthcare and social needs of elderly individuals while fostering innovation in service delivery.

Objective
This study investigates how the quality of community-based elderly care services influences elderly satisfaction and drives innovation within the elderly care industry. Using the SERVQUAL model, it explores the impact of service dimensions—reliability, responsiveness, assurance, empathy, and tangibles—on satisfaction and their role in promoting new care models and technologies.

Methods
The study surveyed 622 residents in Shanghai and analyzed the data with Structural Equation Modeling (SEM). It assessed the relationships between the five service quality dimensions, satisfaction, and innovation in elderly care, offering insights into how these factors interact.

Results
Reliability (β = 0.42), responsiveness (β = 0.35), assurance (β = 0.28), and empathy (β = 0.33) significantly enhance elderly satisfaction, which mediates their impact on innovation. Tangibles (β = 0.09) had minimal influence, suggesting emotional and relational factors are more important than physical care environments. Satisfaction was a key driver of innovation, encouraging the adoption of personalized and technology-driven care models.

Conclusion
High-quality services that improve elderly satisfaction are essential for boosting industry innovative. Smart technology, telemedicine, and remote health monitoring are critical for addressing the challenges of China’s aging population, closing service gaps, and creating responsive and efficient care systems.
Enzymes and Alzheimer's Disease: Pioneering Drug Development Strategies
Authors: Godwa Tejas , Dubey Sonal , Tiwari Prashant 
Published: February 2025

Abstract:
Alzheimer's disease (AD) is a progressive neurodegenerative disorder characterized by
cognitive decline, memory loss, and functional impairment. Despite extensive research, effective
treatments remain elusive, highlighting the need for innovative therapeutic approaches. This review
article explores enzymatic targets and drug development strategies aimed at combating AD. Key
enzymatic targets include beta-secretase (BACE1), gamma-secretase, and tau protein kinases, all of
which play critical roles in the pathogenesis of AD. BACE1 and gamma-secretase are involved in
the production of amyloid-beta (Aβ) peptides, whose aggregation forms the hallmark amyloid
plaques in AD brains. Inhibitors targeting these enzymes aim to reduce Aβ production and accumulation.
Tau protein kinases, such as glycogen synthase kinase-3 (GSK-3) and cyclin-dependent kinase
5 (CDK5), are implicated in tau hyperphosphorylation and subsequent neurofibrillary tangle
formation. Modulating these kinases offers the potential for reducing tau pathology. The review further
discusses various drug development strategies, including small-molecule inhibitors, monoclonal
antibodies, and gene therapy. Small molecule inhibitors, such as BACE1 and gamma-secretase
inhibitors, have shown promise in preclinical studies but face challenges related to specificity and
side effects. Monoclonal antibodies targeting Aβ and tau provide an alternative approach, with several
candidates currently undergoing clinical trials. Gene therapy represents a cutting-edge strategy
aiming to correct or modulate disease-causing genetic mutations. In summary, targeting enzymatic
pathways involved in AD pathogenesis offers a promising avenue for drug development. While significant
challenges remain, ongoing research and clinical trials continue to advance our understanding
and potential treatment options for this debilitating disease.
Fabrication and Optimization of Regorafenib-loaded Solid Lipid Nano Carrier Using Box Behnken Design: In Vitro and Ex Vivo Intra-nasal Permeation Study
Authors: Padhi Priyanka , Kar Nihar Ranjan 
Published: February 2025

Background:
Over the last ten years, there has been little clinical progress in the treatment of glioblastoma, partially because there are no efficient drug delivery techniques that can pass across the blood-brain-tumor-barrier.

Objective:
The present study deals with the development optimization of Regorafenib-loaded Solid lipid na-noparticle(RF-SLN) for intranasal delivery of Regorafenib.

Method:
A Box-Behnken Design was employed to develop and optimize seventeen formulations using the solvent evaporation method, followed by ultrasonication. Each formulation was characterized in terms of particle size, entrapment efficiency (EE%), and zeta potential. The optimized SLN formulation was then con-verted into an in situ gel and assessed for intranasal ex vivo permeation.

Results:
The optimized formulation showed a particle size of 190.1nm, EE% of 81.3%, and zeta potential of -23.8 mV. The optimized Regorafenib-SLN in-situ gel showed enhanced drug permeability as compared to the Regorafenib-in-situ gel.

Conclusion:
It can be concluded that an SLN-based nanocarrier system can be successfully applied for the intranasal delivery of Regorafenib targeting to the brain.
A Study on Privacy Preserving Techniques in Fog Computing: Issues, Challenges, and Solutions
Authors: Banu Sabitha , Mohammed Isbudeen Noor , Ahmad Shabir , Gheisari Mehdi 
Published: February 2025

Abstract:
Privacy plays a substantial role in both public and private, databases especially in
the healthcare industry and government sectors require a high-confidential data transmission
process. Most often, these data contain personal information that must be concealed throughout
data processing and transmission between terminal devices and cloud data centers, such as
username, ID, account information, and a few more sensitive details. Recently, fog computing
highly utilized for such data transmission, storing, and network interconnection processes due
to its low latency, mobility, reduced computational cost, position awareness, data localization,
and geographical distribution. It delivers to cloud computing and the widespread positioning of
IoT applications. Since fog-based service is provided to the massive-scale end-end users by fog
server/node, privacy is a foremost concern for fog computing. Fog computing poses several
challenges if it comes to delivering protected data transfer; making the development of privacy
preservation strategies particularly desirable. This paper exhibits a systematical literature review
(SLR) on privacy preservation methods developed for fog computing in terms of issues,
challenges, and various solutions. The main objective of this paper is to categorize the existing
privacy-related research methods and solutions that have been published between 2012 and
2022 using analytical and statistical methods. The next step is to present specific practical issues
in this area. Depending on the issues, the merits and drawbacks of each suggested fog security
method are explored, and some suggestions are made for how to tackle the privacy concerns
with fog computing. To build, deploy, and maintain fog systems, several imminent motivational
directions and open concerns in this topic were presented.
Exploring Generative AI Applications in Combating COVID-19
Authors: M.A Jabbar , Tasneem Rayeesa , Farhat Shaista 
Published: February 2025

Abstract:
The COVID-19 pandemic has reshaped the world, causing widespread health crises, economic
disruption, and some serious social changes. The initial outbreak in Wuhan saw a rapid escalation
in cases, leading Chinese authorities to enforce stringent lockdown measures. Traditional testing
methods for COVID-19 are time-consuming and can be costly, often taking hours or days to
yield results. To overcome these challenges, researchers have developed automated systems employing
various Artificial Intelligence (AI) techniques to detect coronavirus infections. Over the previous
decade, the integration of AI with medical imaging has revolutionized multiple industries, particularly
healthcare, by improving the diagnosis and treatment of numerous conditions. Generative AI and
Generative Adversarial Networks (GANs) play a significant role in detecting and managing COVID-
19 through various innovative applications. This paper draws attention to the applications of generative
AI systems for COVID-19 detection, highlighting their potential to enhance testing efficiency
and accuracy.
Enhancing GNSS Signal Integrity in Medical Logistics: A Deep Learning Solution
Authors: Ar-Reyouchi El Miloud , Chabel Saâdia 
Published: February 2025

Introduction:
Ensuring the integrity of Global Navigation Satellite System (GNSS) signals
is critical for the timely and accurate delivery of pharmaceuticals within smart medical supply
chain logistics (SMSCL).

Method:
In this study, we propose a novel deep learning (DL) framework that integrates a Bidirectional
Long Short-Term Memory (BiLSTM)-Attention model with Principal Component Analysis
(PCA) and Bayesian Optimization (BO) for feature selection. This approach enhances GNSS signal
reliability by accurately detecting anomalies, especially in environments prone to interference. The
PCA-BO feature selection process optimizes relevant features like signal strength and Doppler shifts,
improving model performance while reducing overfitting.

Result:
Our results demonstrate that the proposed model significantly outperforms conventional
methods, enhancing the precision of pharmaceutical deliveries in critical healthcare settings.

Conclusion:
This work represents a key advancement in using DL to ensure GNSS signal integrity
for SMSCL, contributing to more efficient and secure logistics operations.

conclusion:
The advanced DL approach significantly improves GNSS signal integrity, ensuring reliable and timely delivery of critical medical supplies. These findings suggest that integrating deep learning with feature selection algorithms can enhance the robustness of GNSS applications in medical logistics.

other:
This article extends its impact beyond medical logistics by offering several key contributions. The deep learning approach is applicable across industries like autonomous vehicles and aviation, where GNSS reliability is vital. The PCA-BO method provides an innovative solution for high-dimensional data analysis and is useful in various machine-learning tasks. Additionally, the model improves GNSS resilience against jamming and spoofing, which is crucial for navigation security. Its adaptability across dynamic and static scenarios underscores its scalability. Furthermore, the research advances logistics innovation, demonstrating how deep learning can enhance efficiency and reliability in critical supply chains.
Influence of The Duration of Remission of an Addicted Relative on the Manifestation of Co-Dependent Behavior in The Family
Authors: Ignatenko A. P. , Azarko E. M. 
Published: February 2025

Introduction. Co-dependence in the article is considered as a family problem, where a person, being close to the addict for a long time, learns to suppress his feelings, thoughts and needs. As a consequence, he/she shows low self-esteem, high level of anxiety, aggressiveness, distrust to people and the world. The connection between co-dependence and addiction is considered on the example of the phenomenon of increasing co-dependent care during the increase in the manifestations of dependence of a close relative. The "12 Steps" program as a condition for long-term remission in an addicted person, in our opinion, is an important criterion for reducing the level of co-dependent behavior in his or her close relatives.Objective. To investigate the influence of the duration of remission of an addicted relative on the level of co-dependence manifestation in the family.Materials and methods. The methodological tools of the study included the author's questionnaire, the Kuhn-McPartland test "Who am I?" (Modification by T. V. Rumyantseva), the interpersonal dependence test by R. Hirschfield (adaptation by O. P. Makushina) and the test "Diagnosis of the type of communicative attitude" by V. V. Boyko. Data processing was carried out by comparing mean values and one-sample t-criterion.Results. The empirical object of the study is people exposed to the problem of co-dependent behavior (n=147) and students of the Faculty of Psychology as a control group (n=76). In the course of the study, the data on the state of actualized co-dependence in the study group with a pronounced negative attitude and high level of insecurity were obtained. Along with this, the assumption about the positive influence of long-term remission of a dependent relative on the level of co-dependence manifestation in the family was confirmed.Discussion. It has been empirically proved that respondents subjected to the problem of co-dependence are characterized by an acute need for emotional closeness with another and acceptance from others. This can be traced in the emotional discomfort that co-dependents experience due to perceived rejection. Also, the obtained results indicate a decrease in the level of interpersonal dependence and an increase in personality reflection in respondents who have a co-dependent relative in remission for more than 10 years. In addition, the relatives of persons in remission for a longer period of time are characterized by a decrease in negative communicative attitudes, which favorably affects the process of communication, positive attitude towards the interlocutor, and a decrease in control in communication.
Kontribusi Pembelajaran Berbasis Riset Terhadap Motivasi Berprestasi di Madrasah Unggul Indonesia
Authors: Bulan Sri , Sugianto Hendi , Rusydiyah Evi Fatimatur , Agustina Andre 
Published: February 2025

Research-based learning in superior madrasas is prepared as a means to instill love of knowledge and empathy for the environment and encourage students to reason critically, creatively and independently which is the principle in the independent curriculum. This study aims to examine the contribution of research-based learning at MAN Insan Cendekia Pekalongan to student achievement motivation. The research method used is qualitative with data collection techniques through unstructured interviews, field observations, and documentation. In addition, research instruments were given to 95 students who became the initial population of the study using google forms to ensure the possibility of diversity of perspectives on research-based learning and motivation for achievement. Furthermore, data collection was carried out by purposive sampling to 17 students who participated in the preparation of the national scientific paper competition to dig up achievement data for achievement (nAch) in depth. The results of the study showed that there was a significant contribution of research-based learning to students' motivation for achievement which was shown by the desire to do something more than ordinary learning (nAch1), the desire to solve problems in research (nAch2), the desire to take personal responsibility in research (nAch3), and the desire to show good performance in research (nAch4). Overall, RbL contributes to facilitating students to think critically, creatively and independently which is part of the expected character in the independent curriculum.
Evaluation of Governance and Measurement of Maturity Levels System Plastic Injection Company Information Using COBIT 2019 and Luftman Maturity Model
Authors: Nurtjahjo Ferdinandus Andrew , Harnadi Bernardinus , Koeswoyo G.Freddy 
Published: February 2025

Organization for managing information technology often doesn't work effective so that result the company's workflow becomes irregular . This research initiates governance system PT Amaryllis information Charisma Brilliant by using measurements level IT maturity based on the Luftman Maturity Model and COBIT 2019 framework in the Align, Plan, and Organize (APO) domain. measure by method Luftman maturity model done by share questionnaire related criteria communication , competence , governance , partnership , space scope architecture , and expertise in space scope IT maturity in the company . Evaluation of IT governance using the COBIT method is carried out by opening the APO domain in levels management company to assess weakness system information and can provide recommendation related to improvement performance company . Measurement evaluation results level IT maturity shows company on criteria communication , partnerships , and expertise are included in level 4 ( Improved Process ), meanwhile criteria competence , governance and partnerships enter level 3 ( Established Process ) so this research is recommended company to make structural changes organization under the director directly in implementing service level and focus agreements to business integration using third parties in making IT strategies. Whereas IT governance evaluation results using COBIT 2019 APO domain that companies in sub domains APO02, APO03, APO04, APO07, APO09, APO13 enter level 4 ( Predictable Process ), while sub domains APO01, APO05, APO06, APO08, APO10, APO11, APO12, and APO14 enter level 5 ( Optimizing Process ). Acquired weaknesses that is Not yet fulfillment committee executive IT related and not done testing IT- based by all employees so this research recommends company to form committee special executive in management related to IT and management testing IT- based by a special team formed by the company.
Comparative Analysis of Heterogeneous Adders: Evaluating Performance across 12-bit, 14-bit, and 16-bit Configurations
Authors: Rout Shasanka Sekhar , Patjoshi Rajesh Kumar , Garnaik Sarmila , Rout Ranjita 
Published: February 2025

Abstract
Digital Signal Processing (DSP) heavily relies on repetitive addition and multiplication operations, making adders a crucial component in DSP systems. Likewise, in processor design, an efficient adder circuit is essential for optimizing compactness, achieving high speed, and minimizing power consumption, particularly when utilizing Xilinx technology. This study delves into the exploration and design of an effective adder architecture by examining various parallel, synchronous adders and proposing a novel combination. The presented research introduces heterogeneous adders composed of concatenated homogeneous sub-adders such as Ripple Carry Adder (RCA) and Carry Look Ahead Adder (CLA), which is implemented by using VIVADO 2017.1. The comprehensive assessment involves simulation, synthesis, and Register-Transfer Level (RTL) implementation to generate utilization and power reports. Specifically, this paper focuses on the design of 12-bit, 14-bit, and 16-bit heterogeneous adders. Through a thorough analysis, power consumption and area utilization are scrutinized and compared across each model. The combination of 4-bit RCA + 8-bit CLA, 10-bit RCA + 4-bit CLA, and 4-bit RCA + 12-bit CLA provide the least total on-chip power consumption of 8.141 W, 9.482 W, and 10.827 W in 12-bit, 14-bit and 16-bit heterogeneous adders respectively. Similarly, 6-bit RCA + 6-bit CLA, 6-bit RCA + 8-bit CLA, and 6-bit RCA + 10-bit CLA provides less area utilization. The goal is to identify the model exhibiting the least power consumption and optimal area utilization, contributing to the advancement of efficient adder circuits for DSP systems and processor designs.