bluuebunny/crossref_metadata_2025_split huggingface dataset:
Efficacy of Dyslipidemia Control by Combination Therapy with Rosuvastatin 10 Mg and Ezetimibe 10 Mg Compared with Rosuvastatin 20 Mg Monotherapy in Patients with Chronic Coronary Syndromes: A Randomized, Single-blind Controlled Trial
Authors: Tran An Viet , Tran Bao Lam Thai , Bui Nghia Minh , Le Anh To Tan , Nguyen Diem Thi , Tran Son Kim , Ngo Toan Hoang 
Published: January 2024

Background:
Studies have shown the combination treatment effectiveness of using
rosuvastatin and ezetimibe in patients with chronic coronary artery disease. Our study aim to evaluate
the effectiveness of dyslipidemia treatment with the combination of rosuvastatin and ezetimibe
10mg in patients with chronic coronary artery disease compared with 20 mg rosuvastatin.

Objectives:
To evaluate the effectiveness of dyslipidemia treatment with the target of LDL-c <
1.4 mmol/L between combination therapy with rosuvastatin 10 mg and ezetimibe 10 mg in patients
with chronic coronary artery disease compared with monotherapy increasing the dose of
rosuvastatin 20 mg in Vietnam.

Methods:
A randomized controlled clinical trial, single-blind, parallel-group with a 1:1 randomized
ratio in 103 outpatients with chronic coronary syndromes treated with rosuvastatin 10mg
daily. Group A received the combination therapy with rosuvastatin 10 mg plus ezetimibe 10 mg
daily, and group B received rosuvastatin 20 mg daily. The primary outcome was to assess the
efficacy of low-density lipoprotein – cholesterol (LDL-c) control between rosuvastatin 10 mg
plus ezetimibe 10 mg versus rosuvastatin 20 mg after 4 weeks and 8 weeks.

Results:
After 8 weeks of intervention, the proportion of archived treatment target patients with
LDL-c < 1.4 mmol/L in groups A and B was 69.2% and 44.2%, respectively (Risk ratio (RR) =
1.57, p < 0.01), 50% LDL reduction was 27.9% and 55.8%, respectively (RR = 2.00, p < 0.01),
and archived both targets were 51.9% and 25.6% (RR = 2.03, p < 0.01).

Conclusion:
Group A's LDL-c reduction effect and target achievement proportion (Rosuvastatin
10mg + Ezetimibe 10 mg) were significantly higher than Group B's (Rosuvastatin 20 mg). Both
medication therapies were safe in patients, and the increased dose of monotherapy showed more
side effects than the combination therapy.

Clinical Trail Registration Number:
ISRCTN44571.
Green Synthesis of Silver Nanoparticles using Coriandrum sativum and
Murraya koenigii Leaf Extract and its Thrombolytic Activity
Authors: Pram Priyanca , Mishra Nikita , Vaithilingam Mohanasrinivasan , Samuel Merlyn Keziah , Mohanan Maneesha , Kothari Neeti , Chandrasekaran Subathra Devi 
Published: January 2024

Background:
Plants have been used for ages in traditional medicine, and it is exciting
to perceive how recent research has recognized the bioactive compounds liable for their beneficial
effects. Green synthesis of metal nanoparticles is a hastily emergent research area in nanotechnology.
This study describes the synthesis of silver nanoparticles (AgNPs) using Coriandrum
sativum and Murraya koenigii leaf extract and its thrombolytic activity.

Objective:
The aim of the study was to determine the clot lysis activity of Coriandrum sativum
and Murraya koenigii synthesized silver nanoparticles.

Methods:
Leaves of Coriandrum sativum and Murraya koenigii were collected. Methanolic extraction
of the plant sample was done through a Soxhlet extractor. The methanolic extract obtained
from both the leaves was subjected to GC-MS analysis. The synthesized NPs from leaf
extracts were monitored for analysis, where the typical X-ray diffraction pattern and its diffraction
peaks were identified. 3D image of the NPs was analysed by Atomic Force Microscopy. The
surface charge of nanoparticles was identified by Zeta potential. The Clot lysis activity of Coriandrum
sativum and Murraya koenigii synthesized silver nanoparticles were analysed by the
modified Holmstorm method.

Results:
The thrombolytic property of the methanolic extract of plants Coriandrum sativum
showed clot lysis activity at 2.5 mg/mL with 45.99% activity, and Murraya koenigii extract with
66.56% activity. The nanoparticles (Nps) from Coriandrum sativum showed clot lysis activity at
2.5 mg/mL with 58.29% activity, and NPs from Murraya koenigii with 54.04% activity. Coriandrum
sativum in GC-MS exhibited 3 peaks, whereas Murraya koenigii extract showed five peaks
with notable bioactive compounds.

Conclusion:
These NPs were further used for biomedical applications after being fixed by an
organic encapsulation agent. The present research reveals the usefulness of Coriandrum sativum
and Murraya koenigii for the environmentally friendly manufacture of silver nanoparticles.
In silico Identification of MHC Displayed Tumor Associated Peptides in
Ovarian Cancer for Multi-Epitope Vaccine Construct
Authors: Dobhal Shivashish , Chauhan Kanchan , Kumar Sachin , Shikha Sristy , Jogi Mukesh K. , Kumar Dinesh , Kumar Anuj , Jaiswal Varun K. , Kumar Pramod 
Published: January 2024

Background:
Recognizing the potential of the immune system, immunotherapies
have brought about a revolution in the treatment of cancer. Low tumour mutational burden and
strong immunosuppression in the peritoneal tumor microenvironment (TME) lead to poor outcomes
of immune checkpoint inhibition (ICI) and CART cell therapy in ovarian cancer. Alternative
immunotherapeutic strategies are of utmost importance to achieve sound clinical success.

Introduction:
The development of peptide vaccines based on tumor-associated antigens (TAAs)
for ovarian cancer cells can be a potential target to provoke an anti-tumor immune response and
subsequent clearance of tumour cells. The purpose of this in-silico study was to find potential
epitopes for a multi-epitope vaccine construct using the immunopeptidomics landscape of ovarian
carcinoma.

Methods:
The four TAAs (MUC16, IDO1, FOLR1, and DDX5) were selected for potential
epitopes prediction. The epitopes for B-cells, helper T-lymphocytes (HTL), and Cytotoxic Tlymphocytes
(CTL) were predicted on the basis of antigenic, allergenic, and toxic properties. These
epitopes were combined with suitable linkers and an adjuvant to form a multi-epitope construct.

Results:
Four HTLs, 13 CTLs, and 6 potential B-cell epitopes were selected from the predicted
epitope. The designed multi-epitope construct was potentially immunogenic, non-toxic, and
non-allergenic. Physicochemical properties and higher-order structural analyses of the final construct
revealed a potential vaccine candidate.

Conclusion:
The designed vaccine construct has the potential to trigger both humoral and cellular
immune responses and may be employed as a therapeutic immunization candidate for ovarian
malignancies. However, further in vitro and animal experimentation is required to establish
the efficacy of the vaccine candidate.
On the Improvements of the Performance of Bayes-Net Classification for Diagnosis of Diabetes: A Cross-validation Approach
Authors: Verma Sarvachan , Sharma Anupama , Jain Sachin , Kumar Anuj 
Published: January 2024

Introduction:
This study intends to support medical professionals in the early and precise
detection and diagnosis of type1 and type2 Diabetes. Many complexities occur if Diabetes is
unidentified and remains untreated. 9.3% of individuals worldwide have Diabetes as of 2019, according
to data from the World Health Organization. The anticipated increase would be close to
11% of the global population by 2045.

:
Diabetes mellitus is a chronic, one of the deadliest diseases that causes Body blood glucose levels
to be above average level knowing a blood sugar. Diabetes can be type1 (T1D) and type2(T2D).
An autoimmune condition called Type 1 diabetes is frequently found in young children. Adults
are frequently diagnosed with type 2 diabetes, which is not autoimmune. A branch of artificial
intelligence called machine learning has the potential to be successful in the field of predictions.

Methos:
This research aims to design a machine learning model to predict Diabetes in a patient.
The collective performance of the four algorithms is evaluated on various measures like accuracy,
precision, sensitivity, and specificity. Therefore, four machine learning classification algorithms,
namely Bayes-Net, Naïve-Bayes, J48, and Random Forest, were used to predict Diabetes
in the early stage using 10–fold cross validation.

Results:
For experiments, a standard PIMA Indian diabetes dataset is used. According to the results,
Bayes-Net performs better than other algorithms, with an accuracy rate of 87.72%.

Conclusion:
Diabetes Mellitus may harm kidneys, nerves, eyes, and other organs if it is not diagnosed
in time. Therefore, early diagnosis and treatment could save countless lives. The process
is tedious because patients visit a diagnostic facility and see a doctor, but various machine learning
and deep learning approaches solve this clinical problem earlier.
Optimized Maximum Power Point Tracking using Giza Pyramid Construction Algorithm for Photovoltaic Systems
Authors: Soma Keerthi Sonam , Ramadoss Balamurugan , Natarajan Karuppiah 
Published: January 2024

Background:
One of the key challenges in maximizing the performance of PV systems
is the efficient tracking of the maximum power point (MPP) under varying operational conditions,
including changes in solar irradiance and temperature. Accurate MPP tracking is essential for
achieving optimal energy conversion efficiency and maximizing the electricity generation potential
of the PV array, even during partial shading conditions. Traditional maximum power point
tracking (MPPT) algorithms, such as the incremental conductance (INC) method, often struggle to
efficiently handle partial shading conditions. As a result, there is a need for more sophisticated
and robust optimization techniques that can effectively address this challenge. This study presents
a novel and innovative Giza Pyramid Construction (GPC) algorithm to solve the partial shadinginduced
MPP tracking problem.

Objective:
This study aims to apply the Giza Pyramid Construction (GPC) algorithm for optimized
maximum power point tracking in photovoltaic systems under partial shading conditions,
aiming to enhance energy conversion efficiency and overall system performance.

Methods:
The methodology involves implementing the Giza Pyramid Construction (GPC) algorithm
as the core optimization technique for maximum power point tracking (MPPT) in photovoltaic
(PV) systems. The GPC algorithms are utilized to iteratively adjust the duty cycle of the boost converter,
enabling efficient power extraction from the PV array under varying shading conditions. The
performance of the GPC algorithm is evaluated through simulations in MATLAB/SIMULINK and
compared against conventional MPPT methods like INC and DGO techniques.

Results:
The successful application of the Giza Pyramid Construction (GPC) algorithm for optimized
maximum power point tracking in PV systems under partial shading led to significantly
reduced optimization time, faster settling times, and minimized output ripples. With the proposed
GPC MPPT, optimization time is reduced to 41ms, settling time is reduced to 93ms, and ripples
are minimized to 0.092%.

Conclusion:
the Giza Pyramid Construction (GPC) algorithm demonstrates its effectiveness as a
robust and efficient maximum power point tracking method in photovoltaic systems, particularly
under partial shading conditions. The improved optimization speed, reduced settling times, and
minimized output ripples underscore the GPC algorithm's potential to enhance the overall efficiency
and reliability of PV systems, paving the way for its practical implementation in real-world
renewable energy applications.
Disease Modifying Strategies in Multiple Sclerosis: New Rays of Hope to
Combat Disability?
Authors: Bellanca Carlo Maria , Augello Egle , Mariottini Alice , Bonaventura Gabriele , La Cognata Valentina , Di Benedetto Giulia , Cantone Anna Flavia , Attaguile Giuseppe , Di Mauro Rosaria , Cantarella Giuseppina , Massacesi Luca , Bernardini Renato 
Published: January 2024

Abstract:
Multiple sclerosis (MS) is the most prevalent chronic autoimmune inflammatory- demyelinating
disorder of the central nervous system (CNS). It usually begins in young adulthood, mainly
between the second and fourth decades of life. Usually, the clinical course is characterized by the involvement
of multiple CNS functional systems and by different, often overlapping phenotypes. In the
last decades, remarkable results have been achieved in the treatment of MS, particularly in the relapsing-
remitting (RRMS) form, thus improving the long-term outcome for many patients. As deeper
knowledge of MS pathogenesis and respective molecular targets keeps growing, nowadays, several
lines of disease-modifying treatments (DMT) are available, an impressive change compared to the relative
poverty of options available in the past. Current MS management by DMTs is aimed at reducing
relapse frequency, ameliorating symptoms, and preventing clinical disability and progression. Notwithstanding
the relevant increase in pharmacological options for the management of RRMS, research
is now increasingly pointing to identify new molecules with high efficacy, particularly in progressive
forms. Hence, future efforts should be concentrated on achieving a more extensive, if not exhaustive,
understanding of the pathogenetic mechanisms underlying this phase of the disease in order to characterize
novel molecules for therapeutic intervention. The purpose of this review is to provide a compact
overview of the numerous currently approved treatments and future innovative approaches, including
neuroprotective treatments as anti-LINGO-1 monoclonal antibody and cell therapies, for effective and
safe management of MS, potentially leading to a cure for this disease.
The Urgent Need for an Evidence-Based Digital Mental Health Practice Model of Care for Youth
Authors: Ridout Brad , Forsyth Rowena , Amon Krestina L , Navarro Pablo , Campbell Andrew J 
Published: January 2024

Abstract
Australian providers of mental health services and support for young people include private and public allied health providers, government initiatives (eg, headspace), nongovernment organizations (eg, Kids Helpline), general practitioners (GPs), and the hospital system. Over 20 years of research has established that many young people prefer to seek mental health support online; however, clear client pathways within and between online and offline mental health services are currently lacking. The authors propose a Digital Mental Health Practice model of care for youth to assist with digital mental health service mapping. The proposed model offers accessible pathways for a client to engage with digital mental health services, provides clear navigation to access support for individual needs, and facilitates a seamless connection with offline mental health services using a transferable electronic health records system. This future-looking model also includes emerging technologies, such as artificial intelligence and the metaverse, which must be accounted for as potential tools to be leveraged for digital therapies and support systems. The urgent need for a user-centered Digital Mental Health Practice model of care for youth in Australia is discussed, highlighting the shortcomings of traditional and existing online triage models evident during the COVID-19 pandemic, and the complex challenges that must be overcome, such as the integration of diverse mental health care providers and establishment of a robust electronic health records system. Potential benefits of such a model include reduced pressure on emergency rooms, improved identification of immediate needs, enhanced referral practices, and the establishment of a cost-efficient national digital mental health care model with global applicability. The authors conclude by stressing the consequences of inaction, warning that delays may lead to more complex challenges as new technologies emerge and exacerbate the long-term negative consequences of poor mental health management on the economic and biopsychosocial well-being of young Australians.
Pengaruh edukasi gizi terhadap pengetahuan gizi atlet remaja di Indonesia
Authors: Penggalih Mirza Hapsari Sakti Titis , Sofro Zaenal Mutaqqien , Trisnantoro Laksono , Susila Edi Nurinda , Ernawaty Ernawaty , Rahadian Bayu , Margono Margono , Sujadi Dadi , Isnanta Raden , Bactiar Nia , Anugraheni Ayudiva Rizky , Budiarjo Theresa 
Published: January 2024

The impact of providing nutrition education on the nutritional knowledge of young athletes in IndonesiaBackground: A lack of nutritional knowledge can lead to poor eating habits and nutritional deficiencies in young athletes. One of the issues leading to athletes' need for more knowledge is the absence of professional assistance in educating them about nutrition. Objective: This research was analyzed to investigate the impact of nutrition education through counseling and group-based nutrition education on the nutritional knowledge of young athletes in Indonesia. Methods: This study is a pre-and post-experimental study without control. One hundred eighty-five young athletes who trained in PPLP and SKO received nutrition intervention for two months, including three nutrition education sessions using sports nutrition module media, nutritional counseling, and eating assistance based on eating etiquette. A modified Abridged Nutrition for Sport Knowledge Questionnaire (ANSKQ) questionnaire assessed athletes' nutritional knowledge before and after the intervention. Data was examined by comparing scores before and after the intervention. The Kolmogorov-Smirnov test was used in the normality test, and a paired t-test was used to compare nutritional knowledge before and after the intervention. Results: Before the intervention, 88.65% had a low level of knowledge about nutrition. The scores for sports nutrition and overall nutritional knowledge increased significantly (p<0.05). The scores of general nutrition knowledge increased but were not significant (p>0.05). Conclusions: There was a significant increase in sports nutrition knowledge but not general nutrition knowledge. The provision of nutritional education assisted by nutritionists impacts athletes' nutritional knowledge.
The Relationship Between Learning Readiness and Learning Outcomes of Citizens Learning Package C At Pkbm Titian Amanah Kayu Tanam
Authors: Salman Iza Elvira , Syur'aini Syur'aini 
Published: January 2024

The purpose of study is to ascertain how adult student learning outcomes in Indonesian language classes at PKBM Titian Amanah relate to their readiness for learning. Attention, drive, and the growth of personal preparedness are all components of learning readiness. This study employed a correlational research design and a quantitative methodology. The questionnaire measures the level of learning readiness in terms of attention, motivation and development of individual readiness.Findings of this research indicate the learning readiness of adult students Package C program at PKBM Titian Amanah is in the low category, especially in terms of attention and motivation. This is evident from respondents' propensity to select "disagree" option in the questionnaire. However, research also finds a significant relationship between learning readiness and learning outcomes. Students with a higher level of readiness tend to achieve better learning outcomes. Based on the results of this research, there are several recommendations that can be given. First, PKBM Titian Amanah must provide better facilities to increase adult students' learning readiness. Second, tutors must be more creative in using learning media to increase learning motivation. Additionally, future research could explore the relationship between learning readiness and learning outcomes. Keywords: Learning Readiness, Learning
THE ROLE OF COURSE INSTITUTIONS IN EMPOWERING "KAMPUNG INGGRIS" VILLAGES THROUGH SERVICES BUSINESS
Authors: Rozi Muhamad Fahrur , Sunarti Vevi , Dwi Arini Fitri , Husnul Hayati Ridha , Septia Landa Klara 
Published: January 2024

This study intends to describe the function of the course institutions in empowering communities via roadster bicycle rental services. This study employs a qualitative research method based on case studies. This study utilized in-depth interviews, observation, and documentation studies to collect data. Techniques for data analysis consist of the steps of data management, data description, data classifications, data interpretation, data presentation, and conclusion drawing. Triangulation of the source method and triangulation of approaches are utilized in the process of validating the findings. The results of the study indicate that the course institutions play a crucial role in enhancing the local economy. Student mobility during taking the course in "Kampung Inggris" was high enough that the local community can be used to create economic opportunities. Economic opportunities are created by providing students with teaching courses with roadster bicycle rental services. The potential to boost the economy was utilized by a population for the first time in 1998. Since then, hundreds of roadster bicycle rental services have been formed, and some of them have even become family businesses in Tulungrejo village. Improving the economics of the Tulungrejo village community is inextricable from the role of the Mahesa Institute, which promotes the use of bicycles for student mobility during the "English Village" course.
The influence of gadget usage and maternal attachment on the cognitive and motor development of early childhood in Situbondo Regency
Authors: Damayanti Findya , Supriyono Supriyono 
Published: January 2024

The purpose of this study is to determine the effects of device use and maternal bonding on early childhood cognitive and motor development in Situbondo Regency. In this research, the sample used was 311 respondents using quantitative methods. The multiple linear regression analysis technique was carried out using SPSS version 22 software. The data collection technique used in this research was observation, interviews, filling out Google forms and also direct questionnaires. The results of this research show a partial contribution, namely between the use of gadgets on cognitive development in early childhood, there is a partial contribution, namely between maternal attachment to cognitive development in early childhood, then there is a partial contribution between the use of gadgets and motor development in early childhood. , while there is no partial contribution of maternal attachment to motor development in early childhood. The use of devices in early childhood requires care and supervision, and also the mother’s role in the operation, as overuse of the device by the child can lead to addiction to playing with the device. This negatively affects the child’s growth and development process, especially the development of cognitive and motor skills. Dependence on the use of gadgets hinders the child’s growth and development process. Keywords: Gadget Use, Maternal Attachment, Cognitive Development, Motor Development
Concepts and Practice of Statistics: Part 3 - Graphics
Authors: McLean Alan L. 
Published: January 2024

Abstract
The process of analysis of a set data aims to detect patterns, especially relationships between variables, in the data. Statistical techniques can be described broadly as techniques to detect particular types of patterns, and to test whether such patterns ‘really exist’. The use of graphs to aid in pattern detection is long established. A picture can be used to make a point quickly and easily. In the context of data analysis, it is making the point more powerfully than does a table of data. A well chosen and well drawn graph makes its point - reveals a pattern - very powerfully. Equally, a badly chosen or dishonestly drawn graph makes a point incorectly - but equally powerfully.