bluuebunny/crossref_metadata_2025_split huggingface dataset:
Coronary heart disease classification using deep learning approach with feature selection for improved accuracy
Authors: Muniasamy Anandhavalli , Begum Arshiya , Sabahath Asfia , yaqub Humara , Karunakaran Gauthaman 
Published: December 2024

BACKGROUND: Coronary heart disease (CHD) is one of the deadliest diseases and a risk prediction model for cardiovascular conditions is needed. Due to the huge number of features that lead to heart problems, it is often difficult for an expert to evaluate these huge features into account. So, there is a need of appropriate feature selection for the given CHD dataset. For early CHD detection, deep learning modes (DL) show promising results in the existing studies. OBJECTIVE: This study aimed to develop a deep convolution neural network (CNN) model for classification with a selected number of efficient features using the LASSO (least absolute shrinkage and selection operator) technique. Also, aims to compare the model with similar studies and analyze the performance of the proposed model using accuracy measures. METHODS: The CHD dataset of NHANES (National Health and Nutritional Examination Survey) was examined with 49 features using LASSO technique. This research work is an attempt to apply an improved CNN model for the classification of the CHD dataset with huge features CNN model with feature extractor consists of a fully connected layer with two convolution 1D layers, and classifier part consists of two fully connected layers with SoftMax function was trained on this dataset. Metrics like accuracy recall, specificity, and ROC were used for the evaluation of the proposed model. RESULTS: The feature selection was performed by applying the LASSO model. The proposed CNN model achieved 99.36% accuracy, while previous studies model achieved over 80 to 92% accuracy. CONCLUSION: The application of the proposed CNN with the LASSO model for the classification of CHD can speed up the diagnosis of CHD and appears to be effective in predicting cardiovascular disease based on risk features.
Dinamika dan Tantangan Penegakan Hukum Perampasan Aset Korporasi dalam Tindak Pidana Pencucian Uang
Authors: Usman  , Surono Agus 
Published: December 2024

Tindak pidana pencucian uang atau dikenal dengan istilah Money Laundering sebagai suatu kejahatan mempunyai ciri khas yaitu bahwa kejahatan ini bukan merupakan kejahatan tunggal tetapi kejahatan ganda. Ini ditandai dengan bentuk pencucian uang sebagai kejahatan yang bersifat follow up crime atau kejahatan lanjutan, sedangkan kejahatan utamanya atau kejahatan asalnya disebut sebagai predicate offense atau core crime atau dirumuskan oleh suatu negara sebagai unlawfull actifity, yaitu kejahatan asal yang menghasilkan uang yang dikemudian dilakukan proses pencucian. Pada praktiknya, tindak pidana pencucian uang ini tidak hanya dapat dilakukan oleh perorangan, akan tetapi juga dapat dilakukan oleh korporasi. Tindak pidana oleh Korporasi merupakan tindak pidana yang dilakukan oleh orang berdasarkan hubungan kerja, atau berdasarkan hubungan lain, baik sendiri sendiri maupun bersama-sama yang bertindak untuk dan atas nama Korporasi di dalam maupun di luar Lingkungan Korporasi. Korporasi dapat dimintakan pertanggungjawaban pidana sesuai dengan ketentuan pidana Korporasi dalam undang-undang yang mengatur tentang Korporasi. Proses pencucian uang, selalu memiliki koneksi dengan penyedia jasa keuangan. Pencucian uang merupakan kejahatan yang merugikan kepentingan masyarakat, serta dapat mengakibatkan ketidakstabilan ekonomi suatu negara dan secara ekonomi tidak menguntungkan Negara. Tindakan merampas aset dalam penanganan pencucian uang sangat penting karena perspektif penegakan hukumnya menggunakan pendekatan yang dikenal sebagai "follow the money" untuk menemukan peredaran uang yang terkait dengan kejahatan atau pelanggaran hukum.
Políticas públicas y empleo juvenil en Tungurahua: una visión desde el derecho público
Authors: Bucheli Escobar Carlos Alejandro , Santamaría Velasco Juan Pablo 
Published: December 2024

INTRODUCCIÓN. El empleo juvenil en Tungurahua enfrenta barreras estructurales que limitan la integración socioeconómica de los jóvenes, generando exclusión social y riesgos de violencia. OBJETIVO. Este artículo tiene como objetivo analizar las políticas públicas dirigidas al empleo juvenil desde una perspectiva de derecho público, identificando sus limitaciones y proponiendo mejoras. MÉTODO. El presente trabajo utiliza el método de revisión bibliográfica. DISCUSIÓN Y CONCLUSIONES. Los resultados revelan que las iniciativas implementadas, como los programas "Mi Primer Empleo" y "Empleo Joven", presentan deficiencias en cobertura y sostenibilidad, agravadas por la desconexión con las particularidades del mercado laboral local. Se concluye que fortalecer la integración de marcos normativos locales e internacionales y adoptar enfoques comunitarios participativos son claves para garantizar el ejercicio pleno de los derechos laborales juveniles y fomentar el desarrollo sostenible en Tungurahua.
A Qualitative Study on the Implementation of Prevention and Identification Programs for Acute Respiratory Infections in Depok City, West Java
Authors: Bahasoan Balqis Hiri , Hayati Nisrina , Hamid Endah Nurhalimah , Nurcandra Fajaria 
Published: December 2024

Acute Respiratory Infections (ARI) are among the most frequently reported infectious diseases in healthcare facilities. The ARI Prevention and Control Program aims to reduce ARI prevalence. This study seeks to provide in-depth understanding of the program's implementation at the Cinere Community Health Center, Depok. Using descriptive qualitative approach and rapid assessment test design, data collection occurred through in-depth interviews from April to June 2024. Four participants were selected using purposive sampling: ARI program managers from the Cinere Community Health Center and the Depok City Health Office, along with a teacher and a student from SMAN 9 Depok. The study focused on describing the program, its inputs, and its outputs. Data analysis was conducted using source triangulation.The results indicated that the ARI prevention program primarily focuses on promotive and preventive measures, with collaboration between the Health Office, the Community Health Center, and the community, particularly SMAN 9 Depok. While these activities are conducted regularly, strategies are required to enhance the program's effectiveness. This study concluded that the Cinere Community Health Center’s ARI prevention program has been well-executed, as it has engaged various community groups in promotive, preventive, rehabilitative, and curative efforts, alongside thorough recording and reporting. Nevertheless, further efforts are necessary to increase community participation, strengthen cross-sector collaboration beyond network clinics, expand outreach via digital media, and optimize the health information system for more efficient recording and reporting, ensuring the program's overall success.
Design of Cooling and Monitoring Electric Motorbike Batteries Based On Internet of Things (IoT)
Authors: Dhito Anaga Ozthandrick De Dhito , Amalia Zakiyah 
Published: December 2024

The temperature increase in the battery occurs when the battery is used and charged. This increase in temperature will have a negative impact on the battery, such as reducing battery life, and can even cause the battery to explode. The objective of this research is to design a cooling device with a fan in the form of a fan and equipped with monitoring the condition of electric motorbike batteries using the Blynk application on a smartphone. The research method used in this research is an experimental method with the independent variable in the form of cooling fan rotation speed using PWM with a Duty Cycle of 25% (slow), 50% (medium), and 100% (fast) and charging current variations of 4A and 6A. Testing was carried out on a LiFePO4 battery pack, with a constant battery capacity of 10% and charging up to 100%. Temperature and battery charging time are identified as dependent variables whose values ​​are influenced by the independent variables. The results of this research show that variations in cooling fan rotation speed and variations in charging current have an effect on charging time and battery temperature. The higher the fan rotation speed, the more stable the battery temperature rise when charging. When the fan rotation speed is slow the temperature reaches 37.5°C, while when the fan rotation speed is fast the temperature is 32.04°C. The greater the charging current, the shorter the charging time. When charging the battery capacity to 10% - 100%, a 4A charging current takes 4004.11 seconds, while a 6A charging current takes 2893 seconds. Each increase in battery capacity during charging shows a significant change in temperature, where the average difference in temperature from variations in 4A and 6A charging currents is 1.07°C.
Perspective of 3D culture in medicine: transforming disease research and therapeutic applications
Authors: Park Chan Hum , Park Jung Ho , Suh Yong Joon 
Published: December 2024

3D cell culture is gaining momentum in medicine due to its ability to mimic real tissues (in vivo) and provide more accurate biological data compared to traditional methods. This review explores the current state of 3D cell culture in medicine and discusses future directions, including the need for standardization and simpler protocols to facilitate wider use in research.Purpose3D cell culture develops life sciences by mimicking the natural cellular environment. Cells in 3D cultures grow in three dimensions and interact with a matrix, fostering realistic cell behavior and interactions. This enhanced model offers significant advantages for diverse research areas.MethodsBy mimicking the cellular organization and functionalities found in human tissues, 3D cultures provide superior platforms for studying complex diseases like cancer and neurodegenerative disorders. This enables researchers to gain deeper insights into disease progression and identify promising therapeutic targets with greater accuracy. 3D cultures also play a crucial role in drug discovery by allowing researchers to effectively assess potential drugs’ safety and efficacy.Results3D cell culture’s impact goes beyond disease research. It holds promise for tissue engineering. By replicating the natural tissue environment and providing a scaffold for cell growth, 3D cultures pave the way for regenerating damaged tissues, offering hope for treating burns, organ failure, and musculoskeletal injuries. Additionally, 3D cultures contribute to personalized medicine. Researchers can use patient-derived cells to create personalized disease models and identify the most effective treatment for each individual.ConclusionWith ongoing advancements in cell imaging techniques, the development of novel biocompatible scaffolds and bioreactor systems, and a deeper understanding of cellular behavior within 3D environments, 3D cell culture technology stands poised to revolutionize various aspects of healthcare and scientific discovery.
Machine-learning derived identification of prognostic signature to forecast head and neck squamous cell carcinoma prognosis and drug response
Authors: Li Sha-Zhou , Sun Hai-Ying , Tian Yuan , Zhou Liu-Qing , Zhou Tao 
Published: December 2024

IntroductionHead and neck squamous cell carcinoma (HNSCC), a highly heterogeneous malignancy is often associated with unfavorable prognosis. Due to its unique anatomical position and the absence of effective early inspection methods, surgical intervention alone is frequently inadequate for achieving complete remission. Therefore, the identification of reliable biomarker is crucial to enhance the accuracy of screening and treatment strategies for HNSCC.MethodTo develop and identify a machine learning-derived prognostic model (MLDPM) for HNSCC, ten machine learning algorithms, namely CoxBoost, elastic network (Enet), generalized boosted regression modeling (GBM), Lasso, Ridge, partial least squares regression for Cox (plsRcox), random survival forest (RSF), stepwise Cox, supervised principal components (SuperPC), and survival support vector machine (survival-SVM), along with 81 algorithm combinations were utilized. Time-dependent receiver operating characteristics (ROC) curves and Kaplan-Meier analysis can effectively assess the model’s predictive performance. Validation was performed through a nomogram, calibration curves, univariate and multivariate Cox analysis. Further analyses included immunological profiling and gene set enrichment analyses (GSEA). Additionally, the prediction of 50% inhibitory concentration (IC50) of potential drugs between groups was determined.ResultsFrom analyses in the HNSCC tissues and normal tissues, we found 536 differentially expressed genes (DEGs). Subsequent univariate-cox regression analysis narrowed this list to 18 genes. A robust risk model, outperforming other clinical signatures, was then constructed using machine learning techniques. The MLDPM indicated that high-risk scores showed a greater propensity for immune escape and reduced survival rates. Dasatinib and 7 medicine showed the superior sensitivity to the high-risk NHSCC, which had potential to the clinical.ConclusionsThe construction of MLDPM effectively eliminated artificial bias by utilizing 101 algorithm combinations. This model demonstrated high accuracy in predicting HNSCC outcomes and has the potential to identify novel therapeutic targets for HNSCC patients, thus offering significant advancements in personalized treatment strategies.
Toll-like receptor 4 plays a vital role in irritable bowel syndrome: a scoping review
Authors: Wan Xuemeng , Wang Liyuan , Wang Zhiling , Wan Chaomin 
Published: December 2024

BackgroundIrritable bowel syndrome (IBS) is a common gastrointestinal disease. Recently, an increasing number of studies have shown that Toll-like receptor 4 (TLR4), widely distributed on the surface of a variety of epithelial cells (ECs) and immune sentinel cells in the gut, plays a vital role in developing IBS.ObjectivesWe sought to synthesize the existing literature on TLR4 in IBS and inform further study.MethodsWe conducted a systematic search of the PubMed, Embase (Ovid), Scopus, Web of Science, MEDLINE, and Cochrane Library databases on June 8, 2024, and screened relevant literature. Critical information was extracted, including clinical significance, relevant molecular mechanisms, and therapeutic approaches targeting TLR4 and its pathways.ResultsClinical data showed that aberrant TLR4 expression is associated with clinical manifestations such as pain and diarrhea in IBS. Aberrant expression of TLR4 is involved in pathological processes such as intestinal inflammation, barrier damage, visceral sensitization, and dysbiosis, which may be related to TLR4, NF-κB, pro-inflammatory effects, and CRF. Several studies have shown that many promising therapeutic options (i.e., acupuncture, herbs, probiotics, hormones, etc.) have been able to improve intestinal inflammation, visceral sensitization, intestinal barrier function, intestinal flora, defecation abnormalities, and depression by inhibiting TLR4 expression and related pathways.ConclusionTLR4 plays a crucial role in the development of IBS. Many promising therapeutic approaches alleviate IBS through TLR4 and its pathways. Strategies for targeting TLR4 in the future may provide new ideas for treating IBS.
Association between Alzheimer's disease pathologic products and age and a pathologic product-based diagnostic model for Alzheimer's disease
Authors: Zhen Weizhe , Wang Yu , Zhen Hongjun , Zhang Weihe , Shao Wen , Sun Yu , Qiao Yanan , Jia Shuhong , Zhou Zhi , Wang Yuye , Chen Leian , Zhang Jiali , Peng Dantao 
Published: December 2024

BackgroundAlzheimer's disease (AD) has a major negative impact on people's quality of life, life, and health. More research is needed to determine the relationship between age and the pathologic products associated with AD. Meanwhile, the construction of an early diagnostic model of AD, which is mainly characterized by pathological products, is very important for the diagnosis and treatment of AD.MethodWe collected clinical study data from September 2005 to August 2024 from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Using correlation analysis method like cor function, we analyzed the pathology products (t-Tau, p-Tau, and Aβ proteins), age, gender, and Minimum Mental State Examination (MMSE) scores in the ADNI data. Next, we investigated the relationship between pathologic products and age in the AD and non-AD groups using linear regression. Ultimately, we used these features to build a diagnostic model for AD.ResultsA total of 1,255 individuals were included in the study (mean [SD] age, 73.27 [7.26] years; 691male [55.1%]; 564 female [44.9%]). The results of the correlation analysis showed that the correlations between pathologic products and age were, in descending order, Tau (Corr=0.75), p-Tau (Corr=0.71), and Aβ (Corr=0.54). In the AD group, t-Tau protein showed a tendency to decrease with age, but it was not statistically significant. p-Tau protein levels similarly decreased with age and its decrease was statistically significant. In contrast to Tau protein, in the AD group, Aβ levels increased progressively with age. In the non-AD group, the trend of pathologic product levels with age was consistently opposite to that of the AD group. We finally screened the optimal AD diagnostic model (AUC=0.959) based on the results of correlation analysis and by using the Xgboost algorithm and SVM algorithm.ConclusionIn a novel finding, we observed that Tau protein and Aβ had opposite trends with age in both the AD and non-AD groups. The linear regression curves of the AD and non-AD groups had completely opposite trends. Through a machine learning approach, we constructed an AD diagnostic model with excellent performance based on the selected features.
Seroprevalence of measles antibody among immigrants in Gwangju, South Korea
Authors: Lee Ran , Kim Sarah , Na Hyerin , Seo Ji In , Yoon Jang Gwon , Park A Ram , Bae So Hyun , Park So Yeong , Cho Jun Hwi , Kim Jin , Choi Seong-Woo , Kweon Sun-Seog , Sun Bongkyu , Kim Kyunghak , Park Kyung-Hwa , Kim Seong Eun 
Published: December 2024

IntroductionMeasles remains a public health concern, particularly among populations with suboptimal vaccination coverage, including immigrants. Understanding the seroprevalence of measles antibodies in immigrant populations is essential to inform tailored vaccination strategies and reduce the risk of measles reintroduction.MethodsThis study evaluated measles IgG seroprevalence among 651 immigrants from 30 countries residing in Gwangju, South Korea. Participants were recruited between September 2022 and August 2024, and measles antibody levels were assessed using the LIAISON® XL assay. Statistical analyses included Fisher’s exact and chi-squared tests to identify associations between seropositivity and demographic factors.ResultsMeasles antibody positivity rates varied significantly by age group (p &lt; 0.01). Individuals born after 1995 exhibited the lowest seroprevalence (63.7%), while those born in 1964 or earlier were all seropositive (100%). Seroprevalence was particularly low among immigrants from Russia (68.1%), Kazakhstan (70.6%), Ukraine (72.7%), Mongolia (75.5%), and Cambodia (78.1%). Long-term residents demonstrated higher antibody positivity (92.9%) than other visa categories (p &lt; 0.01), and women had higher seropositivity (85.8%) compared to men (80.0%), with a near-significant difference (p = 0.05).DiscussionThe higher seropositivity observed among long-term residents and women is likely due to prior immunization with the MMR (Measles Mumps Rubella) vaccine, which is recommended to prevent congenital rubella syndrome as part of pre-pregnancy vaccination protocols. This study underscores the importance of implementing tailored vaccination programs based on the characteristics of immigrant populations and focusing on countries with low seroprevalence to effectively prevent measles reintroduction.
The use of patient-generated health data in the management of low anterior resection syndrome: a qualitative study
Authors: Monton Olivia , Smith Allister , Sabboobeh Sarah , Demian Marie , Cornish Julie , Wexner Steven D. , Christensen Peter , Ghuman Amandeep , Bordeianou Liliana G. , Keane Celia , Husain Syed , Gasior Alessandra , Leon Natalie , Savard Julie , Savitt Lieba R. , Majgaard Margit , Sørensen Gitte Kjær , Mills Melanie , Rajabiyazdi Fateme , Boutros Marylise 
Published: December 2024

BackgroundThe cornerstone of low anterior resection syndrome (LARS) treatment is self-management, which requires patient engagement. Colorectal surgeons and nurses may use patient-generated health data (PGHD) to help guide patients in their use of self-management strategies for LARS. However, the perspectives of LARS experts on the use of PGHD remain largely unexplored. The objective of this study was to explore the perspectives and experiences of LARS experts regarding the use of PGHD in the management of LARS.MethodsWe utilized purposive snowball sampling to identify international LARS experts, including surgeons, nurses, and LARS researchers with knowledge and expertise in LARS. We conducted individual semi-structured interviews with these experts between August 2022 and February 2024. We performed thematic analysis using the framework method to identify domains and associated themes.ResultsOur sample included 16 LARS experts from five countries. Thematic analysis identified four domains and associated themes. The domains included: data collection practices, data review practices, perceived usefulness, and future directions. Within the data collection practices domain, we found that most experts asked LARS patients to collect some form of PGHD, including bowel diaries, patient-reported outcome measures, or both. Within the data review practices domain, we found that both surgeons and nurses reviewed PGHD. Most participants described finding it difficult to interpret the data and identified time constraints, legibility, and completeness as the most common barriers to reviewing data in clinic. In terms of perceived usefulness, data collection was felt to help clinicians understand symptoms and their impact and assist patients with self-management. The future directions domain revealed that most experts felt that a clinical tool in the form of an online app or website to support data collection and enhance data visualization would be useful. Finally, some participants saw promise in leveraging PGHD to inform the creation of automated treatment algorithms for LARS management.ConclusionsThis study highlights many gaps in the processes of patient-generated LARS data collection and review. A clinical tool including various data collection templates and data visualization prototypes could help to address these gaps. Future research will focus on incorporating the patient perspective.