bluuebunny/crossref_metadata_2025_split huggingface dataset:
Meta-path-based drug efficacy prediction model for active natural compound discovery from Traditional Chinese medicine
Authors: Li Yingcan , Shen Yu , Cai Yezi , Gao Jiahui , Huang Lei , Si Weinuo , Zhou Kai , Gao Shan , Luo Qichao 
Published: May 2024

Abstract

Identifying the active nature compounds remains a challenge for drug discovery, and new algorithms need to be developed to predict active ingredients from complex natural products. Here, we proposed Meta-DEP, a Meta-paths-based Drug Efficacy Prediction based on drug-protein-disease heterogeneity network, where Meta-paths contains all the shortest paths between drug targets and disease-related proteins in the network and drug efficacy is measured by a predictive score according to drug disease network proximity. Experiments show that Meta-DEP performs better than traditional network topology analysis on drug-disease interaction prediction task. Further investigations demonstrate that the key targets identified by Meta-DEP for drug efficacy are consistent with clinical pharmacological evidence. To prove that Meta-DEP can be used to discover active nature compounds, we apply it to predict the relationship between the monomeric components of traditional Chinese medicine included in the TCMSP database and diseases. Results indicate that Meta-DEP can accurately predict most of the drug-disease pairs included in the TCMSP database. In addition, biological experiments are directly used to demonstrate that the natural compound mined by Meta-DEP from traditional Chinese medicine has a protective effect against myocardial ischemia. Overall, the model developed in this study provides new impetus for driving the nature compound into innovative lead molecule. Code and data are available at https://github.com/t9lex/Meta-DEP.
Gaps in the usage and reporting of multiple imputation for incomplete data: Findings from a scoping review of observational studies addressing causal questions
Authors: Mainzer Rheanna M , Moreno-Betancur Margarita , Nguyen Cattram D , Simpson Julie A , Carlin John B. , Lee Katherine J 
Published: May 2024

Abstract

Background
Missing data are common in observational studies and often occur in several of the variables required when estimating a causal effect, i.e. the exposure, outcome and/or variables used to control for confounding. Analyses involving multiple incomplete variables are not as straightforward as analyses with a single incomplete variable. For example, in the context of multivariable missingness, the standard missing data assumptions (“missing completely at random”, “missing at random” [MAR], “missing not at random”) are difficult to interpret and assess. It is not clear how the complexities that arise due to multivariable missingness are being addressed in practice. The aim of this study was to review how missing data are managed and reported in observational studies that use multiple imputation (MI) for causal effect estimation, with a particular focus on missing data summaries, missing data assumptions, primary and sensitivity analyses, and MI implementation.
Methods
We searched five top general epidemiology journals for observational studies that aimed to answer a causal research question and used MI, published between January 2019 and December 2021. Article screening and data extraction were performed systematically.
Results
Of the 130 studies included in this review, 108 (83%) derived an analysis sample by excluding individuals with missing data in specific variables (e.g., outcome) and 114 (88%) had multivariable missingness within the analysis sample. Forty-four (34%) studies provided a statement about missing data assumptions, 35 of which stated the MAR assumption, but only 11/44 (25%) studies provided a justification for these assumptions. The number of imputations, MI method and MI software were generally well-reported (71%, 75% and 88% of studies, respectively), while aspects of the imputation model specification were not clear for more than half of the studies. A secondary analysis that used a different approach to handle the missing data was conducted in 69/130 (53%) studies. Of these 69 studies, 68 (99%) lacked a clear justification for the secondary analysis.
Conclusion
Effort is needed to clarify the rationale for and improve the reporting of MI for estimation of causal effects from observational data. We encourage greater transparency in making and reporting analytical decisions related to missing data.
Frequency of Immunofluorescence Antinuclear Antibody Patterns: In A Local Population from North India
Authors: Deepak Rani , Agrawal Charu , Kapoor Sunita , Kanotra Ritesh 
Published: May 2024

Background
Autoimmune disorders in India have seen rise in recent times. The first-line test in diagnostic workup of autoimmune disorders is ANA indirect Immunofluorescence (IIF). A positive Antinuclear antibodies (ANA) result in conjunction with clinical findings aids in predicting the diagnosis. ANA test is not widely available in India, probably due to the cost and requirement of technical expertise. In view of rising incidence of autoimmune disorders in India and due to scarcity of data, this study aimed at determining prevalence of ANA positivity, study ANA patterns and their frequency and estimate disease burden in patients among local population of Northern India.
Methods
The study was performed on 2000 suspected autoimmune disorder patients during a period of 01 year (January 2023 till January 2024). Assessment of ANA patterns and titres were carried out by ANA-IIF technique.
Result
Of the 2000 participants, 30.8% were found to be ANA-positive. Nuclear speckled pattern was observed to be the most common, among others. Among mixed ANA patterns, mix of speckled and homogenous patterns were common. Females showed predominance over males in all age groups in ratio of 3:1.
Conclusion:
The study is a preliminary, retrospective study providing overview of ANA positivity and autoimmunity status in Northern India, where despite the heavy disease burden, data is still scarce. Further studies in Indian population are essential to determine relationship of ANA with various etiologic and biochemical factors and association between ANA patterns and specific antibodies in serum for diagnosis of specific autoimmune diseases.
Natural Compounds that Modulate Circadian Rhythms
Authors: TOMITA Tatsunosuke , Wadhwa Renu , Onishi Yoshiaki 
Published: May 2024

Abstract::
Circadian rhythms of innate 24 h cycles comprise well-conserved biological phenomena
from cyanobacteria to mammalian. They are driven by light and regulated by clock genes that
work as transcription factors and control the expression of many other genes and physiological
functions in the cells. The expression of ~ 40% of protein-coding genes shows 24 h oscillation patterns
in mice, implying their importance in normal body functions. Indeed, the physiological and
behavioural rhythmicity generated through clock genes-mediated multiple mechanisms affects the
quality of life at large. Disrupted circadian rhythmicity is associated with several kinds of diseases.
For example, cancer cells show abnormal expression patterns for circadian rhythm genes that
have been shown to regulate oncogenesis, drug responses, and disease prognosis. Furthermore, the
modern globalisation of human lifestyle and business and social activities have disrupted innate
circadian rhythm, resulting in a variety of diseases through disrupted humoral, immunological,
and neuronal pathways. Safe and sustainable modulation of circadian rhythm has become a prevalent
need that warrants basic and interventional research, as well as clinical investigations. Although
traditional systems of medicine suggest some natural compounds with circadian rhythmmodulating
potential, most of these have not been validated in laboratory or clinical studies. Reliable
read-outs of the effects of test compounds on circadian rhythmicity have been limited by the
availability of live cell assays. We have, herein, provided an overview of living cell-embedded real-
time reporter gene assays designed for screening compounds that modulate circadian rhythm,
and discussed the potential of some natural compounds for circadian rhythm modulation as validated
by cell-based assay systems, and their role in disease therapeutics.
Benzimidazole as a Privileged Scaffold in Drug Design and Discovery
Authors: Kumar Ram , Marianesan Arockia Babu , Pathak Shilpi 
Published: May 2024

Abstract:
Benzimidazole is a privileged drug design and discovery scaffold with various phar-macological activities, including antimicrobial, anticancer, antitubercular, anti-inflammatory, an-tidiabetic, antihypertensive, antimalarial, and many more. This scaffold can be observed in the structure of numerous FDA-approved drugs and employed in medicinal chemistry to develop novel bioactive compounds through rational drug design. Its broad pharmacological significance is due to physicochemical attributes, including H-bond donor-acceptor efficiency, π-π stacking interactions, and hydrophobic interactions; these characteristics enable benzimidazole derivatives to bind with macromolecules efficiently. This article emphasizes mechanisms, SAR, and docking studies to unveil benzimidazole's various active hybrids accountable for diversified activities. It will assist researchers in strategically designing various novel benzimidazole-endowed hybrids to develop clinically active therapeutic candidates.
Physicochemical Properties of Quinoa (Chenopodium quinoa Willd.) Protein Isolated from Black, Q12, and Titicaca Seed
Authors: Sekhavatizadeh Seyed Saeed , Hosseinzadeh Saeid 
Published: May 2024

Background:
Quinoa was cultivated in different parts of the world because of the water
crisis. On the other view, proteins from plant sources have attracted significant interest. One of the
sustainable protein sources is quinoa protein.

Objective:
The aim of this study was to isolate quinoa protein and determine physicochemical
properties for its use in the food industry.

Methods:
Quinoa Protein Isolates (QPIs) were separated from Quinoa Seed (QS) varieties (Black-
-QS, Q12-QS, and Titicaca-QS). The particle size, FTIR, SEM, emulsion activity and stability,
protein solubility, and gelation properties were assessed.

Results:
The Q12-QPI had the highest average particle size, 945 μm. The Black-QPI and Titicaca
(T)-QPI had a higher protein content (87.32 ± 1.93, 87.84 ± 1.62% w/w), respectively, and a more
condensed structure. The surface morphology of Black-QPI and T-QPI showed regular flat and
compact surfaces with some small aggregates. Black-QPI had the most negative zeta potential
charge (-38.8 ± 0.03) and stability among the TQPIs. Emulsion capacity was equal among the samples,
but emulsion stability was the greatest value (34.48 ± 8.1) in T-QPI. The protein solubility ratio
was 70.72, 70.0, and 69.27% in Q12-QPI, T-QPI, and Black-QPI, respectively. The higher elastic
performance of Q12-QPI and T-QPI than Black-QPI was seen during the heating steps in the
gelation stage.

Conclusion:
The suitable nutritional and functional resources of Titicaca quinoa protein make it
an appropriate candidate to use as a safe food additive.
Targeted Therapy of Tumors and Cancer Stem Cells based on Oxidant-Regulated Redox Pathway and its Mechanism
Authors: Wang Shunshun , Han Juanjuan , Wang Zijun , Liu Xianqiong , Wang Chunli , Nisar Muhammad Farrukh , Pan Lianhong , Xu Kang 
Published: May 2024

Abstract:
A malignant tumor is a frequent and common disease that severely threatens human
health. Many mechanisms, such as cell signaling pathway, anti-apoptosis mechanism, cell stemness,
metabolism, and cell phenotype, have been studied to explain the reasons for chemotherapy,
radioresistance, and tumor recurrences in antitumor treatment. Cancer stem cells (CSCs) are
important tumor cell subclasses that can potentially organize and regulate stem cell properties.
Growing evidence suggests that CSCs can initiate tumors and constitute a significant factor in
metastasis, recurrence, and treatment resistance. The inability to completely target and remove
CSCs is a considerable obstacle in tumor treatment. Therefore, drugs and therapeutic strategies
that can effectively intervene with CSCs are essential for the treatment of different tumor types.
However, the current strategies and efficacy of targeted elimination of CSCs are very limited.
Oxidative stress has been recognized to play a crucial role in cancer pathophysiology. Moreover,
reactive oxygen species (ROS) production and imbalance of the built-in cellular antioxidant
defense system are hallmarks of tumor and cancer etiology. The current paper will focus on the
regulation and mechanism behind oxidative stress in tumors and cancer stem cells and its tumor
therapy applications. Additionally, the article discusses the role of CSCs in causing tumor
treatment resistance and recurrence based on a redox perspective. The study also emphasizes
that targeted modulation of oxidative stress in CSCs has great potential in tumor therapy,
providing novel prospects for tumor therapy.
The Effects and Safety of Silymarin on β-thalassemia in Children and Adolescents: A Systematic Review based on Clinical Trial Studies
Authors: Rahimi-Dehkordi Nasim , Heidari-Soureshjani Saeid , Sherwin Catherine MT 
Published: May 2024

Background:
β-thalassemia imposes significant complications on affected patients. Silymarin,
a natural flavonoid complex, has potential therapeutic properties.

Objective:
This systematic review aims to comprehensively evaluate the literature on the mechanistic
effects of Silymarin on β-thalassemia outcomes in children and adolescents.

Methods:
A systematic search of electronic databases, including MEDLINE/PubMed, Embase,
Scopus, Cochrane Library, and Web of Science (WOS), was done to identify relevant clinical
trials before January 2024. Various data were extracted, including study characteristics, outcomes
measured (hematological parameters, oxidative stress markers, iron metabolism, and other outcomes),
proposed mechanisms, and safety.

Results:
By iron chelation effects, Silymarin can reduce reactive oxygen species (ROS) production,
increase intracellular antioxidant enzyme glutathione (GSH), and insert antioxidant effects. It
also attenuated inflammation through reduced tumor necrosis factor-alpha (TNF-α), transforming
growth factor-β1 (TGF-β1), interferon-gamma (IFNγ), C-reactive protein (CRP), interleukin 6
(IL-6), IL-17, and IL-23 levels and increase in IL-4 and IL-10 levels. By reducing iron overload
conditions, Silymarin indicates modulatory effects on immune abnormalities, inhibits red blood
cell (RBC) hemolysis, increases RBC count, and minimizes the need for a transfusion. Moreover,
it reduces myocardial and hepatic siderosis, improves liver function tests, and modifies abnormal
enzymes, particularly for aspartate transaminase (AST), alanine transaminase (ALT), alkaline
phosphatase (ALP), total bilirubin, and total protein levels. Silymarin also reduces iron overload,
increases antioxidant and anti-inflammatory capacity in cardiomyocytes, and reveals antioxidant
effects.

Conclusion:
Silymarin indicates promising effects on various aspects of children and adolescents
with β-thalassemia and has no serious side effects on the investigated dosage.
Multimodal Deep Learning for Cancer Survival Prediction: A Review
Authors: Zhang Ge , Ma Chenwei , Yan Chaokun , Luo Huimin , Wang Jianlin , Liang Wenjuan , Luo Junwei 
Published: May 2024

Background::
Cancer has emerged as the "leading killer" of human health. Survival prediction
is a crucial branch of cancer prognosis. It aims to estimate patients' survival risk based on
their disease conditions. Accurate and efficient survival prediction is vital in cancer patients' treatment
and clinical management, preventing unnecessary suffering and conserving precious medical
resources. Deep learning has been extensively applied in cancer diagnosis, prognosis, and treatment
management. The decreasing cost of next-generation sequencing, continuous development of related
databases, and in-depth research on multimodal deep learning have provided opportunities for establishing
more functionally rich and accurate survival prediction models.

Objective::
The current area of cancer survival prediction still lacks a review of multimodal deep
learning methods.

Methods::
We conducted a statistical analysis of the relevant research on multimodal deep learning
for cancer survival prediction. We first filtered keywords from 6 known relevant papers. Then, we
searched PubMed and Google Scholar for relevant publications from 2018 to 2022 using "Multimodal",
"Deep Learning" and "Cancer Survival Prediction" as keywords. Then, we further searched
the related publications through the backward and forward citation search. Subsequently, we conducted
a detailed analysis and review of these studies based on their datasets and methods.

Results::
We present a comprehensive systematic review of the multimodal deep learning research
on cancer survival prediction from 2018 to 2022.

Conclusion::
Multimodal deep learning has demonstrated powerful data aggregation capabilities and
excellent performance in improving cancer survival prediction greatly. It has made a significant positive
impact on facilitating the advancement of automated cancer diagnosis and precision oncology.
Physics tools and methods help fight covid-19
Authors: Sujeetha S. , Arthi V. , Sudha A. , Sowmiya T. , Suganthi A. 
Published: May 2024

Physics based tools and methods play an enormous role in understanding structural features and functions of viral particles along with their impact on the body. For example, X-ray crystallography utilizes electro-magnetic radiation to produce wavelength that can help to generate 3D detailed structure of the virus. Another technique used for obtaining structure information is the Cryo- electron microscopy. Cryo-EM is the electron microscopy method applied to sample cooled to very low temperature. Computed tomography (CT) imaging technology, widely known as CT scan, use a narrow beam of X-ray quickly rotated around the patient to produce cross-sectional images or “slices” of the body. The George Washington university hospital utilized CT images to show the impact of COVID-19 on the lungs of a patient. A ventilator is required to strongly give adequate degrees of oxygen in blood. We learn about all of these physics-related methods and tools to fight SARS-CoV-2 and Covid-19 through UNO’s biomedical physics concentration. Medical physicists by diagnosing and treating patients and as biomedical specialists by designing and building biomedical equipment. Artificial intelligence, nanotechnologies, synthetic biology, drones, robots, Tele health technologies, open-source technologies and gene -editing are the application used in technologies to fight covid-19.
Structural, morphological and magnetic properties of fe3o4 nanoparticles by coprecipitation method
Authors: Padmavathi J. , Anantharaj A. , Gomathi S. , Gokulakumar B. 
Published: May 2024

In recent years nanotechnology has had significant potential in different areas of social interest. In this present thesis work Synthesis Fe3O4 nanoparticles with various ratios of Ammonia solution by Co-Precipitation method and synthesized Fe3O4 nanoparticles analyzed by XRD , SEM, FTIR, and VSM for its Structural, Morphology and Magnetic properties. It has been found that the variations in ammonia solution have a significant effect on the size and magnetic properties of Fe3O4 nanoparticles. The prepared Fe3O4 nanoparticles show applications.
Pengaruh Harga, Kulitas Produk Dan Suasana Terhadap Kepuasan Pelanggan Pada Warkop Kampis Palopo
Authors: Yunus Muhammad Haekal , Rosyalinda Rosyalinda , Reni Nelviana , Meliani Meliani , Arnal Arnal 
Published: May 2024

<p>Penelitian ini bertujuan untuk mengenalisis pangaruh harga,kualitas produk, dan suasana terhadap kepuasan pelanggan dalam konteks ini adalah tingkat kepuasan dan keinginan pelanggan untuk terus menggunakan jasa dari warkop tersebut.Metode penelitian ini menggunakan pendekatan kuntitatif dengan melakukan survey terhadap sampel pelanggan yang mengunjungi warkop kampis palopo. Data-data yang diperoleh kemudian dianalisis menggunakan teknik analisis statistic.Hasil analisis menunjukkan bahwwa harga,kualitas produk dan suasana memiliki pengaruh signifikan terhadap kepuasan pelanggan pada warkop kampis palopo .Harga yang wajar dan sesuai dengan kualitas produk,produk yang berkualitas tinggi,serta suasana yang nyaman dan menyenangkan di warkop kampis tersebut memberikan konstribusi positif terhadap kepuasan pelanggan . Penelitian ini memberikan implikasi penting bagi manajemen warkop kampis palopo dalam mengingkatkan kepuasan pelanggan .Manajemen dapat mempertimbangkan trategi harga yang kompetitif mengingkatkan kulitas produk yang ditawarkan dan menciptakan suasana yang menarik bagi pelanggan. Dengan melakukan hal tersebut warkop dapat memperkuat hubungan dengan pelanggan yang dapat berdampak positif pada kesetiaan pelanggan dan keberhasilan bisnis warkop tersebut .</p>
Pengaruh Keterlibatan Kerja Dan Keseimbangan Kerja Terhadap Turnover Intention Karyawan Pada PT. Makassar Mandiri Putra Utama Mitsubisi Beta Berlian Kendari
Authors: Mulyadi Dwi Zulkifar , Kusumawati Sarah , Razak Abdul , Rahmatia Rahmatia 
Published: May 2024

<span style="font-size: 11.0pt; line-height: 107%; font-family: 'Cambria',serif; mso-fareast-font-family: Calibri; mso-bidi-font-family: Calibri; mso-ansi-language: IN; mso-fareast-language: EN-ID; mso-bidi-language: AR-SA;" lang="IN">Banyak faktor yang dapat mempengaruhi <em>Turnover Intention</em> dari perusahaan pada karyawan, diantaranya adalah keseimbangan kehidupan kerja, komitmen perusahaan, kinerja karyawan, stres kerja, pengembangan karir, kepuasan kerja dan keterlibatan karyawan yang rendah.</span><span style="font-size: 11.0pt; line-height: 107%; font-family: 'Cambria',serif; mso-fareast-font-family: Calibri; mso-bidi-font-family: Calibri; mso-ansi-language: EN-US; mso-fareast-language: EN-ID; mso-bidi-language: AR-SA;" lang="EN-US"> Tujuan dari penelitian ini yakni untuk melihat p</span><span style="font-size: 11pt; line-height: 107%; font-family: Cambria, serif;" lang="IN">engaruh antara keterlibatan kerja karyawan dan keseimbangan kerja karyawan terhadap <em>Turnover Intention</em></span><span style="font-size: 11pt; line-height: 107%; font-family: Cambria, serif;" lang="EN-US">. </span><span style="font-size: 11.0pt; line-height: 107%; font-family: 'Cambria',serif; mso-fareast-font-family: Calibri; mso-bidi-font-family: Calibri; mso-ansi-language: IN; mso-fareast-language: EN-ID; mso-bidi-language: AR-SA;" lang="IN">Penelitian ini menggunakan pendekatan kuantitatif</span><span style="font-size: 11.0pt; line-height: 107%; font-family: 'Cambria',serif; mso-fareast-font-family: Calibri; mso-bidi-font-family: Calibri; mso-ansi-language: EN-US; mso-fareast-language: EN-ID; mso-bidi-language: AR-SA;" lang="EN-US"> dengan menggunakan kuesioner sebagai instrument yang disebar keseluruh karyawan </span><span style="font-size: 11.0pt; line-height: 107%; font-family: 'Cambria',serif; mso-fareast-font-family: Calibri; mso-bidi-font-family: Calibri; mso-ansi-language: IN; mso-fareast-language: EN-ID; mso-bidi-language: AR-SA;" lang="IN">di PT. Makassar Mandiri Putra Utama Mitsubisi Beta Berlian Kendari</span><span style="font-size: 11.0pt; line-height: 107%; font-family: 'Cambria',serif; mso-fareast-font-family: Calibri; mso-bidi-font-family: Calibri; mso-ansi-language: EN-US; mso-fareast-language: EN-ID; mso-bidi-language: AR-SA;" lang="EN-US"> sebanyak 56 responden. Data yang diperoleh dari sebaran questioner akan dianalisis menggunakan regresi linear berganda. Hasil penelitian ini menunjukkan Keterlibatan kerja dan Keseimbangan kerja secara parsial memiliki pengaruh negative dan signifikan. Namun, secara simultan kedua variabel memiliki pengaruh yang signifikan terhadap <em>Turnover Intention</em> Karyawan di PT. Makassar Mandiri Putra Utama Mitsubisi Beta Berlian Kendari. Artinya, Semakin tinggi keterlibatan kerja dan keseimbangan kerja maka <em>Turnover Intention</em> semakin baik.</span>