Review
Pharmacology & Pharmacy
Iman Rahnama, Seyyed Mostafa Arabi, Mahla Chambari, Leila Sadat Bahrami, Vahid Hadi, Sayid Mahdi Mirghazanfari, Manfredi Rizzo, Saeid Hadi, Amirhossein Sahebkar
Summary: This study summarized the effects of Spirulina on the lipid profile and found that Spirulina supplementation significantly reduced LDL-C and TC levels, while significantly increasing HDL-C levels.
PHARMACOLOGICAL RESEARCH
(2023)
Article
Medicine, General & Internal
Rune Matthiesen, Chris Lauber, Julio L. Sampaio, Neuza Domingues, Liliana Alves, Mathias J. Gerl, Manuel S. Almeida, Gustavo Rodrigues, Pedro Araujo Goncalves, Jorge Ferreira, Claudia Borbinha, Joao Pedro Marto, Marisa Neves, Frederico Batista, Miguel Viana-Baptista, Jose Alves, Kai Simons, Winchil L. C. Vaz, Otilia V. Vieira
Summary: The study demonstrates that chronic inflammatory diseases exhibit distinct plasma lipid profiles, which can be accurately distinguished using lipidomics to identify different diseases and subclasses, especially in cardiovascular diseases.
Article
Oncology
Emilia Bevacqua, Salvatore Ammirato, Erika Cione, Rosita Curcio, Vincenza Dolce, Paola Tucci
Summary: Prostate cancer is the most common cancer among men, with PSA testing leading to high false-positive rates. MicroRNAs (miRs) show promise as non-invasive biomarkers for diagnosis, prognosis, and recurrence of prostate cancer.
Article
Endocrinology & Metabolism
Xin Li, Andrea Dai, Richard Tran, Jie Wang
Summary: This study used a text mining-based approach to analyze the role of microRNAs (miRNAs) in diabetes and their potential as biomarkers. The analysis identified 13 distinct topics of miRNA studies in diabetes and miRNAs exhibited a topic-specific pattern. miR-146 emerged as a critical biomarker for diabetes prediction, targeting multiple genes and signal pathways implicated in diabetic inflammation and neuropathy.
FRONTIERS IN ENDOCRINOLOGY
(2023)
Article
Health Care Sciences & Services
Svetlana Tarbeeva, Ekaterina Lyamtseva, Andrey Lisitsa, Anna Kozlova, Elena Ponomarenko, Ekaterina Ilgisonis
Summary: By using automatic text-mining, we identified major trends and key genes in the obesity field, reducing a large set of data to 19 genes for potential personalized medicine applications.
JOURNAL OF PERSONALIZED MEDICINE
(2021)
Article
Infectious Diseases
Samira Yousefinaghani, Rozita Dara, Samira Mubareka, Andrew Papadopoulos, Shayan Sharif
Summary: This study identified public sentiments and opinions towards COVID-19 vaccines on Twitter, showing a dominance of positive sentiments but active discussions on vaccine rejection and hesitancy. Different countries exhibited varying patterns. Additionally, the study found that vaccine opposition content came partly from Twitter bots or political activists, while support for vaccination originated from well-known individuals and organizations.
INTERNATIONAL JOURNAL OF INFECTIOUS DISEASES
(2021)
Review
Pharmacology & Pharmacy
Weilin Chen, Qi Wang, Bin Zhou, Lihua Zhang, Honglin Zhu
Summary: Rheumatic diseases are chronic autoimmune disorders with poorly understood mechanisms, but recent research suggests that cell metabolism plays a crucial role in their pathogenesis. By focusing on lipid metabolism profiles and mechanisms, new insights for clinical treatment of rheumatic diseases can be gained.
FRONTIERS IN PHARMACOLOGY
(2021)
Article
Agriculture, Dairy & Animal Science
Iris B. S. Araujo, Darlinne Amanda S. Lima, Sergio F. Pereira, Rafaella P. Paseto, Marta S. Madruga
Summary: The study demonstrated that collagen gel extracted from chicken feet can replace fat and control the effects of lipid oxidation in chicken sausages. Additionally, the collagen gel showed superior performance in appearance, color sensory parameters, and water holding capacity.
Article
Chemistry, Multidisciplinary
Elham Kariri, Hassen Louati, Ali Louati, Fatma Masmoudi
Summary: Artificial Neural Networks (ANNs) are machine learning algorithms inspired by the human brain, which have gained popularity due to their ability to learn and improve through experience. This paper provides a comprehensive understanding of ANNs by analyzing a large number of articles and keywords, and explores potential future research directions. The analysis shows a high level of interest in topics related to machine learning, deep learning, and ANNs, with a focus on optimization techniques, feature extraction and selection, and clustering. The study aims to guide the continued study and development of ANNs, promote a deeper understanding, and facilitate the development of new techniques and applications.
APPLIED SCIENCES-BASEL
(2023)
Review
Biochemistry & Molecular Biology
Sofia I. R. Conceicao, Francisco M. Couto
Summary: In building biological networks, providing reliable interactions is crucial. Text mining methods can help extract knowledge from scientific literature to overcome the challenge of tracking recent discoveries. These tools can lead to more reliable and personalized networks by identifying relations between entities of interest.
Article
Biology
Leena Nezamuldeen, Mohsin Saleet Jafri
Summary: In this study, an automated system was developed to extract protein-protein interaction networks from scientific research literature using text mining and artificial intelligence techniques. Three models using deep learning and natural language processing methods were created, achieving high accuracy. The automated system provided an improved view compared to manually curated networks, offering valuable insights for physicians and scientists.
Article
Computer Science, Hardware & Architecture
Shengbin Liang, Fuqi Sun, Haoran Sun, Tingting Chen, Wencai Du
Summary: This paper introduces a Chinese medical text classification model using a BERT-based Chinese text encoder, N-gram representations, and a capsule network. The model extracts features using the capsule network and enhances medical text representation and feature extraction through the design of an N-gram medical dictionary. The experimental results demonstrate that the model outperforms the baseline models in terms of precision, recall, and F1-score.
JOURNAL OF SUPERCOMPUTING
(2023)
Article
Environmental Studies
Lee Jae-hyuck, Shin Kyung-hee, Park Jong-mun, Kim Choong-gon, Cho Kongjang
Summary: This study examines the problems and alternatives to the current process of collecting resident opinions during the Environmental Impact Assessment (EIA), using the case of the Dangjin landfill project in Korea. Despite residents' participation in the assessment, they opposed the project and viewed the public participation process as a mere formality. The study suggests the need for more meaningful communication and the use of relatable language during the public participation process to better reflect residents' opinions.
ENVIRONMENTAL IMPACT ASSESSMENT REVIEW
(2022)
Article
Pharmacology & Pharmacy
Juliana Rincon-Lopez, Yara C. Almanza-Arjona, Alejandro P. Riascos, Yareli Rojas-Aguirre
Summary: By analyzing a dataset of CD pharmaceutical patents, a data-driven approach was used to unveil pharmaceutical technologies of cyclodextrins (CDs). Complex networks formed by CD patents demonstrated the supremacy of CDs for solubility enhancement and triggered cutting-edge applications. CDs play a crucial role in formulating aqueous solutions, tablets, and powders, with text-mining showing increasing trends in these dosage forms.
Review
Computer Science, Artificial Intelligence
Gyunam Park, Minsu Cho, Jiyoon Lee
Summary: This article provides an in-depth analysis of process mining using text mining and machine learning techniques, including main research fields, relationships between fields, and future development trends.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Multidisciplinary Sciences
Phasit Charoenkwan, Saeed Ahmed, Chanin Nantasenamat, Julian M. W. Quinn, Mohammad Ali Moni, Pietro Lio, Watshara Shoombuatong
Summary: This study presents a novel meta-predictor, AMYPred-FRL, which utilizes a feature representation learning approach to identify amyloid proteins more accurately. By combining multiple machine learning algorithms and sequence-based feature descriptors, AMYPred-FRL generates 60 probabilistic features and forms a hybrid model. Through cross-validation and independent tests, AMYPred-FRL outperforms existing methods in predictive performance.
SCIENTIFIC REPORTS
(2022)
Article
Biology
Phasit Charoenkwan, Nalini Schaduangrat, Mohammad Ali Moni, Pietro Lio, Balachandran Manavalan, Watshara Shoombuatong
Summary: This study presents a novel computational method, SAPPHIRE, for accurately identifying thermophilic proteins (TPPs) using sequence information. The method combines different feature encodings and machine learning algorithms to train baseline models and extract key information of TPPs. SAPPHIRE outperforms existing methods in terms of predictive performance and achieves higher accuracy and correlation coefficient.
COMPUTERS IN BIOLOGY AND MEDICINE
(2022)
Article
Biology
Phasit Charoenkwan, Nalini Schaduangrat, Pietro Lio, Mohammad Ali Moni, Balachandran Manavalan, Watshara Shoombuatong
Summary: This study proposes a novel computational approach, NEPTUNE, for the accurate and large-scale identification of Tumor Homing Peptides (THPs) from sequence information. The results demonstrate that NEPTUNE achieves superior performance in THP prediction and improves interpretability using the SHapley additive explanations method.
COMPUTERS IN BIOLOGY AND MEDICINE
(2022)
Article
Biology
Phasit Charoenkwan, Chonlatip Pipattanaboon, Chanin Nantasenamat, Md Mehedi Hasan, Mohammad Ali Moni, Pietro Lio, Watshara Shoombuatong
Summary: Despite existing cancer therapies, the development of new and effective treatments is necessary to address the ongoing cancer recurrence and new cases. This study proposes a new machine learning-based approach, PSRTTCA, for improving the identification and characterization of tumor T cell antigens (TTCAs) based on their primary sequences.
COMPUTERS IN BIOLOGY AND MEDICINE
(2023)
Article
Biochemistry & Molecular Biology
Adeel Malik, Watshara Shoombuatong, Chang-Bae Kim, Balachandran Manavalan
Summary: A machine learning-based predictor called GPApred was developed to identify LPXTG-like proteins from their primary sequences. This predictor can be utilized for functional characterization and drug targeting in further research.
INTERNATIONAL JOURNAL OF BIOLOGICAL MACROMOLECULES
(2023)
Article
Anatomy & Morphology
Chanasorn Poodendan, Athikhun Suwannakhan, Tidarat Chawalchitiporn, Yuichi Kasai, Chanin Nantasenamat, Laphatrada Yurasakpong, Sitthichai Iamsaard, Arada Chaiyamoon
Summary: This study investigated the morphometric parameters of the C1 vertebra and evaluated its potential for sex prediction. The results showed that the C1 vertebra was longer in males compared to females. Evaluation of these parameters is important for preoperative assessment and treatment of atlas dislocation, and they can also be used for sex prediction.
SURGICAL AND RADIOLOGIC ANATOMY
(2023)
Review
Environmental Sciences
Norhafiza Mat Lazim, Abdul Hafeez Kandhro, Anna Menegaldo, Giacomo Spinato, Barbara Verro, Baharudin Abdullah
Summary: Autofluorescence and narrow-band imaging have reemerged in the medical field, thanks to advances in technology. These techniques, along with new optical instruments and fluorescence biomolecules, have improved the management of diseases, particularly upper aerodigestive tract tumors. Multispectral imaging and micro-endoscopy have further enhanced tumor management, allowing for early diagnosis and better treatment outcomes. Autofluorescence endoscopy is crucial for screening, diagnosing, and treating these tumors, as it can assess microtumoral deposits that are not visible with traditional endoscopy. Overall, this new technique has led to optimum management, improved treatment outcomes, and better prognosis for patients.
INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH
(2023)
Article
Biology
Phasit Charoenkwan, Pramote Chumnanpuen, Nalini Schaduangrat, Changmin Oh, Balachandran Manavalan, Watshara Shoombuatong
Summary: In this study, a novel computational approach called PSRQSP was developed to improve the prediction and analysis of QSPs. Experimental results showed that PSRQSP outperformed conventional methods in identifying QSPs and demonstrated its predictive capability and effectiveness. PSRQSP also constructed an easy-to-use web server for accelerating the discovery of potential QSPs for drug development.
COMPUTERS IN BIOLOGY AND MEDICINE
(2023)
Article
Biochemistry & Molecular Biology
Phasit Charoenkwan, Nalini Schaduangrat, Nhat Truong Pham, Balachandran Manavalan, Watshara Shoombuatong
Summary: Proposed the first stack-based approach, Pretoria, for accurate and large-scale identification of CD8+ T-cell epitopes (TCEs) of eukaryotic pathogens. Constructed a pool of 144 different machine learning (ML)-based classifiers based on 12 popular ML algorithms and used feature selection method to determine important ML classifiers for building the stacked model. Experimental results demonstrated that Pretoria outperformed several conventional ML classifiers and the existing method, with an accuracy of 0.866, MCC of 0.732, and AUC of 0.921 in the independent test.
INTERNATIONAL JOURNAL OF BIOLOGICAL MACROMOLECULES
(2023)
Article
Biochemistry & Molecular Biology
Tianshi Yu, Tianyang Huang, Leiye Yu, Chanin Nantasenamat, Nuttapat Anuwongcharoen, Theeraphon Piacham, Ruobing Ren, Ying-Chih Chiang
Summary: Researchers studied Cytochrome P450 17A1 (CYP17A1), a key enzyme in steroidogenesis, and its potential as a druggable target for anti-cancer molecule development. They used cheminformatic analyses and quantitative structure-activity relationship (QSAR) modeling on a dataset of CYP17A1 inhibitors. Different models were built for steroidal and nonsteroidal inhibitors, achieving good accuracy. The findings provide valuable insights for further drug discovery efforts targeting CYP17A1 inhibitors.
Article
Chemistry, Multidisciplinary
Nalini Schaduangrat, Nuttapat Anuwongcharoen, Phasit Charoenkwan, Watshara Shoombuatong
Summary: This study proposes a novel deep learning (DL)-based hybrid framework, named DeepAR, to accurately and rapidly identify AR antagonists by using only the SMILES notation. Experimental results indicate that DeepAR is a more accurate and stable approach for identifying AR antagonists, achieving an accuracy of 0.911 and MCC of 0.823 on an independent test dataset. In addition, the framework provides feature importance information and allows for characterization and analysis of potential AR antagonist candidates.
JOURNAL OF CHEMINFORMATICS
(2023)
Article
Chemistry, Multidisciplinary
Tianshi Yu, Chanin Nantasenamat, Supicha Kachenton, Nuttapat Anuwongcharoen, Theeraphon Piacham
Summary: This study used cheminformatic analysis and machine learning modeling to investigate the chemical space, scaffolds, structure-activity relationship, and landscape of human androgen receptor antagonists. The findings revealed differences in physicochemical properties between potent/active class molecules and intermediate/inactive class molecules. Low scaffold diversity was observed, especially in the potent/active class molecules, indicating the need for developing molecules with novel scaffolds. The study also identified significant activity cliff generators and provided insights and guidelines for the development of novel androgen receptor antagonists.
Article
Infectious Diseases
Pornlada Nuchnoi, Pakorn Piromtong, Saranya Siribal, Korrarit Anansilp, Peeradech Thichanpiang, Pilailuk Akkapaiboon Okada
Summary: During the third wave of COVID-19 in Thailand, the rapid spread of infections in high-risk areas necessitates the use of reliable and rapid molecular tests. This study demonstrates that RT-LAMP is a reliable assay for SARS-CoV-2 detection and can be used in emergency response situations.
INTERNATIONAL JOURNAL OF INFECTIOUS DISEASES
(2023)
Article
Multidisciplinary Sciences
Phasit Charoenkwan, Sajee Waramit, Pramote Chumnanpuen, Nalini Schaduangrat, Watshara Shoombuatong
Summary: HCV infection causes chronic liver diseases, and there is no effective vaccine available. This study proposes a novel approach called TROLLOPE to accurately identify TCE-HCVs from sequence information, with superior predictive performance.
