Molecular machines are nanoscale devices capable of performing mechanical works at molecular level. These systems could be a single molecule or a collection of component molecules that interrelate with one another to produce nanomechanical movements and resulting performances. The design of the components of molecular machine with bioinspired traits results in various nanomechanical motions. Some known molecular machines are rotors, motors, nanocars, gears, elevators, and so on based on their nanomechanical motion. The conversion of these individual nanomechanical motions to collective motions integration into suitable platforms yields impressive macroscopic output at varied sizes. Instead of limited experimental acquaintances, the researchers demonstrated several applications of molecular machines in chemical transformation, energy conversion, gas/liquid separation, biomedical use, and soft material fabrication. As a result, the development of new molecular machines and their applications has accelerated over the previous two decades. This review highlights the design principles and application scopes of several rotors and rotary motor systems because these machines are used in real applications. This review also offers a systematic and thorough overview of current advancements in rotary motors, providing in-depth knowledge and predicting future problems and goals in this area.
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http://dx.doi.org/10.1039/d3na00010a | DOI Listing |
Front Chem
January 2025
African Society for Bioinformatics and Computational Biology, Cape Town, South Africa.
Introduction: Treatment of type 2 diabetes (T2D) remains a significant challenge because of its multifactorial nature and complex metabolic pathways. There is growing interest in finding new therapeutic targets that could lead to safer and more effective treatment options. Takeda G protein-coupled receptor 5 (TGR5) is a promising antidiabetic target that plays a key role in metabolic regulation, especially in glucose homeostasis and energy expenditure.
View Article and Find Full Text PDFComput Struct Biotechnol J
December 2024
Key Laboratory of Systems Biology, Center for Excellence in Molecular Cell Science, Shanghai Institute of Biochemistry and Cell Biology, Chinese Academy of Sciences, Shanghai 200031, China.
Persistent infection with high-risk human papillomavirus (hrHPV) is a major cause of cervical cancer. The effectiveness of current HPV-DNA testing, which is crucial for early detection, is limited in several aspects, including low sensitivity, accuracy issues, and the inability to perform comprehensive hrHPV typing. To address these limitations, we introduce MTIOT (Multiple subTypes In One Time), a novel detection method that utilizes machine learning with a new multichannel integration scheme to enhance HPV-DNA analysis.
View Article and Find Full Text PDFPharmgenomics Pers Med
January 2025
Department of Clinical Medicine, North Sichuan Medical College, Nanchong, Sichuan, 637000, People's Republic of China.
Background: Autism spectrum disorder (ASD) is a complex neurodevelopmental condition marked by diverse symptoms affecting social interaction, communication, and behavior. This research aims to explore bacterial lipopolysaccharide (LPS)- and immune-related (BLI) molecular subgroups in ASD to enhance understanding of the disorder.
Methods: We analyzed 89 control samples and 157 ASD samples from the GEO database, identifying BLI signatures using least absolute shrinkage and selection operator regression (LASSO) and logistic regression machine learning algorithms.
Pol J Pathol
January 2025
Department of Stomatology, Northern Jiangsu People's Hospital, China, P.R. China.
Head and neck squamous cell carcinoma (HNSCC) exhibits a poor 5-year survival rate. TNFRSF4 is gaining attention in tumor therapy. The objective of this study was to forecast the expression of TNFRSF4 in HNSCC tissue using analysis of pathological images and investigate its possible molecular mechanisms.
View Article and Find Full Text PDFFood Res Int
February 2025
Izmir Institute of Technology, Department of Food Engineering, Urla-Izmir, Turkiye. Electronic address:
The detection of adulteration in apple juice concentrate is critical for ensuring product authenticity and consumer safety. This study evaluates the effectiveness of artificial neural networks (ANN) and support vector machines (SVM) in analyzing spectroscopic data to detect adulteration in apple juice concentrate. Four techniques-UV-visible, fluorescence, near-infrared (NIR) spectroscopy, and time domain H nuclear magnetic resonance relaxometry (H NMR)-were used to generate data from both authentic and adulterated apple juice samples.
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