KURT SARTORIUS^1,2,3,*, BENN SARTORIUS⁴, CHERIE WINKLER⁵, ANIL CHUTURGOON², ANNA KRAMVIS¹, PING AN⁵, WEIGANG ZHANG⁶, YUNJIE LU^3,6,7,*

BIOCELL, Vol.48, No.11, pp. 1543-1567, 2024, DOI:10.32604/biocell.2024.055505 - 07 November 2024

Abstract Hepatitis B-associated hepatocellular carcinoma (HBV-HCC) remains an intractable high-mortality solid tumor cancer that accounted for 42% of global HCC cases in 2019. Despite some developments in systemic therapy, only a small subset of late-stage HCC patients responds positively to recently developed therapeutic innovations. MicroRNAs (miRNAs) act as an ancillary epigenetic system that can regulate genome expression in all cancer pathways including HCC. The molecular mechanisms of miRNA regulation in cancer pathogenesis offered researchers a new approach that was widely hoped would translate into miRNA-based drugs and diagnostics. Thirty years on, miRNA-based diagnostic and therapeutic agents… More >

STEPHEN E. LANGABEER^*

Oncology Research, Vol.32, No.9, pp. 1423-1427, 2024, DOI:10.32604/or.2024.051218 - 23 August 2024

Abstract Hairy cell leukemia (HCL) is an uncommon mature B-cell malignancy characterized by a typical morphology, immunophenotype, and clinical profile. The vast majority of HCL patients harbor the canonical BRAF V600E mutation which has become a rationalized target of the subsequently deregulated RAS-RAF-MEK-MAPK signaling pathway in HCL patients who have relapsed or who are refractory to front-line therapy. However, several HCL patients with a classical phenotype display non-canonical BRAF mutations or rearrangements. These include sequence variants within alternative exons and an oncogenic fusion with the IGH gene. Care must be taken in the molecular diagnostic work-up of patients More >

PAULINA CHMIEL^1,2, ALEKSANDRA SłOWIKOWSKA^1,2, ŁUKASZ BANASZEK^1,2, ANNA SZUMERA-CIEćKIEWICZ³, BARTłOMIEJ SZOSTAKOWSKI¹, MATEUSZ J. SPAłEK^1,4,*, TOMASZ ŚWITAJ¹, PIOTR RUTKOWSKI¹, ANNA M. CZARNECKA¹

Oncology Research, Vol.32, No.7, pp. 1141-1162, 2024, DOI:10.32604/or.2024.050350 - 20 June 2024

Abstract Inflammatory myofibroblastic tumor (IMT) is a rare neoplasm with intermediate malignancy characterized by a propensity for recurrence but a low metastatic rate. Diagnostic challenges arise from the diverse pathological presentation, variable symptomatology, and lack of different imaging features. However, IMT is identified by the fusion of the anaplastic lymphoma kinase (ALK) gene, which is present in approximately 70% of cases, with various fusion partners, including ran-binding protein 2 (RANBP2), which allows confirmation of the diagnosis. While surgery is the preferred approach for localized tumors, the optimal long-term treatment for advanced or metastatic disease is difficult… More >

C. Vairavel^1,*, N. S. Nithya²

Computer Systems Science and Engineering, Vol.46, No.2, pp. 2159-2175, 2023, DOI:10.32604/csse.2023.036598 - 09 February 2023

Abstract The immune system goes through a profound transformation during pregnancy, and certain unexpected maternal complications have been correlated to this transition. The ability to correctly examine, diagnoses, and predict pregnancy-hastened diseases via the available big data is a delicate problem since the range of information continuously increases and is scalable. Many approaches for disease diagnosis/classification have been established with the use of data mining concepts. However, such methods do not provide an appropriate classification/diagnosis model. Furthermore, single learning approaches are used to create the bulk of these systems. Classification issues may be made more accurate… More >

Milan Tair¹, Nebojsa Bacanin¹, Miodrag Zivkovic¹, K. Venkatachalam^2,*

CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 959-982, 2022, DOI:10.32604/cmc.2022.024989 - 24 February 2022

Abstract There is a growing interest in the study development of artificial intelligence and machine learning, especially regarding the support vector machine pattern classification method. This study proposes an enhanced implementation of the well-known whale optimisation algorithm, which combines chaotic and opposition-based learning strategies, which is adopted for hyper-parameter optimisation and feature selection machine learning challenges. The whale optimisation algorithm is a relatively recent addition to the group of swarm intelligence algorithms commonly used for optimisation. The Proposed improved whale optimisation algorithm was first tested for standard unconstrained CEC2017 benchmark suite and it was later adapted More >

Meenakshi Malik¹, Rainu Nandal¹, Surjeet Dalal², Vivek Jalglan³, Dac-Nhuong Le^4,5,*

Intelligent Automation & Soft Computing, Vol.28, No.3, pp. 887-906, 2021, DOI:10.32604/iasc.2021.016272 - 20 April 2021

Abstract The last several decades have witnessed an exponential growth in the means of transport globally, shrinking geographical distances and connecting the world. The automotive industry has grown by leaps and bounds, with millions of new vehicles being sold annually, be it for personal commuting or for public or commodity transport. However, millions of motor vehicles on the roads also mean an equal number of drivers with varying levels of skill and adherence to safety regulations. Very little has been done in the way of exploring and profiling driving patterns and vehicular usage using real world… More >

Xian Ma¹, Tingyan Lv^2,*, Yingqiang Jin², Rongmin Chen², Dengxian Dong², Yingtao Jia²

Energy Engineering, Vol.118, No.3, pp. 691-705, 2021, DOI:10.32604/EE.2021.012701 - 22 March 2021

Abstract The large number of gas turbines in large power companies is difficult to manage. A large amount of the data from the generating units is not mined and utilized for fault analysis. This study focuses on F-class (9F.05) gas turbine generators and uses unsupervised learning and cloud computing technologies to analyse the faults for the gas turbines. Remote monitoring of the operational status are conducted. The study proposes a cloud computing service architecture for large gas turbine objects, which uses unsupervised learning models to monitor the operational state of the gas turbine. Faults such as More >

Hai-Kun Wang^1,2, Robert Haynes³, Hong-Zhong Huang¹, Leiting Dong^2,4, Satya N. Atluri²

CMES-Computer Modeling in Engineering & Sciences, Vol.105, No.1, pp. 1-24, 2015, DOI:10.3970/cmes.2015.105.001

Abstract In this paper, we propose an approach for diagnostics and prognostics of damaged aircraft structures, by combing high-performance fatigue mechanics with filtering theories. Fast & accurate deterministic analyses of fatigue crack propagations are carried out, by using the Finite Element Alternating Method (FEAM) for computing SIFs, and by using the newly developed Moving Least Squares (MLS) law for computing fatigue crack growth rates. Such algorithms for simulating fatigue crack propagations are embedded in the computer program Safe- Flaw, which is called upon as a subroutine within the probabilistic framework of filter theories. Both the extended… More >

V. Pletser¹, R. Bosch², L. Potthast², R. Kassel³

FDMP-Fluid Dynamics & Materials Processing, Vol.2, No.1, pp. 65-76, 2006, DOI:10.3970/fdmp.2006.002.065

Abstract Orbital weightless conditions have been shown to yield better and larger crystals. The Solution Crystallization Diagnostics Facility (SCDF) is a third generation instrument developed by ESA and dedicated to the observation and study with advanced diagnostics nucleation and crystallisation processes of molecules from solutions on board the International Space Station. The SCDF is intended to be used for studies of proteins and large biomolecules, and more generally of any kind of molecules growing from solutions, using the powerful set of diagnostics means available in the SCDF platform. Several protein crystallisation reactors have been developed to More >