A Review of Advanced Multifunctional Magnetic Nanostructures for Cancer Diagnosis and Therapy Integrated into an Artificial Intelligence Approach

Govindan, Bharath; Sabri, Muhammad Ashraf; Hai, Abdul; Banat, Fawzi; Haija, Mohammad Abu

doi:10.3390/pharmaceutics15030868

Cited by 26 publications

(12 citation statements)

References 264 publications

(289 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For example, the Jurkat cell or blood cancer has a refractive index of 1.39 with an 80% concentration, while its normal cell refractive index is 1.376 [51,52]. Also, the refractive index of Cervical cancer with an 80% concentration is 1.392, as shown in Table . III and its normal cell refractive index is 1.368 with 30-70% concentration of the mentioned sample [47,[53][54][55][56][57]. The refractive index of Adrenal Gland Cancer is 1.395 with an 80% concentration, and its normal cell owns a RI of 1.381 in the 30-70% concentration [58].…”

Section: Resultsmentioning

confidence: 99%

Ultrasensitive and label-free, graphene/CaF2 multilayer-based biosensor for detecting gasses, cancers, viruses, and diabetes, with significant improvement of sensitivity, quality factor, and figure of merit

Jafari,

Gholizadeh,

Zhoulideh

et al. 2023

Preprint

View full text Add to dashboard Cite

One of the primary goals for the researchers is to create a high-quality sensor with a simple structure because of the urgent requirement to identify biomolecules at low concentrations to diagnose diseases and detect hazardous chemicals for health early on. Recently graphene has attracted much interest in the field of improved biosensors. Meanwhile, graphene with new materials such as CaF2 has been widely used to improve the applications of graphene-based sensors. Using the fantastic features of the graphene/CaF2 multilayer, this article proposes an improvement sensor in the sensitivity (S), the figure of merit (FOM), and the quality factor (Q). The proposed sensor is based on the five-layers graphene/dielectric grating integrated with a Fabry-Perot cavity. By tuning graphene chemical potential (µc), due to the semi-metal features of graphene, the surface plasmon resonance (SPR) waves excited at the graphene/dielectric boundaries. Due to the vertical polarization of the source to the gratings and the symmetry of the electric field, both corners of the grating act as electric dipoles, and this causes the propagation of plasmonic waves on the graphene surface to propagate towards each other. Finally, it causes Fabry-Perot (FP) interference on the surface of graphene in the proposed structure's active medium (the area where the sample is located). In this article, using the inherent nature of FP interference and its S to the environment's refractive index (RI), by changing a minimal amount in the RI of the sample, the resonance wavelength (interferometer order) shifts sharply. The proposed design can detect and sense some cancer, such as Adrenal Gland Cancer, Blood Cancer, Breast Cancer I, Breast Cancer II, Cervical Cancer, and skin cancer precisely. By optimizing the structure, we can achieve an S as high as 9000 nm/RIU and a FOM of about 52.14 for the first resonance order (M1). Likewise, the remarkable S of 38000 nm/RIU and the FOM of 81 have been obtained for the second mode (M2). In addition, the proposed label-free SPR sensor can detect changes in the concentration of various materials, including gases and biomolecules, hemoglobin, breast cancer, diabetes, leukemia, and most alloys, with an accuracy of 0.001. The proposed sensor can sense urine concentration with a maximum S of 8500 nm/RIU and cancers with high S in the 6000 nm/RIU range to 7000 nm/RIU. Also, four viruses, such as M13 bacteriophage, HIV type one, Herpes simplex type 1, and influenza, have been investigated, showing Maximum S (for second resonance mode of λR(M2) of 8000 nm/RIU (λR(M2) = 11.2 µm), 12000 nm/RIU (λR(M2) = 10.73 µm), 38000 nm/RIU (λR(M2) = 11.78 µm), and 12000 nm/RIU (λR(M2) = 10.6 µm), respectively, and the obtained S for first resonance mode (λR(M1)) for mentioned viruses are 4740 nm/RIU (λR(M1) = 8.7 µm), 8010 nm/RIU (λR(M1) = 8.44 µm), 8100 nm/RIU (λR(M1) = 10.15 µm), and 9000 (λR(M1) = 8.36 µm), respectively.

show abstract

Section: Resultsmentioning

confidence: 99%

Ultrasensitive and label-free, graphene/CaF2 multilayer-based biosensor for detecting gasses, cancers, viruses, and diabetes, with significant improvement of sensitivity, quality factor, and figure of merit

Jafari,

Gholizadeh,

Zhoulideh

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…The utilization of nano biosensors offers a promising road for early determination, empowering opportune intercession and personalized treatment methodologies. Within the domain of cancer conclusion and treatment, Govindan et al [5] display a comprehensive audit of progressed multifunctional attractive nanostructures coordinates into an artificial intelligence (AI) approach. The study highlights the synergistic potential of attractive nanomaterials focused on sedate conveyance, imaging, and hyperthermia, coupled with AI for upgraded exactness in cancer conclusion and treatment.…”

Section: Related Workmentioning

confidence: 99%

Nanomaterials for Precision Diagnostics and Therapeutic Interventions in Modern Healthcare

Kumar,

Kumari

et al. 2024

E3S Web Conf.

View full text Add to dashboard Cite

The investigation on “Shrewd Nanomaterials for Precision Diagnostics and Therapeutic Interventions in Present day Healthcare” explores the blend, characterization, and applications of temperatureresponsive, pH-responsive, and light-responsive nanomaterials. Results uncover the fruitful amalgamation of well-defined nanomaterials with cruel molecule sizes of 50 nm, 80 nm, and 60 nm, separately. Characterization illustrates their homogeneity with moo polydispersity records (PDIs) of 0.15, 0.20, and 0.18. In vitro studies exhibit the responsiveness of these nanomaterials to shifting physiological conditions, demonstrating their potential for temperature-sensitive diagnostics and controlled medicate discharge. In vivo thinks about illustrates a remarkable focus on productivity, with tall collection in particular target tissues, approving their potential for precise medicate conveyance. Computational modelling provides insights into the dynamic interaction between nanomaterials and biomolecules, thus improving our knowledge on how these materials behave under complex physiological conditions. This work adds to the general scene of nano medicine scope with focus on the competence of keen nanomaterials for customized and targeted therapeutic confessants. Their findings underscore their critical role in the achievement of improved clinical accuracy, targeted effective responses, and reduced side effects. The union parameters, characterization information, and in vitro/in vivo outcomes collectively emphasize the innovative future of these nanomaterials in developing the future of precision pharmaceuticals.

show abstract

“…3 Meanwhile, a comprehensive overview of multifunctional magnetic nanostructures integrated with an AI approach for cancer diagnosis and therapy has been conducted. 18 Indeed, a systematic review and meta-analysis have been conducted on the value of AI in lung cancer diagnosis, highlighting the potential of AI-assisted diagnosis in improving accuracy and efficiency. 4 Another review was conducted on AI applications to find the most critical features extracted from brain cancer patients’ MRI, histopathology, and CT scan images.…”

Section: Artificial Intelligence Application To Cancer Researchmentioning

confidence: 99%

The Application of Artificial Intelligence to Cancer Research: A Comprehensive Guide

Zadeh Shirazi,

Tofighi,

Gharavi

et al. 2024

Technol Cancer Res Treat

View full text Add to dashboard Cite

Advancements in AI have notably changed cancer research, improving patient care by enhancing detection, survival prediction, and treatment efficacy. This review covers the role of Machine Learning, Soft Computing, and Deep Learning in oncology, explaining key concepts and algorithms (like SVM, Naïve Bayes, and CNN) in a clear, accessible manner. It aims to make AI advancements understandable to a broad audience, focusing on their application in diagnosing, classifying, and predicting various cancer types, thereby underlining AI's potential to better patient outcomes. Moreover, we present a tabular summary of the most significant advances from the literature, offering a time-saving resource for readers to grasp each study's main contributions. The remarkable benefits of AI-powered algorithms in cancer care underscore their potential for advancing cancer research and clinical practice. This review is a valuable resource for researchers and clinicians interested in the transformative implications of AI in cancer care.

show abstract

A Review of Advanced Multifunctional Magnetic Nanostructures for Cancer Diagnosis and Therapy Integrated into an Artificial Intelligence Approach

Cited by 26 publications

References 264 publications

Ultrasensitive and label-free, graphene/CaF2 multilayer-based biosensor for detecting gasses, cancers, viruses, and diabetes, with significant improvement of sensitivity, quality factor, and figure of merit

Ultrasensitive and label-free, graphene/CaF2 multilayer-based biosensor for detecting gasses, cancers, viruses, and diabetes, with significant improvement of sensitivity, quality factor, and figure of merit

Nanomaterials for Precision Diagnostics and Therapeutic Interventions in Modern Healthcare

The Application of Artificial Intelligence to Cancer Research: A Comprehensive Guide

Contact Info

Product

Resources

About