Abstract

Cancer vaccines based on tumor cell components have shown promising results in animal and clinical studies. The vaccine system contains abundant tumor antigen components, which can activate the immune system by antigens. However, their efficacy has been limited by the inability of antigens delivery, which are the core components of vaccines, further fail to be presented and activation of effective cells. Nanotechnology offers a novel platform to enhance the immunogenicity of tumor-associated antigens and deliver them to antigen-presenting cells (APCs) more efficiently. In addition, nanotreatment of tumor cells derivate active ingredients could also help improve the effectiveness of cancer vaccines. In this review, we summarize recent advances in the development of cancer vaccines by the combination of nanotechnology and tumor-based ingredients, including liposomes, polymeric nanoparticles, metallic nanoparticles, virus-like particles and tumor cells membrane, tumor lysate, and specific tumor antigens. These nanovaccines have been designed to increase antigen uptake, prolong antigen presentation, and modulate immune responses through codelivery of immunostimulatory agents. We also further discuss challenges and opportunities in the clinical translation of these nanovaccines.

Introduction

Cancer immunotherapy has revolutionized cancer treatment by harnessing the immune system to recognize and attack cancer cells [1–3]. Among various immunotherapeutic approaches, cancer vaccines have attracted significant attention due to their potential to induce long-lasting and specific immune responses against tumor cells [4–7]. By comparison with surgery, it hardly produce large wounds, nor does it cause higher toxicity in normal tissues and drug tolerance like chemotherapy in the treatment of tumor. However, the clinical efficacy of cancer vaccines based on tumor cell components, such as peptides and proteins, is limited by their poor immunogenicity and inefficient delivery to antigen presentation cells (APCs). Although, many cancer vaccines were applied with the immune-stimulatory agents, such as the programmed cell death protein 1 (PD-1), CpG oligonucleotide, etc. The clinical response rates remain low, especially in the hypoimmunogenic tumors. As we all know, pancreatic cancer is a more difficult type for immunotherapy as a low immunogenic tumor. Therefore, there are few effective cancer vaccines that can actively promote the pancreatic cancer treatment. Meanwhile, limited by the specific tumor antigens, the development of cancer vaccines was extremely difficult.

The field of cancer vaccines has seen significant advancements in recent years, with numerous studies focusing on different aspects of vaccine development and application [8–12]. These advances have been facilitated by the exploration of various types of cancer vaccines, including those targeting viral antigens, neoantigens, and other tumor-specific antigens [13–16]. One approach in cancer vaccine research involves targeting shared antigens from viral infections linked to certain cancer types, such as Epstein–Barr virus and HPV. Studies have shown promising results with vaccines targeting viral antigens like Epstein–Barr nuclear antigen 1 and latent membrane proteins, as well as HPV-related antigens, demonstrating favorable antitumor efficacy and the potential to establish durable T cell memory in rapidly progressing HPV-positive tumors [17]. Another significant area of research is neoantigen cancer vaccines. Neoantigens, derived from nonsynonymous cell variants, are only expressed in tumor cells and can bypass thymus-negative selection, leading to robust neoantigen-specific T cell responses [14]. Advances in genomic and transcriptional profiling have made identifying putative neoantigens possible, although challenges remain in predicting which neoantigens can effectively induce neoantigen-specific T cell responses [18–20]. Cancer vaccines are being developed to stimulate antitumor immunity with tumor antigens delivered in various forms, such as cells, peptides, viruses, and nucleic acids [21–24]. These vaccines aim to overcome tumor heterogeneity and reverse the immunosuppressive microenvironment. Challenges in the field include single antigen targeting, weak immunogenicity, off-target effects, and impaired immune response [25–27]. Current research and reviews provide insights into the principle of action, components, classification, delivery methods, and progress of cancer vaccines, offering a broader perspective for future vaccine design.

Recent studies have shown that tumor antigens can be used for the design of cancer vaccines without the necessity to identify, parse, and reconstruct certain antigens [28–31]. A novel concept has been proposed that the whole antigen vaccine systems derived from tumor cells could be designed to conquer cancer treatments [32–34]. The tumor cells themselves are a huge antigen pool with countless tumor antigen epitopes, which can be used to activate antitumor immune response. Among them, more studies have focused on the application of tumor cell membrane as tumor antigens to stimulate APCs and further activate the antitumor immune response of T cells. In addition, the activation of T cells in immune response requires the involvement of costimulatory molecules. Some studies are based on this theoretical foundation, using genetic engineering to modify tumor cell membranes. By retaining antigen on the cell membrane and inducing the expression of costimulatory molecule CD80, T cells can be directly activated, thereby achieving antitumor immune response [35] (Figure 1). Of course, many studies combine tumor cell membranes that retain tumor antigens with other functional molecules to directly activate dendritic cells (DCs) for antigen presentation, leading to the activation of T cell immune response. These are two different approaches aimed at achieving the same goal [36–38]. A series of nanovaccines developed on this basis have been widely used in the treatment of different tumor cells and preclinical experiments.

Abstract

Introduction

Materials and methods

Characterizations

Results and discussion

Optimized reaction conditions

FTIR spectra analysis

SEM–EDS analysis

XRD analysis

TGA analysis

Conclusion

Declarations

References

Abstract

     An interpenetrating polymer network (IPN) containing gum acacia (GA), poly(methacrylic acid) (MAA), and poly(acrylic acid) (AA) was developed using a two-step aqueous polymerization method. Firstly, semi-IPNs were produced by radical polymerization of MAA chains onto GA in the presence of ammonium persulfate as a free radical initiator and N, N′-methylene-bisacrylamide (MBA) as a cross-linking agent using a microwave heating. To obtain a semi-IPN with a higher swelling percentage, several reaction parameters such as initiator, monomer, and crosslinker concentrations were varied. The percentage swelling (%S) was highly dependent upon the reaction conditions. The optimal reaction conditions for maximal %S were 2.55 × 10–2 mol/L initiator concentration, 12 mL solvent, 0.424 × 10–3 mol/L of monomer, and 2.16 × 10–2 mol/L cross-linker concentration, according to the findings. GA-g-poly(MAA) was the name to given to the semi-IPN. Second, IPN was created by grafting AA chains onto a GA-g-poly(MAA) matrix that had been optimized. The IPN was named as a GA-g-poly(MAA-IPN-AA). The reduction of silver ions to silver nanoparticles (AgNPs) was carried out by heating the mixture of flower extract of Koelreuteria apiculate under microwave radiation.

Introduction

     Hydrogels are three-dimensional (3D) networks formed by the crosslinking of hydrophilic polymers [1, 2]. They might swell while maintaining their network structure by absorbing signifcant amounts of water or biological fluids. The inclusion of hydrophilic groups such as -OH, -CONH, -CONH2, -COOH, and -SO3H along the polymer chain contributes to their ability to absorb a large amount of water [1]. Polysaccharide hydrogel networks have received a lot of attention in recent years [3–5]. The physico-chemical properties of these hydrogels frequently differ significantly from those of the macromolecular constituents. Hydrogel characteristics can also be altered utilizing a range of physical and chemical crosslinking techniques [3, 6]. Various researchers have examined a number of polymeric systems to create hydrogels [7]. The polymer composition can be classified as natural polymeric hydrogels, synthetic polymer hydrogels, and or a combination of both [7]. Because of their non-toxicity, low cost, permeability, and biocompatibility, hydrogels have gotten a lot of attention in a variety of sectors [8–10].

Conclusion

     Antimicrobial materials have been studied extensively in response to rising microbial resistance to traditional antibiotics and disinfectants. Natural polysaccharidebased hydrogels and their composites containing metal nanoparticles could be suitable for antibacterial applications in some cases. Using a microwave irradiation approach, we were able to successfully synthesis GA-gpoly(MAA) and GA-g-poly(MAA-IPN-AA) based hydrogels from GA. APS and MBA being used as initiator and cross-linker, respectively. To achieve maximum %S of semi-IPN (588%) and IPN (500%), diferent parameters were optimized. The formation of cross-linked hydrogels and hydrogel composites with AgNPs was successfully demonstrated using FTIR analysis. The SEM investigation of hydrogel networks reveals a variety of morphologies, which could be attributed to structural diferences between semi-IPN, IPN, and their composites with AgNPs. SemiIPN, IPN and their composite showed superb antibacterial potential against E. coli, M. luteus, P. aeruginosa, Rihzobium species, and S. aureus, and may be used to treat a variety of infected efuents. Such interconnected hydrogels will give superior material properties, particularly in biomedical research, to prevent biomaterialassociated infections as well as general pathogenic invasions into the body. The synthesized hydrogel samples will lead to enhanced applications in the future, as well as a deeper knowledge of material interactions. The eforts to improve the property profle of cross-linked hydrogels are still on, with the hope of improving overall performance in terms of water uptake capacity, biocompatibility, and biodegradability.

Abstract

1. Introduction

2. Materials and Methods

3. Proposed Epilepsy Classification Method

4. Results and Discussion

5. Conclusions

Data Availability

Conflicts of Interest

References

Abstract

     The precise detection of epileptic seizure helps to prevent the serious consequences of seizures. As the electroencephalogram (EEG) reflects the brain activity of patients effectively, it has been widely used in epileptic seizure detection in the past decades. Recently, deep learning-based detection methods which automatically learn features from the EEG signals have attracted much attention. However, with deep learning-based detection methods, different input formats of EEG signals will lead to different detection performances. In this paper, we propose a deep learning-based epileptic seizure detection method with hybrid input formats of EEG signals, i.e., original EEG, Fourier transform of EEG, short-time Fourier transform of EEG, and wavelet transform of EEG. Convolutional neural networks (CNNs) are designed for extracting latent features from these inputs. A feature fusion mechanism is applied to integrate the learned features to generate a more stable syncretic feature for seizure detection. The experimental results show that our proposed hybrid method is effective to improve the seizure detection performance in few-shot scenarios.

Introduction

     Approximately one percent of the world’s population, 65 million people, suffer from epilepsy, more than Parkinson’s disease, Alzheimer’s disease, and multiple sclerosis combined [1]. About two-thirds of people with epilepsy can be treated with medication, and the rest may require surgical intervention. Epilepsy has the characteristics of sudden and recurrent seizures, which may lead to falls, asphyxia, and even death. Therefore, seizure detection is very important for early warning and treatment of epilepsy.

     Epileptic seizure detection is mainly based on electroencephalogram (EEG) [2–4]. Single-channel EEG acquisition equipment improves the practicability of EEG in epileptic detection due to its simplicity in implementation. However, the provided information by signal-channel EEG signal is limited because of the small number of channels. Therefore, it is worth studying to establish a model with high accuracy and high robustness for single-channel EEG epileptic detection.

     The traditional methods are mainly based on feature engineering techniques which extract the corresponding features from EEG signals and then complete the detection based on the extracted features [5–9]. These features include time-domain features [10–12], frequency-domain features [8, 9], and time-frequency-domain features [13–15]. Once the features are extracted, EEG signals can be classified using a variety of classifiers. No matter what classifier is used, the quality of designed features will greatly affect the performance of epilepsy detection. In recent years, with the development of deep learning technology, many works have applied deep learning to perform epilepsy detection [16, 17]. Different from traditional feature engineering, deep learning methods automatically learn features from EEG signals and further complete detection tasks with an end-to-end manner without complicated manual feature design process and can achieve better performance than traditional methods in many scenarios.

Conclusions

     In this paper, we focus on epileptic classification in few-shot scenarios. In order to make the classification accuracy higher and more stable, we propose a deep learning method with hybrid input, i.e., original EEG signal, FFT, STFT, and DWT of EEG signal. In order to alleviate the tendency of overfitting, two means are applied. The first is that we replace the traditional convolution by depthwise separable convolution for reducing the parameters in network and then 2 regularization is applied, whose function is decreasing the complexity of the model. We conduct several experiments to distinguish normal and epileptic EEG, and the results show the proposed method with hybrid input has strong advantages in epileptic classification. It benefits from the complementarity of time-domain properties, frequency-domain properties, and time-frequency-domain properties. Our proposed method provides a new perspective to enrich the input information to make improvements for deep learning-based epileptic diagnosis.

Abstract

     Magnetic nanoparticles (MNPs) represent an advanced tool in the medical field because they can be modified according to biomedical approaches and guided by an external magnetic field in the human body. The first objective of this review is to exemplify some promising applications in the medical field, including smart drug-delivery systems, therapies against cancer cells, radiotherapy, improvements in diagnostics using magnetic resonance imaging (MRI), and tissue engineering. Complementarily, the second objective is to illustrate the mechanisms of action and theoretical foundations related to magnetoresponsive materials.

Introduction

     Magnetic nanoparticles (MNPs) are nanoscale particles (1–100 nm) that can be guided through an external magnetic field due to their superparamagnetic, ferrimagnetic, and ferromagnetic properties, which may provide features for biomedical applications. MNPs with superparamagnetic properties are of special interest because they exhibit strong magnetic interactions under an external magnetic field, which disappear once the external magnetic field is removed. This property allows for the design of ferrofluids, since MNPs can be stabilized in solutions because they do not present magnetic interactions when the external magnetic field is switched off; and allows for in vivo performance, as in (i) cell marking [1], (ii) drug systems guided by a magnetic field [2], (iii) image contrast agents [3], and (iv) as heat generators in hyperthermia treatments [4].

     MNPs generally contains two main parts: the core and the coating. The core presents a predominant quantum effect, which commonly incorporates magnetic elements such as Fe, Ni, or Co as well as their corresponding oxides, while the coating is responsible for stabilizing and protecting the core from the chemical effects of the medium [5]. The coating also plays an essential role since it provides specific properties and functions to the nucleus; for example, biocompatible natural polymers, such as chitosan or cellulose, work as cargo vectors of therapeutic agents to release in a controlled manner (Figure 1). Due to these characteristics, biomedical device synthesis has shown great interest in low toxic superparamagnetic MNPs, to avoid embolization or other secondary effects for the patient at the molecular or cellular level [6].

Conclusions

     MNPs, in combination with superconducting magnets, have begun to play an essential role in advances in areas such as MDDS, MRI, and hyperthermia, among other fields. Therefore, it is crucial to continue the synthesis of MNPs and de

Abstract

Keywords

1. Introduction

2. The concept of a metaverse and its possible applications in medicine

3. The medical Internet of Things (MIoT) facilitates important application scenarios for the metaverse

4. The metaverse provides technical support to maximize the value of the MIoT

5. Prospects

Author contributions

Declaration of Competing Interest

Acknowledgements

Availability of data and material

Funding

Consent for publication

Ethics approval and consent to participate

References

Abstract

Background

     Recently, Professor Chunxue Bai and colleagues have proposed a definition of the Metaverse in Medicine as the medical Internet of Things (MIoT) facilitated using AR and/or VR glasses.

Methods

     A multi-disciplinary panel of doctors and IT experts from Asia, the United States, and Europe analyzed published articles regarding expert consensus on the Medical Internet of Things, with reference to study results in the field of metaverse technology.

Findings

     It is feasible to implement the three basic functions of the MIoT, namely, comprehensive perception, reliable transmission, and intelligent processing, by applying a metaverse platform, which is composed of AR and VR glasses and the MIoT system, and integrated with the technologies of holographic construction, holographic emulation, virtuality-reality integration, and virtuality-reality interconnection. In other words, through interactions between virtual and real cloud experts and terminal doctors, we will be able to carry out medical education, science popularization, consultation, graded diagnosis and treatment, clinical research, and even comprehensive healthcare in the metaverse. The interaction between virtual and real cloud experts and terminal users (including terminal doctors, patients, and even their family members) could also facilitate different medical services, such as disease prevention, healthcare, physical examination, diagnosis and treatment of diseases, rehabilitation, management of chronic diseases, in-home care, first aid, outpatient attendance, consultation, etc. In addition, it is noteworthy that security is a prerequisite for the Metaverse in Medicine, and a reliable security system is the foundation to ensure the normal operation of such a platform.

     The rise of the metaverse has brought infinite possibilities to all types of sectors and occupations, such as video game production, leisure, and entertainment. Museum exhibitions have evolved with diverse digital technologies,4 and new metaverse sales models have emerged from traditional retail.5 Some researchers have studied the art community of the 3D virtual world. Concerning social media, on October 28th, 2021, Mark Zuckerberg announced that Facebook had changed its name to “Meta” to align the company with its focus on new computing technologies and the metaverse.6 Others seek to understand how journalism is practiced in the metaverse.7, 8

Conclusion

     The application of a Cloud Plus Terminal platform could enable interaction between virtual and real cloud experts and terminal doctors, in order to realize medical education, science popularization, consultation, graded diagnosis and treatment, clinical research, and even comprehensive healthcare in the metaverse.

Introduction

     Since 2021, the concept of the metaverse has been widely discussed. It refers to the internet accessed via virtual reality (VR) and augmented reality (AR) glasses, and is considered to be the next-generation mobile computing platform that will be widely used in the future.1 Others believe that the metaverse is a ternary digital world established on the basis of digital technology integrating the virtual and the real worlds, which people enter with digital identities. The idea originated from the novel True Names by Professor Vernor Vinge, an American mathematician. In this story, published in 1981, the author creatively conceived a virtual world that enters and obtains sensory experience through a brain-computer interface. Later, in 1992, the term “metaverse” was coined by American science-fiction writer Neal Stephenson in his novel Snow Crash, in which the characters explore an internet world parallel to the real world, using digital avatars of themselves for perception and interaction.2, 3

     The rise of the metaverse has brought infinite possibilities to all types of sectors and occupations, such as video game production, leisure, and entertainment. Museum exhibitions have evolved with diverse digital technologies,4 and new metaverse sales models have emerged from traditional retail.5 Some researchers have studied the art community of the 3D virtual world. Concerning social media, on October 28th, 2021, Mark Zuckerberg announced that Facebook had changed its name to “Meta” to align the company with its focus on new computing technologies and the metaverse.6 Others seek to understand how journalism is practiced in the metaverse.7, 8

Funding

     Science and Technology Commission of Shanghai Municipality (20DZ2254400, 21DZ2200600, 20DZ2261200), National Scientific Foundation of China (82170110), Shanghai Pujiang Program (20PJ1402400).

Abstract

     Wastewater from pharmaceutical industries contains different antibiotics and other effluents that directly affect the environment. For the degradation of antibiotics, nanotechnology is an emerging field in research that majorly focuses on molecules and atoms. Nanoparticles are fine micro-particles with more than one dimension from a size of 1–100 nm. The synthesized nanoparticles can directly be used for the process of degradation of antibiotics in wastewater. Titanium dioxide nanoparticles are majorly used among nanoparticles due to their small surface area and particle size. There are various physical, chemical and conventional (biological) methods for synthesizing titanium dioxide nanoparticles. Green synthesis is an environmentally friendly, less expensive and harmless approach, and these methods are much more efficient than any other physical-chemical means. Titanium dioxide nanoparticles are used for different purposes. In food products, these nanoparticles act as photocatalysts and are also used as a coating material in food packaging. Electrochemical biosensors are combined with titanium dioxide nanoparticles for developing nano sheet matrices. Furthermore, this review describes the usage of biological components (plants, microorganisms like fungi and bacteria) to synthesize titanium dioxide nanoparticles. The present review also focuses on applying synthesized titanium dioxide nanoparticles in various drug delivery systems and other bioactivities.

Introduction

     Water is one of the important components of life used in the domestic, industrial, and agricultural sectors. The increase in consumption leads to an increase in the toxicity of pollutants inside water. The concentrations of emerging pollutants like pharmaceutical compounds have increased [1]. Various types of pharmaceutical compounds are observed in the aqueous environment, including antibiotics. These compounds reach the aquatic habitat through hospital effluents, the pharmaceutical industry and human livestock. The consumption of antibiotics has increased, and the concentration increases inside the aquatic environment, leading to the development of antibiotic-resistant bacteria [2,3].

     To remove antibiotics from wastewater, the traditional sewage treatment method is not appropriate and is not much effective in removing the residuals of antibiotics. Nanoparticles can be used as a photocatalyst for the degradation of antibiotics and other organic pollutants [4]. Nanotechnology is defined as synthesizing and developing various nanomaterials by manipulating the matter at the nanoscale [5]. Nanomaterials are a bridge between molecular material and bulk material. They have a size range of 1–100 nm due to their different shapes, sizes, large surface area, and possess high reactivity [6]. This has become an emerging field in science in the last few years due to its wide range of applications [7]. Because of its small size, the effect can be seen in the physical, optical, chemical properties, melting point, and dielectric constant of various nanoparticles [8].

Conclusion

     The interest in green synthesis is due to its advantages of being innocuous to the environment. Different parts of plants are used for synthesis purposes, and their characterization using various techniques helps form TiO2 NPs of varied shapes and sizes. The green synthesis of TiO2 NPs can be used in biomedical, and the delivery of drugs and antibiotics in wastewater of pharmaceutical industries can be degraded using nanoparticles. Because of the less use of precursors, TiO2 is widely used to remove microbes from water surfaces. The nanoparticles inhibit the growth of microbes and are used in wound dressing. Moreover, nanoparticles synthesized using the green routes are more effective than chemical or physical means.

Abstract

     The global occurrence of oral cancer has increased in recent years. Oral cancer diagnosed in the advanced stages results in morbidity and mortality. The use of technology may be beneficial for early detection and diagnosis, and thus help the clinician with better patient management. The advent of artificial intelligence (AI) has the potential to improve oral cancer screening. AI can precisely analyze an enormous dataset from various imaging modalities and provide assistance in the field of oncology. This review focused on the applications of artificial intelligence in the early diagnosis and prevention of oral cancer. A literature search was conducted in the PubMed and Scopus databases using the search terminology “oral cancer” and “artificial intelligence”. Further information regarding the topic was collected by scrutinizing the reference lists of selected articles. Based on the information obtained, this article reviews and discusses the applications and advantages of AI in oral cancer screening, early diagnosis, disease prediction, treatment planning, and prognosis. Limitations and the future scope of AI in oral cancer research are also highlighted.

Introduction

     The global occurrence of Oral cancer (OC) has increased in recent years, with Oral squamous cell carcinomas (OSCCs) counting for more than 90% of these cancers.[1] OSCCs are also the sixth most common malignancy in the world. In 2012, The World Health Organization reported 529 000 new cases of OC and 300 000 deaths due to OC each year.[2] Oral cancer diagnosed in the advanced stage results in morbidity and mortality. A crucial factor in providing successful treatment is the early detection of cancerous lesions. Inaccessible lesions and the late detection of cancers are associated with low survival, increased symptoms, and a higher treatment cost.[3] Early diagnosis can increase the survival rate to 75–90%.1,4

Early detection includes the diagnosis of oral potentially malignant disorders and regular follow-ups. Oral potentially malignant disorders (OPMDs) have been defined as “any oral mucosal abnormality that is associated with a statistically increased risk of developing oral cancer.”[5] OPMDs include oral leukoplakia, proliferative verrucous leukoplakia, erythroplakia, oral lichen planus, oral submucous fibrosis, palatal lesions in reverse smokers, oral lupus erythematosus, actinic keratosis, and dyskeratosis congenita. Newly included lesions in the recent classification are oral lichenoid lesion and oral chronic graft-versus-host disease.[5]

Conclusion

     In recent years, the use of AI for the diagnosis and prognosis of diseases has evolved. Previous studies have proved that ML produces accurate results for OC detection. It assists clinicians in diagnostic processes and minimizes inadvertent errors. However, previous studies based on deep learning (neural network) provided more accuracy with the early detection of OC as compared to machine learning. AI presents the opportunity to develop new techniques combined with traditional approaches to improve the accuracy of detection of OC and OPMDs, as well as to predict the course of the precancerous/cancerous lesions from retrospective data. Future research could consider the innovation of data fusion algorithms combining various modalities, such as clinical, radiological, histological, and molecular assessments, to support the early diagnosis and outcome estimation of the disease.

Abstract

Study objective

     To determine whether there has been growth in publications on the use of artificial intelligence in cardiology and oncology, we assessed historical trends in publications related to artificial intelligence applications in cardiology and oncology, which are the two fields studying the leading causes of death worldwide. Upward trends in publications may indicate increasing interest in the use of artificial intelligence in these crucial fields.

Design/setting

     To evaluate evidence of increasing publications on the use of artificial intelligence in cardiology and oncology, historical trends in related publications on PubMed (the biomedical repository most frequently used by clinicians and scientists in these fields) were reviewed.

Results

     Findings indicated that research output related to artificial intelligence (and its subcategories) generally increased over time, particularly in the last five years. With some initial degree of vacillation in publication trends, a slight qualitative inflection was noted in approximately 2015, in general publications and especially for oncology and cardiology, with subsequent consistent exponential growth. Publications predominantly focused on “machine learning” (n = 20,301), which contributed to the majority of the accelerated growth in the field, compared to “artificial intelligence” (n = 4535), “natural language processing” (n = 2608), and “deep learning” (n = 4459).

Conclusion

     Trends in the general biomedical literature and particularly in cardiology and oncology indicated exponential growth over time. Further exponential growth is expected in future years, as awareness and cross-disciplinary collaboration and education increase. Publications specifically on machine learning will likely continue to lead the way.

Introduction

     Artificial intelligence (AI) is a broad concept describing computer-performed tasks that would normally require human intelligence. AI applied to medicine has impacted several disciplines, but may have the broadest applications in cutting-edge research-oriented fields such as cardiology and oncology. Cancer and cardiovascular diseases are the leading causes of death [1], [2], and may benefit from applications of AI to elucidate pathophysiology and opportunities for prevention and early diagnosis. Great evidence of growth in a field can come from acceleration in publications indicative of advancement and potential impact in the field. Yet, this has not been objectively assessed for AI publications in the general biomedical literature, nor specifically for the prominent fields of cardiology or oncology.

     AI plays a crucial role in our daily lives and our interactions with technology. There is increasing interest in the implementation of AI in healthcare. A main driver of AI in healthcare is the application of machine learning (ML) algorithms, which are a subset of AI and glean and use insights from training datasets to perform tasks and make predictions without explicit additional programming [3], [4]. Deep learning (DL) is a form of ML that is also based on pattern recognition and uses layered artificial neural networks to assess data at different levels of abstraction, to automate complex cognitive tasks that may not yet be clearly delineated [5], [6]. Natural language processing (NLP) is another subfield of AI, in which computer-based algorithms analyze, process, and transform natural language data into a form ready for computation [7], [8], [9]. All of these forms of AI have utility in healthcare, including in cardiology and oncology [10].

Results

     Publications using the term “artificial intelligence” in the title or abstract were first noted in 1963, “machine learning” in 1964, “natural language processing” in 1978, “deep learning” in general in 1989, and “deep learning” relevant to machine learning in 2011. In the last two decades, publications using all four terms related to AI showed remarkable growth (Fig. 1). Most publications used the term “machine learning” (n = 20,301) with a notable predominance (Fig. 1) compared to the other three distinct terms “artificial intelligence” (n = 4535), “natural language processing” (n = 2608), and “deep learning” (n = 4459) (Table 1). Consequently, the acceleration of publications in AI overall appeared to be due almost entirely to publications on “machine learning”.

Abstract

1. Introduction

2. Fundamentals of nanotechnology

3. The role of nanotechnology in achieving food security

4. Nanotubes in food and agriculture

5. Nanosensors in food and agriculture

6. The challenges of nanotechnology in food and agriculture system

7. Conclusions

References

Abstract

     Ensuring food security in developing countries is highly challenging due to low productivity of the agriculture sector, degradation of natural resources, high post farming losses, less or no value addition, and high population growth. Researchers are striving to adopt newer technologies to enhance supply to narrow the food demand gap. Nanotechnology is one of the promising technologies that could improve agricultural productivity via nano fertilizers, use of efficient herbicides and pesticides, soil feature regulation, wastewater management, and pathogen detection. It is equally beneficial for industrial food processing with enhanced food production with excellent market value, elevated nutritional and sensing property, improved safety, and better antimicrobial protection. Nanotechnology can also reduce post-farming losses by increasing the shelf life with the aid of nanoparticles. However, further investigation is required to solve the safety and health risks associated with the technology.

Introduction

     The foremost universal challenge on our planet is the question of establishing food security for a rapidly increasing population in the world. Predictions show that food demand is likely to rise from 59 to 98% for the world population reaching 9 billion by 2050 (Duro et al., 2020). Despite an increase of the world population particularly in developing countries, the global food supply interrupted by the expenditure of bio-resources for production of energy, manufacturing chemicals, high post farming loss, less value addition, inefficient distribution and marketing systems, and other factors (Barrett, 2021; Sekhon, 2014). Farmers throughout the world will focus on using new innovations and technologies for enhancing the production of crops through intensive and extensive agriculture. The current efforts further boosted through the use of nano-modified stimulants and precision farming. Agricultural efficiency, soil improvement, secure water use, distribution of food in stores, and its quality are basic factors of securing food that may be improved via advances in nanotechnology research (Ashraf et al., 2021; Sastry et al., 2011).

     Newer technology that will increase the production and reduce food wastage is important to maintain sustainable living standards of the nation and improve food security. Nanotechnology can provide a path for producing foods with outstanding quality in highly improved workable form along with induction of nutrients bioavailability. Many research investigations are focusing on increasing the application of nanotechnology for the production of crop and food processing (Abobatta, 2018; Axelos and Van De Voorde, 2017; Dasgupta et al., 2015; Peters et al., 2016). Increment in articles, intellectual property, and patents in nano-agriculture-based food with fresh research tendencies in the processing of food, nutraceutical distribution, packing, quality control, and serviceable food is a highly expanding field in nanotechnology research (Dasgupta et al., 2015).

Conclusions

     Nanotechnology is a newly emerging, but highly expanding technology in many fields related to human activities and benefits worldwide. Its fascinating phenomena have been witnessed through several research findings that the nanoparticles and nanostructure improve various properties due to small size, larger surface area, and highly catalytic nature. Nanotechnology is crucial in achieving food security, especially in the agriculture sector. It can improve crop production by effective microbial, pest, and weed control with high economic value, security, and safety. It also plays a key role in food processing, food modification, stability, sensing, prolonged shelf life, minimized food losses, and provide safe food. Nanotechnology also minimizes post-harvest losses with better stability, safety, and packaging materials. Nanoparticles like Ag, Au, Zn, TiO2, ZnO, SiO2, and MgO commonly used in food processing, may also induce health risks due to easy penetration through the cell for adverse reactions within various humans, and animal organs as well as parts of plants. Such risks due to nanoparticles or nanocomposite could be controlled in future research via using greener synthesis and searching for easy and cheaper protocols for degradation and removal of existing nanomaterial from the sites of attack.

Abstract

Keywords

1. Introduction

2. MFNPs-based magnetoresponsive effects for biomedical applications

3. Conclusion and perspectives

Declaration of competing interest

Acknowledgments

References

Abstract

One of the most attractive features of magnetic ferrite nanoparticles (MFNPs) in biomedical application is that they can mediate external magnetic field to produce local magnetic field, magnetic thermal and magnetic force effects. These generated effects can later be utilized in the diagnosis and treatment of various diseases. The application performance is mainly determined by the nano-magnetism of MFNPs. Therefore, by modulating the magnetic properties, the improved magnetic resonance (MR) signals, magnetothermal, and magnetomechanical effects of the MFNPs can be achieved. In this review, we summarize the strategies used in the engineering of MFNPs to enhance MR imaging sensitivity and magnetic thermal conversion efficiencies. We will also discuss the detailed magnetoresponsive mechanism arising from the critical magnetic properties of MFNPs. Furthermore, we will highlight the recent progresses of the engineered MFNPs in biomedical applications, with emphasis in MR signal amplification, magnetothermal, and magnetomechanical response in biomedical applications.

1. Introduction

Since ancient Greek time, lodestone, i.e., bulk magnetite (Fe3O4), has been used in the treatment of common ailments. With the advancement in nano-biotechnology, the applications of such magnetic ferrite nanoparticles (MFNPs) have evolved, which extends to the modern biomedicine and clinical study [[1], [2], [3], [4], [5], [6], [7]]. As biological tissues are transparent to magnetic fields, the most attractive advantage of MFNPs in biomedical applications is that they can complete various functions triggered by invisible, tissue-penetrating magnetic field [8,9]. For instance, natural magnetotactic bacteria build iron oxide nanoparticles (IONPs) in a chain configuration to generate a permanent dipole in their cells so as to navigate toward their favorable habitats by sensing the Earth’s magnetic field [10]. These MFNPs possess good biocompatibility, which have already been used as imaging and therapeutic agents in preclinical and clinical settings [2,3,11]. As a result of the excellent magnetoresponsive properties of MFNPs, numerous exciting in vivo biomedical applications are created. These include imaging for non-invasive diagnosis of diseases [12], treatment of diseases with hyperthermia [13], magnetically driven nanorobots [14], magnetic activation of ion channel for cell signaling [15], manipulation of cell’s function and fate [16,17], and so on. Despite the good establishment of these concepts, there is a significant challenge in obtaining effective MFNPs that can achieve full functionality via the efficient conversion of the external energy of magnetic field. As such, due to their poor conversion ability, this greatly hinders their transition from bench to bed.

1- Introduction

2- pH-responsive polymeric nanocarriers

3- Temperature-responsive polymeric nanocarriers

4- Redox potential-responsive polymeric nanocarriers

5- Enzyme-responsive polymeric nanocarriers

6- Light-responsive polymeric nanocarriers

7- Dual-responsive polymeric nanocarriers

8- Future perspectives

9- Conclusions

References

Abstract

In the last decade, considerable attention has been devoted to the use of biodegradable polymeric materials as potential drug delivery carriers. However, bioavailability and drug release at the disease site remain uncontrollable even with the use of polymeric nanocarriers. To address this issue, successful methodologies have been developed to synthesize polymeric nanocarriers incorporated with regions exhibiting a response to stimuli such as redox potential, temperature, pH, and light. The resultant stimuli-responsive polymeric nanocarriers have shown tremendous promise in drug delivery applications, owing to their ability to enhance the bioavailability of drugs at the disease site. In such systems, drug release is controlled in response to specific stimuli, either exogenous or endogenous. This review reports recent advances in the design of stimuli-responsive nanocarriers for drug delivery in cancer therapy. In particular, the synthetic methodologies investigated to date to introduce different types of stimuli-responsive elements within the biomaterials are described. The sufficient understanding of these stimuli-responsive nanocarriers will allow the development of a better drug delivery system that will allow us to solve the challenges encountered in targeted cancer therapy.

Conclusions

In this review, it was focused on the most recent advances in the development of stimuli-responsive nanocarriers for drug delivery in cancer therapy. These smart drug delivery systems respond to the distinct changes in cancer cells, such as changes in pH gradient and elevated secretion of certain enzymes, rather than the conditions in normal cells. Targeting tumors with stimuliresponsive nanocarriers could not only increase the therapeutic benefit and minimize associated toxicity, but could also enhance the curative effect by specifically releasing the anti-cancer drug in a powerful precise mode, both temporally and spatially. The different approaches adopted to incorporate stimuli responsive elements within the polymeric nanocarriers were discussed, with a particular focus on polymeric materials with stimuliresponsive mechanisms in response to pH, redox potential, enzymes, temperature, and light. Despite the tremendous progress that has been made regarding the engineering of new stimuli-responsive materials, several challenges still remain to be addressed with respect to nanomedicine applications. For example, their biodegradability and biocompatibility profiles should be critically investigated prior to utilization in human clinical trials. Many of these systems have only been reported as an in vitro proof-of-concept and follow-up work in vivo preclinical models has been described for only a few. However, the translation of these stimuli-responsive delivery systems from the bench to the bedside might be facilitated, to some extent, if the regulatory requirements for human clinical trials are considered in light of the key features that render a biopolymer suitable for biomedical application, such as biocompatibility, biodegradability, high drug loading capability, programmable release, excellent in vivo stability, non-cytotoxicity, and ability to support effective targeting. Given the ongoing developments in the field of bionanotechnology along with the wide knowledge accumulated over recent years, it is convinced that the collaborative efforts of chemists, biologists, and medicinal and pharmaceutical scientists will revolutionize the design of responsive polymeric materials for cancer therapy to significantly improve both the quality and duration of the lives of patients with cancer.

1- A brief history of CRISPR

2- CRISPR/Cas9: biology, functionality, and applications

3- Therapeutic targets for ex vivo cell-based CRISPR/Cas9 genome editing

4- CRISPR therapy in clinical trials

5- Challenges and opportunities

References

Abstract

The recently developed CRISPR/Cas9 technology has revolutionized the genome engineering field. Since 2016, increasing number of studies regarding CRISPR therapeutics have entered clinical trials, most of which are focusing on the ex vivo genome editing. In this review, we highlight the ex vivo cell-based CRISPR/Cas9 genome editing for therapeutic applications. In these studies, CRISPR/Cas9 tools were used to edit cells in vitro and the successfully edited cells were considered as therapeutics, which can be introduced into patients to treat diseases. Considering a large number of previous reviews have been focused on the CRISPR/Cas9 delivery methods and materials, this review provides a different perspective, by mainly introducing the targeted conditions and design strategies for ex vivo CRISPR/Cas9 therapeutics. Brief descriptions of the history, functionality, and applications of CRISPR/Cas9 systems will be introduced first, followed by the design strategies and most significant results from previous research that used ex vivo CRISPR/Cas9 genome editing for the treatment of conditions or diseases. The last part of this review includes general information about the status of CRISPR/Cas9 therapeutics in clinical trials. We also discuss some of the challenges as well as the opportunities in this research area.

A brief history of CRISPR

The term CRISPR, or the clustered regularly interspaced palindromic repeats, was first used by Jansen et al., in 2002 to describe a novel family of repetitive DNA sequences presented in the genomes of prokaryotes [1]. These unusual repeated sequences were first detected in 1987 by Nakata et al. in Escherichia coli [2], and then recognized to be widespread in archaea and bacteria by Mojica et al., in 2000 [3]. After Jansen et al. identified the CRISPR-associated (Cas) genes in 2002 [1], Mojica et al. [4], Vergnaud et al. [5], and Bolotin et al. [6] revealed the exrtachromosomal and phage-associated origins of the spacers that separate the individual direct repeats, in 2005. Speculations about CRISPR arrays as immune memory and defense mechanism against virus envisions were then proposed [6,7], while the first experimental evidence for CRISPR/Cas system based adaptive immunity was reported by Horvath et al., in 2007 [8]. Following the series discoveries on the basic function and mechanism of CRISPR systems [9–12], in 2010 Moineau et al. demonstrated the Cas9 enzyme (formerly named Cas5, COG3513, Csn1 or Csx12), which is guided by spacer sequences, cleaves target DNA [13]. In 2012, Doudna, Charpentier et al. [14], and Siksnys et al. [15] proved that Streptococcus pyogenes and Streptococcus thermophiles Cas9 can be guided by CRSIPR RNAs (crRNAs) to cleave target DNA in vitro, through forming a double-strand break (DSB). In 2013, Zhang et al. [16], Church et al. [17], and Doudna et al. [18] independently demonstrated the applications of engineered CRISPR/Cas9 systems for genome editing in mammalian cells. This represented a giant leap forward in the genome engineering field and triggered a tremendous number of studies regarding the use of the CRISPR/Cas9 platform for eukaryotic gene editing in a wide range of species, including flies [19,20], zebrafish [21–24], frogs [25], mice [26–28], rats [26,29], pigs [30], and monkeys [31,32].