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

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

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”.

1- Introduction

2- Multi-layer NPs for CRISPR-Cas9 delivery

3- Novel building blocks for multi-layer NPs

4- Conclusions and perspective

References

Abstract

The clustered regularly interspaced short palindromic repeat (CRISPR) has great potential to revolutionize biomedical research and disease therapy. The specific and efficient genome editing strongly depends on high efficiency of delivery of the CRISPR payloads. However, optimization of CRISPR delivery vehicles still remains a major obstacle. Recently, various non-viral vectors have been utilized to deliver CRISPR tools. Many of these vectors have multi-layer structures assembled. In this review, we will introduce the development of CRISPR-Cas9 systems and their general therapeutic applications by summarizing current CRISPR-Cas9 based clinical trials. We will highlight the multi-layer nanoparticles (NPs) that have been developed to deliver CRISPR cargos in vitro and in vivo for various purposes, as well the potential building blocks of multi-layer NPs. We will also discuss the challenges in making the CRISPR tools into viable pharmaceutical products and provide potential solutions on efficiency and biosafety issues.

Introduction

In recent years, a series of programmable DNA nuclease-dependent genome editing techniques have been developed to enable efficient genetic manipulation in eukaryotes, particularly in mammalian cells [1-3]. These genome-editing techniques used for gene knock-out or knock-in predominately include meganucleases, zinc-finger nucleases (ZFNs), transcription activator-like effector nucleases (TALENs), and the CRISPR-Cas system [4-7]. These technologies all employ endonucleases to induce double strand breaks (DSB) at intended sites in the genome, followed by DSB repair through different mechanisms including non-homologous end joining (NHEJ), homologous recombination (HR), homology-mediated end joining (HMEJ) and microhomology-mediated end joining (MMEJ) [8]. Among these genome editing techniques, CRISPR-Cas9 is a gene editing system derived from the bacterial adaptive immune system, which is capable of combating the invasive viral genome under the guidance of short sequence RNA. CRISPR-Cas9 system is highly versatile because of its simplicity and high efficiency, moreover, any genome sequence theoretically can be edited by this system. In principle, the whole genome can be site-specifically modified at the genetic level in cells and tissues with CRISPR-Cas9 system, which further allows researchers to study the relationship between gene mutations and biological phenotypes. CRISPR-Cas9 system has driven innovative applications from basic biology research to biotechnology and medicine [9].

1- Introduction

2- Materials and methods

3- Results and discussion

4- Conclusion

References

Abstract

In this paper, we report on spatial redistribution of bone marrow mesenchymal stem cells loaded with magnetic nanoparticles under the influence of continuously applied magnetic field. Semiconductor nanoparticles were synthesized by epitaxial growth of a GaN thin layer on magnetic sacrificial core consisting of ZnFe2O4 nanoparticles. Different quantities of nanoparticles were incubated in vitro with mesenchymal stem cells. High density of nanoparticles (50 μg/ml) leads to a decrease in the number of cells during incubation, while the density of nanoparticles as low as 10 μg/ml is enough to drag cells in culture and rearrange them according to the spatial distribution of the magnetic field intensity.

Introduction

Stem cells are defined by their ability to self-renewal and differentiation into more specialized cells, depending on their level of potency [1]. Being easily harvested and cultured, Mesenchymal Stem Cells (MSC) are among the most used stem cells in regenerative medicine. MSC can differentiate into bone, cartilage, fat, tendon, and other different cell lines depending on the given conditions and growth factors [2]. MSC therapeutic potential is enormous and covers a wide medical area, from bone marrow transplantation in blood cancers [3] to the treatment of various diseases like osteoarthritis [4], Crohn`s disease [5], diabetes [6], cardiovascular diseases [7], otorhinolaryngologic pathologies [8], etc. There are several major issues in the therapeutic processes including the non-invasive cellular imaging in high quality transport of therapeutic agent, as well as the remote manipulation in the desired area. The success rate of the therapy based on stem cells depends on the accuracy the cells are delivered to the site of interest. Magnetic resonance imaging (MRI) represents an important tool in cells tracking [9]. The MRI usually includes gadolinium-based contrast agents. Over the last years, however, different types of paramagnetic nanoparticles have been used for tracking cells [10]. The advantage of using nanoparticles for cells tracking relies on the possibility of tuning with the material concentration and chemical composition. Besides tracking, nanoparticles offer the possibility to manipulate with cells and control their spatial position in vivo [11]. The superparamagnetic iron oxide (SPIO), usually stabilized by coating with dextran, gelatine, chitosan or other polymers, is among the most commonly used nanoparticles for cells tracking [12, 13]. Our previous investigations have shown that uncoated gallium nitride nanoparticles (GaN) do not affect the viability and proliferation of endothelial cells [14] and can be used for multifunctional therapeutic purposes which include cells tracking [15]. The use of piezoelectric nanoparticles in combination with magnetic ones could increase the impact of smart materials not only in remote cells imaging but also in the control of the cellular metabolic activity [16-19].

Lentiviral vectors are increasingly utilized in cell and gene therapy applications because they efficiently transduce target cells such as hematopoietic stem cells and T cells. Large-scale production of current Good Manufacturing Practices-grade lentiviral vectors is limited because of the adherent, serum-dependent nature of HEK293T cells used in the manufacturing process. To optimize large-scale clinical-grade lentiviral vector production, we developed an improved production scheme by adapting HEK293T cells to grow in suspension using commercially available and chemically defined serum-free media. Lentiviral vectors with titers equivalent to those of HEK293T cells were produced from SJ293TS cells using optimized transfection conditions that reduced the required amount of plasmid DNA by 50%. Furthermore, purification of SJ293TSderived lentiviral vectors at 1 L yielded a recovery of 55% ± 14% (n = 138) of transducing units in the starting material, more than a 2-fold increase over historical yields from adherent HEK293T serum-dependent lentiviral vector preparations. SJ293TS cells were stable to produce lentiviral vectors over 4 months of continuous culture. SJ293TS-derived lentiviral vectors efficiently transduced primary hematopoietic stem cells and T cells from healthy donors. Overall, our SJ293TS cell line enables high-titer vector production in serum-free conditions while reducing the amount of input DNA required, resulting in a highly efficient manufacturing option.

Introduction

HIV-1-derived lentiviral vectors (LVs) are efficient gene transfer vehicles used in both basic and clinical research settings. The ability of LVs to efficiently shuttle DNA into mammalian cells enables researchers to explore the function of various genes of interest.1–5 Clinically, LVs are used ex vivo to deliver therapeutic genes or expression cassettes to primary target cells, such as hematopoietic stem cells (HSCs), to treat genetic disorders or infectious diseases.6–9 LVs are well suited for use in ex vivo gene therapy because they can deliver a relatively large payload (>9 kb, including intron-containing genomes), have the ability to transduce dividing and non-dividing cells, and stably integrate into the genome of a target cell to provide lifelong correction to that cell and its progeny.10–13 Further, the integration profile of LVs suggests that they are safer than gamma-retroviral vectors, and there have been no product-related malignant transformations reported to date with LVs used in clinical trials.14–16 The promise of LV cell and gene therapy has led to the initialization of nearly 200 clinical trials, with notable successes in the treatment of hemoglobinopathies, primary immunodeficiencies, and leukemias.15,17–25 In fact, LV-based cell and gene therapies have already been approved by the US Food and Drug Administration, Kymriah, and by the European Medicines Agency, Zynteglo. Furthermore, cancer immunotherapy, which currently includes the use of LV to modify T cell function, is an area of intense research. Thus, additional LV-based cellular therapies are likely to be approved in the future.

2- Standard Model

3- Solution Method

4- Study Case

5- Conclusions

References

Introduction

Treatment for chronic hepatitis C infection began in the early 1990s with interferon-alpha [1]. ,is injectable drug worked by improving the immune system, rather than by specifically attacking the virus. In 1998, the oral drug ribavirin was added to interferon [2]. ,e development of the treatment occurred in 2002 with the approval of pegylated interferon-alpha, a process that makes interferon more durable and effective [3]. In 2011, antiviral medications that stop the virus reproducing, which called direct-acting antivirals (DAAs), appeared [4]. Sofosbuvir, commercial name Sovaldi, was approved in the United States in December 2013. Sovaldi-based oral therapy offers high cure rates for HCV infection with excellent tolerability [5]. Currently, the recommended DAA regimens are combinations of multidrug treatments, containing in particular NS5A, NS5B, NS4A inhibitors, such as glecaprevir + pibrentasvir, sofosbuvir and ribavirin (SOF + RBV), telaprevir + pegylated interferon and ribavirin (TVR + PR), sofosbuvir + simeprevir (SOF + SIM), and sofosbuvir + velpatasvir. For more details, see [6, 7, 8]. ,ese regimens offer high cure rates with a rate of curation near 100%.

The quick step of HCV drug development has led to the hopeful estimate that fully eradication of HCV is possible. Although there is still no vaccine for HCV, cure and eradication happened, but further study for the factors that increase this eradication rate is needed. ,e World Health Organization (WHO) has formulated the Global Health Sector Strategy on Viral Hepatitis, 2016–2021, and established service coverage targets to eliminate HCV as a public health threat by 2030 [9, 10]. Meanwhile, there remain many barriers that need to be overcome. Such barriers include the development of simplified and highly effective drug regimens, improving the rates of detection of infection, and the availability of funds including financial and medical expertise.

Strategies for biotechnology must take account of opportunities for research, innovation and business growth. At a regional level, public-private collaborations provide potential for such growth and the creation of centres of excellence. By considering recent progress in areas such as genomics, healthcare diagnostics, synthetic biology, gene editing and bio-digital technologies, opportunities for smart, strategic and specialised investment are discussed. These opportunities often involve convergent or disruptive technologies, combining for example elements of pharma-science, molecular biology, bioinformatics and novel device development to enhance biotechnology and the life sciences. Analytical applications use novel devices in mobile health, predictive diagnostics and stratified medicine. Synthetic biology provides opportunities for new product development and increased efficiency for existing processes. Successful centres of excellence should promote publicprivate business partnerships, clustering and global collaborations based on excellence, smart strategies and innovation if they are to remain sustainable in the longer term.

Introduction

The biotechnology sector operates on an increasingly global basis, with the 25 biggest drug and biotech companies coming from eight different countries (Morrison & Lhäteenmäki 2017; NASDAQ Biotech Index, 2018). The sector has however, undergone considerable consolidation, including the $130 Bn merger of Dow Chemical with Du Pont and Chem China’s bid of $43 Bn for Syngenta, leading to a smaller number of truly global players. Major revenue earners include Abvie’s Humira monoclonal antibody for arthritis, psoriasis, and Crohn’s disease therapy ($15.9 Bn), Gilead’s Harvoni small molecule for hepatitis C antiviral therapy ($13.8 Bn) and Celgene’s Revlimid for multiple myeloma and mantle cell lymphoma ($6.9 Bn; 1, illustrated in Fig. 1). Approximately 26% of revenue sales are currently spent on research and development, in what is increasingly an expensive escalating biotechnological arms race, with upwards of 40% of venture capital centred on just two areas: Boston Bay and San Francisco (Morrison & Lhäteenmäki, 2017). Amongst European companies, one of the biggest recent changes has seen Bayer bid $66 Bn for Monsanto, to create a global entity dedicated to innovation in healthcare, including pharmaceuticals, consumer & animal health and agriculture. Digital farming combining big data with the internet of things, is one example of how Bayer will meet the challenges posed by world population increasing to 10 Bn by 2050. Farmland per capita will decline by 17% and climate change is forecast to reduce yields by a further 17%. This means an effective 60% productivity increase will be necessary to meet the needs of 10 Bn global citizens (Gartland & Gartland 2016; Food & Agriculture Organisation, 2016). Sustainably producing more food with fewer inputs will require precision data on needs and opportunities for growers. Bayer hope to provide an improved quality of life by combining aspects of their current crop protection portfolio with seed, trait and climate change platforms from Monsanto (Bayer, 2017). At a national level, EU28’s largest biotechnology products pipeline comes from the UK, where €34.66 Bn was generated from pharma, medtech and biotechnology products in 2015 (PWC, 2017). This activity provides 482,000 UK jobs, €9.81 Bn tax take and equates to a Gross Value Added of €118,500 per employee, being the highest in Europe. What then are the areas of biotechnology where opportunities exist for smart, specialized strategies to emerge and be successful in research and innovation, including economic and future employment prospects?

1- Introduction

2- Differential evolution algorithm

3- Chaotic systems

4- Chaotic based differential evolution algorithm

5- Benchmark functions

6- Optimization of baker’s yeast drying process

7- Results and discussion

8- Conclusion

References

Abstract

Chaotic based Differential Evolution (CDE) algorithm is presented to determine the optimal control variables for the optimization of Baker’s Yeast drying process. The chaotic system is proposed to determine the initial population, to select the trial individuals from the population in the mutation operation instead of the random number generator. The random values produced by the random number generator are likely to be similar or same values with each other. In this study, four different chaotic systems, such as Lorenz attractor, Rössler attractor, Chua circuit and Mackey-Glass equation, are solved by Runge-Kutta method to produce the random values of the initial individuals. To demonstrate the performance of the CDE algorithms, ten optimization problems are taken from the literature. Furthermore, the performances of the proposed CDE algorithms are compared with the classic Differential Evolution (DE) algorithm, Particle Swarm Optimization (PSO) algorithm, Artificial Bee Colony (ABC) algorithm, Simulated Annealing (SA) algorithm, Touring Ant Colony Optimization (TACO) algorithm in terms of the mean best solution, the number of function evaluations (NFE) and CPU-time metrics. At the same time, the proposed CDE algorithms are implemented for numerical optimization problems based on the IEEE Congress on Evolutionary Computation (CEC) 2014 test suite. For the optimization of baker’s yeast drying process, there are four significant parameters, such as product quality, drying total time, energy cost of air and the final moisture content. The proposed CDE algorithms and classic DE algorithm are applied for the same optimization problem that is taken from a baker’s yeast producer in Turkey. The experimental results prove that the proposed CDE algorithms are able to provide very competitive results.

Introduction

Differential Evolution (DE) algorithm is a powerful heuristic method for global optimization problems, was introduced by Storn and Price [31,32,37]. This population based heuristic optimization algorithm has drawn the interest of researchers in many scientific fields. The DE algorithm has happened to more popular step by step and it has been used in a lot of useful cases due to ease and the good convergence in the optimization problems [4]. The principle of DE algorithm is basically based on adding the difference between two individuals to a third individual in population. It differs from other heuristic algorithms in the mutation, crossover and selection stages. Unlike the procedures based on random number generator in evolutionary algorithms such as genetic algorithms, DE algorithm uses the differences between individuals in the population to form the next generation [10]. Furthermore, DE algorithm has got few control parameters, such as scaling factor, crossover probability constant and population size, which are used during the optimization process like the other evolutionary algorithms. These control parameters have to be determined carefully to increase the solution quality and the algorithm efficiency. The robustness and effectiveness of DE algorithm are based on the suitable settings of the control parameters [44]. In addition to these parameters, the other important thing is determining the initial population by random number generator. In DE algorithm, the individuals’ initial values in the population which are produced by the random number generator are likely to be similar or same values with each other. This is an undesirable situation because of reducing the diversity in the population. In this paper, the new methods based on the chaotic functions were proposed instead of the classic random procedure. Chaos functions have got applications, such as observing the weather in meteorology area [38], cryptography in computer science area [43], predicting gas solubility in chemical engineering [39], finance modeling in economics area [14] and hydrology in biology area [40]. Chaotic functions have the behavior of dynamic systems which are highly sensitive to initial conditions. Each point in a chaotic system is arbitrarily close to other points with different future trajectories. As a result, an small change in the existing trajectory can lead to considerably different behavior.