International Journal of Nanotechnology (57 papers in press)
Fabrication of super hydrophobic duo-structures
by X.Y. Zhang
Methyltrimethoxysilane silica aerogel composite with carboxyl-functionalised multi-wall carbon nanotubes
by Kyu-Yeon Lee
Experimental investigations on direct absorption solar flat plate collector using Al2O3 nanofluid
by Rahul Khatri, Rajesh Jangid, Pranay Singh Tomar, Shyam Sunder Sharma
Abstract: Solar flat plate collectors have lower thermal conversion efficiencies due to high thermal losses and lower heat transfer. Direct absorption solar collector (DASC) works on the concept of volumetric absorption i.e. solar energy will be absorbed by a thin layer of fluid flowing over a flat surface. The glass plate with reflective surface was used as base for fluid film formation Experimental investigations were carried out with water and Al2O3 nanofluid. Nanoparticles of size 20 nm was used to prepare nanofluid with ultrasonic mixers. Three flow rates i.e. 2, 3 and 4 lpm were worked out with water and 0.001%, 0.005%, and 0.01% volume concentrations of nanofluid. Improvement in efficiency of the collector with nanofluid was recorded when compared with water as a working fluid. Optimal flow rate of 2 lpm recorded better thermal efficiencies for water and nanofluids both. Higher efficiencies were recorded with 0.01% volume concentration of nanofluid when compared with water, 0.001% & 0.005%. For constant mass flow of 2 lpm, the maximum efficiency improvement i.e. 13% was achieved with 0.01% volume concentration of nanoparticles compared with water. Maximum single pass temperature difference of 2.7
Keywords: Nanofluid; Al2O3; DASC; Volumetric absorption; Thermal performance.
A simulation-based study on the disc brake temperature distribution for optimizing hole geometry
by Shyam Sunder Sharma, Hariharan Raju, Pranay Singh Tomar, Rajesh Jangid, Rahul Khatri
Abstract: Disc brakes used in automotive are responsible for braking to ensure a smooth and safe ride. This study deals with the thermal analysis of a disc brake rotor under various geometry of holes cut on the disc rotor surface. The friction on the disc escapes in the form of heat from the surface of the disc rotor. The temperature observed on the surface of the rotor, because of the friction developed between the brake pads and the rotor is analysed using ANSYS 18.1. The rotor is designed by assuming appropriate parameters in SOLIDWORKS 17. The temperature distribution and total heat flux were observed using ANSYS 18.1. The analysis was carried out on different hole geometries i.e. circular, square, 3/4th circular, straight slots, and rotor without holes. The dissipation of heat was found better in disc rotor with holes as compared to rotor without holes. The simulation study shows that the slotted holes on the disc rotor has surface temperature i.e. 89.356
Keywords: Automotive disc brake; Simulation; Hole geometry; Heat dissipation.
Deep learning-based feature extraction coupled with multi class SVM for COVID-19 detection in the IoT era
by Mubarak Auwalu Saleh, Sertan Serte, Fadi Al-Turjman, R.A. Abdulkadir, Zubaida Sa’id Ameen, Mehmet Ozsoz
Abstract: The deadly coronavirus virus (COVID-19) was confirmed as a pandemic by the World Health Organisation (WHO) in December 2019 Prompt and early identification of suspected patients is necessary to monitor the transmission of the disease, increase the effectiveness of medical treatment and as a result, decrease the mortality rate. The adopted method to identify COVID-19 is the Reverse-Transcription Polymerase Chain Reaction (RT-PCR), the method is affected by the shortage of RT-PCR kits and complexity. Medical imaging using deep learning has proved to be one of the most efficient methods of detecting respiratory diseases, but efficient deep learning architecture and low data are affecting the performance of the deep learning models. To detect COVID-19 efficiently, a deep learning model based feature extraction coupled with support vector machine (SVM) was employed in this study, Seven pre-trained models were employed as feature extractors and the extracted features are classified by multi-class SVM classifier to classify COVID-19, Common Pneumonia and Healthy individuals CT scan images, to
improve the performance of the models and prevent overfitting, training was also carried out on augmented images. To generalised the models performance and robustness, three datasets were merged in the study. The model with the best performance is the VGG19 which was trained with augmented images, the VGG19 achieved an accuracy of 96%, the sensitivity of 0.936, specificity 0f 0.967, F1 score of 0.935, the precision of 0.934, Yonden Index of 0.903 and AUC of 0.952. the best model shows that COVID-19 can be detected efficiently on CT scan images.
Keywords: artificial intelligence; COVID-19; SVM; support vector machine; feature extraction.
Special Issue on: Intelligent Nano-Biotechnology for the Future Emerging Challenges and Advancements
Research on the application of new nanomaterial -Al2O3 in industrial product modelling design
by Wei Bi, Zidong He
Abstract: Gamma-Al2O3 is one of many aluminas. It has a series of remarkable characteristics, such as porosity, large specific surface area, high activity, and good thermal stability. Therefore, -Al2O3 is often used as adsorbents, catalysts and catalyst carriers, and has extremely wide applications in petrochemical industries, environmental protection and other fields. In industrial design, owing to its different physical properties, it is often widely used in different design products. In this research, aluminium nitrate and glycine were used as raw materials, and nano--Al2O3 was synthesised by low-temperature combustion. XRD and TEM test methods were used to analyse and characterise the reaction products, investigate the pH value of the solution, the molar ratio of the reactants, the calcination temperature, and the calcination time of the formation, particle size, crystal transformation and morphology of the reaction product and other aspects. Relevant data were obtained through experiments.
Keywords: nano-γ-Al2O3; environmental protection materials; new nanomaterials; industrial product modelling design.
Image-based automatic segmentation of leaf using clustering algorithm
by Shubham Mahajan, Shivalika Sharma, Davinder Paul Singh, Chinmay Chakraborty, Dr. Amit Kant Pandit
Abstract: This paper proposes a productive strategy to separate the leaf area from different plant images. The image analysis in plants plays a critical job in reasonable and gainful farming. Plant image analysis is mainly used to record the plant development plant area and leaf zone etc. Plant leaf segmentation has become a significant task to be examined among these plant characters. For this reason, a strategy for leaf area segmentation from different plant images is made. In this paper we have proposed an algorithm which will segment plant on the basis of L*a*b colour spaces and cluster formation. When our proposed technique was compared with the rosette tracker it was found that our proposed algorithm was more efficient in plant leaf segmentation. The results of our proposed algorithm are: accuracy is 79.23%, sensitivity is 85.84%, and specificity is 97.40%.
Keywords: colour space; leaf segmentation; plant development; plant image analysis.
Protective effect of nano-electrostatic composite material on miners skin and its influence on working state
by Shuicheng Tian, Ying Chen, Yuan Zou
Abstract: A nano-electrostatic composite material is proposed to promote the rapid healing of miners skin injuries and maintain their good working state in the harsh underground environment. The poly(lactic-co-glycolic acid) (PLGA) nano-particle therapy has a strong effect on maintaining the biological stability of the drug, so it is undertaken as a carrier to load the basic fibroblast growth factor (bFGF) into PLGA. The electrospinning technology is adopted to form a nano-electrospinning composite material (NECM), which is obviously interwoven into a network with uniform thickness. Sixty miners with skin injuries were treated with different dressings. It was found after 10 days that the wound-healing rate of miners using NECM reached 86.67%, which is much higher in contrast to that of the traditional medical dressings (43.3%). The main reason is that bFGF can promote the proliferation of wound granulation tissue, and loading it into nanofibre dressings can increase its bioavailability and promote wound healing. Therefore, the application of bFGF/PLGA NECM has a significant effect on promoting the healing of miners skin injuries, so it plays an important role in alleviating the psychological pressure of miners and enhancing their enthusiasm for work.
Keywords: nano-electrospinning composite material; skin protection; miner; working state.
Preparation of hydrophobic honeycomb films with an amphiphilic copolymer via the breath figure method
by Dejie Zhou, Qingzhao Wang
Abstract: Fabrication of honeycomb films with amphiphilic fluorinated ABC-type triblock copolymer via the breath figure method is introduced in this paper. The triblock copolymer poly(ethylene oxide)-block-polystyrene-block-poly(perfluorohexylethyl acrylate) (MeOPEO-b-PSt-b-PFHEA) was synthesised by atom transfer radical polymerisation (ATRP). The film with ordered honeycomb structure was fabricated under selective solvent, controlled humidity and polymer concentration. We analysed the influence of different factors on the film morphology. The triblock polymer we prepared exhibited a regular honeycomb surface structure in a carbon disulphide solution at 5 g/L concentration and 75% humidity. Surface wetting properties indicate that the amphiphilic triblock polymer can achieve high hydrophobicity by varying the concentration and humidity. The obtained polymer of the honeycomb pore structure is expected to be used in hydrophobic materials.
Keywords: honeycomb film; amphiphilic block copolymer; hydrophobic effect; ATRP.
Characteristic analysis of discontinuous function modelling-based nano-biosensor and its detection for hyaluronidase
by Fangfang Zhang
Abstract: This study draws attention to the characteristics of a nano-biosensor based on a discontinuous function algorithm and its application in hyaluronidase detection. The discontinuous algorithm model is introduced in the construction of sensor. Gold nanoparticles (AuNPs) and nanocomposite materials of Nation-Ru(bpy)32+-AuNPs and Nation-Ru(bpy)32+-AuNPs-TTX are prepared for chemical modification of the electrode. Finally, a new nano-electrochemical immunosensor is successfully constructed. Twenty ICR male mice aged 6-8 months were selected as research subjects. They were randomly divided into control group and observation group, with their sperm protein extracted for Western blot and enzyme activity detection. The results show that AuNPs nanomaterials have good biocompatibility and conductivity, and demonstrate good sensitivity in the detection process. The enzyme detection results show that the enzyme activity of the observation group is higher than that of control group, indicating that the prepared sensor system has relatively high accuracy, with the detection being simple, more sensitive, and quicker.
Keywords: discontinuous function; nano-biosensor; HAase detection; nano-particle.
Nano-drug release control microchip combined with biomaterials in the recovery of shoulder joint movement and contraction
by Zhigang Tong
Abstract: This study explores the effect of a new type of biological tissue engineering stent of kartogenin (KGN) loaded with polycaprolactone (PCL) combined with a microarray polylactic acid (PLA) nano-drug release (NDR) microchip based on magnetic nanoparticles to promote the healing of rotator cuff injury (RCI). Firstly, the ferroferric oxide (Fe3O4) magnetic nanoparticles (MNPs) were prepared and applied to PLA-loaded microchips to realise the remote magnetic control (RMC) of 1% lidocaine. Secondly, the directional PCL stent loaded with KGN (KGN-PCL) was prepared using the electrospinning technology. The results showed that the KGN-PCL biomaterial showed excellent in vitro drug release ability. The microarray PLA NDR microchip based on Fe3O4 MNPs combined with the KGN-PCL biological stent can promote the regeneration of cartilage in the tendon bone structure of the rotator cuff, and elevate the healing quality of rotator cuff tendon-bone by improving the biomechanical properties of the rotator cuff.
Keywords: Fe3O4 magnetic nanoparticles; nanodrug release microchip; kartogenin; biological tissue engineering stent; rotator cuff injury; biomechanical properties.
Cytological mechanism of nanowire reinforced biological bone cement in the treatment of vertebral osteoporotic fractures
by Sheng Xu
Abstract: This study analyses the effects of calcium phosphate cement (CPC) with magnetic composite nanomaterials on the adhesion, proliferation, apoptosis, and osteogenic differentiation of mesenchymal stem cells (MSC) in vitro. In this study, CNTs/Fe3O4 composites were prepared using different surfactants, and the best materials were selected through characterisation and analysis and then added to CPC to prepare the magnetic CPC (MCPC). The bone marrow MSCs of rats was selected to culture with CPC (CPC group), MCPC (MCPC group), and MCPC under 60 mT static magnetic field (MS group), respectively. Subsequently, the differences in the proliferation, apoptosis, and alkaline phosphatase (ALP) activity of MSCs in each group were detected. The results showed that the CNTs/Fe3O4 nanomaterials prepared by the combination of sodium acetate + polyethylene glycol 2000 surfactant had excellent properties; MCPC prepared with 9 wt% CNTs/Fe3O4 addition had the best stress-strain performance. Compared with the CPC group, the MSCs adhesion rate, proliferation activity, and ALP activity of the MCPC group and the MS group were obviously increased (P < 0.05), while the apoptosis rate was greatly reduced (P < 0.05). In contrast to the MCPC group, MSCs adhesion rate, proliferation activity, and ALP activity in MS group increased greatly (P < 0.05), and cell apoptosis rate was observably decreased (P < 0.05). It showed that CPC with magnetic composite material can promote MSC adhesion, proliferation, and osteogenic differentiation in vitro and reduce cell apoptosis, and the effect was more obvious under 60mT static magnetic field. The above results could provide an important cytological mechanism for the treatment of patients with vertebral osteoporotic fracture (VOF).
Keywords: magnetic composite nanomaterials; calcium phosphate cement; mesenchymal stem cells; vertebral osteoporotic fracture.
Effect of sodium hyaluronate-based nanoprobe in rehabilitation of tendon injury and tendon adhesion
by Siqi Lv
Abstract: This paper aimed to investigate the effect of nanoprobe fibres based on sodium hyaluronate in the rehabilitation of tendon injury and adhesion. A desolventisation method was adopted to prepare nanoparticle-encapsulated hyaluronic acid chitosan nanoprobes. There was an analysis on the cumulative release of basic fibroblast growth factor in different nanoprobe fibres, the difference in mechanical properties, its effect on the proliferation of human amniotic mesenchymal stem cells, and the difference in expression of tenomodulin and scleraxis proteins in hAMSCs cells. It was found that the NPs/HA-CS had a smooth surface with an average particle size of 136
Keywords: sodium hyaluronate; nanoprobe; tendon injury; tendon repair.
Graphene oxide nanomaterials in the recovery of shoulder joint movement Contraction
by Yayun Zhu
Abstract: Graphene oxide (GO) and pure titanium (Ti) samples were adopted to prepare the titanium dioxide (TiO2) nanotube films based on GO (GO-TiO2) by electrochemical anodisation. Then, the drug loading capacity, the drug releasing capacity, and the antibacterial ability of the GO-TiO2 nanotubes were analysed. The results showed that the number of MSCs on the surface of GO-TiO2 nanotubes was higher than that of pure Ti; the MSCs on GO-TiO2 nanotubes surface were in the form of polymerised colonies, while the MSCs on the surface of pure Ti were in the form of dispersed particles. Compared with pure Ti, the GO-TiO2 nanotubes could effectively prolong the drug release time. The antibacterial ability of pure Ti, GO-TiO2 nanotubes, and drug-loaded GO-TiO2 nanotubes was compared as pure Ti < GO-TiO2 nanotubes < drug-loaded GO-TiO2 nanotubes. GO-TiO2 nanotubes could promote the differentiation of MSCs such as osteoblasts, and also had a strong drug loading capacity for macromolecular drugs.
Keywords: graphene oxide; titanium dioxide nanotube; antibacterial ability; mesenchymal stem cells; shoulder joint.
Electrospinning nanofibres in exercise rehabilitation after fracture of anterior cruciate ligament
by Haiying Wang
Abstract: This study aimed to analyse the application of electrospinning nanofibres in the rehabilitation of fracture of anterior cruciate ligament (ACL). In this study, the polycaprolactone fibres were prepared by dissolving the PCL in a mixed solution of dichloromethane and N,N-dimethylformamide through an EP machine. The silk fibroin and PCL were dissolved and blended to prepare a nanofibre stent. Ninety patients with ACL injury were selected as the research objects and randomly rolled into a blank group, a control group, and an experimental group, with 30 cases in each group. The ACL was reconstructed through exercise rehabilitation therapy, ozone therapy combined with exercise rehabilitation therapy, and EP nanofibre stent, respectively. Results showed that the nanofibre stent was successfully prepared, which had good mechanical properties and hydrophilicity. The EP nanofibre material could effectively promote the proliferation and differentiation of stem cells, thereby increasing the rate of tissue regeneration at the injured site.
Keywords: electrospinning; anterior cruciate ligament fracture; polycaprolactone; silk fibroin.
Experimental and theoretical validation studies of ASnO3 (A = Ba, Ca, Sr) nanofibres for bioactivity applications
by Bradha Madhavan, A. Suvitha, Anand Steephen
Abstract: Barium, calcium and strontium stannate nano strands were blended through an electrospinning process. The as-prepared samples were calcined at 1000
Keywords: barium stannate; perovskite; nanofibres; bioactivity.
Preparation of reduced graphene oxide/nano hydroxyapatite nanocomposite scaffold and its adoption in sports fracture injury
by Danya Yang, Jianfang Wang, Jianquan Yang, Danya Fu
Abstract: This paper aimed to study the preparation of a nanocomposite scaffold composed of reduced graphene oxide and nano hydroxyapatite, and its adoption value in patients with sports injuries. First, graphene oxide and nHA nanocrystals were prepared, which were then combined to form RGO/nHA nanocomposites. Transmission electron microscopy and scanning electron microscopy were employed to characterise nanocomposites. The in vitro and in vivo experiments in mice were performed to detect the toxicity of the prepared RGO/nHA nanocomposite. Moreover, the physiological characteristics of the formed RGO/nHA nanocomposite in the tissues of mice were analysed, such as HE staining images. Then, the prepared nanocomposites were adopted to prepare RGO/nHA nano gel composite scaffolds, which were used in surgery of patients with bone injuries. The results showed that the prepared RGO/nHA nanocomposite was non-toxic in mice, and the RGO/nHA nanocomposite had good biocompatibility in mice.
Keywords: nanomaterials; composite scaffolds; sports injuries; reduced RGO-nHA.
Clinical value of injected calcium phosphate cement/recombined bone xenograft-based hybrid nano-biomaterials in the knee joint sports rehabilitation ability
by Wenting Zhou
Abstract: This paper aimed to analyse the application value of poly nano-microspheres and injected calcium phosphate cement/recombined bone xenograft hybrid biomaterials in knee joint sports rehabilitation. In this study, different types of hybrid nano-biomaterial, such as ICPC/RBX, ICPC/RBX + 4% PLGA, and ICPC/RBX + 8% PLGA, were prepared based on ICPC raw materials, and their physical and chemical properties were tested. The results showed that ICPC/RBX material, ICPC/RBX + 4% PLGA material, ICPC/RBX + 8% PLGA material could be formed in phosphate buffer saline buffer, and there was no collapse phenomenon. The initial setting time and final setting time of ICPC/RBX, ICPC/RBX + 4% PLGA, and ICPC/RBX + 8% PLGA materials increased compared with ICPC raw materials, but the difference was not marked. The compressive strength of ICPC/RBX + 4% PLGA and ICPC/RBX + 8% PLGA materials was always lower than the strength of ICPC and ICPC/RBX materials.
Keywords: PLGA nanospheres; ICPC/RBX hybrid biomaterials; new bone formation rate; biomechanics; ultimate compressive strength.
Preparation of nano biosensors for detection of intestinal flora and cellulose molecular weight
by Xiaoqian Liu
Abstract: This paper describes the preparation of a Fe2O3@Au nanoparticles (NPs)/glassy carbon electrode (GCE) biosensor to detect intestinal flora and cellulose molecular weight. Specifically, Fe2O3@Au and DNA-loaded NPs were prepared first, which were then attached to GCE to prepare Fe2O3@Au/GCE biosensor. The results showed that when the biosensor was hybridised with a base-matched target sequence, no oxidation peak was obtained; when the biosensor was hybridised with a mismatched target sequence and a completely non-complementary target sequence, an oxidation peak was obtained. The biosensor had a potential peak around 0.9 V at different polymerization temperatures, and the potential peak intensity results were expressed as 40
Keywords: biosensor; intestinal flora; cellulose molecule.
Neurotrophic factors combined with ordered collagen nano-modified materials in the recovery of exercise ability of patients with peripheral nerve transection
by Yiming Gu, Chaoyang Yang
Abstract: This study aimed to analyse the application of neurotrophic factor (NTF) combined with ordered collagen modified material (OCMM) (taking graphene oxide (GO) as example) modified nanofibre stent (NFS) in the recovery of exercise ability of patients with peripheral nerve transection (PNT). The graphene NFS was prepared by dissolving the graphite powder and preparing graphene nanomaterials by ultrasonic and centrifugal treatment methods. It indicated that the NFS was successfully modified, with good mechanical properties, hydrophilicity, and biocompatibility; the modified NFM could effectively enhance the migration and proliferation of neuronal cells, thereby promoting the nerve regeneration and recovery of exercise ability in patients with nerve injury.
Keywords: neurotrophic factor; peripheral nerve transection; water contact angle; silk fibroin; nerve regeneration.
Influence of gold nanoclusters biomaterials combined with upper limb rehabilitation robot on patients with limb ischemic injury
by Yan Zhai, Xiaobo Guo, Yong Zhang, Yongzheng He, Zhiqiang Li
Abstract: The reverse microemulsion was prepared and gold nanoparticles (AuNPs) were synthesised from it. Then, AuNC biomaterials were prepared by modifying folic acid with dendrimers, which were applied to patients with limb ischemic injury. It was found that the peak of AuNCs near the wavelength of 1700cm-1 corresponded to the infrared characteristic peak of C-O. The peak near the wavelength of 3400cm-1 corresponded to the N-H stretching vibration peak, and the peak near the wavelength of 3200cm-1 corresponded to the C-H deformation vibration peak. The arm supination score (40.2
Keywords: gold nanoclusters; sodium borohydride; surfactants; ischemic injury; reverse microemulsion.
Adoption of carbon nanomaterial-based flexible sensors in human exercise health monitoring
by Zhenwen Xu, Liou Liu
Abstract: To analyse the adoption of nanomaterials in flexible sensors and the monitoring value in human exercise health, carbon nanotube (CNT) was first prepared. Four different types of CNT sensors were subsequently prepared. After performance analysis, the optimal flexible CNT sensor was selected and used in human exercise health monitoring. The results showed that the diameter of the undoped nitrogen CNT was about 35.9 nm, and that of nitrogen-doped CNT was 51.42 nm. The height of the two CNT arrays was similar, and the arrangement was relatively uniform. The infrared response monitoring results of four different types of sensor revealed that the infrared sensor with silica substrate had a faster response speed than that with PMMA substrate, with remarkable difference (P < 0.05). The fitting curve of the bending angle of the wrist and knee and the resistance change showed that the greater the bending angle of the knee, the greater the resistance received, with a positive correlation between them. In short, the flexible sensor based on carbon nanomaterials had ideal infrared signal sensing and monitoring capabilities, and had high sensitivity.
Keywords: carbon nanomaterials; sensors; motion monitoring; smart products.
Adoption of three-resonant terahertz nano-biosensor in human sports health monitoring
by Xueqin Zhang, Fan Jiang, Benyu Xi, Yan Ma
Abstract: This work aims to analyse the adoption effect of three-resonant terahertz (THz) in nano-biosensors and its monitoring value in human sports health. First, the absorber-based three-resonant THz nanomaterial was prepared, which was applied to the preparation of sensors after its physical and chemical properties were detected. Then, three kinds of resonant sensor under modes A, B, and C were prepared, and the best nano-biosensor was selected and used in the monitoring of human sports health. The prepared flexible sensor was used for human motion detection, and the results showed that the sensor can accurately monitor the amplitude of human motion. Nano-biosensor based on three-resonant THz had good electrical signal monitoring capabilities and high sensitivity, which can conduct a comprehensive assessment of the human sports health status and is worthy of clinical promotion and adoption.
Keywords: multi-resonance terahertz; sensor; metamaterial; sports health.
Adoption of porous nanocomposite fibre scaffold in cartilage injury healing and rehabilitation
by Jucui Wang, Mingzhi Li
Abstract: To evaluate the effect of a porous nanofibre scaffold on the healing and repair of cartilage defects in animal models, three different PNFSs were prepared, namely gelatin/polylactic acid scaffold, thermally cross-linked Gel/PLA scaffold, and hyaluronic acid modified Gel/PLA-T scaffold. After characterisation and analysis, bone marrow stromal cells were taken as the research object. The detection of cell proliferation and apoptosis was performed to evaluate the biocompatibility of each scaffold after scaffold culture. Then, a New Zealand rabbit model of cartilage defect was constructed. The results showed that the density, mechanical properties, and in vitro degradation rate of HA@Gel/PLA-T scaffolds were remarkably increased relative to Gel/PLA and Gel/PLA-T scaffolds, while the porosity and water absorption rate decreased. It was verified that the prepared HA@Gel/PLA-T composite PNFS can promote the proliferation and chondrogenic differentiation of BMSCs cells, thereby promoting the repair and regeneration of cartilage damaged tissues.
Keywords: nanofibre scaffold; cartilage defect; wound healing; chondrogenic differentiation.
Effect observation and nursing of nano-silver antibacterial dressings in preventing catheter-related bloodstream infection
by Yan Zhang, Jing Wang
Abstract: This thesis takes patients with severe burns as a research case to discuss the therapeutic effects and nursing methods of nano-silver antibacterial dressings on patients with severe burns caused by central venous catheterisation through trauma. We found that the per-thousand-day infection rates of catheter-related bloodstream infections in the sterile application group and the Aner iodine dressing group were 25.48% and 20.83%, respectively. A total of 16 cases of catheter-related bloodstream infections occurred in all patients in the three groups, and a total of 16 pathogenic bacteria were isolated from the microbial culture of the catheter tip attachment and the blood microbial culture. For this reason, we can conclude that the use of nano-silver antibacterial dressings for maintenance of central venous catheters placed through the wound in patients with severe burns can effectively reduce the rate of central venous catheter-related infections and extend the number of days of catheter indwelling.
Keywords: nano-silver antibacterial dressing; catheter-related bloodstream infection; catheterisation; central vein; burns.
Polyacrylic acid coated nanomaterials and sports rehabilitation training on the therapeutic effect of patients with stroke
by Dan He, Yu Bai
Abstract: This article explores the protective effect of alteplase-loaded dextran-polyacrylic acid nanoparticles on hippocampal neurons in stroke rats. We randomly divided 24 stroke rats into sham operation group, stroke group, stroke + alteplase group, stroke + alteplase-loaded dextran-polyacrylic acid nanoparticle intervention group. We used the HE stains method to detect the number of neurons in the hippocampus and calculate the survival rate of neurons under an inverted microscope. The survival rates of neurons in the sham operation group, stroke group, alteplase group, and nanoparticle group were 100%, 11.0%, 16.6%, and 57.6%. The ischemic encephalopathy group and the neuron survival rate of the general enzyme group and the nanoparticle group were lower than that of the sham operation group. The neuron survival rate of the nanoparticle group was higher than that of the stroke group and alteplase group (P<0.05). There was no statistical difference between the alteplase group and the stroke group.
Keywords: stroke; polyacrylic acid nanoparticles; exercise rehabilitation; alteplase.
Nanomaterials in diagnosis and treatment of cardiovascular diseases
by Xiao-Qi Zhao
Abstract: This paper prepared a nucleic acid aptamer functionalized magnetic ball/gold nanorod composite material. Thrombin and its pair of nucleic acid aptamers can be built into a closed-loop structure based on the hybridisation technology of adjacent surfaces. The measurement of human serum coagulation time proves that this inhibition and recovery method has very good clinical application prospects. In the application of multi-functional nanomaterials to assist sports rehabilitation training, the paper analyses the level of serum N-terminal brain natriuretic peptide precursor (NT-pro BNP), high-sensitivity myocardial troponin T (hscTnT) and heart function indexes in patients with chronic heart failure influences. The results show that the use of multifunctional nanomaterials combined with exercise rehabilitation therapy in patients with chronic heart failure can significantly reduce serum NT-pro BNP and hscTnT levels and improve cardiovascular function.
Keywords: cardiovascular disease; thrombin; nucleic acid aptamers; near infrared light; gold nanorods; exercise rehabilitation.
Study of the in vitro transdermal administration of surface anaesthetics based on dextran-based nano-injection lipid lidocaine
by Na He
Abstract: The paper explores the in vitro transdermal performance and in vivo characteristics of surface anaesthetics containing lidocaine (LID-HLD-BNs) using dextran-based nanoliposome as a carrier. Lidocaine-encapsulated traditional liposomes (LID-CLs) and lidocaine hydrochloride injection (LID-IJ) were used as controls, using isolated mouse abdominal skin. Using a Franz diffusion cell, high performance liquid chromatography was used to conduct in vitro transdermal experiments to observe the transdermal performance of LID-HLD-BNs. Using calcein as a probe and laser confocal microscopy (CLSM), with calcein-loading traditional liposomes (calcein-CLs) and free calcein as controls, the liposome preparation (calcein-HLD -BNs) fluorescence penetration in the abdomen of living mice was observed. The study found that the cumulative transdermal volume per unit area and transdermal rate of LID-HLD-BNs were higher than LID-CLs and LID-IJ. The transdermal rates of LID-IJ, LID-CLs, and LID-HLD-BNs were 39.15, 79.04 and 142.86 g/(cm2
Keywords: dextran-based nanoparticles; liposomes; lidocaine; chromatography; transdermal administration; in vitro studies.
Nano-targeted drugs in combined treatment and nursing of liver cancer
by Lan Li, Tingting Qiu, Yuqin Ni
Abstract: This article uses the new polymer material N-glycyrrhetinic acid (GA)-polyethylene glycol (PEG)-chitosan (NGPC) as the carrier material, and uses the ion cross-linking method to prepare Brucine-loaded chitosan nanoparticles (Brucine/NGPC-NPs) ).The uptake of Brucine solution and Brucine/NGPC-NPs by hepatocellular carcinoma cells is both time- and concentration-dependent. The uptake of NGPC-NPs is significantly stronger than that of the solution. It has significant active transport characteristics, and the intake follows the order of exogenous licorice. The addition of acid decreases, and its uptake pathway mainly depends on clathrin-mediated endocytosis and then internalisation by the cell. Therefore, we can conclude that NGPC-NPs can be used as a carrier for liver cell targeting, significantly increasing the amount of drugs entering liver cancer cells, and achieving the purpose of reducing toxicity and increasing efficiency.
Keywords: Brucine; nano-targeted drugs; chitosan nanoparticles; liver cancer cells; cell uptake; liver cancer care.
New nanoparticle computed tomography contrast agent in the treatment of neonatal hypoxic ischemic encephalopathy
by Yulin Chen, Wei Liu, Jingwei Sun, Linbao Wen
Abstract: The paper describes the preparation of iodine-containing polymer nanoparticles contrast agent by precipitation polymerisation method, used in the diagnosis of head CT in children with HIE, and the analysis of the CT manifestations of children with HIE. The article selected 36 children with HIE admitted from January 2019 to December 2020 as the observation group. In addition, we selected 36 patients who were born at the same time and had clinical symptoms but no abnormalities in CT examination as the control group. The brain CT values of the severe group were lower than those of the moderate group and the mild group, and the difference was statistically significant (P<0.05). For this reason, we have concluded that cranial CT examination with iodine-containing polymer nanoparticles as a contrast agent can reflect the scope, location and extent of neonatal ischemic and hypoxic encephalopathy, and has guiding significance for early diagnosis and treatment.
Keywords: head CT performance; new nanoparticles; iodine-containing polymer nanoparticles; hypoxic ischemic encephalopathy.
Application of bio-scaffold materials in the recovery of sports health of injured parts of fracture patients
by Min Li, Hong Ye, Xuefei Hu
Abstract: In order to analyse the application effect of bio-scaffold materials in the treatment of patients' fractures, the study first performed computed tomography scans of fracture patients to obtain fracture data. Secondly, the paper imports the fracture data of the patient into the model based on the finite element analysis software, and uses the finite element analysis software to analyse the stress situation of the fracture site. The study found that the orientation of peak strain on the loading surface of fracture patients appeared on the outside of the bone and stress concentration occurred. Finally, under the condition of finite element simulation, we installed the bio-stent at the fracture site and modelled and simulated it again, which better reflected the biomechanical situation of the fixed stent in the fracture. The research results provide relevant theoretical and practical support for the optimisation of clinical fracture stent installation materials.
Keywords: bio-scaffold material; finite element; fracture; biomechanics; sports injury; structural optimisation.
Study on preparation and technology of nano-porous platinum-based metal materials by dealloying method
by Chao Gao, Ping Yan, Zhongliang Tang, Shulong Liu
Abstract: Nano-porous metal materials have nano-scale pores and ligaments, which have the characteristics and excellent properties of both nano-porous (NP) materials and metal materials. Therefore, they have great application value in optics, chemistry, physics and so on. Compared with single metal catalysts, NP Pt-Ru alloy shows excellent activity for ammonia borane hydrolysis at room temperature. In this paper, a series of NP Pt-Ru-Al alloys with different components were prepared by the dealloying method, in which the active component Al was selectively corroded in acidic solution. The morphology and composition of the prepared NP Pt-Ru alloy were characterised, and the activity of the samples was tested.
Keywords: nano-porous; platinum; dealloying; Pt-Ru alloy; metal materials.
Antibacterial properties of PCL/silver NP-based nano-composites for potential food packaging applications
by Blessy Melapurakkal, Ahmed John
Abstract: A large number of non-renewable materials have been used for preparation of food packaging materials. These non-renewable based food packaging materials create negative impact on the eco system. Thus, biodegradable and biocompatible polymeric materials for food packaging materials are preferred. The addition of silver nanoparticles in biocompatible polymeric matrices enhances the physicochemical properties, and therefore, the food shelf life. Silver NPs were prepared by a green chemical approach, and the prepared silver nanoparticles were characterised by XRD, HRTEM and EDAX analysis. The composite film was prepared by incorporating silver nanoparticles in polycaprolactone. The composite film containing polycaprolactone (PCL) with various concentrations (0.1, 0.5, 1.0, and 1.5 wt%) of silver nanoparticles was prepared by the solution casting method. Furthermore, the composite film exhibited strong antibacterial activity against pathogenic micro-organisms Bacillus cereus, Bacillus pumilius, Bacillus subtilis, Clostridium perfringens and Pseudomonas aeruginosa. The enhanced properties were attributed to the incorporation of silver into the PCL matrix.
Keywords: silver nanoparticles; polycaprolactone; solution casting; antibacterial; food packaging.
Application of nanomaterials in martial arts sports injury patients
by Bin Wang
Abstract: This paper discusses the curative effect and influencing factors of the implantation of nano-hydroxyapatite rods in the treatment of martial arts sports Schatzker type III tibial plateau fractures. Methods: The thesis research included 36 martial arts Schatzker type III tibial plateau fracture patients who were admitted to the hospital from July 2017 to July 2019. In the control group, 18 patients received traditional incision bone plate screw internal fixation. The HSS knee function scores at 1, 3, and 6 months after surgery were all higher than those of the control group. Arthroscopic nano-hydroxyapatite rod implantation for minimally invasive treatment of Schatzker type III tibial plateau fracture injury in martial arts sports can promote fracture healing, relieve pain, and promote joint function recovery.
Keywords: nanomaterials; fracture injury; arthroscopy; hydroxyapatite; tibial plateau fracture injury.
Research on application of new nanomaterials in repairing ligament injury of wushu athletes
by Mengjian Miao, Yan Lou, Changcheng Xin
Abstract: This paper explores the application value of nano-hydroxyapatite/polyamide 66 composite bioactive artificial vertebral body in anterior surgery of thoracolumbar ligament injury in martial arts athletes. In the experiment, 81 patients with thoracolumbar ligament injury who were treated by anterior menstrual route from January 2016 to September 2019 and were followed up, were divided into titanium mesh group and artificial vertebral body group according to different intervertebral bone graft support materials. All 81 cases were followed up for more than 12 months. There was no significant difference in the Cobb angle and the height of adjacent intervertebral space between the two groups at 1 week and 3 months after surgery (P > 0.05). Thoracolumbar ligament injury anterior decompression is thorough, and the reconstruction method conforms to the principle of spine biomechanical distribution, and the spinal cord function recovery is better.
Keywords: martial arts athletes; artificial vertebral body; nanomaterials; titanium mesh; thoracolumbar ligament injury; anterior surgery.
Research on gene therapy of neurons based on narcotic nanoparticles intervention in cerebral ischemia
by Fan Zhang, Fan Lei, Xingpeng Xiao
Abstract: To detect the efficiency of gene therapy of anesthetic nanoparticle drug materials -CD-PAMAM and -CD-PAMAM/pEGFP-N3 in vitro to intervene in cerebral ischemic neurons, in preparation for further in vivo gene therapy. Standard non-viral vector polyamidoamine dendrimer PAMAM was used as a positive control group. Cytotoxicity test: in the two types of cell, when N/P < 10, all anesthetic nanoparticle drug material carriers and their complexes showed low toxicity, and the cell survival rate was above 80% (P < 0.05); when N/P > 20, the cell survival rate of -CD-PAMAM and -CD-PAMAM/pEGFPN3 complex was higher than that of PAMAM and PAMAM/pEGFP-N3 complex (P<0.05). The cytotoxicity and gene transfection efficiency of -CD-PAMAM have been clarified, and it has been confirmed that -CD-PAMAM can be used as a carrier for gene transfer in Hek293 and SH-SY5Y cells, and can further interfere with gene therapy of cerebral ischemic neurons.
Keywords: anaesthetic nano-particle pharmaceutical materials; intervention of cerebral ischemic neuron gene therapy; cytotoxicity test.
Special Issue on: Eco-Friendly and Sustainable Cognitive Green Nano-Technologies for the Mitigation of Emerging Environmental Pollutants
Preparation of titanium dioxide composite nanomaterials using copper catalysis and their dynamic adsorption and photocatalytic performance in water treatment
by Ye Tian
Abstract: The aim is to investigate the dynamic adsorption performance of titanium dioxide (TiO2) nanocomposite materials in water treatment, providing direction for water purification. The copper-catalyzed living free-radical polymerization method polymerizes the prepared TiO2 particles with tertiary amine polymer to manufacture the TiO2 polymer nanocomposite materials. The prepared TiO2 nanocomposite materials are then modified to obtain the quaternised TiO2 polymer nanocomposite materials (quaternised TiO2@poly(DEAEMA)), which are characterized and analysed. Finally, the water treatment performance of quaternized TiO2@poly(DEAEMA) is judged through photocatalysis and adsorption experiments, while the antibacterial performance of the prepared materials is judged using the common Escherichia coli and Staphylococcus aureus. Results demonstrate that the quaternised TiO2@poly(DEAEMA) polymer nanocomposite materials are completely and tightly wrapped, presenting a flower-like appearance, with a significantly-increased diameter and an average size of about 600nm, which can be utilized as the pollutant adsorbent. Water treatment simulation reveals the fastest adsorption rate and the highest adsorption capacity of quaternised TiO2@poly(DEAEMA), reaching 265 mg/g given the same reaction time. The catalytic removal rate in ultraviolet and visible light reaches 94%, and the photocatalysis of visible light reaches 69%. Until the reaction lasts for 45 minutes, its antibacterial activity is optimal, and the diameter of the inhibition zone against Escherichia coli and Staphylococcus aureus exceeds 16 mm. Therefore, the prepared TiO2 nanomaterials have high adsorption properties, good photocatalysis performance, and excellent antibacterial properties, which can provide an experimental basis for the treatment and purification of water resources in the industry.
Keywords: titanium dioxide; water treatment; dynamic adsorption; photocatalysis; nanocomposite material.
Special Issue on: Smart Bio-Signal Acquisition System
Smart approach to the impact of cumin powder on obesity among adults in the urban area of Puducherry, India
by J. Suganya, S. Singaravelu Ramasamy, U. Dinesh Babu, R. Vijayaraghavan, K. Emayavarman, T.P. Latchoumi
Abstract: This research study aimed to discover the effect of cumin powder on physical and biological parameters among adults with obesity in the urban area of Puducherry. Cumin may be effective in lowering cholesterol and in weight loss. Cumin may be helpful for people trying to lose weight and lower total cholesterol, LDL or "bad" cholesterol, and triglyceride levels with type 2 diabetes and also to help the body to handle stress. The following experimental design is used. A total of 40 adults with obesity for control and experimental groups, BMI value of 25, total cholesterol >200 mg/dl, and age group of 20 to 60 years were selected. After evaluating the lipid profile, a bag of cumin powder with warm water was administered for 70 days (10 weeks). Prior to and post action, all anthropometric and biochemical parameters were measured. Warm water and cumin powder decreased serum and total cholesterol, and slightly reduced BMI (p<0.05). In both anthropometric and biochemical parameters of overweight in adults, cumin powder with hot water showed a drop.
Keywords: overweight; obesity; cholesterol; body mass index.
Bioanalytical method devolpment and validation of a novel antiseizure agent Canobamate, using LC-MS/MS
by Yamarthi Venkateswara Rao, Rasheed Ahemad Shaik, Jithendra Chimakurthy
Abstract: To quantify Ceobamate in human plasma, a precise and sensitive electroionisation procedure in tandem mass spectrometry has been developed. The study was successfully confirmed by using Cenobamate-D4 as the internal norm and tert-butyl methyl ether in the testing of several positive ionic reactions. It has been used in liquid-liquid extraction to prepare samples as an extraction solvent. Cenobamate and Cenobamate-D4 (internal standard) were separated on an Eclipse C18 column (150 mm, 4.6 mm I.D., 5
Keywords: Cenobamate; human plasma; epilepsy; robustness; validation.
Analysing behavioral and academic attributes of students using educational data mining
by Muhammad Umer, Saima Sadiq, Arif Mehmood, Imran Ashraf, Gyu Sang Choi, Sadia Din
Abstract: Educational data mining has attracted significant consideration over the last few years. Information that is stored online using educational systems is increasing tremendously. The online learning environment can be improved by analysing and mining this information to extract representative features about students' behaviour and academic skills. Various classifiers are investigated to analyse the prediction of students' academic performance based on the attributes from the Kalboard360 learning management system (LMS). The selection of significant features can substantially improve the prediction, hence, ANOVA and chi-square filter approaches and forward feature selection and backward feature elimination wrapper approaches are examined for their efficacy. Results reveal that an extra tree classifier can achieve an accuracy of 0.8755 when trained on backward feature elimination selected features. Wrapper approaches prove to be effective in determining the most significant attributes for student performance prediction. 'Visited resources', 'raised hands', 'relation', 'parent answering survey', and 'student absent days' are regarded as the most significant attributes to determine student performance. Education specialists and institutions can leverage these findings to improve the student learning process and enhance their academic performance.
Keywords: metadata; educational data mining; feature extraction; ANOVA; chi-square filter; forward feature selection; backward feature elimination; extra tree classifier.
Multi to binary class size based imbalance handling technique in wireless sensor networks
by Neha Singh, Deepali Virmani, Gaurav Dhiman, S. Vimal
Abstract: Wireless sensor networks are used various disciplines including healthcare, banking, transportation, ocean and wildlife monitoring, earthquake monitoring, and numerous military applications. Now-a-days, there is escalation in size of data which makes it unfeasible to analyze it with accuracy. There are numerous problems that are faced when detecting patterns between structured and unstructured data that are unworkable by humans, so to make computation fast, easy and accurate, Machine Learning came in existence. Machine learning is extensively used in Wireless sensor networks. To make a machine learn, a training dataset is required and output is predicted by testing the dataset. A dataset in wireless sensor network has multi-class in its dependent variable. This multi-class classification causes class imbalance problem. This paper proposes MBSCIH (multi to binary class size based imbalance handling) technique in wireless sensor networks to solve the class imbalance problem in multi-class classification. MBSCIH converts multi-class classification into binary-class classification. MBSCIH is applied on WSN-DS, NSL-KDD and KDD-Cup 99 datasets and is tested with five major machine learning algorithm: Naive Bayes, Random Forest, Decision Tree, Support Vector Machine (SVM) and k-Nearest Neighbour (KNN). The test method used for testing is 10-fold cross validation. Results indicate that the proposed method increases the existing efficiency by 15.13%, 0.28%, 0.01%, 0.01%, 0.12% for Na
Keywords: wireless sensor network; multiclass classification; binary classification; intrusion detection; WSN-DS dataset.
A dynamic programming approach for accurate content-based retrieval of ordinary and nano-scale medical images
by Jinhong Sun, Liang Qi, Yinglei Song, Junfeng Qu, Mohammad Khosravi
Abstract: Recently, with the explosive growth in the number of available medical images generated by medical imaging systems, content-based retrieval of medical images has become an important method for the diagnosis and study of many diseases. Most existing methods find medical images similar to a given one based on the extraction and comparison of crucial image features. However, similarity values computed with low level visual features of an image generally do not match the similarity obtained from human observation well. The overall performance of these methods is thus often unsatisfactory. This paper proposes a dynamic programming approach for content-based retrieval of medical images. The approach represents an image with three different histograms that contain both crucial intensity and textural features of the image. The similarity between two images is evaluated with a dynamic programming approach that can optimally align the peaks in the corresponding histograms from both images. Experiments show that the proposed approach is able to generate retrieval results with high accuracy. A comparison with state-of-the-art approaches for content-based medical image retrieval shows that the proposed approach can achieve higher retrieval accuracy in both ordinary and nano-scale medical images. As a result, higher retrieval accuracy may lead to more reliable results for the diagnosis and treatment of many diseases. The proposed approach is thus potentially useful for improving the reliability of many applications in health informatics.
Keywords: medical image retrieval; similarity; alignment of histograms; dynamic programming.
Small cell lung tumour differentiation using fluorine-18 PET and smoothening using Gaussian 3D convolution operator
by J. Vijayaraj, D. Loganathan, T.P. Latchoumi, M.V. Pavan, P.J. Lakshmi
Abstract: Nowadays, the most common disease for smokers is lung cancer. The deadly type of lung cancer is Small Cell Lung Cancer (SCLC). Tumour identification is complicated. It is only in the final stage that this form of lung cancer can be detected. When the patient has some of the earlier symptoms of SCLC, they can be subjected to preliminary tests for cancer. Two composite images, the maximum intensity projection and artificial intelligence, were automatically generated from the 4D image datasets. So, this paper presents the part of the identification of lung cancer by differentiating identified tumour cells using fluorine-18 Positron Emission Tomography (F-18 PET) and that can be smoothened using the smart Gaussian 3D convolution operator. The performance analysis shows the lung image dataset showing differentiation of tumour cells by applying this technique and simulation graphs for accuracy, sensitivity, and precision.
Keywords: small cell lung cancer; tumour cell; fluorine-18 PET; Gaussian 3D convolution operator.
Analysis of high-dimensional data using feature selection models
by Shubham Mahajan, Shubham Mahajan
Abstract: The determination of features assumes a significant part in the enhancement of output of AI models, limiting the computational time taken to make a learning model and improves the exactness of the learning cycle. Hence, the analysts give more consideration to the determination of features to expand the exhibition of AI calculations. The choice of the proper technique for the determination of features is significantly for a specific AI task through high-dimensional information. It is consequently important to complete an examination of various strategies for character determination for the exploration network, specifically with the end goal of improving effective techniques for choice. The method for choosing features improves the effectiveness of AI undertakings for high-dimensional information. To accomplish this target, this paper surveys the many different techniques for the choice of features for high-dimensional information.
Keywords: feature selection; signal processing; artificial intelligence; high-dimensional data; classification.
Improved generalized fuzzy peer group with modified trilateral filter to remove mixed impulse and adaptive white Gaussian noise from colour images
by Akula Suneetha, E. Srinivasa Reddy
Abstract: In image processing applications, image denoising is an emerging area that recovers the original image from noisy image, which is essential in the applications like pattern analysis. The main aim of this study is to propose an effective filtering technique with peer groups for effective image restoration processes. In this research paper, a new approach is proposed that includes fuzzy-based approach and similarity function for filtering the mixed noise. In a peer group, the similarity function was adaptive to edge information and local noise level, which was utilized for detecting the similarity among pixels. In addition, a new filtering method (modified trilateral filter) was combined with improved generalised fuzzy peer group (IGFPG). The modified trilateral filter includes Kikuchi algorithm and loopy belief propagation to solve the inference issues on the basis of passing local message. In this research work, the images were collected from KODAK dataset and a few real time multimedia images like Lena were also used for testing the effectiveness of the proposed methodology. The collected colour images were contaminated with Adaptive White Gaussian Noise (AWGN) of standard deviation [05,20] and impulse noise of probability [0.05,0.20]. From the simulation, the proposed approach attained better performance related to the existing approaches in light of Normalised Color Difference (NCD), Peak Signal-to-Noise Ratio (PSNR), and Mean Absolute Error (MAE). Hence, the proposed approach achieved PSNR of almost 36 dB, which shows 0.2 dB to 2 dB improvement over the existing methods.
Keywords: adaptive white Gaussian noise; belief propagation technique; impulse noise; improved generalised fuzzy peer group; Kikuchi algorithm; modified trilateral filter.
Optimising pharmacokinetics via ADMET, bioactivity of Zr substituted samarium-doped ceria nanomaterials
by Bradha Madhavan, Suvitha A, Nagaraj Balakrishnan, Ananth Steephen, Ajay P
Abstract: The properties of ADMEs, bioactivity and restricted molecular medicinal chemistry were analysed via Swiss ADME in order to facilitate drug development. The important activities of Absorption, Distribution, Metabolism, Excretion and Toxicity (ADMET) in optimising pharmacokinetics and assessing the Ce0.8-xZrxSm0.2O2?? compounds were estimated. Six physico-chemical characteristics, bioactivity radar, donor acceptors and molar refractivity are being tested. In search of significant improvement in CeO2 fluorite structure, a promising doping, co doping approach was adopted. By varying the mole ratios of samarium dopant and Zr substitution, the Ce0.8-x ZrxSm0.2O2 nanomaterials were synthesised through simple chemical processes. Basic electronic structure calculation, bonding graph and crystallographic representation were analysed via a materials project tool. From the bonding graph, material design, synthesis glitches and stability assessments from the energies were calculated. ADMET Predictor is a product tool that rapidly and reliably forecasts more than 40 properties, including dissolvability, log P, CYP digestion positions and mutagenicity of Ames. Hence, these efforts are promising in monitoring pharmacokinetics via bio-electronics sensors and interfaces.
Keywords: bio sensors; bio-potential ceria; bio-electronics; bioactivity; pharmacokinetics.
EFFECT OF DIALYTIC STRETCH EXERCISES ON MUSCLE CRAMPS AND QUALITY OF LIFE AMONG HEMODIALYSIS PATIENTS
by P. Gophi, V. Shruthikamal, R. Danasu
Abstract: Chronic kidney disease is a serious scourge of non-communicable infections that influences the global population. The pervasiveness of end-stage kidney disease is increasing primarily because of diabetes mellitus and hypertension. Among hemodialysis patients, the most widely recognised difficulty is muscular cramps. It can occur anywhere in the body, however normally in the lower muscles of the legs, feet, toes, thigh, and middle section. This review evaluated the impact of dialytic stretching on muscle problems and the personal satisfaction of hemodialysis patients. A semi-exploratory scan pattern was selected. The investigation members were chosen by using the purposive inspecting method and during the second hour of hemodialysis the members were told to perform dialytic stretch exercise includes straight leg extension exercise (thigh hamstring muscle), seated marching (thigh front and back quadriceps muscle), leg stretch (leg front and back ankle), calf muscle stretching (gastronomies muscle) for a period of 12 sittings. The results of the muscle problems and personal satisfaction survey demonstrated improvement and were critical at a p < 0.05 level in light of mediation.
Keywords: muscle cramps; dialytic stretch exercises; hemodialysis.
A novel and intelligent decision-making system for real-time healthcare tracking using commercial wearable data
by Anudeep Peddi, Teppala Venkata Ramana
Abstract: Wearable health devices became popular these days and have become genuinely intertwined with society. Smartwatches and other fitness devices fulfill the consumer needs in continuously tracking human activity, which can further decode to analyze the health parameters like heart rate, blood pressure, blood glucose levels, and many more. Internet of things (IoT) enabled techniques, mobile and desktop-based applications are ameliorating the ease of using these techniques. The applications of the wearables are also transforming the quality of virtual and tele-health care to improve, which is a substitute to the conventional medical practices. In this paper, we reported a descriptive analysis on the progress in modeling the healthcare wearable sensors that impact the imminent health care applications in different domains. Also, we made a comparative study on consumer fitness wearable devices to analyze how the device facilitates the ease of usage with other specification comparisons. We recorded data from a consumer wearable fitness device to observe and envisage the users effort to accomplish the activity goals each day for maintaining good health. We reported the exploratory analysis of the data obtained from the recordings. Supervised machine learning algorithms are applied to the recorded data and compared the results. Among the supervised algorithms applied, the random forest regression gave us the highest accuracy of 97.88% in predicting the subjects activity goal is perpetrated for the respective day.
Keywords: health care; wearables; smartwatches; fitness trackers; wellness activity trackers; wireless sensors; data processing; supervised machine learning; prediction; decision-making system.
Predictive protein module based on PPI network and double clustering algorithm
by Sicong Huo, Quansheng Liu, Tao Lu
Abstract: Protein-protein interaction (PPI) is a kind of biomolecular network which plays an important role in biological activities. In order to improve the accuracy of protein function module prediction, obtain the protein function module and run timely, this paper proposes a predictive protein module based on PPI network and double clustering algorithm (ISCC), which considers the characteristics of PPI network and considers nodes as two-dimensional data points. First of all, improved Density-Based Spatial Clustering of Applications with Noise (IDBSCAN) determines the central cluster, and then uses Spectral Clustering (SC) to redivide the weight; secondly, CFSFDP and chameleon algorithm are used to filter the similarity of the central cluster, and Support Vector Machine (SVM) is used to get the final clustering result. Finally, the experiments are compared with CDUN, EA, MCL and MCODE in terms of accuracy, sensitivity, F value and the number of protein functional modules. The experimental results show that the F value of ISCCD is 70% higher than that of EA, the number of recognition modules is 257 higher than that of CDUN, and the running time is 494 s faster than that of MCODE
Keywords: PPI network; protein function module prediction; clustering; SVM; IDBSCAN.
Diagnosing cardiovascular disease via intelligence in multimedia healthcare: a novel approach
by Geeitha Senthilkumar, Fadi Al-Turjman, Rajagopal Kumar, Jothilakshmi Ramakrishnan
Abstract: Diagnosing cardiovascular disease (CVD) in its early stages remains a challenge despite the existence of all the medical technologies and devices that are being used. Besides the digitized form of collecting and organizing data, prediction and diagnosis are two stumbling blocks in CVD. This study explores statistical machine learning models with a multimedia healthcare approach using artificial intelligence to predict risk factors of heart diseases associated with type 2 diabetes mellitus (T2DM). This study investigates the efficacy of a mathematical model to perform attribute evaluation using information criteria-based selection in LASSO regression. The present study implements a deep learning algorithm using a MultiLayer Perceptron (MLP) classifier with Gaussian Process Classification (GPC) that provides probabilistic predictions in terms of linear and nonlinear functions. The performance of the classifier is evaluated using precision, recall and accuracy metrics. The proposed classification model yields 93.59% accuracy of 10 cross-validations assorted with sigmoid function for better analysis.
Keywords: artificial intelligence; cardiovascular disease; multimedia healthcare; feature selection; type 2 diabetes.
Fishier mantis optimiser: a swarm intelligence algorithm for clustering images of COVID-19 pandemic
by Javad Rahebi
Abstract: In this study, an automated segmentation method is used to increase the speed of diagnosis and reduce the segmentation error of CT scans of the lung. In the proposed technique, the Fishier mantis optimiser (FMO) algorithm is modelled and formulated based on the intelligent behaviour of mantis insects for hunting to create an intelligent algorithm for image segmentation. In the second phase of the method, the proposed algorithm is used to cluster scanned image images of COVID-19 patients. Implementation of the proposed technique on CT scan images of patients shows that the similarity index of the proposed method is 98.36%, accuracy is 98.45%, and sensitivity is 98.37%. The proposed algorithm is more accurate in diagnosing COVID-19 patients than the falcon algorithm, the spotted hyena optimiser (SHO), the grasshopper optimisation algorithm (GOA), the grey wolf optimisation algorithm (GWO), and the black widow optimisation algorithm (BWO).
Keywords: meta-heuristic algorithms; Fishier mantis optimiser; COVID 19; coronavirus; clustering.
Deep learning-based feature extraction coupled with multi-class SVM for COVID-19 detection in the IoT era
by Auwalu Mubarak, Sertan Serte, Fadi Al-Turjman, Rabiu Aliyu, Zubaida Said, Mehmet Ozsoz
Abstract: The deadly coronavirus virus (COVID-19) was confirmed as a pandemic by the World Health Organisation (WHO) in December 2019. Prompt and early identification of suspected patients is necessary to monitor the transmission of the disease, increase the effectiveness of medical treatment and as a result, decrease the mortality rate. The adopted method to identify COVID-19 is the Reverse-Transcription Polymerase Chain Reaction (RT-PCR), the method is affected by the shortage of RT-PCR kits and complexity. Medical imaging using deep learning has proved to be one of the most efficient methods of detecting respiratory diseases, but efficient deep learning architecture and low data are affecting the performance of the deep learning models. To detect COVID-19 efficiently, a deep learning model based feature extraction coupled with Support Vector Machine (SVM) was employed in this study, Seven pre-trained models were employed as feature extractors and the extracted features are classified by multi-class SVM classifier to classify CT scan images from COVID-19, common pneumonia and healthy individuals. To improve the performance of the models and prevent overfitting, training was also carried out on augmented images. To generalise the model's performance and robustness, three datasets were merged in the study. The model with the best performance is the VGG19 which was trained with augmented images: it achieved an accuracy of 96%, a sensitivity of 0.936, a specificity 0f 0.967, an F1 score of 0.935, a precision of 0.934, a Yonden Index of 0.903 and AUC of 0.952. The best model shows that COVID-19 can be detected efficiently on CT scan images.
Keywords: artificial intelligence; COVID-19; SVM; feature extraction.
Power optimisation of wireless sensing network through quantum deep learning
by Zhongzhen Yan, Kewei Kewei Zhou, Feng Guo, Na Hou, Jiangyi Du
Abstract: In current research, a secure wireless communication system with high data rate obtained by quantum computing (QC) is used. To speed up the data rate of transfer, the QC has been combined with machine learning algorithms. Then the large volume of data that are transferred, stored and processed in wireless systems leads high energy of the system. This article proposes a power minimisation approach with QC-based approach called improved sequential parametric convex approximation (ISPCA) trained by deep learning algorithm called Graph Convolutional Neural Network (GCNN). The proposed approach has been used to minimise the power and enhance the energy efficiency of wireless communication systems, such as LTE/5G. Evaluated results show that our proposed approach consumes only 44.82J energy for computing 1000 samples. The proposed technique outperforms existing ones by consuming much less energy.
Keywords: wireless communication system; 5G; SPCA; convolution network; deep learning; energy efficiency; power optimisation.
Special Issue on: Nano Impact on Next-Gen Biomedical and Environmental Research
Application of energy saving and environmental protection material in landscape construction of urban landscape wetland system
by Yifei Zhang, Yi Liu
Abstract: with the strengthening of energy saving and environmental protection requirements of buildings in China, the limitations of traditional landscape construction in energy saving and environmental protection in China are becoming more and more obvious. Based on this, this paper studies the application of energy saving and environmental protection materials in the landscape design of urban garden wetland system, and constructs a material performance analysis model based on logical regression algorithm. From six aspects, the information collection of materials in energy conservation and environmental protection is realized, and the logical regression algorithm is used for comprehensive analysis and evaluation. The material analysis model can make the construction of urban garden wetland system more ecological by comprehensive analysis of different types of materials, according to the environmental protection requirements of landscape construction and the planning scheme of urban garden wetland system. The experimental results show that the material analysis model based on logical regression algorithm has the advantages of quantifiable evaluation and good stability, which can improve the application efficiency of energy saving and environmental protection materials in landscape construction and strengthen the ecological nature of garden wetland system.
Keywords: logical regression algorithm; urban garden wetland system; energy saving and environmental protection materials; landscape construction.
Application analysis of green building materials in urban three dimensional landscape design
by Hongji Yue, Xuchen Jia
Abstract: The current urban three-dimensional landscape design process is mainly based on the three-dimensional simulation of an urban landscape for the overall design. How to achieve the innovation of urban three-dimensional landscape design process with the help of green building materials is the current development trend. Based on this, this paper studies the application of green building materials in urban three-dimensional landscape design. Firstly, the three-dimensional model of urban landscape based on SFM (Structure From Motion) algorithm is proposed, and the autocorrelation function is used to simulate the landscape signal. Through the maximum value of autocorrelation function curve in the process of three-dimensional reconstruction, the restoration of three-dimensional landscape is realised, and then the error of three-dimensional model is analysed. Secondly, the fuzzy evaluation method is used in the simulation evaluation, and the improved SFM three-dimensional reconstruction model is used to analyse the results of the three-dimensional landscape design. Finally, the application value of different types of green building material in urban three-dimensional landscape design is analysed through the design of three-dimensional simulation experiment.
Keywords: urban three-dimensional landscape design; environmental protection materials; SFM algorithm; three-dimensional model.
Effect of surfactants on mechanical and environmental geotechnical properties of loess and bentonite
by Kui Liu, Kangze Yuan
Abstract: In recent years, research on the influence of surfactants on the mechanical and environmental geotechnical properties of loess and bentonite has become a focus of the environmental geotechnical field. Based on this, this paper proposes a mechanical analysis model of loess and bentonite based on the mixed washing leapfrog algorithm, and studies the influence of surfactants on the mechanical and environmental geotechnical properties of loess and bentonite and its quantitative evaluation. Firstly, the data of mechanical strength, composition change, permeability, density change and load-bearing change of loess after adding surfactant are analysed, and then the comprehensive comparative analysis and evaluation of environmental geotechnical characteristics with or without surfactant are carried out by using the mixed washing frog leaping algorithm. Finally, the mechanical analysis model is verified by design experiment. The results show that the mechanical analysis model of loess and bentonite based on the mixed washing leapfrog algorithm has the advantages of high feasibility, high data accuracy and high analysis efficiency, which can realise the quantitative evaluation of the influence of surfactants on the environmental geotechnical characteristics.
Keywords: shuffle leapfrog algorithm; surfactant; environmental geotechnical characteristics; loess and bentonite; mechanical analysis model.
Application of micro and nano bubble technology in water level recovery of water conservancy construction engineering
by Dongling Zhang, Yuzhen Wang, Fei Luo
Abstract: In order to improve the removal efficiency of groundwater pollutants, and improve the ability of groundwater restoration and water level recovery, a micro and nano bubble enhanced remediation technology is proposed. A micro and nano bubble micro observation system was designed, including camera, micro lens, laser and image processing software. Combined with a particle size analyser and interface potential analyser, the basic physical characteristics and migration characteristics of micro and nano bubbles were analysed. According to the obtained characteristics, the particle size distribution and interfacial potential characteristics of micro bubbles are obtained, the existence time and gas mass transfer effect of microbubbles in water body are verified, and the migration rule of microbubbles in water bodies and porous media is obtained. The numerical simulation results show that the micro and nano bubbles have high mass transfer efficiency and long existence time, which can effectively improve the dissolved oxygen of groundwater, improve the activity of microorganisms, achieve the effective removal of pollutants, and promote the in-situ remediation of polluted groundwater.
Keywords: microbubble technology; water conservancy construction engineering; water level recovery; mass transfer effect; ozone; observation system.