This PDF file contains the front matter associated with SPIE Proceedings Volume 12179, including the Title Page, Copyright information, Table of Contents, and Committee Page.

Ophthalmic ultrasonic B-mode diagnostic device is widely used in medical institutions. “penetration depth”, “resolution”, “dead zone” and “geometric position accuracy” are the key technical parameters to evaluate the metrological performance of the device. However, until now no national or local metrological specifications applicable to ophthalmic ultrasonic B-mode diagnostic device has been issued in China, and the corresponding traceability system of the device has not been established either. The novel calibration procedure presented in this article has been performed on several typical types of ophthalmic ultrasonic B-mode diagnostic devices widely used in China, and the experimental results show that the calibration method presented in this article can be adopted for the periodic calibration of ophthalmic ultrasonic B-mode diagnostic device, in order to establish the metrological traceability system of the device.

With the increasingly mature and in-depth use of computer image processing technology in clinical medicine in China, the role of computer image processing technology in clinical medicine is also expanding. This paper mainly carries out data mining from three aspects: computer software analysis, system model and association prediction in clinical medicine. In the development of computer technology in clinical medicine, network security system also plays a great role. However, the existing computer network system can not meet the security requirements of clinical medical computer system, so it is necessary to carry out targeted model design and system construction of computer software defense system with the help of data mining technology. This paper mainly expounds the specific application of network security system and image processing technology in clinical medicine under computer technology, and analyzes the design process and function module of computer system based on data mining technology.

Liver cancer segmentation is an important process for doctors to diagnose liver tumors and the patient's subsequent treatments. Computed tomography (CT) scan images are often used because of their high spatial resolution, clear image and speediness. However, most of existing liver tumor segmentation algorithms perform feature extraction from singlephase CT images, ignoring the rich supplementary information provided by multi-phase CT images. Therefore, combined with the attention mechanism, we proposed a multi-channel attention gate V-Net (MCAGV-Net) based on the attention gate module for the automatic liver tumor segmentation by fusing multi-phase CT images information. The algorithm was verified by a multi-phase CT dataset. To further illustrate the performance of this algorithm, accuracy of segmentation by our algorithm was compared with previous algorithm. For liver segmentation, the Dice score is 90.46%, which was 3.92% higher than the single-channel V-Net. For liver tumor segmentation, the Dice score is 69.65%, which was increased 2.41% compared with the single-channel V-Net. In this work, we show that MCAGV-Net can effectively solve the problem of liver tumor segmentation, and for single-channel networks, the accuracy has also been improved.

This study was aimed to investigate the value of logistic regression analysis model of ultrasound characteristics in differentiating nodular goiter and papillary thyroid carcinoma. Methods: A total of 184 patients with nodular goiter and thyroid cancer confirmed by surgery and pathology in our hospital were collected. Among them, 102 patients with nodular goiter and 82 patients with papillary thyroid carcinoma. All patients underwent conventional ultrasonography and contrast enhanced ultrasonography before surgery. The ultrasound image features were compared and analyzed, and univariate analysis was performed by χ2 test. With statistically significant indicators as independent variables, then multiple logistic regression analysis was performed, and receiver operating characteristic (ROC) curves were constructed to analyze the diagnostic performance of the logistic regression model. Results: Univariate analysis showed that age, lesion number, shape, margin, aspect ratio, calcification, micro calcification, enhancement mode, internal echo, internal structure, cystic degeneration and other factors were statistical significance (P<0.05).Multivariate analysis showed that irregular shape, mass effect, aspect ratio greater than 1, internal nodule echo, micro calcification and contrast-enhanced ultrasound were independent predictors for differentiating nodular goiter from thyroid cancer (P<0.05). The area under the ROC curve constructed by the independent predictors was 0.954, and the sensitivity and specificity were 95.1% and 83.3%, respectively. Conclusion: The logistic regression analysis model of ultrasound features has important value in the identification of nodular goiter and thyroid cancer.

Uremia is a serious disease in the stage of renal failure, so it is very important to detect uremia in time. In order to overcome the shortcomings of insufficient sensitivity of traditional ELISA methods, we report a digital ELISA detection platform composed of three parts of microsphere operating system, optical imaging system and computer processing system, which can realize the automatic detection of biomarkers in serum. The platform can combine fluorescent coding microspheres with microporous array, and apply DEP electric field externally, which can effectively increase the recognition efficiency of fluorescent coding microspheres and realize the ultra-high sensitivity detection of two important cytokines of IL-6 and IL-4 in uremic serum. The experiment successfully and accurately detected the IL-4 and IL-6 in the serum of uremic patients. At the end of the article, we summarize and prospect the experiment.

Pulmonary medical image processing is an effective diagnostic method for COVID-19, and CapsNet-based methods have achieved good performance. However, as cost-blind methods, these diagnostic methods only consider immediate and deterministic decisions, which easily lead to misdiagnosis and high costs. Therefore, based on a revised CapsNet, we propose a cost-sensitive three-way decision (3WD) method for COVID-19 diagnosis, named as Caps-3WD. To enhance the feature extraction ability for pneumonia areas, we introduce a Restage module to improve convolution layer of the original CapsNet. Further, to lighten the model, we introduce depth wise separable convolution to reconstruct decoder. Additionally, three options are considered in the decision set: infected, normal, and suspected, which are given different costs, respectively. The lowest-cost decision is chosen for each input. In the experimental analysis, we compare Caps- 3WD with CNN-based and CapsNet-based methods on COVID-CXR dataset, which proves the effectiveness of 3WD and the superiority of Caps-3WD in COVID-19 diagnosis.

Skin cancer is one of the diseases which affect large population in the world. Artificial Intelligence (AI) has been introduced in skin cancer diagnosis in decades. The convolutional neural networks (CNNs) is a deep learning technology used in image classification for skin cancer diagnosis. The study in this article explores show data size and learning rate influence the accuracy of the CNN model. In the study, the best validation accuracy of the CNN model can reach 92.05%. The result shows that the CNN model can effectively diagnose skin cancer with the adjusted learning rate and data size.

Lung cancer and Colon cancer are a leading cause of death worldwide, accounting for nearly 10 million deaths in 2020 which is the second leading death worldwide. Cancer can be reduced through early detection and appropriate treatment. Many cancers have a high chance of cure if diagnosed early and treated appropriately. This paper proposes an easy-to-use interactive Lung And Colon Cancer Detecting Platform (LANCET) to detect lung and colon cancers and communicate the diagnostic results with the users. The proposed system includes a Lung and Colon Cancer Classification Module (LACONIC), a Diagnostic Information and Health Suggestion Providing Chatbot (NINTENDO), and an Easy-To-Use Lung and Colon Cancer Detection Web Application (ANACONDA). For the LACONIC, we resize and split the dataset, and then we use transfer learning to classify lung and colon histopathological images and yield predictions. Using Natural Language Toolkit (NLTK) for the NINTENDO, we train a chatbot that can generate human-like conversation and communicate with users fluently. Finally, the ANACONDA allows patients to select images and get feedback along with suggestions. The systematic tests and validations show that our integrated system is an easy-to-use way of detecting potential cancer and providing health suggestions with high accuracy.

The automation of the medical industry has become one of the necessary conditions in today's medical field. Radiologists or physicians need this automation technology for accurate diagnosis and treatment. Automatic segmentation of tumor parts from magnetic resonance (MR) brain images is a challenging task. Several methods have been developed to improve the segmentation efficiency of automation systems. However, in the process of medical image segmentation, there is always room for improvement. This paper proposes a brain tumor image segmentation method based on deep learning, which includes the concepts of stationary wavelet transform (SWT) and growing convolutional neural network (GCNN). The vital goal of this work is to improve the accuracy of traditional systems. In this paper, the support vector machine (SVM) and convolutional neural network (CNN) are compared and analyzed. The experimental results show that this method is superior to SVM and CNN inaccuracy, peak signal-to-noise ratio, mean square error, and other performance indicators.

With the continuous maturity of medical image information and digitization technology, the Picture Archiving and Communication Systems (PACS) developed based on this technology has been deployed and applied as the main module in most medical institutions in China. Through the introduction and application of the system, high integration, convenient transmission, and mass storage of digital medical image information can be achieved within and a cross medical institutions. Due to the particularity of stomatology, PACS plays a crucial role in the clinical decision support of stomatology. According to a questionnaire survey conducted among 191 dentists from a Class A tertiary stomatological hospital in China, PACS has been widely applied in this hospital, and the system function and quality have been well recognized. However, there is still room for improvement in the overall construction.

Quercetin is a kind of flavonoid with important bioactivity, which is unstable. Quercetin liposomes were prepared to improve the stability of quercetin through a green thin-film dispersion method. We optimized the formulation of quercetin liposomes using response surface methodology. Results showed that the particle size and encapsulation efficiency(EE) of quercetin liposomes were 192.1 nm±3.24 nm and 65.82%±1.35%, respectively. Additionally, we evaluated the stability of quercetin and quercetin liposomes under different storage conditions. The results showed that light, temperature and metal ions had obvious effects on the stability of quercetin and quercetin liposomes. Meanwhile liposomes have ameliorative effects on the stability of quercetin.

In this study, the GH4169 samples were prepared by Laser Melting Deposition(LMD) using a SMAT 5M-060 HC fiber laser. By studying the effect of different powder feeding rates on the samples manufactured by LMD, adjusting the process parameters, and comparing the deposition state with the heat-treated state, the effect of heat treatment process on the microstructure and properties of GH4169 was studied. The samples were characterized by microscope, mechanical experiment and scanning electron microscope. The results showed that the microstructur e of GH4169 alloy deposited by laser melting deposition was mainly composed of γ matrix phase, γ" phase, γ' phase, δ phase and some carbides dispersed in the grain. After heat treatment, the microstructure of GH4169 sample was smaller and compact, equiaxed grain area was larger, columnar grain was less, some bad Laves phase and carbide were dissolved, δ phase was precipitated at grain boundary, which improved the properties of GH4169 on the whole. With the increase of powder feeding rate, the fracture of the deposited sample was ductile fracture, and the elongation increased first and then decreased. The tensile strength and yield strength of heat-treated sample increased, but the elongation decreased. The microhardness of deposition was 314~323 HV and the heat-treated samples were 469~498 HV.

Despite the current high level of drug development and pharmaceutical technology, bacterial infections continue to pose a threat to human health. The increasing ineffectiveness of traditional antibiotics, the misuse of antibiotics, which is accelerating the development of bacterial resistance, the multiplication of drug doses due to ineffective infection control and the rapid increase in adverse drug reactions, as well as the contrast between the huge investment in the development of new drugs (in terms of money, staff and time) and the delay in research and development and the lack of results, make the future of antibiotics for infectious diseases a worrying one. With the development of nanotechnology, particularly the grapheme family and its successful application in the biological field (biosensors, drug delivery, bioimaging, targeted therapies, etc.), scientists have begun to focus on its antibacterial applications, offering new ideas to address the problem of anti-infection. Graphene oxide (GO), a derivative of graphene, not only retains the original nano properties of graphene, but also has the advantages of high activity, good dispersion in solution and easy chemical modification. This paper addresses the advantages of graphene oxide and its related materials as well as its proven antibacterial effects on substances such as bacteria and fungi through a comparative approach, and therefore there is great scope for graphene oxide and its related materials in the pharmaceutical industry.

Carbon nanotubes have 100 times the tensile strength of steel, low density, about 1.35g/cm3, low expansion rate and strong acid and alkali resistance. Its application fields include electrochemical devices, catalyst supports, electrode materials, hydrogen storage materials and composites.At present, there are many carbon nanotubes and their composites. In order to facilitate the future research direction, a variety of carbon nanotechnologies are discussed. This paper first introduces the definition and preparation methods of carbon nanotubes, and then expounds the preparation methods of metal composites and polymer composites of carbon nanotubes, their advantages and disadvantages. Carbon nanotube composites are considered as the final form of composite reinforcement because of their excellent properties. Therefore, they have very broad application prospects in the field of nanotechnology manufacturing in the future.Through this study, we can more intuitively understand the changes from carbon nanotubes to carbon nanotube composites, and infer the future development trend from the current situation.

The reduction and control of arsenic emissions from coal-fired power plants has become a global environmental issue. In this study, F-TiO2 photocatalytic adsorbent with special flower-like structure was prepared by a modified hydrothermal method using tetrabutyl titanate and propanetriol as precursors for the removal of liquid-phase arsenic from coal-fired power plants. The physical and chemical properties of the photocatalysts were characterized using characterization tools such as X-ray diffraction (XRD), scanning electron microscopy (SEM), and X-ray photoelectron spectroscopy (XPS). The results showed that the samples showed nanoflower clusters at microscopic level with the size around 1-2 μm, which are typical mesoporous structures and favorable for the adsorption and diffusion of reactants. UV photocatalytic arsenic removal experiments done on a homemade bench showed that F-TiO2 has a photocatalytic oxidation effect on liquid phase As(III), which can oxidize As(III) to As(V), thus reducing the toxicity of arsenic in desulfurization wastewater. This work provides a new method for the removal of liquid-phase arsenic from coal-fired power plants.

Nanomaterials have some remarkable thermal, optical, electrical properties showing excellent prospects in the application of cancer therapy, where nanomaterials can be ultilised to not only detect and inhibit tumors but also deliver the drugs. Organic and inorganic nanomaterials are mentioned in this research, including noble metal nanomaterials, magnetic nanomaterials, carbon nanomaterials, quantum dots, liposomes and polymeric nanomaterials. According to the features of different kinds of nanomaterials, different functionalisations can be designed such as the photoactive or photodynamics treating platforms to damage the tumor. The strengths and challenges of each types of nanomaterials applied in cancer treating indicates the a broad development space and research direction in the future.

Cancer is a serious and incurable disease. Rresearch has been devoted to cancer treatment for many years using a diverse of different treatment methods. However, traditional cancer therapies have many serious side effects. Bionanomaterials have the advantages of small volume, good biocompatibility and good biodegradability. Therefore, bionanomaterials have good application prospects in cancer treatment. Bionanomaterials can be used as cancer vaccines, checkpoint proteins inhibitors and the tumor microenvironment modulators for the cancer immunotherapy. Besides, bionanomaterials with photothermal conversion capacity can be prepared and used for photothermal cancer therapy. Although the number of the cancer therapies based on nanomaterials is increasing, the shortcomings of this type of cancer therapy such as low targeting abilities, large side effects and few clinical applications are still evident. The research summarizes the application of bionanomaterials in cancer immunotherapy and cancer photothermal therapy, and their limitations and future development directions will be analyzed.

Cellulose is the most abundant and widely distributed natural and renewable organic polymer in the world. The applications of cellulose derivatives in energy, electronics, environmental protection, biomedicine and food industries were reviewed, and the future development trend of cellulose was prospected.

To research and explore novel bio-based materials can effectively reduce the energy and environmental problems caused by the use of petrochemical products, and natural plant fiber materials are one of the novel bio-based materials. Cellulose is the most abundant and widely distributed renewable biological resource in the world. It is a low cost resource with good biological performance. However, due to its compact structure and the existence of hydrogen bonds between molecules, it is insoluble in most solvents, making it unable to be used well. Pre-treatment and modification of plant fibers is conducive to the realization of high-value utilization of plant fiber materials, and also helps to promote the sustainable development of human society.

Cancer has complex pathological mechanisms and is one of the leading diseases causing death. Traditional treatments (such as radiotherapy, chemotherapy and immunotherapy) have the limitations of low specificity and high cytotoxicity. As one of the emerging methods for cancer treatment, nano targeted drug delivery technology provides new options and prospects for cancer treatment. Nanoparticles (NPs) have higher specificity and can deliver drugs to the target site more efficiently. NPs have two modes of action on the tumor region: active targeting and passive targeting. Drug-delivery nanocarriers can also be classified into various types, organic nanocarriers and inorganic nanocarriers, which have been more extensively studied in cancer therapy. This research discusses the different delivery modes of nanodrugs in cancer therapy and a discussion of the different types of nanocarriers.

As a new semiconductor material, CsPbBr3 nanocrystal scintillator is widely used in light-emitting diodes, lasers and Xray imaging and detection fields. Compared with the traditional single crystal scintillators, it has many advantages, such as high absorption rate, photoluminescence intensity, short fluorescence lifetime (photoluminescence decay time), strong X-ray absorption capacity and radioluminescence (RL) intensity. The results show that CsPbBr3 nanocrystal has a strong absorption ability in ultraviolet region. The photoluminescence peak is located at 525 nm, which can be well matched with the photoelectric detection device. The width of half-peak is only 15 nm, which is a good candidate for laser materials. The photoluminescence decay time is 7.29 ns by fitting fluorescence decay curve, which is important for its application in dynamic fluorescence imaging. It can emit uniform green fluorescence under X-ray excitation, which has great application potential in the field of X-ray non-destructive testing.

Breast cancer has become a high incidence of cancer in women. The detection of breast cancer has become more and more important in recent years. A diverse of different treatment methods have widely used to detect breast cancer. The existing detection methods shows their own advantages and disadvantages. For example, the traditional chemotherapy has many side effects and great harm, which has seriously affected the quality of life of patients. The development of new cancer treatment methods has received increasing attention. Therefore, cancer treatment methods based on nanomaterials have been introduced, and good treatment effects have been achieved. This research describes three improved methods based on different nanomaterials that can replace chemotherapy. One is to improve trans-ferulic acid’s (TFA) efficiency by loading folate-receptor-targeted-poly lactic-co-glycolic acid nanoparticles (FA-PLGA-TFA NPs). Dox/FA-PLGA-TFA NPs reduced the side-effects of drugs, and at the same time showed superior anti-cancer performance and safety profile. The other is synthesized with biodegradable chitosan nanoparticles (CSNPs), which can selectively release DOX in the environment of breast tumors. In addition, CSNPs surface was modified with polyethylene glycol (PEG) to enhance its circulation time in the blood. Through this targeted nano drug delivery platform, it can be used to control dose-dependent system toxicity of freely ingested DOX.

Currently, the emergence of resistant pathogenic strains of conventional antibiotics poses an obstacle to disease treatment and an increasingly serious threat to human health. Antimicrobial peptides (AMPs) are considered to have promising applications in the pharmaceutical industry due to their advantages such as high antimicrobial activity, broad antimicrobial spectrum and the unlikelihood of resistance mutations in the target strains. Antimicrobial peptides, also known as host defense peptides (HDPs), are naturally occurring biomolecules. Certain antimicrobial peptides have great potential in the fight against microbial infections due to their ability to directly kill or inhibit bacterial activity and also potentially have the capacity to modulate the host's immune response. However, many limitations to the efficacy of antimicrobial peptides, including rapid degradation, systemic toxicity, and low bioavailability, make the search for improved approaches an urgent one. Advances in nanomedicine have provided ideas for the optimization of delivery systems for antimicrobial peptides, and research on a variety of targeted delivery systems using nanomaterials as carriers has made progress in the fight against microbial infections. This nanomaterial-based delivery method combines the good biocompatibility of delivery carriers with the unhindered activity of free AMP, which contributes greatly to the advancement of AMP therapy for clinical applications.

In order to investigate the influence of pressure holding time on certain burning tear gas mixtures’ aerosol particles characteristics, this paper uses a laser particle size analyzer to measure the aerosol particle size and its distribution, etc. The testing results can provide a theoretical basis for the evaluation of its performance.

In recent years, heavy metal pollution in water bodies has become more serious and has already had a significant impact on the ecological environment. Therefore, research on inorganic adsorbent materials has become very important. The author sorted out and analyzed the principle preparation and related applications of the current three inorganic adsorption materials, and summarized their applications in industrial production. And also discussed the current limitations of the magnetic nano-adsorption materials, Metal-based adsorbent and 4A zeolite adsorption material and their vision for future research.

In this study, the surface of waste rubber powder was activated and modified by sodium silicate through the chemical modification method, and the hydrophilic properties of the modified rubber powder were comprehensively analyzed based on characterization methods including the contact angle test, scanning electron microscopy (SEM), and infrared spectroscopy. According to the research results, the rubber powder modified by sodium silicate has a contact angle of 59.41°, indicating that the powder surface has changed from hydrophobic to hydrophilic after modification. The analysis of infrared spectroscopy, SEM and energy spectrum reveals that a semi-transparent resin film, which is hydrophilic, has been formed on the surface of modified rubber powder. The rubber powder activated and modified by sodium silicate can be added into composite materials.

According to the determination method of titration after distillation in Appendix C of JC 1066-2008 "Limits of Harmful Substances in Building Waterproof Coating", 5g sample was dissolved, the pH of solution was adjusted to alkaline, ammonia was distilled from alkaline solution, and the distillate containing excess sulfuric acid was titrated with sodium hydroxide standard solution. In order to explore whether the experimental factors in this standard affect the determination of ammonia content in waterproof coating, this paper carried out several validation tests on sample type, distillate temperature and pH factors.

In this paper, styrene-acrylic emulsion cement-based composite joint sealant is the research object, and the leveling test, the filling consistency test and the fixed extension test are carried out. The effect of the powder-liquid ratio (0.30, 0.35, 0.40, 0.45, 0.50, 0.55) on the working property and bonding property of the joint sealant is studied through indicators such as leveling, filling consistency, fixed elongation form and elastic recovery rate. Combined with the results of the mercury intrusion (MIP) test, the pore structure changes of joint sealant with different powder-liquid ratios are analyzed. The results show that the leveling property of joint sealant is not affected by changes in the powder-liquid ratio, and all have good leveling property. With the increase of powder-liquid ratio, the filling consistency of the joint sealant continues to increase, the bonding property gradually deteriorates, and the elastic recovery rate generally shows a downward trend. Comprehensive comparison of the working property and bonding property of different powder-liquid ratio joint sealant, the best powder-liquid ratio range of joint sealant is 0.35~0.40. With the increase of powder-liquid ratio, the pore volume of the joint sealant decreases, the pore size is refined, and the pore size distribution is optimized.

A modified composite ultrafiltration membrane was prepared using polyethersulfone as a polymer, polyvinylpyrrolidone as a pore-forming agent, N-N dimethylacetamide as a solvent, silver phosphate and polyhexamethylene biguanidine hydrochloride as antimicrobial agents. The morphology, hydrophilicity and water flux of the membrane materials were investigated by scanning electron microscopy, surface contact angle determination and antibacterial test. Their anti-fouling and antibacterial effects were investigated. The results showed that when the mass of silver phosphate was 0.5% of solvent mass and polyvinylpyrrolidone was 0.5% of solvent mass, the contact angle of the modified ultrafiltration membrane was 68.2° and the hydrophilicity was obviously improved and the antibacterial property was 90.54%.

Nano cellulose generally takes natural cellulose fermented by higher plants, algae and microorganisms as raw materials, and has many incomparable excellent properties, such as high crystallinity, ultra-fine nano network structure, high elastic modulus, high tensile strength, high permeability, degradability in the natural environment and good biocompatibility. At the same time, it has the chiral nematic properties of polymer materials. In recent years, nano cellulose has been widely used in electronic energy, medical field, composite adsorption materials, reinforcement materials and other fields. In this paper, the physical and chemical properties and applications of nano cellulose at home and abroad are summarized, in order to provide reference for scientific and technological research and development based on nano cellulose.

In this paper, we first introduce different types of natural species that can prevent hard and soft fouling or have self cleaning ability. Then, we state several theories that may be helpful to fabricate different kinds of fouling resisted surfaces. After that we introduce different types of fouling classified by a framework that based on the modulus, including hard fouling, marine fouling, and other types of fouling, and for each type, we state how to fabricate surfaces that can prevent it or lower the adhesion. Overall, we summarize different ways that can prevent fouling stick on the surfaces and it can also be a guide to direct manufacturing different fouling-resistant surfaces.

The clay components in machine-made sand mainly include 1:1 layer kaolinite, 2:1 layer montmorillonite and illite. In this paper, through experiments of mortar and concrete mixed with different clays, the influence of different clays on the dispersion performance of polycarboxylate superplasticizer and the strength and durability of concrete is studied, which provides a reference for the quality control of concrete using machine-made sand. It can be seen from the experimental results that montmorillonite, due to its multi-layer structure and large interlayer spacing, has the greatest impact on the dispersion performance of polycarboxylate superplasticizer and the strength and durability of concrete. In a certain amount, adding illite can improve the fluidity of mortar and concrete, and has little effect on the strength and durability of concrete.

Nanoparticle play an important role in drug delivery system. Traditional drug delivery methods have too many hinders that can overcome by embellishing nanopaticle. For those advantage, nanoparticle application in drug delivery system as research interest in great demand. This paper overview gold nanoparticle used as in protein nanocarriers and used in nucleic acid delivery. Meanwhile use less length to introduce the mesoporous nanoparticle in drug delivery to offer another think about nanoparticle using in drug delivery. Protein expression disorder or dysfunctional protein production leads to disease state. Restoring biological function by supplementing protein is the safest, most direct and most effective way to treat diseases, but their therapeutic use is limited because they are vulnerable to endosomal internalization, instability and immunogenicity. Efficient and universal system based on gold nanoparticle DNA has characteristics similar to nanoblocks, allowing any recombinant protein to be loaded and transported to mammalian living systems without additional modification. And as report, nucleic acid can treat some genetic disease and cancer, gold nanoparticle can be a good tool to delivery nucleic acid. In addition, mesoporous nanoparticle also can help some drug delivery system to control their release.

Polylactic acid (PLA) is a biodegradable material widely used in biomedical field. In order to improve the performance of pure PLA, 316 L stainless steel content of 10 vol% to 60 vol % was added to the PLA matrix. Powder physical characteristics tester was used to explore the angle of repose, the angle of collapse, the angle of flat plate and compressibility, the integrated thermal analyzer was for detecting thermal properties of PLA/316 L stainless steel composite. The results showed that the angle of repose and angle of collapse of the composite decreases first and then increases with the increase of 316 L stainless steel powder. The angle of flat plate and compressibility was reduced by the addition of steel powders. When the 316 L stainless steel powder content is 50 vol %, the fluidity of the composite is the best. The high melting point of 316 L stainless steel powder increases the melting point temperature of composites. The prepared PLA/316 L composites can be applied to 3D printing technology represented by fused deposition modeling (FDM).

In order to better predict the prognosis of lung adenocarcinoma (LUAD) and develop targeted gene therapy, we need to further understand the molecular mechanism of lung adenocarcinoma. The purpose of this study is to screen out biomarkers that may be related to the pathogenesis and prognosis of lung adenocarcinoma. Use limma package to find the differentially expressed genes (DGEs) related to LUAD from GSE7660, GSE32863, GSE75037, GSE116959 and LUAD data in the TCGA database downloaded from GEO. Using differential gene analysis method, a total of 189 overlapping differential genes were screened out. The KEGG pathway enrichment analysis using R clusterProfiler package found that 189 differential genes were concentrated in focal adhesion and proteoglycan enrichment in cancer. Using the protein-protein interaction (PPI) network, 10 pivot genes (TOP2A, AURKA, TK1, UBE2C, PRC1, ASPM, AURKB, TRIP13, CCNB2, MELK) were identified. Compared with normal tissues, the expressions of 10 hub genes in LUAD tissues are all up-regulated. Survival analysis of 10 hub genes showed that the high expression of TOP2A, PRC1, ASPM, TRIP13, and MELK in patients with lung adenocarcinoma was associated with survival (OS). In conclusion, our research shows that survival-related hub genes are highly correlated with the occurrence and development of lung cancer. Therefore, TOP2A, PRC1, ASPM, TRIP13, and MELK may play an important role in the progression of lung adenocarcinoma, and may become potential biomarkers for diagnosis and treatment in the future.

3D ultrasonic diagnostic equipment is widely used in medical institutions. “volume reconstruction” and “slice thickness” are the key technical parameters to evaluate the metrological performance of the equipment. However, until now no national or local metrological specifications applicable to 3D ultrasonic diagnostic equipment have been issued in China, and the corresponding traceability system of the equipment has not been established either. The novel calibration procedure presented in this article has been performed on several typical types of 3D ultrasonic diagnostic equipment widely used in China. The experimental results show that the calibration method presented in this article can be adopted for the periodic calibration of 3D ultrasonic diagnostic equipment, in order to establish the metrological traceability system of the equipment.

In order to solve the problem that the model volume in 3D printing exceeds the working volume of 3D printer, a 3D mesh model segmentation method based on skeleton and concave point plane fitting was proposed. Firstly, the skeleton is extracted based on Laplacian mesh smoothing method, and the segmentation points set is obtained according to the adjacency relation of points. Secondly, according to the concave-convex principle of three-dimensional mesh model and the curvature of points, the concave points that meet the conditions are solved and the concave point fitting plane is obtained by the least square method. Finally, the concave plane normal vector is used to complete the plane segmentation optimization of 3d model feature points. Experimental results show that the decomposition results of the algorithm can keep the integrity of the typical feature parts of the model, have good surface accuracy and can reduce the printing support consumables.

The problem of surface temperature measurement in metal additive manufacturing was investigated, and the metal heating process was monitored and tested using a color CCD camera based on the colorimetric temperature method. Stainless steel samples of 304 and 316 were selected and tested in oxygen and argon environments, respectively, while the surface temperature was measured using a pyrometer. The acquired images were processed and the emissivity ratios of the metals were calculated from the measured temperatures. The MRT(Mathematical multispectral radiation thermometry) model of metal emissivity was modified based on the acquired data to obtain a more suitable emissivity ratio model for the colorimetric temperature method and to reduce the temperature measurement error caused by the change of metal emissivity at different temperatures.

Lung cancer is one of the main reasons for death globally, with an impressive rate of about five million deadly cases per year. Detection of lung cancer at an early stage is necessary to prevent deaths and increase the survival rate. However, most of the current works on lung cancer detection stop at the algorithm level, leading to a lack of a platform that allows patients to get diagnosis results by simply uploading their CT scans. To slightly fill up the vacancy, this paper designs an auxiliary lung cancer diagnosis system based on deep learning techniques to detect lung cancer, provide associated knowledge and send out personal reports. Specifically, our system consists of two main parts, the lung cancer diagnosis assistant and the lung cancer diagnosis center. The lung cancer diagnosis assistant allows patients to upload CT scans and get their reports in the mailbox. Meanwhile, it encourages patients to learn medical knowledge about lung cancer. When detecting lung cancer, we first resample and resize the uploaded images to avoid the impact of picture quality differences and reduce memory consumption brought by 3D CT scans. Then, we construct our 3D convolutional neural network (3D CNN) model based on processed images. We verify the effectiveness of our model compared with a baseline model on the Data Science Bowl 2017 (DSB17) dataset, which is a lung cancer dataset consisting of over a thousand low-dose CT images from high-risk patients. The baseline model is a relatively simple model with only 2 convolutional layers. Our improved edition gets an accuracy of around 64%, 5% higher than the baseline model, which illustrates the effectiveness of our 3DCNN model. The experiment results and analyses demonstrate that our system is of practical value to detect lung cancer and provide extra services for patients.

With the improvement of the quality of human life, more and more people pay more attention to dental health. Panoramic CT image is an important method for studying teeth. The existing technology simply classifies or segment the teeth, and cannot accurately segment each tooth independently. The reason is that they did not consider the relationship between tooth type and location. Therefore, this article proposes a method that combines tooth position and tooth type. Mainly by adding the LSTM network to the extracted features to perform feature screening, and then perform classification, detection, and segmentation tasks. Experimental results show that the effective combination of CNN and RNN can accurately detect and segment teeth.

In recent years, to better distinguish skin cancer from common skin diseases, the CNN model, known for its precise classification, has been widely used in the clinical field to recognize skin cancer. However, since this model relies entirely on the given dataset used to train the model, it is hard for the model to recognize features, not in the given dataset. To tackle this issue, this paper proposed a CNN model to classify skin cancer, which has higher accuracy in classifying skin cancers with most of the features. This paper also proposed a web page where the CNN model and a chatbot (using Natural Language Processing) were used, providing people an interface for skin cancer diagnosis. In this experiment, we trained a CNN model with skin lesion images, enabling the model to classify malignant and benign skin cancers. The CNN model is trained with a dataset provided by ISIC, which includes 3297 images of skin cancer, consisting of 1800 benign samples and 1497 malignant samples. The experimental results demonstrated that the accuracy of this model in the classification of the test set achieves 88.04%. Furthermore, we note that the AUC value of this model also achieves 0.874, which seems to show that the model performs well.

Early diagnosis of skin cancer plays an important role in cure rate increasement, and deep learning models adopting neural networks for malignant & benign skin mole image classification obtained great accuracy. However, most of the existing works used the skin data in a brute force way that pays equal attention to both the skin lesion and irrelevant features that compromise overall accuracy and bring severe bias to the result, for instance, skin colour and hair around. To tackle these issues, this paper proposed a SE-CNN model, which adopted squeeze-and-excitation attention and can be utilized to accurately classify the property of skin moles between benign and malignant without severe bias efficiently. SE-CNN obtained similar performance using fewer parameters than other state-of-the-art models we evaluated in the experiment, including Resnet, Dense Net, Efficient Net. In this paper, the attention mechanisms we included in the experiment were introduced. After that, we developed our own CNN model, adopted different attention modules into our model, and analysed the performance. Experiments of performance comparison with widely used neural networks models are shown after the development section. At last, we embedded our SE-CNN model into a skin cancer doctor web application for malignant & benign skin mole image classification and adopted an Albert NLP model for symptoms discussion with the user.

In recent years, cancer has become one of the main causes of human death. If the disease develops to the middle and advanced stage, it is diffuclt to treat because the spread and mutation of cancer cells. But if the disease is detected and diagnosed at the early stage, the cure will become much easier. Therefore, research and development of early detection methods for cancer is of great importance. In view of some current cancer detection methods, electrochemical biosensors will be a good choice. Electrochemical biosensing technology has the characteristics of simple operation, low background signal, high sensitivity and easy miniaturization. It is a kind of diagnostic method which can replace the existing technology and has a good application prospect. This paper introduces several feasible electrochemical biosensors for detection of cancer biomarkers, including electrochemical biosensor constructed by peptide hydrogel, nanostructured superhydrophilic and superhydrophobic electrochemical biosensors, amperometric and impedance electrochemical biosensors, and electrochemical enzyme biosensor combined with various biological enzymes.

Plasmonic nanobiosensors have an enormous application range. It has the capacity to detect a wide variety of substances including metal, protein and even nucleic acids due to the superiority of SPR and LSPR. Plasmonic biosensors have been widely applied in the field of disease diagnosis, environmental conservation and food safety, eliminating barriers of traditional diagnosis methods and providing sensitive, quick and label-free devices. The applications of plasmonic biosensors in detection of many concerned diseases like cancer and SARS-CoV-2 are making an improvement on our medical condition. In the field of environmental protection, plasmonic-based biosensors also show great potential. They can efficiently detect two main types of contaminants, inorganic heavy metals involving Pb, Cd, As and Hg, and organic pollutants like polycyclic aromatic hydrocarbons (PAHs). Plasmonic biosensors could also overcome challenges on food allergen detection. This paper mainly focusses on SPR and LSPR-based nanobiosensors’ application in environmental protection, food safety and health-care.

Acupuncture is a critical part of Chinese Traditional medicine, and its efficacy has been proved by long-term clinical practice. However, the function effect on the neural system and mechanism of acupuncture manipulation (MA) is still unclear. The paper explores the mechanism of lifting-thrusting manipulations of acupuncture from an electroencephalogram (EEG) perspective. An experiment is implemented that acupuncture at Quchi point (LI11) is stimulated with two different manipulations (reinforcing and reducing manipulation), and EEG was collected during the stimulation. Then EEG features of brain rhythm are calculated to analyze the response of different acupuncture manipulation before and after stimulation. The research shows that compared with the pre-acupuncture period to the post-acupuncture period, the energy of the EEG signal decreased in the theta rhythm and increased in beta rhythm. Overall, this paper provides an insight into the quantification of brain electrical activity response of acupuncture manipulation in Qu Chi acupoint. The research results could present a quantitative study method for the neural response of MA stimulation．

Diabetic retinopathy (DR) severity grade depends on lesion types. Automatic lesion segmentation of DR on fundus image plays a key role in the diagnosis of DR. It is increasingly common that a model is trained by the images from different sources. While a model trained on the source domain is transferred to another (target domain), the performance of the model generally decreases. In this paper, a novel method was proposed for cross-domain segmentation of DR lesions by applying cycle-consistent adversarial networks (CycleGAN) and an improved Xception-based UNet named AttXUNet. To enhance the generalization ability of AttXUNet, the AttXUNet was trained on transformed dataset generated by CycleGAN for reducing the distribution difference between source domain and the target domain. We tested the proposed model on three datasets of fundus images, and the results demonstrated that our model could accurately segment DR lesions on fundus images and alleviate the degradation of segmentation performance on multiple target domains.