Characterization and in-vitro assessment of silicon-based apatite microspheres for bone tissue engineering applications.

In the field of bone tissue engineering, silicon (Si) has been found as an essential element for bone growth. However, the use of silicon in bioceramics microspheres remains limited. In this work, different weight percentages (0.8, 1.6, and 2.4 wt %) of silicon was incorporated into hydroxyapatite and fabricated into microspheres. 2.4 wt % of Si incorporated into HAp microspheres (2.4 SiHAp) were found to enhance functional properties of the microspheres which resulted in improved cell viability of human mesenchymal stem cells (hMSCs), demonstrating rapid cell proliferation rates resulting in high cell density accumulated on the surface of the microspheres which in turn permitted better hMSCs differentiation into osteoblasts when validated by bone marker assays (Type I collagen, alkaline phosphatase, osteocalcin, and osteopontin) compared to apatite microspheres of lower wt % of Si incorporated and non-substituted HAp (2.4 SiHAp >1.6 SiHAp >0.8 SiHAp > HAp). SEM images displayed the densest cell population on 2.4 SiHAp surfaces with the greatest degree of cell stretching and bridging between neighboring microspheres. Incorporation of silicon into apatite microspheres was found to accelerate the rate and number of apatite nucleation sites formed when subjected to physiological conditions improving the interface between the microsphere scaffolds and bone forming cells, facilitating better adhesion and proliferation.

PTC-MAS: A Deep Learning-Based Preoperative Automatic Assessment of Lymph Node Metastasis in Primary Thyroid Cancer.

BACKGROUND: Identifying cervical lymph node metastasis (LNM) in primary thyroid cancer preoperatively using ultrasound is challenging. Therefore, a non-invasive method is needed to assess LNM accurately. PURPOSE: To address this need, we developed the Primary Thyroid Cancer Lymph Node Metastasis Assessment System (PTC-MAS), a transfer learning-based and B-mode ultrasound images-based automatic assessment system for assessing LNM in primary thyroid cancer. METHODS: The system has two parts: YOLO Thyroid Nodule Recognition System (YOLOS) for obtaining regions of interest (ROIs) of nodules, and LMM assessment system for building the LNM assessment system using transfer learning and majority voting with extracted ROIs as input. We retained the relative size features of nodules to improve the system's performance. RESULTS: We evaluated three transfer learning-based neural networks (DenseNet, ResNet, and GoogLeNet) and majority voting, which had the area under the curves (AUCs) of 0.802, 0.837, 0.823, and 0.858, respectively. Method III preserved relative size features and achieved higher AUCs than Method II, which fixed nodule size. YOLOS achieved high precision and sensitivity on a test set, indicating its potential for ROIs extraction. CONCLUSIONS: Our proposed PTC-MAS system effectively assesses primary thyroid cancer LNM based on preserving nodule relative size features. It has potential for guiding treatment modalities and avoiding inaccurate ultrasound results due to tracheal interference.

Analysis and Prediction of Influence Factors of Green Computing on Carbon Cycle Process in Smart City.

Global warming has become the focus of attention of the international community, and the control of carbon dioxide emissions has become one of the necessary choices for the development strategies of countries around the world. Cities are places where carbon dioxide emissions are concentrated. The key to controlling carbon emissions is to control the carbon emissions of cities. My country is currently in the process of rapid urbanization. Quantitative studies of the carbon cycle at the city level will help to take stock of carbon dioxide emissions in cities. On the other hand, it is helpful to understand the status and role of the urban carbon cycle in the process of the regional carbon cycle. Through the analysis and prediction of the elements influencing the carbon cycle of smart cities, this paper first determines the factors affecting smart cities in the carbon cycle process as industrial carbon emission strength factors, industrial structure effects, economic development factors, and population elements. It is found that the major positive factors affecting the significant add of CO2 emissions in smart cities from 2010 to 2019 are economic development factors and demographic factors, including economic development factors GDP/per capita GDP. The per capita contribution to CO2 emissions is higher than the model established by adjusting the affecting elements of overall CO2 emissions, except that the proportion of economic development factors in total CO2 emissions from 2013 to 2015 was lower than the increase in total CO2 emissions. The comparison can better reflect the relation between CO2 emissions and influencing elements. The main determinants affecting CO2 emissions are the expansion of the financial condition, the increase in the average daily population, and the increase in construction work. The adaptation index is judged to be consistent, indicating that the model adjustment effect is good; finally, the green computing in the smart city predicts the carbon cycle process, and the actual value trend line and the predicted value trend line are not much different from the practical value, the forecast error is small, and the prediction results are credible. Global warming has become the focus of attention of the international community, and carbon emission control has become one of the necessary options in the development strategies of countries around the world. Cities are the places where carbon emissions are concentrated. The key to controlling carbon emissions is to control urban carbon emissions. At present, my country is in the process of rapid urbanization. Quantitative research on the carbon cycle at the city level will help to establish an inventory accounting of urban carbon emissions. On the other hand, it is convenient to deeply understand the status and role of the urban carbon cycle in the process of the regional carbon cycle.