Application of Three-Dimensional Printing Technology in the Perioperative Management of Cardiac Tumours: A Review and Analysis.

Background: Multimodal imaging plays a crucial role in evaluating suspected cardiac tumours. In recent years, three-dimensional (3D) printing technology has continued to advance such that image-based 3D-printed models have been incorporated into the auxiliary diagnosis and treatment of cardiac tumour diseases. The purpose of this review is to analyze the existing literature on the application of 3D printing in cardiac tumour surgery to examine the current status of the application of this technology. Methods: By searching PubMed, Cochrane, Scopus and Google Scholar, as well as other resource databases, a completed review of the available literature was performed. Effect sizes from published studies were investigated, and results are presented concerning the use of 3D surgical planning in the management of cardiac tumours. Results: According to the reviewed literature, our study comes to the point that 3D printing is a valuable technique for planning surgery for cardiac tumours. As shown in the review report, Mucinous and sarcomatous tumours are the most commonly used tumours for 3D printing, magnetic resonance imaging (MRI) and computed tomography (CT) are the most commonly used technologies for preparing 3D printing models, the main printing technology is stereolithography, and the most used 3D modeling software is Mimics. The printing time and cost required for 3D printing are affected by factors such as the size of the type, complexity, the printed material and the 3D printing technology used. The reported research shows that 3D printing can understand the anatomy of complex tumour cases, virtual surgical simulation, as well as facilitate doctor-patient communication and clinical teaching. Conclusions: These results show that the development of 3D printing technology has brought more accurate and safe perioperative treatment options for patients with cardiac tumours. Therefore, 3D printing technology is expected to become a routine clinical diagnosis and treatment tool for cardiac tumours.

Predictive Model for National Minimal CFR during Spontaneous Initial Outbreak of Emerging Infectious Disease: Lessons from COVID-19 Pandemic in 214 Nations and Regions.

The minimal case fatality rate (CFR) is one of the essential fundaments for the establishment of a diverse national response strategy against the COVID-19 epidemic, but cannot be quantitatively predicted. The aim of the present study was to explore the applicable quantitative parameters labeling integrating responding capacity from national daily CFR curves, and whether the minimal CFR during initial emerging epidemic outbreaks can be predicted. We analyzed data from 214 nations and regions during the initial 2020 COVID-19 epidemic and found similar falling zones marked with two turning points within a fitting three-day-moving CFR curve which occurred for many nations and regions. The turning points can be quantified with parameters for the day duration (T1, T2, and ΔT) and for the three-day moving arithmetic average CFRs (CFR1, CFR2, and ΔCFR) under wave theory for 71 nations and regions after screening. Two prediction models of minimal CFR were established with multiple linear regressions (M1) and multi-order curve regressions (M2) after internal and external evaluation. Three kinds of falling zones could be classified in the other 71 nations and regions. Only the minimal CFR showed significant correlations with nine independent national indicators in 65 nations and regions with CFRs less than 7%. Model M1 showed that logarithmic population, births per 1000 people, and household size made significant positive contributions, and logarithmic GDP, percentage of population aged 65+ years, domestic general government health expenditure, physicians per 1000 people, nurses per 1000 people, and body mass index made negative contributions to the minimal CFR against COVID-19 epidemics for most nations and regions. The spontaneous minimal CFR was predicted well with model M1 for 57 nations and regions based on the nine national indicators (R2 = 0.5074), or with model M2 for 59 nations and regions based on the nine national indicators (R2 = 0.8008) at internal evaluation. The study confirmed that national spontaneous minimal CFR could be predicted with models successfully for most nations and regions against COVID-19 epidemics, which provides a critical method to predict the essential early evidence to evaluate the integrating responding capacity and establish national responding strategies reasonably for other emerging infectious diseases in the future.

A Ni-Doped Carbon Nanotube Sensor for Detecting Oil-Dissolved Gases in Transformers.

C2H2, C2H4, and C2H6 are important oil-dissolved gases in power transformers. Detection of the composition and content of oil-dissolved gases in transformers is very significant in the diagnosis and assessment of the state of transformer operations. The commonly used oil-gas analysis methods have many disadvantages, so this paper proposes a Ni-doped carbon nanotube (Ni-CNT) gas sensor to effectively detect oil-dissolved gases in a transformer. The gas-sensing properties of the sensor to C2H2, C2H4, and C2H6 were studied using the test device. Based on the density functional theory (DFT) the adsorption behaviors of the three gases on intrinsic carbon nanotubes (CNTs) and Ni-CNTs were calculated. The adsorption energy, charge transfer, and molecular frontier orbital of the adsorption system were also analyzed. Results showed that the sensitivity of the CNT sensor to the three kinds of gases was in the following order: C2H2 > C2H4 > C2H6. Moreover, the doped Ni improved the sensor response, and the sensor response and gas concentration have a good linear relationship.

Theoretical calculation of the gas-sensing properties of Pt-decorated carbon nanotubes.

The gas-sensing properties of Pt-decorated carbon nanotubes (CNTs), which provide a foundation for the fabrication of sensors, have been evaluated. In this study, we calculated the gas adsorption of Pt-decorated (8,0) single-wall CNTs (Pt-SWCNTs) with SO2, H2S, and CO using GGA/PW91 method based on density functional theory. The adsorption energies and the changes in geometric and electronic structures after absorption were comprehensively analyzed to estimate the responses of Pt-SWCNTs. Results indicated that Pt-SWCNTs can respond to the three gases. The electrical characteristics of Pt-SWCNTs show different changes after adsorption. Pt-SWCNTs donate electrons and increase the number of hole carriers after adsorbing SO2, thereby enhancing its conductivity. When H2S is adsorbed on CNTs, electrons are transferred from H2S to Pt-SWCNTs, converting Pt-SWCNTs from p-type to n-type sensors with improved conductivity. However, Pt-SWCNTs obtain electrons and show decreased conductivity when reacted with CO gas.