Modeling of Interstitial Microwave Hyperthermia for Hepatic Tumors Using Floating Sleeve Antenna

PurposeMicrowave hyperthermia is a treatment modality that uses microwaves to destroy cancer cells by increasing their temperature to 41- 45°C. This study aims to design, modeling, and simulation of a microwave sleeve antenna for hepatic (liver) hyperthermia. MethodThe designed antenna resonated at 2.45 GHz. The antenna was tested in six different 3D liver models: Model A: without a tumor and blood vessels; Model B: with a realistic tumor (2x3 cm) and without blood vessels; Model C: created by adding blood vessels to model B; Model D: created by adding a small tumor (1.5x1.5 cm) to model C and changed its location; Model E: same as model C with a different tumor size; Model F: model with a simple spherical tumor (1.5x1.5 cm).ResultsThe return loss of the antenna varied from -45 dB to -25 dB for the 6 models. The Specific Absorption Rate (SAR) was between 29 W/kg to 30W/kg in the tumors and below 24 W/Kg in the surrounding tissues. The tumors’ temperature elevated to 43- 45°C, while the temperature of the surrounding tissues was below 41°C.ConclusionsThe results showed the capability of the designed antenna to raise the temperature of hepatic tumors to the therapeutic ranges of hyperthermia.

Artificial Neural Networks and Deep Learning for Genomic Prediction of Binary, Ordinal, and Mixed Outcomes

In this chapter, we provide the main elements for implementing deep neural networks in Keras for binary, categorical, and mixed outcomes under feedforward networks as well as the main practical issues involved in implementing deep learning models with binary response variables. The same practical issues are provided for implementing deep neural networks with categorical and count traits under a univariate framework. We follow with a detailed assessment of information for implementing multivariate deep learning models for continuous, binary, categorical, count, and mixed outcomes. In all the examples given, the data came from plant breeding experiments including genomic data. The training process for binary, ordinal, count, and multivariate outcomes is similar to fitting DNN models with univariate continuous outcomes, since once we have the data to be trained, we need to (a) define the DNN model in Keras, (b) configure and compile the model, (c) fit the model, and finally, (d) evaluate the prediction performance in the testing set. In the next section, we provide illustrative examples of training DNN for binary outcomes in Keras R (Chollet and Allaire, Deep learning with R. Manning Publications, Manning Early Access Program (MEA), 2017; Allaire and Chollet, Keras: R interface to Keras’, 2019).

Speleonymia Slovenského rudohoria z pohľadu teórie modelovania

The article presents model analysis of non-standardized names of caves and chasms of the Slovenské rudohorie mountains which follows the model analysis of anoikonyms by Jana Pleskalová, as well as the work devoted to modelling of Slovak hydronymy. The model analysis pointed out the dominance of the relational model C which expresses “properties, symptoms (and circumstances)” and the relational model A which is associated with the expression of “position, the location of underground object in the field”. The most common structural model in the given relational models is the two-member ADd+S, consisting of a derivative adjective and a noun such as Jelšavská jaskyňa, Gajdova štôlňa. Relational models (VM) referring to possessivity (VM D), especially to the immediate expression of the type of object (VM B), only have a marginal position in the proposed model analysis.

Determination of the convective heat transfer constant (c and n) in a solar still

The geometry of a solar still determines the convection constants C and n, which in turn affect the convection heat transfer coefficient’s value and mass. A method for determining the value of convection heat transfer constants C and n has already been developed by the researchers. Therefore, this study aimed to use several methods and theories to find the value of convection heat transfer constants C and n. The results are then compared with the results of the study. The solar still used in this study has one slope. To reduce variables that cannot be controlled, the data collection was conducted indoors using a halogen lamp that can be regulated as a heat source for 24 hours nonstop. The sea surface height in the solar still was maintained at a height of 20 mm, using a height regulator. Temperature was measured using a data logger set to enter data every hour. The desalinised clean water was stored in bottles placed on scales that were recorded every one hour. Room temperature was maintained in the range of 35 to 36 oC. The data in this study were used to calculate the heat transfer constants C and n to obtain the value of the convection heat transfer coefficient and mass calculation. This study compares the calculation models of Tiwari, Dunkle and Power. The following calculation model results: Tiwari model, C = 0.082 and n = 0.612; Dunkle model, C = 0.075 and n = 1/3; Power model, C = 0.815 and n = 0.611. The C and n values obtained with these four approaches reveal that the results from the Power model calculation are the closest to the actual mass, showing a percentage deviation of 1.63%.

Description of Limit States in the Subsurface Layer of Loosened Subsoil in View of Critical State Soil Mechanics

The article aims to present an effective numerical method for the behaviour analysis and safety assessment of a subsurface layer of subsoil in the existing or predicted states of mining and post-mining deformations. Based on our own analytical record, using the equations of the Modified Cam-Clay model, the description of limit states in the subsurface layer of subsoil was validated, making it consistent with in situ observations. The said effect was demonstrated by comparing numerical analyses of the subsoil layer subjected to the limit state, using the Modified Cam-Clay (MCC) model and the Coulomb-Mohr model (C-M). The article also presents the applicability potential of the numerical analysis of the loosened subsoil layer for the assessment of protection elements (e.g., geo-matresses) used under linear structures in the areas subjected to mining and post-mining impacts.

Evolution of Chemical Composition and Modeling of Growth Nonmetallic Inclusions in Steel Containing Yttrium

The programs WYK_Stal and Bi-Growth, developed at AGH-UST, Kraków, Poland, were used for simulating the refining process, the formation of non-metallic inclusions, and their growth. The Fe-Y-Al-O-S-Ca system in pre-oxidized steel was analyzed, where yttrium formed precipitates from both O and S. When first Al and second Y were added to steel, the proportion of Al2O3 inclusions remained constant. This resulted in higher yttrium losses for oxide formation, whereas the sulfur content promoted sulfide phase formation. The introduction of yttrium at the end of refining contributed to reducing the consumption of this element in the non-metallic phase formation. The addition of aluminum and then calcium were sufficient to achieve a high degree of deoxidation and desulfurization. Calculations performed with WYK_Stal for both (a) and (c) versions of the model showed that the sulfide phase was constituted by CaS and FeS (model c) and CaS (model (a)). The participation of the calcium sulfide phase turned out to be dominant in the inclusions. Their presence was also identified in the slag phase. Simulations of the growth of complex oxide and oxo-sulfide inclusions using the Bi_Growth program showed that the yttrium content of the steel has a decisive role in the formation of complex oxide inclusions and the final oxygen content of the steel. In contrast, for the growth of oxide-sulfide inclusions, the character of growth is determined by the sulfur content of steel.

Displacement and stress distribution of the craniomaxillofacial complex under different surgical conditions: a three-dimensional finite element analysis of fracture mechanics

Objective To provide a simplified treatment strategy for patients with maxillary transverse deficiency. We investigated and compared the fracture mechanics and stress distribution of a midline palatal suture under dynamic loads during surgically-assisted rapid palatal expansion. Methods Based on the cone-beam computed tomography (CBCT) data of a 21-year-old female volunteer, a three-dimensional model of the cranio-maxillofacial complex (including the palatal suture) was constructed. A finite element analysis model was constructed based on meshwork. After the yield strength of the palatal suture was set, an increasing expansion force (0–500 N) was applied within 140 ms to calculate the time–load curve, which mimicked nonsurgical bone expansion (model A). The same method was used to evaluate the fracture process, time and stress distribution of the palatal suture in maxillary lateral osteotomy-assisted (model B) and LeFort osteomy I (LFIO)-assisted expansion of the maxillary arch (model C). Results Compared with model A, the palatal suture of model B and model C showed a faster stress accumulation rate and shorter fracture time, and the fracture time of model B and model C was almost identical. Compared with model A, we discovered that model B and model C showed greater lateral extension of the maxilla, and the difference was reflected mainly in the lower part of the maxilla, and there was no difference between model B and model C in lateral extension of the maxilla. Conclusions Compared with arch expansion using nonsurgical assistance (model A), arch expansion using maxillary lateral wall-osteotomy (model B) or LFIO had a faster rate of stress accumulation, shorter time of fracture of the palatal suture and increased lateral displacement of the maxilla. Compared with arch expansion using LFIO (model C), arch expansion using lateral osteotomy (model B) had a similar duration of palatal suture rupture and lateral maxillary extension. In view of the trauma and serious complications associated with LFIO, maxillary lateral wall-osteotomy could be considered a substitute for LFIO.

Research-ready data for multi-cohort analyses: The Dementias Platform UK (DPUK) C-Surv data model

Research-ready data (that curated to a defined standard) increases scientific opportunity and rigour by integrating the data environment. The development of research platforms has highlighted the value of research-ready data, particularly for multi-cohort analyses. Following user consultation, a standard data model (C-Surv), optimised for data discovery, was developed using data from 12 Dementias Platform UK (DPUK) population and clinical cohort studies. The model uses a four-tier nested structure based on 18 data themes selected according to user behaviour or technology. Standard variable naming conventions are applied to uniquely identify variables within the context of longitudinal studies. The data model was used to develop a harmonised dataset for 11 cohorts. This dataset populated the Cohort Explorer data discovery tool for assessing the feasibility of an analysis prior to making a data access request. It was concluded that developing and applying a standard data model (C-Surv) for research cohort data is feasible and useful.

EVALUATION OF ANTICONVULSANT ACTIVITY OF CALLISTEMON CITRINUS (CURTIS) SKEELS (MYRTACEAE) VOLATILE OIL

The use of medicinal plants is on the rise due to the increase of various diseases and shortcomings of orthodox medicine. For many ailments including convulsion, conventional medicine has not been able to find a lasting solution. This study was directed towards assessing the ethnomedicinal use of Callistemon citrinus leaves in the management of convulsion. The volatile oil of the leaves was extracted and an acute toxicity test was carried out following Lorke’s description. Maximal electroshock (MES), strychnine and pentylenetetrazol anticonvulsant methods were used. Separate groups of albino mice were given 200, 400 and 800 mg/kg doses of the volatile oil. Drug solutions; 30 mg/kg phenobarbitone for MES and 2 mg/kg diazepam for strychnine and pentylenetetrazol models were administered as a positive control. The start of tonic leg extension, duration and percentage mortality was recorded. Doses of 200 and 400 mg/kg significantly (P<0.05) inhibited seizure in the mice with scores of 40 % each in the MES model. There was a dose-dependent reduction in the duration of seizures with 68.47, 70.27 and 81.08 % reductions in the pentylenetetrazol model. No significant coverage was given in the strychnine model. C. citrinus oil protected the mice against pentylenetetrazol and maximal electroshock-induced convulsion hence could contribute to the medical treatment of epilepsy.

Abstract 1122‐000046: A Latin American Model of the PHASES Score

Introduction : The PHASES score was developed to predict the 5‐year risk of rupture for intracranial aneurysms (IAs). However, only populations from North America, Europe, and Japan were included in the original study. As the population of origin is an item in the score, it has yet to be applied in a Latin American population. We aimed to determine the best approximation to employ this model in this previously unstudied population. Methods : We extracted the data of 848 Peruvian patients with ruptured (n = 486) and unruptured (n = 362) IAs from 2010 to 2020. According to the PHASES score, the North American and European (other than Finish), Japanese, and Finnish populations are rated with 0, 3 and 5 points, respectively. Therefore, we developed three PHASES‐derived models in which our Peruvian population is rated with 0 (Model A), 3 (Model B), and 5 (Model C) points. We compared the observed probability of each model to the expected probability reported by the original PHASES score using a scatter plot. We then compared the goodness‐of‐fit of each model using the Hosmer‐Lemeshow test in STATA version 14. Results : Nineteen percent of the patients were female. Hypertension was found in 34% of patients and 15% were >70 years. Fifty‐four percent of the aneurysms were smaller than 7mm, 25% ranged between 7 and 9.9mm, 18% were between 10 and 19.9mm, and 3% were larger than 20mm. Previous subarachnoid hemorrhage was found in 4%. The location of the aneurysms was the internal carotid artery in 4%, the middle cerebral artery in 4%, and arteries of the anterior and posterior circulation (including the anterior and posterior communicating artery) in 92%. When Model A was applied, 63% of the patients among the ruptured subgroup have an estimated 5‐year risk of rupture of <3% while 77% of the patients have an estimated risk of <3% in the unruptured subgroup. When Model B was applied, 30% of the patients among the ruptured subgroup have an estimated 5‐year risk of rupture of <3% and 42% of patients among the unruptured subgroup have an estimated risk of <3%. When Model C was applied, 96% of the patients among the ruptured subgroup have an estimated 5‐year risk of rupture of >3% while in the unruptured subgroup an estimated risk of <3% was observed only in 4% of the patients. When comparing observed to expected frequencies, model B presented a better calibration to the values reported by the original PHASES score. Additionally, the Hosmer‐Lemeshow showed Model B to have improved goodness‐of‐fit, compared to other models, although all presented adequate fit. Conclusions : We found that rating the Peruvian population with 3 points was the best approximation to the estimated risk calculated by the PHASES score to predict the 5‐year risk of rupture for IAs.