Zero-contrast percutaneous coronary interventions to preserve kidney function in patients with severe renal impairment and hemodialysis subjects

Introduction: Zero-contrast percutaneous coronary intervention (zero-PCI) is a new method for prevention of contrast-induced acute kidney injury (AKI) in patients with chronic kidney disease (CKD). However, evidence for its feasibility, safety and clinical utility is limited to reports of single cases or series of patients. Aim: To present outcomes of zero-PCI in patients with severe CKD, including hemodialysis subjects, who were treated with this procedure in order to preserve their renal function. Material and methods: Twenty-nine zero-PCIs were performed, mostly as a staged procedure, in 20 patients with advanced CKD. In this group, 4 patients were treated with hemodialysis but presented preserved residual renal function. The estimated median risk for contrast-induced AKI in non-dialysis patients was 26% (26–57%). Results: Zero-PCI was feasible in each intended patient, including those with complex left main stenosis or lesion within a saphenous vein graft, and there was no specific complication associated with this technique. After the procedure, the factual AKI prevalence was 10% and no patient required renal replacement therapy. Three of 4 hemodialysis patients preserved their residual renal function. During the median follow-up of 3.2 (1.2–5.3) months no patient experienced an acute coronary event or required revascularization. Conclusions: Zero-PCI is a safe and promising method to preserve renal function in patients with CKD and hemodialysis patients. Such an approach is feasible even in complex coronary lesions and yields good clinical outcomes in mid-term observation

Application of electronic dental dynamometer in biomechanics

The paper presents a microprocessor force recorder, whose design allows the measurement of occlusal forces on a continuous basis with a maximum frequency of 250 Hz. The electronic dental dynamometer records the results of clinical trials in the form of graphic images and text files which are presented directly onto the computer. The recorded results can be processed digitally, as well as compared with other measurements, resulting in the ability to monitor the progress and potential advances in the treatment of masticatory organ diseases. An important advantage of the proposed solution is the simple and intuitive design. In addition, the dynamometer requires no power coming directly from the electricity network, as it is powered through a 5 V USB port. This feature not only determines the comfort of use, but also the safety as the voltage does not pose a risk to the patient during examination. The results recorded during clinical trials using the electronic dental dynamometer are consistent with those obtained using a calibrated mechanical dental dynamometer

Application of electronic dental dynamometer in biomechanics

The paper presents a microprocessor force recorder, whose design allows the measurement of occlusal forces on a continuous basis with a maximum frequency of 250 Hz. The electronic dental dynamometer records the results of clinical trials in the form of graphic images and text files which are presented directly onto the computer. The recorded results can be processed digitally, as well as compared with other measurements, resulting in the ability to monitor the progress and potential advances in the treatment of masticatory organ diseases. An important advantage of the proposed solution is the simple and intuitive design. In addition, the dynamometer requires no power coming directly from the electricity network, as it is powered through a 5 V USB port. This feature not only determines the comfort of use, but also the safety as the voltage does not pose a risk to the patient during examination. The results recorded during clinical trials using the electronic dental dynamometer are consistent with those obtained using a calibrated mechanical dental dynamometer

The use of transfer learning with very deep convolutional neural network in quality management

PURPOSE: The aim of the article is to develop an algorithm for classifying cracks in the analyzed images using modern methods of deep machine learning and transfer learning based on pretrained convolutional neural network - Inception-ResNet-v2.DESIGN/METHODOLOGY/APPROACH: Transfer learning based on the pretrained convolutional neural network was used to categorize the images. The fully conected layer of the InceptionResNet-v2 network has been modified. The last layer was trained using a two-class (binary) linear SVM (Support Vector Machine). In total, 20,000 training cases (images) were used to train the fully connected layer within transfer learning process. The research analyzed the possibility of using the deep neural networks for quick and fully automatic identification of cracks / defects on the surface of analyzed parts.FINDINGS: The results indicate that pretrained convolutional neural network using SVM to train a fully connected layer is a very effective solution for visual crack / fault detection. In the analyzed model, a positive classification was obtained at the level of 99.89%.PRACTICAL IMPLICATIONS: The model presented in the article can be used in quality control carried out by process monitoring. An effective model for identifying defective parts can be used in both logistics and production processes.ORIGINALITY/VALUE: A novelty is the use of a freely available, deep neural network trained to classify 1000 categories of various images for binary categorization of faults (cracks). The algorithm was adjusted by replacing the primary, 1000-output fully connected layer in the Inception-ResNet-v2 network with a binary layer (2 categories). The fully connected layer has been trained using the classification version of the popular SVM learner, but thanks to the combination of this layer with the sophisticated fearure extraction ability of the pre-trained Inception-ResNet-v2 deep network, the resulting predictive model enables the classification of defects with a very high level of accuracy.peer-reviewe

Prevalence of the Onodi cell in the Polish adult population: an anatomical computed tomography study

Background: Onodi cell is a posterior ethmoid air cell with the optic canal bulging into it; the common position of the bulge is into the sphenoid sinus, usually immediately posterior to the posterior ethmoid air cells. Variable pneumatization patterns lead to various structures of lamellae and sinuses occasionally exposing important nerves and vessels, such as the optic and vidian nerves, internal carotid artery and cavernous sinus. In clinical practice, special imaging techniques are used to navigate through the paranasal sinuses and hence avoid injury to these structures. This study is aimed to determine the prevalence of the Onodi cell in the Polish population and compare it with other reported occurrences. Materials and methods: A retrospective analysis of 296 computed tomography (CT) scans of patients treated in Cracow, Poland, using a Siemens Somatom Sensation 16 spiral CT scanner. No contrast medium was administered. Results: The Onodi cell was found in 31 out of the 296 patients, or approximately 10.5%, consistent with the majority of research reporting on Onodi variants. Additionally, there was one presentation of a bilateral Onodi cell in a male patient. No statistically significant difference was found between the male and female populations with a positive identification of the variant (p = 0.095, Chi2 test). Conclusions: This study helped approximate the Onodi variant prevalence of 10.47%, falling within a commonly reported range 8-14%. This gives clinicians and surgeons a better understanding of this variant's structure and significance, and therefore an opportunity to improve treatment outcomes and research

Optimisation of milling parameters using neural network

The purpose of this study was to design and test an intelligent computer software developed with the purpose of increasing average productivity of milling not compromising the design features of the final product. The developed system generates optimal milling parameters based on the extent of tool wear. The introduced optimisation algorithm employs a multilayer model of a milling process developed in the artificial neural network. The input parameters for model training are the following: cutting speed vc, feed per tooth fz and the degree of tool wear measured by means of localised flank wear (VB3). The output parameter is the surface roughness of a machined surface Ra. Since the model in the neural network exhibits good approximation of functional relationships, it was applied to determine optimal milling parameters in changeable tool wear conditions (VB3) and stabilisation of surface roughness parameter Ra. Our solution enables constant control over surface roughness parameters and productivity of milling process after each assessment of tool condition. The recommended parameters, i.e. those which applied in milling ensure desired surface roughness and maximal productivity, are selected from all the parameters generated by the model. The developed software may constitute an expert system supporting a milling machine operator. In addition, the application may be installed on a mobile device (smartphone), connected to a tool wear diagnostics instrument and the machine tool controller in order to supply updated optimal parameters of milling. The presented solution facilitates tool life optimisation and decreasing tool change costs, particularly during prolonged operation

Implementation of artificial intelligence in optimisation of technological processes

This article introduces an algorithm for determining optimal parameters of a technological process. The objective function is the processing time of operations (efficiency) at the constraint of quality requirements of finish according to the designer specification. The problem of selecting a correct combination of processing parameters may only be solved when the cause-and-effect relationship between the finish quality and the machining settings is known. If the process considered for optimisation is repeatable, it appears economically viable to invest resources in the development of a model that would describe these relationships. To this end, we propose employing the artificial neural network trained on the progressions obtained from the tests. In the second stage, the Multiple-Input-Multiple-Output (MIMO) system, capable of representing relationships of nonlinear nature, was implemented for the optimisation of the objective function. The paper presents the application of the developed algorithm in determination of optimal parameters for the roller burnishing process of surface treatment. A technologist/software user defines the range of acceptable surface finishes. The optimisation algorithm determines a set of modifiable parameters that ensure minimal processing time at a specified surface finish requirements constraint