Emergence Of New Classes Research Articles

Molecular diagnostics of familial hypercholesterolemia in Russia: yesterday, today and tomorrow

Familial hypercholesterolemia is a severe hereditary disease leading to the development of atherosclerosis and its complications in the form of angina pectoris, myocardial infarction, cerebral stroke, or even leading to sudden death. Since the description of the disease, the concept of it has undergone significant evolution. First, it became clear that the prevalence of this disease was significantly higher than originally thought (1:300 for heterozygous familial hypercholesterolemia and not as 1:500 as estimated earlier). Secondly, it has been established that it is not based on the pathology of the low-density lipoprotein receptor gene alone, but includes at least four monogenic forms (defects of the APOB, PCSK9, ARH genes) and may also have a multigenic nature. Thirdly, with the development of DNA analysis methods from the initially available Southern hybridization to next generation DNA sequencing, the exceptional molecular heterogeneity of familial hypercholesterolemia became obvious and, accordingly, the need to establish national spectra of mutations leading to the development of familial hypercholesterolemia was established. Researchers have moved from characterizing individual mutations to creating national registries and databases. Finally, research into the genetics of familial hypercholesterolemia has led to the emergence of new classes of cholesterol-lowering drugs. In Russia, molecular diagnostics of familial hypercholesterolemia has also undergone significant changes since the beginning of the study of familial hypercholesterolemia in 1987 and to the present, consideration of these changes formed the basis of this review.

Nematode Extracellular Protein Interactome Expands Connections between Signaling Pathways.

Multicellularity was accompanied by the emergence of new classes of cell surface and secreted proteins. The nematode C. elegans is a favorable model to study cell surface interactomes, given its well-defined and stereotyped cell types and intercellular contacts. Here we report our C. elegans extracellular interactome dataset, the largest yet for an invertebrate. Most of these interactions were unknown, despite recent datasets for flies and humans, as our collection contains a larger selection of protein families. We uncover new interactions for all four major axon guidance pathways, including ectodomain interactions between three of the pathways. We demonstrate that a protein family known to maintain axon locations are secreted receptors for insulins. We reveal novel interactions of cystine-knot proteins with putative signaling receptors, which may extend the study of neurotrophins and growth-factor-mediated functions to nematodes. Finally, our dataset provides insights into human disease mechanisms and how extracellular interactions may help establish connectomes.

Diffuse large B-cell lymphoma and follicular lymphoma: problem state in Russia

Background. Non-Hodgkin's lymphomas (NHL) are a heterogeneous group of hematological malignancies, the vast majority of which are B-cell tumors. The most common variant of NHL is diffuse large B-cell lymphoma (DLBCL), characterized by an aggressive course, which accounts for 30–40% of all NHL. The second most common is follicular lymphoma (FL), traditionally classified as an indolent variant accounting for up to 25% of all NHL. Current therapies for these lymphoproliferative disorders which includes innovative drugs in the 1st line of therapy (LT) have demonstrated high efficacy. However, some patients develop a relapse or a refractory disease. Despite recent significant progress in the development and implementation of innovative targeted drugs, in most cases, it is not possible to achieve persistent long-term remissions after disease relapse, which leaves patients with an unmet need for effective and well-tolerated treatment options. Aim. To obtain objective data on the incidence, clinical course, and effectiveness of therapy for the most common variants of NHL in real-world practice in Russia. Materials and methods. From February to March 2023, 130 hematologists and oncologists from 30 regions of Russia were surveyed to update the data on DLBCL and FL. Results. Over the past 12 months, 5,689 patients with NHL were observed, of which 56% had DLBCL; 62% of them received the 1st LT, 22% received the 2nd LT, 10% received the 3rd LT, and only a few reached later lines. Analysis of the administered treatment options in the 2nd and 3rd LTs shows that there is no standard of care for this population, and the effectiveness of the regimens used in real-world Russian practice is extremely low. FL accounted for 23% of all 5,689 patients with NHL, of which 56% were newly diagnosed and 44% received treatment for relapse. The majority of patients with FL who received ≥3 LTs had an inferior prognosis and rapid disease progression: the median time from diagnosis to the beginning of the 3rd LT was only 26.4 months. The analysis of treatment options for patients with relapsed FL indicates a lack of standard and effective therapies. Conclusion. Relapsed and refractory DLBCL and FL represent a complex clinical situation where the main goal of treatment is disease control due to the impossibility of achieving stable remissions with existing treatment options. Clinicians with great hope are looking to the emergence of new classes of drugs that will be able to improve the prognosis for this complex population.

Online learning for streaming data classification in nonstationary environments

AbstractIn this article, we implement the classification of nonstationary streaming data. Due to the inability to obtain full data in the context of streaming data, we adopt a strategy based on clustering structure for data classification. Specifically, this strategy involves dynamically maintaining clustering structures to update the model, thereby updating the objective function for classification. Simultaneously, incoming samples are monitored in real‐time to identify the emergence of new classes or the presence of outliers. Moreover, this strategy can also deal with the concept drift problem, where the distribution of data changes with the inflow of data. Regarding the handling of novel instances, we introduce a buffer analysis mechanism to delay their processing, which in turn improves the prediction performance of the model. In the process of model updating, we also introduce a novel renewable strategy for the covariance matrix. Numerical simulations and experiments on datasets show that our method has significant advantages.

Audio signal transmission method in network-based audio analytics system

The subject matter of the article is аudio signal transmission method in network-based audio analytics system. The creation of a network-based audio analytics system leads to the emergence of new classes of load sources that transmit packetized sound data. Therefore, without constructing adequate mathematical models, it is impossible to build a well-functioning network-based audio analytics system. A fundamental question in traffic theory is the question of load source models. The development of an method for transmitting audio signals in a network-based audio analytics system becomes necessary. Based on this, the goal of the work is to create methods an method for transmitting audio signals in a network-based audio analytics system to ensure efficiency and accuracy in audio analytics. The following tasks were solved in the article: the formation of a model for the system's load sources, investigation of connection and traffic management, implementation of control and traffic monitoring functions in the network, research of methods to ensure the quality of audio signal transmission and the development of a method of transmitting an audio signal by virtual routes switching. To achieve these goals, the following methods are used: mathematical signal processing, data compression algorithms, optimization of network protocols, and the use of high-speed network connections. The obtained results include modeling of the system's load sources, examination of connection and traffic management, investigation of methods to ensure the quality of audio signal transmission and a method of transmitting an audio signal by virtual routes switching was proposed. In conclusion, the possibilities of using simulation modeling of nodes in the network-based audio analytics system are highly limited. This is explained by the fact that the acceptable level of information loss in data centers is very low. The use of the developed method enables effective control and processing of sound information in real-time. This method can find broad applications in various fields, including security, healthcare, management systems, and other industries where the analysis of audio signals is a crucial element.

An adaptive classification and updating method for unknown network traffic in open environments

In recent years, to address the classification problem of unknown traffic in open network environments, numerous deep learning (DL)-based classification techniques have been proposed. Among these techniques, the single threshold-based DL method has gained significant popularity. However, the classification boundary constructed by this method is inaccurate, thus leading to the misclassification of known and unknown traffic. In this paper, we propose an adaptive classification and updating method for accurate application-level classification of known and unknown traffic in open environments. We adaptively construct individualized and accurate decision boundaries for each class of known traffic, which avoids misclassification cases caused by a single threshold and achieves accurate separation of known and unknown traffic. To provide a more accurate feature representation for the construction of the decision boundary, we also introduce an additional angular loss function to guide the model in learning more accurate flow features. In addition, to cope with the constant emergence of new classes of traffic in real network environments, our proposed method adaptively updates the model to respond to changes in network traffic classes. The proposed method is evaluated on two datasets, and the experimental results show that our proposed method has excellent classification accuracy and outperforms the state-of-the-art unknown traffic classification methods.

Contemporary Multiple Myeloma Treatment Patterns Among HCPs and Concordance with Expert Recommendations: Analysis of an Interactive Online Decision Support Tool

Background: The management of multiple myeloma (MM) continues to evolve with the emergence of new classes of therapy, most recently including BCMA-targeted therapies. Considering the expanding MM treatment armamentarium, we sought to identify contemporary practice patterns among healthcare professionals (HCPs) for MM and compare them with those of experts using an online decision support tool. Methods: An online decision support tool was developed with input from 5 experts providing therapy recommendations for 192 unique patient case scenarios based on considerations including disease setting, treatment history, access to therapy, and specific comorbidities. HCP tool users entered specific patient criteria to define a case along with their intended management for that case. The tool then showed the 5 expert recommendations for that same case scenario, and users were asked if the recommendations impacted their intended approach. An analysis of expert recommendations and user-selected therapy was performed. Results: Between April 2023 and July 2023, 207 patient cases were entered by 144 participating HCPs. HCP and expert treatment recommendations were frequently discordant, regardless of treatment setting (Table). The highest concordance between HCP and expert recommendations (47%; P <.0001) was in the induction setting (n = 111). In this setting, experts were more likely to recommend quadruplet regimens compared with HCPs (62% vs 27%, respectively; P <.0001). Treatment selection concordance was lower in the maintenance (n = 17; 24%; P <.0001) and relapsed/refractory settings (n = 79; 20%; P <.0001). The lowest concordance (10%; P <.0001) between HCP and expert treatment recommendations was noted in the relapsed/refractory setting specifically among patients who were refractory to earlier lines of therapy (n = 41; P <.0001). For patients who are refractory to earlier lines of therapy, experts were more likely to recommend an anti-CD38-based combination regimen compared with HCPs (81% vs 32; P <.0001). When recommending treatment for patients with triple- or penta-refractory disease, experts were significantly more likely to recommend BCMA-targeted agents (83% vs 39%; P <.0001). For HCPs whose treatment plan did not match expert recommendations in these scenarios, 53% of respondents indicated that expert recommendations confirmed or changed their intended therapy, but 31% indicated that there were barriers to implementing those recommendations. Conclusions: These data suggest ongoing challenges with incorporating the newest therapies and combinations across the spectrum of MM into patient care, particularly multidrug regimens in the induction and maintenance settings and BCMA-targeted therapies in those with triple- or penta-refractory disease. Continued education and development of resources for HCPs, including online decision support tools, may be increasingly important as the treatment of MM continues to grow in complexity.

In vitro comparison of the acetylcholinesterase inhibition caused by V- and A-series nerve agents’ surrogates

Nerve agents (NA) pose as a great risk in the modern world. NA from the V-series, such as VX, are currently recognized as the most toxic among those compounds. However, the emergence of new classes of toxicants recently included in the Chemical Weapons Convention (CWC), such as the A-series NA, a class of organophosphorus compounds related to phosphoramidates, pose a new source of concern due to the lack of information. In order advance in the investigation on the toxicity of such toxic chemicals, we performed in vitro studies to compare representatives of the V- and A-series using affordable surrogates. Results suggest a similar inhibition potency between both agents.

Update of the interstate standard “Reinforced concrete slabs for tramway track pavements”

Introduction. Development of urban public transport networks in Russian cities involves modernization and improvement of rail transport to increase its traveling comfort, carrying capacity and reliability. Tram transport is environmentally friendly and has a high passenger capacity. The combination as well as the intersection of rail and pneumatic transport in the urban environment is considered to be an urgent issue. In this regard, State Standard 19231 “Reinforced concrete slabs for tramway track pavements. Specifications” is needed to be revised and updated due to changes in the requirements for concrete and emergence of new classes of steel reinforcement.Aim. To update State Standard 19231 in order to bring products for tram infrastructure to the proper regulatory and technological parameters in compliance with current requirements.Materials and methods. The present study involves an analysis of existing regulatory documents in the field of reinforced concrete materials and products, as well as a bibliographical research of works and expert opinions.Results. Updating the standard resulted in the establishment of specifications that meet the current demand, both in terms of regulations and scientific research.Conclusions. The updated interstate State Standard 19231 will contribute to a higher quality design and production of reinforced concrete slabs for tramway pavements, which will surely enhance their reliability and durability.

ПРО КОНЦЕПЦІЮ ОРГАНІЗАЦІЇ БОРОТЬБИ З БЕЗПІЛОТНИМИ ЛІТАЛЬНИМИ АПАРАТАМИ В МІСЦЯХ ЗБЕРІГАННЯ ЗАСОБІВ УРАЖЕННЯ

The history of small-sized, low-speed unmanned aerial vehicles, in contrast to the history of unmanned aerial vehicles, is not counted for half a century, but only for two dozen years. The first publications devoted to small-sized and low-speed unmanned aerial vehicles were published after 2008. The number of publications on the specified subject significantly increased in 2017-2018. It is during this period that military operations using small-sized and low-speed unmanned aerial vehicles become widespread. As an example, we can mention the attacks on the oil refinery in Saudi Arabia in September 2019, the attack on the airport in the city of Jizan in the same Saudi Arabia in July 2020, the attacks on the Russian air base in the Syrian city of Khmeimi in 2019-2020. Almost every day, from the regions of the planet where armed conflict is taking place, there are reports of the destruction of storage places of weapons and means of destruction, which affects the course of armed conflicts. It is significant that unmanned aircraft are increasingly being used to destroy weapons. The article highlights the factors that determine the effectiveness of the use of unmanned aerial systems for the destruction of weapons storage sites, and predicted dynamic changes in these factors. The rapid development of microelectronic technologies, which is currently observed, became the basis for the improvement of unmanned aircraft systems, the expansion of the range of tasks performed by unmanned aircraft, the emergence of new classes of devices and the improvement of the tactics of their use. The rapid development of microelectronics is also considered from the point of view of confirming Moore's law, forecasts for the near future, clarifications of the law for the present time, clarifications of forecasts for the next 15 years. Conceptually, the opinions of the authors regarding the minimization of losses of weapons of destruction are presented, the basis of which are their own observations of the statistics of losses from storage sites of weapons of destruction of the Armed Forces of Ukraine for the period from 2003 to 2018, the causes of losses. Keywords: means of defeat, unmanned aerial vehicle, storage places, logistic security.

DirectVoxGO++: Grid-based fast object reconstruction using radiance fields

In recent years, the computer vision and computer graphics communities have seen the emergence of new classes of models for representing objects based on Neural Radiance Fields (NeRFs). These techniques use ideas based on traditional methods from computer graphics, for example, radiance fields, and combine them with implicit neural representations and neural image-based rendering (IBR). However, one significant drawback of this class of techniques was that they were too slow, taking in the range of hours in high-end GPUs. Due to these limitations, new techniques have been created for the fast reconstruction of scenes, such as Direct Voxel Grid Optimization (DirectVoxGO). Alongside this limitation, initially, NeRFs had limited compositing and modeling capabilities. For example, a simple task such as separating the background from the foreground could not be modeled with NeRFs until the emergence of new techniques such as NeRF++. Our method extends DirectVoxGO to allow the handling of unbounded scenes inspired by some ideas from NeRF++ and also adapts it to incorporate elements from a neural hashing approach employed by other works. Our technique improved photorealism compared with DirectVoxGO and Plenoxels on a subset of the Light Field Dataset on average in at least 2%, 8%, and 8% for Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM), and Learned Perceptual Image Patch Similarity (LPIPS) metrics, respectively, while also being an order of magnitude faster than NeRF++. Also, we demonstrate that, for the evaluated scenes, our technique has comparable training time and memory consumption than previous works. Code is available in https://github.com/danperazzo/dvgoplusplus.

A Data Stream Dynamic Classification Method with Control of the Decision-making Area

A dynamic classification problem in which a change with time in the data classified is assumed is considered. Data streams, such as computer network data, sensor data, bank transactions, etc., are characterized by problems of data drift, the emergence of new classes, and anomalies. The existing data streams classification methods are analyzed. It is pointed out that there is no a single and effective classification method that would simultaneously take into account the problems of anomaly detection, drift, and model adaptation to new data. The importance of controlling the decision-making area of classifiers for obtaining a high-quality solution of the problem is noted. A dynamic classification method based on a scalable ensemble of autoencoders with a decision-making area controlled using the EDCAP criterion is proposed. The autoencoder properties are used to solve the problems of detecting drift, anomalies and new classes. The ensemble autoencoders were trained to recognize a single class. Based on the EDCAP criterion, the size of the recognition area of each autoencoder was controlled. The classification result is based on analyzing the responses of the ensemble's all autoencoders. When a new class of data is detected, the ensemble is scaled by adding a new autoencoder. When a drift is detected, only the corresponding autoencoders are retrained. The qualities of the proposed dynamic classifier and an incremental algorithm based on an adaptive Hoeffding tree are compared. The advantages of the proposed method are demonstrated on the example of a synthetic data stream that includes drift, a new class, and anomalies.

A federated learning and blockchain framework for physiological signal classification based on continual learning

A key challenge of physiological signal processing in the Internet of Medical Things is that physiological signals usually have dynamic distribution changes. Another challenge is that patients' data is vulnerable to disclosures. To address these problems, we propose a meta continual learning method, called MetaCL. MetaCL consists of a shared feature extractor, a knowledge base, a micro classifier module and a blockchain module. The shared feature extractor adopts a horizontal federated learning mechanism to prevent data leakage. The knowledge base is built and updated based on a Splite-based method to overcome catastrophic forgetting. The micro classifier module uses a mean-based model transfer method to adapt to the emergence of new classes. It integrates a Kullback-Leibler divergence regularization to the loss function to deal with fuzzy boundaries of classes. The blockchain module is designed based on the Alliance chain to protect the privacy of the classification results. Experiment results show that refinement classification performance of MetaCL is 98.35%, which outperforms the compared state-of-the-art works. The backward transfer under four increment scenarios is all within -2.6%. At last, a blockchain-based engineering application is presented to show that MetaCL can prevent privacy protection in the Internet of Medical Things (IoMT).

THE PROBLEMS OF CONSTRUCTING THE FUTURE: LEFT-WING PHILOSOPHERS VS NEOLIBERALISM

The purpose of this study is to analyze the different projects of the future by thinkers who adhere to the left-wing ideology. Two directions have been established for the development of an image of the future that is intended to replace the unjust socio-economic relations of today. The first direction is related to the demonstration of the shortcomings of the practical implementation of liberalism and neoliberalism, which has taken the outlines of neo-feudalism. The second direction emphasizes the gradual rebirth of capitalism into post-capitalism through the emergence of new classes of precariat, personhood and impersonality, and the emergence of people who exist as individuals. This situation actualizes the search for new ideological approaches in accordance with the changed reality. In addition, more and more evidence confirms the need to reassess the nature and functions of the economy in favor of the principles of post-growth. The possibility of combining the ideas of P. Mason, N. Srnicek and A. Williams to replace capitalism with post-capitalism by expanding the use of information and communication technologies and automation on this basis of all economic activities; reducing working time without reducing its pay; introducing an unconditional basic income and eliminating outdated approaches to work ethics is shown. The implementation of the above approaches can contribute to the establishment of a new counter-hegemonic social order, which will be an alternative to the market economy.

Detection of Load-Altering Cyberattacks Targeting Peak Shaving Using Residential Electric Water Heaters

The rapid adoption of the smart grid’s nascent load-management capabilities, such as demand-side management and smart home systems, and the emergence of new classes of controllable high-wattage loads, such as energy storage systems and electric vehicles, magnify the smart grid’s exposure to load-altering cyberattacks. These attacks aim at disrupting power grid services by staging a synchronized activation/deactivation of numerous customers’ high-wattage appliances. A proper defense plan is needed to respond to such attacks and maintain the stability of the grid, and would include prevention, detection, mitigation, incident response, and/or recovery strategies. In this paper, we propose a solution to detect load-altering cyberattacks using a time-delay neural network that monitors the grid’s load profile. As a case study, we consider a cyberattack scenario against demand-side management programs that control the loads of residential electrical water heaters in order to perform peak shaving. The proposed solution can be adapted to other load-altering attacks involving different demand-side management programs or other classes of loads. Experiments verify the proposed solution’s efficacy in detecting load-altering attacks with high precision and low false alarm and latency.

Making Social Knowledge in the Victorian City: The Visiting Mode in Manchester, 1832-1914, by Marin Hewitt

Reviewed by: Making Social Knowledge in the Victorian City: The Visiting Mode in Manchester, 1832–1914 by Martin Hewitt Mark Crinson (bio) Making Social Knowledge in the Victorian City: The Visiting Mode in Manchester, 1832–1914, by Martin Hewitt; pp. viii + 114. Abingdon and New York: Routledge, 2020, $59.95, $22.95 paper, $19.50 ebook. Quite a lot of people visited quite a lot of other people in Victorian Manchester. The visitors were philanthropists, clergymen, missionaries, doctors, the odd architect, distributors of tracts and readers of scripture, temperance agents, sanitary and nuisance inspectors, and officials from school boards and Boards of Guardians. Some were paid, many were voluntary. Those they visited were the laboring classes, the poor and indigent—some 35,000 of them in 1842 alone. Apart from relief and reformation, the visits gathered information and impressions about housing conditions, schooling, income and rent, and diseases like cholera. The visitor was thus granted "the authority to speak on social matters" (17). It is this visiting mode that is the subject of Martin Hewitt's new book, Making Social Knowledge in the Victorian City: The Visiting Mode in Manchester, 1832–1914. For historians in the 1990s and early 2000s, industrial Manchester stood for something very different from what Asa Briggs had named the "shock city" with its associated debates about the emergence of new classes and the economic causes and social impact of the Industrial Revolution (Victorian Cities [Pelican, 1963], 96). In the work of Patrick Joyce, Mary Poovey, and others, the emphasis shifted to social surveillance, particularly as found in the work of experts, medical reports, maps, the Manchester Statistical Society and, overall, in a society of bodies and populations framed by Foucauldian ideas of governmentality and biopower. Hewitt's deep research and careful arguments challenge the Foucauldians on their own ground, using the statisticians and their opponents in the [End Page 310] nineteenth century as a mirror to the Foucauldians and their critics today, and arguing instead that "social knowledge was material, morphological and multidimensional" (94). The visiting mode, both an ecology of knowledge and a hermeneutics, is usefully divided into four stages or forms: exploration or penetration, encounter, the tableau, and the specimen or case. Visitors and their committees, far from being made up of the new experts, were mostly members of groups traditionally interested in the poor. This is less a class difference than a difference of attitude between those wielding competences, in Michel Foucault's terms, and those closer to settled forms of power; both could be found in the same individual. The work of the statisticians, Hewitt demonstrates, was usually crude and partial, a limited and flawed version of social knowledge. By contrast, the knowledge gathered by the visitors sometimes overlapped with statistics, but it was also highly localized and highly narrativized, populated with stock characters (often racialized, though this is not brought out by Hewitt), and concerned with specificities of place and time that marked their difference from the flattened social space of statistical abstraction. A chapter on maps explores the "cartographic imaginary" as a way of managing "ignorance and impotence," rather than (in the somewhat circular arguments of Joyce) as the production of a standardized and homogenized urban space (37). As functionally and visually differentiated objects whose efficacy is difficult to establish, maps represented a "chequerboard of distinctive districts" and "a complexly configured material topography" (33–34). Here, art historians might have emphasized not the "failure of precision" in a bird's eye view, but rather the significance of its suggestiveness (38). Another chapter looks at "knowledge transactions" in Elizabeth Gaskell's fiction, revealing just how steeped this author and her work were in the culture of visiting. Again, the overtness of the anti-statistical stance is clear. In Gaskell's fiction the chaos and opacity of the city are always present, but also, and complementarily, we find the idea that social knowledge could best come from direct observation, particularly in the homes of the poor (Gaskell's work was compared to that of naturalists, photographers, and genre painters). Confrontation, recognition of shared interests, revelation, vindication, and active remediation: all are enacted by Gaskell's visitors and expected of her readers...

Learning to Classify With Incremental New Class

New class detection and effective model expansion are of great importance in incremental data mining. In open incremental data environments, data often come with novel classes, e.g., the emergence of new classes in image classification or new topics in opinion monitoring, and is denoted as class-incremental learning (C-IL) in literature. There are two main challenges in C-IL: how to conduct novelty detection and how to update the model with few novel class instances. Most previous methods pay much attention to the former challenge while ignoring the problem of efficiently updating models. To solve this problem, we propose a novel framework to handle the incremental new class, named learning to classify with incremental new class (LC-INC), which can process these two challenges automatically in one unified framework. In detail, LC-INC utilizes a novel structure network to consider the prototype information between class centers of known classes and newly incoming instances, which can dynamically combine the prediction information with structure information to detect novel class instances efficiently. On the other hand, the proposed structure network can also act as a meta-network, which can learn to expand the model much faster and more efficiently with inadequate novel class instances. Experiments on synthetic and real-world datasets successfully validate the effectiveness of our proposed method.

Somethings About Biological Prostheses

Finite element models of the female biofidel were developed using a specific combination of segmentation with computed tomography and solid modeling tools capable of representing bone physiology and structural behavior. This biofidel finite element (FEM) model is used to evaluate the change in the physiological distribution of stress in the femoral prosthesis and to evaluate the new design criteria for biopsy. Biomimetics, biomechanics, and tissue engineering are three multidisciplinary fields that have been considered in this research to achieve the goal of improving the reliability of prosthetic implants. The authors took these studies to gather the untapped potential of such advanced materials and design technologies by developing finite models of Biofidel elements capable of correctly mimicking the biomechanical behavior of the femur. The new remodeling of the tetrahedral elements was performed in 3Matic looking for the congruence of the node at the bone-implant interfaces, where the material was defined for the new configuration of the finite elements. The evaluation of the mechanical properties was made taking into account the mechanical characteristics of the cortical and trabecular bone. For biomechanical integration of the implant, a custom material with an improved combination of strength and rigidity that matches the bone should be used. This greater biomechanical compatibility will avoid weakening the implant and increase lifespan, avoiding additional surgery for revision and allowing good biological integration (bone growth). Innovative biomimetic materials for tissue engineering based on hydrophilic polymers were developed by our research group and presented attractive physical, biological, and mechanical properties for biomedical applications. For use with metal prostheses, the authors have developed a hybrid biocompatible material, extremely biocompatible, based on hydrophilic chemicals and hydroxy-ethyl-methacrylate type. The structural metal composition of the new prostheses will be made of titanium alloys using additive technology based on melting thin layers of titanium powder (about 50 microns) on each other until the desired component is obtained (sandwich method). Then, the biomaterial and osteoconductive nanostructured material developed in our previous studies can cover the titanium structural prosthetic skeleton. These hybrid biological prostheses, which are made using synthetic materials capable of inducing the growth of biological networks and structural steel scaffolding, may favor the emergence of new classes of orthopedic hybrids in the medical field. The new hybrid bio-prosthesis could drastically reduce protection against stress while providing an advantageous improvement in the life of the prosthesis compared to traditional solutions. Recovering optimal joint functionality will improve the patient's quality of life, which perceives a significant reduction in the risk of the new surgery. The requirement to predict potential structural changes that could be induced by improper use of biologically compatible prostheses in bone structure and morphology has forced our studies to evaluate fictitious models that could be considered for efficient bone distribution and orthotropic behavior.

Possibilities of a new glucagon-like peptide-1 receptor agonist Semaglutide in improving left ventricular diastolic function in a patient with arterial hypertension and type 2 diabetes mellitu

The emergence of new classes of glucose-lowering drugs that can further reduce cardiovascular risk (CVR) have fundamentally changed the approach to the management of patients with type 2 diabetes mellitus. The most significant changes concerned the strategy of choosing glucose-lowering therapy with a change in priorities from the glycemia level to CVR, which plays a key role in choosing a class of antidiabetic drugs. At the same time, the mechanisms responsible for lowering CVR remain largely unclear. Of undoubted interest is the study of effects of a new glucagon-like peptide-1 receptor agonist, Semaglutide, on the preclinical stage of diastolic dysfunction. The article presents a clinical case of the use of Semaglutide in a patient with arterial hypertension and type 2 diabetes mellitus.

The Display Week 2021 Symposium Preview

The Display Week 2021 Symposium Preview