570 Biowissenschaften; Biologie
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The aim was to implement uncertainty calculation for detecting the negative effects of smoking on the bone density of the paranasal sinus. Materials and Methods: A total of 100 male participants aged 20 to 44 were included in the study and divided into two groups. The first group comprised individuals with minimal harmful habits, while the second group consisted of individuals who had been smoking for at least 10 years, consuming 1 to 2 packs of cigarettes per day. Results Bone density has a negative impact on the bone tissue of the upper wall of the maxillary sinus. The findings suggest that individuals with a pronounced decrease in minimum density, as well as those with a marked difference between minimum and maximum density values, may require heightened medical attention due to potential associations with undiagnosed diseases or specific structural characteristics in the skull. Conclusions. The uncertainty calculation was implemented for the detection of negative effect of smoking on the bone density of paranasal sinuses. The calculated difference between maximum and minimum density during the research suggests significant medical implications, especially considering the heterogeneity of the trabecular bone structure in the skull. Individuals with a marked difference may require heightened medical attention, potentially associated with undiagnosed diseases or specific structural characteristics in the skull.
Evaluation of medical images is of key importance in the work of medical staff today. Especially this problem pays important role in otolaryngology.
The aim of our work was to develop an automatic complex method for assessing the state of the human paranasal sinuses.
Our research included 10 people of different sex and age, who were divided into groups, taking into account the recommendations of the WHO for 2019-2021. The structure of the mucous membrane of the maxillary sinus were calculated and compared.
In the course of our research, an algorithm was developed for the automatic assessment of the state of the mucous membrane of the maxillary sinus and its bone walls according to the data of the spiral computed tomography. The difference between obtained data in the manual and automatic mode is minimal.
Solutions to the 3D Model Problem of Pressure Measurement in the Area of Maxillary Sinus Anastomosis
(2020)
The ostiomeatal complex (OMC) is a key area that determines the occurrence of inflammatory processes in the paranasal sinuses (PNSs). The aim of our work was to develop a procedure for studying the OMC components in the preoperative period that allows for identification the impact of anatomical peculiarities on change of physiological pressure in the maxillary sinus. Materials and methods: The study was carried out on the basis of the otorhinolaryngological department of Kharkiv Regional Clinical Hospital in 2019-2020. It involved 100 patients of both sexes aged 20-59 years with chronic non-polyposis maxillary sinusitis. Results: The sizes of the uncinate process, the middle turbinate and the natural anastomosis were determined using the calculation of uncertainty. Basing on the data obtained, all the patients were divided into three groups. Conclusions: Changes in the size of the natural anastomosis (both an increase and its narrowing) lead to changes in pressure in the area of the anastomosis, and a decrease in ventilation in the paranasal sinuses. SCT study with subsequent 3D modeling is an informative, accurate and effective method for assessment of OMC and PNSs condition. It allows surgeons to presume the method and volume of surgery as early as at the preoperative stage, without resorting to invasive research methods.
The aim of our study was to investigate the impact of insulin resistance on the bone density of the upper wall of the maxillary sinus. Materials and Methods: The study included 100 female participants aged 18 to 44 years, divided into two groups. The first group consisted of individuals with insulin resistance, while the control group comprised individuals without signs of insulin resistance. In each group, we conducted an investigation of the radiological density of the upper wall of the maxillary sinus using uncertainty calculations. Results of the study suggest a potential influence of insulin resistance on the density of bone tissue around the nasal sinuses, specifically the upper wall of the maxillary sinus in our case. This parameter was found to be minimal in the group of individuals with insulin resistance. It is particularly noteworthy that both minimum and maximum bone density decreased in this group. Conclusions. The research focused on how insulin resistance affects the density of the upper wall of the maxillary sinus. By employing uncertainty calculations, the study revealed that insulin resistance is associated with a decrease in the minimum density of the upper wall of the maxillary sinus. This tendency may act as a catalyst for the emergence of significant inflammatory alterations in the nasal sinuses, serving as a foundation for the initiation of complications.
The human microbiome has become an area of intense research due to its potential impact on human health. However, the analysis and interpretation of this data have proven to be challenging due to its complexity and high dimensionality. Machine learning (ML) algorithms can process vast amounts of data to uncover informative patterns and relationships within the data, even with limited prior knowledge. Therefore, there has been a rapid growth in the development of software specifically designed for the analysis and interpretation of microbiome data using ML techniques. These software incorporate a wide range of ML algorithms for clustering, classification, regression, or feature selection, to identify microbial patterns and relationships within the data and generate predictive models. This rapid development with a constant need for new developments and integration of new features require efforts into compile, catalog and classify these tools to create infrastructures and services with easy, transparent, and trustable standards. Here we review the state-of-the-art for ML tools applied in human microbiome studies, performed as part of the COST Action ML4Microbiome activities. This scoping review focuses on ML based software and framework resources currently available for the analysis of microbiome data in humans. The aim is to support microbiologists and biomedical scientists to go deeper into specialized resources that integrate ML techniques and facilitate future benchmarking to create standards for the analysis of microbiome data. The software resources are organized based on the type of analysis they were developed for and the ML techniques they implement. A description of each software with examples of usage is provided including comments about pitfalls and lacks in the usage of software based on ML methods in relation to microbiome data that need to be considered by developers and users. This review represents an extensive compilation to date, offering valuable insights and guidance for researchers interested in leveraging ML approaches for microbiome analysis.
Advancing microbiome research with machine learning: key findings from the ML4Microbiome COST action
(2023)
The rapid development of machine learning (ML) techniques has opened up the data-dense field of microbiome research for novel therapeutic, diagnostic, and prognostic applications targeting a wide range of disorders, which could substantially improve healthcare practices in the era of precision medicine. However, several challenges must be addressed to exploit the benefits of ML in this field fully. In particular, there is a need to establish “gold standard” protocols for conducting ML analysis experiments and improve interactions between microbiome researchers and ML experts. The Machine Learning Techniques in Human Microbiome Studies (ML4Microbiome) COST Action CA18131 is a European network established in 2019 to promote collaboration between discovery-oriented microbiome researchers and data-driven ML experts to optimize and standardize ML approaches for microbiome analysis. This perspective paper presents the key achievements of ML4Microbiome, which include identifying predictive and discriminatory ‘omics’ features, improving repeatability and comparability, developing automation procedures, and defining priority areas for the novel development of ML methods targeting the microbiome. The insights gained from ML4Microbiome will help to maximize the potential of ML in microbiome research and pave the way for new and improved healthcare practices.
Microbiomic analysis of human gut samples is a beneficial tool to examine the general well-being and various health conditions. The balance of the intestinal flora is important to prevent chronic gut infections and adiposity, as well as pathological alterations connected to various diseases. The evaluation of microbiome data based on next-generation sequencing (NGS) is complex and their interpretation is often challenging and can be ambiguous. Therefore, we developed an innovative approach for the examination and classification of microbiomic data into healthy and diseased by visualizing the data as a radial heatmap in order to apply deep learning (DL) image classification. The differentiation between 674 healthy and 272 type 2 diabetes mellitus (T2D) samples was chosen as a proof of concept. The residual network with 50 layers (ResNet-50) image classification model was trained and optimized, providing discrimination with 96% accuracy. Samples from healthy persons were detected with a specificity of 97% and those from T2D individuals with a sensitivity of 92%. Image classification using DL of NGS microbiome data enables precise discrimination between healthy and diabetic individuals. In the future, this tool could enable classification of different diseases and imbalances of the gut microbiome and their causative genera.
Diagnosis of cardiovascular diseases is an urgent task because they are the main cause of death for 32% of the world’s population. Particularly relevant are automated diagnostics using machine learning methods in the digitalization of healthcare and introduction of personalized medicine in healthcare institutions, including at the individual level when designing smart houses. Therefore, this study aims to analyze short 10-s electrocardiogram measurements taken from 12 leads. In addition, the task is to classify patients with suspected myocardial infarction using machine learning methods. We have developed four models based on the k-nearest neighbor classifier, radial basis function, decision tree, and random forest to do this. An analysis of time parameters showed that the most significant parameters for diagnosing myocardial infraction are SDNN, BPM, and IBI. An experimental investigation was conducted on the data of the open PTB-XL dataset for patients with suspected myocardial infarction. The results showed that, according to the parameters of the short ECG, it is possible to classify patients with a suspected myocardial infraction as sick and healthy with high accuracy. The optimized Random Forest model showed the best performance with an accuracy of 99.63%, and a root mean absolute error is less than 0.004. The proposed novel approach can be used for patients who do not have other indicators of heart attacks.
Microscopic examination of human blood samples is an excellent opportunity to assess general health status and diagnose diseases. Conventional blood tests are performed in medical laboratories by specialized professionals and are time and labor intensive. The development of a point-of-care system based on a mobile microscope and powerful algorithms would be beneficial for providing care directly at the patient's bedside. For this purpose human blood samples were visualized using a low-cost mobile microscope, an ocular camera and a smartphone. Training and optimisation of different deep learning methods for instance segmentation are used to detect and count the different blood cells. The accuracy of the results is assessed using quantitative and qualitative evaluation standards.
Today, academic researchers benefit from the changes driven by digital technologies and the enormous growth of knowledge and data, on globalisation, enlargement of the scientific community, and the linkage between different scientific communities and the society. To fully benefit from this development, however, information needs to be shared openly and transparently. Digitalisation plays a major role here because it permeates all areas of business, science and society and is one of the key drivers for innovation and international cooperation. To address the resulting opportunities, the EU promotes the development and use of collaborative ways to produce and share knowledge and data as early as possible in the research process, but also to appropriately secure results with the European strategy for Open Science (OS). It is now widely recognised that making research results more accessible to all societal actors contributes to more effective and efficient science; it also serves as a boost for innovation in the public and private sectors. However for research data to be findable, accessible, interoperable and reusable the use of standards is essential. At the metadata level, considerable efforts in standardisation have already been made (e.g. Data Management Plan and FAIR Principle etc.), whereas in context with the raw data these fundamental efforts are still fragmented and in some cases completely missing. The CHARME consortium, funded by the European Cooperation in Science and Technology (COST) Agency, has identified needs and gaps in the field of standardisation in the life sciences and also discussed potential hurdles for implementation of standards in current practice. Here, the authors suggest four measures in response to current challenges to ensure a high quality of life science research data and their re-usability for research and innovation.