Medical Genetics, Genomics and Bioinformatics-2022 Klimontov, Vadim V; Koshechkin, Konstantin A; Orlova, Nina G ...
International journal of molecular sciences,
05/2023, Letnik:
24, Številka:
10
Journal Article
Recenzirano
Odprti dostop
The analysis of molecular mechanisms of disease progression challenges the development of bioinformatics tools and omics data integration ....
Applications of Artificial Intelligence in medical informatics solutions risk sharing have social value. At a time of ever-increasing cost for the provision of medicines to citizens, there is a need ...to restrain the growth of health care costs. The search for computer technologies to stop or slow down the growth of costs acquires a new very important and significant meaning. We discussed the two information technologies in pharmacotherapy and the possibility of combining and sharing them, namely the combination of risk-sharing agreements and Machine Learning, which was made possible by the development of Artificial Intelligence (AI). Neural networks could be used to predict the outcome to reduce the risk factors for treatment. AI-based data processing automation technologies could be also used for risk-sharing agreements automation.
Recent developments in Digital Medicine approaches concern pharmaceutical product optimization. Artificial Intelligence (AI) has multiple applications for pharmaceutical products’ lifecycle, ...increasing development speed, quality of the products, and efficiency of the therapy. Here, we systematically review the overall approach for AI implementation in pharmaceutical products’ lifecycle. The published studies in PubMed and IEEE Xplore were searched from inception to March 2022. The papers were screened for relevant outcomes, publication types, and data sufficiency, and a total of 73 (1.2%) out of 6131 studies were retrieved after the selection. We extracted the data according to the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) statement. All Artificial Intelligence systems could be divided into multiple overlapping categories by implementation. For the 177 projects found, the most popular areas of AI implementation are clinical trials and pre-clinical tests (34%). In second place are novel small molecule design systems, with 33% of the total. The third most popular scope for AI implementation is target identification for novel medicines. More than 25% of the systems provide this functionality. It is interesting that most of the systems specialize in only one area (102 systems—57%). None of the systems provide functionality for full coverage of the lifecycle and function in all categories of the tasks. This meta-analysis demonstrated that Artificial Intelligence solutions in pharmaceutical products’ lifecycle could find numerous implementations, and none of the available market solutions covers them all.
Introduction: Nowadays an increase in the amount of information creates the need to replace and update data processing technologies. One of the tasks of clinical pharmacology is to create the right ...combination of drugs for the treatment of a particular disease. It takes months and even years to create a treatment regimen. Using machine learning (in silico) allows predicting how to get the right combination of drugs and skip the experimental steps in a study that take a lot of time and financial expenses. Gradual preparation is needed for the Deep Learning of Drug Synergy, starting from creating a base of drugs, their characteristics and ways of interacting.
Aim: Our review aims to draw attention to the prospect of the introduction of Deep Learning technology to predict possible combinations of drugs for the treatment of various diseases.
Materials and methods: Literary review of articles based on the PUBMED project and related bibliographic resources over the past 5 years (2015–2019).
Results and discussion: In the analyzed articles, Machine or Deep Learning completed the assigned tasks. It was able to determine the most appropriate combinations for the treatment of certain diseases, select the necessary regimen and doses. In addition, using this technology, new combinations have been identified that may be further involved in preclinical studies.
Conclusions: From the analysis of the articles, we obtained evidence of the positive effects of Deep Learning to select “key” combinations for further stages of preclinical research.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
Brain-computer interface (BCI) technologies have developed as a game changer, altering how humans interact with computers and opening up new avenues for understanding and utilizing the power of the ...human brain. The goal of this research study is to assess recent breakthroughs in BCI technologies and their future prospects. The paper starts with an outline of the fundamental concepts and principles that underpin BCI technologies. It examines the many forms of BCIs, including as invasive, partially invasive, and non-invasive interfaces, emphasizing their advantages and disadvantages. The progress of BCI hardware and signal processing techniques is investigated, with a focus on the shift from bulky and invasive systems to more portable and user-friendly options. Following that, the article delves into the important advances in BCI applications across several fields. It investigates the use of BCIs in healthcare, particularly in neurorehabilitation, assistive technology, and cognitive enhancement. BCIs’ potential for boosting human capacities such as communication, motor control, and sensory perception is being thoroughly researched. Furthermore, the article investigates developing BCI applications in gaming, entertainment, and virtual reality, demonstrating how BCI technologies are growing outside medical and therapeutic settings. The study also gives light on the problems and limits that prevent BCIs from being widely adopted. Ethical concerns about privacy, data security, and informed permission are addressed, highlighting the importance of strong legislative frameworks to enable responsible and ethical usage of BCI technologies. Furthermore, the study delves into technological issues such as increasing signal resolution and precision, increasing system reliability, and enabling smooth connection with existing technology. Finally, this study paper gives an in-depth examination of the advances and future possibilities of BCI technologies. It emphasizes the transformative influence of BCIs on human-computer interaction and their potential to alter healthcare, gaming, and other industries. This research intends to stimulate further innovation and progress in the field of brain-computer interfaces by addressing problems and imagining future possibilities.
The main difficulty in practical work with data obtained via immunosignature analysis is high dimensionality and the presence of a significant number of uninformative or false-informative features ...due to the specific character of the technology. To ensure practically relevant quality of data analysis and classification, it is necessary to take due account of this specific character.
is to create and test the technology for effective reduction of immunosignature data dimensionality, which provides practically relevant and high quality of classification with due regard for the properties of the data obtained.
The study involved the use of two normalized data sets obtained from the public biomedical repository and containing the results of immunosignature analysis.The technology for selecting informative features was proposed within the framework of the study. It consisted of three successive steps: 1) breaking a multiclass task into a series of binary tasks using the "one vs all" strategy; 2) screening of false-informative features is performed for each binary comparison by comparing the values of the median of the sets "one" and "all"; 3) ranking of the remaining features according to their informative value and selection of the most informative ones for each binary comparison.To assess the quality of the proposed technology for informative feature selection, we used the results obtained after application of classification based on the filtered data. Support vector method that proved itself in the problems of high-dimensional data classification was used as a classification model.
Effectiveness of the proposed technology for informative feature selection was determined. This technology allows us to provide high quality of classification while significantly reducing the feature space. The number of features eliminated in the second step is approximately 50% for each data set under consideration, which greatly simplifies subsequent data analysis. After the third step, when the feature space is reduced to 15 features, the quality of classification by the macro-average F1-score metric is assessed as 98.9% for the GSE52581 dataset. For the GSE52581 dataset, with the feature space reduced to 266 features, the quality of classification by the macro-average F1-score metric is 91.3%.
The results of the work demonstrate the promising outlook of the proposed technology for informative feature selection as applied to the data of immunosignature analysis.
BACKGROUND: Non-invasive diagnosis of diabetes is one of the major problems of contemporary medicine. The system being planned could be a new technology for measuring hemoglobin A1c (HbA1c) ...accurately and non-invasively. Therefore, a series of studies are to be conducted to assess the efficiency of the method under study and determine its potential for medical diagnosis and monitoring of HbA1c.
AIMS:
Investigation of the feasibility of Raman spectroscopy for non-invasive measurement of HbA1c.
Development and design of a portable analyzer using this technology.
Assessment of the efficiency and accuracy of the developed device.
METHODS: Neural network creation requires collecting a training sample of measurements for subsequent application of TensorFlow library tools and performing laboratory measurements to calibrate the system for determining HbA1c. The device will use a 785-nm laser to take spectra according to the Raman spectroscopy. The obtained data will be fed to the input of the neural network based on the architecture of convolutional neural networks. Experiments will be conducted to train the model to determine the accuracy and efficiency of the device. A two-step data collection procedure is planned. First, a preliminary test will be done on 50 patients to see how the proposed method handles different age and gender groups and different HbA1c levels. Later, the data will continue to be collected on a larger scale, including patients with different types of diabetes and healthy individuals. Data will be collected using a portable spectrophotometer and monitored by high-performance liquid chromatography. Various metrics will be used to assess the efficiency and accuracy of the device such as accuracy, precision, recall, and F1-score.
RESULTS: An analysis of the available literature was conducted and the following conclusions were drawn. In addition, a neural network model was developed using HbA1c measurements. Currently, our model is optimized to improve the accuracy and reliability of the results.
CONCLUSIONS: The non-invasive Raman spectroscopy-based method has several advantages in measuring HbA1c levels. The procedure is faster and non-traumatic, and HbA1c levels can be monitored continuously. In particular, the non-invasive method eliminates errors associated with protein leakage outside the bloodstream.
The Baikal Gigaton Volume Detector (Baikal-GVD) is a km
3
-scale neutrino detector currently under construction in Lake Baikal, Russia. The detector consists of several thousand optical sensors ...arranged on vertical strings, with 36 sensors per string. The strings are grouped into clusters of 8 strings each. Each cluster can operate as a stand-alone neutrino detector. The detector layout is optimized for the measurement of astrophysical neutrinos with energies of
∼
100 TeV and above. Events resulting from charged current interactions of muon (anti-)neutrinos will have a track-like topology in Baikal-GVD. A fast
χ
2
-based reconstruction algorithm has been developed to reconstruct such track-like events. The algorithm has been applied to data collected in 2019 from the first five operational clusters of Baikal-GVD, resulting in observations of both downgoing atmospheric muons and upgoing atmospheric neutrinos. This serves as an important milestone towards experimental validation of the Baikal-GVD design. The analysis is limited to single-cluster data, favoring nearly-vertical tracks.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Scientific relevance.
Medication adherence is an important condition for effective and safe treatment. The adherence of patients to prescriptions is tracked by assessing their condition, counting the ...pills taken, and using other indirect methods. Digital technologies can help healthcare providers improve their patients’ medication adherence.
Aim.
The authors aimed to review literature describing the medication adherence impact on treatment effectiveness, as well as digital solutions accompanying pharmacotherapy.
Discussion.
Poor adherence to treatment is a significant risk factor for patients. The most common examples of poor adherence are omissions and delays in the timing of doses. Compared with classical daily dosing, individualised regimens significantly increase the risk of adherence errors. Significant consequences of non-adherence include exacerbation of the disease, insufficient effectiveness of treatment, adverse drug reactions, and drug resistance. Promising hardware and software approaches to supporting medication adherence include innovative technological solutions (pillboxes, bottles with electronic reminder systems, digital pills, and smart medication adherence monitoring systems), mobile apps, and chatbots.
Conclusions.
Digital solutions to support pharmacotherapy help improve patients’ adherence to their dosing regimens and individualise their treatment. Further research is needed to select the most promising areas and develop novel digital technologies.
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