Development of technologies to support the diagnosis of infectious diseases and cancer to support the primary health care

54/2017). Publisher Copyright: © 2022, The Author(s).Purpose: Primary Health Care (PHC) is the coordinator of health care in Brazil and needs to be strengthened in the diagnostic field to increase health care quality. Aiming to improve the diagnostic tools currently available in PHC, this work describes the process of development and validation of two point-of-care biomedical devices for screening patients with syphilis or different kinds of cancer. Methods: The development of these devices followed nine stages of action based on the requirements established by the Ministry of Health. During development, both systems followed the stages of circuit planning, software simulation to verify the components used, cost assessment for the acquisition of features, simulation in contact matrix, development of the embedded system, and planning of the printed circuit board and storage box. Results: Both devices underwent preliminary functionality tests to assess their quality. The performance tests applied on the device to diagnose syphilis performed 8,733,194 requests, with a flow of 2426 requests/second, reaching the desired parameters of robustness, integrity, durability, and stability. In addition, functioning tests on the cancer-screening device indicated the ability to detect standard fluorescence in a minimal (150 uL) sample volume. Conclusions: Together, the methodology used for developing the devices resulted in promising equipment to improve the diagnosis and meet the requirements for executing technologies for testing and triaging patients in PHC.publishersversionpublishe

Development of a Cyclic Voltammetry-Based Method for the Detection of Antigens and Antibodies as a Novel Strategy for Syphilis Diagnosis

54/2017). Publisher Copyright: © 2022 by the authors.The improvement of laboratory diagnosis is a critical step for the reduction of syphilis cases around the world. In this paper, we present the development of an impedance-based method for detecting T. pallidum antigens and antibodies as an auxiliary tool for syphilis laboratory diagnosis. We evaluate the voltammetric signal obtained after incubation in carbon or gold nanoparticle-modified carbon electrodes in the presence or absence of Poly-L-Lysine. Our results indicate that the signal obtained from the electrodes was sufficient to distinguish between infected and non-infected samples immediately (T0′) or 15 min (T15′) after incubation, indicating its potential use as a point-of-care method as a screening strategy.publishersversionpublishe

Artificial intelligence on the identification of risk groups for osteoporosis, a general review

Abstract Introduction The goal of this paper is to present a critical review on the main systems that use artificial intelligence to identify groups at risk for osteoporosis or fractures. The systems considered for this study were those that fulfilled the following requirements: range of coverage in diagnosis, low cost and capability to identify more significant somatic factors. Methods A bibliographic research was done in the databases, PubMed, IEEExplorer Latin American and Caribbean Center on Health Sciences Information (LILACS), Medical Literature Analysis and Retrieval System Online (MEDLINE), Cumulative Index to Nursing and Allied Health Literature (CINAHL), Scopus, Web of Science, and Science Direct searching the terms “Neural Network”, “Osteoporosis Machine Learning” and “Osteoporosis Neural Network”. Studies with titles not directly related to the research topic and older data that reported repeated strategies were excluded. The search was carried out with the descriptors in German, Spanish, French, Italian, Mandarin, Portuguese and English; but only studies written in English were found to meet the established criteria. Articles covering the period 2000–2017 were selected; however, articles prior to this period with great relevance were included in this study. Discussion Based on the collected research, it was identified that there are several methods in the use of artificial intelligence to help the screening of risk groups of osteoporosis or fractures. However, such systems were limited to a specific ethnic group, gender or age. For future research, new challenges are presented. Conclusions It is necessary to develop research with the unification of different databases and grouping of the various attributes and clinical factors, in order to reach a greater comprehensiveness in the identification of risk groups of osteoporosis. For this purpose, the use of any predictive tool should be performed in different populations with greater participation of male patients and inclusion of a larger age range for the ones involved. The biggest challenge is to deal with all the data complexity generated by this unification, developing evidence-based standards for the evaluation of the most significant risk factors