Computational methods applied to syphilis: where are we, and where are we going?

Syphilis is an infectious disease that can be diagnosed and treated cheaply. Despite being a curable condition, the syphilis rate is increasing worldwide. In this sense, computational methods can analyze data and assist managers in formulating new public policies for preventing and controlling sexually transmitted infections (STIs). Computational techniques can integrate knowledge from experiences and, through an inference mechanism, apply conditions to a database that seeks to explain data behavior. This systematic review analyzed studies that use computational methods to establish or improve syphilis-related aspects. Our review shows the usefulness of computational tools to promote the overall understanding of syphilis, a global problem, to guide public policy and practice, to target better public health interventions such as surveillance and prevention, health service delivery, and the optimal use of diagnostic tools. The review was conducted according to PRISMA 2020 Statement and used several quality criteria to include studies. The publications chosen to compose this review were gathered from Science Direct, Web of Science, Springer, Scopus, ACM Digital Library, and PubMed databases. Then, studies published between 2015 and 2022 were selected. The review identified 1,991 studies. After applying inclusion, exclusion, and study quality assessment criteria, 26 primary studies were included in the final analysis. The results show different computational approaches, including countless Machine Learning algorithmic models, and three sub-areas of application in the context of syphilis: surveillance (61.54%), diagnosis (34.62%), and health policy evaluation (3.85%). These computational approaches are promising and capable of being tools to support syphilis control and surveillance actions.

Digital health solution for monitoring and surveillance of Amyotrophic Lateral Sclerosis in Brazil.

Amyotrophic Lateral Sclerosis (ALS) is a complex and rare neurodegenerative disease given its heterogeneity. Despite being known for many years, few countries have accurate information about the characteristics of people diagnosed with ALS, such as data regarding diagnosis and clinical features of the disease. In Brazil, the lack of information about ALS limits data for the research progress and public policy development that benefits people affected by this health condition. In this context, this article aims to show a digital health solution development and application for research, intervention, and strengthening of the response to ALS in the Brazilian Health System. The proposed solution is composed of two platforms: the Brazilian National ALS Registry, responsible for the data collection in a structured way from ALS patients all over Brazil; and the Brazilian National ALS Observatory, responsible for processing the data collected in the National Registry and for providing a monitoring room with indicators on people diagnosed with ALS in Brazil. The development of this solution was supported by the Brazilian Ministry of Health (MoH) and was carried out by a multidisciplinary team with expertise in ALS. This solution represents a tool with great potential for strengthening public policies and stands out for being the only public database on the disease, besides containing innovations that allow data collection by health professionals and/or patients. By using both platforms, it is believed that it will be possible to understand the demographic and epidemiological data of ALS in Brazil, since the data will be able to be analyzed by care teams and also by public health managers, both in the individual and collective monitoring of people living with ALS in Brazil.

Osteoporosis screening using machine learning and electromagnetic waves.

Osteoporosis is a disease characterized by impairment of bone microarchitecture that causes high socioeconomic impacts in the world because of fractures and hospitalizations. Although dual-energy X-ray absorptiometry (DXA) is the gold standard for diagnosing the disease, access to DXA in developing countries is still limited due to its high cost, being present only in specialized hospitals. In this paper, we analyze the performance of Osseus, a low-cost portable device based on electromagnetic waves that measures the attenuation of the signal that crosses the medial phalanx of a patient's middle finger and was developed for osteoporosis screening. The analysis is carried out by predicting changes in bone mineral density using Osseus measurements and additional common risk factors used as input features to a set of supervised classification models, while the results from DXA are taken as target (real) values during the training of the machine learning algorithms. The dataset consisted of 505 patients who underwent osteoporosis screening with both devices (DXA and Osseus), of whom 21.8% were healthy and 78.2% had low bone mineral density or osteoporosis. A cross-validation with k-fold = 5 was considered in model training, while 20% of the whole dataset was used for testing. The obtained performance of the best model (Random Forest) presented a sensitivity of 0.853, a specificity of 0.879, and an F1 of 0.859. Since the Random Forest (RF) algorithm allows some interpretability of its results (through the impurity check), we were able to identify the most important variables in the classification of osteoporosis. The results showed that the most important variables were age, body mass index, and the signal attenuation provided by Osseus. The RF model, when used together with Osseus measurements, is effective in screening patients and facilitates the early diagnosis of osteoporosis. The main advantages of such early screening are the reduction of costs associated with exams, surgeries, treatments, and hospitalizations, as well as improved quality of life for patients.

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

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.

Biomedical signals and machine learning in amyotrophic lateral sclerosis: a systematic review.

INTRODUCTION: The use of machine learning (ML) techniques in healthcare encompasses an emerging concept that envisages vast contributions to the tackling of rare diseases. In this scenario, amyotrophic lateral sclerosis (ALS) involves complexities that are yet not demystified. In ALS, the biomedical signals present themselves as potential biomarkers that, when used in tandem with smart algorithms, can be useful to applications within the context of the disease. METHODS: This Systematic Literature Review (SLR) consists of searching for and investigating primary studies that use ML techniques and biomedical signals related to ALS. Following the definition and execution of the SLR protocol, 18 articles met the inclusion, exclusion, and quality assessment criteria, and answered the SLR research questions. DISCUSSIONS: Based on the results, we identified three classes of ML applications combined with biomedical signals in the context of ALS: diagnosis (72.22%), communication (22.22%), and survival prediction (5.56%). CONCLUSIONS: Distinct algorithmic models and biomedical signals have been reported and present promising approaches, regardless of their classes. In summary, this SLR provides an overview of the primary studies analyzed as well as directions for the construction and evolution of technology-based research within the scope of ALS.

Radiation dose comparison between V/P-SPECT and CT-angiography in the diagnosis of pulmonary embolism.

PURPOSE: The aim of this study is to compare two routine protocols at our institution, CTPA and V/P-SPECT, in terms of radiation dose to the most exposed organs (lungs and breast) and to the embryo/fetus in the case of pregnant patients. METHODS: At our institution, the CTPA protocol includes a contrast enhanced CT (scan parameters: 100kVp, 700mA, 0.5s/rot, pitch 0.984) and in some cases a non-contrast enhanced CT acquisition (120kVp, 400mA, 0.5s/rot, pitch 1.375). In the V/P-SPECT protocol, ventilation SPECT was performed after inhalation of 99mTc-Technegas, reaching 30MBq in the lungs; perfusion was performed after intravenous administration of 60-120MBq of 99mTc-MAA. The absorbed doses (mGy) to lungs and breast from CTPA were estimated using the "ImPACT CT Patient Dosimetry Calculator". The embryo/fetus dose was estimated for different gestational stages (0-7, 8-12, 13-25 and 26-40weeks) using the web based calculation tool "COnceptus Dose Estimation" (CODE). Doses to organs and embryo/fetus from V/P-SPECT were estimated based on published dose data normalized to administered activity (mGy/MBq). RESULTS: Embryo/fetus absorbed doses are similar for CTPA and V/P-SPECT and bellow 1mGy. The calculated dose to the lungs (breast) was 1.3-10.6 (27-136) times higher from CTPA when compared with V/P-SPECT. CONCLUSION: For the diagnosis of PE in women, if both imaging modalities are available, it is recommended to proceed with V/P-SPECT rather than CTPA due to the considerably lower radiation dose to the breast.

Avaliação de escore corporal em equinos por meio da ultrassonografia / Body condition score evaluation in horses using ultrasonography

Objetivando estudar a relação do escore de condição corporal (ECC) com medidas de espessura de gordura e músculo em três raças distintas, foram utilizados 31 animais divididos conforme a raça, sendo 14 da raça Puro Sangue Inglês (PSI), oito machos e seis fêmeas, com idade média de 3,5 anos e peso médio de 462,70 kg; sete da raça Quarto de Milha (QM), dois machos e cinco fêmeas, com idade média de 2,5 anos e peso médio de 510,40 kg; e 10 da raça Puro Sangue Árabe (PSA), machos, com idade média de 3,5 anos e peso médio de 357,50 kg. Os animais foram avaliados através de ultrassonografia em três regiões paralelas a colina vertebral, onde foram mensuradas: espessura de gordura lombar, espessura do músculo glúteo e espessura da gordura na base da cauda. Foram realizadas trÇes mesurações ultrassonográficas a cada 30 dias (D0, D30 e D60), durante 60 dias. Os resultados ondocaram comportamento diferente entre as raças, porém foi observada maior correlação do escore corporal com a espessura de gordura na base de cauda. A avaliação ultrassonográfica pode ser uma metodologia utilizada como ferramenta de avaliação de ECC de raças de cavalos de esporte.

Aiming to study the relationship of body condition score (BCS) with thickness measurements of fat and muscle in three distinct breeds, 31 animals were distributed according to race: 14 Thoroughbred (PSI) with a mean age of 3.5 yearsand mean weight of 462.70 kg, 7 Quarter Horse (QM) with mean age 2.5 years and mean weight of 510.40 kg, and 10 purebred Arabian (PSA) with a mean age of 3.5 years and mean weight of 357.50 kg. The animals were evaluated by ultrasound in three regions parallel to the spine, where they were measured for thickness of backfat, thickness of gluteus muscle and fat thickness at the base of the tail. The ultrasound measurements were performed every 30 days (D0, D30 and D60) for 60 days. The results showed different responses between races, although there was a higher correlation score with body fat thickness at the base of the tail. The ultrasound evaluation showed to be a reliable tool for assessment of BCS in sport horse breeds.