Digital dual test syphilis/HIV detection based on Fourier Descriptors of Cyclic Voltammetry curves.

BACKGROUND: Effective and timely detection is vital for mitigating the severe impacts of Sexually Transmitted Infections (STI), including syphilis and HIV. Cyclic Voltammetry (CV) sensors have shown promise as diagnostic tools for these STI, offering a pathway towards cost-effective solutions in primary health care settings. OBJECTIVE: This study aims to pioneer the use of Fourier Descriptors (FDs) in analyzing CV curves as 2D closed contours, targeting the simultaneous detection of syphilis and HIV. METHODS: Raw CV signals are filtered, resampled, and transformed into 2D closed contours for FD extraction. Essential shape characteristics are captured through selected coefficients. A complementary geometrical analysis further extracts features like curve areas and principal axes lengths from CV curves. A Mahalanobis Distance Classifier is employed for differentiation between patient and control groups. RESULTS: The evaluation of the proposed method revealed promising results with classification performance metrics such as Accuracy and F1-Score consistently achieving values rounded to 0.95 for syphilis and 0.90 for HIV. These results underscore the potential efficacy of the proposed approach in differentiating between patient and control samples for STI detection. CONCLUSION: By integrating principles from biosensors, signal processing, image processing, machine learning, and medical diagnostics, this study presents a comprehensive approach to enhance the detection of both syphilis and HIV. This setts the stage for advanced and accessible STI diagnostic solutions.

Ensemble-imbalance-based classification for amyotrophic lateral sclerosis prognostic prediction: identifying short-survival patients at diagnosis.

Prognosticating Amyotrophic Lateral Sclerosis (ALS) presents a formidable challenge due to patients exhibiting different onset sites, progression rates, and survival times. In this study, we have developed and evaluated Machine Learning (ML) algorithms that integrate Ensemble and Imbalance Learning techniques to classify patients into Short and Non-Short survival groups based on data collected during diagnosis. We aimed to identify individuals at high risk of mortality within 24 months of symptom onset through analysis of patient data commonly encountered in daily clinical practice. Our Ensemble-Imbalance approach underwent evaluation employing six ML algorithms as base classifiers. Remarkably, our results outperformed those of individual algorithms, achieving a Balanced Accuracy of 88% and a Sensitivity of 96%. Additionally, we used the Shapley Additive Explanations framework to elucidate the decision-making process of the top-performing model, pinpointing the most important features and their correlations with the target prediction. Furthermore, we presented helpful tools to visualize and compare patient similarities, offering valuable insights. Confirming the obtained results, our approach could aid physicians in devising personalized treatment plans at the time of diagnosis or serve as an inclusion/exclusion criterion in clinical trials.

Artificial intelligence applied to analyzes during the pandemic: COVID-19 beds occupancy in the state of Rio Grande do Norte, Brazil.

The COVID-19 pandemic is already considered one of the biggest global health crises. In Rio Grande do Norte, a Brazilian state, the RegulaRN platform was the health information system used to regulate beds for patients with COVID-19. This article explored machine learning and deep learning techniques with RegulaRN data in order to identify the best models and parameters to predict the outcome of a hospitalized patient. A total of 25,366 bed regulations for COVID-19 patients were analyzed. The data analyzed comes from the RegulaRN Platform database from April 2020 to August 2022. From these data, the nine most pertinent characteristics were selected from the twenty available, and blank or inconclusive data were excluded. This was followed by the following steps: data pre-processing, database balancing, training, and test. The results showed better performance in terms of accuracy (84.01%), precision (79.57%), and F1-score (81.00%) for the Multilayer Perceptron model with Stochastic Gradient Descent optimizer. The best results for recall (84.67%), specificity (84.67%), and ROC-AUC (91.6%) were achieved by Root Mean Squared Propagation. This study compared different computational methods of machine and deep learning whose objective was to classify bed regulation data for patients with COVID-19 from the RegulaRN Platform. The results have made it possible to identify the best model to help health professionals during the process of regulating beds for patients with COVID-19. The scientific findings of this article demonstrate that the computational methods used applied through a digital health solution, can assist in the decision-making of medical regulators and government institutions in situations of public health crisis.

Half dose ChAdOx1 nCoV-19 vaccine was equivalent to full doses to reduce moderate and severe COVID-19 cases.

Objectives: Previously, we presented the effectiveness of ChAdOx1 nCoV-19 half-dose (HD) immunization for preventing new COVID-19 cases. Here, we evaluated the administration of an HD of ChAdOx1 nCoV-19 in the primary immunization protocol (up to two doses) in reducing moderate and severe cases, hospitalizations, and deaths when compared to the administration of full doses (FD) after a long-term follow-up. Methods: We evaluated data from 29,469 participants between January 2021 and November 2022 who received an HD or FD vaccine and crossed this information with their medical records to identify those who developed moderate or severe cases. All participants were classified into four groups according to their immunization status and followed 500 days after the last vaccine administration. Results: The propensity-score matching analysis indicates that the administration of the two HDs of ChAdOx1 nCoV-19 was equivalent to the use of two FDs to reduce moderate and severe COVID-19 cases. The relative risk of being infected and developing moderate or severe conditions after the administration of at least one HD or FD was similar 150 or 500 days after the administration of the immunizers. Conclusion: Administering two HDs can be used safely as a cost-effective alternative to the primary immunization protocol.

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.

The impact of nintedanib and pirfenidone on lung function and survival in patients with idiopathic pulmonary fibrosis in real-life setting.

BACKGROUND: Idiopathic pulmonary fibrosis (IPF) is a chronic, fibrosing interstitial pneumonia of unknown cause that is associated with radiological and/or histological features of usual interstitial pneumonia (UIP). A mean survival of 2-5 years was reported previously to the advent of antifibrotics. According to clinical trials, nintedanib and pirfenidone induce a significant delay in functional decline, with a favorable impact on survival. METHODS: A real-life retrospective and longitudinal study was conducted to assess the efficacy and tolerability of antifibrotics in IPF patients, between January 2014 and December 2020. Two groups (under nintedanib or pirfenidone) were analyzed at diagnosis through their clinical features and radiological patterns. Lung function was assessed at diagnosis (time 0) and after 6, 12 and 24 months of treatment. We also compared this antifibrotic cohort with an older naïve antifibrotic cohort, mainly treated with immunosuppressive drugs and/or N- acetylcysteine. Survival was analyzed and prognostic features were also studied. Statistical analysis was performed with IBM® SPSS®. RESULTS: A cohort of 108 patients under antifibrotics (nintedanib n = 54; pirfenidone n = 54) was assessed. Lung function analysis showed an overall stabilization in FVC and DLCO mean predicted percentages at 6, 12 and 24 months of treatment. The mean decline in FVC and DLCO, at 12 months, was -40.95 ± 438.26 mL and -0.626 ± 1.31 mL/min/mmHg, respectively. However, during this period, 34.2% of the patients died mostly due to acute exacerbation associated with a poorer lung function at diagnosis. Mean survival in the naïve antifibrotic cohort was significantly lower than in the antifibrotic cohort (39.9 months versus 58.2 months; p < 0.005). Regarding lung function evolution and survival, we found no differences between definitive or probable UIP radiological patterns, both on patients under nintedanib and pirfenidone (p = 0.656). CONCLUSIONS: In this real-life observational study, the positive impact of antifibrotic therapy on the IPF clinical course and on survival was corroborated. Regarding efficacy, there was no difference between patients taking nintedanib or pirfenidone. The need for an early treatment was also demonstrated, since a worse outcome is clearly associated with lower lung volumes and lower diffusing capacity at diagnosis.

Digital Alternative Communication for Individuals with Amyotrophic Lateral Sclerosis: What We Have.

Amyotrophic Lateral Sclerosis is a disease that compromises the motor system and the functional abilities of the person in an irreversible way, causing the progressive loss of the ability to communicate. Tools based on Augmentative and Alternative Communication are essential for promoting autonomy and improving communication, life quality, and survival. This Systematic Literature Review aimed to provide evidence on eye-image-based Human-Computer Interaction approaches for the Augmentative and Alternative Communication of people with Amyotrophic Lateral Sclerosis. The Systematic Literature Review was conducted and guided following a protocol consisting of search questions, inclusion and exclusion criteria, and quality assessment, to select primary studies published between 2010 and 2021 in six repositories: Science Direct, Web of Science, Springer, IEEE Xplore, ACM Digital Library, and PubMed. After the screening, 25 primary studies were evaluated. These studies showcased four low-cost, non-invasive Human-Computer Interaction strategies employed for Augmentative and Alternative Communication in people with Amyotrophic Lateral Sclerosis. The strategies included Eye-Gaze, which featured in 36% of the studies; Eye-Blink and Eye-Tracking, each accounting for 28% of the approaches; and the Hybrid strategy, employed in 8% of the studies. For these approaches, several computational techniques were identified. For a better understanding, a workflow containing the development phases and the respective methods used by each strategy was generated. The results indicate the possibility and feasibility of developing Human-Computer Interaction resources based on eye images for Augmentative and Alternative Communication in a control group. The absence of experimental testing in people with Amyotrophic Lateral Sclerosis reiterates the challenges related to the scalability, efficiency, and usability of these technologies for people with the disease. Although challenges still exist, the findings represent important advances in the fields of health sciences and technology, promoting a promising future with possibilities for better life quality.

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.

Does the placement of a collagen-fibrin sealant reduce complications of radical inguinal lymph node dissection? - Comparative study in patients with penile cancer.

OBJECTIVE: Management of patients with penile cancer (PeC) with palpable inguinal lymph nodes (ILNs) relies on radical ILN dissection (RILND). Low burden of nodal metastatic disease may lead to long-lasting survival with surgical management. Nevertheless, RILND involves significant postoperative morbidity. We compared the complications of patients undergoing RILND with (RILND-T) and without (RILND-0T) placement of a collagen-fibrin sealant patch on the resection bed. MATERIALS AND METHODS: We conducted an observational retrospective study. Data from men submitted to RILND-T and RILND-0T from Jan/2001 to Feb/2022, in a tertiary care centre were compared. The primary endpoint was the overall incidence of complications until 1 month after the procedure and their respective severity in both cohorts (Clavien-Dindo classification system). Secondarily, length of hospital stay (LOHS) was analysed. The placement of a collagen-fibrin sealant patch was left at the surgeon's discretion. RESULTS: Seven patients underwent RILND-T and 20 underwent RILND-0T, respectively. There were no differences in pathologic TNM stage nor in the total number of ILNs removed (17 ± 4 vs. 20 ± 8, p = 0.37). Overall, 23 (85.2%) patients had complications. The complication rate was similar in both cohorts (85.7% vs 85%, p = 0.73). Surgical wound infection (3/7 vs. 11/20) and lymphocele (4/7 vs. 11/20) were the most reported complications. Patients undergoing RILND-T were discharged faster (mean length of hospital stay 9 ± 3 vs 19 ± 20 days, p = 0.22). CONCLUSIONS: The application of a collagen-fibrin sealant patch on the resection bed does not seem to reduce the postoperative complication rate in patients undergoing RILND. Nevertheless, a trend towards a shorter LOHS in patients with RILND-T cannot be excluded and should be validated by further studies with a higher number of patients.

Mortality risk prediction with ILD-GAP index in a fibrotic hypersensitivity pneumonitis cohort.

BACKGROUND: Fibrotic hypersensitivity pneumonitis (fHP) is associated with significant morbidity and mortality. Interstitial lung disease-gender-age-physiology (ILD-GAP) performance in fHP outside the initial cohort was never performed. AIM: To assess the ILD-GAP index's ability to predict mortality in a Portuguese cohort of patients with fHP and analyse whether other clinical variables add value. METHODS: Retrospective analysis of fHP cohort in two Portuguese ILD centres. The baseline ILD-GAP index was calculated. Survival was analysed in months; mortality was the primary outcome. Univariate and multivariate analyses to identify mortality risk factors were performed. RESULTS: A total of 141 patients were included. Fifty-three patients (37.6%) died during the follow-up. The usual interstitial pneumonia (UIP) pattern was found in 49.6%, and their survival was inferior to non-UIP [32 months (interquartile range, IQR = 19, 60) versus 52 months (IQR = 28, 98), p = 0.048]. Patients with an ILD-GAP index higher than three double their risk of mortality [hazard ratio (HR) = 6.48, 95% confidence interval (CI) = (3.03-13.96)] when compared with the patients with an index between 2 and 3 [HR = 3.04, 95% CI = (1.62-5.71)] adjusting for acute exacerbation history. Even though UIP patients had worse survival, it did not reach statistical significance when UIP pattern was added to this model. Acute exacerbation history was an independent risk factor for mortality; however, ILD-GAP still predicted mortality after adjusting for this factor. PaO2 and 6-minute walk test desaturation were not significant risk factors. CONCLUSION: ILD-GAP index is a good predictor for mortality in fHP, even after adjusting for other mortality risk factors.

Electronic health records in Brazil: Prospects and technological challenges.

Electronic Health Records (EHR) are critical tools for advancing digital health worldwide. In Brazil, EHR development must follow specific standards, laws, and guidelines that contribute to implementing beneficial resources for population health monitoring. This paper presents an audit of the main approaches used for EHR development in Brazil, thus highlighting prospects, challenges, and existing gaps in the field. We applied a systematic review protocol to search for articles published from 2011 to 2021 in seven databases (Science Direct, Web of Science, PubMed, Springer, IEEE Xplore, ACM Digital Library, and SciELO). Subsequently, we analyzed 14 articles that met the inclusion and quality criteria and answered our research questions. According to this analysis, 78.58% (11) of the articles state that interoperability between systems is essential for improving patient care. Moreover, many resources are being designed and deployed to achieve this communication between EHRs and other healthcare systems in the Brazilian landscape. Besides interoperability, the articles report other considerable elements: (i) the need for increased security with the deployment of permission resources for viewing patient data, (ii) the absence of accurate data for testing EHRs, and (iii) the relevance of defining a methodology for EHR development. Our review provides an overview of EHR development in Brazil and discusses current gaps, innovative approaches, and technological solutions that could potentially address the related challenges. Lastly, our study also addresses primary elements that could contribute to relevant components of EHR development in the context of Brazil's public health system. Systematic review registration: PROSPERO, identifier CRD42021233219, https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42021233219.

Diagnostic yield and safety of transbronchial lung cryobiopsy and surgical lung biopsy in interstitial lung diseases: a systematic review and meta-analysis.

INTRODUCTION: Transbronchial lung cryobiopsy (TBLC) is increasingly being used as an alternative to video-assisted thoracoscopic surgery (VATS) biopsy to establish the histopathologic pattern in interstitial lung disease (ILD). METHODS: A systematic literature search of the PubMed and Embase databases, from October 2010 to October 2020, was conducted to identify studies that reported on diagnostic yield or safety of VATS or TBLC in the diagnosis of ILD. RESULTS: 43 studies were included. 23 evaluated the diagnostic yield of TBLC after multidisciplinary discussion, with a pooled diagnostic yield of 76.8% (95% confidence interval (CI) 70.6-82.1), rising to 80.7% in centres that performed ≥70 TBLC. 10 studies assessed the use of VATS and the pooled diagnostic yield was 93.5% (95% CI 88.3-96.5). In TBLC, pooled incidences of complications were 9.9% (95% CI 6.8-14.3) for significant bleeding (6.9% for centres with ≥70 TBLC), 5.6% (95% CI 3.8-8.2) for pneumothorax treated with a chest tube and 1.4% (95% CI 0.9-2.2) for acute exacerbation of ILD after TBLC. The mortality rates were 0.6% and 1.7% for TBLC and VATS, respectively. CONCLUSIONS: TBLC has a fairly good diagnostic yield, an acceptable safety profile and a lower mortality rate than VATS. The best results are obtained from more experienced centres.

Neoadjuvant chemotherapy for muscle-invasive bladder cancer: does variant histology matter?

PURPOSE: The most frequent histology of bladder tumors is urothelial carcinoma. Most are pure urothelial carcinomas (PUC) but up to one-third of the cases present variant histological (VH) features. The aim of this study was to evaluate the role of variant histology in neoadjuvant chemotherapy (NAC) response in patients with urothelial muscle-invasive bladder cancer. METHODS: We retrospectively analyzed data from 77 patients with bladder cancer who performed neoadjuvant chemotherapy at two institutions. RESULTS: Complete pathological response (ypT0) was higher in patients with PUC (38.5%), comparing with VH (12%). Logistic regression analysis demonstrated that variant histology is associated with an 89% lesser likelihood of tumor downstaging, with advanced clinical T stages and positive smoking history as independent predictors. The estimated mean cancer-specific survival was 68.91 months for PUC patients and 50.23 months for VH patients (log rank test, P = 0.024). Multivariate Cox regression analysis demonstrated that VH and clinical T stage were independent predictors of cancer-specific survival, indicating a worse outcome for patients with VH and advanced clinical T stages. CONCLUSIONS: There are only a few retrospective studies evaluating the clinical impact of variant histology tumors, which are mainly managed as PUC. Our results demonstrate that VH is associated with a worse likelihood of tumor downstaging after NAC and a worse cancer-specific survival in bladder cancer patients. There is a need for further studies and genetic analysis to identify the patients most likely to achieve ypT0 status and downstaging after NAC.

Predictive value of common genetic variants in idiopathic pulmonary fibrosis survival.

Idiopathic pulmonary fibrosis (IPF) is a chronic, progressive, fibrosing interstitial pneumonia of unknown etiology. The role of genetic risk factors has been the focus of numerous studies probing for associations of genetic variants with IPF. We aimed to determine whether single-nucleotide polymorphisms (SNPs) of four candidate genes are associated with IPF susceptibility and survival in a Portuguese population. A retrospective case-control study was performed with 64 IPF patients and 74 healthy controls. Ten single-nucleotide variants residing in the MUC5B, TOLLIP, SERPINB1, and PLAU genes were analyzed. Single- and multi-locus analyses were performed to investigate the predictive potential of specific variants in IPF susceptibility and survival. Multifactor dimensionality reduction (MDR) was employed to uncover predictive multi-locus interactions underlying IPF susceptibility. The MUC5B rs35705950 SNP was significantly associated with IPF: T allele carriers were significantly more frequent among IPF patients (75.0% vs 20.3%, P < 1.0 × 10-6). Genotypic and allelic distributions of TOLLIP, PLAU, and SERPINB1 SNPs did not differ significantly between groups. However, the MUC5B-TOLLIP T-C-T-C haplotype, defined by the rs35705950-rs111521887-rs5743894-rs5743854 block, emerged as an independent protective factor in IPF survival (HR = 0.37, 95% CI 0.17-0.78, P = 0.009, after adjustment for FVC). No significant multi-locus interactions correlating with disease susceptibility were detected. MUC5B rs35705950 was linked to an increased risk for IPF, as reported for other populations, but not to disease survival. A haplotype incorporating SNPs of the MUC5B-TOLLIP locus at 11p15.5 seems to predict better survival and could prove useful for prognostic purposes and IPF patient stratification. KEY MESSAGES : The MUC5B rs35705950 minor allele is associated with IPF risk in the Portuguese. No predictive multi-locus interactions of IPF susceptibility were identified by MDR. A haplotype defined by MUC5B and TOLLIP SNPs is a protective factor in IPF survival. The haplotype may be used as a prognostic tool for IPF patient stratification.