Telemedicine allowed greater access to the healthcare system, reduced the need to employ emergency services, improved assessment/control of symptoms, and provided greater orientation and confidence in the care given by family members through early and proactive interventions. Web conferencing proved to be a good adjuvant to home monitoring of symptoms, complementing in-person assistance.

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Lung cancer is a disease with significant prevalence in several countries around the world. Its difficult treatment and rapid progression make the mortality rates among people affected by this illness to be very high. Aiming to offer a computational alternative for helping in detection of nodules, serving as a second opinion to the specialists, this work proposes a totally automatic methodology based on successive detection refining stages. The automated lung nodules detection scheme consists of six stages: thorax extraction, lung extraction, lung reconstruction, structures extraction, tubular structures elimination, and false positive reduction. In the thorax extraction stage all the artifacts external to the patient's body are discarded. Lung extraction stage is responsible for the identification of the lung parenchyma. The objective of the lung reconstruction stage is to prevent incorrect elimination of portions belonging to the parenchyma. Structures extraction stage comprises the selection of dense structures from inside the lung parenchyma. The next stage, tubular structures elimination eliminates a great part of the pulmonary trees. Finally, the false positive stage selects only structures with great probability to be nodule. Each of the several stages has very specific objectives in detection of particular cases of lung nodules, ensuring good matching rates even in difficult detection situations. We use 33 exams with diversified diagnosis and slices numbers for validating the methodology. We obtained a false positive per exam rate of 0.42 and false negative rate of 0.15. The total classification sensitivity obtained, measured out of the nodule candidates, was 84.84%. The specificity achieved was 96.15% and the total accuracy of the method was 95.21%.

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Oesophageal cancer is one of the most common and lethal malignancies in the world. Despite many efforts, treatment is still ineffective for most cases; thus, the development of preventive strategies is crucial for decreasing the burden presented by this disease. Environmental factors, particularly nitrosamines, are thought to be involved in the genesis of oesophageal tumours, and knowledge about the expression of enzymes capable of activating pre-carcinogens in human oesophagus is very important for the development of preventive measures. We analysed the expression of CYP1A1, CYP1A2, CYP2A6/2A7, CYP2E1 and CYP3A4 mRNA in oesophageal mucosa of 50 patients by semi-quantitative RT-PCR. In five patients, who suffered from squamous cell carcinoma, we measured Nnitrosodimethylamine and N-nitrosodiethylamine metabolism in normal and tumorous tissue. CYP2A6/2A7 mRNA was expressed in 61% and CYP2E1 mRNA in 96% of the patients, but in the latter a lower degree of inter-individual variation was observed. These enzymes were expressed either in the distal or middle portions of the oesophagus of 90% of the patients. CYP1A1, CYP1A2 and CYP3A4 mRNA expression was not detected in any portion of the oesophagus. Oesophageal microsomes activated N-nitrosodimethylamine with a low degree of inter-individual variation and microsomes prepared from the tumour of a patient who strongly expressed CYP2A6/2A7 mRNA activated N-nitrosodiethylamine. We conclude that the human oesophagus expresses CYP2A6/2A7 and CYP2E1 and can activate nitrosamines. Notably, the expression of these enzymes is preferentially localized to the most common sites where tumours arise.

Lung cancer is the major cause of death among patients with cancer worldwide. This work is intended to develop a methodology for the diagnosis of lung nodules using images from the Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI). The proposed methodology uses image processing and pattern recognition techniques. To differentiate the patterns of malignant and benign forms, we used a Minkowski functional, distance measures, representation of the vector of points measures, triangulation measures, and Feret diameters. Finally, we applied a genetic algorithm to select the best model and a support vector machine for classification. In the test stage, we applied the proposed methodology to 1405 (394 malignant and 1011 benign) nodules from the LIDC-IDRI database. The proposed methodology shows promising results for diagnosis of malignant and benign forms, achieving accuracy of 93.19 %, sensitivity of 92.75 %, and specificity of 93.33 %. The results are promising and demonstrate a good rate of correct detections using the shape features. Because early detection allows faster therapeutic intervention, and thus a more favorable prognosis for the patient, herein we propose a methodology that contributes to the area.

Background COVID-19 is characterized by a rapid change in the patient’s condition, with major changes occurring over a few days. We aimed to develop and evaluate an emergency system for monitoring patients with COVID-19, which may be useful in hospitals where more severe patients stay in their homes. Methodology/Principal findings The system consists of the home-based patient unit, which is set up around the patient and the hospital unit, which enables the medical staff to telemonitor the patient’s condition and help to send medical recommendations. The home unit allows the data transmission from the patient to the hospital, which is performed using a cell phone application. The hospital unit includes a virtual instrument developed in LabVIEW® environment that can provide a real-time monitoring of the oxygen saturation (SpO2), beats per minute (BPM), body temperature (BT), and peak expiratory flow (PEF). Abnormal events may be fast and automatically identified. After the design details are described, the system is validated by a 30-day home monitoring study in 12 controls and 12 patients with COVID-19 presenting asymptomatic to mild disease. Patients presented reduced SpO2 (p<0.0001) and increased BPM values (p<0.0001). Three patients (25%) presented PEF values between 50 and 80% of the predicted. Three of the 12 monitored patients presented events of desaturation (SpO2<92%). The experimental results were in close agreement with the involved pathophysiology, providing clear evidence that the proposed system can be a useful tool for the remote monitoring of patients with COVID-19. Conclusions An emergency system for home monitoring of patients with COVID-19 was developed in the current study. The proposed system allowed us to quickly respond to early abnormalities in these patients. This system may contribute to conserving hospital resources for those most in need while simultaneously enabling early recognition of patients under acute deterioration, requiring urgent assessment.