Effect of Photobiomodulation With Different Wavelengths on Radiodermatitis Treatment.

Approximately 80% of patients submitted to radiotherapy develop radiodermatitis. Photobiomodulation based on light-emitted diode (LED) is one of the therapeutic strategies for treating inflammation. This study aimed to investigate the effect of the photobiomodulation with two wavelengths, in an acute radiodermatitis animal model. Methods: Twenty rats were submitted to one radiotherapy session. After 15 days, the rats that developed radiodermatitis were divided into control groups, LED-630 nm, LED-850 nm, and LED-630 + 850 nm. The treatment regimen was one session lasting 10 minutes on alternate days for 21 days. We analyzed macroscopy aspects (RTOG scale), vascular density, dermal appendages, VEGF-a, TNF-alpha, MMP-9, and MMP-9 genic expression level. Results: All LED groups revealed a two-point reduction on the radiodermatitis severity grade compared with the baseline classification. Dermal appendage and vascular analysis showed a higher counting in all LED groups compared to control. This study showed dermal appendages twice in the 630/850 nm group compared with the control group. The 630/850 nm group showed six times more arterioles than the control group. Regarding genic expression, this study showed a 10-fold decrease between LED-630 nm versus LED-630 + 850 nm (P = 0.02) interleukin-10 expression and a 12-fold decrease between control versus LED-630 nm (P = 0.006) and LED-850 nm (P = 0.002) in TNF-alpha. Conclusion: LED (630 nm, 850 nm, and 630 nm + 850 nm) showed benefit in the treatment of radiodermatitis, and the association of the 630 nm + 850 nm and 630 nm parameters demonstrated the best macroscopic and microscopic results.

Water tank and swimming pool detection based on remote sensing and deep learning: Relationship with socioeconomic level and applications in dengue control.

Studies have shown that areas with lower socioeconomic standings are often more vulnerable to dengue and similar deadly diseases that can be spread through mosquitoes. This study aims to detect water tanks installed on rooftops and swimming pools in digital images to identify and classify areas based on the socioeconomic index, in order to assist public health programs in the control of diseases linked to the Aedes aegypti mosquito. This study covers four regions of Campinas, São Paulo, characterized by different socioeconomic contexts. With mosaics of images obtained by a 12.1 MP Canon PowerShot S100 (5.2 mm focal length) carried by unmanned aerial vehicles, we developed deep learning algorithms in the scope of computer vision for the detection of water tanks and swimming pools. An object detection model, which was initially created for areas of Belo Horizonte, Minas Gerais, was enhanced using the transfer learning technique, and allowed us to detect objects in Campinas with fewer samples and more efficiency. With the detection of objects in digital images, the proportions of objects per square kilometer for each region studied were estimated by adopting a Chi-square distribution model. Thus, we found that regions with low socioeconomic status had more exposed water tanks, while regions with high socioeconomic levels had more exposed pools. Using deep learning approaches, we created a useful tool for Ae. aegypti control programs to utilize and direct disease prevention efforts. Therefore, we concluded that it is possible to detect objects directly related to the socioeconomic level of a given region from digital images, which encourages the practicality of this approach for studies aimed towards public health.