Radiochromic film has become an important tool to verify dose distributions for intensity‐modulated radiotherapy (IMRT) and quality assurance (QA) procedures. A new radiochromic film model, EBT3, has recently become available, whose composition and thickness of the sensitive layer are the same as those of previous EBT2 films. However, a matte polyester layer was added to EBT3 to prevent the formation of Newton's rings. Furthermore, the symmetrical design of EBT3 allows the user to eliminate side‐orientation dependence. This film and the flatbed scanner, Epson Perfection V750, form a dosimetry system whose intrinsic characteristics were studied in this work. In addition, uncertainties associated with these intrinsic characteristics and the total uncertainty of the dosimetry system were determined. The analysis of the response of the radiochromic film (net optical density) and the fitting of the experimental data to a potential function yielded an uncertainty of 2.6%, 4.3%, and 4.1% for the red, green, and blue channels, respectively. In this work, the dosimetry system presents an uncertainty in resolving the dose of 1.8% for doses greater than 0.8 Gy and less than 6 Gy for red channel. The films irradiated between 0 and 120 Gy show differences in the response when scanned in portrait or landscape mode; less uncertainty was found when using the portrait mode. The response of the film depended on the position on the bed of the scanner, contributing an uncertainty of 2% for the red, 3% for the green, and 4.5% for the blue when placing the film around the center of the bed of scanner. Furthermore, the uniformity and reproducibility radiochromic film and reproducibility of the response of the scanner contribute less than 1% to the overall uncertainty in dose. Finally, the total dose uncertainty was 3.2%, 4.9%, and 5.2% for red, green, and blue channels, respectively. The above uncertainty values were obtained by minimizing the contribution to the total dose uncertainty of the film orientation and film homogeneity.PACS number(s): 87.53.Bn

Tuberculosis (TB) of the central nervous system (CNS) presents high mortality due to brain damage and inflammation events. The formation and deposition of immune complexes (ICs) in the brain microvasculature during Mycobacterium tuberculosis (Mtb) infection are crucial for its pathobiology. The relevance of ICs to Mtb antigens in the pathogenesis of CNS-TB has been poorly explored. Here, we aimed to establish a murine experimental model of ICs-mediated brain vasculitis induced by cell wall antigens of Mtb. We administered a cell wall extract of the prototype pathogenic Mtb strain H37Rv to male BALB/c mice by subcutaneous and intravenous routes. Serum concentration and deposition of ICs onto blood vessels were determined by polyethylene glycol precipitation, ELISA, and immunofluorescence. Histopathological changes in the brain, lung, spleen, liver, and kidney were evaluated by hematoxylin and eosin staining. Our results evidenced that vasculitis developed in the studied tissues. High serum levels of ICs and vascular deposition were evident in the brain, lung, and kidneys early after the last cell wall antigen administration. Cell wall Mtb antigens induce strong type III hypersensitivity reactions and the development of systemic vasculitis with brain vascular changes and meningitis, supporting a role for ICs in the pathogenesis of TB.

Objective. To classify digital mammograms based on radiological findings using morphology and texture descriptors with artificial neural networks (ANN) for breast cancer detection. Approach. The mammography dataset from High Specialty Regional Hospital of Oaxaca (HRAEO) (median patient age (mpa), 48 years [interquartile range (IQR), 41-54 years]) with radiological findings was retrospectively analyzed. All patients underwent breast biopsy and were not previously treated. External testing was performed using mammograms from the National Cancer Institute (INCAN) (mpa: 47 years [IQR, 37-62 years]). The morphology was analyzed using a circularity descriptor (), and the texture was analyzed using the mean height/width ratio of the extrema descriptor (). These results were compared with cancer/benign histopathology, which was binarily classified using ANNs. The F1-score, Cohen’s kappa (K), and area under the ROC curve (AUC) were employed as evaluation metrics, and the Wilcoxon rank-sum test was used for statistical analysis (h = 0, with p > 0.05, was considered as not statistically significant). Main results. 216 raw mammograms from HRAEO and 33 mammograms from INCAN (95+16 breast cancer and 121+17 benign findings) were included. The best internal testing results were obtained with a one-hidden-layer ANN with 100 neurons, achieving a F1-score of 0.95, K of 0.91, and an AUC of 0.953 (95% confidence interval [CI]: 0.917, 0.977) (h=0, p>0.99). However, the external testing results were significantly lower: 0.38 F1-score, 0.02 K, and 0.509 AUC (95% CI: 0.344, 0.664) (h=0, p=0.14) due to not exactly meeting the inclusion criteria and possible demographic and spectrum bias, or domain-adaptation issues. Significance. The proposed morphology () and texture () descriptors show promise for detecting breast cancer in raw mammograms, with radiological findings, in a local context. However, their poor external performance highlights the need for substantial further work before this approach can be deemed suitable for broader diagnostic applications.