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In order to examine whether intratracheal instillation studies can be useful for determining the harmful effect of nanoparticles, we performed inhalation and intratracheal instillation studies using samples of the same nanoparticles. Nickel oxide nanoparticles (NiO) and titanium dioxide nanoparticles (TiO2) were used as chemicals with high and low toxicities, respectively. In the intratracheal instillation study, rats were exposed to 0.2 or 1 mg of NiO or TiO2. Cell analysis and chemokines in bronchoalveolar lavage fluid (BALF) were analyzed from 3 days to 6 months following the single intratracheal instillation. In the inhalation study, rats were exposed to inhaled NiO or TiO2 (1.65, 1.84 mg/m3, respectively) for 4 weeks. The same endpoints were examined from 3 days to 3 months after the end of exposure. Inhalation of NiO induced an increase in the number of neutrophils in BALF and concentrations of cytokine-induced neutrophil chemoattractant (CINC)-1, CINC-2 and heme oxygenase (HO)-1. Intratracheal instillation of NiO induced persistent inflammation and upregulation of these cytokines was observed in the rats. However, inhalation of TiO2 did not induce pulmonary inflammation, and intratracheal instillation of TiO2 transiently induced an increase in the number of neutrophils in BALF and the concentrations of CINC-1, CINC-2 and HO-1. Taken together, a difference in pulmonary inflammation was observed between the high and low toxicity nanomaterials in the intratracheal instillation studies, as in the inhalation studies, suggesting that intratracheal instillation studies may be useful for ranking the harmful effects of nanoparticles. [ABSTRACT FROM AUTHOR]

Objectives: We performed the two inhalation exposures, whole-body inhalation and nose-only inhalation, to investigate the pulmonary deposition and health effects of the two inhalation methods. Methods: In both methods, we exposed rats to the same TiO nanoparticles at almost the same exposure concentration for 6 h and compared the deposited amounts of nanoparticles and histopathological changes in the lungs. Rats were exposed to rutile-type TiO nanoparticles generated by the spray-dry method for 6 h. The exposure concentration in the whole-body chamber was 4.10 ± 1.07 mg/m, and that in nose-only chamber was 4.01 ± 1.11 mg/m. The particle sizes were 230 and 180 nm, respectively. A control group was exposed to fresh air. Results: The amounts of TiO deposited in the lungs as measured by ICP-AES after acid digestion just after the exposure were: 42.6 ± 3.5 μg in the whole-body exposure and 46.0 ± 7.7 μg in the nose-only exposure groups. The histopathological evaluation was the same in both exposure groups: no infiltration of inflammatory cells in the alveolar space and interstitium, and no fibrosis. Conclusion: The two inhalation methods using the same material under the same exposure conditions resulted in the same particle deposition and histopathology in the lung. [ABSTRACT FROM AUTHOR]

In the last decades, many publicly available large fundus image datasets have been collected for diabetic retinopathy, glaucoma, and age-related macular degeneration, and a few other frequent pathologies. These publicly available datasets were used to develop a computer-aided disease diagnosis system by training deep learning models to detect these frequent pathologies. One challenge limiting the adoption of a such system by the ophthalmologist is, computer-aided disease diagnosis system ignores sight-threatening rare pathologies such as central retinal artery occlusion or anterior ischemic optic neuropathy and others that ophthalmologists currently detect. Aiming to advance the state-of-the-art in automatic ocular disease classification of frequent diseases along with the rare pathologies, a grand challenge on "Retinal Image Analysis for multi-Disease Detection" was organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI - 2021). This paper, reports the challenge organization, dataset, top-performing participants solutions, evaluation measures, and results based on a new "Retinal Fundus Multi-disease Image Dataset" (RFMiD). There were two principal sub-challenges: disease screening (i.e. presence versus absence of pathology - a binary classification problem) and disease/pathology classification (a 28-class multi-label classification problem). It received a positive response from the scientific community with 74 submissions by individuals/teams that effectively entered in this challenge. The top-performing methodologies utilized a blend of data-preprocessing, data augmentation, pre-trained model, and model ensembling. This multi-disease (frequent and rare pathologies) detection will enable the development of generalizable models for screening the retina, unlike the previous efforts that focused on the detection of specific diseases., Competing Interests: Declaration of competing interest The authors have no conflicts of interest to declare., (Copyright © 2024 Elsevier B.V. All rights reserved.)