ZrO₂ thin film samples were produced by the sol-gel dip coating method. Four different absorbed dose levels (such as ~ 0.4, 0.7, 1.2 and 2.7 Gray-Gy) were applied to ZrO₂ thin films. Hence, the absorbed dose of ZrO₂ thin film was examined as physical dose quantity representing the mean energy imparted to the thin film per unit mass by gamma radiation. Modification of the grain size was performed sensitively by the application of the absorbed dose to the ZrO₂ thin film. Therefore the grain size reached from ~50 nm to 87 nm at the irradiated ZrO₂ thin film. The relationship of the grain size, the contact angle, and the refractive index of the irradiated ZrO₂ thin film was investigated as being an important technical concern. The irradiation process was performed in a hot cell by using a certified solid gamma ray source with 0.018021 Ci as an alternative technique to minimize the utilization of extra toxicological chemical solution. Antireflection and hydrophilic properties of the irradiated ZrO₂ thin film were slightly improved by the modification of the grain size. The details on the optical and structural properties of the ZrO₂ thin film were examined to obtain the optimum high refractive index, self-cleaning and anti-reflective properties.

The cost of diagnostic errors has been high in the developed world economics according to a number of recent studies and continues to rise. Up till now, a common process of performing image diagnostics for a growing number of conditions has been examination by a single human specialist (i.e., single-channel recognition and classification decision system). Such a system has natural limitations of unmitigated error that can be detected only much later in the treatment cycle, as well as resource intensity and poor ability to scale to the rising demand. At the same time Machine Intelligence (ML, AI) systems, specifically those including deep neural network and large visual domain models have made significant progress in the field of general image recognition, in many instances achieving the level of an average human and in a growing number of cases, a human specialist in the effectiveness of image recognition tasks. The objectives of the AI in Medicine (AIM) program were set to leverage the opportunities and advantages of the rapidly evolving Artificial Intelligence technology to achieve real and measurable gains in public healthcare, in quality, access, public confidence and cost efficiency. The proposal for a collaborative AI-human image diagnostics system falls directly into the scope of this program.

Objective: To determine the presence of bacteria by means of microbiological analysis on the surfaces contacted by the operator during the taking and processing of intraoral radiographs at different times of the day in the Oral Radiology Service of the UPCH. Materials and methods: Nine surfaces of the oral radiology service were sampled. The samples were taken at two times by the same investigator; at the beginning and the end of the activities in the service, the surfaces were swabbed with Trypticase Soy Broth (TSB). The samples were inoculated and incubated in three culture media (Plate Count Agar, Lamb’s Blood Agar and Cetrimide Agar). Then the respective Colony Forming Unit (CFU) count was performed and Gram staining was also performed. Results: A high concentration of bacteria (4180 CFU/mL) and fungi was found in the oral radiology service. Gram-positive cocci were the most frequently found microorganisms and gram-negative bacilli were less frequently found. Conclusions: There is a high contamination of bacteria in the oral radiology service. When the activities are completed, the number of bacteria decreases, but the variety of bacteria increases.

In the last several decades, cardiovascular diseases (CVDs) have emerged as a major hazard to human life and health. Conventional formulations for the treatment of CVD are available, but they are far from ideal because of poor water solubility, limited biological activity, non-targeting, and drug resistance. With the advancement of nanotechnology, a novel drug delivery approach for the treatment of CVDs has emerged: nano-drug delivery systems (NDDSs). NDDSs have shown significant advantages in tackling the difficulties listed above. Cytotoxicity is a difficulty with the use of non-destructive DNA sequences. NDDS categories and targeted tactics were outlined, as well as current research advancements in the diagnosis and treatment of CVDs. It’s possible that gene therapy might be included into nano-carriers in the delivery of cardiovascular medications in the future. In addition, the evaluation addressed the drug’s safety.