The objective of this work was to study the morphometry and morphology of the round window (RW) and its relationships with the internal carotid artery, jugular bulb (JB), facial nerve and oval window (OW). Fifty cadaveric temporal bones were microdissected to expose the medial wall of the middle ear. The areas around the RW were cleared and its shape, height and width were noted. Its distances from the carotid canal (CC), jugular fossa (JF), facial canal (FC), and OW were measured. Oval, round, triangular, comma, quadrangular, and pear shapes of RW were observed. The average height and width of the RW were 1.62 ± 0.77 mm and 1.15 ± 0.39 mm, respectively. There was a statistically significant correlation (r = 0.4, P < 0.01) between the height and width. The distances between the RW and the CC, JF, FC, and OW were in the ranges 4.39-11.05 mm, 0.38-8.65 mm, 2.99-6.3 mm, and 1.39-3.57 mm, respectively. In 8% of cases, the distance between the RW and the JF was <1 mm. There were no statistically significant differences with regard to age group, gender, or side. Electrode insertion can be challenging in cases where the height and width of the RW are <1 mm. The thin bone separating the roof of the JF from the RW (<1 mm in 8%) highlights a potential risk of injury to the JB during cochleostomy placement. This information could be useful for selecting cochlear implant electrodes in order to avoid potential risks to vital neurovascular structures during implant surgery.

Aims: The aim of this study was to analyze the current trend in the use of antidiabetes as well as other drugs for comorbidities along the duration of diabetes. The study also aimed to analyze the direct drug cost to patients. Settings and Design: Retrospective cross-sectional study. Subjects and Methods: Data captured in clinic electronic medical records of an endocrine practice was analyzed. Statistical Analysis Used: Data was analyzed descriptively using machine learning codes on python platform. Results: Records of 489 people who attended the clinic during the 6-month period were retrieved. Data of 403 people with diabetes were analyzed after exclusion of incomplete data. Use of antidiabetic drug increased from 1.44 (0.78) [mean (standard deviation)] in people with a duration of diabetes <5 years to 3.18 (1.05) in people with 20+ years of diabetes. The mean number of antidiabetic drug usage seems to plateau at 15 years of diabetes. About 46% of people with 20+ years of diabetes required insulin therapy. Prescription patterns involving a combination of different drug classes in patients were also analyzed. The cost of diabetes therapy increases linearly along the duration of diabetes. Conclusion: This study provides valuable insights on temporal prescription patterns of antidiabetic drugs from an endocrine practice. Metformin remains the most preferred drug across the entire duration of diabetes. Dipeptidyl peptidase-4 inhibitors seem to be fast catching up with sulfonylureas as a second-line treatment after metformin. After 20 years or more of diabetes duration, 46% people would require insulin for glycemic control.

Health monitoring of civil infrastructures is a key application of Internet of things (IoT), while edge computing is an important component of IoT. In this context, swarms of autonomous inspection robots, which can replace current manual inspections, are examples of edge devices. Incorporation of pretrained deep learning algorithms into these robots for autonomous damage detection is a challenging problem since these devices are typically limited in computing and memory resources. This study introduces a solution based on network pruning using Taylor expansion to utilize pretrained deep convolutional neural networks for efficient edge computing and incorporation into inspection robots. Results from comprehensive experiments on two pretrained networks (i.e., VGG16 and ResNet18) and two types of prevalent surface defects (i.e., crack and corrosion) are presented and discussed in detail with respect to performance, memory demands, and the inference time for damage detection. It is shown that the proposed approach significantly enhances resource efficiency without decreasing damage detection performance.