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Purpose: To study the clinical features, causative factors and treatment outcomes in patients with infectious scleritis seen in India. Methods: A retrospective study of all patients examined at a tertiary care center between August 2012 and March 2021. Results: Forty-five patients (45 eyes; mean age 52.7 ± 17.5 years) were included in the study. The mean duration of symptoms was 3.1 ± 4.4 months. Inciting factors were found in 53.3% (injury: 33.3%; ocular surgery: 20.0%). The scleritis was predominantly anterior (97.8%), with multiple lesions in 40.0%, a solitary lesion in 31.1%, and diffuse in 28.9%. Associated features included uveitis (51.1%), keratitis (37.8%), hypopyon (15.6%), and endophthalmitis (6.7%). Causative organisms included bacteria (53.3%), fungi (35.6%), and presumed herpes virus (11.1%). All patients were treated with antimicrobial agents along with systemic corticosteroids where indicated. Surgical treatment included scleral debridement (37.8%), patch grafts (4.4%), and penetrating keratoplasty (2.2%). Complete resolution of scleritis was seen in 86.7%, with a mean duration of therapy of 2.9 ± 2.5 months. The mean follow-up was 8.3 ± 14.3 months. 51.1% of patients lost functional vision (<6/60). Causes of decreased vision included corneal scar, cataract, macular scar, glaucomatous optic atrophy, and phthisis bulbi. On bivariate analysis, poor visual acuity at presentation was associated with a worse visual outcome (p = 0.02). Other risk factors included necrotizing scleritis, multifocal scleritis, the presence of keratitis and uveitis. Conclusion: In our study, infectious scleritis resulted from bacterial and fungal infections. The scleritis resolved in most subjects, however, vision loss was frequent due to infection-related complications. [ABSTRACT FROM AUTHOR]

Inverter configurations for multiple-load induction cooking applications need development. Inverter configurations for induction cooking applications are used in home appliances based on single coil inverters. For multiple-load configurations, multiple coils are used. They require proper inverters, which provide independent control for each load and have fewer components. This paper presents a three-leg inverter configuration for three load induction cooking applications. Each induction coil powers one induction cooking load. This configuration operates with constant switching frequency and powers individual loads. The output power of the required load is controlled with a phase-shift control technique. This configuration is simulated and experimentally tested with three induction loads. The simulation and experimental results are in good agreement. This configuration can be extended to more loads. [ABSTRACT FROM AUTHOR]

In this paper an effective and simple power control scheme is presented for two load induction cooking application with constant switching frequency in class-E series resonant inverter. The proposed configuration operates in synchronization with load switch switching pulse and inverter output voltage. Each load output power is controlled individually by duty ratio variation with each load switching device separately. The proposed configuration can be extended to multiple loads. It is more reliable for multiple load induction cooking application with output power control of each load independently. The class-E series resonant inverter operates with constant switching frequency and constant duty ratio. The proposed class-E series resonant inverter configuration can be operating at stable and efficient ZVS operation. The proposed configuration for multiple load induction cooking application with independent control of each load is simulated in MATLAB/Simulink environment. [ABSTRACT FROM AUTHOR]

Background Since the introduction of TotalSegmentator CT, there has been demand for a similar robust automated MRI segmentation tool that can be applied across all MRI sequences and anatomic structures. Purpose To develop and evaluate an automated MRI segmentation model for robust segmentation of major anatomic structures independent of MRI sequence. Materials and Methods In this retrospective study, an nnU-Net model (TotalSegmentator MRI) was trained on MRI and CT scans to segment 80 anatomic structures relevant for use cases such as organ volumetry, disease characterization, surgical planning, and opportunistic screening. Images were randomly sampled from routine clinical studies to represent real-world examples. Dice scores were calculated between the predicted segmentations and expert radiologist segmentations to evaluate model performance on an internal test set and two external test sets and against two publicly available models and TotalSegmentator CT. The Wilcoxon signed rank test was used to compare model performance. The proposed model was applied to a separate internal dataset containing abdominal MRI scans to investigate age-dependent volume changes. Results A total of 1143 scans (616 MRI, 527 CT; median patient age, 61 years [IQR, 50-72 years]) were split into a training set ( n = 1088; CT and MRI) and an internal test set ( n = 55; MRI only). The two external test sets (AMOS and CHAOS) contained 20 MRI scans each, and the aging-study dataset contained 8672 abdominal MRI scans (median patient age, 59 years [IQR, 45-70 years]). The proposed model had a Dice score of 0.839 for the 80 anatomic structures in the internal test set and outperformed two other models (Dice score of 0.862 vs 0.759 for 40 anatomic structures and 0.838 vs 0.560 for 13 anatomic structures; P < .001 for both). On the TotalSegmentator CT test set (89 CT scans), the performance of the proposed model almost matched that of TotalSegmentator CT (Dice score, 0.966 vs 0.970; P < .001). The aging study demonstrated a strong correlation between age and organ volume (eg, age and liver volume: ρ = -0.096; P < .0001). Conclusion The proposed open-source, easy-to-use model allows for automatic, robust segmentation of 80 structures, extending the capabilities of TotalSegmentator to MRI scans from any MRI sequence. The ready-to-use online tool is available at https://totalsegmentator.com ; the model, at https://github.com/wasserth/TotalSegmentator ; and the dataset, at http://zenodo.org/records/14710732 . © RSNA, 2025 Supplemental material is available for this article. See also the editorial by Kitamura in this issue.