Publications by authors named "S B Tummala"
Background: Neurologic symptoms seen in patients receiving immune checkpoint inhibitors (ICI) may not be entirely caused by immunotoxicity. We aim to highlight these confounding conditions through clinical cases to encourage early recognition and management.
Methods: We describe a series of seven cases from our institution that were treated with ICI and presented with Neurologic symptoms and were diagnosed with superimposed conditions beyond immunotoxicity.
Background: Tobacco use is a known modifiable risk factor for postoperative complications and revision surgery after anterior cruciate ligament reconstruction (ACLR). Previous studies focus on tobacco as a broad categorization of traditional smoking, smokeless tobacco, and other forms of nicotine use. It is unclear if differences in the type of nicotine used lead to similar adverse outcomes after ACLR.
View Article and Find Full Text PDFBackground: Patent ductus arteriosus (PDA) is a congenital heart defect that requires closure to prevent complications like heart failure and pulmonary hypertension. Catheter-based closure using devices such as the Amplatzer duct occluder is the preferred method due to its minimally invasive nature. However, device embolization is a rare but recognized complication, particularly in small children or high-flow PDAs.
View Article and Find Full Text PDFArticle Synopsis
- The study aimed to assess trends in the use and reimbursement of open carpal tunnel release (OCTR) and endoscopic carpal tunnel release (ECTR) surgeries among Medicare patients from 2013 to 2021.
- Results showed a notable increase in ECTR utilization (50%) compared to OCTR (6%), with regional differences in usage and reimbursement amounts; the Midwest had the highest OCTR usage but lowest ECTR usage.
- Despite the increased utilization of both procedures, reimbursement rates fell (10.3% for OCTR and 11.8% for ECTR), and the patient demographics revealed fewer patients with serious comorbidities or dual Medicare-Medicaid coverage, suggesting stricter criteria for surgery eligibility
Quick and accurate structural damage detection is essential for maintaining the safety and integrity of infrastructure, especially following natural disasters. Traditional methods of damage assessment, which rely on manual inspections, can be labor-intensive and subject to human error. This paper introduces a hybrid deep learning model that combines the capabilities of ResNet50 and GoogLeNet, further enhanced by a convolutional block attention module (CBAM), proposed to improve both the accuracy and performance in detecting structural damage.
View Article and Find Full Text PDF