RESUMEN
BACKGROUND: Urinary tract infections (UTIs) are among the most common bacterial infections, and antibiotic resistance complicates empiric treatment. This study aimed to describe recent resistance patterns among uropathogens in a tertiary-care teaching hospital to optimize empiric UTI management. METHODS: This retrospective observational study included 280 patients diagnosed with UTIs at the Dr. Patnam Mahender Reddy Institute of Medical Sciences, Hyderabad, over a six-month period from June 2023 to November 2023. Urine culture and antibiotic susceptibility data were collected from electronic medical records. Patient demographics, including age, sex, and comorbid diabetes, were recorded. Causative uropathogens and their resistance rates to commonly prescribed UTI antibiotics were analyzed. Empiric antibiotic treatment patterns and outcomes were talked about. These included clinical cure, recurrence, susceptibility match, and microbiologic eradication. RESULTS: The mean age of patients was 43.5 years, with 196 (70%) being female and 70 (25%) having diabetes. Escherichia coli caused 210 (75%) of UTIs, Klebsiella pneumoniae 42 (15%), Proteus mirabilis 14 (5%), Enterococcus faecalis 8 (3%), and Staphylococcus saprophyticus 6 (2%). E. coli resistance rates were 48% for ampicillin, 25% for ciprofloxacin, 18% for trimethoprim/sulfamethoxazole (TMP/SMX), and 5% for nitrofurantoin. K. pneumoniae resistance rates were 89% for ampicillin, 67% for ciprofloxacin, 44% for TMP/SMX, and 22% for nitrofurantoin. The most frequently prescribed antibiotic was nitrofurantoin (45%), then ciprofloxacin (35%). Clinical cure was achieved in 75% of cases. Recurrent UTIs within four weeks occurred in 25% of cases. Treatment matched urine culture susceptibility in 82% of patients. CONCLUSION: The rising fluoroquinolone resistance highlights the need for current local data to guide empiric UTI treatment. Nitrofurantoin had low resistance rates and was an effective first-line therapy. Ongoing monitoring of resistance patterns in UTIs is essential to optimize antibiotic selection.
RESUMEN
Contrast-enhanced computed tomography (CE-CT) images are vital for clinical diagnosis of focal liver lesions (FLLs). However, the use of CE-CT images imposes a significant burden on patients due to the injection of contrast agents and extended shooting. Deep learning-based image synthesis models offer a promising solution that synthesizes CE-CT images from non-contrasted CT (NC-CT) images. Unlike natural images, medical image synthesis requires a specific focus on certain organs or localized regions to ensure accurate diagnosis. Determining how to effectively emphasize target organs poses a challenging issue in medical image synthesis. To solve this challenge, we present a novel CE-CT image synthesis model called, Organ-Aware Generative Adversarial Network (OA-GAN). The OA-GAN comprises an organ-aware (OA) network and a dual decoder-based generator. First, the OA network learns the most discriminative spatial features about the target organ (i.e. liver) by utilizing the ground truth organ mask as localization cues. Subsequently, NC-CT image and captured feature are fed into the dual decoder-based generator, which employs a local and global decoder network to simultaneously synthesize the organ and entire CECT image. Moreover, the semantic information extracted from the local decoder is transferred to the global decoder to facilitate better reconstruction of the organ in entire CE-CT image. The qualitative and quantitative evaluation on a CE-CT dataset demonstrates that the OA-GAN outperforms state-of-the-art approaches for synthesizing two types of CE-CT images such as arterial phase and portal venous phase. Additionally, subjective evaluations by expert radiologists and a deep learning-based FLLs classification also affirm that CE-CT images synthesized from the OA-GAN exhibit a remarkable resemblance to real CE-CT images.
