Hasil untuk "United States"

P. Ravdin, K. Cronin, N. Howlader et al.

G. Naccarelli, H. Varker, Jay Lin et al.

D. Dabelea, R. Bell, R. D'Agostino et al.

D. Angus, A. Barnato, W. Linde‐Zwirble et al.

Stephanie Staras, S. Dollard, Kay W. Radford et al.

S. Preston, E. Kitagawa, P. Hauser

Narcotics Anonymous

L. Finer, S. Henshaw

M. Greenwood

E. L. Little

D. Friedman, R. Wolfs, Benita J. O’Colmain et al.

E. Finkelstein, P. Corso, T. Miller

D. Cayan, S. A. Kammerdiener, M. Dettinger et al.

R. Bailey, J. Gahche, Cindy V Lentino et al.

E. Weber, P. Stern

V. Jennings, I. Sinai

J. Moorman, Lara J. Akinbami, C. Bailey et al.

Timothy R Dewitt

Kaviya Devaraja, Anjali Sachdeva, Karina Gandhi et al.

Purpose: Pelvic radiotherapy (RT) is an effective treatment for pelvic malignancies but often leads to sexual and reproductive health (SRH) challenges, particularly among adolescent and young adult (AYA) patients assigned female at birth (AFAB). Despite known toxicities, no study has systematically explored the SRH experiences, challenges, and unmet care needs of this population post-RT. This study aimed to assess SRH impacts and identify gaps in care among AYA AFAB cancer survivors following pelvic RT. Patients and Methods: We conducted a simultaneous mixed-methods study at a tertiary cancer center. AYA AFAB patients aged 18–39 who completed pelvic RT between 2018 and 2023 completed a 23-item survey assessing SRH experiences and care satisfaction; a subset participated in in-depth interviews. Quantitative data were analyzed descriptively, and qualitative data were examined using thematic analysis. Triangulation enabled a comprehensive understanding of patient experiences. Results: Fifty-two participants completed the survey (median age 33), and 15 completed interviews. Most reported sexual dysfunction (90%), intimacy disruption (90%), and fertility-related distress (87%). Three themes emerged: (1) RT-induced sexual dysfunction negatively affected relationships and intimacy, (2) fertility and reproductive disruptions were compounded by inadequate education and support, and (3) chronic pelvic discomfort and body image changes caused emotional and psychosocial distress. Participants emphasized limited provider communication, scarce RT-specific SRH resources, and lack of post-treatment supports. Conclusions: This first study characterizing RT-specific SRH disruptions among AYA AFAB survivors underscores the urgent need for age-appropriate survivorship care integrating SRH education, counseling, and partner-inclusive supports.

Meng Tan, Zhe Guo, Yanyi Wang et al.

ABSTRACT Rapid and accurate identification of Aspergillus species in clinical microbiology laboratories is crucial for aspergillosis diagnosis and antifungal therapy. However, traditional methods still face challenges in distinguishing phylogenetically related species due to their morphological similarities. This study presents FungalNet, a deep learning model integrating ResNet-50 architecture with Focal Loss algorithm, specifically designed to enhance feature extraction for Aspergillus identification. A total of 12,000 high-resolution images were obtained from lactophenol cotton blue-stained slide preparations under a 100× oil immersion objective, among which 311 images were excluded through a novel quality control approach combining fivefold cross-validation and expert manual review. The performance of four deep learning models (FungalNet and three established models) for identifying Aspergillus species and sections was evaluated using the remaining 11,689 qualified images. FungalNet demonstrated superior classification performance, achieving overall accuracies of 98.45% and 97.85% at the section and species levels, respectively. These results indicate that FungalNet shows significant promise for rapid and accurate identification of Aspergillus species. With further optimization and multicenter validation, this tool could potentially be integrated into routine diagnostic workflows to enhance the efficiency and reliability of fungal identification in clinical settings.IMPORTANCEThis study integrates microscopic morphology identification with deep learning to address the challenge of accurate Aspergillus species identification. Twelve clinically isolated Aspergillus species belonging to eight different sections were included. From touch-tape slide preparations with lactophenol cotton blue staining under a 100× oil immersion objective, 11,689 qualified images were collected and analyzed using FungalNet (our proposed model) along with three established models (GoogLeNet, ResNet-50, and Xception). The results showed that FungalNet demonstrated superior performance in Aspergillus identification, achieving the highest classification accuracy at both section (98.45%) and species (97.85%) levels. Given its rapid turnaround time and cost-effectiveness, this AI-based image analysis approach shows promising potential for the rapid and accurate identification of Aspergillus species in clinical microbiology laboratories.