Background: While the potential anti-cancer effects of antidepressants have been investigated, limited research has been conducted incorporating age-specific analyses for individual tricyclic antidepressants (TCAs) and selective serotonin reuptake inhibitors (SSRIs). This study aims to elucidate the associations between TCAs and SSRIs with gynecologic cancers and to identify specific age groups and cancer types that may benefit from the chemopreventive effects of these medications. Methods: A case-control study comprised 97,736 female patients diagnosed with gynecologic cancers between 2002 and 2016. Each newly diagnosed case of cervical, ovarian, or uterine cancer was matched with four controls. Both cases and controls were then stratified by age to perform subgroup analyses. Associations between antidepressant use and cancer risk were evaluated using multivariable conditional logistic regression models. Results: The TCA class was significantly associated with reduced risks of cervical, ovarian, and uterine cancers, displaying adjusted odds ratios (aORs) of 0.799, 0.775, and 0.813, respectively. The SSRI class also indicated reduced risks, with aORs of 0.736, 0.638, and 0.567 for the same cancer types. Particularly noteworthy were females aged 40–64, who demonstrated the most significant associations between gynecologic cancers and using TCAs or SSRIs. Conclusions: TCAs and SSRIs are associated with reduced risks of developing cervical, ovarian, and uterine cancers. The middle-aged population may have the most significant potential for future research on drug repurposing against gynecologic cancers, and both cervical and uterine cancers are potential targets for drug repurposing involving TCAs or SSRIs.

Accurate diagnosis of knee joint injuries from magnetic resonance (MR) images is critical for patient care. Background/Objectives: While deep learning has advanced 3D MR image analysis, its reliance on extensive labeled datasets is a major hurdle for diverse knee pathologies. Few-shot learning (FSL) addresses this by enabling models to classify new conditions from minimal annotated examples, often leveraging knowledge from related tasks. However, creating robust 3D FSL frameworks for varied knee injuries remains challenging. Methods: We introduce MedNet-FS, a 3D FSL framework that effectively classifies knee injuries by utilizing domain-specific pre-trained weights and generalized end-to-end (GE2E) loss for discriminative embeddings. Results: MedNet-FS, with knee-MRI-specific pre-training, significantly outperformed models using generic or other medical pre-trained weights and approached supervised learning performance on internal datasets with limited samples (e.g., achieving an area under the curve (AUC) of 0.76 for ACL tear classification with k = 40 support samples on the MRNet dataset). External validation on the KneeMRI dataset revealed challenges in classifying partially torn ACL (AUC up to 0.58) but demonstrated promising performance for distinguishing intact versus fully ruptured ACLs (AUC 0.62 with k = 40). Conclusions: These findings demonstrate that tailored FSL strategies can substantially reduce data dependency in developing specialized medical imaging tools. This approach fosters rapid AI tool development for knee injuries and offers a scalable solution for data scarcity in other medical imaging domains, potentially democratizing AI-assisted diagnostics, particularly for rare conditions or in resource-limited settings.