Pranav Sompalle

Introduction: Nitazenes are an emerging class of highly potent synthetic opioids not approved for clinical use. Knowledge of nitazene-associated practices and clinical effects remains limited. Reddit contains nitazene-related discussions describing self-reported experiences. We analyzed nitazene mentions to characterize these experiences. Methods: Nitazene-mentioning posts between 01/2019 and 10/2025 were extracted via natural language processing-based lexical filters, and a random sample of 293 mentions was qualitatively reviewed and annotated for themes consolidated via consensus discussion. Repeated findings more specific than themes were synthesized as subthemes, and a validated GPT-4o-based pipeline was applied to all mentions to forecast the theme distribution. Results: A total of 31,638 mentions were extracted; frequency increased between 2022 and 2024 (mean 584 mentions/month, P <0.01 increase compared with 2019–2021’s mean of 197 mentions/month). Ten themes were curated, with source (31.3%), potency and dose (26.5%), and uncertainty (25.9%) most prevalent in the annotated sample. Across the full dataset, automated characterization predicted that general experience (44.7%), general comment (37.9%), and potency and dose (27.6%) were the most prevalent thematic labels. Nine subthemes included information about counterfeit pills, withdrawal management difficulties, motivating factors for use, benzodiazepine adulteration, and vascular injury associated with intravenous administration. Discussion: Increasing nitazene mentions on Reddit may reflect increasing interest and use. Thematic characterization identified patterns in nitazene use, effects, and distribution relevant to clinical practice and public health. Insights provided by subthemes align with laboratory potency assessments and emerging evidence from case series and health alerts. Conclusion: These findings can inform public health monitoring, overdose prevention, and clinical characterizations of nitazene use.

Objectives: Accurate identification of xylazine-associated wounds (XAWs) is critical to providing timely and optimal management; however, discerning the etiology of wounds by appearance alone poses a clinical challenge. This study sought to develop an accessible and accurate approach for XAW diagnosis using a deep learning tool applied to wound photographs. Methods: Publicly accessible wound photographs were curated from academic publications, Reddit, and news media to develop, train, and test the deep learning tool. XAWs were defined by provided clinical confirmation or self-reported descriptions associated with each image. The data set included images of 114 xylazine-associated and 1710 nonxylazine wounds from 17 distinct pathologies. Four deep learning models (DenseNet121, EfficientNetB0, ResNet34, and SENet154) were trained on 1185 images (65%) and 163 for validation (9%) to predict xylazine exposure and tested using 476 unseen wound images (26%). Results: All 4 deep learning models achieved consistent diagnostic performance on 476 unseen wound images (accuracy: 97.5%–98.5%; AUROC: 96.8%–99.7%; weighted F1 score: 97.2%–98.5%). High specificity, reaching 100.0%, was observed across the 4 models. Sensitivity ranged from 60.0% to 80.0% across the 4 models, with SENet154 demonstrating robust performance across all metrics. Qualitative assessment demonstrated accurate identification of XAWs with high-confidence exclusion of xylazine exposure in wounds attributed to trauma, surgery, pressure, or venous ulcers. Conclusions: This novel deep learning tool can enable accurate identification of XAW. With further validation, this tool may offer an accessible and automated approach to guide wound care, augment bedside clinical medicine assessments, and equip public health efforts to monitor xylazine’s geographic distribution.