Samuel Stumo

The use of ketamine for the management of neuropsychiatric conditions outside clinical settings has rapidly expanded, creating a critical need to understand diverse individual experiences. We conducted a qualitative content analysis of posts from the r/TherapeuticKetamine subreddit. From 3302 threads, the 500 highest-engagement threads (12,852 comments) were analyzed by independent coders across six domains: perceived positive effects, adverse effects, reasons for use, route of administration, polydrug use, and dose amounts. Mood-related concerns were the primary reason for use (53%). Users reported positive effects, most often improvements in emotional well-being (65%). Adverse effects were predominantly psychological or mood-related (56%). A total of 70% of reported doses exceeded 149 mg, suggesting a trend toward higher dose use. Intravenous administration (40%) and sublingual troches (23%) were the most frequently reported routes. Concurrent use of prescribed psychotropics, cannabis, and psychedelics was also reported. This analysis identified substantial heterogeneity in individual-reported experiences. Frequent high-dose use, dose escalation, and polydrug exposure underscores the importance of clinical monitoring and attention to addiction potential and drug–drug interactions. The findings should be interpreted with caution, as follow-up and clinical verification were not possible; however, the data provide an unfiltered view of individual experiences in relation to ketamine use outside the clinical setting.

Kidney and renal pelvic cancer are a significant cause of cancer-related deaths, with the most common malignant kidney tumor being renal cell carcinoma (RCC). Chromophobe renal cell carcinoma is a rarer form of RCC that poses significant challenges to accurate diagnosis, as it shares many histologic features with Oncocytoma, a benign renal tumor. Biopsies for histopathological and immunohistochemical analysis have limitations in distinguishing chromophobe RCC from Oncocytoma. Syndromic cases may also have tumors with overlapping features. Techniques such as infrared (IR) spectroscopic imaging have shown promise as an alternative approach to tissue diagnostics. In this study, we propose a deep-learning-based framework for automating classification in kidney tumor tissue microarrays (TMAs) using an IR dataset. Feature selection algorithms reduce data dimensionality, followed by a deep learning classification approach. A classification accuracy of 91.3% was observed for validation data, even with the use of 13.6% of all wavelengths, thereby reducing training time by 21% compared to using the entire spectrum. Through the integration of scalable deep learning models coupled with feature selection, we have developed a classification pipeline with high predictive power, which could be integrated into a high-throughput real-time IR imaging system. This would create an advanced diagnostic tool for the detection and classification of renal tumors, namely chromophobe RCC and Oncocytoma. This may impact patient outcomes and treatment strategies.