Background: Complications arising from acute traumatic spinal cord injury (SCI) are routinely managed by various pharmacological interventions. Despite decades of clinical application, the potential impact on neurological recovery has been largely overlooked. This study aims to highlight commonly administered drugs with potential disease-modifying effects. Methods: This systematic literature review included studies referenced in PubMed, Scopus and Web of Science from inception to March 31st, 2021, which assess disease-modifying properties on neurological and/or functional recovery of drugs routinely administered following spinal cord injury. Drug effects were classified as positive, negative, mixed, no effect, or not (statistically) reported. Risk of bias was assessed separately for animal, randomized clinical trials, and observational human studies. Results: We analyzed 394 studies conducting 486 experiments that evaluated 144 unique or combinations of drugs. 195 of the 464 experiments conducted on animals (42%) and one study in humans demonstrate positive disease-modifying properties on neurological and/or functional outcomes. Methylprednisolone, melatonin, estradiol, and atorvastatin are the most common drugs associated with positive effects. Two studies on morphine and ethanol report negative effects on recovery. Conclusion: Despite a large heterogeneity observed in study protocols, research from bed to bench and back to bedside provides an alternative approach to identify new candidate drugs in the context of SCI. Future research in human populations is warranted to determine if introducing drugs like melatonin, estradiol, or atorvastatin would contribute to enhancing neurological outcomes after acute SCI.

PURPOSE: Breast cancer relapses are rarely collected by cancer registries because of logistical and financial constraints. Hence, we investigated natural language processing (NLP), enhanced with state-of-the-art deep learning transformer tools and large language models, to automate relapse identification in the text of computed tomography (CT) reports. METHODS: We analyzed follow-up CT reports from patients diagnosed with breast cancer between January 1, 2005, and December 31, 2014. The reports were curated and annotated for the presence or absence of local, regional, and distant breast cancer relapses. We performed 10-fold cross-validation to evaluate models identifying different types of relapses in CT reports. Model performance was assessed with classification metrics, reported with 95% confidence intervals. RESULTS: In our data set of 1,445 CT reports, 799 (55.3%) described any relapse, 72 (5.0%) local relapses, 97 (6.7%) regional relapses, and 743 (51.4%) distant relapses. The any-relapse model achieved an accuracy of 89.6% (87.8-91.1), with a sensitivity of 93.2% (91.4-94.9) and a specificity of 84.2% (80.9-87.1). The local relapse model achieved an accuracy of 94.6% (93.3-95.7), a sensitivity of 44.4% (32.8-56.3), and a specificity of 97.2% (96.2-98.0). The regional relapse model showed an accuracy of 93.6% (92.3-94.9), a sensitivity of 70.1% (60.0-79.1), and a specificity of 95.3% (94.2-96.5). Finally, the distant relapse model demonstrated an accuracy of 88.1% (86.2-89.7), a sensitivity of 91.8% (89.9-93.8), and a specificity of 83.7% (80.5-86.4). CONCLUSION: We developed NLP models to identify local, regional, and distant breast cancer relapses from CT reports. Automating the identification of breast cancer relapses can enhance data collection about patient outcomes.

Purpose: Quantification of metabolite concentrations using MRS requires tissue‐dependent signal corrections. Accurate estimation of voxel tissue composition is therefore essential. Commonly used brain tissue segmentation tools differ in their algorithms and implementation, potentially introducing variability in MRS‐derived concentration estimates. This study investigates the impact and reliability of tissue segmentation on metabolite quantification. Methods: Three segmentation tools (ANTs, FSL, SPM) were evaluated using an in vivo test–retest MRI/MRS dataset. Voxelwise GM/WM/CSF fractions were applied to compute tissue‐corrected total creatine (tCr) concentrations. Linear mixed‐effects modeling, variance‐component partitioning, and intraclass correlation coefficients (ICCs) quantified tool‐, session‐, and participant‐related variance under permutation scenarios that isolated segmentation‐ and MRS‐related effects. As a benchmark for segmentation performance, comparisons with manually segmented data were conducted across three brain regions. Results: Segmentation tools produced systematically different tissue fractions that propagated into differences in tCr concentration estimates. Variance partitioning attributed 56.8%, 50.0%, and 51.3% of total tCr concentration variability to segmentation tool across the three permutations, with participant‐specific factors accounting for 34.7%, 36.2%, and 28.5%, respectively. When segmentation variability was held constant, test–retest reliability was high (ICC > 0.8) but dropped to ∼0.5 when both segmentation and MRS variability varied. Agreement with manual segmentation was region‐ and tool‐dependent, with the lowest agreement in the thalamus. Conclusion: Tissue segmentation contributes substantially to the variability in MRS‐derived metabolite concentration estimates. These results underscore the need for transparent segmentation reporting and data sharing to ensure reproducibility and cross‐study comparability in MRS research.

The utilization of structural, functional, and biochemical data from the human brain has grown in addressing inquiries related to neurodegenerative and neuropsychiatric conditions. However, the normal variability within these measures has not been systematically reported. In this work, a database comprising these outcome measures in a healthy population (n=51) was established to potentially serve as a comparative reference. Healthy individuals underwent standardized procedures to ensure consistent collection of magnetic resonance imaging (MRI) and spectroscopy data. The MR data was acquired using a 3T scanner with various sequences, including MPRAGE 3D T1w, pseudo-continuous arterial spin labelling (pCASL), and single voxel proton magnetic resonance spectroscopy (1H-MRS). Established and custom software tools were employed to analyze outcome measures such as tissue segmentation, cortical thickness, cerebral blood flow, metabolite levels, and temperature estimated using MRS. This study provides a comprehensive overview of the data analysis process, aiming to facilitate future utilization of the collected data through an interactive dashboard developed in R using the Shiny framework.