We have developed a hyperspectral microscopic imaging (HMI) platform that can precisely identify and quantify 10 molecular markers in individual cancer cells in a single pass. Exploitation of an improved separation of circulating tumor cells and the application of HMI has provided an opportunity to identify molecular changes in these cells, the recognition of co-expression of these markers, and poses an important opportunity for non-invasive diagnosis, and the use of targeted therapy. We have balanced the intensity of 10 fluorochromes bound to 10 different antibodies, each specific to a particular tumor marker, so that the intensity of each fluorochrome can be discerned from overlapping emissions. Using 2 touch preps from each primary breast cancer, the average molecular marker-intensities of 25 tumor cells gave a representative molecular signature for the tumor despite some cellular heterogeneity. The intensities determined by the HMI correlate well with the conventional 0-3+ analysis by experts in cellular pathology. Since additional multiplexes can be developed using the same fluorochromes but different antibodies, this analysis allows quantification of a large number of molecular markers on individual tumor cells. HMI can be completely automated and, eventually, could allow standardization of protein biomarkers and improve reproducibility among clinical pathology laboratories.

Traumatic spinal cord injury often leads to loss of sensory, motor, and autonomic function below the level of injury. Recent advancements in spinal cord electrical stimulation (SCS) for spinal cord injury have provided potential avenues for restoration of neurologic function in affected patients. This review aims to assess the efficacy of spinal cord stimulation, both epidural (eSCS) and transcutaneous (tSCS), on the return of function in individuals with chronic spinal cord injury. The current literature on human clinical eSCS and tSCS for spinal cord injury was reviewed. Seventy-one relevant studies were included for review, specifically examining changes in volitional movement, changes in muscle activity or spasticity, or return of cardiovascular pulmonary, or genitourinary autonomic function. The total participant sample comprised of 327 patients with spinal cord injury, each evaluated using different stimulation protocols, some for sensorimotor function and others for various autonomic functions. One hundred eight of 127 patients saw improvement in sensorimotor function, 51 of 70 patients saw improvement in autonomic genitourinary function, 32 of 32 patients saw improvement in autonomic pulmonary function, and 32 of 36 patients saw improvement in autonomic cardiovascular function. Although this review highlights SCS as a promising therapeutic neuromodulatory technique to improve rehabilitation in patients with SCI, further mechanistic studies and stimulus parameter optimization are necessary before clinical translation.

Surgical management for gynecologic malignancies often involves hysterectomy, often constituting the most common gynecologic surgery worldwide. Despite maximal surgical and medical care, gynecologic malignancies have a high rate of recurrence following surgery. Current machine learning models use advanced pathology data that is often inaccessible within low-resource settings and are specific to singular cancer types. There is currently a need for machine learning models to predict non-clinically evident residual disease using only clinically available health data. Here we developed and tested multiple machine learning models to assess the risk of residual disease post-hysterectomy based on clinical and operative parameters. Data from 3656 hysterectomy patients from the NSQIP dataset over 14 years were used to develop models with a training set of 2925 patients and a validation set of 731 patients. Our models revealed the top postoperative predictors of residual disease were the initial presence of gross abdominal disease on the diaphragm, disease located on the bowel mesentery, located on the bowel serosa, and disease located within the adjacent pelvis prior to resection. There were no statistically significant differences in performances of the top three models. Extreme gradient Boosting, Random Forest, and Logistic Regression models had comparable AUC ROC (0.90) and accuracy metrics (87–88%). Using these models, physicians can identify gynecologic cancer patients post-hysterectomy that may benefit from additional treatment. For patients at high risk for disease recurrence despite adequate surgical intervention, machine learning models may lay the basis for potential prospective trials with prophylactic/adjuvant therapy for non-clinically evident residual disease, particularly in under-resourced settings.

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