Citations

Abstract Rationale Stereotactic body radiation therapy (SBRT) represents an effective therapeutic strategy for early-stage non–small cell lung cancer (NSCLC); however, local and systemic recurrences represent ongoing challenges. Computed tomography (CT) radiomics-based risk models can potentially be used to predict the risk of local recurrence on pretreatment CT scans. Objective Development of a radiomics model to predict local recurrence after SBRT in patients with NSCLC. Methods This single-institution study includes a retrospective case-control training set (20 patients with local recurrence and 40 control subjects) and an independent validation set (198 consecutive cases) of patients with early-stage NSCLC treated with SBRT. Tumors were semiautomatically segmented, and 102 quantitative radiomic features, including texture, landscape, spatial, nodule shape, and nodule surface features, were extracted. These features were included in three separate multivariable models to predict the risk of recurrence on the basis of pre-SBRT, post-SBRT, and the difference between the pre-SBRT and post-SBRT scans (Delta model). The pre-SBRT model was subsequently validated in an independent validation set. Results Thirteen independent variables were selected for the models using the Boruta algorithm. The sensitivity, specificity, and area under the curve of the pre-SBRT, post-SBRT, and Delta models were 85%, 90%, and 0.91; 85%, 92.5%, and 0.92; and 85%, 92.5%, and 0.94, respectively. The pre-SBRT model was validated in the independent validation set (area under the curve, 0.89; confidence interval, 0.83–0.92), because this model was believed to be the most useful to assist in individualized treatment planning. Conclusions Radiomic analysis facilitated the development of three high-performing models predicting local recurrence using either pre-SBRT CT, post-SBRT CT, or the change between these two. We successfully validated the most clinically relevant model, the pre-SBRT model. Although this model needs further validation, it may facilitate individualized surveillance, treatment planning, and selection of adjuvant therapy.

Angiotensin converting enzyme-2 receptor (ACE2) present on human cell membrane surfaces is a critical receptor for binding of SARS-CoV-2 to invade cells. Severe burden of recent COVID-19 pandemic was of global public health importance. Using bioinformatics tools, protein sequence and structural features of ACE2 protein can be obtained. Protein sequences can be compared with structures of ACE2 proteins from same individuals (or patients) of different clinical status. This can either be from data obtained from disease states or already deposited annotated data. Then assess for effectiveness of a pool of bio-molecular attributes such structural neighbor profiles which help describe micro-environment of single amino polymorphisms (SAPs). Then engage predictive and modeling tools to predict key mutational loci- particularly involving SAPs and their functions in relation to disease states. Generated data can contribute to ontology, screens to identify biomarkers and open up paths to development of research protocols in therapeutics and diagnostics.

Abstract Miliary tuberculosis (TB) is a rare and potentially fatal form of disseminated TB. Disseminated TB involving the central nervous system (CNS) may be seen in up to nearly a third of miliary TB cases. We describe a case of miliary TB with CNS involvement and suspected hepatobiliary involvement in an immunocompetent patient after an elective spine surgery. Given the patient's unusual presentation in the absence of risk factors for TB, diagnosis was delayed. We were later informed that the bone graft he received during spine surgery, FiberCel (“FiberCel”) Fiber Viable Bone Matrix (Aziyo Biologics, Inc, Richmond, Calif), was contaminated with TB. This patient is 1 of almost 2 dozens reported to be affected. This case represents a novel presentation of TB due to contaminated cadaveric bone allograft matrix implantation, with notable rapid dissemination and CNS involvement.