Editorial Commentary: Biceps Management During Rotator Cuff Tear: Confounding Variables Result in Unclear Indications.

Is "killing the biceps" during rotator cuff repair a capital crime or a lawful act? One of the most passionately debated topics in shoulder surgery is what to do with the biceps during rotator cuff repair: save it, tenotomize it, or perform tenodesis. Results of repair are not very successful, and given that repair of massive rotator cuff tears shows a 40% to 57% failure rate, there is renewed interest in sparing the biceps tendon as a humeral head depressor-or so that it may be used as a local graft for revision rotator cuff repair. The literature regarding tenodesis versus biceps sparing during rotator cuff repair is controversial. There are so many confounding variables affecting rotator cuff repair outcomes (tear size, comorbidities, age, tissue quality, etc.) that we do not believe that anything less than a randomized, prospective study that matches groups is likely to provide a conclusive verdict.

Failing to Make the Grade: Conventional Cardiac Allograft Rejection Grading Criteria Are Inadequate for Predicting Rejection Severity.

BACKGROUND: Cardiac allograft rejection is the leading cause of early graft failure and is a major focus of postheart transplant patient care. While histological grading of endomyocardial biopsy samples remains the diagnostic standard for acute rejection, this standard has limited diagnostic accuracy. Discordance between biopsy rejection grade and patient clinical trajectory frequently leads to both overtreatment of indolent processes and delayed treatment of aggressive ones, spurring the need to investigate the adequacy of the current histological criteria for assessing clinically important rejection outcomes. METHODS: N=2900 endomyocardial biopsy images were assigned a rejection grade label (high versus low grade) and a clinical trajectory label (evident versus silent rejection). Using an image analysis approach, n=370 quantitative morphology features describing the lymphocytes and stroma were extracted from each slide. Two models were constructed to compare the subset of features associated with rejection grades versus those associated with clinical trajectories. A proof-of-principle machine learning pipeline-the cardiac allograft rejection evaluator-was then developed to test the feasibility of identifying the clinical severity of a rejection event. RESULTS: The histopathologic findings associated with conventional rejection grades differ substantially from those associated with clinically evident allograft injury. Quantitative assessment of a small set of well-defined morphological features can be leveraged to more accurately reflect the severity of rejection compared with that achieved by the International Society of Heart and Lung Transplantation grades. CONCLUSIONS: Conventional endomyocardial samples contain morphological information that enables accurate identification of clinically evident rejection events, and this information is incompletely captured by the current, guideline-endorsed, rejection grading criteria.

Inferring super-resolution tissue architecture by integrating spatial transcriptomics with histology.

Spatial transcriptomics (ST) has demonstrated enormous potential for generating intricate molecular maps of cells within tissues. Here we present iStar, a method based on hierarchical image feature extraction that integrates ST data and high-resolution histology images to predict spatial gene expression with super-resolution. Our method enhances gene expression resolution to near-single-cell levels in ST and enables gene expression prediction in tissue sections where only histology images are available.