Human wingless-type MMTV integration site family member 10A (WNT10A) is a secreted glycoprotein that is involved in signaling pathways essential to ectodermal organogenesis and tissue regeneration. WNT10A was first linked to human disorders in 2006, demonstrating a WNT10a variant to be associated with cleft lip with/without cleft palate. Numerous publications have since then identified the importance of WNT10A in the development of ectodermal appendages and beyond. In this review, we provide information on the structure of the WNT10A gene and protein, summarize its expression patterns in different animal models and in human, and describe the identified roles in tissue and organ development and repair in the different animal model organisms. We then correlate such identified functions and working mechanisms to the pathophysiology of a spectrum of human diseases and disorders that result from germline loss-of-function mutations in WNT10A, including ectodermal dysplasia (ED) syndromes Odonto-oncho-dermal dysplasia (OODD), Schöpf-Schulz-Passarge syndrome (SSPS), and selective tooth agenesis, as well as pathological conditions like fibrosis and carcinogenesis that can be correlated with increased WNT10A activity (Section 5).

This study aimed at predicting the filtration surgery (FS) outcome using a machine learning (ML) approach. 102 glaucomatous patients undergoing FS were enrolled and underwent ocular surface clinical tests (OSCTs), determination of surgical site-related biometric parameters (SSPs) and conjunctival vascularization. Break-up-time, Schirmer test I, corneal fluorescein staining, Meibomian gland expressibility; conjunctival hyperemia, upper bulbar conjunctiva area of exposure, limbus to superior eyelid distance; and conjunctival epithelial and stromal (CET, CST) thickness and reflectivity (ECR, SCR) at AS-OCT were considered. Successful FS required a 30% baseline intraocular pressure reduction, with values ≤ 18 mmHg with or without medications. The classification tree (CT) was the ML algorithm used to analyze data. At the twelfth month, FS was successful in 60.8% of cases, whereas failed in 39.2%. At the variable importance ranking, CST and SCR were the predictors with the greater relative importance to the CART tree construction, followed by age. CET and ECR showed less relative importance, whereas OSCTs and SSPs were not important features. Within the CT, CST turned out the most important variable for discriminating success from failure, followed by SCR and age, with cut-off values of 75 µm, 169 on gray scale, and 62 years, respectively. The ROC curve for the classifier showed an AUC of 0.784 (0.692-0.860). In this ML approach, CT analysis found that conjunctival stroma thickness and reflectivity, along with age, can predict the FS outcome with good accuracy. A pre-operative thick and hyper-reflective stroma, and a younger age increase the risk of FS failure.

Simulated standardized patients (SSP) have emerged as close to a 'gold standard' for measuring the quality of clinical care. This method resolves problems of patient mix across healthcare providers and allows care to be benchmarked against preexisting standards. Nevertheless, SSPs are not real patients. How, then, should data from SSPs be considered relative to clinical observations with 'real' patients in a given health system? Here, we reject the proposition that SSPs are direct substitutes for real patients and that the validity of SSP studies therefore relies on their ability to imitate real patients. Instead, we argue that the success of the SSP methodology lies in its counterfactual manipulations of the possibilities available to real careseekers - especially those paths not taken up by them - through which real responses can be elicited from real providers. Using results from a unique pilot study where SSPs returned to providers for follow-ups when asked, we demonstrate that the SSP method works well to elicit responses from the provider through conditional manipulations of SSP behavior. At the same time, observational methods are better suited to understand what choices real people make, and how these can affect the direction of diagnosis and treatment. A combination of SSP and observational methods can thus help parse out how quality of care emerges for the "patient" as a shared history between care-seeking individuals and care providers.