Vertebral cancellous bone turn-over: microcallus and bridges in backscatter electron microscopy

Backscatter electron microscopy (BSE) is a powerful technique for investigating cancellous bone structure. Its main function is to offer information regarding the degree of mineralization of the tissue within individual trabeculae.

To illustrate the qualitative information that can be drawn from BSE imaging technique, we present a study on human vertebral cancellous bone. This tissue is continuously remodeled through osteoclastic resorption and osteoblastic new bone apposition. It is thought that osteoclastic resorption pits are especially deleterious for vertebral bone architecture since they often perforate the thin trabeculae; the osteoblasts being unable to repair the gap. In addition, excessive stress may also disrupt the architecture in case of trabecular fracture or damage accumulation.

Waves of new bone formation were easy to identify in BSE. Often these waves were connecting both edges of a perforation and called bridges. Additionally, we present a few images of microcallus formations. A microcallus is described as a small mass of woven bone that generally repairs a trabecula. The microstructural aspects of different microcalluses are presented and discussed. Both bridges and microcallus should be considered as examples of the repair porcess since they obviously preserve the connectivity of the trabeculae. However, bridges were much more frequent than microcallus (396 vs 15). Both mechanisms probably illustrate the normal response to different local stimuli.     

Co-crystal Prediction by Artificial Neural Networks**

A significant amount of attention has been given to the design and synthesis of co-crystals by both industry and academia because of its potential to change a molecule's physicochemical properties. Yet, difficulties arise when searching for adequate combinations of molecules (or coformers) to form co-crystals, hampering the efficient exploration of the target's solid-state landscape. This paper reports on the application of a data-driven co-crystal prediction method based on two types of artificial neural network models and co-crystal data present in the Cambridge Structural Database. The models accept pairs of coformers and predict whether a co-crystal is likely to form. By combining the output of multiple models of both types, our approach shows to have excellent performance on the proposed co-crystal training and validation sets, and has an estimated accuracy of 80 % for molecules for which previous co-crystallization data is unavailable.