Despite the huge body of research on osteogenic differentiation and bone tissue engineering, the translation potential of in vitro results still does not match the effort employed. One reason might be that the protocols used for in vitro research have inherent pitfalls. The synthetic glucocorticoid dexamethasone is commonly used in protocols for trilineage differentiation of human bone marrow mesenchymal stromal cells (hBMSCs). However, in the case of osteogenic commitment, dexamethasone has the main pitfall of inhibiting terminal osteoblast differentiation, and its pro-adipogenic effect is well known. In this work, we aimed to clarify the role of dexamethasone in the osteogenesis of hBMSCs, with a particular focus on off-target differentiation. The results showed that dexamethasone does induce osteogenic differentiation by inhibiting SOX9 expression, but not directly through RUNX2 upregulation as it is commonly thought. Rather, PPARG is concomitantly and strongly upregulated, leading to the formation of adipocyte-like cells within osteogenic cultures. Limiting the exposure to dexamethasone to the first week of differentiation did not affect the mineralization potential. Gene expression levels of RUNX2, SOX9, and PPARG were simulated using approximate Bayesian computation based on a simplified theoretical model, which was able to reproduce the observed experimental trends but with a different range of responses, indicating that other factors should be integrated to fully understand how dexamethasone influences cell fate. In summary, this work provides evidence that current in vitro differentiation protocols based on dexamethasone do not represent a good model, and further research is warranted in this field.

The study aims to create a preoperative model from baseline demographic and health-related quality of life scores (HRQOL) to predict a good to excellent early clinical outcome using a machine learning (ML) approach. A single spine surgery center retrospective review of prospectively collected data from January 2016 to December 2020 from the institutional registry (SpineREG) was performed. The inclusion criteria were age ≥ 18 years, both sexes, lumbar arthrodesis procedure, a complete follow up assessment (Oswestry Disability Index—ODI, SF-36 and COMI back) and the capability to read and understand the Italian language. A delta of improvement of the ODI higher than 12.7/100 was considered a “good early outcome”. A combined target model of ODI (Δ ≥ 12.7/100), SF-36 PCS (Δ ≥ 6/100) and COMI back (Δ ≥ 2.2/10) was considered an “excellent early outcome”. The performance of the ML models was evaluated in terms of sensitivity, i.e., True Positive Rate (TPR), specificity, i.e., True Negative Rate (TNR), accuracy and area under the receiver operating characteristic curve (AUC ROC). A total of 1243 patients were included in this study. The model for predicting ODI at 6 months’ follow up showed a good balance between sensitivity (74.3%) and specificity (79.4%), while providing a good accuracy (75.8%) with ROC AUC = 0.842. The combined target model showed a sensitivity of 74.2% and specificity of 71.8%, with an accuracy of 72.8%, and an ROC AUC = 0.808. The results of our study suggest that a machine learning approach showed high performance in predicting early good to excellent clinical results.

Molecular phenomena driving pathological aggregation in neurodegenerative diseases are not completely understood yet. Peculiar is the case of Spinocerebellar Ataxia 3 (SCA3) where the conformational properties of the AT-3 N-terminal region, also called Josephin Domain (JD), play a key role in the first step of aggregation, having the JD an amyloidogenic propensity itself. For this reason, unraveling the intimate relationship between JD structural features and aggregation tendency may lead to a step forward in understanding the pathology and rationally design a cure. In this connection, computational modeling has demonstrated to be helpful in exploring the protein molecular dynamics and mechanism of action. Conformational dynamics of the JD is here finely investigated by replica exchange molecular dynamics simulations able to sample the microsecond time scale and to provide both a thermodynamic and kinetic description of the protein conformational changes. Accessible structural conformations of the JD have been identified in: open, intermediate and closed like arrangement. Data indicated the closed JD arrangement as the most likely protein arrangement. The protein transition from closed toward intermediate/open states was characterized by a rate constant higher than 700 ns. This result also explains the inability of classical molecular dynamics to explore transitions from closed to open JD configuration on a time scale of hundreds of nanoseconds. This work provides the first kinetic estimation of the JD transition pathway from open-like to closed-like arrangement and vice-versa, indicating the closed-like arrangement as the most likely configuration for a JD in water environment. More widely, the importance of our results is also underscored considering that the ability to provide a kinetic description of the protein conformational changes is a scientific challenge for both experimental and theoretical approaches to date. This article was reviewed by Oliviero Carugo, Bojan Zagrovic.