The levels of nerve growth factor (NGF) and its mRNA in the rat central nervous system were determined by two‐site enzyme immunoassay and quantitative Northern blots, respectively. Relatively high NGF levels (0.4‐1.4 ng NGF/g wet weight) were found both in the regions innervated by the magnocellular cholinergic neurons of the basal forebrain (hippocampus, olfactory bulb, neocortex) and in the regions containing the cell bodies of these neurons (septum, nucleus of the diagonal band of Broca, nucleus basalis of Meynert). Comparatively low, but significant NGF levels (0.07‐0.21 ng NGF/g wet weight) were found in various other brain regions. mRNANGF was found in the hippocampus and cortex but not in the septum. This suggests that magnocellular cholinergic neurons of the basal forebrain are supplied with NGF via retrograde axonal transport from their fields of innervation. These results, taken together with those of previous studies showing that these neurons are responsive to NGF, support the concept that NGF acts as trophic factor for magnocellular cholinergic neurons.

Background Achievement of low-risk status is a treatment goal in pulmonary arterial hypertension (PAH). Risk assessment is often performed using multiparameter tools, such as the Registry to Evaluate Early and Long-term PAH Disease Management (REVEAL) risk calculator. Risk calculators that assess fewer variables without compromising validity may expedite risk assessment in the routine clinic setting. We describe the development and validation of REVEAL Lite 2, an abridged version of REVEAL 2.0. Research Questions To develop and validate a simplified version of the REVEAL 2.0 risk assessment calculator for patients with PAH. Study Design and Methods: REVEAL Lite 2 includes six non-invasive variables: functional class (FC), vital signs (systolic blood pressure [SBP] and heart rate), six-minute walk distance (6MWD), brain natriuretic peptide (BNP)/ N -terminal prohormone of brain natriuretic peptide (NT-proBNP), and renal insufficiency (by estimated glomerular filtration rate [eGFR]) and was validated in a series of analyses (Kaplan–Meier, concordance index, Cox proportional-hazard model and multivariate analysis). Results REVEAL Lite 2 approximates REVEAL 2.0 at discriminating low, intermediate, and high risk for 1-year mortality in patients in the REVEAL registry. The model indicated that the most highly predictive REVEAL Lite 2 parameter was BNP/NT-proBNP, followed by 6MWD and FC. Even if multiple, less predictive variables (heart rate, SBP, eGFR) were missing, REVEAL Lite 2 still discriminated among risk groups. Interpretation REVEAL Lite 2, an abridged version of REVEAL 2.0, provides a simplified method of risk assessment that can be implemented routinely in daily clinical practice. REVEAL Lite 2 is a robust tool that provides discrimination between patients at low, intermediate, and high risk of 1-year mortality.

BackgroundCurrent risk stratification tools in pulmonary arterial hypertension (PAH) are limited in their discriminatory abilities, partly due to the assumption that prognostic clinical variables have an independent and linear relationship to clinical outcomes. We sought to demonstrate the utility of Bayesian network (BN) based machine learning in enhancing the predictive ability of an existing state-of-the-art risk stratification tool, REVEAL 2.0.MethodsWe derived a Tree Augmented Naïve Bayes model (titled PHORA) to predict one-year survival in PAH patients included in the REVEAL registry, using the same variables and cut-points found in REVEAL 2.0. PHORA models were validated internally (within the REVEAL registry) and externally (in COMPERA and PHSANZ registry). Patients were classified as low, intermediate and high-risk (<5%, 5-20% and>10% 12-month mortality, respectively) based on the 2015 ESC/ERS guidelines.ResultsPHORA had an AUC of 0.80 for predicting one-year survival, which was an improvement over REVEAL 2.0 (AUC of 0.76). When validated in COMPERA and PHSANZ registries, PHORA demonstrated an AUC of 0.74 and 0.80 respectively. One-year survival rates predicted by PHORA were greater for patients with lower risk scores and poorer for those with higher risk scores (P<.001), with excellent separation between low-, intermediate-, and high-risk groups in all three registries.ConclusionOur BN derived risk prediction model, PHORA, demonstrated an improvement in discrimination over existing models. This is reflective of BN based model's ability to account for the interrelationships between clinical variables on outcome, and tolerance to missing data elements when calculating predictions.

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A patient's capacity for tissue regeneration varies based on age, nutritional status, disease state, lifestyle, and gender. Because regeneration cannot be predicted prior to biomaterial implantation, there is a need for responsive biomaterials with adaptive, personalized degradation profiles to improve regenerative outcomes. This study reports a new approach to use therapeutic ultrasound as a means of altering the degradation profile of silk fibroin biomaterials noninvasively postimplantation. By evaluating changes in weight, porosity, surface morphology, compressive modulus, and chemical structure, it is concluded that therapeutic ultrasound can trigger enhanced degradation of silk fibroin scaffolds noninvasively. By removing microbubbles on the scaffold surface, it is found that acoustic cavitation is the mechanism responsible for changing the degradation profile. This method is proved to be safe for human cells with no negative effects on cell viability or metabolism. Sonication through human skin also effectively triggers scaffold degradation, increasing the clinical relevance of these results. These findings suggest that silk is an ultrasound-responsive biomaterial, where the degradation profile can be adjusted noninvasively to improve regenerative outcomes.

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Introduction: Pulmonary arterial hypertension (PAH) is a rare disease with poor prognosis. Clinical trials for PAH now use time to morbidity/mortality event (clinical worsening) as the primary endpoint instead of six-minute walk distance (6MWD). These trials require nearly four times the sample size and 10 times the trial length versus trials with 6MWD as their endpoint. Reduction in trial samples sizes would improve trial efficiency. To this end, registry risk algorithms were investigated as candidates for trial enrichment by identifying patients with high-risk of clinical worsening using baseline characteristics. Methods: COMPERA, French, REVEAL 2.0 were investigated as trial enrichment candidates. Patient-level data from three PAH phase 3 trials (AMBITION, SERAPHIN, and GRIPHON) were pooled and standardized. Receiver-operating curves (ROC) were generated for each algorithm to determine predictive capability for clinical worsening. Survival tree analysis was performed to identify algorithm cut-points that created unique risk-level groups. Hazard ratios between the pooled treatment and pooled placebo patients were recalculated for each risk group. Power analysis was conducted by bootstrapping to estimate sample size reductions for multiple enrichment methods. Results: ROC analysis revealed that REVEAL 2.0 and COMPERA performed reasonably (both with AUC 0.70) at predicting clinical worsening, while the French score was less accurate (AUC 0.66). Survival tree analysis demonstrated that all algorithms were able to identify a unique low, intermediate, and high-risk group, but REVEAL 2.0 was more precise, further separating the population into very low, low, intermediate, and high-risk. A treatment effect was found in each unique risk group (all p-values < 0.05). Using power analyses with bootstrapping, enriching trials by enrolling only high-risk patients substantially decreased estimated sample size versus enrichment by NYHA class (Figure 1). Enrollment of intermediate and high-risk patients still lowered estimated sample size versus NYHA enrichment, but only when REVEAL 2.0 was applied in AMBITION and GRIPHON trials. Similar results were found for enrollment of 50% high risk, 50% all other risk levels. Conclusion: Registry risk algorithms are better predictors of clinical worsening than NYHA class alone. REVEAL 2.0 outperformed all registry risk algorithms, except for in the SERAPHIN trial, which is attributable to SERAPHIN using a non-standard NT-proBNP assay. Further investigations should consider availability of high-risk patients and cost of screening when enrichment is used.