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BACKGROUND: While autografts to date remain the “gold standard” for bone void fillers, synthetic bone grafts have garnered attention due to their advantages such as ability to be tailored in terms of its physical and chemical properties. Bioactive glass (BG), an inorganic material, has the capacity to form a strong bond with bone by forming a bone-like apatite surface, enhancing osteogenesis. Coupled with three-dimensional printing it is possible to maximize bone regenerative properties of the BG. OBJECTIVE: The objective of this study was to synthesize and characterize 3D printed mesoporous bioactive glass (MBG), BG 45S5, and compare to β-Tricalcium phosphate (β-TCP) based scaffolds; test cell viability and osteogenic differentiation on human osteoprogenitor cells in vitro. METHODS: MBG, BG 45S5, and β-TCP were fabricated into colloidal gel suspensions, tested with a rheometer, and manufactured into scaffolds using a 3D direct-write micro-printer. The materials were characterized in terms of microstructure and composition with Thermogravimetric Analyzer/Differential Scanning Calorimeter (TGA/DSC), Fourier Transform Infrared Spectroscopy (FTIR), X-ray Diffraction (XRD), Micro-Computed Tomography (μ-CT), Scanning Electron Microscopy (SEM), Energy Dispersive X-ray Spectroscopy (EDS), and Mattauch–Herzog-Inductively Coupled Plasma-Mass Spectrometry (MH-ICP-MS). RESULTS: Scaffolds were tested for cell proliferation and osteogenic differentiation using human osteoprogenitor cells. Osteogenic media was used for differentiation, and immunocytochemistry for osteogenic markers Runx-2, Collagen-I, and Osteocalcin. The cell viability results after 7 days of culture yielded significantly higher (p

Aims Through this proof of concept, we studied the potential added value of machine learning (ML) methods in building cardiovascular risk scores from structured data and the conditions under which they outperform linear statistical models. Methods and results Relying on extensive cardiovascular clinical data from FOURIER, a randomized clinical trial to test for evolocumab efficacy, we compared linear models, neural networks, random forest, and gradient boosting machines for predicting the risk of major adverse cardiovascular events. To study the relative strengths of each method, we extended the comparison to restricted subsets of the full FOURIER dataset, limiting either the number of available patients or the number of their characteristics. When using all the 428 covariates available in the dataset, ML methods significantly (c-index 0.67, P-value 2e−5) outperformed linear models built from the same variables (c-index 0.62), as well as a reference cardiovascular risk score based on only 10 variables (c-index 0.60). We showed that gradient boosting—the best performing model in our setting—requires fewer patients and significantly outperforms linear models when using large numbers of variables. On the other hand, we illustrate how linear models suffer from being trained on too many variables, thus requiring a more careful prior selection. These ML methods proved to consistently improve risk assessment, to be interpretable despite their complexity and to help identify the minimal set of covariates necessary to achieve top performance. Conclusion In the field of secondary cardiovascular events prevention, given the increased availability of extensive electronic health records, ML methods could open the door to more powerful tools for patient risk stratification and treatment allocation strategies.

The aim of the study is to describe ultrasound features of fetal ovarian cysts as reported by the original ultrasound examiner. International Ovarian Tumor Analysis (IOTA) terminology was also applied after retrospective analysis of ultrasound images. Management and evolution of fetal cysts during pregnancy and after delivery were also described.This is a retrospective observational study. All pregnant women diagnosed with fetal ovarian cyst at ultrasound examination were enrolled at Prenatal Diagnosis Division of the Bambino Gesù Children's Hospital in Rome, between March 2011 and May 2020. The cysts were classified by the original ultrasound examiner as "simple" (unilocular anechoic cyst) or "complex" (cyst with other morphology). In addition, three ultrasound examiners, experienced in gynecologic oncology, retrospectively classified the fetal ovarian cysts according to IOTA terminology, by reviewing ultrasound images. The evolution of fetal ovarian cysts during pregnancy and after birth was recorded.51 ovarian cysts were included. 29/51 (56.9%) were classified as "simple", whereas 22/51 (43.1%) were classified as "complex". The vast majority of "simple" cysts resolved spontaneously after delivery (20/29, 69.0%), 2/29 (6.9%) resolved following intrauterine aspiration, 2/29 (6.9%) resolved after post-natal aspiration, and 5/29 (17.2%) were triaged to surgery due to persistence after delivery and normal ovarian parenchyma without signs of necrosis was observed at histology. The "complex" cysts showed spontaneous resolution in 7/22 (31.8%), whereas 15/22 (68.2%) were surgically removed. Necrosis was observed in most of them (12/15, 80.0%) whereas in 3/15 (20%) benign epithelial cyst with normal ovarian parenchyma was observed at histology. After reviewing the ultrasound images with the application of the IOTA terminology, all 51 (100%) fetal cysts were described as unilocular; 29/51 (56.8%) cysts showed anechoic content, 10/51 (19.6%) low level, 1/51 (82.0%) ground glass, 9/51 (17.6%) hemorrhagic, 1/51 (2.0%) mixed, and 1/51 (2%) undefined content. Among 29 anechoic ovarian cysts, resolution was observed in most of the cases (24/29, 82.7%). Similarly, resolution was observed in 7/10 (70.0%) low level cysts. Resolution was not observed in any of the other cysts described as ground glass, hemorrhagic, mixed, or undefined and all of them underwent surgery with evidence of necrosis in 11/12 (91.7%).Anechoic cysts (described as "simple" cysts by the original ultrasound examiner) and low level cysts (described as "complex" by the original ultrasound examiner) frequently resolved spontaneously. Cysts with ground glass, hemorrhagic, mixed or undefined cyst content (described as "complex" by the original ultrasound examiner) were frequently associated with necrosis at final histology. This article is protected by copyright. All rights reserved.

Poor medication adherence in people with epilepsy (PwE) increases mortality, hospitalization, and poor quality of life, representing a critical challenge for clinicians. Several demographic, clinical, and neuropsychological factors were singularly found associated with medication adherence in several studies, but the literature lacks a comprehensive study simultaneously assessing all these variables.We performed a multicenter and cross-sectional study using online questionnaires with the following clinical scales: Morisky Medication Adherence Scale (MMAS-8), Quality of Life in Epilepsy Inventory 31 (QoLIE-31), Beck Depression Inventory-II (BDI-II), Generalized Anxiety Disorder-7 (GAD-7) and 14-item Resilience scale (RES14) in a population of 200 PwE. We used the ANOVA test and Spearman's correlation to evaluate the relationship between medication adherence and demographic, clinical (seizure frequency, number of anti-seizure medications), and neuropsychological characteristics. We trained separate machine learning models (logistic regression, random forest, support vector machine) to classify patients with medium-high adherence (MMAS-8 ≥ 6) and poor adherence (MMAS-8 6) and to identify the main features that influence adherence.Women were more adherent to medication (p-value = 0.035). Morisky Medication Adherence Scale -8 showed a direct correlation with RES14 (p-value = 0.001) and age (p-value = 0.001), while was inversely correlated with BDI-II (p-value = 0.001) and GAD-7 (p-value = 0.001). In our model, the variables mostly predicting treatment adherence were QoLIE-31 subitems, followed by age, resilience, anxiety, years of school, and disease duration.Our study confirms that gender, age, and neuropsychological traits are relevant factors in predicting medication adherence to PwE. Furthermore, our data provided the first evidence that machine learning on multidimensional self-report questionnaires could help to develop a decisional support system in outpatient epilepsy clinics.

Pancreatic cancer has a complex tumor microenvironment which engages in extensive crosstalk between cancer cells, cancer-associated fibroblasts, and immune cells. Many of these interactions contribute to tumor resistance to anti-cancer therapies. Here, new therapeutic strategies designed to modulate the cancer-associated fibroblast and immune compartments of pancreatic ductal adenocarcinomas are described and clinical trials of novel therapeutics are discussed. Continued advances in our understanding of the pancreatic cancer tumor microenvironment are generating stromal and immune-modulating therapeutics that may improve patient responses to anti-tumor treatment.

Background In patients at risk of cardiovascular (CV) events, the effectiveness of lipid-lowering therapies (LLT) is affected by both intensity and adherence. Our study evaluated the association between LLT intensity (statin and/or ezetimibe) and adherence, and CV events in patients with a history of myocardial infarction (MI) in France. Methods Using the French national healthcare database (SNDS), we included patients with a history of MI, an initial LLT prescription in 2011–2013, and a second prescription within one year. LLT intensity was defined using the expected percent reduction in low-density lipoprotein cholesterol; adherence was measured as the proportion of days covered. Cox proportional hazards models were used to assess associations between intensity and/or adherence, and the risk of major adverse CV event (MACE). Results 164,565 patients were included; mean (SD) age, 66·3 (13·8) years; 73·6% men. Following an MI, only half of patients were treated with high-intensity LLT and approximately 40% of those on LLT remained non-adherent during follow-up (mean (SD) follow-up, 2·6 (1·4) years). Each 10% increase in treatment intensity, adherence, or adherence-adjusted intensity was respectively associated with a 16% (HR 0.84, 95%CI 0.84–0.85), 7% (HR 0.93, 95%CI 0.93–0.94), and 15% (HR 0.85, 95%CI 0.84–0.86) decrease in the risk of MACE. Conclusions Among patients with a history of MI, prescriptions of high-intensity LLT were limited and adherence to LLT was low. Higher intensity and/or adherence to statins was associated with a significantly lower risk of MACE, highlighting the importance of compliance with clinical guidelines to improve patient outcomes.