Fate and transformation of silver nanoparticles in different biological conditions

The exploitation of silver nanoparticles (AgNPs) in biomedicine represents more than one third of their overall application. Despite their wide use and significant amount of scientific data on their effects on biological systems, detailed insight into their in vivo fate is still lacking. This study aimed to elucidate the biotransformation patterns of AgNPs following oral administration. Colloidal stability, biochemical transformation, dissolution, and degradation behaviour of different types of AgNPs were evaluated in systems modelled to represent biological environments relevant for oral administration, as well as in cell culture media and tissue compartments obtained from animal models. A multimethod approach was employed by implementing light scattering (dynamic and electrophoretic) techniques, spectroscopy (UV-vis, atomic absorption, nuclear magnetic resonance) and transmission electron microscopy. The obtained results demonstrated that AgNPs may transform very quickly during their journey through different biological conditions. They are able to degrade to an ionic form and again reconstruct to a nanoparticulate form, depending on the biological environment determined by specific body compartments. As suggested for other inorganic nanoparticles by other research groups, AgNPs fail to preserve their specific integrity in in vivo settings

Rational positioning of 3D printed micro-bricks to realize high-fidelity, multi-functional soft-hard interfaces

peer reviewedLiving organisms have developed design principles, such as functional gradients (FGs), to interface hard materials with soft ones (e.g., bone and tendon). Mimicking such design principles can address the challenges faced when developing engineered constructs with soft-hard interfaces. To date, implementing these FG design principles has been primarily performed by varying the ratio of the hard phase to that of the soft phase. Such design approaches, however, lead to inaccurate mechanical properties within the transition zone. That is due to the highly nonlinear relationship between the material distribution at the microscale and the macroscale mechanical properties. Here, we 3D print micro-bricks from either a soft or a hard phase and study the nonlinear relationship between their arrangements within the transition zone and the resulting macroscale properties. We carry out experiments at the micro- and macroscales as well as finite element simulations at both scales. Based on the obtained results, we develop a co-continuous power-law model relating the arrangement of the micro-bricks to the local mechanical properties of the micro-brick composites. We then use this model to rationally design FGs at the individual micro-brick level and create two types of biomimetic soft-hard constructs, including a specimen modeling bone-ligament junctions in the knee and another modeling the nucleus pulposus-annulus fibrosus interface in intervertebral discs. We show that the implemented FGs drastically enhance the stiffness, strength, and toughness of both types of specimens as compared to non-graded designs. Furthermore, we hypothesize that our soft-hard FGs regulate the behavior of murine preosteoblasts and primary human bone marrow-derived mesenchymal stromal cells (hBMSCc). We culture those cells to confirm the effects of soft-hard interfaces on cell morphology as well as on regulating the expression of focal adhesion kinase, subcellular localization, and YAP nuclear translocation of hBMSCs. Taken together, our results pave the way for the rational design of soft-hard interfaces at the micro-brick level and (biomedical) applications of such designs

Dependence of intracellular magnetic nanoparticles amount in the apoptotic death pathway triggered by magnetic hyperthermia treatment

Trabajo presentado a la 4th Spanish Conference on Biomedical Applications of Nanomaterials, celebrada online del 2 al 4 de junio de 2021.Peer reviewe

A mixed-methods feasibility study of a novel AI-enabled, web-based, clinical decision support system for the treatment of major depression in adults

Background:- The objective of this paper is to discuss perceived clinical utility and impact on physician-patient relationship of a novel, artificial-intelligence (AI) enabled clinical decision support system (CDSS) for use in treating adults with major depression. Methods:- A single arm, naturalistic follow-up study aimed at assessing the acceptability and useability of the software. Patients had a baseline appointment, followed by a minimum of two appointments with the CDSS. Study exit questionnaires and interviews were conducted to assess perceived clinical utility, impact on patient-physician relationship, and understanding and trust. 7 physicians and 17 patients, of which 14 completed, consented to participate. Results:- 86 % of physicians (6/7) felt the information provided by the CDSS provided more comprehensive understanding of patient situations. 71 % (5/7) felt the information was helpful. 86 % of physicians (6/7) reported the AI/predictive model was useful when deciding treatment. 62 % of patients (8/13) reported improved care due to the tool, and 46 %(6/13) reported a significantly or somewhat improved physician-patient relationship 54 % reported no change. 71 % of physicians (5/7) and 62 % of patients (8/13) rated trusting the tool. Limitations:- Small sample size and treatment changes prior to CDSS introduction limits ability to verify impact on outcomes. Conclusions:- Qualitative results from 12 patient exit interviews are analyzed and presented. Findings suggest physicians perceived the tool as useful in conducting appointments and used it while deciding treatment. Physicians and patients generally found the tool trustworthy, and it may have positive effects on physician-patient relationships. (Study identifier: NCT04061642)

In vitro degradation behavior and cytocompatibility of Mg–Zn–Zr alloys

Zinc and zirconium were selected as the alloying elements in biodegradable magnesium alloys, considering their strengthening effect and good biocompatibility. The degradation rate, hydrogen evolution, ion release, surface layer and in vitro cytotoxicity of two Mg–Zn–Zr alloys, i.e. ZK30 and ZK60, and a WE-type alloy (Mg–Y–RE–Zr) were investigated by means of long-term static immersion testing in Hank’s solution, non-static immersion testing in Hank’s solution and cell-material interaction analysis. It was found that, among these three magnesium alloys, ZK30 had the lowest degradation rate and the least hydrogen evolution. A magnesium calcium phosphate layer was formed on the surface of ZK30 sample during non-static immersion and its degradation caused minute changes in the ion concentrations and pH value of Hank’s solution. In addition, the ZK30 alloy showed insignificant cytotoxicity against bone marrow stromal cells as compared with biocompatible hydroxyapatite (HA) and the WE-type alloy. After prolonged incubation for 7 days, a stimulatory effect on cell proliferation was observed. The results of the present study suggested that ZK30 could be a promising material for biodegradable orthopedic implants and worth further investigation to evaluate its in vitro and in vivo degradation behavior

Evaluating the clinical feasibility of an artificial intelligence–powered, web-based clinical decision support system for the treatment of depression in adults: longitudinal feasibility study

Background:- Approximately two-thirds of patients with major depressive disorder do not achieve remission during their first treatment. There has been increasing interest in the use of digital, artificial intelligence–powered clinical decision support systems (CDSSs) to assist physicians in their treatment selection and management, improving the personalization and use of best practices such as measurement-based care. Previous literature shows that for digital mental health tools to be successful, the tool must be easy for patients and physicians to use and feasible within existing clinical workflows. Objective:- This study aims to examine the feasibility of an artificial intelligence–powered CDSS, which combines the operationalized 2016 Canadian Network for Mood and Anxiety Treatments guidelines with a neural network–based individualized treatment remission prediction. Methods:- Owing to the COVID-19 pandemic, the study was adapted to be completed entirely remotely. A total of 7 physicians recruited outpatients diagnosed with major depressive disorder according to the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition criteria. Patients completed a minimum of one visit without the CDSS (baseline) and 2 subsequent visits where the CDSS was used by the physician (visits 1 and 2). The primary outcome of interest was change in appointment length after the introduction of the CDSS as a proxy for feasibility. Feasibility and acceptability data were collected through self-report questionnaires and semistructured interviews. Results:- Data were collected between January and November 2020. A total of 17 patients were enrolled in the study; of the 17 patients, 14 (82%) completed the study. There was no significant difference in appointment length between visits (introduction of the tool did not increase appointment length; F2,24=0.805; mean squared error 58.08; P=.46). In total, 92% (12/13) of patients and 71% (5/7) of physicians felt that the tool was easy to use; 62% (8/13) of patients and 71% (5/7) of physicians rated that they trusted the CDSS. Of the 13 patients, 6 (46%) felt that the patient-clinician relationship significantly or somewhat improved, whereas 7 (54%) felt that it did not change. Conclusions:- Our findings confirm that the integration of the tool does not significantly increase appointment length and suggest that the CDSS is easy to use and may have positive effects on the patient-physician relationship for some patients. The CDSS is feasible and ready for effectiveness studies