Nanomechanical Mapping of Hard Tissues by Atomic Force Microscopy: An Application to Cortical Bone

Force mapping of biological tissues via atomic force microscopy (AFM) probes the mechanical properties of samples within a given topography, revealing the interplay between tissue organization and nanometer-level composition. Despite considerable attention to soft biological samples, constructing elasticity maps on hard tissues is not routine for standard AFM equipment due to the difficulty of interpreting nanoindentation data in light of the available models of surface deformation. To tackle this issue, we proposed a protocol to construct elasticity maps of surfaces up to several GPa in moduli by AFM nanoindentation using standard experimental conditions (air operation, nanometrically sharp spherical tips, and cantilever stiffness below 30 N/m). We showed how to process both elastic and inelastic sample deformations simultaneously and independently and quantify the degree of elasticity of the sample to decide which regime is more suitable for moduli calculation. Afterwards, we used the frequency distributions of Young’s moduli to quantitatively assess differences between sample regions different for structure and composition, and to evaluate the presence of mechanical inhomogeneities. We tested our method on histological sections of sheep cortical bone, measuring the mechanical response of different osseous districts, and mapped the surface down to the single collagen fibril level

NSCLC Biomarkers to Predict Response to Immunotherapy with Checkpoint Inhibitors (ICI): From the Cells to In Vivo Images

SIMPLE SUMMARY: Lung cancer and in particular non-small cell lung cancer (NSCLC) remains the leading cause of cancer-related death. The development of new therapeutic approaches, including immunotherapy, has led to substantial improvement in survival time and quality of life. However, the clinical benefit of immunotherapy-based strategies is still limited to a minority of patients, reflecting the need to identify predictive biomarkers of response, which are any substance, structure, or process or its products that can be measured in the body and that can influence or predict clinical response. In this work, we provide an overview of the approved and the most promising investigational biomarkers, which have been assessed in vitro/ex vivo and in vivo, to identify patients who could benefit the most from immunotherapy-based treatment. ABSTRACT: Lung cancer remains the leading cause of cancer-related death, and it is usually diagnosed in advanced stages (stage III or IV). Recently, the availability of targeted strategies and of immunotherapy with checkpoint inhibitors (ICI) has favorably changed patient prognosis. Treatment outcome is closely related to tumor biology and interaction with the tumor immune microenvironment (TME). While the response in molecular targeted therapies relies on the presence of specific genetic alterations in tumor cells, accurate ICI biomarkers of response are lacking, and clinical outcome likely depends on multiple factors that are both host and tumor-related. This paper is an overview of the ongoing research on predictive factors both from in vitro/ex vivo analysis (ranging from conventional pathology to molecular biology) and in vivo analysis, where molecular imaging is showing an exponential growth and use due to technological advancements and to the new bioinformatics approaches applied to image analyses that allow the recovery of specific features in specific tumor subclones

A biologically inspired neural network controller for ballistic arm movements

<p>Abstract</p> <p>Background</p> <p>In humans, the implementation of multijoint tasks of the arm implies a highly complex integration of sensory information, sensorimotor transformations and motor planning. Computational models can be profitably used to better understand the mechanisms sub-serving motor control, thus providing useful perspectives and investigating different control hypotheses. To this purpose, the use of Artificial Neural Networks has been proposed to represent and interpret the movement of upper limb. In this paper, a neural network approach to the modelling of the motor control of a human arm during planar ballistic movements is presented.</p> <p>Methods</p> <p>The developed system is composed of three main computational blocks: 1) a parallel distributed learning scheme that aims at simulating the internal inverse model in the trajectory formation process; 2) a pulse generator, which is responsible for the creation of muscular synergies; and 3) a limb model based on two joints (two degrees of freedom) and six muscle-like actuators, that can accommodate for the biomechanical parameters of the arm. The learning paradigm of the neural controller is based on a pure exploration of the working space with no feedback signal. Kinematics provided by the system have been compared with those obtained in literature from experimental data of humans.</p> <p>Results</p> <p>The model reproduces kinematics of arm movements, with bell-shaped wrist velocity profiles and approximately straight trajectories, and gives rise to the generation of synergies for the execution of movements. The model allows achieving amplitude and direction errors of respectively 0.52 cm and 0.2 radians.</p> <p>Curvature values are similar to those encountered in experimental measures with humans.</p> <p>The neural controller also manages environmental modifications such as the insertion of different force fields acting on the end-effector.</p> <p>Conclusion</p> <p>The proposed system has been shown to properly simulate the development of internal models and to control the generation and execution of ballistic planar arm movements. Since the neural controller learns to manage movements on the basis of kinematic information and arm characteristics, it could in perspective command a neuroprosthesis instead of a biomechanical model of a human upper limb, and it could thus give rise to novel rehabilitation techniques.</p

The ANTENATAL multicentre study to predict postnatal renal outcome in fetuses with posterior urethral valves: objectives and design

Abstract Background Posterior urethral valves (PUV) account for 17% of paediatric end-stage renal disease. A major issue in the management of PUV is prenatal prediction of postnatal renal function. Fetal ultrasound and fetal urine biochemistry are currently employed for this prediction, but clearly lack precision. We previously developed a fetal urine peptide signature that predicted in utero with high precision postnatal renal function in fetuses with PUV. We describe here the objectives and design of the prospective international multicentre ANTENATAL (multicentre validation of a fetal urine peptidome-based classifier to predict postnatal renal function in posterior urethral valves) study, set up to validate this fetal urine peptide signature. Methods Participants will be PUV pregnancies enrolled from 2017 to 2021 and followed up until 2023 in >30 European centres endorsed and supported by European reference networks for rare urological disorders (ERN eUROGEN) and rare kidney diseases (ERN ERKNet). The endpoint will be renal/patient survival at 2 years postnatally. Assuming α = 0.05, 1–β = 0.8 and a mean prevalence of severe renal outcome in PUV individuals of 0.35, 400 patients need to be enrolled to validate the previously reported sensitivity and specificity of the peptide signature. Results In this largest multicentre study of antenatally detected PUV, we anticipate bringing a novel tool to the clinic. Based on urinary peptides and potentially amended in the future with additional omics traits, this tool will be able to precisely quantify postnatal renal survival in PUV pregnancies. The main limitation of the employed approach is the need for specialized equipment. Conclusions Accurate risk assessment in the prenatal period should strongly improve the management of fetuses with PUV

Near-Surface Nanomechanics of Medical-Grade PEEK Measured by Atomic Force Microscopy

Detecting subtle changes of surface stiffness at spatial scales and forces relevant to biological processes is crucial for the characterization of biopolymer systems in view of chemical and/or physical surface modification aimed at improving bioactivity and/or mechanical strength. Here, a standard atomic force microscopy setup is operated in nanoindentation mode to quantitatively mapping the near-surface elasticity of semicrystalline polyether ether ketone (PEEK) at room temperature. Remarkably, two localized distributions of moduli at about 0.6 and 0.9 GPa are observed below the plastic threshold of the polymer, at indentation loads in the range of 120&ndash;450 nN. This finding is ascribed to the localization of the amorphous and crystalline phases on the free surface of the polymer, detected at an unprecedented level of detail. Our study provides insights to quantitatively characterize complex biopolymer systems on the nanoscale and to guide the optimal design of micro- and nanostructures for advanced biomedical applications

Stance postural strategies in patients with chronic inflammatory demyelinating polyradiculoneuropathy

Introduction: Polyneuropathy leads to postural instability and an increased risk of falling. We investigated how impaired motor impairment and proprioceptive input due to neuropathy influences postural strategies. Methods: Platformless bisegmental posturography data were recorded in healthy subjects and patients with chronic inflammatory demyelinating polyradiculoneuropathy (CIDP). Each subject stood on the floor, wore a head and a hip electromagnetic tracker. Sway amplitude and velocity were recorded and the mean direction difference (MDD) in the velocity vector between trackers was calculated as a flexibility index. Results: Head and hip postural sway increased more in patients with CIDP than in healthy controls. MDD values reflecting hip strategies also increased more in patients than in controls. In the eyes closed condition MDD values in healthy subjects decreased but in patients remained unchanged. Discussion: Sensori-motor impairment changes the balance between postural strategies that patients adopt to maintain upright quiet stance. Motor impairment leads to hip postural strategy overweight (eyes open), and prevents strategy re-balancing when the sensory context predominantly relies on proprioceptive input (eyes closed)

Burnout following moral injury and dehumanization: A study of distress among Italian medical staff during the first COVID-19 pandemic period

Clinical Impact Statement The COVID-19 pandemic also presents high mental health challenges for medical staff. The present study highlights how the COVID-19 pandemic has stressed Italian medical staff and how dehumanization of patients and colleagues has been associated with distress, alienation, and moral injury. The results show that the processes involved in humanizing care in medicine strengthen the patient-centered relationship but may have been affected by the impact of the pandemic. It is necessary to generate more in-depth research on the relationship between dehumanization, humanization, and medical staff well-being. Regarding humanization of care in medicine, the results suggest that training needs to be both implemented to enable medical staff to learn how to properly manage relationships and communication with patients and colleagues and conducted by mental health professionals.Background: Italy was the first country outside Asia to deal with the early phase of the COVID-19 pandemic, and health care facilities and medical staff were not fully prepared. Research worldwide has documented the enormous effect of the COVID-19 pandemic on health care providers' mental health, including experiences of dehumanization, but less work has focused on factors which may influence the development of these outcomes in response to COVID-19-related stress. Objective: This study examined the association of dehumanization, self-efficacy, and alienation to burnout, depression, and PTSD among medical staff. Potential moderators included moral injury, professional role, COVID workload, and work in a critical care unit (CCU). Method: Participants were recruited through the Internet. The sample consisted of 270 medical staff members who completed a self-report survey online. Instruments included: Human Traits Attribution Scale for dehumanization; NYP-Queens Survey - Self-Efficacy Subscale for self-efficacy; Moral Injury Events Scale for moral injury; Alienation Scale for alienation; PTSD-8 for posttraumatic stress disorder; Patient Health Questionnaire-9 for depression; and a single item for burnout. The analytic plan included ANOVAs, zero-order correlations, logistic regression analyses, multiple linear regression models, and parallel mediation. Results: Results show that dehumanization was associated with higher levels of burnout, PTSD, and depressive symptoms and effects were consistent across professional role and work context. Dehumanization was significantly associated with PTSD symptoms only among those who had increased COVID-19-related caseloads. Moral injury was positively associated with dehumanization, displayed an independent association with all 3 mental health outcomes, over and above dehumanization, and tended to exacerbate the effects of dehumanization. The effect sizes across analyses were small to medium. Conclusion: This research confirms that the COVID-19 pandemic stressed Italian medical staff in a way not documented in the prepandemic literature. There is a need to support staff in their complex relationships and communication with patients