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Mechanical stimuli are a fundamental player in the pathophysiology of the brain influencing its physiological development and contributing to the onset and progression of many diseases. In some pathological states, the involvement of mechanical and physical stimuli might be extremely subtle; in others, it is more evident and particularly relevant. Among the latter pathologies, one of the most serious life-threatening condition is the brain abscess (BA), a focal infection localized in the brain parenchyma, which causes large brain mechanical deformations, giving rise to a wide range of neurological impairments. In this paper, we present the first nano-mechanical characterization of surgically removed human brain abscess tissues by means of atomic force microscopy (AFM) in the spectroscopy mode. Consistently with previous histological findings, we modeled the brain abscess as a multilayered structure, composed of three main layers: the cerebritis layer, the collagen capsule, and the internal inflammatory border. We probed the viscoelastic behavior of each layer separately through the measure of the apparent Young’s modulus (E), that gives information about the sample stiffness, and the AFM hysteresis (H), that estimates the contribution of viscous and dissipative forces. Our experimental findings provide a full mechanical characterization of the abscess, showing an average E of (94 ± 5) kPa and H of 0.37 ± 0.01 for the cerebritis layer, an average E = (1.04 ± 0.05) MPa and H = 0.10 ± 0.01 for the collagen capsule and an average E = (9.8 ± 0.4) kPa and H = 0.57 ± 0.01 for the internal border. The results here presented have the potential to contribute to the development of novel surgical instruments dedicated to the treatment of the pathology and to stimulate the implementation of novel constitutive mechanical models for the estimation of brain compression and damage during BA progression.

Atomic force microscopy (AFM) in spectroscopy mode receives a lot of attention because of its potential in distinguishing between healthy and cancer tissues. However, the AFM translational process in clinical practice is hindered by the fact that it is a time-consuming technique in terms of measurement and analysis time. In this paper, we attempt to address both issues. We propose the use of neural networks for pattern recognition to automatically classify AFM force&ndash, distance (FD) curves, with the aim of avoiding curve-fitting with the Sneddon model or more complicated ones. We investigated the applicability of this method to the classification of brain cancer tissues. The performance of the classifier was evaluated with receiving operating characteristic (ROC) curves for the approach and retract curves separately and in combination with each other. Although more complex and comprehensive models are required to demonstrate the general applicability of the proposed approach, preliminary evidence is given for the accuracy of the results, and arguments are presented to support the possible applicability of neural networks to the classification of brain cancer tissues. Moreover, we propose a possible strategy to shorten measurement times based on the estimation of the minimum number of FD curves needed to classify a tissue with a confidence level of 0.005. Taken together, these results have the potential to stimulate the design of more effective protocols to reduce AFM measurement times and to get rid of curve-fitting, which is a complex and time-consuming issue that requires experienced staff with a strong data-analysis background.

The intrinsically disordered tau protein plays a pivotal role in the pathogenesis of Alzheimer's disease (AD) and other human tauopathies. Abnormal post-translational modifications of tau, such as truncation, are causally involved in the onset/development of these neurodegenerative diseases. In this context, the AD-relevant N-terminal fragment mapping between 26 and 44 amino acids of protein (tau26-44) is interesting, being endowed with potent neurotoxic effects in vitro and in vivo. However, the understanding of the mechanism(s) of tau26-44 toxicity is a challenging task because, similarly to the full-length tau, it does not have a unique 3D structure but exists as dynamic ensemble of conformations. Here we use Atomic Force Spectroscopy, Small Angle X-ray Scattering and Molecular Dynamics simulation to gather structural and functional information on the tau26-44. We highlight the presence, the type and the location of its temporary secondary structures and we unveil the occurrence of relevant transient tertiary conformations that could contribute to tau26-44 toxicity. Data are compared with those obtained on the biologically-inactive, reverse-sequence (tau44-26 peptide) which has the same mass, charge, aminoacidic composition as well as the same overall unfolded character of tau26-44.

Many ocular diseases are associated with an alteration of the mechanical and the material properties of the eye. These mechanically-related diseases include macular hole and pucker, two ocular conditions due to the presence of abnormal physical tractions acting on the retina. A complete relief of these tractions can be obtained through a challenging microsurgical procedure, which requires the mechanical peeling of the internal limiting membrane of the retina (ILM). In this paper, we provide the first comparative study of the nanoscale morphological and mechanical properties of the ILM in macular hole and macular pucker. Our nanoscale elastic measurements unveil a different bio-mechanical response of the ILM in the two pathologies, which correlates well to significant differences occurring during microsurgery. The results here presented pave the way to the development of novel dedicated microsurgical protocols based on the material ILM properties in macular hole or pucker. Moreover, they contribute to clarify why, despite a common aetiology, a patient might develop one disease or the other, an issue which is still debated in literature.

Forensic estimation of post-mortem interval relies on different methods, most of which, however, have practical limitations or provide insufficient results, still lacking a gold standard method. In order to better understand the phenomenon of rigor mortis and its applicability to the post-mortem interval estimation, we decided to use atomic force microscopy, a tool often employed to measure mechanical properties of adherent cells. Thus, we surgically removed skeletal muscle samples of three forensic cases from 0 to 120 h post-mortem and quantitatively evaluate two parameters: the Young’s modulus (E), which gives information about the sample stiffness, and the hysteresis (H), which estimates the contribution of viscous forces. Despite being a preliminary study, the obtained results show that the temporal behavior of E well correlates with the expected evolution of rigor mortis between 0 and 48 h post-mortem, and then monotonically decreases over time. Unfortunately, it is strongly affected by inter-individual variability. However, we found that H provides measurable data along a time-dependent curve back to the starting point, and these data measured on different subjects collapse onto a single master curve, getting rid of the inter-individual variability. Although a larger sampling should be performed to improve the result reliability, this finding is strongly suggestive that the evaluation of rigor mortis should involve the measure of the nanoscale dissipative behavior of muscular tissues.

Glioblastoma (GBM) and meningothelial meningioma (MM) are the most frequent malignant and benign brain lesions, respectively. Mechanical cues play a major role in the progression of both malignancies that is modulated by the occurrence of aberrant physical interactions between neoplastic cells and the extracellular matrix (ECM). Here we investigate the nano-mechanical properties of human GBM and MM tissues by atomic force microscopy. Our measures unveil the mechanical fingerprint of the main hallmark features of both lesions, such as necrosis in GBM and dural infiltration in MM. These findings have the potential to positively impact on the development of novel AFM-based diagnostic methods to assess the tumour grade. Most importantly, they provide a quantitative description of the tumour-induced mechanical modifications in the brain ECM, thus being of potential help in the search for novel ECM targets for brain tumours and especially for GBM that, despite years of intense research, has still very limited therapeutic options.

Atomic Force Microscopy (AFM) has the unique capability of probing the nanoscale mechanical properties of biological systems that affect and are affected by the occurrence of many pathologies, including cancer. This capability has triggered growing interest in the translational process of AFM from physics laboratories to clinical practice. A factor still hindering the current use of AFM in diagnostics is related to the complexity of AFM data analysis, which is time-consuming and needs highly specialized personnel with a strong physical and mathematical background. In this work, we demonstrate an operator-independent neural-network approach for the analysis of surgically removed brain cancer tissues. This approach allowed us to distinguish—in a fully automated fashion—cancer from healthy tissues with high accuracy, also highlighting the presence and the location of infiltrating tumor cells.

Colorectal cancer (CRC) development represents a multistep process starting with specific mutations that affect proto-oncogenes and tumour suppressor genes. These mutations confer a selective growth advantage to colonic epithelial cells that form first dysplastic crypts, and then malignant tumours and metastases. All these steps are accompanied by deep mechanical changes at the cellular and the tissue level. A growing consensus is emerging that such modifications are not merely a by-product of the malignant progression, but they could play a relevant role in the cancer onset and accelerate its progression. In this review, we focus on recent studies investigating the role of the biomechanical signals in the initiation and the development of CRC. We show that mechanical cues might contribute to early phases of the tumour initiation by controlling the Wnt pathway, one of most important regulators of cell proliferation in various systems. We highlight how physical stimuli may be involved in the differentiation of non-invasive cells into metastatic variants and how metastatic cells modify their mechanical properties, both stiffness and adhesion, to survive the mechanical stress associated with intravasation, circulation and extravasation. A deep comprehension of these mechanical modifications may help scientist to define novel molecular targets for the cure of CRC.

Professor Giulio C. Sarti has provided outstanding contributions to the modelling of fluid sorption and transport in polymeric materials, with a special eye on industrial applications such as membrane separation, due to his Chemical Engineering background. He was the co-creator of innovative theories such as the Non-Equilibrium Theory for Glassy Polymers (NET-GP), a flexible tool to estimate the solubility of pure and mixed fluids in a wide range of polymers, and of the Standard Transport Model (STM) for estimating membrane permeability and selectivity. In this review, inspired by his rigorous and original approach to representing membrane fundamentals, we provide an overview of the most significant and up-to-date modeling tools available to estimate the main properties governing polymeric membranes in fluid separation, namely solubility and diffusivity. The paper is not meant to be comprehensive, but it focuses on those contributions that are most relevant or that show the potential to be relevant in the future. We do not restrict our view to the field of macroscopic modelling, which was the main playground of professor Sarti, but also devote our attention to Molecular and Multiscale Hierarchical Modeling. This work proposes a critical evaluation of the different approaches considered, along with their limitations and potentiality.