Innovative Biomaterials for Tissue Engineering

In the field of regenerative medicine, biomaterials play a crucial role since they may serve as a support (scaffold) to promote cell growth and differentiation in order to promote the healing of tissue lesion. The aim of this chapter will be to analyze the properties of more recent biomaterials suitable for tissue engineering strategies, to end to define better and innovative materials for scaffold production. To this purpose, we will analyze the main materials (natural and synthetic) and their characteristics, such as biocompatibility, bioactivity, and biodegradation, and it will be discussed how their chemical-physical properties (surface morphology, porosity, stiffness, and mechanical strength) could affect the interaction with cells and living system. Moreover, the chapter will be focused on methods of extraction or production of biomaterial suitable for scaffolds

A miniaturized silicon based device for nucleic acids electrochemical detection

In this paper we describe a novel portable system for nucleic acids electrochemical detection. The core of the system is a miniaturized silicon chip composed by planar microelectrodes. The chip is embedded on PCB board for the electrical driving and reading. The counter, reference and work microelectrodes are manufactured using the VLSI technology, the material is gold for reference and counter electrodes and platinum for working electrode. The device contains also a resistor to control and measuring the temperature for PCR thermal cycling. The reaction chamber has a total volume of 20 μL. It is made in hybrid silicon–plastic technology. Each device contains four independent electrochemical cells.Results show HBV Hepatitis-B virus detection using an unspecific DNA intercalating redox probe based on metal–organic compounds. The recognition event is sensitively detected by square wave voltammetry monitoring the redox signals of the intercalator that strongly binds to the double-stranded DNA. Two approaches were here evaluated: (a) intercalation of electrochemical unspecific probe on ds-DNA on homogeneous solution (homogeneous phase); (b) grafting of DNA probes on electrode surface (solid phase).The system and the method here reported offer better advantages in term of analytical performances compared to the standard commercial optical-based real-time PCR systems, with the additional incomes of being potentially cheaper and easier to integrate in a miniaturized device. Keywords: Electrochemical detection, Real time PCR, Unspecific DNA intercalato

Novel β-cyclodextrin–eosin conjugates

Eosin B (EoB) and eosin Y (EoY), two xanthene dye derivatives with photosensitizing ability were prepared in high purity through an improved synthetic route. The dyes were grafted to a 6-monoamino-β-cyclodextrin scaffold under mild reaction conditions through a stable amide linkage using the coupling agent 4-(4,6-dimethoxy-1,3,5-triazin-2-yl)-4-methylmorpholinium chloride. The molecular conjugates, well soluble in aqueous medium, were extensively characterized by 1D and 2D NMR spectroscopy and mass spectrometry. Preliminary spectroscopic investigations showed that the β-cyclodextrin–EoY conjugate retains both the fluorescence properties and the capability to photogenerate singlet oxygen of the unbound chromophore. In contrast, the corresponding β-cyclodextrin–EoB conjugate did not show either relevant emission or photosensitizing activity probably due to aggregation in aqueous medium, which precludes any response to light excitation

Early detection of hip periprosthetic joint infections through CNN on Computed Tomography images

Early detection of an infection prior to prosthesis removal (e.g., hips, knees or other areas) would provide significant benefits to patients. Currently, the detection task is carried out only retrospectively with a limited number of methods relying on biometric or other medical data. The automatic detection of a periprosthetic joint infection from tomography imaging is a task never addressed before. This study introduces a novel method for early detection of the hip prosthesis infections analyzing Computed Tomography images. The proposed solution is based on a novel ResNeSt Convolutional Neural Network architecture trained on samples from more than 100 patients. The solution showed exceptional performance in detecting infections with an experimental high level of accuracy and F-score

Carbon-dots conductometric sensor for high performance gas sensing

In this paper the first example of using C-dots (CDs) as sensing nanomaterial for monitoring low concentrations of NO2 in ambient air is reported. In the logic to support a green circular economy, CDs were prepared from a natural low cost precursor consisting in olive solid waste (OSW) by a simple pyrolysis process combined with chemical oxidation. Characterization data showed the formation of spherical CDs with dimensions in the narrow size range from 0.5 to 5 nm and charged with functional groups (COO- (carboxylate), C-O-C (epoxide) and C-OH (hydroxyl) imprinting excellent water colloidal dispersion. The nanomaterial was used to fabricate and test a conductometric gas sensor (CDs-sensor) that was found to exhibit excellent performances in terms of high and selective response to sub-ppm concentration of NO2 at low temperature (150 °C), low limit of detection (LOD) of 50 ppb, good reproducibility and stability over use and aging. To the best of our knowledge, this is the first example reported in the literature of CDs high performances gas sensing material. Results here presented pave the way for a new class of a carbon nanomaterial for gas sensing to be applied in the field of environmental monitoring

Development of Si-based electrical biosensors: Simulations and first experimental results

In this work, we simulated and experimentally assessed the possibility to detect, through electrical transduction, hybridization of DNA molecules on MOS-like devices, having different dielectrics: SiO2, Si3N4 and SiO2/Si3N4/SiO2 (ONO). The electrical characterization was performed after the various functionalization steps, consisting of dielectric activation, silanization, DNA spotting and anchoring, and after the hybridization process, to test the devices effectiveness as DNA recognition biosensors. The experimental results were used to validate device simulations. The comparison shows the ability to determine a priori the DNA probe density needed to maximize the response. The results confirm that the structures analyzed are sensitive to the immobilization of DNA and its hybridization

An Explainable AI System for Automated COVID-19 Assessment and Lesion Categorization from CT-scans

COVID-19 infection caused by SARS-CoV-2 pathogen is a catastrophic pandemic outbreak all over the world with exponential increasing of confirmed cases and, unfortunately, deaths. In this work we propose an AI-powered pipeline, based on the deep-learning paradigm, for automated COVID-19 detection and lesion categorization from CT scans. We first propose a new segmentation module aimed at identifying automatically lung parenchyma and lobes. Next, we combined such segmentation network with classification networks for COVID-19 identification and lesion categorization. We compare the obtained classification results with those obtained by three expert radiologists on a dataset consisting of 162 CT scans. Results showed a sensitivity of 90\% and a specificity of 93.5% for COVID-19 detection, outperforming those yielded by the expert radiologists, and an average lesion categorization accuracy of over 84%. Results also show that a significant role is played by prior lung and lobe segmentation that allowed us to enhance performance by over 20 percent points. The interpretation of the trained AI models, moreover, reveals that the most significant areas for supporting the decision on COVID-19 identification are consistent with the lesions clinically associated to the virus, i.e., crazy paving, consolidation and ground glass. This means that the artificial models are able to discriminate a positive patient from a negative one (both controls and patients with interstitial pneumonia tested negative to COVID) by evaluating the presence of those lesions into CT scans. Finally, the AI models are integrated into a user-friendly GUI to support AI explainability for radiologists, which is publicly available at http://perceivelab.com/covid-ai

Bio-Inspired Deep-CNN Pipeline for Skin Cancer Early Diagnosis

Skin cancer is the most common type of cancer, as also among the riskiest in the medical oncology field. Skin cancer is more common in people who work or practice outdoor sports and those that expose themselves to the sun. It may also develop years after radiographic therapy or exposure to substances that cause cancer (e.g., arsenic ingestion). Numerous tumors can affect the skin, which is the largest organ in our body and is made up of three layers: the epidermis (superficial layer), the dermis (middle layer) and the subcutaneous tissue (deep layer). The epidermis is formed by different types of cells: melanocytes, which have the task of producing melanin (a pigment that protects against the damaging effects of sunlight), and the more numerous keratinocytes. The keratinocytes of the deepest layer are called basal cells and can give rise to basal cell carcinomas. We are interested in types of skin cancer that originate from melanocytes, i.e., the so-called melanomas, because it is the most aggressive. The dermatologist, during a complete visit, evaluates the personal and family history of the patient and carries out an accurate visual examination of the skin, thanks to the use of epi-luminescence (or dermoscopy), a special technique for enlarging and illuminating the skin. This paper mentions one of the most widely used diagnostic methods due to its simplicity and validity—the ABCDE method (Asymmetry, edge irregularity, Color Variegation, Diameter, Evolution). This methodology, based on “visual” investigation by the dermatologist and/or oncologist, has the advantage of not being invasive and quite easy to perform. This approach is affected by the opinion of who (physicians) applies it. For this reason, certain diagnosis of cancer is made, however, only with a biopsy, a procedure during which a portion of tissue is taken and then analyzed under a microscope. Obviously, this is particularly invasive for the patient. The authors of this article have analyzed the development of a method that obtains with good accuracy the early diagnosis of skin neoplasms using non-invasive, but at the same time, robust methodologies. To this end, the authors propose the adoption of a deep learning pipeline based on morphological analysis of the skin lesion. The results obtained and compared with previous approaches confirm the good performance of the proposed pipeline

Image data analysis in qPCR: A method for smart analysis of DNA amplification

In this paper, a method for the direct quantitative analysis of amplified DNA via q-Polymerase Chain Reaction (qPCR) in miniaturised silicon-based chip system is presented. The designed tool presented here allows the automatic extraction of meaningful information from input fluorescent images by means of digital image processing algorithm. In particular, a smart mathematical model, optimizing the integration of all the analysis steps of the fluorescence data from on chip multiple real time PCR, is described. Such a tool is able to load the digital input images, select and smartly detect the region of interest for fluorescence, elaborate the data input, calculate the average fluorescence values and finally, plot the fitted curve as output, giving also, for each well, both the Cycle Threshold (CT) and Slope parameters. Keywords: Image analysis, qPCR, LabonChip, Edge detection, Hough transfor

Advanced Bio-Inspired System for Noninvasive Cuff-Less Blood Pressure Estimation from Physiological Signal Analysis

Blood Pressure (BP) is one of the most important physiological indicators that provides useful information in the field of health-care monitoring. Blood pressure may be measured by both invasive and non-invasive methods. A novel algorithmic approach is presented to estimate systolic and diastolic blood pressure accurately in a way that does not require any explicit user calibration, i.e., it is non-invasive and cuff-less. The approach herein described can be applied in a medical device, as well as in commercial mobile smartphones by an ad hoc developed software based on the proposed algorithm. The authors propose a system suitable for blood pressure estimation based on the PhotoPlethysmoGraphy (PPG) physiological signal sampling time-series. Photoplethysmography is a simple optical technique that can be used to detect blood volume changes in the microvascular bed of tissue. It is non-invasive since it takes measurements at the skin surface. In this paper, the authors present an easy and smart method to measure BP through careful neural and mathematical analysis of the PPG signals. The PPG data are processed with an ad hoc bio-inspired mathematical model that estimates systolic and diastolic pressure values through an innovative analysis of the collected physiological data. We compared our results with those measured using a classical cuff-based blood pressure measuring device with encouraging results of about 97% accuracy