Engineering three-dimensional (3D) scaffolds for functional tissue and organ regeneration is a major challenge of the tissue engineering (TE) community. Great progress has been made in developing scaffolds to support cells in 3D, and to date, several implantable scaffolds are available for treating damaged and dysfunctional tissues, such as bone, osteochondral, cardiac and nerve. However, recapitulating the complex extracellular matrix (ECM) functions of native tissues is far from being achieved in synthetic scaffolds. Modular TE is an intriguing approach that aims to design and fabricate ECM-mimicking scaffolds by the bottom-up assembly of building blocks with specific composition, morphology and structural properties. This review provides an overview of the main strategies to build synthetic TE scaffolds through bioactive modules assembly and classifies them into two distinct schemes based on microparticles (µPs) or patterned layers. The µPs-based processes section starts describing novel techniques for creating polymeric µPs with desired composition, morphology, size and shape. Later, the discussion focuses on µPs-based scaffolds design principles and processes. In particular, starting from random µPs assembly, we will move to advanced µPs structuring processes, focusing our attention on technological and engineering aspects related to cell-free and cell-laden strategies. The second part of this review article illustrates layer-by-layer modular scaffolds fabrication based on discontinuous, where layers’ fabrication and assembly are split, and continuous processes.

The availability of standardized guidelines regarding the use of electronic fetal monitoring (EFM) in clinical practice has not effectively helped to solve the main drawbacks of fetal heart rate (FHR) surveillance methodology, which still presents inter- and intra-observer variability as well as uncertainty in the classification of unreassuring or risky FHR recordings. Given the clinical relevance of the interpretation of FHR traces as well as the role of FHR as a marker of fetal wellbeing autonomous nervous system development, many different approaches for computerized processing and analysis of FHR patterns have been proposed in the literature. The objective of this review is to describe the techniques, methodologies, and algorithms proposed in this field so far, reporting their main achievements and discussing the value they brought to the scientific and clinical community. The review explores the following two main approaches to the processing and analysis of FHR signals: traditional (or linear) methodologies, namely, time and frequency domain analysis, and less conventional (or nonlinear) techniques. In this scenario, the emerging role and the opportunities offered by Artificial Intelligence tools, representing the future direction of EFM, are also discussed with a specific focus on the use of Artificial Neural Networks, whose application to the analysis of accelerations in FHR signals is also examined in a case study conducted by the authors.

Many activities may elicit a biomechanical overload. Among these, lifting loads can cause work-related musculoskeletal disorders. Aspiring to improve risk prevention, the National Institute for Occupational Safety and Health (NIOSH) established a methodology for assessing lifting actions by means of a quantitative method based on intensity, duration, frequency and other geometrical characteristics of lifting. In this paper, we explored the machine learning (ML) feasibility to classify biomechanical risk according to the revised NIOSH lifting equation. Acceleration and angular velocity signals were collected using a wearable sensor during lifting tasks performed by seven subjects and further segmented to extract time-domain features: root mean square, minimum, maximum and standard deviation. The features were fed to several ML algorithms. Interesting results were obtained in terms of evaluation metrics for a binary risk/no-risk classification; specifically, the tree-based algorithms reached accuracies greater than 90% and Area under the Receiver operating curve characteristics curves greater than 0.9. In conclusion, this study indicates the proposed combination of features and algorithms represents a valuable approach to automatically classify work activities in two NIOSH risk groups. These data confirm the potential of this methodology to assess the biomechanical risk to which subjects are exposed during their work activity.