Healthy cornea guarantees the refractive power of the eye and the protection of the inner components, but injury, trauma or pathology may impair the tissue shape and/or structural organization and therefore its material properties, compromising its functionality in the ocular visual process. It turns out that biomechanical research assumes an essential role in analysing the morphology and biomechanical response of the cornea, preventing pathology occurrence, and improving/optimising treatments. In this review, ex vivo, in vivo and in silico methods for the corneal mechanical characterization are reported. Experimental techniques are distinct in testing mode (e.g., tensile, inflation tests), samples\' species (human or animal), shape and condition (e.g., healthy, treated), preservation methods, setup and test protocol (e.g., preconditioning, strain rate). The meaningful results reported in the pertinent literature are discussed, analysing differences, key features and weaknesses of the methodologies adopted. In addition, numerical techniques based on the finite element method are reported, incorporating the essential steps for the development of corneal models, such as geometry, material characterization and boundary conditions, and their application in the research field to extend the experimental results by including further relevant aspects and in the clinical field for diagnostic procedure, treatment and planning surgery. This review aims to analyse the state-of-art of the bioengineering techniques developed over the years to study the corneal biomechanics, highlighting their potentiality to improve diagnosis, treatment and healing process of the corneal tissue, and, at the same, pointing out the current limits in the experimental equipment and numerical tools that are not able to fully characterize in vivo corneal tissues non-invasively and discourage the use of finite element models in daily clinical practice for surgical planning.

The placenta provides the vital nutrients and removal of waste products required for fetal growth and development. Understanding and quantifying the differences in structure and function between a normally functioning placenta compared to an abnormal placenta is vital to provide insights into the aetiology and treatment options for fetal growth restriction and other placental disorders. Computational modelling of blood flow in the placenta allows a new understanding of the placental circulation to be obtained. This structured review discusses multiple recent methods for placental vascular model development including analysis of the appearance of the placental vasculature and how placental haemodynamics may be simulated at multiple length scales.

OBJECTIVE: Assumptions on the natural history of ductal carcinoma in situ (DCIS) are necessary to accurately model it and estimate overdiagnosis. To improve current estimates of overdiagnosis (0-91%), the purpose of this review was to identify and analyse assumptions made in modelling studies on the natural history of DCIS in women.
METHODS: A systematic review of English full-text articles using PubMed, Embase, and Web of Science was conducted up to February 6, 2023. Eligibility and all assessments were done independently by two reviewers. Risk of bias and quality assessments were performed. Discrepancies were resolved by consensus. Reader agreement was quantified with Cohen\'s kappa. Data extraction was performed with three forms on study characteristics, model assessment, and tumour progression.
RESULTS: Thirty models were distinguished. The most important assumptions regarding the natural history of DCIS were addition of non-progressive DCIS of 20-100%, classification of DCIS into three grades, where high grade DCIS had an increased chance of progression to invasive breast cancer (IBC), and regression possibilities of 1-4%, depending on age and grade. Other identified risk factors of progression of DCIS to IBC were younger age, birth cohort, larger tumour size, and individual risk.
CONCLUSIONS: To accurately model the natural history of DCIS, aspects to consider are DCIS grades, non-progressive DCIS (9-80%), regression from DCIS to no cancer (below 10%), and use of well-established risk factors for progression probabilities (age). Improved knowledge on key factors to consider when studying DCIS can improve estimates of overdiagnosis and optimization of screening.

Analysis of the glutamine metabolic pathway has taken a special place in metabolomics research in recent years, given its important role in cell biosynthesis and bioenergetics across several disorders, especially in cancer cell survival. The science of metabolomics addresses the intricate intracellular metabolic network by exploring and understanding how cells function and respond to external or internal perturbations to identify potential therapeutic targets. However, despite recent advances in metabolomics, monitoring the kinetics of a metabolic pathway in a living cell in situ, real-time and holistically remains a significant challenge.
This review paper explores the range of analytical approaches for monitoring metabolic pathways, as well as physicochemical modeling techniques, with a focus on glutamine metabolism. We discuss the advantages and disadvantages of each method and explore the potential of label-free Raman microspectroscopy, in conjunction with kinetic modeling, to enable real-time and in situ monitoring of the cellular kinetics of the glutamine metabolic pathway.
Given its important role in cell metabolism, the ability to monitor and model the glutamine metabolic pathways are highlighted. Novel, label free approaches have the potential to revolutionise metabolic biosensing, laying the foundation for a new paradigm in metabolomics research and addressing the challenges in monitoring metabolic pathways in living cells.

Pre-procedural planning of thoracic endovascular aortic repair (TEVAR) may implement computational adjuncts to predict technical and clinical outcomes. The aim of this scoping review was to explore the currently available TEVAR procedure and stent graft modelling options.
PubMed (MEDLINE), Scopus, and Web of Science were systematically searched (English language, up to 9 December 2022) for studies presenting a virtual thoracic stent graft model or TEVAR simulation.
The Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) was followed. Qualitative and quantitative data were extracted, compared, grouped, and described. Quality assessment was performed using a 16 item rating rubric.
Fourteen studies were included. Among the currently available in silico simulations of TEVAR, severe heterogeneity exists in study characteristics, methodological details, and evaluated outcomes. Ten studies (71.4%) were published during the last five years. Eleven studies (78.6%) included heterogeneous clinical data to reconstruct patient specific aortic anatomy and disease (e.g., type B aortic dissection, thoracic aortic aneurysm) from computed tomography angiography imaging. Three studies (21.4%) constructed idealised aortic models with literature input. The applied numerical methods consisted of computational fluid dynamics analysing aortic haemodynamics in three studies (21.4%) and finite element analysis analysing structural mechanics in the others (78.6%), including or excluding aortic wall mechanical properties. The thoracic stent graft was modelled as two separate components (e.g., graft, nitinol) in 10 studies (71.4%), as a one component homogenised approximation (n = 3, 21.4%), or including nitinol rings only (n = 1, 7.1%). Other simulation components included the catheter for virtual TEVAR deployment and numerous outcomes (e.g., Von Mises stresses, stent graft apposition, drag forces) were evaluated.
This scoping review identified 14 severely heterogeneous TEVAR simulation models, mostly of intermediate quality. The review concludes there is a need for continuous collaborative efforts to improve the homogeneity, credibility, and reliability of TEVAR simulations.

The geometrical details and biomechanical relationships of the mitral valve-left ventricular apparatus are very complex and have posed as an area of research interest for decades. These characteristics play a major role in identifying and perfecting the optimal approaches to treat diseases of this system when the restoration of biomechanical and mechano-biological conditions becomes the main target. Over the years, engineering approaches have helped to revolutionize the field in this regard. Furthermore, advanced modelling modalities have contributed greatly to the development of novel devices and less invasive strategies. This article provides an overview and narrative of the evolution of mitral valve therapy with special focus on two diseases frequently encountered by cardiac surgeons and interventional cardiologists: ischemic and degenerative mitral regurgitation.

Cell proliferation is vital for the development and homeostasis of the human body. For such to occur, cells go through the cell cycle during which they replicate their genetic material and ultimately complete cellular division, when one cell divides into two new cells with equal genetic material. However, if there are some errors or abnormalities during the cell cycle that disrupt the balance between cell death and proliferation, severe problems can occur, such as tumour development, which is currently one of the leading causes of death in the world. Nowadays, mathematical and computational models are used to understand and study several biological mechanisms and processes, namely cellular proliferation. Over the last forty-five years, several models have attempted to describe cell proliferation and its regulation. Due to the complexity of the process, numerous assumptions and simplifications have been considered. This work presents a review of some of these models, focusing mainly on mammalian or generic eukaryotic models. Previously published continuum, discrete and hybrid approaches are presented and compared, in order to understand and highlight the relevance and capabilities of these models, their shortcomings and future challenges.

Bringing precision to the understanding and treatment of mental disorders requires instruments for studying clinically relevant individual differences. One promising approach is the development of computational assays: integrating computational models with cognitive tasks to infer latent patient-specific disease processes in brain computations. While recent years have seen many methodological advancements in computational modelling and many cross-sectional patient studies, much less attention has been paid to basic psychometric properties (reliability and construct validity) of the computational measures provided by the assays. In this review, we assess the extent of this issue by examining emerging empirical evidence. We find that many computational measures suffer from poor psychometric properties, which poses a risk of invalidating previous findings and undermining ongoing research efforts using computational assays to study individual (and even group) differences. We provide recommendations for how to address these problems and, crucially, embed them within a broader perspective on key developments that are needed for translating computational assays to clinical practice.

Alterations in belief updating are proposed to underpin symptoms of psychiatric illness, including psychosis, depression, and anxiety. Key parameters underlying belief updating can be captured using computational modelling techniques, aiding the identification of unique and shared deficits, and improving diagnosis and treatment. We systematically reviewed research that applied computational modelling to probabilistic tasks measuring belief updating in stable and volatile (changing) environments, across clinical and subclinical psychosis (n = 17), anxiety (n = 9), depression (n = 9) and transdiagnostic samples (n = 9). Depression disorders related to abnormal belief updating in response to the valence of rewards, evidenced in both stable and volatile environments. Whereas psychosis and anxiety disorders were associated with difficulties adapting to changing contingencies specifically, indicating an inflexibility and/or insensitivity to environmental volatility. Higher-order learning models revealed additional difficulties in the estimation of overall environmental volatility across psychosis disorders, showing increased updating to irrelevant information. These findings stress the importance of investigating belief updating in transdiagnostic samples, using homogeneous experimental and computational modelling approaches.

Post-operative bone growth and long-term bone adaptation around the orthopaedic implants are simulated using the mechanoregulation based tissue-differentiation and adaptive bone remodelling algorithms, respectively. The primary objective of these algorithms was to assess biomechanical feasibility and reliability of orthopaedic implants. This article aims to offer a comprehensive review of the developments in mathematical models of tissue-differentiation and bone adaptation and their applications in studies involving design optimization of orthopaedic implants over three decades. Despite the different mechanoregulatory models developed, existing literature confirm that none of the models can be highly regarded or completely disregarded over each other. Not much development in mathematical formulations has been observed from the current state of knowledge due to the lack of in vivo studies involving clinically relevant animal models, which further retarded the development of such models to use in translational research at a fast pace. Future investigations involving artificial intelligence (AI), soft-computing techniques and combined tissue-differentiation and bone-adaptation studies involving animal subjects for model verification are needed to formulate more sophisticated mathematical models to enhance the accuracy of pre-clinical testing of orthopaedic implants.