Institut Catholique d'Arts et Métiers

The blood-brain barrier (BBB) limits the delivery of systemically administered drugs to the brain. Methods to circumvent the BBB have been developed, but none are used in standard clinical practice. The lack of adoption of existing methods is due to procedural invasiveness, serious adverse effects, and the complications associated with performing such techniques coincident with repeated drug administration, which is customary in chemotherapeutic protocols. Pulsed ultrasound, a method for disrupting the BBB, was shown to effectively increase drug concentrations and to slow tumor growth in preclinical studies. We now report the interim results of an ultrasound dose-escalating phase 1/2a clinical trial using an implantable ultrasound device system, SonoCloud, before treatment with carboplatin in patients with recurrent glioblastoma (GBM). The BBB of each patient was disrupted monthly using pulsed ultrasound in combination with systemically injected microbubbles. Contrast-enhanced magnetic resonance imaging (MRI) indicated that the BBB was disrupted at acoustic pressure levels up to 1.1 megapascals without detectable adverse effects on radiologic (MRI) or clinical examination. Our preliminary findings indicate that repeated opening of the BBB using our pulsed ultrasound system, in combination with systemic microbubble injection, is safe and well tolerated in patients with recurrent GBM and has the potential to optimize chemotherapy delivery in the brain.

Three-dimensional bioprinting of biomaterials shows great potential for producing cell-encapsulated scaffolds to repair nerves after injury or disease. For this, preparation of biomaterials and bioprinting itself are critical to create scaffolds with both biological and mechanical properties appropriate for nerve regeneration, yet remain unachievable. This paper presents our study on bioprinting Schwann cell-encapsulated scaffolds using composite hydrogels of alginate, fibrin, hyaluronic acid, and/or RGD peptide, for nerve tissue engineering applications. For the preparation of composite hydrogels, suitable hydrogel combinations were identified and prepared by adjusting the concentration of fibrin based on the morphological spreading of Schwann cells. In bioprinting, the effects of various printing process parameters (including the air pressure for dispensing, dispensing head movement speed, and crosslinking conditions) on printed structures were investigated and, by regulating these parameters, mechanically-stable scaffolds with fully interconnected pores were printed. The performance of Schwann cells within the printed scaffolds were examined in terms of viability, proliferation, orientation, and ability to produce laminin. Our results show that the printed scaffolds can promote the alignment of Schwann cells inside scaffolds and thus provide haptotactic cues to direct the extension of dorsal root ganglion neurites along the printed strands, demonstrating their great potential for applications in the field of nerve tissue engineering.

Severe brain injuries can lead to long-lasting disorders of consciousness (DoC) such as vegetative state/unresponsive wakefulness syndrome (VS/UWS) or minimally conscious state (MCS). While behavioral assessment remains the gold standard to determine conscious state, EEG has proven to be a promising complementary tool to monitor the effect of new therapeutics. Encouraging results have been obtained with invasive electrical stimulation of the brain, and recent studies identified transcranial direct current stimulation (tDCS) as an effective approach in randomized controlled trials. This non-invasive and inexpensive tool may turn out to be the preferred treatment option. However, its mechanisms of action and physiological effects on brain activity remain unclear and debated. Here, we stimulated 60 DoC patients with the anode placed over left-dorsolateral prefrontal cortex in a prospective open-label study. Clinical behavioral assessment improved in twelve patients (20%) and none deteriorated. This behavioral response after tDCS coincided with an enhancement of putative EEG markers of consciousness: in comparison with non-responders, responders showed increases of power and long-range cortico-cortical functional connectivity in the theta-alpha band, and a larger and more sustained P300 suggesting improved conscious access to auditory novelty. The EEG changes correlated with electric fields strengths in prefrontal cortices, and no correlation was found on the scalp. Taken together, this prospective intervention in a large cohort of DoC patients strengthens the validity of the proposed EEG signatures of consciousness, and is suggestive of a direct causal effect of tDCS on consciousness.

Industry 4.0 concepts have brought about a wind of renewal in the organization of companies and their production methods. However, this integration is subject to obstacles when it comes to Small and Medium sized Enterprises—SMEs: the costs of new technologies to be acquired, the level of maturity of the company regarding its level of digitization and automation, human aspects such as training employees to master new technologies, reluctance to change, etc. This article provides a new framework and presents an intelligent support system to facilitate the digital transformation of SMEs. The digitalization is realized through physical, informational, and decisional points of view. To achieve the complete transformation of the company, the framework combines the triptych of performance criteria (cost, quality, time) with the notions of sustainability (with respect to social, societal, and environmental aspects) and digitization through tools to be integrated into the company’s processes. The new framework encompasses the formalisms developed in the literature on Industry 4.0 concepts, information systems and organizational methods as well as a global structure to support and assist operators in managing their operations. In the form of a web application, it will exploit reliable data obtained through information systems such as Enterprise Resources Planning—ERP, Manufacturing Execution System—MES, or Warehouse Management System—WMS and new technologies such as artificial intelligence (deep learning, multi-agent systems, expert systems), big data, Internet of things (IoT) that communicate with each other to assist operators during production processes. To illustrate and validate the concepts and developed tools, use cases of an electronic manufacturing SME have been solved with these concepts and tools, in order to succeed in this company’s digital transformation. Thus, a reference model of the electronics manufacturing companies is being developed for facilitating the future digital transformation of these domain companies. The realization of these use cases and the new reference model are growing up and their future exploitation will be presented as soon as possible.

Almost one third of all food produced in the world currently goes to waste. One of the targets under the ‘Responsible consumption and production’ sustainable development goal calls for halving the per capita food waste at the consumer level as well as across the supply chain from manufacturing, storage and retail by 2030. While numerous strategies have been recommended and implemented to address this problem, major challenges remain to be overcome. The paper presents an in-depth review of current state-of-art practices in food waste management. The solutions and recommendations presented to reduce food waste at the household, retail, restaurant, manufacturing and supply chain levels are reviewed. Regulations and regional variations in food waste management practices are also examined. The findings are used to identify research gaps and propose a conceptual framework to increase closed-loop material flow for more circular food systems that can reduce food waste. Potential areas for application of engineering and management principles to develop analytical models for food waste reduction are also discussed.

Tissue scaffolds with living cells fabricated by three-dimensional bioprinting/plotting techniques are becoming more prevalent in tissue repair and regeneration. In the bioprinting process, cells are subject to process-induced forces (such as shear force) that can result in cell damage and loss of cell function. The flow behavior of the biomaterial solutions that encapsulate living cells in this process plays an important role. This study used a rheometer to examine the flow behavior of alginate solution and alginate-Schwann cell (RSC96), alginate-fibroblast cell (NIH-3T3), and alginate-skeletal muscle cell (L8) suspensions during shearing with respect to effects on cell viability and proliferation. The flow behavior of all the alginate-cell suspensions varied with alginate concentration and cell density and had a significant influence on the viability and proliferation of the cells once sheared as well as on the recovery of the sheared cells. These findings provide a mean to preserve cell viability and/or retain cell proliferation function in the bioprinting process by regulating the flow behavior of cell-biomaterial suspensions and process parameters.

Abstract While most firms do not grow, a small number of firms grow and enhance their equity and debt capital intensity. Researchers, managers and policymakers question the role that digital technologies play in propelling firm growth and resource mobilization. Using a longitudinal dataset from emerging industries in the United Kingdom during 2010–2019, we distinguish three types of firms and examine their growth and resource mobilization. First, we find that digitally advanced firms grow faster and enhance equity capital intensity while reducing debt capital intensity. Second, we find that the relationship between digitally advanced firms and firm growth is mediated by equity capital intensity. Third, firm size positively moderates the effect of digitally advanced firms on firm growth. Firm age does not moderate this relationship. Other firm‐level characteristics, such as number of digital tools, firm productivity, accelerator experience and stage of growth, may either impede or facilitate a firm's growth and resource mobilization. This study helps policymakers and firm managers in emerging industries better understand the role of digitalization and resources in firm growth.

BACKGROUND: Congenital heart disease accounts for almost a third of all major congenital anomalies. Congenital heart defects have a significant impact on morbidity, mortality and health costs for children and adults. Research regarding the risk of pre-surgical mortality is scarce. OBJECTIVES: Our goal is to generate a predictive model calculator adapted to the regional reality focused on individual mortality prediction among patients with congenital heart disease undergoing cardiac surgery. METHODS: Two thousand two hundred forty CHD consecutive patients' data from InCor's heart surgery program was used to develop and validate the preoperative risk-of-death prediction model of congenital patients undergoing heart surgery. There were six artificial intelligence models most cited in medical references used in this study: Multilayer Perceptron (MLP), Random Forest (RF), Extra Trees (ET), Stochastic Gradient Boosting (SGB), Ada Boost Classification (ABC) and Bag Decision Trees (BDT). RESULTS: The top performing areas under the curve were achieved using Random Forest (0.902). Most influential predictors included previous admission to ICU, diagnostic group, patient's height, hypoplastic left heart syndrome, body mass, arterial oxygen saturation, and pulmonary atresia. These combined predictor variables represent 67.8% of importance for the risk of mortality in the Random Forest algorithm. CONCLUSIONS: The representativeness of "hospital death" is greater in patients up to 66 cm in height and body mass index below 13.0 for InCor's patients. The proportion of "hospital death" declines with the increased arterial oxygen saturation index. Patients with prior hospitalization before surgery had higher "hospital death" rates than who did not required such intervention. The diagnoses groups having the higher fatal outcomes probability are aligned with the international literature. A web application is presented where researchers and providers can calculate predicted mortality based on the CgntSCORE on any web browser or smartphone.

The competitiveness of companies in emerging countries implies many European countries must transform their production systems to be more efficient. Indeed, the new context created by the COVID-19 pandemic increases the necessity of digital transformation and focuses attention on its limited uptake by manufacturing companies. In France, the Industry 4.0 concepts are already implemented in large companies. Despite the demonstration and validation of their benefits, SMEs are reluctant to move towards implementation. This problem of SME performance improvement increases with the current geopolitical situation in Europe (raw materials and gasoil cost). It is thus urgent and paramount to find a better solution for encouraging SMEs in their transformation. Taking note of the brakes on uptake of Industry 4.0 concepts in SMEs, the objectives of this paper are to find levers to accelerate implementation of Industry 4.0 concepts in SMEs, through the development and the deployment of a sustainable Industry 4.0 methodology, and to develop an intelligent system for supporting companies’ digital transformation in order to improve their performance. After a literature review, focused on Industry 4.0 concepts, theory of systems, organizational methods, and artificial intelligence, a sustainable methodology will be presented. The SME performance model that has been elaborated will then be shown and the structure of the intelligent system (mainly the decision aided tool) being developed for supporting the digital transformation of SMEs will be described. An illustrative example relating to a food elaboration SME will be presented for validating the concepts that have been developed. The proposed framework helped the company to formulate guidelines and transition towards a sustainable 4.0 company.

European countries actually adopt industry 4.0 and supply chain 4.0 philosophy. Enterprise modelling methodologies define enterprise as a system, which can integrate new technologies, Internet of things, automation and robotics in collaboration with people. GRAI methodology and its supporting tool GRAIMOD are used for modelling, analysing and improving enterprise supply chain performance. QCD (quality, cost and lead time) criteria are combined (in GRAIMOD) to social, societal and environmental dimensions for improving the company supply chain. This paper presents how lead-time criterion could be implemented for increasing supply chain performance. A real application is given for illustrating the concepts presented.

Percutaneous nephrolithotomy (PCNL) is considered the gold standard for the treatment of patients with renal stones larger than 20 mm in diameter. The success and treatment outcomes of the surgery are very well known to be highly dependent on the precision and accuracy of the puncture step, since it must allow to reach the stone with a precise and direct path. Thus, performing the renal access during PCNL is the most crucial and challenging step of the procedure with the steepest learning curve. In this letter, we propose an innovative solution, based on an AR application combined with a robotic system, that can assist both an expert surgeon in improving the performance of the surgical operation and a novel surgeon in strongly reducing his/her learning curve. The proposed system is validated on a setup including a KUKA LWR 4+ robot and the Microsoft HoloLens as augmented reality headset, through experiments performed by a sample of 11 users.

Three-dimensional (3D) bioprinting is an emerging fabrication technique to create 3D constructs with living cells. Notably, bioprinting bioinks are limited due to the mechanical weakness of natural biomaterials and the low bioactivity of synthetic peers. This paper presents the development of a natural bioink from chicken eggwhite and sodium alginate for bioprinting cell-laden patches to be used in endothelialized tissue engineering applications. Eggwhite was utilized for enhanced biological properties, while sodium alginate was used to improve bioink printability. The rheological properties of bioinks with varying amounts of sodium alginate were examined with the results illustrating that 2.0–3.0% (w/v) sodium alginate was suitable for printing patch constructs. The printed patches were then characterized mechanically and biologically, and the results showed that the printed patches exhibited elastic moduli close to that of natural heart tissue (20–27 kPa) and more than 94% of the vascular endothelial cells survived in the examination period of one week post 3D bioprinting. Our research also illustrated the printed patches appropriate water uptake ability (>1800%).

The driving and charging patterns of battery electric vehicles can have a significant impact on the load profile of the distribution grid. Renewable resources and controlled charging can mitigate this effect by reducing the energy imported from the grid. The smart real-time electric vehicle charging scheduling proposed in this paper reduced grid peak demand through dynamic programming that uses future renewable supply and electric vehicle demand predictions to optimize the balance of energy in the electric grid. The large-scale driving, charging and parking availability patterns used in the simulations were synthesized from an actual experiment made up of a commercial fleet of battery electric vehicles. Numerical simulations showed that for commercial-use vehicles, solar photovoltaic and hybrid photovoltaic +wind technologies can reduce annual grid demand by 24%. Smart scheduling of electric vehicles and the integration of renewable energy resulted in lower peaks but no significant shift in the peak hour demand was observed.

The covid pandemic points out inconsistencies and points to improve in the organization of healthcare logistics. Indeed, the dangerousness and the propagation process of the virus imply to increase health security (patient and personal health). In this context, healthcare logistics flows require a new and safety organization improving the hospital performance. The purpose of this paper consists in optimizing healthcare logistics flows by solving problems associated to the internal logistics such as reduction of the personal health wasting time and the protection of both patients and personal health. Then, the methodology corresponds to the use of the hospital sustainable digital transformation as a response to healthcare flows and safety problems. Indeed, social, societal and environmental aspects have to be considered in addition to new technologies such as artificial intelligence (AI), Internet of Things (IoTs), Big data and analytics. These parameters could be used in the healthcare for increasing doctor, nurse, caregiver performance during their daily operations, and patient satisfaction. Indeed, this hospital digital transformation requires the use of large data associated to patients and personal health, algorithms, a performance measurement tool (actual and future state) and a general approach for transforming digitally the hospital flows. The paper findings show that the healthcare logistics performance could be improved with a sustainable digital transformation methodology and an intelligent software tool. This paper aims to develop this healthcare logistics 4.0 methodology and to elaborate the intelligent support system. After an introduction presenting the common hospital flows and their main problems, a literature review will be detailed for showing how existing concepts could contribute to the elaboration of a structured methodology. The structure of the intelligent software tool for the healthcare digital transformation and the tool development processes will be presented. An example will be given for illustrating the development of the tool.

This paper presents the first documented investigation of a flow-induced vibration phenomenon referred to as dual cantilever flutter (DCF). The purpose of this research is to introduce the concept of DCF and to help understand the key components that cause it. If unaccounted for, vibration caused by DCF has the potential to cause catastrophic structural damage or unwanted acoustic excitation. DCF may also be used as an effective energy harvesting method. The most attractive feature of DCF for energy harvesting is that it provides a robust type of broadband, flow-induced excitation. This paper will present an experimental and analytical study on a novel, solidstate, DCF energy harvesting device. Results will include CFD simulations that were setup and executed using ANSYS-CFX.

Objective: This study aims to develop a predictive model using artificial intelligence to estimate the ICU length of stay (LOS) for Congenital Heart Defects (CHD) patients after surgery, improving care planning and resource management. Design: We analyze clinical data from 2240 CHD surgery patients to create and validate the predictive model. Twenty AI models are developed and evaluated for accuracy and reliability. Setting: The study is conducted in a Brazilian hospital's Cardiovascular Surgery Department, focusing on transplants and cardiopulmonary surgeries. Participants: Retrospective analysis is conducted on data from 2240 consecutive CHD patients undergoing surgery. Interventions: Ninety-three pre and intraoperative variables are used as ICU LOS predictors. Measurements and main results: Utilizing regression and clustering methodologies for ICU LOS (ICU Length of Stay) estimation, the Light Gradient Boosting Machine, using regression, achieved a Mean Squared Error (MSE) of 15.4, 11.8, and 15.2 days for training, testing, and unseen data. Key predictors included metrics such as "Mechanical Ventilation Duration", "Weight on Surgery Date", and "Vasoactive-Inotropic Score". Meanwhile, the clustering model, Cat Boost Classifier, attained an accuracy of 0.6917 and AUC of 0.8559 with similar key predictors. Conclusions: Patients with higher ventilation times, vasoactive-inotropic scores, anoxia time, cardiopulmonary bypass time, and lower weight, height, BMI, age, hematocrit, and presurgical oxygen saturation have longer ICU stays, aligning with existing literature.

Autoclave curing is a manufacturing process for high-performance parts based on carbon fiber–reinforced polymers (CFRPs) used for large aircraft parts. Today, this manufacturing process is the reference in terms of quality and, therefore, the manufactured parts’ mechanical performance and robustness. However, several parameters can impact the quality of the parts resulting from this process, which requires optimizing key manufacturing parameters. In this study, the effect of autoclave process parameters (i.e., temperature, pressure, and vacuum-pressure) on the glass transition temperature (Tg), laminate compressive modulus (LCM), laminate compressive strength (LCS), and interlaminar shear strength (ILSS) was investigated using three factors, three-level Box–Behnken design (BBD) and response surface methodology (RSM). In addition, the interactions of processing parameters with Tg, LCM, LCS, and ILSS were investigated, making this study an essential investigation for accurately selecting processing parameters. Thus, there is a functionally non-linear relationship between the interaction of the autoclave process parameters. Therefore, these parameters were optimized using RSM with the maximum Tg, LCM, LCS, and ILSS. The optimization and validation of the obtained models were carried out with an average relative error below 3% for all thermomechanical and mechanical properties, indicating that the BBD and optimization were correct. Because of this, the established regression models can accurately predict the Tg, LCM, LCS, and ILSS in autoclaved epoxy/carbon composite laminates.

This paper aims to present the dilemma of simulation tool selection. Authors discuss the examples of methodologies of enterprises architectures (CIMOSA and GRAI) where agent approach is used to solve planning and managing problems. Actually simulation is widely used and practically only one tool which can enable verification of complex systems. Many companies face the problem, which simulation tool is appropriate to use for verification. Selected tools based on ABS and DES are presented. Some tools combining DES and ABS approaches are described. Authors give some recommendation on selection process.

The economic growth of developed or emerging countries through globalization has prompted them to increase their supply chain performance. A large number of concepts, tools, and methodologies have been proposed in support of this performance improvement. They are mainly based on the use of classical optimization or enterprise modeling methods. However, environmental and social issues, not to mention digital transformation, are often ignored or not sufficiently integrated. Indeed, the world geopolitical situation, the increase in oil prices, and the commitment to protect our earth require the integration of sustainability aspects and Industry 4.0 concepts like digital twin and artificial intelligence in transforming the supply chain. This paper focuses on defining a conceptual framework to support sustainable supply chain management and digital transformation. It aims to exploit the sustainability and digital maturity of companies to transform their supply chains and enhance their performance to meet the challenges of Industry 5.0. Several practices related to sustainability, as well as two use cases on optimization and digital twin, are presented to illustrate this framework. Finally, based on the previous practices and use cases, an adapted framework for the supply chain manager to support the transition from Industry 4.0 to Industry 5.0 has been developed, as well as a performance dashboard.

BACKGROUND AND AIM OF THE WORK: Uncovering the barriers and facilitators of antibiotic prescribing is crucial in order to develop effective strategies for promoting responsible and evidence-based antibiotic use, thereby combating antibiotic resistance and enhancing patient care. This qualitative study, informed by the Theoretical Domains Framework (TDF) - specifically designed to understand and analyze the factors that influence human behavior, with a focus on identifying barriers and facilitators to behavior change, was aimed to explore the determinants (barriers and facilitators) of antibiotic prescribing behaviors from the perspective of doctors. RESEARCH DESIGN AND METHODS: Semi-structured interviews were conducted with healthcare professionals, and data analysis followed a theory-driven approach guided by the TDF. RESULTS: The analysis identified eight TDF domains influencing antibiotic prescribing, including memory, attention, and decision processes; knowledge; skills; belief about capabilities; goals; belief about consequences; emotions; and environmental context and resources. These domains were clustered into three overarching themes according to a bottom-up logic: the decision-making prescribing process itself, intrinsic factors related to the physician, and extrinsic factors influencing the decision. CONCLUSIONS: This research provides a comprehensive understanding of the complex interactions between these determinants in antibiotic prescribing. The evidence gained from the study valuable information for developing targeted interventions to improve antibiotic prescribing practices and combat antimicrobial resistance considering psychosocial and environmental variables impacting on antibiotic prescription decision making.