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Abstract Several artificial intelligence (AI)-driven tools have emerged for the hemodynamic evaluation of critically ill and surgical patients. This article provides an overview of current developments and potential clinical applications of machine learning (ML) for blood pressure measurements, hypotension prediction, hemodynamic profiling, and echocardiography. ML algorithms have shown promise in enabling continuous, non-invasive blood pressure monitoring by analyzing pulse oximetry waveforms, though these methods require periodic calibration with traditional oscillometric brachial cuffs. Additionally, a variety of ML models have been trained to forecast impending hypotension. However, clinical research indicates that these algorithms often primarily rely on mean arterial pressure, leading to questions about their added predictive value. The issue of false-positive alerts is also significant and can result in unwarranted clinical interventions. In terms of hemodynamic profiling, ML algorithms have been proposed to automatically classify patients into specific hemodynamic endotypes. However, current evidence suggests these models tend to replicate conventional hemodynamic profiles found in medical textbooks or depicted on advanced hemodynamic monitors. This raises questions about their practical clinical utility, especially given occasional discrepancies that could impact treatment decisions. Point-of-care ultrasound (POCUS) has gained traction for evaluating cardiac function in patients experiencing circulatory shock. ML algorithms now embedded in some POCUS systems can assist by recognizing ultrasound images, guiding users for optimal imaging, automating and reducing the variability of key echocardiographic measurements. These capabilities are especially beneficial for novice operators, potentially enhancing accuracy and confidence in clinical decision-making. In conclusion, while several AI-based technologies show promise for refining hemodynamic assessment in both critically ill and surgical patients, their clinical value varies. Comprehensive validation studies and real-world testing are essential to identify which innovations will genuinely contribute to improving the quality of care.

Abstract Acute circulatory shock is a life-threatening emergency requiring an efficient and timely management plan, which varies according to shock etiology and pathophysiology. Specific guidelines have been developed for each type of shock; however, there is a need for a clear timeline to promptly implement initial life-saving interventions during the early phase of shock recognition and management. A simple, easily memorable bundle of interventions could facilitate standardized management with clear targets and specified timeline. The authors propose the “MINUTES” acronym which summarizes essential interventions which should be performed within the first 30 min following shock recognition. All the interventions in the MINUTES bundle are suitable for any patient with undifferentiated shock. In addition to the acronym, we suggest a timeline for each step, balancing the feasibility and urgency of each intervention. The MINUTES acronym includes seven sequential steps which should be performed in the first 30 min following shock recognition: Maintain “ABCs”, INfuse vasopressors and/or fluids (to support hemodynamic/perfusion) and INvestigate with simple blood tests, Ultrasound to detect the type of shock, Treat the underlying Etiology, and Stabilize organ perfusion.

Cardiovascular disease is the leading cause of death worldwide, including critical conditions such as blood vessel blockage, heart failure, and stroke. Accurate and early prediction of heart disease remains a significant challenge due to the complexity of symptoms and the variability of contributing factors. This study proposes a novel hybrid model integrating a Stacked Convolutional Neural Network (SCNN) with the Levy Flight-based Grasshopper Optimization Algorithm (LFGOA) to address this challenge. The SCNN provides robust feature extraction, while LFGOA enhances the model by optimizing hyperparameters, improving classification accuracy, and reducing overfitting. The proposed approach is evaluated using four publicly available heart disease datasets, each representing diverse clinical and demographic features. Compared to traditional classifiers, including Regression Trees, Support Vector Machine, Logistic Regression, K-Nearest Neighbors, and standard Neural Networks, the SCNN-LFGOA consistently outperforms these methods. The results highlight that the SCNN-LFGOA achieves an average accuracy of 99%, with significant improvements in specificity, sensitivity, and F1-Score, showcasing its adaptability and robustness across datasets. This study highlights the SCNN-LFGOA's potential as a transformative tool for early and accurate heart disease prediction, contributing to improved patient outcomes and more efficient healthcare resource utilization. By combining deep learning with an advanced optimization technique, this research introduces a scalable and effective solution to a critical healthcare problem.

This paper examines the integration of Industry 5.0 principles with advanced predictive maintenance (PdM) and condition monitoring (CM) practices, based on Industry 4.0's enabling technologies. It provides a comprehensive review of the roles of Machine Learning (ML), Digital Twins (DT), the Internet of Things (IoT), and Big Data (BD) in transforming PdM and CM. The study proposes a six-layered framework designed to enhance sustainability, human-centricity, and resilience in industrial systems. This framework includes layers for data acquisition, processing, human-machine interfaces, maintenance execution, feedback, and resilience. A case study on a boiler feed-water pump is also presented which demonstrates the framework's potential benefits, such as reduced downtime, extended lifespan, real-time equipment monitoring and improved efficiency. The findings of this study emphasises the importance of integrating human intelligence with advanced technologies for a collaborative and adaptive industrial environment, and suggest areas for future research.

Renewable energy sources, particularly photovoltaic and wind power, are essential in meeting global energy demands while minimising environmental impact. Accurate photovoltaic (PV) and wind power (WP) forecasting is crucial for effective grid management and sustainable energy integration. However, traditional forecasting methods encounter challenges such as data privacy, centralised processing, and data sharing, particularly with dispersed data sources. This review paper thoroughly examines the necessity of forecasting models, methodologies, and data integrity, with a keen eye on the evolving landscape of Federated Learning (FL) in PV and WP forecasting. Commencing with an introduction highlighting the significance of forecasting models in optimising renewable energy resource utilisation, the paper delves into various forecasting techniques and emphasises the critical need for data integrity and security. A comprehensive overview of non-Federated Learning-based PV and WP forecasting is presented based on high-quality journals, followed by in-depth discussions on specific non-Federated Learning approaches for each power source. The paper subsequently introduces FL and its variants, including Horizontal, Vertical, Transfer, Cross-Device, and Cross-Silo FL, highlighting the crucial role of encryption mechanisms and addressing associated challenges. Furthermore, drawing on extensive investigations of numerous pertinent articles, the paper outlines the innovative horizon of FL-based PV and wind power forecasting, offering insights into FL-based methodologies and concluding with observations drawn from this frontier.This review synthesises critical knowledge about PV and WP forecasting, leveraging the emerging paradigm of FL. Ultimately, this work contributes to the advancement of renewable energy integration and the optimisation of power grid management sustainably and securely.

Background: Out-of-hospital cardiac arrest (OHCA) is a major health concern in Europe, leading to significant morbidity and mortality. Survivors often suffer from cognitive deficits, anxiety, and depression, that affect significantly their quality of life. Current post-discharge care is inconsistent and frequently overlooks subtle but disabling symptoms. The ENFORCER trial aims to significantly enhance the health and quality of life of OHCA survivors by providing a comprehensive, accessible, and user-friendly internet-based lifestyle intervention. Methods: ENFORCER is a multicentre, parallel group randomized controlled trial involving OHCA survivors aged 18–80 years with cognitive impairment or anxiety/depression measured through validated instruments.Participants will be randomized 1:1 to the intervention or the control group. The intervention group will receive a one-year program via a secure web application, offering cognitive, emotional, and physical rehabilitation support. The control group will receive standard care.The primary outcome is the difference in the proportion of patients without cognitive or emotional symptoms between the two groups after one year.Secondary outcomes include changes in the level of patients’ cognitive and emotional symptoms, quality of life, sleep quality, sexual interest and satisfaction, and caregivers’ burden, quality of life, sleep quality and emotional symptoms in the two groups. Discussion: The trial addresses the need for consistent post-discharge care, and the timely detection and treatment of cognitive and emotional problems. The internet-based approach allows to potentially reach many patients, ensuring cost-effectiveness and high adherence rates.The study results could establish a standard for post-OHCA care, improving long-term recovery and quality of life for survivors.Trial registration.The trial is registered at clinicaltrials.gov (NCT06395558).

The growing interest in Digital Twin (DT) Technology represents a significant advancement in academic research and industrial applications. Leveraging advancements in Internet of Things (IoT), sensors, and communication devices, DTs are increasingly utilised across different sectors, notably in the energy domain such as Power Systems and Smart Grids. DT concepts facilitate the creation of virtual models mirroring physical assets, streamlining real-time data management and analysis. Driven by the potential of DTs to revolutionise energy systems, this paper offers a comprehensive review of DT applications in the power sector, specifically within next-generation energy systems like Smart Grids. TThe integration of DT technology with Machine Learning (ML) algorithms is highlighted as a key factor in significantly enhancing the performance and capabilities of these advanced energy systems. In contrast to prior reviews, our study meticulously investigates all of the crucial components of energy systems, including forecasting, anomaly detection, and security, which are fundamental for improving the management of operational grids. In addition, the study examines the seamless incorporation of Renewable Energy into current grids and investigates how DT technology could contribute to Electric Vehicles for increased sustainability and reliability within the Smart Grid framework. This review underlines that DTs significantly enhance the management of real-time data and analysis, consequently improving operational grid management. There are ample opportunities into further research and development to design a more advanced and digital system as compared to conventional power systems. The findings are presented in clear and concise tables, highlighting current limitations, proposing effective solutions, and identifying potential future research directions in academia and industry.

Abstract Background Propofol is the most commonly used hypnotic agent used during sedation and general anesthesia (GA) practice, offering faster recovery compared to benzodiazepines. However, cardiovascular impact of propofol and pain at injection are commonly encountered side effects. Ciprofol is a novel disubstituted phenol derivative, and there is growing evidence regarding its clinical use. Methods We conducted a systematic literature search (updated on 23 July 2023) to evaluate safety and efficacy of ciprofol in comparison to propofol in patients undergoing procedures under sedation or GA. We focused on randomized controlled trials (RCTs) only, extrapolating data on onset and offset, and on the side effects and the pain at injection. Results The search revealed 14 RCTs, all conducted in China. Eight RCTs studied patients undergoing sedation, and six focused on GA. Bolus of ciprofol for sedation or induction of GA varied from 0.2 to 0.5 mg/kg. In four studies using ciprofol for maintenance of GA, it was 0.8–2.4 mg/kg/h. Ciprofol pharmacokinetics seemed characterized by slower onset and offset as compared to propofol. Pain during injection was less frequent in the ciprofol group in all the 13 studies reporting it. Eight studies reported “adverse events” as a pooled outcome, and in five cases, the incidence was higher in the propofol group, not different in the remaining ones. Occurrence of hypotension was the most commonly investigated side effects, and it seemed less frequent with ciprofol. Conclusion Ciprofol for sedation or GA may be safer than propofol, though its pharmacokinetics may be less advantageous. Graphical Abstract

Background: Prolonged preoperative fasting may worsen postoperative outcomes. Cardiac surgery has higher perioperative risk, and longer fasting periods may be not well-tolerated. We analysed the postoperative metabolic and hemodynamic variables in patients undergoing elective coronary artery bypass grafting (CABG) according to their morning or afternoon schedule. Methods: Single-centre retrospective study at University teaching hospital (1-year data collection from electronic medical records). Using a mixed-effects linear regression model adjusted for several covariates, we compared metabolic (lactatemia, pH, and base deficit [BD]) and haemodynamic values (patients on vasoactive support, and vasoactive inotropic score [VIS]) at 7 prespecified time-points (admission to intensive care, and 1st, 3rd, 6th, 12th, 18th, and 24th postoperative hours). Results: 339 patients (n = 176 morning, n = 163 afternoon) were included. Arterial lactatemia and BD were similar (overall P = 0.11 and P = 0.84, respectively), while pH was significantly lower in the morning group (overall P < 0.05; mean difference −0.01). Postoperative urine output, fluid balance, mean arterial pressure, and central venous pressure were similar (P = 0.59, P = 0.96, P = 0.58 and P = 0.53, respectively). A subgroup analysis of patients with diabetes (n = 54 morning, n = 45 afternoon) confirmed the same findings. The VIS values and the proportion of patients on vasoactive support was higher in the morning cases at the 18th (P = 0.002 and p=0.04, respectively) and 24th postoperative hours (P = 0.003 and P = 0.04, respectively). Mean intensive care length of stay was 1.94 ± 1.36 days versus 2.48 ± 2.72 days for the afternoon and morning cases, respectively (P = 0.02). Conclusions: Patients undergoing elective CABG showed similar or better metabolic and hemodynamic profiles when scheduled for afternoon surgery.

Background and Objectives: A tracheostomy is a frequently performed surgical intervention in intensive care units (ICUs) for patients requiring prolonged mechanical ventilation. This procedure can offer significant benefits, including reduced sedation requirements, improved patient comfort, and enhanced airway management. However, it is also associated with various risks, and the absence of standardized clinical guidelines complicates its implementation. This study aimed to determine the prevalence of tracheostomy among ICU patients, while also evaluating patient characteristics, complication rates, and overall outcomes related to the procedure. Materials and Methods: We conducted an observational, cross-sectional, point-prevalence survey across eight ICUs in Italy. Data were collected over two 24 h periods in March and April 2024, with a focus on ICU characteristics, patient demographics, the details of tracheostomy procedures, and associated complications. Results: Among the 92 patients surveyed in the ICUs, 31 (33.7%) had undergone tracheostomy. The overall prevalence of tracheostomy was found to be 9.1%, translating to a rate of 1.8 per 1000 admission days. The mean age of patients with a tracheostomy was 59.5 years (SD = 13.8), with a notable predominance of male patients (67.7%). Neurological conditions were identified as the most common reason for ICU admission, accounting for 48.4% of cases. Tracheostomy procedures were typically performed after a mean duration of 12.9 days of mechanical ventilation, primarily due to difficulties related to prolonged weaning (64.5%). Both early and late complications were observed, and 19.35% of tracheostomized patients did not survive beyond one month following the procedure. The average length of stay in the ICU for these patients was significantly extended, averaging 43.0 days (SD = 34.3). Conclusions: These findings highlight the critical role of tracheostomy in the management of critically ill patients within Italian ICUs. The high prevalence and notable complication rates emphasize the urgent need for standardized clinical protocols aimed at optimizing patient outcomes and minimizing adverse events. Further research is essential to refine current practices and develop comprehensive guidelines for the management of tracheostomy in critically ill patients.

With the growing demand for battery-powered devices and electric vehicles, the need for improved battery performance and safety is paramount. A key determinant of battery health is the accurate monitoring of surface temperature (ST). Conventional ST estimation often depends on direct sensor measurements, which may not be cost-effective and can impact system reliability. This paper presents DeepTimeNet, a novel approach leveraging deep learning (DL) architectures for sensorless ST prediction in lithium-ion batteries. DeepTimeNet combines Convolutional Neural Networks (CNN), ResNet blocks, Inception modules, Bidirectional LSTM, and GRU layers to precisely model the time-dependent behaviour of batteries. A comprehensive evaluation against traditional models, across temperatures ranging from -20 °C to 25 °C and under various driving profiles, including US06 and Urban Dynamometer Driving Schedule (UDDS), is conducted. DeepTimeNet’s performance is quantified by metrics such as mean absolute error (MAE), surpassing that of models like Gated Recurrent Unit-Recurrent Neural Network (GRU-RNN), Convolutional Neural Network-Long Short Term Memory Network (CNN-LSTM), and Long Short Term Memory Network (LSTM). The results demonstrate DeepTimeNet’s superior performance, with an RMSE of 0.0971, MSE of 0.0099, MAE of 0.0912, and MAXE of 0.3963, validating it as an advanced tool for enhancing the efficacy of battery management systems and underscoring its potential as a benchmark for future innovations.

Collaborative robots, or cobots, have become popular due to their ability to safely operate alongside humans in shared environments. These robots use compliant actuators as a key design element to prevent damage during unintended collisions. In prosthetic and orthotic applications, compliant actuators are crucial for ensuring user safety and comfort. However, most compliant cobots for these applications are excessively expensive and complex to construct. Our study introduces an innovative, cost-effective, and sensorised elastic actuator design tailored for prosthetics and orthotics. The design uses a modular approach and leverages 3D printing technology for rapid customisation, enabling efficient and affordable fabrication. Both hardware and software components are open-source, facilitating unrestricted access for students, researchers, and practitioners. Our design supports impedance and admittance control techniques, enhancing the system’s capabilities. Validation results show a standard deviation of 9.67 Nm between calculated and measured torque in impedance control and 0.2563 radians between calculated and measured angles in admittance control. This allows for improved adaptability to varying operational requirements in prosthetics and orthotics. By introducing this educational framework encompassing a low-cost, sensorised elastic actuator design, we aim to address the need for accessible solutions in the field of collaborative robotics for prosthetics and orthotics.

Abstract Purpose The prevalence of hyperdynamic left ventricular (LV) systolic function in septic patients and its impact on mortality remain controversial. In this systematic review and meta-analysis, we investigated the prevalence and association of hyperdynamic LV systolic function with mortality in patients with sepsis. Methods We searched MEDLINE, Cochrane Central Register of Controlled Trials, and Embase. Primary outcomes were the prevalence of hyperdynamic LV systolic function in adult septic patients and the associated short-term mortality as compared to normal LV systolic function. Hyperdynamic LV systolic function was defined using LV ejection fraction (LVEF) of 70% as cutoff. Secondary outcomes were heart rate, LV end-diastolic diameter (LVEDD), and E/e’ ratio. Results Four studies were included, and the pooled prevalence of hyperdynamic LV systolic function was 18.2% ([95% confidence interval (CI) 12.5, 25.8]; I 2 = 7.0%, P

Abstract Background Blood pressure has become one of the most important vital signs to monitor in the perioperative setting. Recently, the Italian Society of Anesthesia Analgesia Resuscitation and Intensive Care (SIAARTI) recommended, with low level of evidence, continuous monitoring of blood pressure during the intraoperative period. Continuous monitoring allows for early detection of hypotension, which may potentially lead to a timely treatment. Whether the ability to detect more hypotension events by continuous noninvasive blood pressure (C-NiBP) monitoring can improve patient outcomes is still unclear. Here, we report the rationale, study design, and statistical analysis plan of the niMON trial, which aims to evaluate the effect of intraoperative C-NiBP compared with intermittent (I-NiBP) monitoring on postoperative myocardial and renal injury. Methods The niMon trial is an investigator-initiated, multicenter, international, open-label, parallel-group, randomized clinical trial. Eligible patients will be randomized in a 1:1 ratio to receive C-NiBP or I-NiBP as an intraoperative monitoring strategy. The proportion of patients who develop myocardial injury in the first postoperative week is the primary outcome; the secondary outcomes are the proportions of patients who develop postoperative AKI, in-hospital mortality rate, and 30 and 90 postoperative days events. A sample size of 1265 patients will provide a power of 80% to detect a 4% absolute reduction in the rate of the primary outcome. Conclusions The niMON data will provide evidence to guide the choice of the most appropriate intraoperative blood pressure monitoring strategy. Clinical trial registration Clinical Trial Registration: NCT05496322, registered on the 5th of August 2023.

Extracorporeal cardiopulmonary resuscitation (ECPR) is a complex, life-saving procedure that uses mechanical support for patients with refractory cardiac arrest, representing the pinnacle of extracorporeal membrane oxygenation (ECMO) applications. Effective ECPR requires precise patient selection, rapid mobilization of a multidisciplinary team, and skilled cannulation techniques. Establishing a program necessitates a cohesive ECMO system that promotes interdisciplinary collaboration, which is essential for managing acute cardiogenic shock and severe pulmonary failure. ECPR is suited for selected patients, emphasizing the need to optimize every step of cardiac arrest management—from public education to advanced post-resuscitation care. The flexibility of ECMO teams allows them to manage various emergencies such as cardiogenic shock, massive pulmonary embolism, and severe asthma, showcasing the program’s adaptability. Launching an ECPR program involves addressing logistical, financial, and organizational challenges. This includes gaining administrative approval, assembling a diverse team, and crafting detailed protocols and training regimens. The development process entails organizing teams, refining protocols, and training extensively to ensure operational readiness. A systematic approach to building an ECPR program involves establishing a team, defining patient selection criteria, and evaluating caseloads. Critical elements like patient transport protocols and anticoagulation management are vital for the program’s success. In conclusion, initiating an ECPR program demands thorough planning, collaborative effort across specialties, and ongoing evaluation to improve outcomes in critical cardiac emergencies. This guide offers practical insights to support institutions in navigating the complexities of ECPR program development and maintenance.

The integration of Smart Grid technology and conceptual Industry 5.0 has paved the way for advanced energy management systems that enhance efficiency and revolutionized the parallel integration of power sources in a sustainable manner. However, this digitization has opened a new stream of the threat and opportunities of electricity theft posing a significant challenge to the security and reliability of Smart Grid networks. In this paper, we propose a secure and reliable theft detection technique using deep federated learning (FL) mechanism. The technique leverages the collaborative power of FL to train a Convolutional Gated Recurrent Unit (ConvGRU) model on distributed data sources without compromising data privacy. The training deep learning model backbone consists of a ConvGRU model that combines convolutional and gated recurrent units to capture spatial and temporal patterns in electricity consumption data. An improvised preprocessing mechanism and hyperparameter tuning is done to facilitate FL mechanism. The halving randomized search algorithm is used for hyperparameters tuning of the ConvGRU model. The impact of hyperparameters involved in the ConvGRU model such as number of layers, filters, kernel size, activation function, pooling, GRU layers, hidden state dimension, learning rate, and the dropout rate is elaborated. The proposed technique achieves promising results, with high accuracy, precision, recall, and F1 score, demonstrating its efficacy in detecting electricity theft in Smart Grid networks. Comparative analysis with existing techniques reveal the superior performance of the deep FL-based ConvGRU model. The findings highlight the potential of this approach in enhancing the security and efficiency of Smart Grid systems while preserving data privacy.

In this study, we address the critical need for enhanced situational awareness and victim detection capabilities in Search and Rescue (SAR) operations amidst disasters. Traditional unmanned ground vehicles (UGVs) often struggle in such chaotic environments due to their limited manoeuvrability and the challenge of distinguishing victims from debris. Recognising these gaps, our research introduces a novel technological framework that integrates advanced gesture-recognition with cutting-edge deep learning for camera-based victim identification, specifically designed to empower UGVs in disaster scenarios. At the core of our methodology is the development and implementation of the Meerkat Optimization Algorithm—Stacked Convolutional Neural Network—Bi—Long Short Term Memory—Gated Recurrent Unit (MOA-SConv-Bi-LSTM-GRU) model, which sets a new benchmark for hand gesture detection with its remarkable performance metrics: accuracy, precision, recall, and F1-score all approximately 0.9866. This model enables intuitive, real-time control of UGVs through hand gestures, allowing for precise navigation in confined and obstacle-ridden spaces, which is vital for effective SAR operations. Furthermore, we leverage the capabilities of the latest YOLOv8 deep learning model, trained on specialised datasets to accurately detect human victims under a wide range of challenging conditions, such as varying occlusions, lighting, and perspectives. Our comprehensive testing in simulated emergency scenarios validates the effectiveness of our integrated approach. The system demonstrated exceptional proficiency in navigating through obstructions and rapidly locating victims, even in environments with visual impairments like smoke, clutter, and poor lighting. Our study not only highlights the critical gaps in current SAR response capabilities but also offers a pioneering solution through a synergistic blend of gesture-based control, deep learning, and purpose-built robotics. The key findings underscore the potential of our integrated technological framework to significantly enhance UGV performance in disaster scenarios, thereby optimising life-saving outcomes when time is of the essence. This research paves the way for future advancements in SAR technology, with the promise of more efficient and reliable rescue operations in the face of disaster.

Abstract Background Tissue Doppler-derived left ventricular systolic velocity (mitral S’) has shown excellent correlation to left ventricular ejection fraction (LVEF) in non-critically patients. However, their correlation in septic patients remains poorly understood and its impact on mortality is undetermined. We investigated the relationship between mitral S’ and LVEF in a large cohort of critically-ill septic patients. Methods We conducted a retrospective cohort study between 01/2011 and 12/2020. All adult patients (≥ 18 years) who were admitted to the medical intensive care unit (MICU) with sepsis and septic shock that underwent a transthoracic echocardiogram (TTE) within 72 h were included. Pearson correlation test was used to assess correlation between average mitral S’ and LVEF. Pearson correlation was used to assess correlation between average mitral S’ and LVEF. We also assessed the association between mitral S’, LVEF and 28-day mortality. Results 2519 patients met the inclusion criteria. The study population included 1216 (48.3%) males with a median age of 64 (IQR: 53–73), and a median APACHE III score of 85 (IQR: 67, 108). The median septal, lateral, and average mitral S’ were 8 cm/s (IQR): 6.0, 10.0], 9 cm/s (IQR: 6.0, 10.0), and 8.5 cm/s (IQR: 6.5, 10.5), respectively. Mitral S’ was noted to have moderate correlation with LVEF (r = 0.46). In multivariable logistic regression analysis, average mitral S’ was associated with an increase in both 28-day ICU and in-hospital mortality with odds ratio (OR) 1.04 (95% CI 1.01–1.08, p = 0.02) and OR 1.04 (95% CI 1.01–1.07, p = 0.02), respectively. Conclusions Even though mitral S’ and LVEF may be related, they are not exchangeable and were only found to have moderate correlation in this study. LVEF is U-shaped, while mitral S’ has a linear relation with 28-day ICU mortality. An increase in average mitral S’ was associated with higher 28-day mortality. Graphical Abstract

Abstract Background Variation of inferior vena cava (IVC) is used to predict fluid-responsiveness, but the IVC visualization with standard sagittal approach (SC, subcostal) cannot be always achieved. In such cases, coronal trans-hepatic (TH) window may offer an alternative, but the interchangeability of IVC measurements in SC and TH is not fully established. Furthermore, artificial intelligence (AI) with automated border detection may be of clinical value but it needs validation. Methods Prospective observational validation study in mechanically ventilated patients with pressure-controlled mode. Primary outcome was the IVC distensibility (IVC-DI) in SC and TH imaging, with measurements taken both in M-Mode or with AI software. We calculated mean bias, limits of agreement (LoA), and intra-class correlation (ICC) coefficient. Results Thirty-three patients were included. Feasibility rate was 87.9% and 81.8% for SC and TH visualization, respectively. Comparing imaging from the same anatomical site acquired with different modalities (M-Mode vs AI), we found the following IVC-DI differences: (1) SC: mean bias − 3.1%, LoA [− 20.1; 13.9], ICC = 0.65; (2) TH: mean bias − 2.0%, LoA [− 19.3; 15.4], ICC = 0.65. When comparing the results obtained from the same modality but from different sites (SC vs TH), IVC-DI differences were: (3) M-Mode: mean bias 1.1%, LoA [− 6.9; 9.1], ICC = 0.54; (4) AI: mean bias 2.0%, LoA [− 25.7; 29.7], ICC = 0.32. Conclusions In patients mechanically ventilated, AI software shows good accuracy (modest overestimation) and moderate correlation as compared to M-mode assessment of IVC-DI, both for SC and TH windows. However, precision seems suboptimal with wide LoA. The comparison of M-Mode or AI between different sites yields similar results but with weaker correlation. Trial registration Reference protocol: 53/2022/PO, approved on 21/03/2022

Cobots are robots that are built for human-robot collaboration (HRC) in a shared environment. In the aftermath of disasters, cobots can cooperate with humans to mitigate risks and increase the possibility of rescuing people in distress. This study examines the resilient and dynamic synergy between a swarm of snake robots, first responders and people to be rescued. The possibility of delivering first aid to potential victims dispersed around a disaster environment is implemented. In the HRC simulation framework presented in this study, the first responder initially deploys a UAV, swarm of snake robots and emergency items. The UAV provides the first responder with the site planimetry, which includes the layout of the area, as well as the precise locations of the individuals in need of rescue and the aiding goods to be delivered. Each individual snake robot in the swarm is then assigned a victim. Subsequently an optimal path is determined by each snake robot using the A* algorithm, to approach and reach its respective target while avoiding obstacles. By using their prehensile capabilities, each snake robot adeptly grasps the aiding object to be dispatched. The snake robots successively arrive at the delivering location near the victim, following their optimal paths, and proceed to release the items. To demonstrate the potential of the framework, several case studies are outlined concerning the execution of operations that combine locomotion, obstacle avoidance, grasping and deploying. The Coppelia-Sim Robotic Simulator is utilised for this framework. The analysis of the motion of the snake robots on the path show highly accurate movement with and without the emergency item. This study is a step towards a holistic semi-autonomous search and rescue operation.

Advancements in materials science and 3D printing technologies have opened up new avenues for developing low-cost robotic grippers with high-performance capabilities, making them suitable for various biomechatronic applications. In this research, it has been explored the utilization of high-performance polymer materials, such as Polyetherketoneketone (PEKK), Polyethylene Terephthalate Glycol (PET-G) and MED 857 (DraftWhite), in the designing and developing of customized robotic grippers. The primary focus of made analyses was oriented on materials characterization, both experimentally and analytically. Computer-Aided Engineering (CAE) methods were employed to simulate bending experiments, allowing for a comprehensive analysis of the mechanical behavior of the selected materials. These simulations were validated through physical bending experiments using samples fabricated via 3D printing technologies, including Fused Filament Fabrication (FFF) for PET-G and PEKK, as well as Jetted Photopolymer (PolyJet) technology employing UV Resin for MED 857. The findings of this research provided advantages of utilizing advanced materials like PEKK in low-cost robotic grippers for biomechatronic applications. The experimental and analytical approaches offer valuable insights into material selection, design optimization, and the development of cost-effective high-performing robotic systems with a wide range of applications in the field of biomechatronics.

Abstract Purpose Assessment of the inferior vena cava (IVC) respiratory variation may be clinically useful for the estimation of fluid-responsiveness and venous congestion; however, imaging from subcostal (SC, sagittal) region is not always feasible. It is unclear if coronal trans-hepatic (TH) IVC imaging provides interchangeable results. The use of artificial intelligence (AI) with automated border tracking may be helpful as part of point-of-care ultrasound but it needs validation. Methods Prospective observational study conducted in spontaneously breathing healthy volunteers with assessment of IVC collapsibility (IVCc) in SC and TH imaging, with measures taken in M-mode or with AI software. We calculated mean bias and limits of agreement (LoA), and the intra-class correlation (ICC) coefficient with their 95% confidence intervals. Results Sixty volunteers were included; IVC was not visualized in five of them (n = 2, both SC and TH windows, 3.3%; n = 3 in TH approach, 5%). Compared with M-mode, AI showed good accuracy both for SC (IVCc: bias − 0.7%, LoA [− 24.9; 23.6]) and TH approach (IVCc: bias 3.7%, LoA [− 14.9; 22.3]). The ICC coefficients showed moderate reliability: 0.57 [0.36; 0.73] in SC, and 0.72 [0.55; 0.83] in TH. Comparing anatomical sites (SC vs TH), results produced by M-mode were not interchangeable (IVCc: bias 13.9%, LoA [− 18.1; 45.8]). When this evaluation was performed with AI, such difference became smaller: IVCc bias 7.7%, LoA [− 19.2; 34.6]. The correlation between SC and TH assessments was poor for M-mode (ICC = 0.08 [− 0.18; 0.34]) while moderate for AI (ICC = 0.69 [0.52; 0.81]). Conclusions The use of AI shows good accuracy when compared with the traditional M-mode IVC assessment, both for SC and TH imaging. Although AI reduces differences between sagittal and coronal IVC measurements, results from these sites are not interchangeable.

Abstract Background Acute non-variceal gastrointestinal bleedings (GIBs) are pathological conditions associated with significant morbidity and mortality. Embolization without angiographic evidence of contrast media extravasation is proposed as an effective procedure in patients with clinical and/or laboratory signs of bleeding. The purpose of this systematic review is to define common clinical practice and clinical and technical outcomes of blind and preventive embolization for upper and lower gastrointestinal bleeding. Main body Through the PubMed, Embase and Google Scholar database, an extensive search was performed in the fields of empiric and preventive embolization for the treatment of upper and lower gastrointestinal bleedings (UGIB and LGIB). Inclusion criteria were: articles in English for which it has been possible to access the entire content; adults patients treated with empiric or blind transcatheter arterial embolization (TAE) for UGIB and/or LGIB. Only studies that analysed clinical and technical success rate of blind and empiric TAE for UGIB and/or LGIB were considered for our research. Exclusion criteria were: recurrent articles from the same authors, articles written in other languages, those in which the entire content could not be accessed and that articles were not consistent to the purposes of our research. We collected pooled data on 1019 patients from 32 separate articles selected according to the inclusion and exclusion criteria. 22 studies focused on UGIB (total 773 patients), one articles focused on LGIB (total 6 patients) and 9 studies enrolled patients with both UGIB and LGIB (total 240 patients). Technical success rate varied from 62% to 100%, with a mean value of 97.7%; clinical success rate varied from 51% to 100% with a mean value of 80%. The total number of complications was 57 events out of 1019 procedures analysed. Conclusion TAE is an effective procedure in the treatment of UGIB patients in which angiography does not demonstrate direct sign of ongoing bleeding. The attitude in the treatment of LGIBs must be more prudent in relation to poor vascular anastomoses and the high risk of intestinal ischemia. Blind and preventive procedures cumulatively present a relatively low risk of complications, compared to a relatively high technical and clinical success.

Abstract Background Capillary Refill Time (CRT) is a marker of peripheral perfusion usually performed at fingertip; however, its evaluation at other sites/position may be advantageous. Moreover, arm position during CRT assessment has not been fully standardized. Methods We performed a pilot prospective observational study in 82 healthy volunteers. CRT was assessed: a) in standard position with participants in semi-recumbent position; b) at 30° forearm elevation, c and d) at earlobe site in semi-recumbent and supine position. Bland–Altman analysis was performed to calculate bias and limits of agreement (LoA). Correlation was investigated with Pearson test. Results Standard finger CRT values (1.04 s [0.80;1.39]) were similar to the earlobe semi-recumbent ones (1.10 s [0.90;1.26]; p = 0.52), with Bias 0.02 ± 0.18 s (LoA -0.33;0.37); correlation was weak but significant (r = 0.28 [0.7;0.47]; p = 0.01). Conversely, standard finger CRT was significantly longer than earlobe supine CRT (0.88 s [0.75;1.06]; p

Abstract Background Sepsis is a leading cause of death and it is characterized not only by profound vasoplegia but also by myocardial dysfunction. Critical care echocardiography is the preferred modality for the initial assessment of the cause of shock. Moreover, it can be extremely helpful in the identification of progressing myocardial dysfunction during the course of sepsis, also known as septic cardiomyopathy. Main body One of the issues in the identification of septic cardiomyopathy is that it can be manifest with different clinical phenotypes, from overt biventricular dysfunction to isolated left ventricular (LV) systolic and/or diastolic dysfunction, from right ventricular (RV) systolic dysfunction to RV failure and dilatation. However, the commonly used echocardiography parameters for the assessment of LV and/or RV function are not always entirely reliable. Indeed, these are influenced by variable preload and afterload conditions imposed by critical illness such as fluid shifts, sedation level and mechanical ventilation with positive pressure. Conclusions Strain echocardiography is a promising tool for the early identification of myocardial dysfunction in the context of sepsis. Studies reporting data on strain echocardiography should be particularly detailed in order to increase the reproducibility of results and to favor comparison with future studies.

Stress affects individual of all ages as a regular part of life, but excessive and chronic stress can lead to physical and mental health problems, decreased productivity, and reduced quality of life. By identifying stress at an early stage, individuals can take steps to manage it effectively and improve their overall well-being. Feature selection is a critical aspect of early stress detection because it helps identify the most relevant and informative features that can differentiate between stressed and non-stressed individuals. This paper firstly proposes a variance based feature selection technique that uses q-learning embedded Starling Murmuration Optimiser (QLESMO) to choose relevant features from a publicly available dataset in which stresses experienced by nurses working during the Covid’19 Pandemic is recorded using bio-signals and user surveys. Furthermore, a comparative study with other metaheuristic based feature selection techniques have been demonstrated. Next, to evaluate the efficacy of the proposed algorithm, 10 benchmark test functions have been used. The reduced feature subset is then classified through a 1D convolutional neural network (CNN) model (QLESMO-CNN) and is seen to perform well in terms of the evaluation metrics in comparison to other competitive algorithms. Finally, the proposed technique is compared with the State-of-the-Art methodologies present in literature. The experiments provide a strong basis to determine features that are most relevant for early mental stress classification using a hybrid model combining CNN, Reinforcement Learning and metaheuristic algorithms.

In this paper, a novel approach using a Henry Gas Solubility-based Stacked Convolutional Neural Network (HGS-SCNN) for hand gesture recognition using surface electromyography (sEMG) sensors is proposed. The stacked architecture of the CNN model helps to capture both low-level and high-level features, enabling effective representation learning. To begin, we generated a dataset comprising 600 samples of hand gestures. Next, we applied the Discrete Wavelet Transform (DWT) technique to extract features from the filtered sEMG signal. This step allowed us to capture both spatial and frequency information, enhancing the discriminative power of the extracted features. Extensive experiments are conducted to evaluate the performance of the proposed HGS-SCNN model. In addition, the obtained results are compared with state-of-the-art techniques, namely AOA-SCNN, GWO-SCNN, and WOA-SCNN. The comparative analysis demonstrates that the HGS-SCNN outperforms these existing methods, achieving an impressive accuracy of 99.3%. The experimental results validate the effectiveness of our proposed approach in accurately detecting hand gestures. The combination of DWT-based feature extraction and the HGS-SCNN model offers robust and reliable hand gesture recognition, thereby opening new possibilities for intuitive human-machine interaction and applications requiring gesture-based control.

Abstract Critical care ultrasonography (US) is widely used by intensivists managing critically ill patients to accurately and rapidly assess different clinical scenarios, which include pneumothorax, pleural effusion, pulmonary edema, hydronephrosis, hemoperitoneum, and deep vein thrombosis. Basic and advanced critical care ultrasonographic skills are routinely used to supplement physical examination of critically ill patients, to determine the etiology of critical illness and to guide subsequent therapy. European guidelines now recommend the use of US for a number of practical procedures commonly performed in critical care. Full training and competence acquisition are essential before significant therapeutic decisions are made based on the US assessment. However, there are no universally accepted learning pathways and methodological standards for the acquisition of these skills. Therefore, in this review, we aim to provide a methodological approach of the head to toe ultrasonographic evaluation of critically ill patients considering different districts and clinical applications.

Abstract Background Optimal oxygen targets in patients resuscitated after cardiac arrest are uncertain. The primary aim of this study was to describe the values of partial pressure of oxygen values (PaO2) and the episodes of hypoxemia and hyperoxemia occurring within the first 72 h of mechanical ventilation in out of hospital cardiac arrest (OHCA) patients. The secondary aim was to evaluate the association of PaO2 with patients’ outcome. Methods Preplanned secondary analysis of the targeted hypothermia versus targeted normothermia after OHCA (TTM2) trial. Arterial blood gases values were collected from randomization every 4 h for the first 32 h, and then, every 8 h until day 3. Hypoxemia was defined as PaO2 300 mmHg. Mortality and poor neurological outcome (defined according to modified Rankin scale) were collected at 6 months. Results 1418 patients were included in the analysis. The mean age was 64 ± 14 years, and 292 patients (20.6%) were female. 24.9% of patients had at least one episode of hypoxemia, and 7.6% of patients had at least one episode of severe hyperoxemia. Both hypoxemia and hyperoxemia were independently associated with 6-month mortality, but not with poor neurological outcome. The best cutoff point associated with 6-month mortality for hypoxemia was 69 mmHg (Risk Ratio, RR = 1.009, 95% CI 0.93–1.09), and for hyperoxemia was 195 mmHg (RR = 1.006, 95% CI 0.95–1.06). The time exposure, i.e., the area under the curve (PaO2-AUC), for hyperoxemia was significantly associated with mortality (p = 0.003). Conclusions In OHCA patients, both hypoxemia and hyperoxemia are associated with 6-months mortality, with an effect mediated by the timing exposure to high values of oxygen. Precise titration of oxygen levels should be considered in this group of patients. Trial registration: clinicaltrials.gov NCT02908308 , Registered September 20, 2016.

This paper presents a novel methodology that utilizes gesture recognition data, which are collected with a Leap Motion Controller (LMC), in tandem with the Spotted Hyena-based Chimp Optimization Algorithm (SSC) for feature selection and training of deep neural networks (DNNs). An expansive tabular database was created using the LMC for eight distinct gestures and the SSC algorithm was used for discerning and selecting salient features. This refined feature subset is then utilized in the subsequent training of a DNN model. A comprehensive comparative analysis is conducted to evaluate the performance of the SSC algorithm in comparison with established optimization techniques, such as Particle Swarm Optimization(PSO), Grey Wolf Optimizer(GWO), and Sine Cosine Algorithm(SCA), specifically in the context of feature selection. The empirical findings decisively establish the efficacy of the SSC algorithm, consistently achieving a high accuracy rate of 98% in the domain of gesture recognition tasks. The feature selection approach proposed emphasizes its intrinsic capacity to enhance not only the accuracy of gesture recognition systems and its wider suitability across diverse domains that require sophisticated feature extraction techniques.

Abstract Background The coronavirus disease 2019 (COVID-19) pandemic has caused over 530 million infections to date (June 2022), with a high percentage of intensive care unit (ICU) admissions. In this context, relatives have been restricted from visiting their loved ones admitted to hospital. This situation has led to an inevitable separation between patients and their families. Video communication could reduce the negative effects of such phenomenon, but the impact of this strategy on levels of anxiety, depression, and PTSD disorder in caregivers is not well-known. Methods We conducted a prospective study (6 October 2020–18 February 2022) at the Policlinico University Hospital in Catania, including caregivers of both COVID-19 and non-COVID-19 ICU patients admitted during the second wave of the pandemic. Video-calls were implemented twice a week. Assessment of anxiety, depression, and PTSD was performed at 1-week distance (before the first, T1, and before the third, video-call, T2) using the following validated questionnaires: Impact of Event Scale (Revised IES-R), Center for Epidemiologic Studies Depression Scale (CES-D), and Hospital Anxiety and Depression Scale (HADS). Results Twenty caregivers of 17 patients completed the study (T1 + T2). Eleven patients survived (n = 9/11 in the COVID-19 and n = 2/6 in the “non-COVID” group). The average results of the questionnaires completed by caregivers between T1 and T2 showed no significant difference in terms of CES-D (T1 = 19.6 ± 10, T2 = 22 ± 9.6; p = 0.17), HADS depression (T1 = 9.5 ± 1.6, T2 = 9 ± 3.9; p = 0.59), HADS anxiety (T1 = 8.7 ± 2.4, T2 = 8.4 ± 3.8; p = 0.67), and IES-R (T1 = 20.9 ± 10.8, T2 = 23.1 ± 12; p = 0.19). Similar nonsignificant results were observed in the two subgroups of caregivers (COVID-19 and “non-COVID”). However, at T1 and T2, caregivers of “non-COVID” patients had higher scores of CES-D (p = 0.01 and p = 0.04, respectively) and IES-R (p = 0.049 and p = 0.02, respectively), while HADS depression was higher only at T2 (p = 0.02). At T1, caregivers of non-survivors had higher scores of CES-D (27.6 ± 10.6 vs 15.3 ± 6.7, p = 0.005) and IES-R (27.7 ± 10.0 vs 17.2 ± 9.6, p = 0.03). We also found a significant increase in CES-D at T2 in ICU-survivors (p = 0.04). Conclusions Our preliminary results showed that a video-call implementation strategy between caregivers and patients admitted to the ICU is feasible. However, this strategy did not show an improvement in terms of the risk of depression, anxiety, and PTSD among caregivers. Our pilot study remains exploratory and limited to a small sample.

Abstract Background Spinal anesthesia (SA) is widely used for anesthetic management of patients undergoing hip surgery, and hypotension is the most common cardiovascular side effect of SA. This paper aims to assess the lowest effective dose of SA that reduces the occurrence of intraoperative hypotension in elderly patients scheduled for major lower limb orthopedic surgery. Methods We conducted a systematic review of randomized controlled trials (RCTs) performed in elderly patients scheduled for surgical hip repair and a meta-analysis with meta-regression on the occurrence of hypotensive episodes at different effective doses of anesthetics. We searched PUBMED®, EMBASE®, and the Cochrane Controlled Clinical trials registered. Results Our search retrieved 2085 titles, and after screening, 6 were finally included in both the qualitative and quantitative analysis, including 344 patients [15% (10–28) males], with a median (25th to 75th interquartile) age of 82 (80–85). The risk of bias assessment reported “low risk” for 5 (83.3%) and “some concerns” for 1 (16.7%) of the included RCTs. The low dose of SA of [mean 6.5 mg (1.9)] anesthetic was associated with a lower incidence of hypotension [OR = 0.09 (95%CI 0.04–0.21); p = 0.04; I 2 = 56.9%], as compared to the high-dose of anesthetic [mean 10.5 mg (2.4)]. Conclusions In the included studies of this meta-analysis, a mean dose of 6.5 mg of SA was effective in producing intraoperative comfort and motor block and associated with a lower incidence of hypotension as compared to a mean dose of 10.5 mg. Trial registration CRD42020193627

Background: Tracheal intubation in patients with coronavirus disease-19 is a high-risk procedure that should be performed with personal protective equipment (PPE). The influence of PPE on operator’s performance during tracheal intubation remains unclear. Methods: We conducted a systematic review and meta-analysis of simulation studies to evaluate the influence of wearing PPE as compared to standard uniform regarding time-to-intubation (TTI) and success rate. Subgroup analyses were conducted according to device used and operator’s experience. Results: The TTI was prolonged when wearing PPE (eight studies): Standard Mean Difference (SMD) -0.54, 95% Confidence Interval [-0.75, -0.34], p

Background: Recognition of postoperative infection after cardiac surgery is challenging. Biomarkers may be very useful to recognize infection at early stage, but the literature is controversial. Methods: We conducted a retrospective study at two large University Hospitals, including adult patients undergoing cardiac surgery (excluding those with preoperative infections, cirrhotic or immunocompromised). We evaluated the kinetics of C-Reactive Protein (CRP) and White Cell Count (WCC) during the first postoperative week. Primary outcomes were CRP and WCC changes according to the development of postoperative infection. In order to evaluate the influence of cardiopulmonary bypass on biomarker kinetics, we also studied CRP and WCC changes in patients without postoperative infection and undergoing on- vs off-pump coronary-artery bypass grafting. Results: Among 429 included, 45 patients (10.5%) had evidence of postoperative infection. Patients with postoperative infection had higher CRP and WCC values than those without infection, with between-groups difference becoming significant from postoperative day 2 for CRP (120.6 ± 3.6 vs. 134.6 ± 7.9, P < 0.01), and from postoperative day 3 for WCC (10.5 ± 0.5 vs. 9.9 ± 0.2, P = 0.02). Over the postoperative period, CRP and WCC showed significant within-group changes regardless of development of postoperative infection (P < 0.001 for both). We found no differences in CRP and WCC kinetics between patients undergoing on- vs off-pump procedure. Conclusions: During the first week after cardiac surgery, CRP increases one day earlier than WCC in patients developing postoperative infections, with such difference becoming significant on the second postoperative day. In not infected patients, use of cardiopulmonary bypass does not influence CRP and WCC kinetics.

This paper presents the outcomes of investigations conducted on the development procedure of a personalized prosthetic device for an adult patient. The individualization is achieved through 3D scanning, followed by semi-automated design using the AutoMedPrint system, and low-cost fused deposition modelling (FDM) technology for 3D printing. The prosthesis is aimed for use during bicycle riding and other sport activities. During the conducted experiments outlined in this manuscript, the prosthesis is equipped with force and movement sensors. The purpose is to collect data on its functionality across different scenarios and dynamic activities, aiming to assess potential harm, refine the design, and serve as an initial step before activating the prosthesis end effector. This article describes the methodology in detail, including the process of designing, producing, and programming, as well as laboratory and field test results (including testing performed with and without a patient). Overall, the design and prototype are implemented successfully. A discussion about the need for particular improvements in both the mechanical and electrical areas is finally presented.

Background and Objectives: Redistribution hypothermia occurs during anesthesia despite active intraoperative warming. Prewarming increases the heat absorption by peripheral tissue, reducing the central to peripheral heat gradient. Therefore, the addition of prewarming may offer a greater preservation of intraoperative normothermia as compared to intraoperative warming only. Materials and Methods: A single-center clinical trial of adults scheduled for non-cardiac surgery. Patients were randomized to receive or not a prewarming period (at least 10 min) with convective air devices. Intraoperative temperature management was identical in both groups and performed according to a local protocol. The primary endpoint was the incidence, the magnitude and the duration of hypothermia (according to surgical time) between anesthetic induction and arrival at the recovery room. Secondary outcomes were core temperature on arrival in operating room, surgical site infections, blood losses, transfusions, patient discomfort (i.e., shivering), reintervention and hospital stay. Results: In total, 197 patients were analyzed: 104 in the control group and 93 in the prewarming group. Core temperature during the intra-operative period was similar between groups (p = 0.45). Median prewarming lasted 27 (17–38) min. Regarding hypothermia, we found no differences in incidence (controls: 33.7%, prewarming: 39.8%; p = 0.37), duration (controls: 41.6% (17.8–78.1), prewarming: 45.2% (20.6–71.1); p = 0.83) and magnitude (controls: 0.19 °C · h−1 (0.09–0.54), prewarming: 0.20 °C · h−1 (0.05–0.70); p = 0.91). Preoperative thermal discomfort was more frequent in the prewarming group (15.1% vs. 0%; p < 0.01). The interruption of intraoperative warming was more common in the prewarming group (16.1% vs. 6.7%; p = 0.03), but no differences were seen in other secondary endpoints. Conclusions: A preoperative prewarming period does not reduce the incidence, duration and magnitude of intraoperative hypothermia. These results should be interpreted considering a strict protocol for perioperative temperature management and the low incidence of hypothermia in controls.

Randomized controlled trials (RCTs) may sometimes be underpowered to detect differences between interventions. Meta-analyses may reduce such risk by pooling together the available evidence from studies with similar inclusion criteria. However, this approach does not entirely rule out the risks of type-1 and type-2 statistical errors. Trial sequential analysis (TSA) is a statistical method developed for the assessment of the robustness of meta-analyses findings. Similar to the sample size calculation for a RCT, the calculation of the required “information size” for any TSA requires the outcome estimation (incidence or value) for the control and intervention groups, and the acceptable risks for type-1 and type-2 statistical errors (significance threshold and statistical power, respectively). Once performed, TSA graphically allows evaluation of whether the meta-analysis findings (cumulative effect size described in terms of Z-curve) are robust enough, or if there is need for further research. We briefly discuss the rationale and interpretation of TSA, identifying three main areas of the graph if the “information size” has not yet been reached. No further research is needed when the Z-curve crosses the adjusted significance thresholds or stands in the futility area. Conversely, if the Z-curve sits between adjusted significance thresholds and the futility boundary, more research is needed to establish differences between the interventions. In summary, similar to the interim analyses performed during any RCTs, the TSA graph can be helpful for scientists to understand if a meta-analysis finding is robust, and if further research would be desirable or futile on that topic.

This paper presents results from experimental studies that assess the utilization of virtual, augmented, and mixed reality (VR, AR, MR) at different stages of developing personalized 3D printed upper limb prostheses for adult patients. The prostheses are designed automatically using the AutoMedPrint system, leveraging 3D scans as described in various prior publications. Various stages of development of the prosthesis are made as applications of different extended reality technologies. An assembly instruction is implemented as an immersive VR application, a configurator is designed as AR application and a configurator and try-on application is prepared and deployed in MR. The applications are tested by an international group of experts during a scheduled experiment. The experts then participate to surveys and comparatively evaluate the potential of all the XR technologies. The paper presents the development of these applications, provides a detailed account of the experimental process, including the rankings of XR technologies for different applications throughout the lifecycle of a prosthetic device.

Abstract Background Inappropriate authors’ self-citation (A-SC) is a growing mal-practice possibly boosted by the raising importance given to author’s metrics. Similarly, also excessive journals’ self-citation (J-SC) practice may factitiously influence journal’s metrics (impact factor, IF). Evaluating the appropriateness of each self-citation remains challenging. Main body We evaluated the presence of policies discouraging A-SC in Critical Care Medicine (CCM) journals with IF. We also calculated the J-SC rate of these journals. In order to evaluate if J-SC rates are influenced by the focus of interest of CCM journals, we separated them in three sub-categories (“multidisciplinary”, “broad” or “topic-specific” CCM journals). We analyzed 35 CCM journals and only 5 (14.3%) discouraged excessive and inappropriate A-SC. The median IF was higher in CCM journals with A-SC policies [4.1 (3–12)] as compared to those without [2.5 (2–3.5); p = 0.02]. The J-SC rate was highly variable (0–35.4%), and not influenced by the presence of A-SC policies (p = 0.32). However, J-SC rate was different according to the focus of interest (p = 0.01): in particular, it was higher in “topic-specific” CCM journals [15.3 (8.8–23.3%)], followed by “broad” CCM [11.8 (4.8–17.9%)] and “multidisciplinary” journals [6.1 (3.6–9.1%)]. Conclusions A limited number of CCM journals have policies for limiting A-SC, and these have higher IF. The J-SC rate among CCM journals is highly variable and higher in “topic-specific” interest CCM journals. Excluding self-referencing practice from scientific metrics calculation could be valuable to tackle this scientific malpractice.

This paper proposes a new non-stationary three-dimensional (3D) channel model for a physical millimeter wave (mm-Wave) multiple-input multiple-output (MIMO) channel. This MIMO channel model is driven by the trajectory of a moving point scatterer, which allows us to investigate the impact of a single moving point scatterer on the propagation characteristics in an indoor environment. Starting from the time-variant (TV) channel transfer function, the temporal behavior of the proposed non-stationary channel model has been analyzed by studying the TV micro-Doppler characteristics and the TV mean Doppler shift. The proposed channel model has been validated by measurements performed in an indoor environment using a MIMO radar kit operating at 24 GHz. For the measurement campaign, we used a single swinging pendulum as a model for a moving point scatterer. The trajectory of the pendulum has been captured by an inertial measurement unit attached to the pendulum and by a motion capture camera system. The measured trajectories are fed into the proposed mm-Wave MIMO channel model. The results obtained for the micro-Doppler characteristics show an excellent agreement between the proposed MIMO channel model and real-world measured channels in the presence of a moving point scatterer. We believe that our model can serve as a basis for the development of novel non-stationary MIMO channel models capturing the effects caused by moving objects and people.

RESUMO Objetivo: Realizar uma revisão sistemática para sumarizar o conhecimento relativo à prevalência de burnout entre médicos atuantes na unidade de terapia intensiva. Métodos: Conduzimos uma revisão sistemática nas bases de dados MEDLINE e PubMed® (última atualização em 4 de fevereiro de 2019), com o objetivo de resumir a evidência a respeito de burnout entre médicos atuantes em unidades de terapia intensiva. Incluímos todos os estudos que relatavam burnout em trabalhadores na unidade de terapia intensiva, segundo o Inventário de Burnout de Maslach e, a seguir, triamos os estudos quanto a dados relativos a burnout especificamente em médicos atuantes na unidade de terapia intensiva. Resultados: Encontramos 31 estudos que descreviam burnout em membros da equipe da unidade de terapia intensiva e incluíam diferentes perfis de profissionais de saúde. Dentre estes, cinco estudos se focalizavam apenas em médicos, e 12 outros investigavam burnout em uma mescla de profissionais atuantes na unidade de terapia intensiva, mas forneciam dados à parte relativos aos médicos. A prevalência de burnout teve grande variação entre os estudos (variando entre 18% e 49%), porém diversas discrepâncias metodológicas, dentre elas os critérios de corte para definição de burnout e variabilidade da escala de Likert, impediram uma análise agrupada significativa. Conclusão: A prevalência da síndrome de burnout entre médicos atuantes na unidade de terapia intensiva é relativamente alta, porém heterogeneidades metodológicas significantes exigem precauções na interpretação de nossos resultados. Os níveis mais baixos de burnout relatados parecem mais elevados do que os identificados em estudos que investigaram uma mescla de profissionais da unidade de terapia intensiva. Há uma necessidade urgente de consenso que recomende o uso consistente do Inventário de Burnout de Maslach para triar a presença de burnout a fim de fornecer dados precisos a respeito de burnout entre médicos atuantes na unidade de terapia intensiva.

Every year, especially in urban areas, the population density rises quickly. The effects of catastrophes (i.e., war, earthquake, fire, tsunami) on people are therefore significant and grave. Assisting the impacted people will soon involve human-robot Search and Rescue (SAR) operations. Therefore, it is crucial to connect contemporary technology (i.e., robots and cognitive approaches) to SAR to save human lives. However, these operations also call for careful consideration of several factors, including safety, severity, and resources. Hence, ethical issues with technologies in SAR must be taken into consideration at the development stage. In this study, the most relevant ethical and design issues that arise when using robotic and cognitive technology in SAR are discussed with a focus on the response phase. Among the vast variety of SAR robots that are available nowadays, snake robots have shown huge potential; as they could be fitted with sensors and used for transporting tools to hazardous or confined areas that other robots and humans are unable to access. With this perspective, particular emphasis has been put on snake robotics in this study by considering ethical and design issues. This endeavour will contribute to providing a broader knowledge of ethical and technological factors that must be taken into account throughout the design and development of snake robots.

The reptile search algorithm is a newly developed optimization technique that can efficiently solve various optimization problems. However, while solving high-dimensional nonconvex optimization problems, the reptile search algorithm retains some drawbacks, such as slow convergence speed, high computational complexity, and local minima trapping. Therefore, an improved reptile search algorithm (IRSA) based on a sine cosine algorithm and Levy flight is proposed in this work. The modified sine cosine algorithm with enhanced global search capabilities avoids local minima trapping by conducting a full-scale search of the solution space, and the Levy flight operator with a jump size control factor increases the exploitation capabilities of the search agents. The enhanced algorithm was applied to a set of 23 well-known test functions. Additionally, statistical analysis was performed by considering 30 runs for various performance measures like best, worse, average values, and standard deviation. The statistical results showed that the improved reptile search algorithm gives a fast convergence speed, low time complexity, and efficient global search. For further verification, improved reptile search algorithm results were compared with the RSA and various state-of-the-art metaheuristic techniques. In the second phase of the paper, we used the IRSA to train hyperparameters such as weight and biases for a multi-layer perceptron neural network and a smoothing parameter (σ) for a radial basis function neural network. To validate the effectiveness of training, the improved reptile search algorithm trained multi-layer perceptron neural network classifier was tested on various challenging, real-world classification problems. Furthermore, as a second application, the IRSA-trained RBFNN regression model was used for day-ahead wind and solar power forecasting. Experimental results clearly demonstrated the superior classification and prediction capabilities of the proposed hybrid model. Qualitative, quantitative, comparative, statistical, and complexity analysis revealed improved global exploration, high efficiency, high convergence speed, high prediction accuracy, and low time complexity in the proposed technique.