Mortality from gastrointestinal congenital anomalies at 264 hospitals in 74 low-income, middle-income, and high-income countries: a multicentre, international, prospective cohort study

Summary Background Congenital anomalies are the fifth leading cause of mortality in children younger than 5 years globally. Many gastrointestinal congenital anomalies are fatal without timely access to neonatal surgical care, but few studies have been done on these conditions in low-income and middle-income countries (LMICs). We compared outcomes of the seven most common gastrointestinal congenital anomalies in low-income, middle-income, and high-income countries globally, and identified factors associated with mortality. Methods We did a multicentre, international prospective cohort study of patients younger than 16 years, presenting to hospital for the first time with oesophageal atresia, congenital diaphragmatic hernia, intestinal atresia, gastroschisis, exomphalos, anorectal malformation, and Hirschsprung’s disease. Recruitment was of consecutive patients for a minimum of 1 month between October, 2018, and April, 2019. We collected data on patient demographics, clinical status, interventions, and outcomes using the REDCap platform. Patients were followed up for 30 days after primary intervention, or 30 days after admission if they did not receive an intervention. The primary outcome was all-cause, in-hospital mortality for all conditions combined and each condition individually, stratified by country income status. We did a complete case analysis. Findings We included 3849 patients with 3975 study conditions (560 with oesophageal atresia, 448 with congenital diaphragmatic hernia, 681 with intestinal atresia, 453 with gastroschisis, 325 with exomphalos, 991 with anorectal malformation, and 517 with Hirschsprung’s disease) from 264 hospitals (89 in high-income countries, 166 in middleincome countries, and nine in low-income countries) in 74 countries. Of the 3849 patients, 2231 (58·0%) were male. Median gestational age at birth was 38 weeks (IQR 36–39) and median bodyweight at presentation was 2·8 kg (2·3–3·3). Mortality among all patients was 37 (39·8%) of 93 in low-income countries, 583 (20·4%) of 2860 in middle-income countries, and 50 (5·6%) of 896 in high-income countries (p<0·0001 between all country income groups). Gastroschisis had the greatest difference in mortality between country income strata (nine [90·0%] of ten in lowincome countries, 97 [31·9%] of 304 in middle-income countries, and two [1·4%] of 139 in high-income countries; p≤0·0001 between all country income groups). Factors significantly associated with higher mortality for all patients combined included country income status (low-income vs high-income countries, risk ratio 2·78 [95% CI 1·88–4·11], p<0·0001; middle-income vs high-income countries, 2·11 [1·59–2·79], p<0·0001), sepsis at presentation (1·20 [1·04–1·40], p=0·016), higher American Society of Anesthesiologists (ASA) score at primary intervention (ASA 4–5 vs ASA 1–2, 1·82 [1·40–2·35], p<0·0001; ASA 3 vs ASA 1–2, 1·58, [1·30–1·92], p<0·0001]), surgical safety checklist not used (1·39 [1·02–1·90], p=0·035), and ventilation or parenteral nutrition unavailable when needed (ventilation 1·96, [1·41–2·71], p=0·0001; parenteral nutrition 1·35, [1·05–1·74], p=0·018). Administration of parenteral nutrition (0·61, [0·47–0·79], p=0·0002) and use of a peripherally inserted central catheter (0·65 [0·50–0·86], p=0·0024) or percutaneous central line (0·69 [0·48–1·00], p=0·049) were associated with lower mortality. Interpretation Unacceptable differences in mortality exist for gastrointestinal congenital anomalies between lowincome, middle-income, and high-income countries. Improving access to quality neonatal surgical care in LMICs will be vital to achieve Sustainable Development Goal 3.2 of ending preventable deaths in neonates and children younger than 5 years by 2030

Analyse temps–fréquence à haute résolution du signal HRV : Application à la télésurveillance des arythmies cardiaques

The alteration of the content of the heart rate variability signal HRV (Heart Rate Variability) makes it possible to detect several cardiovascular pathologies such as acute myocardial infarction, congestive heart failure, and diabetic neuropathy. The high–resolution time–frequency (TF) analysis characterizes non–stationarities within HRV signals. This analysis is affected by cross-terms in the TF plane. Quadratic TF analysis with high–resolution kernels reduces the effect of these cross–terms in favor of an adequate representation of the time–frequency content of the HRV signal. We also developed a client–server model implemented as a telemedical platform for real-time remote monitoring of cardiovascular function in patients suffering from arrhythmia. This platform detects and classifies cardiac arrhythmia using time-frequency analysis methods. We gathered all the developed functionalities such as extraction and selection of features and classification of the Heart Rate Variability (HRV) signal ina Graphical user interface (GUI). A Raspberry Pi Zero acquires data and communicates with a server through the TCP/IP protocol involving a connection secured by the transport layer security (TLS) for a reliably safe connection between the client and the server. This telemedical platform adopts continuous control and monitoring of the heart rhythm. Using this system, in case of an alarm, medical staff can easily communicate with their patients in the hospital or at home.L’altération du contenu du signal de variabilité cardiaque HRV (Heart Rate Variability) permet de détecter plusieurs pathologies cardiovasculaires telles que l’infarctus du myocarde aigu, l’insuffisance cardiaque congestive, la neuropathie diabétique, etc. L’analyse du signal HRV par des méthodes d’analyse temps–fréquence (TF) à haute résolution permet de mieux caractériser la non–stationnarité de ce signal, mais à uneperformance affectée par les termes croisés qui apparaissent dans le plan TF. L’analyse TF par des méthodes quadratiques à des noyaux d’analyse à haute résolution permet de réduire l’effet de ces termes croisés au profit d’une représentation adéquate du contenu du signal HRV. Dans le cadre de cette thèse, nous avons développé un modèle client–serveur mis en œuvre comme plateforme de télémédecine pour lasurveillance à distance en temps réel de la fonction cardiovasculaire chez les patients souffrant d’arythmie. Cette plateforme est capable de détecter et de classer une arythmie cardiaque en temps réel via une interface utilisateur graphique (GUI : Graphical User Interface) à l’aide de méthodes d’analyse temps–fréquence, d’extraction et de sélection de caractéristiques, et de classification de la variabilité du rythme cardiaque(VRC) enregistré à l’aide d’un système d’acquisition de données. Le système d’acquisition de données que nous avons développé est conçu autour du Raspberry Pi Zero qui communique avec un serveur à travers un protocole TCP/IP via une connexion sécurisée par la couche de sécurité de transport (TLS : Transport Layer Security) pour une connexion fiable entre le client et le serveur. Cette plateforme télémédicale adopte un contrôle et une surveillance continus du rythme cardiaque

Analyse temps–fréquence à haute résolution du signal HRV : Application à la télésurveillance des arythmies cardiaques

The alteration of the content of the heart rate variability signal HRV (Heart Rate Variability) makes it possible to detect several cardiovascular pathologies such as acute myocardial infarction, congestive heart failure, and diabetic neuropathy. The high–resolution time–frequency (TF) analysis characterizes non–stationarities within HRV signals. This analysis is affected by cross-terms in the TF plane. Quadratic TF analysis with high–resolution kernels reduces the effect of these cross–terms in favor of an adequate representation of the time–frequency content of the HRV signal. We also developed a client–server model implemented as a telemedical platform for real-time remote monitoring of cardiovascular function in patients suffering from arrhythmia. This platform detects and classifies cardiac arrhythmia using time-frequency analysis methods. We gathered all the developed functionalities such as extraction and selection of features and classification of the Heart Rate Variability (HRV) signal ina Graphical user interface (GUI). A Raspberry Pi Zero acquires data and communicates with a server through the TCP/IP protocol involving a connection secured by the transport layer security (TLS) for a reliably safe connection between the client and the server. This telemedical platform adopts continuous control and monitoring of the heart rhythm. Using this system, in case of an alarm, medical staff can easily communicate with their patients in the hospital or at home.L’altération du contenu du signal de variabilité cardiaque HRV (Heart Rate Variability) permet de détecter plusieurs pathologies cardiovasculaires telles que l’infarctus du myocarde aigu, l’insuffisance cardiaque congestive, la neuropathie diabétique, etc. L’analyse du signal HRV par des méthodes d’analyse temps–fréquence (TF) à haute résolution permet de mieux caractériser la non–stationnarité de ce signal, mais à uneperformance affectée par les termes croisés qui apparaissent dans le plan TF. L’analyse TF par des méthodes quadratiques à des noyaux d’analyse à haute résolution permet de réduire l’effet de ces termes croisés au profit d’une représentation adéquate du contenu du signal HRV. Dans le cadre de cette thèse, nous avons développé un modèle client–serveur mis en œuvre comme plateforme de télémédecine pour lasurveillance à distance en temps réel de la fonction cardiovasculaire chez les patients souffrant d’arythmie. Cette plateforme est capable de détecter et de classer une arythmie cardiaque en temps réel via une interface utilisateur graphique (GUI : Graphical User Interface) à l’aide de méthodes d’analyse temps–fréquence, d’extraction et de sélection de caractéristiques, et de classification de la variabilité du rythme cardiaque(VRC) enregistré à l’aide d’un système d’acquisition de données. Le système d’acquisition de données que nous avons développé est conçu autour du Raspberry Pi Zero qui communique avec un serveur à travers un protocole TCP/IP via une connexion sécurisée par la couche de sécurité de transport (TLS : Transport Layer Security) pour une connexion fiable entre le client et le serveur. Cette plateforme télémédicale adopte un contrôle et une surveillance continus du rythme cardiaque

Telemedical transport layer security based platform for cardiac arrhythmia classification using quadratic time-frequency analysis of HRV signal

International audienceThe heart rate variability signal is a valuable tool for cardiovascular system diagnostics. Processing this signal detects arrhythmia during long-term cardiac monitoring. It is also analyzed to recognize abnormalities within the autonomic nervous system. Processing this signal helps in detecting various pathologies, such as atrial fibrillation (AF), supraventricular tachycardia (SVT), and congestive heart failure (CHF). As a beneficial alternative to the commonly used HRV spectrum analysis, quadratic time-frequency analysis of HRV signals could be helpful in heart pathology detection. Indeed, in this study, we have created a client-server paradigm deployed as a telemedical platform for real-time remote monitoring of the cardiovascular function in patients suffering from arrhythmia. This platform detects arrhythmia in real-time by deploying time-frequency analysis, feature extraction, feature selection, and classification of Heart Rate Variability (HRV) signals. We gathered all these functionalities in a Graphical User Interface (GUI) in addition to data acquisition. As a client, a Raspberry Pi Zero ensures data acquisition and connects to a server over TCP/IP that involves a 4G/3G connection encrypted through the transport layer security (TLS). This telemedical tool continuously monitors the heart rate variability. In the case of an alarm, medical professionals may immediately interact with their patients in the hospital or at home

Characterization and technological properties of Staphylococcus xylosus strains isolated from a Tunisian traditional salted meat.

International audienceThe technological properties of strains of Staphylococcus xylosus were studied to select the most suitable for use as starter cultures for the production of dried fermented meat products. Strains of S. xylosus were isolated from traditional salted Tunisian meat and were identified by biochemical and molecular methods. Thirty strains of S. xylosus were studied to evaluate their catalase, nitrate reductase, lipolytic, proteolytic and antibacterial activities as well as growth ability at different temperatures, pH's and NaCl concentrations. All strains of S. xylosus had catalase activity and were able to reduce nitrates to nitrites. The nitrate reductase activity increased when the strains were kept under anaerobic conditions. Proteolytic activity on milk and on gelatin agar was demonstrated for 100% and 83.3% of the S. xylosus isolates, respectively. However extracellular proteolytic activity as assessed by the azocasein method was poor in all the strains. Lipolytic activity as assessed by the agar method showed that 76.6% of strains of S. xylosus could hydrolyze Tween 20 against 33.3% that could hydrolyze tributyrin. Tween 80 was hydrolyzed by only 10% of strains. Strains of S. xylosus hydrolyzed pork fat better than beef and lamb fat. The majority of strains had antibacterial activity against Salmonella arizonae, Staphylococcus aureus, Pseudomonas aeuroginosa, Escherichia coli and Enterococcus faecalis

Integration of Renewable-Energy-Based Green Hydrogen into the Energy Future

There is a growing interest in green hydrogen, with researchers, institutions, and countries focusing on its development, efficiency improvement, and cost reduction. This paper explores the concept of green hydrogen and its production process using renewable energy sources in several leading countries, including Australia, the European Union, India, Canada, China, Russia, the United States, South Korea, South Africa, Japan, and other nations in North Africa. These regions possess significant potential for “green” hydrogen production, supporting the transition from fossil fuels to clean energy and promoting environmental sustainability through the electrolysis process, a common method of production. The paper also examines the benefits of green hydrogen as a future alternative to fossil fuels, highlighting its superior environmental properties with zero net greenhouse gas emissions. Moreover, it explores the potential advantages of green hydrogen utilization across various industrial, commercial, and transportation sectors. The research suggests that green hydrogen can be the fuel of the future when applied correctly in suitable applications, with improvements in production and storage techniques, as well as enhanced efficiency across multiple domains. Optimization strategies can be employed to maximize efficiency, minimize costs, and reduce environmental impact in the design and operation of green hydrogen production systems. International cooperation and collaborative efforts are crucial for the development of this technology and the realization of its full benefits

Combined Economic Emission Dispatch with and without Consideration of PV and Wind Energy by Using Various Optimization Techniques: A Review

Combined economic emission dispatch (CEED) problems are among the most crucial problems in electrical power systems. The purpose of the CEED is to plan the outputs of all production units available in the electrical power system in such a way that the cost of fuel and polluted emissions are minimized while respecting the equality and inequality constraints of the system and efficiently responding to the power load required. The rapid depletion of these sources causes limitation and increases the price of fuel. It is therefore very important that scientific research in the last few decades has been oriented toward the integration of renewable energy systems (RES) such as wind and PV as an alternative source. Furthermore, the CEED problem including RES is the most important problem with regard to electrical power field optimization. In this study, a classification of optimization techniques that are widely used, such as traditional methods, non-conventional methods, and hybrid methods, is summarized. Many optimization methods have been presented and each of them has its own advantages and disadvantages for solving this complex CEED problem, including renewable energy. A review of different optimization techniques for solving this CEED problem is explored in this present paper. This review will encourage researchers in the future to gain knowledge of the best approaches applicable to solve CEED problems for practical electrical systems

Optimized FACTS Devices for Power System Enhancement: Applications and Solving Methods

The use of FACTS devices in power systems has become increasingly popular in recent years, as they offer a number of benefits, including improved voltage profile, reduced power losses, and increased system reliability and safety. However, determining the optimal type, location, and size of FACTS devices can be a challenging optimization problem, as it involves mixed integer, nonlinear, and nonconvex constraints. To address this issue, researchers have applied various optimization techniques to determine the optimal configuration of FACTS devices in power systems. The paper provides an in-depth and comprehensive review of the various optimization techniques that have been used in published works in this field. The review classifies the optimization techniques into four main groups: classical optimization techniques, metaheuristic methods, analytic methods, and mixed or hybrid methods. Classical optimization techniques are conventional optimization approaches that are widely used in optimization problems. Metaheuristic methods are stochastic search algorithms that can be effective for nonconvex constraints. Analytic methods involve sensitivity analysis and gradient-based optimization techniques. Mixed or hybrid methods combine different optimization techniques to improve the solution quality. The paper also provides a performance comparison of these different optimization techniques, which can be useful in selecting an appropriate method for a specific problem. Finally, the paper offers some advice for future research in this field, such as developing new optimization techniques that can handle the complexity of the optimization problem and incorporating uncertainties into the optimization model. Overall, the paper provides a valuable resource for researchers and practitioners in the field of power systems optimization, as it summarizes the various optimization techniques that have been used to solve the FACTS optimization problem and provides insights into their performance and applicability

Antimicrobial Efficiency of Essential Oils from Traditional Medicinal Plants of Asir Region, Saudi Arabia, over Drug Resistant Isolates

Antimicrobial resistance (AMR) is a recurring global problem, which constantly demands new antimicrobial compounds to challenge the resistance. It is well known that essential oils (EOs) have been known for biological activities including antimicrobial properties. In this study, EOs from seven aromatic plants of Asir region of southwestern Saudi Arabia were tested for their antimicrobial efficacy against four drug resistant pathogenic bacterial isolates (Staphylococcus aureus, Streptococcus pyogenes, Escherichia coli, and Streptococcus typhimurium) and one fungal isolate (Candida albicans). Chemical compositions of EOs were determined by gas chromatography-mass spectrometry (GC-MS). The results revealed that EOs from Mentha cervina, Ocimum basilicum, and Origanum vulgare proved most active against all isolates with inhibitory zone range between 17 and 45 mm. The lowest minimum inhibitory concentration (MIC) of 0.025mg/ml was observed for Staph. aureus and Streptococcus pyogenes with EO of Origanum vulgare. All the three EOs showed significant anticandida activity. The results related to EOs from Mentha cervina, Ocimum basilicum, and Origanum vulgare demonstrated significant antimicrobial efficacy against drug resistant microorganisms