Transport Delay Research Articles

Prehospital Trauma Compendium: Prehospital Administration of Antibiotics in Trauma Patients – an NAEMSP Resource Document

ABSTRACT OBJECTIVES To conduct a literature review and provide a summary of the evidence surrounding prehospital administration of antibiotics for open fractures and other major open wounds. METHODS We performed a literature search and summarized the evidence following the methodology established for the NAEMSP Prehospital Trauma Compendium. We searched PubMed from inception to 23 December 2022 for articles relevant to Emergency Medical Services, trauma, and antibiotics. Due to resource limitations, the Grading of Recommendations, Assessment, Development and Evaluation (GRADE) methodology was not used during review of the evidence, and no formal assessment of bias or strength of evidence was performed. RESULTS Of 105 articles identified in the initial search, 13 articles were included in the final evidence review and synthesis. Prehospital administration of antibiotics in combat and civilian trauma patients consists of mostly observational, retrospective studies that describe use as likely safe, but with uncertainty as to its effect on important clinical outcomes. Both combat and civilian analyses of protocol adherence and inter-rater reliability for recognition and treatment of injuries have also produced variable and inconsistent results. These results pose a challenge for implementation and highlight the inherent limitations and external validity of efficacy outcomes published to date. CONCLUSIONS Prehospital administration of prophylactic antibiotics for trauma appears safe and may be considered in some specific patient populations. Universal and widespread adoption of this intervention needs further study to identify the true impact on patient-centered outcomes and identification of patients who might confer greatest benefit. Local practice characteristics may support adoption of multidisciplinary-developed prudent and practicable protocols incorporating the use of prophylactic antibiotics for some trauma patients such as those with open fractures or those with significant delays in transport to definitive care. Future research should attempt to address the appropriate identification of wounds and injury patterns that have the highest likelihood of benefit from prehospital administration of antibiotics, the ideal timing of administering the antibiotic(s) following initial injury, impact on infection rates, and other important patient outcomes.

A Novel Artificial Intelligence-Enhanced Digital Network for Prehospital Emergency Support: Community Intervention Study.

Efficient emergency patient transport systems, which are crucial for delivering timely medical care to individuals in critical situations, face certain challenges. To address this, CONNECT-AI (CONnected Network for EMS Comprehensive Technical-Support using Artificial Intelligence), a novel digital platform, was introduced. This artificial intelligence (AI)-based network provides comprehensive technical support for the real-time sharing of medical information at the prehospital stage. This study aimed to evaluate the effectiveness of this system in reducing patient transport delays. The CONNECT-AI system provided 3 key AI services to prehospital care providers by collecting real-time patient data from the scene and hospital resource information, such as bed occupancy and the availability of emergency surgeries or procedures, using 5G communication technology and internet of things devices. These services included guidance on first aid, prediction of critically ill patients, and recommendation of the optimal transfer hospital. In addition, the platform offered emergency department medical staff real-time clinical information, including live video of patients during transport to the hospital. This community-based, nonrandomized controlled intervention study was designed to evaluate the effectiveness of the CONNECT-AI system in 2 regions of South Korea, each of which operated an intervention and a control period, each lasting 16 weeks. The impact of the system was assessed based on the proportion of patients experiencing transfer delays. A total of 14,853 patients transported by public ambulance were finally selected for analysis. Overall, the median transport time was 10 (IQR 7-14) minutes in the intervention group and 9 (IQR 6-13) minutes in the control group. When comparing the incidence of transport time outliers (>75%), which was the primary outcome of this study, the rate was higher in the intervention group in region 1, but significantly reduced in region 2, with the overall outlier rate being higher in the intervention group (27.5%-29.7%, P=.04). However, for patients with fever or respiratory symptoms, the group using the system showed a statistically significant reduction in outlier cases (36.5%-30.1%, P=.01). For patients who received real-time acceptance signals from the hospital, the reduction in the percentage of 75% outliers was statistically significant compared with those without the system (27.5%-19.6%, P=.02). As a result of emergency department treatment, 1.5% of patients in the control group and 1.1% in the intervention group died (P=.14). In the system-guided optimal hospital transfer group, the mortality rate was significantly lower than in the control group (1.54%-0.64%, P=.01). The present digital emergency medical system platform offers a novel approach to enhancing emergency patient transport by leveraging AI, real-time information sharing, and decision support. While the system demonstrated improvements for certain patient groups facing transfer challenges, further research and modifications are necessary to fully realize its benefits in diverse health care contexts. ClinicalTrials.gov NCT04829279; https://clinicaltrials.gov/study/NCT04829279.

Implementing Advanced Analytics for Optimizing Food Supply Chain Logistics and Efficiency

The global food supply chain faces significant challenges in ensuring efficiency and minimizing disruptions, especially in the face of unexpected events, fluctuating demand, and logistical complexities. Implementing advanced analytics within food supply chains can transform operational dynamics, enabling precise prediction of potential disruptions and enhancing logistics efficiency. This study proposes a robust framework that leverages machine learning algorithms to forecast disruptions—such as delays in transport, shifts in consumer demand, or adverse weather conditions—and to optimize the logistics flow accordingly. By incorporating predictive modeling and real-time data analysis, this framework aims to enhance decision-making capabilities across various stages of the supply chain, from production and warehousing to transportation and delivery. The proposed framework utilizes a combination of supervised learning for demand forecasting and unsupervised learning for anomaly detection, enabling early identification of potential logistical challenges. Furthermore, integrating these analytics with real-time data sources, such as IoT sensors and satellite imagery, facilitates a comprehensive view of supply chain status, thereby increasing responsiveness and adaptability to unforeseen events. The potential future integration of this framework with autonomous delivery systems, such as drones or autonomous vehicles, could further streamline last-mile logistics, reducing both cost and delivery time while maintaining the quality and safety of perishable goods. This study not only demonstrates the immediate benefits of machine learning-driven predictive analytics but also discusses the scalability and flexibility of this approach for broader applications within global food systems. Future research can explore enhancements in algorithmic accuracy and integration with blockchain for traceability, supporting transparent, resilient, and highly efficient food supply chains. The integration of autonomous delivery systems will additionally be a key focus for ensuring seamless, reliable, and sustainable logistics operations in a rapidly evolving food industry landscape.

A robust single-machine scheduling problem with scenario-dependent processing times and release dates

Many uncertainties arise during the manufacturing process, such as changes in the working environment, traffic transportation delays, machine breakdowns, and worker performance instabilities. These factors can cause job processing times and ready times to change. In this study, we address a scheduling model for a single machine where both job release dates and processing times are scenario dependent. The objective is to minimize the total completion time across the worst-case scenarios. Even without the uncertainty factor, this problem is NP-hard. To solve it, we derive several properties and a lower bound used in a branch-and-bound method to find an optimal solution. We propose nine heuristics based on a linear combination of scenario-dependent processing times and release times for approximate solutions. Additionally, we offer an iterated greedy population-based algorithm that efficiently solves this problem by taking advantage of the diversity of solutions. We evaluate the performance of the proposed nine heuristics and the iterated greedy population-based algorithm.

ON THE ISSUE OF ASSESSING THE EFFECTIVENESS OF DESIGN SOLUTIONS TO ENSURE THE SUSTAINABILITY OF THE URBAN TRANSPORT SYSTEM

Modern transport problems in many cities around the world are caused by the high level of motorisation of their population and the high frequency of private car use. Despite a number of advantages of using private cars as a means of transport (high speed of communication, the ability to choose a route and travel at a convenient time, high comfort, etc.), the mismatch between the development of motorisation and the existing transport infrastructure leads to traffic congestion, which has a number of negative consequences that make the use of private cars less efficient. These consequences include increased transport delays, reduced traffic speeds and, consequently, increased travel time, and more intensive consumption of non-renewable energy sources. Equally urgent are the issues of road safety and improving the environmental situation in cities. At present, a promising way to solve urban transport problems is to apply the concept of sustainable development of urban transport systems and the methodology of transport demand management. The justification of design solutions to ensure the sustainability of urban transport systems should be based on a model approach and include a comprehensive assessment of a number of social, economic and environmental performance indicators. The adequacy of transport models and the accuracy of the assessment of the consequences of design decisions are crucial for choosing the most appropriate set of measures to ensure the sustainability of transport systems. The article proposes an efficiency criterion for evaluating design solutions to ensure the sustainability of the city's transport system, which contribute to reducing the use of private car. This criterion provides for a comprehensive assessment of the effectiveness of design solutions in terms of economic, social and environmental performance indicators. Based on the analysis of statistical data, it was found that the following distribution of passenger cars by type of fuel consumed is typical for Kharkiv: cars with petrol engines - 48.58%; diesel cars - 14.63%; cars with gas cylinder equipment - 33.58%; electric cars - 3.21%. The established distribution of passenger cars by type of fuel consumed is the basis for establishing average values of variable and fixed operating costs of private car owners and should be taken into account when assessing the environmental impact of the transport process. This is the direction of further research.

Analysis of Preanalytical Errors in Clinical Biochemistry Laboratory of a Tertiary Care Hospital: A Retrospective Study

Introduction: Pre-analytical phase of laboratory testing is the most susceptible phase, as errors in this phase leads to more than 50% of erroneous results and often breaches the trust of the stakeholders on the quality of the laboratory results. Many pre-analytical errors occur during the pre-analytical phase, encompassing sample collection, labelling and transportationfactors often beyond the laboratory’s direct control. Aim: To determine the type and frequency of pre-analytical errors leading to sample rejection in clinical biochemistry laboratory. Materials and Methods: Being a retrospective descriptive study, convenient sampling was used to analyse sample rejection due to pre-analytical errors in clinical biochemistry laboratory of a tertiary care teaching hospital- PSG Institute of Medical Sciences and Research, Coimbatore, Tamil Nadu, India for a period of six months from May 2023 to October 2023. All the cases/blood samples from Outpatient Department (OPD) and Inpatient Department (IPD), received and rejected during this period were included under study. The data collection and analysis was done over a period of five months using the sample rejection and resample description from Laboratory Information System (LIS). Using Statistical Package for the Social Sciences (SPSS) version 28.0, data were summarised using descriptive statistics such as numbers and percentages. Results: During the six months period out of the total of 667454 samples, 1505 (0.23%) samples were rejected due to pre-analytical errors. The majority of the samples which were rejected were from IPD than OPD. Among the pre-analytical errors, haemolysis accounted for 806 (53.6%), clotted samples 256 (17%), delta check 217 (14.4%), insufficient sample 129 (8.6%), contamination 74 (4.9%), identification error 14 (0.9%), sample without request form 3 (0.2%) while missing samples, billing error, inappropriate tube, delay in transport and wrong test selection accounted for <3 (0.1%). Conclusion: Haemolysis and clotted samples were the most common pre-analytical causes for sample rejection in the laboratory. The samples from IPD were rejected more often than OPD due to incorrect phlebotomy techniques. This accentuates the need for proper hands-on phlebotomy training sessions for novice nurses following their recruitment, as their competency will be instrumental in bringing down the errors in pre-analytical phase.

Enhancing Supply Chain Resilience in Bengaluru’s Restaurant Industry through Data Analytics and Strategic Vendor Management

The restaurant industry in Bengaluru faces unique challenges in managing supply chains due to seasonal demand fluctuations, vendor reliability issues, and external risk factors such as transportation delays and labor shortages. This project analyzes supply chain dynamics in the city’s restaurant sector, focusing on demand patterns, vendor performance, and risk management strategies. Using a combination of primary survey data and secondary data from a comprehensive restaurant dataset, the study identifies critical factors impacting operational efficiency and customer satisfaction. Through detailed analysis and data visualization using Power BI, this study highlights seasonal demand peaks, vendor performance metrics, and the most significant supply chain risks, such as ingredient shortages and price volatility. Key findings suggest the importance of data-driven demand forecasting, flexible inventory management, multi-vendor sourcing, and real-time risk monitoring. By implementing these strategies, restaurants can enhance supply chain resilience, improve service quality, and mitigate operational disruptions, ultimately supporting a more stable and customer-oriented business model. The project provides actionable recommendations for restaurant managers, offering insights into strategic supply chain practices that align with industry demands and consumer expectations.

Reconstructing functional networks of air transport delay propagations with minimal information

Reconstructing functional networks of air transport delay propagations with minimal information

Emergency thoracotomy of chest trauma: A cohort of 30 case series

Background: Chest trauma is one of the leading causes of death in all age groups and accounts for 25–50% of all traumatic injuries. A small but significant proportion of individuals with thoracic injuries need an emergency thoracotomy (ET) during their primary resuscitation, although the majority can be managed conservatively. Surgical intervention has been recommended for the withdrawal of pericardial tamponade, control of large air leaks, open cardiac massage, direct management of pulmonary hemorrhage, and cross-clamping of the thoracic aorta. Thoracotomy can be used to define emergency thoracotomies. The aim of the study was to evaluate the demographic characteristics and outcomes of ET for chest trauma. The study also examined the indications for ET and the outcomes of mortality and prognosis in chest trauma patients who underwent ET in the operating room. Methodology: A cross-sectional study was conducted on 30 patients who were randomly chosen and admitted to Al-Imamain Al-Kadhymain Teaching Hospital between March 2022 and March 2023. Results: The mean age of the sample population was 31.1 years. Males made up 21 (70%) of the sample, while females made up 9 (30%). There were 22 penetrating chest injuries (73.3%), compared with 8 blunt chest injuries (26.7%): 26 (86.7%) were alive, while 4 (13.3%) were dead. Conclusion: The need for an ET to save the life of a trauma patient must be made better known. Patient delays in transport to the hospital play a crucial role in saving the lives of trauma victims. We must provide the emergency department with all the necessary equipment to perform an urgent thoracotomy.

Effect of hypernatremia on protein reabsorption in renal proximal tubules of the lake frog <I>Pelophylax ridibundus</I>

Protein reabsorption in the kidney proximal tubules occurs simultaneously with the transport of ions and water, but little is known about the dependence of receptor-mediated protein endocytosis on water-salt balance changes. The aim of the study was to investigate tubular reabsorption and intracellular vesicular transport of various proteins in a model of hypernatremia in lake frogs (Pelophylax ridibundus). Frogs were injected with hypertonic sodium chloride solution (0.75 M NaCl) 1 hour before injection of green or yellow fluorescent proteins (GFP or YFP), as well as lysozyme. The method of fluorescent immunohistochemistry was used for detection of lysozyme and endocytic receptor megalin in kidney sections. Specimens were investigated using laser scanning confocal microscopy. The intensity of fluorescent signals of proteins and megalin in proximal tubular cells was determined on the images obtained. To study the dynamics of endocytosis, an automated method for quantifying colocalized protein and megalin signals was used. A statistically significant decrease in the reabsorption of GFP, YFP and lysozyme in the proximal tubules after 0.75 M of NaCl injection was found. The accumulation of proteins in the early endocytic compartment and decrease in their entry into late endosomes and lysosomes are shown, that is considered as evidence of a delay in intracellular vesicular transport in hypernatremia. The data obtained were analyzed in connection with changes in blood parameters and kidney activity during osmoregulation, and also with the role of chloride channels in receptor-mediated protein endocytosis. It can be assumed that increased ion transport in the proximal tubules cells in hypernatremia leads to decreased reabsorption capacity of epitheliocytes and delayed intracellular transport of proteins.

Compact Analysis of the Necessity of Padé Approximation for Delayed Continuous-time Models in LQR, H-Infinity and Root Locus Control Strategies

This paper presents a comprehensive analysis of the need for the Padé approximation for continuous-time models with delays, focusing on its critical role in addressing the control challenges posed by time delays. Time delays, often referred to as dead times, transport delays or time lags, are inherent in a wide range of industrial and engineering processes. These delays introduce phase shifts that degrade control performance by reducing control bandwidth and threatening the stability of closed-loop systems. Accurate modelling and compensation of these delays is essential to maintain system stability and ensure effective control. This paper highlights the difficulties that arise when using advanced control techniques such as root locus (RL), linear quadratic regulator (LQR) and H-infinity (H_∞) control in systems with delays. Representing delays in exponential form leads to an infinite number of state problems, complicating the design and analysis of controllers in such systems. To address these challenges, the Padé approximation is proposed as an effective method for approximating time delays with rational polynomials of appropriate order. This approach allows for more accurate simulation, system analysis and controller design, thereby mitigating the problems caused by delays. The paper also provides a detailed comparative analysis between the Padé approximation and Taylor polynomials, demonstrating the superiority of the former in achieving accurate delay modelling and control performance. The results show that the use of Padé approximation not only improves the accuracy of system models, but also improves the robustness and stability of control strategies such as RL, LQR, and H_∞. These results highlight the importance of the Padé approximation as a valuable tool in the design of delay-affected control systems, offering significant advantages for both theoretical and practical applications.

From the Operating Theater to the Pathology Laboratory: Failure Mode, Effects, and Criticality Analysis of the Biological Samples Transfer.

Introduction: The frozen section intra-operative consultation is a pathology procedure that provides real-time evaluations of tissue samples during surgery, enabling quick and informed decisions. In the pre-analytical phase, errors related to sample collection, transport, and identification are common, and tools like failure mode, effects, and criticality analysis help identify and prevent risks. This study aims to enhance patient safety and diagnostic quality by analyzing risks and optimizing sample management. Materials and Methods: The failure mode, effects, and criticality analysis was conducted by a multidisciplinary team to analyze the workflow of frozen section sample handling from collection in the operating theater to acceptance at the pathology lab. Six steps were identified, each assigned tasks and responsibilities, with risks assessed through the risk priority number, calculated from severity, occurrence, and detectability. Severity was classified based on the WHO framework, ranging from "No Harm" to "Death", to prioritize risks effectively. Results: The study identified 12 failure modes across 11 sub-processes, prioritized by risk. Key failures included missing patient identification, incorrect sample retrieval, missing labels, misdirected samples, and samples sent to the wrong lab. Discussion: Pre-analytical errors in pathology pose risks to diagnosis and patient care, with most errors occurring in this phase. A multidisciplinary team identified key issues, such as sample mislabeling and delays due to staff unavailability, and implemented corrective actions, including improved signage, staff re-training, and sample tracking systems. Monitoring and regular checks ensured ongoing adherence to protocols and reduced the risks of misidentification, transport delays, and procedural errors. Conclusions: The frozen section intra-operative consultation is vital in surgical pathology, with the pre-analytical phase posing significant risks due to potential errors in sample handling and labeling. Failure mode, effects, and criticality analysis has proven effective in identifying and prioritizing these failures, despite resource demands, by allowing corrective actions that enhance patient safety and healthcare quality.

Ethnic Disparities of Arrival Following ST Elevation Myocardial Infarction in South Israel.

Background: Early activation of the emergency medical service (EMS) is crucial for the care of patients with STEMI. Methods: A retrospective cohort study of patients hospitalized with STEMI was conducted. Two groups of patients were compared: Bedouins and Jews. The primary outcome was one-year mortality. The secondary endpoints were 30-day mortality, mode of transportation and door-to-balloon time. Results: There were 445 Bedouin patients (BPs) and 1103 Jewish patients (JPs). BPs with STEMI were significantly younger than JPs, had more diabetes and higher rates of smoking. More JPs arrived at the hospital by ambulance compared to BPs (56.9% vs. 31.9%, p < 0.001). Direct transportation to the catheterization laboratory was observed in 51.9% of JPs and in 43.6% of BsP, p = 0.003. Door-to-balloon time was longer in BPs compared to JPs (median 65 min vs. 62 min, p = 0.044). There were no differences in one-year, 30-day and in-hospital mortality between the two groups. After adjustment by propensity score analysis for JPs vs. BPs, there was a protective factor for one-year mortality (hazard ratio (HR), 0.68; 95% CI 0.48-0.97), p = 0.034. Thirty-day and one-year mortality in the subgroup of BPs that arrived at the hospital from the outpatient clinic was higher compared to JPs (7.1% vs. 4.4%, p = 0.004 and 10.4% vs. 5.6%, p < 0.001, relatively) Conclusions: Underuse of EMS by BPs with STEMI compared to JPs resulted in a delay in direct transportation to the catheterization laboratory, longer door-to-balloon time and a higher 30-day and one-year mortality in the subgroup of BPs who arrived at the hospital after visiting an outpatient clinic.

Human Vision as A Multi-Circuit Mathematical Model of the Automated Control System

Abstract The paper contains a proposal an original, extended mathematical model of an automatic system of human vision reaction to a forcing light pulse. A comprehensive mathematical model of the vision process was proposed in the form of an equation described in the frequency (dynamics) domain. Mathematical modelling of human senses is very important. It enables better integration of automation systems with a human cooperating with them, also as an automation system. This provides the basis for reasoning based on a mathematical model instead of intuitive reasoning about human reactions to visual stimuli. A block diagram of the proposed system with five human reaction paths is given. The following can be distinguished in the scheme: the main track consisting of: the transport delay of the eye reaction, the transport delay of the afferent nerves, the inertia of the brain with a preemptive action, the transport delay of the centrifugal nerves and the inertial and transport delay of the neuromotor system. In addition, the scheme of the system includes four tracks of negative feedback of motor and force reactions: upper eyelid, lower eyelid, pupil and lens. In the proposed model, the components of each path along with their partial mathematical models are given and discussed. For each reaction path, their overall mathematical models are also given. Taking into account the comprehensive models of all five reaction paths, a complete mathematical model of the automatic system of human reaction to a forcing light impulse is proposed. The proposed mathematical model opens up many possibilities for synchronizing it with mathematical models of many mechatronics and automation systems and their research. Optimizing the parameters of this model and its synchronization with specific models of automation systems is difficult and requires many numerical experiments. This approach enables the design of automation systems that are better synchronized with human reactions to existing stimuli and the selection of optimal parameters of their operation already in the design phase. The proposed model allows, for example, accurate determination of difficulty levels in computer games. Another example of the use of the proposed model is the study of human reactions to various situations generated virtually, for example in flight simulators and other similar ones.

Public Transport Delay Prediction using Deep Learning

A system of trains, buses, subways, ferries, and other vehicles that are accessible to the public is referred to as public transportation, also known as public transit or mass transit. These networks are usually run by private or public organisations are made to efficiently transport large numbers of people in urban and suburban areas. It usually operates on set schedules and routes and has a listed fare for each journey. Long wait periods and lengthy travel times resulting from delays are two problems that frequently plague public transportation services. There are several things that can cause a delay in public transport, such as an excess of passengers, heavy traffic, accidents, and other unforeseen circumstances. The availability of a more accurate delay prediction for public transportation might increase users’ confidence and their willingness to pay more for transit services. Over the past two decades, a number of studies on prediction algorithms for transportation data have been proposed. Most of the work is on machine learning model development, focusing on delay prediction and taking into account several factors such as weather conditions and infrastructure issues. This paper proposes a deep learning model to predict public transportation delays using data from public transportation and the weather. The results obtained from this research work are compared with several other existing works. Our experiment has demonstrated that the deep neural network (DNN) is the best model to predict transit delay compared to several other machine learning and deep learning models.

Preselection of Applicants for Small UAV Remote Pilot Training

In this article a new method is proposed for the preselection and objective evaluation of applicants for UAV training based on HMI models and mathematical analysis. With the rising number of UAV users there is a need for their preselection and qualification based on objective evaluation of their performance to assure the safety of air operations. A system consisting of a UAV RC controller, a display, and a software solution for the measurement of potential UAV pilot reaction was created. 59 participants were given the task to move the sticks of the RC controller to the random positions shown on the display as quickly and precisely as possible multiple times in a row. Based on the recorded stick movements, the performance of the participants was evaluated using HMI models and mathematical analysis. Four key aspects were identified for the preselection and continual evaluation of the UAV pilots: Transport delay, Time to 90% regulation, Overshoot, Deviation from the target in the stabilized state. For these parameters, limit values were proposed for the preselection of UAV pilot applicants. The created system with possible expansions can be used for effective UAV applicant preselection. The research supports the aim of better preselection and effective training of UAV pilots.

Unveiling risk factors influencing the selection of the Northern Sea Route: A conjoint analysis approach for Japanese shippers

This paper explores how Japanese corporations assess risks associated with using the Northern Sea Route (NSR) as a new maritime transport route in Asia and identifies measures to promote its usage. We address two key questions: (1) What risks influence shippers' route choices in maritime transport? (2) Does risk evaluation vary by cargo type and company size? Using choice-based conjoint analysis and a conditional logit model with online survey data, we identified five main risk factors influencing shippers: route unavailability rate, delay probability, piracy probability, transport costs, and war conditions, ranked in importance from highest to lowest as war conditions, delay probability, transport costs, route unavailability rate, and piracy probability. Additionally, the risk factors significantly influencing route selection were found to be robust, showing minimal variation across company size, type of trade (import/export), and the nature of the goods transported. Our findings suggest several policy implications. Diplomatic efforts are crucial for safe vessel navigation on the NSR. Government initiatives should focus on reducing transport delays through technology investments and implementing strategies to decrease NSR delay rates without increasing shippers' costs. Additionally, emphasizing the lower piracy risks on the NSR compared to Suez Canal Route could boost its attractiveness.

Mobile Critical Care in Resource-Limited Settings: An Unmet Need.

Care of the critically ill in resource-limited areas, inside or outside the intensive care unit (ICU), is indispensable. Murthy and Adhikari noted that about 70% of patients in low-middle income (LMIC) areas could benefit from good critical care. Many patients in resource-limited settings still die before getting to the hospital. Investing in capacity building by strengthening and expanding ICU capability and training intensivists, critical care nurses, respiratory therapists, and other ICU staff is essential, but this process will take years. Also, having advanced healthcare facilities that are still far from remote areas will not do much to alleviate distance and mode of transportation as barriers to achieving good critical care. This paper discusses the importance of mobile critical care units (MCCUs) in supporting and enhancing existing emergency medical systems. MCCUs will be crucial in addressing critical delays in transportation and time to receive appropriate lifesaving critical care in remote areas. They are incredibly versatile and could be used to transfer severely ill patients to a higher level of care from the field, safely transfer critically ill patients between hospitals, and, sometimes, almost more importantly, provide standalone short-term critical care in regions where ICUs might be absent or immediately inaccessible. MCCUs should not be used as a substitute for primary care or to bypass readily available services at local healthcare centers. It is essential to rethink the traditional paradigm of 'prehospital care' and 'hospital care' and focus on improving the care of critically ill patients from the field to the hospital.

Management of Patients With Cardiac Arrest Requiring Interfacility Transport: A Scientific Statement From the American Heart Association.

People who experience out-of-hospital cardiac arrest often require care at a regional center for continued treatment after resuscitation, but many do not initially present to the hospital where they will be admitted. For patients who require interfacility transport after cardiac arrest, the decision to transfer between centers is complex and often based on individual clinical characteristics, resources at the presenting hospital, and available transport resources. Once the decision has been made to transfer a patient after cardiac arrest, there is little direct guidance on how best to provide interfacility transport. Accepting centers depend on transferring emergency departments and emergency medical services professionals to make important and nuanced decisions about postresuscitation care that may determine the efficacy of future treatments. The consequences of early care are greater when transport delays occur, which is common in rural areas or due to inclement weather. Challenges of providing interfacility transfer services for patients who have experienced cardiac arrest include varying expertise of clinicians, differing resources available to them, and nonstandardized communication between transferring and receiving centers. Although many aspects of care are insufficiently studied to determine implications for specific out-of-hospital treatment on outcomes, a general approach of maintaining otherwise recommended postresuscitation care during interfacility transfer is reasonable. This includes close attention to airway, vascular access, ventilator management, sedation, cardiopulmonary monitoring, antiarrhythmic treatments, blood pressure control, temperature control, and metabolic management. Patient stability for transfer, equity and inclusion, and communication also must be considered. Many of these aspects can be delivered by protocol-driven care.

Factors Affecting Maternal Mortality Rates in Nigeria: A Review of Challenges and Potential Solutions

Maternal mortality is a significant public health issue in Nigeria, accounting for approximately 23% of global maternal deaths. This review explored the main factors affecting maternal mortality rates in Nigeria and proposed potential solutions. Accurate data collection on maternal health in Nigeria, especially in rural areas, remains challenging. Aims: this paper aimed to identify the key factors contributing to maternal mortality in Nigeria and proposed solutions to address this issue. Study design: A qualitative analysis of literature review narration was employed taking into account the research methodology, instrument interpretation, and data analysis in order to avoid bias and misinterpretation of result findings. Methodology: The methodology was designed to systematically gather, analyse, and synthesise data from multiple sources, including the peer-reviewed academic literature, government reports, and NGO publications. To ensure rigor and reproducibility, in the literature search were utilised several databases and search engines beyond Google, including PubMed, Scopus, Web of Science, Google Scholar, Semantic Scholar, and Research Gate. 50 published papers and articles from credible sources were reviewed in the study, identifying the key issues such as inadequate healthcare infrastructure, socio-economic challenges, cultural and behavioural practices, as well as political factors. Results: The findings revealed that the major causes of maternal mortality in Nigeria included haemorrhage, infection, unsafe abortion, hypertensive diseases of pregnancy, and obstructed labour. There were cases of socio-economic factors such as unavailability of electricity, transportation delay, the lack of skilled medical personal and cultural factors such as traditional beliefs. Despite various interventions, progress in reducing maternal mortality has been limited. Conclusion: This paper emphasised the need for continuous investment in maternal and child health, improvements in healthcare infrastructure, education, and effective policy implementation. A holistic approach that integrates organisational structures, technical expertise, human experiences, and patient needs is essential for effectively reducing maternal mortality rates.