Advancing the Understanding of Clinical Sepsis Using Gene Expression-Driven Machine Learning to Improve Patient Outcomes

Sepsis remains a major challenge that necessitates improved approaches to enhance patient outcomes. This study explored the potential of Machine Learning (ML) techniques to bridge the gap between clinical data and gene expression information to better predict and understand sepsis. We discuss the application of ML algorithms, including neural networks, deep learning, and ensemble methods, to address key evidence gaps and overcome the challenges in sepsis research. The lack of a clear definition of sepsis is highlighted as a major hurdle, but ML models offer a workaround by focusing on endpoint prediction. We emphasize the significance of gene transcript information and its use in ML models to provide insights into sepsis pathophysiology and biomarker identification. Temporal analysis and integration of gene expression data further enhance the accuracy and predictive capabilities of ML models for sepsis. Although challenges such as interpretability and bias exist, ML research offers exciting prospects for addressing critical clinical problems, improving sepsis management, and advancing precision medicine approaches. Collaborative efforts between clinicians and data scientists are essential for the successful implementation and translation of ML models into clinical practice. ML has the potential to revolutionize our understanding of sepsis and significantly improve patient outcomes. Further research and collaboration between clinicians and data scientists are needed to fully understand the potential of ML in sepsis management

A Transcriptomic Appreciation of Childhood Meningococcal and Polymicrobial Sepsis from a Pro-Inflammatory and Trajectorial Perspective, a Role for Vascular Endothelial Growth Factor A and B Modulation?

This study investigated the temporal dynamics of childhood sepsis by analyzing gene expression changes associated with proinflammatory processes. Five datasets, including four meningococcal sepsis shock (MSS) datasets (two temporal and two longitudinal) and one polymicrobial sepsis dataset, were selected to track temporal changes in gene expression. Hierarchical clustering revealed three temporal phases: early, intermediate, and late, providing a framework for understanding sepsis progression. Principal component analysis supported the identification of gene expression trajectories. Differential gene analysis highlighted consistent upregulation of vascular endothelial growth factor A (VEGF-A) and nuclear factor κB1 (NFKB1), genes involved in inflammation, across the sepsis datasets. NFKB1 gene expression also showed temporal changes in the MSS datasets. In the postmortem dataset comparing MSS cases to controls, VEGF-A was upregulated and VEGF-B downregulated. Renal tissue exhibited higher VEGF-A expression compared with other tissues. Similar VEGF-A upregulation and VEGF-B downregulation patterns were observed in the cross-sectional MSS datasets and the polymicrobial sepsis dataset. Hexagonal plots confirmed VEGF-R (VEGF receptor)–VEGF-R2 signaling pathway enrichment in the MSS cross-sectional studies. The polymicrobial sepsis dataset also showed enrichment of the VEGF pathway in septic shock day 3 and sepsis day 3 samples compared with controls. These findings provide unique insights into the dynamic nature of sepsis from a transcriptomic perspective and suggest potential implications for biomarker development. Future research should focus on larger-scale temporal transcriptomic studies with appropriate control groups and validate the identified gene combination as a potential biomarker panel for sepsis

Advancing sepsis clinical research: harnessing transcriptomics for an omics-based strategy - a comprehensive scoping review

Sepsis continues to be recognized as a significant global health challenge across all ages and is characterized by a complex pathophysiology. In this scoping review, PRISMA-ScR guidelines were adhered to, and a transcriptomic methodology was adopted, with the protocol registered on the Open Science Framework. We hypothesized that gene expression analysis could provide a foundation for establishing a clinical research framework for sepsis. A comprehensive search of the PubMed database was conducted with a particular focus on original research and systematic reviews of transcriptomic sepsis studies published between 2012 and 2022. Both coding and non-coding gene expression studies have been included in this review. An effort was made to enhance the understanding of sepsis at the mRNA gene expression level by applying a systems biology approach through transcriptomic analysis. Seven crucial components related to sepsis research were addressed in this study: endotyping (n = 64), biomarker (n = 409), definition (n = 0), diagnosis (n = 1098), progression (n = 124), severity (n = 451), and benchmark (n = 62). These components were classified into two groups, with one focusing on Biomarkers and Endotypes and the other oriented towards clinical aspects. Our review of the selected studies revealed a compelling association between gene transcripts and clinical sepsis, reinforcing the proposed research framework. Nevertheless, challenges have arisen from the lack of consensus in the sepsis terminology employed in research studies and the absence of a comprehensive definition of sepsis. There is a gap in the alignment between the notion of sepsis as a clinical phenomenon and that of laboratory indicators. It is potentially responsible for the variable number of patients within each category. Ideally, future studies should incorporate a transcriptomic perspective. The integration of transcriptomic data with clinical endpoints holds significant potential for advancing sepsis research, facilitating a consensus-driven approach, and enabling the precision management of sepsis

Application of a gene modular approach for clinical phenotype genotype association and sepsis prediction using machine learning in meningococcal sepsis

Sepsis is a major global health concern causing high morbidity and mortality rates. Our study utilized a Meningococcal Septic Shock (MSS) temporal dataset to investigate the correlation between gene expression (GE) changes and clinical features. The research used Weighted Gene Co-expression Network Analysis (WGCNA) to establish links between gene expression and clinical parameters in infants admitted to the Pediatric Critical Care Unit with MSS. Additionally, various machine learning (ML) algorithms, including Support Vector Machine (SVM), Naive Bayes, K-Nearest Neighbors (KNN), Decision Tree, Random Forest, and Artificial Neural Network (ANN) were implemented to predict sepsis survival. The findings revealed a transition in gene function pathways from nuclear to cytoplasmic to extracellular, corresponding with Pediatric Logistic Organ Dysfunction score (PELOD) readings at 0, 24, and 48 h. ANN was the most accurate of the six ML models applied for survival prediction. This study successfully correlated PELOD with transcriptomic data, mapping enriched GE modules in acute sepsis. By integrating network analysis methods to identify key gene modules and using machine learning for sepsis prognosis, this study offers valuable insights for precision-based treatment strategies in future research. The observed temporal-spatial pattern of cellular recovery in sepsis could prove useful in guiding clinical management and therapeutic interventions

Using System Dynamics Simulation to Understand the Feedback Process in a Construction Project

The fast changing, complex, and dynamic environment in the construction project nowadays has led to frequent occurrences of last minute changes and reworks. Such frequent occurrences can influence performance in terms of project completion time, which the completion time will result in delay and have an impact on the quality of the project. Usually, reworks occur because of last minute changes in order to fulfill the client’s request or because of a decision made by the project manager. The Project Management Institute (PMI) has introduced the Project Management Body of Knowledge (PMBOK) as a standard guide that explains the different knowledge areas involved and needed to be taken into account when managing a project. As managing a construction project is complex and complicated, in addition to the project manager’s lack of understanding on how different knowledge areas may influence other knowledge areas, the problem of project delay due to rework become inevitable. Therefore, the aim of this paper is to identify the interconnectivity and interrelationship between different knowledge areas in PMBOK using the System Dynamics (SD) simulation model. A construction project model of simulation was developed using Causal Loop Diagrams (CLD) as well as Stock and Flow Diagram (SFD). With the model, it is anticipated the project manager will be able to understand the feedback process of the project in order to prevent delays. The development of simulation model can be used to help and support the project manager in the process of planning and managing the construction project efficiently and making timely effective decision-making

Using System Dynamics Simulation To Understand The Feedback Process In A Construction Project

The fast changing, complex, and dynamic environment in the construction project nowadays has led to frequent occurrences of last minute changes and reworks. Such frequent occurrences can influence performance in terms of project completion time, which the completion time will result in delay and have an impact on the quality of the project. Usually, reworks occur because of last minute changes in order to fulfill the client’s request or because of a decision made by the project manager. The Project Management Institute (PMI) has introduced the Project Management Body of Knowledge (PMBOK) as a standard guide that explains the different knowledge areas involved and needed to be taken into account when managing a project. As managing a construction project is complex and complicated, in addition to the project manager’s lack of understanding on how different knowledge areas may influence other knowledge areas, the problem of project delay due to rework become inevitable. Therefore, the aim of this paper is to identify the interconnectivity and interrelationship between different knowledge areas in PMBOK using the System Dynamics (SD) simulation model. A construction project model of simulation was developed using Causal Loop Diagrams (CLD) as well as Stock and Flow Diagram (SFD). With the model, it is anticipated the project manager will be able to understand the feedback process of the project in order to prevent delays. The development of simulation model can be used to help and support the project manager in the process of planning and managing the construction project efficiently and making timely effective decision-making

A system dynamics model to assess the interrelationship between different knowledge areas of PMBOK in a construction project

Successful construction projects are difficult to achieve in the dynamic and complex construction industry. Successful project management requires the application of knowledge areas in project management while managing projects and completing projects as scheduled. The Project Management Institute (PMI) has issued ten areas of knowledge that are interconnected with each other as a guide to project management. It has also explained step by step clearly for every area of knowledge in the Project Management Body of Knowledge (PMBOK). Existing research has focused on these areas of knowledge, either separately or individually to solve problems in a project. This situation can cause results taken in one area of knowledge to bring problems to other knowledge areas because the areas of knowledge related to the problem are seen separately. Therefore, this study was set up to solve the problem of project managers who made ineffective decision making due to looking at areas of knowledge related separately with the proposal to identify the connectivity and the relationship between different knowledge areas of PMBOK in construction projects. The areas of knowledge used in this study are procurement management, scope management, stakeholder management, integrated management, and human resource management. These five areas of knowledge are derived from a case study. Then, a causal loop is constructed to see the relationship between the knowledge field and is translated into stock and flow diagrams to develop a system dynamic model. After that, the system dynamic model was built and the model was tested. Only then, the required information is included and the recommendation strategy is formulated. Through the system dynamic model, project managers can link between different and relevant areas of knowledge, simulate and view them in larger descriptions. Simulation explains the behavior of the project and its relation to the pre-construction project process with different areas of knowledge in project management. After analyzing all the important data using the system dynamic model, it was found that pre-construction projects had been postponed around 38.7% from the schedule. This is due to several activities that can be done simultaneously, but done one by one. Additionally, project managers put inadequate number of employees to complete the project as scheduled. Through the system dynamic model, models can suggest possible strategies for reducing project completion time by combining several activities that can be carried out simultaneously and increase the number of employees sufficient to complete the project in a given time. Finally, the system dynamic model in this study can show forecasts if the results of the combined activities and the employees are added, the problem of completion of the project completion can be reduced

Understanding the interrelationship between different knowledge areas in PMBOK through the development of system dynamics model

Last minute changes, error, rework, cost overrun and delays are common issues in project management. Besides that project managers also face the pressure of completing projects within the given time and allocated budget. Project Management Body of Knowledge (PMBOK) by Project Management Institute (PMI) compiles guidelines for project management through the introduction of ten knowledge areas. Although, PMBOK provide detailed, step-by-step guidance through the project management process, there is no discussion on the interrelationship and interdependencies between the knowledge areas. Frequent changes in the projects also lead to uncertainly and unpredictable outcome as project manager’s well intentioned efforts to solve a problem sometimes make it worse. This is because the action’s outcome are delayed, diluted or defeated by unforeseen reactions of other factors due to the interconnected factors in project management. System dynamics methodology will be used in this study to capture the interdependencies between different knowledge areas that occurred during the pre-construction phase of a residential housing construction project. The developed model can be used by project managers to understand the interconnectivity and interrelationship between different knowledge areas. Also, the model can be extended as a learning tool where project managers can test extreme conditions or strategies to the model and observe its impact before implementing it to the real project

Pediatric chylous ascites treatment with combined ultrasound and fluoroscopy-guided intranodal lymphangiography

Chylous ascites is a form of ascites resulting from the leakage of lymph into the peritoneal cavity, which is particularly rare in children, most common etiology being an iatrogenic injury to lymphatics during surgery. Initial conservative management options include medium-chain triglycerides-based diet, somatostatin analogs, and total parenteral nutrition. If these fail, then interventions such as paracentesis with sclerotherapy, surgical ligation, or peritoneal shunts have been described. This study reports a case of a 7-year-old child with refractory chylous ascites to demonstrate a minimally invasive technique of intranodal lymphangiography with lipiodol as a viable treatment option for chylous ascites in children, particularly in cases of minor and undetectable leaks

Persistent Antibodies to HPV Virus-Like Particles Following Natural Infection Are Protective Against Subsequent Cervicovaginal Infection with Related and Unrelated HPV

Whether persistent human papillomavirus (HPV) IgG antibodies following natural infection are protective against subsequent infection is unknown. In a cohort of 508 college women followed for 3 y, persistent seropositivity was defined as the presence of type-specific HPV virus-like particle (VLP) antibodies at ≥2 consecutive visits 1 y apart. Protection from incident infection with any HPV was conferred by persistent antibodies to HPV16 (p = 0.02), HPV31 (p < 0.001), HPV33 (p = 0.03), HPV35 (p = 0.002), HPV52 (p = 0.007), HPV45 (p = 0.003), and HPV53 (p = 0.01). The risk of incident infection with species-specific HPV types was also decreased in women with persistent antibodies to any HPV type in that group, suggesting that exposure to HPV with persistent development of antibody response can be protective, and may explain the decreased efficacy of HPV vaccine in women with prior exposure