However, inferring causal relationships between all genes is challenging due to the large number of genes in the transcriptome. Here, we present SIGNET , a flexible software package that reveals networks of causal regulation between genes built upon large-scale transcriptomic and genotypic data at the population level. Like Mendelian randomization, SIGNET uses genotypic variants as natural instrumental variables to establish such causal relationships but constructs a transcriptome-wide gene regulatory network with high confidence. SIGNET makes such a computationally heavy task feasible by deploying a well-designed statistical algorithm over a parallel computing environment. It also provides a user-friendly interface allowing for parameter tuning, efficient parallel computing scheduling, interactive network visualization, and confirmatory results retrieval. The Open source SIGNET software is freely available ( Many methods have been widely applied to construct GRNs based on gene co-expression5. However, co-expression-based methods only infer association rather than direct causation. Hence the activator or repressor role of genes will remain ambiguous. On the other hand, unmeasured confounding variables and possible reverse causation challenge the utility of directed acyclic graphs for plausible causal interpretations between genes8.Recently, gene regulatory networks (GRNs) have attracted increasing attention due to the availability of high-throughput gene expression data. GRNs can elucidate the disease mechanisms when cells are under dysregulation and greatly accelerate the wet lab experiments by precise predictions9. 2SPLS employs genotypic variants as instrumental variables, which also enables the practice of Mendelian randomization10. While Mendelian randomization can only establish a local causal relationship between a pair of genes, 2SPLS can construct a transcriptome-wide gene regulatory network11. This is a significant improvement, as it allows us to understand the complex interactions between genes in a more comprehensive way. 2SPLS is able to handle large amounts of transcriptomic and genotypic data by designing gene-based tasks of parallel computing at each of its two sequential stages9. This makes it possible to construct the transcriptome-wide gene regulatory networks that would otherwise be intractable.A two-stage penalized least squares approach (2SPLS) has been developed to simultaneously conduct causal inference on all genes for their regulation with each other12 to automatically identify regulatory structures, prune for better accuracy, and report confidence on each constructed regulation. It also provides interactive visualization of the transcriptome-wide GRN and its subnetworks, and connects with public databases to provide validatory information on the identified causal relationships.We developed SIGNET, a handy and flexible platform for constructing transcriptome-wide GRNs. SIGNET takes advantage of 2SPLS and the computational power provided by clustered computers. SIGNET can be applied to transcriptomic and genotypic data for all tissues regardless of species. Driven purely by collected data, SIGNET applies the state-of-art 2SPLS as well as the greedy algorithm for clustering13 and the Genotype-Tissue Expression (GTEx)14 project. It also provides an interface to apply to user-preprocessed transcriptomic and genotypic data. We illustrated the use and capability of SIGNET by applying it to the Lung Adenocarcinoma (LUAD) data from TCGA and healthy lung tissue data from GTEx. With the LUAD data, we identified 4079 regulations for 4904 genes in each of the 1000 bootstrapped datasets. Similarly, with the healthy lung tissue data from GTEx, we identified 4301 regulations for 3603 genes. Many of these identified regulations have been reported in biological pathways with validated protein-protein interactions, however many others have not been reported before.SIGENT is ready to apply to transcriptomic and genotypic data from The Cancer Genome Atlas (TCGA) projectSIGNET is publicly available on GitHub with detailed documentation and example data for illustration. It allows users to quickly pick up the analysis and provides options for customization. A singularity container is provided for SIGNET, which can be used to run SIGNET on local servers or high-performance computing (HPC) clusters without having to install any additional software or libraries. This makes it easy for users to get started with SIGNET and reproduce their results.15, Bioconductor16, and PLINK17. Options are also provided at each step to fine-tune, e.g., cutoffs for filtering genotypic variants, and significance levels for identifying gene regulatory effects. In summary, SIGNET provides a flexible platform to meet users’ diverse demands in constructing transcriptome-wide GRNs with their own data or with data from TCGA and GTEx but with self-defined criteria. It is noteworthy that SIGNET can be applied to transcriptomic and genotypic data from any tissue of any taxonomy as long as both are preprocessed to pass quality control.SIGNET has four main components, (i) preprocessing transcriptomic and genotypic data; (ii) identifying instrumental variables for each gene; (iii) causal inference of gene regulations; and (iv) visualizing constructed GRN with validatory information from public databases Fig. . The fou19. SIGNET streamlines the data preprocessing procedures for transcriptomic and genotypic data provided by TCGA and GTEx21. It provides separate functions for each type of data, making it easy to process data from either source.Both transcriptomic and genotypic data need to be processed before they can be used to construct GRNs. This processing includes removing low-quality data, imputing missing values, and correcting for confounding effects. There are well-established protocols and pipelines for processing each type of data20 for TCGA data and TMM21 for GTEx data, respectively. SIGNET allows users to select only protein-coding genes to work in the downstream analysis. As confounding factors may lead to spurious association and result in false regulation, SIGNET provides functions to adjust for confounding effects from race, gender, and possible population stratification. An interactive interface is provided to help identify necessary principal components to account for the genetic differences in the population.For transcriptomic data, SIGNET filters out genes with low reads to improve the statistical power. It handles the heteroscedasticity in the count data and normalizes the data by taking account of the library sizes. SIGNET transforms the transcriptional abundance with a base-2 logarithm for the downstream analysis, using the variance stabilizing transformation (VST)17, removal of genotypic variants and samples with high missing rates, and disposal of single nucleotide polymorphisms (SNPs) discordant with Hardy Weinberg equilibrium (HWE). SIGNET streamlines the SNP imputation procedure in parallel using IMPUTE222. This significantly speeds up the process by simultaneously imputing missing values of multiple genetic regions. SIGNET also provides a function that combines and streamlines the procedures in the GTEx pipeline14. This function starts with the phased genotypic data which are directly available at dbGaP23 and have passed through quality control.For genotypic data, SIGNET provides a function for preprocessing TCGA data. This function assembles procedures such as quality control using PLINK25. As shown in Mendelian randomization, genotypic variants in a gene’s genetic region, that is, the gene’s cis genotypic variants, have the random assignment nature during meiosis, so they have the aforementioned properties and serve naturally as instrumental variables for their host genes. SIGNET can detect significant cis-acting genotypic variants, i.e., genetic variants in a gene’s genetic region, for each gene at a prespecified significance level (0.05 by default) and may use multiple cis-acting genotypic variants as instrumental variables.Possible confounding factors and reverse causation make it challenging to conduct causal inference in observational studies. For a successful inference, we need instrumental variables (IVs) which (i) are associated with the exposure; (ii) are independent of the confounders of both exposure and outcome; (iii) affect the outcome only through the exposure9, which takes advantage of IVs identified in the previous stage to infers transcriptome-wide causal regulatory networks. Unlike many methods constructing directed acyclic graphs (DAGs) to decipher causality in gene regulations, SIGNET builds non-recursive yet directed cyclic graphs (DCGs) to realistically describe causal regulations between genes. This is because DCGs can capture reciprocal regulation between genes or regulatory feedback loops among a group of genes. Statistically, for any gene with its cis genotypic variants as instrumental variables, the rank condition26, serving as a necessary and sufficient condition for uniquely estimating parameters in a system of equations, assures that SIGNET can systematically explore and identify all causal effects on other genes.SIGNET implements 2SPLSWith the parallel nature of 2SPLS, SIGNET is computationally fast in constructing a transcriptome-wide GRN from a set of transcriptomic and genotypic data. This allows SIGNET to bootstrap the original dataset, construct a causal regulatory network for each bootstrap dataset, and aggregate all networks to infer a transcriptome-wide GRN with desired confidence Fig. .27. With multi-nodes with multi-cores in an HPC cluster, SIGNET can construct a transcriptome-wide GRN from hundreds of samples in a day. With two stages of parallel computing in 2SPLS, SIGNET instantly summarizes the results upon the completion of the first stage and submits the jobs for the second stage. It also allows users to customize the parallel computing at each of the two stages.SIGNET can estimate the total execution time and automatically optimize the allocation of available computing resources for distributed computing over server clusters. It can submit jobs and collect the results on any high performance computing (HPC) cluster with SLURM (Simple Linux Utility for Resource Management) Workload Manager28, Cytoscape29, and Ingenuity Pathway Analysis (IPA)30.SIGNET reports its constructed causal network using an adjacency matrix, where each entry encodes the confidence of the corresponding regulation. With a customized confidence level, the whole network may be broken down into disconnected subnetworks. SIGNET provides functions to output and further inspect these subnetworks. These subnetworks can be saved in files with various formats, allowing users to conduct downstream analysis using other packages such as STRING31 and allows users to explore the rich results in constructed networks and incorporate biological interpretation from STRING28. With an adjacency matrix recapitulating the bootstrap results from the above network construction, users may customize the confidence level and take SIGNET to summarize the constructed network, e.g., reporting the numbers of genes and regulations involved as well as hub genes and pivotal interactions. Such hub genes and pivotal interactions together with the underlying sub-networks may direct users to the most relevant protein complex for further investigation. For example, pertinent confirmatory information may be obtained from the STRING database28 which reports protein-protein interaction (PPI) scores, indicating confidence shown in biochemical experiments, co-expressions, and other databases.The causal network constructed by SIGNET may involve thousands of genes and hence tens of thousands of possible regulations. The huge size of such networks makes it challenging to visualize and interpret. To address this challenge, SIGNET provides a web-based interactive interface that is developed upon R package Shiny32. By characterizing the divisibility of a network, SIGNET can identify densely connected communities within the network, where regulations are much denser than interactions between subnetworks. SIGNET utilizes STRING to extract enriched pathways, which are subsequently employed to partition the network. In fact, SIGNET provides a bar plot of these enriched pathways shown in decreasing order of p values. Genes can be selectively highlighted for investigation of their specific function, facilitating the study of their enriched effect. SIGNET also accentuates transcript factors for their important roles in the network. With the interactive interface provided by SIGNET, users can select two connected genes and check for pertinent confirmatory information in other databases. The interactive plots on GRN are also accessible in a portable HTML format, allowing for effortless sharing and dissemination of the plots among fellow researchers  at the first stage and 16,761,000 tasks  at the second stage using Purdue HPC with 448 nodes of Two Rome CPUs (2.0GHz), each having 128 cores. Without parallel computing, it would take more than 3 years to complete. Moreover, SIGNET can set up parallel tasks adaptable to available cores, memories, and wall time of HPC, alleviating the burden for users unfamiliar with parallel computing.Although the task of transcriptome-wide causal inference is formidable, SIGNET implements 2SPLS2SPLS is built upon the assumption of linear causal systems and well-developed identification results on such systems. The recent development of ensemble models and deep learning methods makes it appealing to explore nonlinear causal systems, e.g., constructing GRNs without the linear assumption. However, such an endeavour is still challenged by the model identification issue, i.e., when and how we are assured that a constructed relationship is truly causal. Furthermore, building ensemble models implies more computational burdens, and deep learning methods also require a sufficient amount of data.SIGNET is available with a Singularity container, which includes all the required software and packages. The container is user-friendly and can save users a huge amount of time setting up the required computational environment. SIGNET is also a flexible command-line tool that allows users to adjust multiple parameters to customize their analysis. Additionally, SIGNET provides independent functional units that advanced users can easily modify and integrate with their own analyses. We also provide the example data and a detailed document with step-by-step instructions.21. The function for preprocessing GTEx data assumes input files including gene count data and gene TPM (transcript per million), directly from the GTEx portal . The function for preprocessing TCGA data takes the log-transformed HTSeq gene count data, available at UCSC Xena37, and transforms them back to the original gene counts.SIGNET sets up the preprocessing procedures for transcriptomic data following two studies, i.e., GTEx and TCGA, and provides template functions for each study14 to conduct the quality control for GTEx data. It selects genes with TPM greater than 0.1 in at least 20% of the samples and at least six reads in at least 20% of samples. For TCGA data, SIGNET filters out genes with total counts less than 2.5 million or missing in more than 38. Both types of transcriptomic data are normalized via base-2 logarithm transformation, with GTEx data normalized via the TMM method21 available in the edgeR package39 and TCGA data normalized via the variance stabilizing transformation available in the DESeq2 package40.SIGNET follows the GTEx pipeline42, and provides corresponding functions for each study. The function for preprocessing GTEx data assumes phased genotypic data after quality control, which are directly available at dbGaP (https://www.ncbi.nlm.nih.gov/gap/). It filters out genetic variants with the total counts of minor alleles across samples of fewer than five. The function for preprocessing TCGA data takes the input of genotypic data in PLINK file format17, which can be converted from the BAM files available in the GDC data portal . It follows the GDC bioinformatics pipeline42. The more detailed data information and data preprocessing procedures, which include conversion from whole-exome sequencing BAM files to PLINK format, are available in Supplementary Note.SIGNET streamlines the preprocessing procedure for genotypic data from both GTEx and TCGAp-value cutoff at 0.0001 by default. The missing values are imputed via IMPUTE222 with 1000 Genomes Phase 3 as the reference genome43. This may be time-consuming, so SIGNET speeds up the process by simultaneously imputing multiple genetic regions, e.g., each region with SIGNET excludes samples and genetic variants with high missing rates. By default, it excludes samples with a missing rate of more than 10% across genetic variants and genetic variants with a missing rate of more than 10% across samples. It then filters out genetic variants discordant with the Hardy-Weinberg equilibrium, tested via PLINK with a 14, but only races and sex from gene expression in TCGA data13.SIGNET uses linear regression to remove the effects of potential confounding factors from the gene expression data for subsequent causal inference. It provides separate functions for GTEx and TCGA because there are different factors available in the two studies. Specifically, SIGNET removes the confounding effects of sex, sequencing platform (Illumina Hiseq2000 or Illumina HiseqX), and library construction protocol (PCR based or PCR free) from gene expression in GTEx data44. SIGNET removes the effects of top PCs (top three PCs by default) from the gene expression data before identifying IVs. This is because SIGNET is conducting local association studies for IVs. On the other hand, the gene expression data used for the transcriptome-wide causal inference are not adjusted for these PCs. This is because the transcriptome-wide causal inference is designed to identify global patterns of gene regulation, and adjusting for PCs would remove some of this information.Population stratification via principal components (PCs) of genotypic data is an important step in local association studies. PCs can be used to control the confounding effects of other genetic variants45, which aggregately constructs possible IVs to avoid loss of power caused by opposite effects of variants.Similar to Mendelian randomization, SIGNET leverages available genetic polymorphisms in a gene’s genetic region as its potential IVs. By default, SIGNET scans the cis-acting genetic polymorphisms located within both the start and end sites of genes, as well as 1000 base pairs upstream and downstream of these regions. SIGNET then categorize the polymorphisms according to their minor allele frequency (MAF): common variants with MAF no less than 0.05, low-MAF variants with MAF no less than 0.01, and rare variants with MAF less than 0.01. Common variants are scanned directly for their qualification of serving as IVs. Both low-MAF and rare variants go through a data-adaptive burden test, aSum testSIGNET provides a platform to identify genotypic IVs in parallel by conducting association studies of expression traits of many genes on their own genetic regions. In particular, SIGNET implements the aSum test as a permutation test, which is computationally intensive. SIGNET provides a simple portal that automatically divides the whole genome into separate regions and uses parallel computing to efficiently conduct the related tests. A full list of identified IVs for healthy lung tissue and LUAD is available in Supplementary Tables p genes and q genotypic variants, which are observed in a sample of n observations. Let We focus on a linear system with k-th gene, we can deal with the following limited information model,k-th structural equation in the model  and k-th columns of k.With tens of thousands of genes and even more genotypic variants available in a system, directly maximizing the likelihood for model  is a formentclass2pt{minimmentclass2pt{minim9 to predict the expression levels of each potential regulatory gene using all available IVs, which are first screened via the iterative sure independence screening method (ISIS)46. Such a prediction, say j, is optimized by ridge regression47 combined with the generalized cross validation method (GCV)48 selecting the best tuning parameters.The success of the multiple Mendelian randomization on the model  relies ok does not have any IVs, Because the majority of elements in 49 to the following high-dimensional regression,k is causally regulated by the corresponding gene. It has been proved that the estimated regulatory effects have well-bounded errors and identified gene regulatory causality is statistically consistent with the underlying gene regulatory network9.With predicted entclass1pt{minimak, in two stages: (1) predicting The above multiple Mendelian randomization will identify all regulatory genes and estimate relevant regulatory effects for each gene, say gene 50 for the prediction purpose, with the tuning parameter optimized by GCV. SIGNET uses R package parcor51 to implement adaptive lasso. At the completion of construction, SIGNET outputs the results as a sparse adjacency matrix, with each -th component including the regulatory effect of j-th gene on i-th gene.SIGNET constructs model  to depic9 to conduct transcriptome-wide causal inference of gene regulation. The regulatory effects are stored in matrix b-th bootstrap dataset, with its component j on gene i. The corresponding transcriptome-wide GRN can be described by an adjacency matrix B networks constructed from B bootstrap datasets, SIGNET averages across all adjacency matrices componentwise for the frequencies of regulations identified between each pair of genes,The parallel scalability of 2SPLS makes it possible to employ the bootstrap method to evaluate the reliability of each constructed regulation. This is usually a challenging task because of the enormous parameters involved in a transcriptome-wide GRN. For each bootstrap dataset, we will apply 2SPLSSIGNET automates the divide-and-combine steps for parallel computing. For each stage involving parallel computing, SIGNET randomly selects 10 genes from the input data set and runs with them to evaluate the computational burden. Specifically, SIGNET records the maximum running time and memory consumption for these genes and employ this information to determine the optimal number of genes for each batch of the task. SIGNET then configures batch scripts and submits them via the SLURM scheduler. Upon completion of the jobs, SIGNET collects the results, which include coefficient matrices and adjacency matrices as mentioned in the previous section, and automatically summarizes the regulatory relationships of all genes identified from all bootstrap datasets.p, we can derive a directed graph  describing the GRN, with V including all the involved genes and E calculated with its componentWith the averaged adjacency matrix lated in , we can i, SIGNET can calculate its total degree asi. With a partition V, E) into certain subgraphs, we denote i, and N the total number of regulations in the graph , the modularity, under partition 12, which is implemented in the R packages igraph52.For each gene Supplementary Information 1.Supplementary Information 2.Supplementary Information 3.Supplementary Information 4.Supplementary Information 5.Supplementary Information 6.Supplementary Information 7.Supplementary Information 8.Supplementary Information 9.Supplementary Information 10.Supplementary Information 11.Supplementary Information 12.Supplementary Information 13.


Background and Objectives: Chronic critical illness (CCI) is a syndrome characterized by persistent organ dysfunction that requires critical care therapy for ≥14 days. Sepsis and respiratory failure constitute the two primary causes of CCI. A better understanding of this patient population and their clinical course may help to risk-stratify them early during hospitalization. Our objective was to identify whether the source of sepsis  affected clinical trajectory and prognosis in patients developing CCI. Materials and Methods: We describe a cohort of patients having acute respiratory failure and sepsis and requiring critical care therapy in the medical (MICU) or surgical (SICU) critical care units for ≥14 days. Given the relative infrequency of CCI, we use a case series design to examine mortality, functional status, and place of residence (home versus non-home) at one year following their index hospitalization. Results: In medical patients developing CCI (n = 31), the severity of initial organ dysfunction, by SOFA score, was significantly associated with the development of CCI (p = 0.002). Surgical patients with CCI (n = 7) experienced significantly more ventilator-free days within the first 30 days following sepsis onset (p = 0.004), as well as less organ dysfunction at day 14 post-sepsis (p < 0.0001). However, one-year mortality, one-year functional status, and residency at home were not statistically different between cohorts. Moreover, 57% of surgical patients and 26% of medical patients who developed CCI were living at home for one year following their index hospitalization (p = 0.11). Conclusions: While surgical patients who develop sepsis-related CCI experience more favorable 30-day outcomes as compared with medical patients, long-term outcomes do not differ significantly between groups. This suggests that reversing established organ dysfunction and functional disability, regardless of etiology, is more challenging compared to preventing these complications at an earlier stage. Advances in the treatment of acute sepsis, such as early antibiotic therapy, infectious source control, and goal-directed fluid resuscitation, have reduced short-term mortality rates . NonetheIn 2009, the healthcare costs associated with CCI amounted to USD 26 billion, and over 60% of these patients had sepsis upon ICU admission . In factMedical and surgical patients who develop sepsis-related CCI have distinct risk factors and comorbidity profiles, and their clinical trajectory would, therefore, be expected to differ ,10. PatiTo gain a better understanding of the clinical trajectory of this patient population with high morbidity and healthcare burden, we conducted an analysis of long-term outcomes in patients with medical CCI following sepsis. We then compared these outcomes with those of patients who developed surgical CCI following sepsis. Given that mechanical ventilation and sepsis are the top two known causes of CCI , we focuAll septic, critically ill adult patients described in this case series were treated in the medical intensive care unit (MICU) or surgical intensive care unit (SICU) at a quaternary care academic medical center between May 2013 and August 2022. Surgical ICU patients were defined as patients who have undergone a prior surgical procedure and were receiving critical care therapy in the SICU at the time of enrollment, whereas medical ICU patients did not undergo a prior surgical procedure during their hospitalization and were receiving critical care therapy in the MICU. For the purposes of this case series, the following inclusion criteria were used: (1) age ≥ 18 years, (2) acute respiratory failure requiring mechanical intubation or bilevel positive airway pressure (BiPAP) at the time of recruitment, and (3) sepsis or septic shock by Sepsis-3 criteria at the time of recruitment .Chronic critical illness (CCI) was defined as an ICU length of stay ≥14 days with evidence of persistent organ dysfunction, determined by using the components of the Sequential Organ Failure Assessment (SOFA) score  . Early dDescriptive statistics were used to characterize the study population, with continuous variables reported as mean ± standard deviation and categorical variables reported as counts with proportions. Statistical tests were two-tailed with a level of significance set at α = 0.05. Inter-cohort differences were analyzed using Analysis of Variance or Student’s t-test for continuous variables and Chi-Square Test or Fisher’s exact test for categorical variables. Nonparametric, pair-wise comparisons were performed using Wilcoxon method. The survival curves were calculated using the Kaplan-Meier method with a 95% confidence interval and end point of 365 days after ICU admission. Analyses were performed using Stata 14.5 . p = 0.0017).Eighty-two patients met the criteria for medical sepsis, of which 31 (38%) developed CCI A. Seventp-value was not calculated due to sample size.We report both short-term  and longWithin the confines of our sample size, we performed multivariable regression analyses to correct for clinically relevant variables that may affect long-term survival, including age, APACHE II, Charlson comorbidity index, and presence of septic shock .Patients suffering from CCI experience prolonged disability and impose a disproportionately large burden on healthcare resources. While patients who eventually develop CCI represent only a minority among septic patients, the number of these patients is increasing ,9. To reThe primary objective of the present analysis was to describe the trajectory of the most gravely ill patients with sepsis-induced CCI and to examine whether clinical trajectories differed between sepsis of medical versus surgical origin. Given the infrequency of CCI, a descriptive case series was the most feasible way to accomplish this. Our investigation is the first, to our knowledge, to compare medical versus surgical sepsis-induced CCI in this manner. It demonstrated that short-term clinical outcomes were better in patients having surgical CCI, while one-year mortality and the proportion of patients living at home were similar in both groups.Septic surgical patients who develop CCI often exhibit a well-recognized phenotype known as persistent inflammation, immunosuppression, and catabolism syndrome (PICS) ,21,22. CThe term CCI is uncommonly encountered in the medical  literature, especially as it relates to sepsis. Studies that focus on medical patients do not have a universally accepted definition of CCI, most likely due to the heterogeneity of the patient population . HoweverBeing a case series description, our report has several limitations. First, cohort sizes are relatively small, which may limit the generalizability of the findings to a broader population. Generalizability to other critically ill patients may also be reduced by our description of the ‘sickest of the sick’ patients . While the number of patients in this cohort precluded our ability to perform robust statistical queries such as regression analyses, the disproportionate healthcare burden imposed by this small number of severely ill patients nonetheless mandates a better understanding of their clinical trajectory. Secondly, as a retrospective case series, there is a potential for selection bias, as cases were identified based on available medical records. Additionally, the absence of a control group  restricts our ability to establish causal relationships. Lastly, the study design inherently carries the risk of incomplete or missing data, and although efforts were made to minimize this through meticulous data collection, there is a possibility of data incompleteness or measurement error. These limitations should be considered when interpreting the results of this case series.In conclusion, septic patients with acute respiratory failure who develop CCI represent a minority of septic patients but a tremendous healthcare burden. While the infrequency of this condition makes randomized and controlled trials unfeasible, our study adds value to the body of literature about this condition and may assist patients and healthcare providers in better understanding the epidemiology and short- and long-term outcomes of this disease. Long-term outcomes in CCI patients following sepsis of medical versus surgical origin may not differ significantly despite the different clinical profiles of these two patient populations. Rather, the persistence of organ dysfunction for 14 days or more following sepsis onset appears to be the most significant prognostic predictor.


Background: Plate osteosynthesis is a widely used technique for bone fracture fixation; however, complications such as plate bending remain a significant clinical concern. A better understanding of the failure mechanisms behind plate osteosynthesis is crucial for improving treatment outcomes. This study aimed to develop finite element (FE) models to predict plate bending failure and validate these against in vitro experiments using literature-based and experimentally determined implant material properties.Methods: Plate fixations of seven cadaveric tibia shaft fractures were tested to failure in a biomechanical setup with various implant configurations. FE models of the bone-implant constructs were developed from computed tomography (CT) scans. Elasto-plastic implant material properties were assigned using either literature data or the experimentally derived data. The predictive capability of these two FE modelling approaches was assessed based on the experimental ground truth.Results: The FE simulations provided quantitatively correct prediction of the in vitro cadaveric experiments in terms of construct stiffness , yield load , and maximum force , when including the experimentally determined material properties. Literature-based properties led to inferior accuracies for both stiffness , yield load , and maximum force .Conclusion: The validated FE model allows for accurate prediction of plate osteosynthesis construct behaviour beyond the elastic regime but only when using experimentally determined implant material properties. Literature-based material properties led to inferior predictability. These validated models have the potential to be utilized for assessing the loads leading to plastic deformation in vivo, as well as aiding in preoperative planning and postoperative rehabilitation protocols. Plate osteosynthesis is a well-established technique applied for fixation of bone fractures with use of metallic plates and screws to stabilize the reduced fracture, promote bone healing, and restore function . Trauma Finite element (FE) modelling has emerged as a valuable tool for prediction of construct behaviour in various fields, including traumatology, orthopaedics and biomechanics , providiin vivo implant failures, the first step is to understand the construct behaviour in a more controlled in vitro setting. Therefore, the main aim of this study was to investigate the elastic and plastic biomechanical behaviour of plate fixation constructs using experimental testing and to establish validated specimen-specific FE models predicting construct stiffness, yield and maximum load. A secondary aim was to compare the predictive ability of the FE models using either experimentally determined elastic and plastic implant material properties or literature-based properties.Towards the overarching goal to describe It was hypothesized that the FE models can more accurately predict fixation construct behaviour with use of experimentally determined elastic and plastic implant material properties versus use of literature-based properties.Lit) or measured experimentally via uniaxial tensile testing (MatExp) and integrated into the models  scans of the intact tibiae were acquired with scanning settings of 120 kV voltage, 200 mA current and 0.625 mm slice thickness, and calibrated to volumetric bone mineral density (vBMD) units using a density phantom . Five specimens were instrumented with a veterinary stainless steel locking compression plate (LCP)  and two with human titanium LCP  creating a relative stability construct according to the bridge plating principle . After in = 10, 1.4441, Robert Mathys Co., Bettlach, Switzerland) and titanium  were determined by uniaxial tensile tests using a material testing machine . Dog bone shaped samples were harvested from raw material plate profiles scaled to maximum possible dimensions  (Exp) based on the averaged stress-strain curve of all samples of a given material. Engineering strain of the test data was converted to true strain and an elasto-plastic behaviour was defined with Young’s modulus, yield stress, ultimate stress, and the plastic stress-strain behaviour. Poisson’s ratio was defined as 0.35 for both implant materials and as 0.3 for bone. Tie constraints were applied at the bone-screw and the screw-plate interfaces. Boundary conditions were defined to replicate the experimental setup as follows. The PMMA embedding was not included in the models. Instead, reference nodes located at the centers of the proximal and distal cardan joints were kinematically coupled with the PMMA-embedded bone surfaces, and the experimentally measured displacements of the cardan joints were applied to these nodes. To ensure that the loading axis of the specimen was the same as in the experimental setup, the digitized four landmarks at the osteotomy site and the two markers at the proximal and distal end of the specimen—where the embedding plane intersected the axis between the two cardan joints—were used to align the FE model with the experimental specimen using an Iterative Closest Point (ICP) algorithm. The FE analyses were solved using the implicit solver of Abaqus  considering geometrical nonlinearities. Construct stiffness was defined from the initial slope of the force-displacement curve below 300 N. Yield load was determined by shifting the initial slope by 1 mm and identifying the intersection point. Furthermore, the maximum reaction force was evaluated. The FE predictions of all three outcome measures were compared with the corresponding experimental outcomes using linear regression analysis involving all seven specimens and the concordance correlation coefficients (CCC), standard error of estimate (SEE), and relative standard error , All seven ectively . First, ectively  that wasly (Lit) . In the The cadaveric experiments induced overloading failure of the constructs resulting in permanent deformation of the plate . ConstruAll dog bone shaped samples failed within the midsection during uniaxial tensile testing. The stress-strain curves  and all Exp) were able to accurately predict experimental construct stiffness  for construct stiffness (CCC = 0.92, SEE = 27.62 N/mm, RSE = 19.6%, Specimen-specific FE models with experiment-based implant material properties (Matin vitro cadaveric construct test, has demonstrated promising results in accurately predicting the post-plastic behaviour of osteosynthesis constructs. By incorporating experimentally determined values of elastic and plastic properties of locking plates, the models’ predictive capabilities have been enhanced compared to literature-base properties, offering a more accurate prediction of the mechanical behaviour of the fracture fixation construct.FE simulations are widely used in orthopaedics, mainly to analyse the biomechanical behaviour and stability of bone-implant constructs . Howeverin vitro cadaveric experiments highly accurately, proving the validity of the modelling approach and thus confirming the initial main hypothesis. Kopec et al. investigated broken implants and hypothesized that these failed most likely due to mechanical overloading resulting from either repetitive or excessive limb loading (The FE simulations were able to predict the construct stiffness, yield load, and maximum load of the  loading . They co loading . Similar loading , thus liImplant material properties evaluated via uniaxial tensile testing of dog bone shaped samples were highly reproducible. When comparing the results to the used values in the literature, differences could be observed. Others used solely elastic properties, usually ranging between 195–210 GPa for stainless steel  and 96–1in vivo loading before translating the models to the clinics. Nonetheless, this validation will contribute to the development of more reliable and effective treatment strategies for bone fracture management.Several limitations apply to the current study. The evaluated implant material properties were based on a single raw material profile bar of stainless steel or titanium. Even though this resulted in a small variance between the samples, the properties might vary between different suppliers and manufacturing series. To accurately determine the range of the different properties, multiple series from different suppliers would need to be analysed. Furthermore, the cadaveric experiments consisted of seven specimens of which only two were instrumented with titanium plates. However, the aim of the study was not to compare the differences in construct behaviour between the two implant materials but to validate the FE model. The fixation constructs were tested in compression-bending only and it was not investigated whether the behaviour in other loading modes could be predicted accurately. No post-elastic material behavior of the bone was incorporated in the simulations. However, no bone damage was observed around the screws after testing. Furthermore, a tied interface between screw and bone without the existence of press-fit forces was assumed. However, MacLeod et al. have demonstrated that the load-deformation characteristics of a construct exhibit minimal sensitivity to the utilization of a tied interface between the screw and bone, thereby justifying the chosen modeling approach . It is iin vivo or guide preoperative planning and postoperative rehabilitation protocols. However, to maximize prediction accuracy, potential errors at each step of the modeling workflow should be eliminated in an isolated manner, as it was presented here for the implant material properties.In conclusion, FE models of cadaveric ovine tibia shaft fracture fixations using relative stability via bridge plating with experimentally determined elasto-plastic material properties of stainless steel and titanium LCPs were able to predict construct stiffness and failure with high accuracy. The findings that the use of these properties significantly enhanced the predictive performance of the FE model compared to literature-based values emphasize the importance of carefully determining the appropriate elasto-plastic implant material properties either from the manufacturer or by experimental testing. Consequently, these models can be used to determine the forces needed to cause plastic deformations—i.e., overloading failure—in osteosynthesis constructs. In the future, these validated models could be used to predict the level of loads that cause the observed plate deformations


Increased O2●− and H2O2 may contribute to the overall progression of renal injury as well as catalyze the onset of comorbidities. In this review, we discuss the role of mitochondrial oxidative metabolism in the pathology of CKD and the recent developments in treating CKD progression specifically targeted to the mitochondria. Recently published results from a Phase 2b clinical trial by our group as well as recently released data from a ROMAN: Phase 3 trial (NCT03689712) suggest avasopasem manganese (AVA) may protect kidneys from cisplatin-induced CKD. Several antioxidants are under investigation to protect normal tissues from cancer-therapy-associated injury. Although many of these antioxidants demonstrate efficacy in pre-clinical models, clinically relevant novel compounds that reduce the severity of AKI and delay the progression to CKD are needed to reduce the burden of kidney disease. In this review, we focus on the various metabolic pathways in the kidney, discuss the role of mitochondrial metabolism in kidney disease, and the general involvement of mitochondrial oxidative metabolism in CKD progression. Furthermore, we present up-to-date literature on utilizing targets of mitochondrial metabolism to delay the pathology of CKD in pre-clinical and clinical models. Finally, we discuss the current clinical trials that target the mitochondria that could potentially be instrumental in advancing the clinical exploration and prevention of CKD.Chronic kidney disease (CKD) predisposes one toward end-stage renal disease (ESRD) and its associated morbidity and mortality. Significant metabolic perturbations in conjunction with alterations in redox status during CKD may induce increased production of reactive oxygen species (ROS), including superoxide (O Mitochondria are crucial for their role as redox sensors and act by interrupting oxidative stress signals, thereby triggering compensatory as well as adaptive responses in target cells. In addition to their role in oxidative phosphorylation and redox regulation, mitochondria mediate several other processes, including inflammation and apoptosis . Since tThe relationship between mitochondrial perturbations and CKD progression has been studied extensively with the aim to identify and define specific biomarkers that can be followed longitudinally and be therapeutically targeted to delay the onset of CKD. Since mitochondrial dysfunction, depending on its nature, degree, and presentation, may affect the extent of CKD pathology, the interplay between the two often remains abstract. Nevertheless, several hallmark features that define mitochondrial alterations, including mitochondrial oxidative metabolism, biogenesis, and remodeling, are a focus of CKD development. One of the concepts linking CKD and mitochondrial alterations is the unifying hypothesis that focuses on the mitochondria and the pathobiology of diabetic complications . The uniThis review will discuss mitochondrial metabolism regarding CKD progression and risk factors that could affect CKD progression. Furthermore, this review will also provide insight into mitochondrially targeted therapeutic interventions that are currently clinically available and may improve CKD management. The prevalence of CKD is around 10–13% worldwide . The incTraditionally, the risk factors for CKD include adverse outcomes of kidney failure, diabetes, hypertension, age, and gender which are causally related to cardiovascular complications . CKD canDiabetes mellitus is a major risk factor for the development of CKD and ESRD. The NIDDK report estimates a total of 34.2 million people (10.5% of the U.S. population) have diabetes with an estimated 26.9 million (8.2%) diagnosed cases . The num2•− and thus oxidative damage thiazol-6-yl]phenyl]quinoxaline-2-carboxamide), an SIRT1 activator, has an essential role in preventing the progression of CKD by transforming growth factor-beta1 (TGF-beta1)/connective tissue growth factor (CTGF) pathway inhibition in UUO-induced tubulointerstitial fibrosis  Table 1Table 1. Sulforaphane (SFN) modulates mitochondrial function and dynamics . SFN actSeveral other small-molecule pharmacological compounds have been utilized to target mitochondrial metabolism in the context of CKD and renal injury in general including celastrol (CLT) a.k.a tripterygium wilfordii, a traditional Chinese medicinal plant that exerts anti-inflammatory effects by inhibiting pro-inflammatory cytokines and chemokines and reduces endothelial damage to alleviate the severity of CKD  Table 1Table 1. Understanding the various pathways that govern the mitochondrial function and deciphering its role in kidney diseases in conjunction with mitochondrially targeting agents could identify a therapeutic window that could be targeted to significantly impact the treatment of renal diseases and CKD progression.In the recent past, unified preclinical and clinical studies have facilitated the understanding of CKD risks, development, and pathogenesis, thereby revealing novel therapeutic targets. The attribution of increased ROS levels to CKD development, although debatable, is a common theme of mitochondrial dysfunction, and alterations in mitochondrial oxidative metabolism do emerge from past studies. Assessing specific biomarkers of renal function during CKD development and progression using pharmacological interventions could help prevent and delay CKD pathology. Several therapeutic interventions that are mitochondrially targeted are currently in clinical trials to prevent, treat, as well as delay CKD progression.