Recalcitrant groundwater contamination is a common problem at hazardous waste sites worldwide. Groundwater contamination persists despite decades of remediation efforts at many sites because contaminants sorbed or dissolved within low-conductivity zones can back diffuse into high-conductivity zones, and therefore act as a continuing source of contamination to flowing groundwater. A review of the available literature on remediation of plume persistence due to back diffusion was conducted, and four sites were selected as case studies. Remediation at the sites included pump and treat, enhanced bioremediation, and thermal treatment. Our review highlights that a relatively small number of sites have been studied in sufficient detail to fully evaluate remediation of back diffusion; however, three general conclusions can be made based on the review. First, it is difficult to assess the significance of back diffusion without sufficient data to distinguish between multiple factors contributing to contaminant rebound and plume persistence. Second, high-resolution vertical samples are decidedly valuable for back diffusion assessment but are generally lacking in post-treatment assessments. Third, complete contaminant mass removal from back diffusion sources may not always be possible. Partial contaminant mass removal may nonetheless have potential benefits, similar to partial mass removal from primary DNAPL source zones. DiffuPlume persistence at contaminated groundwater sites often refers to the continued presence of contaminants at concentrations exceeding a remedial goal. It generally signifies a source of contaminant mass that has eluded initial characterization or remedial efforts. Plume persistence may also reflect rate-limited processes associated with contaminant mass removal and limitations in naturally occurring abiotic/biotic degradation mechanisms. Contaminant rebound typically refers to an increase in contaminant concentrations immediately following remedial treatment induced reductions in contaminant concentration. Rebound results from remaining contaminant, despite remedial treatment, and one or more processes acting to delay equilibrium conditions at the sampling device, such as rate-limited desorption, rate-limited DNAPL dissolution , or conProper characterization of site hydrogeology and contaminant distribution provides crucial information to evaluate the significance of back diffusion. Soil cores have traditionally provided rigorous characterization data and continue to do so e.g., ,18,19). ,19. 18,1Forward diffusion results in the accumulation of contaminants in LCZs where they cannot be easily reached by remedial strategies that depend exclusively on groundwater flow for flushing contaminants or delivering reactants e.g., ,30). Con. Con30])Amendment injection remediation technologies commonly used in HCZs, such as in situ chemical oxidation (ISCO), have had limited success  becauseHorst et al.  and BrooThe focus herein is on pilot- and field-scale demonstrations of LCZ remediation schemes to mitigate back diffusion and plume persistence. Explored are remedial approaches, levels of success, and lessons learned. Technology costs and general applicability considerations were not considered. The description of our approach for identifying peer reviewed literature and other sources is followed by a review of four salient case studies. Improving remedial efficiency for sites with back diffusion promises to save costs, decrease greenhouse gas emissions associated with remedial activities, and—most important—provides greater certainty that groundwater resources are not imperiled by persistent contamination. Therefore, this work should be of interest to remedial site managers, regulators, and remedial technology developers.2.A literature search was conducted using Google, Google Scholar, Web of Science, Science Direct, and Wiley. The searches were restricted to the English language and used variations and combinations of three key search terms to identify potential case studies: groundwater, remediation, and back diffusion. A focus was placed on identifying peer-reviewed literature and technical reports from government programs , The Department of Defense’s Strategic Environmental Research and Development Program (SERDP) and the Environmental Security Technology Certification Program (ESTCP), etc.). Gray literature, such as presentations, conference abstracts, and non-peer-reviewed papers and reports by state and federal agencies, were also considered. Two main criteria were used to prioritize search results for the selection of case studies: (1) sites with characterization data to support the existence of plume persistence due to back diffusion and (2) sites where one or more remedial strategies were applied to address back diffusion, at either pilot or field scales. Interviews with site-responsible parties were also conducted in some cases to collect more information.3.The literature review showed that back diffusion appears to be a factor impeding remediation at many sites. Moreover, varying remedial actions have been taken to address the problem. However, the level of information available describing site contamination, evidence for the importance of and initiation of back diffusion, remedial actions taken to address it (by targeting contamination in LCZs), and the performance of the remedial technologies used at most of these sites was too limited for extensive discussion. Consequently, many sites were eliminated as case studies because they lacked data to specifically identify back diffusion or focus on back diffusion in the context of a remedial technology. Another challenge in selecting case studies was that some sites lacked identification , which prevented evaluation of current conditions.3.1.Located in Cocoa, Florida, the Precision Fabricating & Cleaning (PFC) site is a small metal fabricating and cleaning facility with a history of trichloroethylene (TCE) DNAPL releases. This site has been the subject of several studies, including one on the nature of DNAPL contaminants in the subsurface , mass fl3.1.1.The site geology results from a complex sequence of coastal deposition processes. The uppermost 8.5 to 9.1 m below ground surface (bgs) is composed of homogenous, well-sorted beach sand with scattered lenses of coquina (limestone composed of cemented shell fragments). In the source area, upper and lower clay layers are found at 9.1 and 10.7 m bgs, respectively, separated by a sandy layer. Towards the southwest end of the site, the upper and lower clay beds seem to converge . HoweverDirectly under the deeper clay layer, there is a softer, shell-rich clay unit that coarsens with depth, extending to about 12 m bgs. From about 12 to 15 m bgs, there is an organic-rich unit consisting of poorly sorted brown sands, silts, and sandy, shell-rich layers. Interbedded with the sandy, shell-rich beds are poorly sorted sand and silt layers . Depth t3.1.2.Multiple spill events occurred between 1964 and 1977 while TCE was used at the site. In addition to smaller, routine spills, two larger spills contributed to the contamination of the underlying aquifer. In 1966, two 55-gallon drums spilled, and in 1977, a hose burst . A DNAPL3.1.3.The treatment system selected for the site consisted of hydraulic isolation of the DNAPL source zone with monitored natural attenuation in the downgradient plume. Source-zone hydraulic isolation was achieved using a PAT system composed of 12 extraction wells located along a north–south transect immediately downgradient of the primary source zone. Half of the extraction wells were screened above the uppermost clay layer (above ~9 m bgs), or in the shallow zone, and the remaining extraction wells were screened below the uppermost clay layer, or in the deep zone. Water extracted from these wells was treated and injected into nineteen injection wells  located along another north–south transect ~20 m downgradient from the pumping well transect. The intent of this design was to hydraulically isolate the primary source zone, facilitate plume detachment with a wedge of clean water, and enhance flushing of the downgradient plume.3/day, until November 2006, when adjustments were made to optimize the operation, including termination of wells that were consistently below MCLs and increasing extraction rates in areas with higher volatile organic compound (VOC) concentrations. The total modified pumping rate after these adjustments was 163 m3/day. Groundwater VOC concentrations downgradient of the source area initially declined but remained above remedial goals in most cases. In 2008, the potential causes of plume persistence observed at the site were investigated )Dhc was added to both electrode and amendment injection wells to provide a microbial consortium capable of reductive dehalogenation of COVCs. Following bioaugmentation, EK operation resumed and continued for about five months. Monitoring samples were then collected, followed by a 6-month incubation period. Similarly to Stage 1, Stage 2 included injection of additional electron donors, buffers, supplemental acids, and supplemental bases but differed in that the KB-1 microbial culture was not added. Stage 2 took five months, after which there was a monitoring event and 3 months of incubation.The EK demonstration began with a 60-day period for pH conditioning of the treatment zone via sodium carbonate solution addition. Subsequently, there were two operational stages with six months of incubation between them. The stages used different anode and cathode configurations to orient the electrical fields 90 degrees from each other and thereby promote amendment distribution. In Stage 1, lactate, an electron donor to support microbial reductive dehalogenation, was supplied to the electrode wells and amendment supply wells in short pulses several times per day. Also injected were pH buffer, acid, and base additions as needed to maintain pH in a favorable range. Following approximately 75 days of operation, the system was shut down for 48 h for KB-1 bioaugmentation. The KB-1 microbial culture that contains Dhc and vcrA) increased by a factor of 1000 from levels near or at the detection limit.The groundwater chemistry and soil analyses showed that after bioaugmentation, the pH and redox conditions within the treatment area remained in a range that promotes biodegradation ). In addition, decreased sulfate concentrations at all wells were consistent with active sulfate reduction in the treatment area, an indication of anaerobic conditions needed for reductive dehalogenation. Groundwater data indicated that reductive dechlorination occurred in the treatment area. Initial PCE concentrations at the upgradient edge  decreased by 95% after Stage 2 treatment. While contaminant concentrations in most wells remained low after treatment, concentrations in one well increased from a low of 180 μg/L after Stage 2 to 3540 μg/L. The cause was not clearly identified, but it suggests that treatment was not uniform throughout the treatment area. Ethene, the end product of dechlorination, increased in the treatment area from non-detectable to tens to hundreds of mg/L, and biomarkers , with significant improvements apparent by Stage 1. There were, however, no clear patterns of increased daughter products from the dehalogenation , VC). Post-Stage 2 samples showed quantifiable levels of 3.2.4.n = 9); range: 78% to 99%) over a duration of 22 months. Similarly, reductions in groundwater PCE concentrations averaged 81% .The data from the Jacksonville NAS site indicate that EK treatment is promising as a technology for diffusion-limited media. This is because electrokinetic flux is much less susceptible to limitations arising from subsurface heterogeneity and because the flux in low-conductivity materials is larger than can be achieved by hydraulic means. Consistent with the intended remedial mechanism, this study showed that reductive dechlorination was responsible for reductions in soil PCE concentrations in the LCZ  often applied to DNAPL source zones . In the Practical, on-site factors also come into play when making a final remedial decision. For example, upscaling the technology can present challenges. At Jacksonville NAS, the EK bioaugmentation treatment area was very small relative to the entire CVOC contamination. Deploying the appropriate density of electrode, injection, and monitoring wells for a much larger area may not be practical , although application at hotspots could be an advantageous use of this technology. Although EK bioaugmentation was not pursued for larger deployment at Jacksonville, the demonstration project provided proof of concept, valuable information on operational parameters, and data to support exploration of the mechanisms causing dispersal of the amendments.3.3.Located in West Trenton, NJ, the Naval Air Warfare Center (NAWC) is a former U.S. Navy facility that was used for jet engine testing from 1953 to 1998. Chlorinated solvent leakage and waste disposal practices resulted in CVOC contamination (primarily TCE) in fractures and the rock matrix at the site ,68, in aResearch on groundwater contamination and remediation at the facility has been ongoing since the 1980s. The U.S. Geological Survey (USGS) and several other federal, state, and private cooperating organizations , New Jersey Department of Environmental Protection, universities, consultants) have been studying the fate and transport of chlorinated solvents at NAWC ,66–72. T3.3.1.The NAWC site geology was described by Lacombe  and cons3.3.2.Leakage and disposal of TCE over the course of decades resulted in subsurface contamination in both DNAPL and dissolved phases . The conThe geology at the NAWC site results in a high degree of contaminant sorption to the rock matrix and thereby creates a substantial contaminant reservoir . Core an3.3.3.3 [The facility’s PAT system was installed in 1995 to remove the CVOCs from the groundwater and to hydraulically contain the contamination in the two main source areas . It is h3 ,98,99.13C relative to the pure product, and this was considered indicative of natural biodegradation [Besides PAT, natural attenuation through biodegradation has been considered an important part of the overall site remediation. Natural attenuation is estimated to have removed about 500 kg/year of TCE beyond the 630 kg/year removed by the PAT system  over rouradation . Nonetheradation  note thaLacombe  summarizResults from Allen-King et al. , who ana2H) acted as a conservative tracer. Although the augmenting agents were expected to act primarily in the HCZ fractures because of microbial pore size exclusion [After a smaller-scale pilot test in 2005, another bioaugmentation test was conducted between 2008 and 2010 ,71. The xclusion , the bio13C values and decreased TCE concentrations were observed in the injection well and in monitoring well 73BR (~20 m from the injection well), where δ13C is an isotopic signature based on the ratio of 13C to 12C in the sample to that in a standard. Changes were seen seven months later in monitoring well 71BR (~30 m from the injection well) but not in the pumping well 15BR (~50 m from the injection well). DCE and VC showed initial depletion in 13C, indicating that they were formed as TCE degraded, followed by enrichment in 13C as they in turn were degraded. This pattern persisted for a month after injection, after which TCE concentrations remained too low for analysis at the injection well and eventually too low at monitoring well 73BR. The weighted average δ13C values over time, along with ethene concentrations, suggest that bioaugmentation led to reductive dechlorination that proceeded past VC to ethene at least at the injection well and monitoring well 71BR during the year after amendment injection [During the bioaugmentation test, increased δnjection .Three weeks after bioaugmentation/biostimulation, the concentration of cDCE in well 36BR rose from less than 10,000 μg/L to approximately 150,000 μg/L, significantly higher than that of the TCE before injection . Less pr2H tracer at 36BR plateaued for about a year, declined, and returned to pre-bioaugmentation levels approximately two years after injection. Daughter products VC and cDCE remained elevated in the first monitoring well (73BR) for about two years. At the second monitoring well (71BR), cDCE and VC remained elevated for two years while TCE was lower relative to prebioaugmentation, although δ13C for the CVOCs were at pre-bioaugmentation values [A delayed peak in total CVOCs (TCE + cDCE + VC) was observed and was interpreted to be the result of the biodegradation of TCE that entered the system from the LCZ after the initial rapid biodegradation. The concentrations of CVOCs and the n values . For TCEing well BR, cDCE n values .3-year before bioaugmentation to 13.3 kg TCE/m3-year after (based on 3 years of data). At well 73BR, partway towards the pumping well, the transport rates similarly increased from 1.25 kg TCE/m3-year to 10.2 kg TCE/m3-year. This one order of magnitude increase persisted over five years of monitoring after bioaugmentation started. Shapiro et al. [Shapiro et al.  present o et al.  attributo et al. .3.3.4.Work at the NAWC site shows the importance of site characterization in a fractured rock aquifer because groundwater flowpaths will be affected by the fracture network as well as pumping. There are also several other site-specific factors affecting contaminant transport—including migration into and out of LCZs, matrix organic carbon content, matrix porosity, and CVOC mass in the matrix. As shown by Allen-King et al. , underst2H tracer, microbial populations, and electron donor concentrations) can be used to evaluate both effectiveness and mechanisms of bioaugmentation.The bioaugmentation pilot test was deemed successful at decreasing CVOC concentrations in the fractures. The resulting change in concentration gradient between the fractures and matrix promoted back diffusion; TCE was then degraded in the fractures after leaving the matrix. This experimental work illustrated how multiple lines of monitoring data  Yard is in Brandywine, Maryland, approximately 80 km south of Baltimore. The DRMO site, ~3.2 hectares in size, is currently inactive but was used by the U.S. Navy and the U.S. Air Force (USAF) between 1943 and 1987 as a waste storage area. Environmental investigations began at the site in the mid-1980s and it was placed on the National Priorities List in 1999 . It was Following more than a decade of soil and groundwater remedial activities, the persisting contaminants at this site have been identified in two distinct hydrogeologic horizons ,102,103:3.4.1.The water-bearing Brandywine Formation overlies the Calvert Formation, an aquitard. The Brandywine Formation ranges from 6.4 to 9.1 m in thickness and is composed of heterogeneous layers of clay, silt, sand, and gravel. This formation is composed of four layers (from top to bottom): the Shallow Brandywine, a continuous clay with some silt, sand, and gravel 0.6 to 3.7 m thick; the Upper Intermediate Brandywine, a discontinuous fine sand with minor silt and clay 0 to 2.3 m thick; the Lower Intermediate Brandywine, a continuous water-bearing stratum of poorly graded sand with gravel 2.4 to 7 m thick; and the Deep Brandywine, a discontinuous oxidized and poorly graded sand and gravel less than 0.8 m thick. The depth to the top of the Calvert Formation ranges from 6.4 to 9.1 m and the boundary between it and the Brandywine Formation above is highly variable ,102,104.−7 cm/s) [Groundwater at the site is typically less than 3.1 m deep and can be as shallow as 0.9 m. Prior to groundwater remediation activities that started in 2000, groundwater flow at the site was generally to the northwest ,103. The−7 cm/s) .3.4.2.Since the mid-1980s, multiple phases of site investigation and remediation have been conducted –107. TheInvestigations in 2011 and 2012  includedThe MIP/ECD findings identified high concentrations of TCE, along with cDCE and VC, located primarily at the top of the Calvert Formation over an area of approximately 0.42 hectares, with the highest concentrations approximately 1 m into it. The highest mass flux of TCE at the site also corresponded to the Brandywine–Calvert boundary at a depth of approximately 6.7 m. Data collected during a rebound study  indicate3.4.3.The first of two PAT systems consisted of a groundwater extraction trench and an extraction well in the northwestern corner of the site and removed approximately 230 kg of VOCs from 2000 to 2005 ,108. In A subsequent site remediation goal was to reduce contaminant concentrations to below CVOC MCLs in the Calvert Formation and thereby eliminate back diffusion into the overlying Brandywine Formation . ElectriAll vertical bored electrodes and sheet pile electrodes were installed to a depth of approximately 11.9 m bgs, approximately 2.4 to 3.1 m into the Calvert Formation. The effective thermal treatment interval was continuous from approximately the top of the aquifer to at least a meter into the underlying aquitard. A total of eight groundwater monitoring wells  and 17 tThe ERH system at the Brandywine DRMO Yard was shut down in October 2019 when multiple lines of evidence indicated site cleanup criteria had been achieved. The average subsurface temperature at the site from June 2019 to October 2019 was 106.3 °C. Mass removal rates declined in mid-August, indicating that the contaminant mass had significantly decreased, and that ERH had reached diminishing returns. Based on photoionization detector analysis of vapors recovered during ERH operations, an estimated 798 kg of VOCs were removed during remediation .Groundwater contaminant sampling results ,78 from 3.4.4.Treatment by ERH was successful in meeting the remedial criteria during and immediately after the treatment. Groundwater data showed that MCLs were reached in the Calvert Formation (the LCZ) at almost every location. Thermal treatment by ERH on average reduced concentrations by more than two orders of magnitude, which is consistent with previous thermal treatment evaluations . HoweveArguably, certain treatment endpoint criteria were set fairly low, i.e., an area was considered sufficiently treated when a sample from one well in the area indicated contaminant concentrations below MCLs . It is l4.It may be appropriate to assume back diffusion is a relevant factor with a site hydrogeologic conceptual model that identifies HCZs and LCZs, coupled with tailing in concentration–time series data from either monitoring or pumping wells. However, other mechanisms besides back diffusion can contribute to contaminant rebound and sustain contaminant concentrations in groundwater, including slow advection, desorption, and DNAPL dissolution. Moreover, it is likely that more than one of these mechanisms may be a significant factor in plume persistence. This is particularly true at DNAPL sites where it is likely the case that both DNAPL dissolution and back diffusion are relevant and concurrent features over some finite duration as the site ages. As noted by Schaefer et al. , withoutThe four case studies evaluated herein were in large part selected because of the extensive efforts invested to assess the spatial distribution of contaminant mass relative to the hydraulic conductivity distribution. Diffusion is a slow process and contaminant distributions due to diffusive flux occur over relatively small distances; consequently, high-resolution vertical samples are important for back diffusion assessment. In particular, high-resolution soil cores are valuable for defining contaminant profiles as illustrated at the PFC , JacksonBiostimulation and bioaugmentation were used at the PFC, Jacksonville NAS, and NAWC sites. Advective injection of amendments into the HCZs was used in the case of the first and last, while EK was used in the case of Jacksonville NAS. Results from the NAWC site indicate that reductions in HCZ concentrations promote mass transfer from the LCZ, but that repeated treatments would be needed over many years to effectively remediate the site ,71. A siSeveral citations have suggested or demonstrated that degradation in the LCZ can have a significant effect on reducing plume persistence due to back diffusion –112. ShaAs highlighted in the EK pilot study at Jacksonville NAS and ERH treatment at Brandywine, complete contaminant mass removal from secondary sources responsible for back diffusion may not always be possible, even in the case of treatment technologies that are less susceptible to limitations arising from low hydraulic conductivity. While the reasons for this may vary depending on the site-specific conditions under which the remedial technology was applied, the result is nonetheless the need for another remedial treatment application with the same or different technology, or a different remedial management strategy altogether. While the potential need for a phased approach to remediation at complex sites is not new , it doe5.A review of the available literature for case studies on remediation of plume persistence due to back diffusion was conducted, and four sites were highlighted: the PFC and Jacksonville NAS sites in Florida, the NAWC in New Jersey, and the Brandywine DRMO Yard in Maryland. For the first three case studies, information was integrated across multiple publications in the peer-reviewed literature to focus on the topic of back diffusion remediation. Moreover, recent monitoring data were summarized for the PFC and NAWC sites, which adds to the previously published results. Three general conclusions are made based on this review. First, it is difficult to assess the significance of back diffusion without sufficient data to distinguish between multiple factors contributing to contaminant rebound and plume persistence. Second, high-resolution vertical samples are decidedly valuable for back diffusion assessment. Third, complete contaminant mass removal from back diffusion sources may not always be possible. Partial contaminant mass removal may nonetheless have potential benefits, similar to partial mass removal from primary DNAPL source zones.The case studies summarize remedial strategies used at sites where back diffusion of CVOCs is occurring. They highlight the outcomes and lessons learned for a range of remediation approaches and geological settings, as well as important limitations. As current and new remediation technologies are implemented, the lessons noted here improve the likelihood that site managers can remediate sites when back diffusion hampers cleanup efforts. Altogether, these advances will contribute to sustainable water supplies and healthier ecosystems.Finally, our review highlights that a relatively small number of sites have been studied in sufficient detail to fully evaluate remediation of back diffusion. Relying too heavily on just a few sites hampers the assessment of back diffusion as a nationwide issue. Further, assessing the extent to which more innovative remedial strategies that target LCZs have been successful at reducing back diffusion is challenging without sufficient data for a wide variety of geographically diverse sites. Our review also highlights that assessment of LCZ spatial contaminant distributions after remediation treatment is lacking. Such information is needed to fully evaluate benefits of partial contaminant mass removal from LCZs. Consequently, we recommend the development of more case studies, with a focus on changes in LCZ contaminant distributions in sufficient detail after remediation to evaluate the effects on back diffusion flux and longevity.


Epidemiological studies show that high circulating cystatin C is associated with risk of cardiovascular disease (CVD), independent of creatinine-based renal function measurements. However, the relationship between serum cystatin C level and coronary atherosclerotic plaque burden is limited. We aimed to evaluate the relationship between circulating cystatin C and coronary atherosclerotic plaque burden.This study was a cross-sectional study based on China community population. Measurements of plaque burden were based on the segment-involvement score (SIS) and segment stenosis score (SSS), which derived from the Coronary Artery Tree Model Depicting Coronary Artery Plaque Scores. Logistic regression model was used to demonstrate the association between cystatin C level and coronary artery plaque burden. Mendelian randomization (MR) analyses were conducted to assess the causal effect of cystatin C level on coronary atherosclerosis risk.A total of 3,043 objects were included in the present study. The odds risks (OR) of severe plaque burden in the highest serum cystatin C levels  and medium-level cystatin C levels  were significantly higher after fulled adjusted confounders compared with the lowest levels of serum cystatin C by SSS. The MR analysis showed that genetic predicted cystatin C levels was associated with an increased risk of coronary atherosclerosis  .Elevated serum cystatin C levels were associated with coronary atherosclerotic plaque burden. Cystatin C levels had a causal effect on an increased risk of coronary atherosclerosis at the genetic level.Coronary artery disease is currently the most common cardiovascular disease and the leading global cause of mortality. Previous studies reported that higher serum cystatin C levels were associated with an increased risk for future cardiovascular events, independent of the normal creatinine levels or estimated glomerular filtration rate (eGFR) values. The presence of high-risk coronary atherosclerotic plaque burden is associated with increased risk of cardiovascular events. However, the association between serum cystatin C and coronary atherosclerotic plaque burden is not very clear.Our study demonstrated that the elevated serum cystatin C levels were associated with coronary atherosclerotic plaque burden. In addition, we found that serum cystatin C levels had a causal effect on an increased risk of coronary atherosclerosis at the genetic level.Current research finds that serum cystatin C levels were associated with coronary atherosclerosis. The metabolic pathway of cystatin C could be a target for new therapies against CAD.The online version contains supplementary material available at 10.1186/s12872-023-03506-2. Due to the aging global population, coronary artery disease (CAD) is currently the most common cardiovascular disease worldwide . It has Cystatin C is a important cysteine protease inhibitor which has essential function in maintaining vascular function and structure especially through regulate cathepsins S and cathepsins K . Previou2 (m2).The study data were derived from the PRECISE study (NCT03178448) and the protocol has been reported in previous study . We extrParticipants with a potential risk of using contrast media were excluded, and the remained participants were requested to underwent CTA for major arteries of the body . All scans and readings were performed by clinicians blinded to the trial. Plaque burden was defined as the sum of calcified plaque burden and non-calcified plaque burden. Measurements of plaque burden were based on the segment-involvement score (SIS) and segment stenosis score (SSS), which derived from the Coronary Artery Tree Model Depicting Coronary Artery Plaque Scores . The segContinuous variables that exhibited a normal distribution were documented as the mean ± standard deviation (SD). Otherwise, they were documented as medians with upper and lower quartiles. Categorical variables were documented as frequencies with percentages. Group comparisons were pooled using Chi-square, 1-way ANOVA, and Kruskal-WallisH tests. For each clinical outcome measures, three multivariate logistic regression models were constructed on the basis of cystatin C group inclusion according to tertiles. The first tertile was treated as the reference group. In the model I, covariates were adjusted for age, gender, BMI, diabetes, smoking, waistline and hipline. In the model II, we adjusted for creatinine, UA, homocysteine, cholesterol, LDL-C, HDL-C, triglyceride, HbA1c. In the model III, we further adjusted for covariates age, gender, BMI, diabetes, smoking, hypertension, creatinine, homocysteine, UA, LDL-C, HbA1c. Additionally, we performed a subgroup analysis for further investigating the association between serum cystatin C levels and coronary atherosclerotic plaque burden based on the model III. All tests were 2-tailed tests, and P ≤ 0.05 was considered statistically significant. Statistical analyses were performed using R versions 3.4.2 .− 8) . The summary statistics data for coronary atherosclerosis was derived from the GWAS database . Studies contributing data to these GWAS meta-analyses had received ethical approval from relevant institutional review boards. In the present study, we only made use of the summarized data from these studies; hence, no additional ethics approval was required.Summary level data on the associations of cystatin C was obtained from recently published a large-scale genome-wide association studies (GWAS) . We seleThe primary MR analysis was performed using the fixed effects inverse variance weighted (IVW) and random effects inverse variance weighted (IVW) methods. As the presence of horizontal pleiotropy in IVW estimates, MR-Egger and weighted median were also conducted to test the robustness of the results. In the sensitivity analysis, the heterogeneity and pleiotropy of individual SNPs were evaluated using IVW methods with Cochran’s Q statistics and MR Egger intercept, respectively. Also, a leave-one-out analysis was performed to evaluate the robustness of MR analysis results through any outlier SNP. All statistical analyses were undertaken using the “TwoSampleMR” package in R version 3.4.2  and a two-tailed p value < 0.05 was considered statistically significant.A total of 3,043 participants  with an average age of 61.1 ± 7.5 years old were included in our study. The mean SIS and SSS was highest in tertiles 3 and lowest in tertiles 1 (P < 0.001). Atherosclerotic plaques appeared in 1186 (38.97%) individuals. Among the objects, 900 objects had mild-to-moderate plaque burden with the prevalence of 29.6%, and 286 objects had severe plaque burden with the prevalence of 9.4% according to the SSS. The prevalence of severe plaque burden according to SSS among each cystatin C group was 3.4% (33/979), 8.4% (85/1016), and 16% (168/1048), respectively. Mean age, BMI, waist and hip circumference, SBP, prevalence of men, hypertension, diabetes, dyslipidemia, stroke, smoking, creatinine, UA, BUN and Triglyceride significantly increased with serum cystatin C tertiles. By contrast, total cholesterol, and HDL cholesterol were significantly decreased with serum cystatin C tertiles. However, there were no significant difference in ankle brachial index between the various group. More information about the study population characteristics is shown in Table Serum cystatin C levels had a positive association with coronary atherosclerotic plaque burden. In the model I, after adjusted for the general information  of participants, we have not observed the significant association between serum cystatin C tertiles and mild-to-moderate coronary atherosclerotic plaque burden , however, for the severe coronary atherosclerotic plaque burden (SSS ≥ 4), the risk of severe coronary atherosclerotic plaque burden (SSS ≥ 4) was significantly higher with increasing serum cystatin C tertiles than with the first serum cystatin C tertiles . In the model II, we only adjusted for the risk factors of laboratory examination . Both mild-to-moderate and severe coronary atherosclerotic plaque burden were all significant associated with increasing serum cystatin C tertiles (P < 0.001). In the model III, after full adjustment for cardiovascular disease risk factors , the risk of severe coronary atherosclerotic plaque burden gradually increased in the second , and third serum cystatin C tertiles  compared with the first serum cystatin C tertiles  was significantly associated with severe plaque burden in the objects who over 65 years old. Nevertheless, there was no significant association between serum cystatin C level and severe plaque burden in the objects who under 55 years old. The subgroup analyses results about serum cystatin C level and severe plaque burden as shown in Table  was signA total of 358 SNPs were genome-wide significant with serum cystatin C level. Ultimately, 358 SNPs as the instruments were included in the two-sample MR analysis after matching the coronary atherosclerosis data. Five MR analysis methods, inverse-variance weighted fixed-effect, inverse-variance weighted random-effect, simple median, weighted median, and MR-Egger (bootstrap) were performed to analyze the final results, as shown in Fig. In this large-scale population-based study, we investigated the association between serum cystatin C level and coronary atherosclerotic plaque burden. To the best of our knowledge, it’s the first study to research the association between serum cystatin C level and coronary atherosclerotic plaque burden. Our results demonstrated that serum cystatin C was strongly associated with severe plaque burden independent of traditional cardiovascular disease predictors. Furthermore, cystatin C provides incremental information for the risk stratification of female objects age range 55 to 65 years old, accompany with hypertension or with higher BMI. In two-sample MR analyses, we found a potential association between genetic determinants of cystatin C level and coronary atherosclerosis.Cystatin C is an endogenous cysteine proteinase inhibitor which is filtered by the renal glomerulus, and metabolized by the proximal tubule . ComparePrevious studies included objects with acute coronary syndrome, objects with acute coronary syndromes tend to have a stronger inflammatory response than the general CAD population. Our study objects were community population with not serious inflammation, while, cystatin C was also strongly associated with severe coronary atherosclerotic plaque burden. Of note, Yamashita H et al. observed a significant association between cystatin C and cardio-ankle vascular index, which is a marker of early-stage arteriosclerosis . TherebyCathepsin S–deficient mice with attenuated atherosclerosis provided convincing evidence for cysteine protease involvement in atherogenesis . SubsequIn our study, it was found that cystatin C was closely correlated with multi-vessel lesions and coronary atherosclerotic plaque burden. Furthermore, serum cystatin C levels had a causal effect on an increased risk of coronary atherosclerosis at the genetic level. These results provide objective evidence that cystatin C may be involved in the early stage of CAD. However, more pathological mechanisms are still needs to be explored by related basic pathology studies. Further, our study subjects derive from cluster sampling of Lishui living communities , they have similar demographics and medical histories as nationwide sample survey data. The representativeness of our study population allows us to better assess the prevalence of clinical or subclinical CAD.Several study limitations should also be stated here. First, our sample size was not large enough from an epidemiological perspective, but this is the largest study ever conducted on the coronary atherosclerotic plaque burden. Second, most of the population being composed in our study were Han people, therefore, selection bias was inevitable. Given the many elements involved in CAD, we are cautious of the extension of our findings to different regions or races. Third, our study was a cross-sectional study based on community population. We demonstrated an association between cystatin C and coronary atherosclerotic plaque burden, but unable to reveal the causal relationship between them, this should be further research in the future.Elevated serum cystatin C levels were associated with coronary atherosclerotic plaque burden independent of traditional cardiovascular disease predictors as assessed by computed tomography coronary angiography. Furthermore, serum cystatin C levels had a causal effect on an increased risk of coronary atherosclerosis at the genetic level. The metabolic pathway of cystatin C could be a target for new therapies against CAD.Below is the link to the electronic supplementary material.Supplementary Material 1


The formal mechanism of linear mixed and non-competitive enzyme inhibition implies the binding of inhibitors to both the active site of the free enzyme in competition with the substrate, and to an allosteric site on the enzyme-substrate complex. However, it is evident from a review of the scientific literature that the two-site mechanism is frequently mistaken as the actual underlying mechanism of mixed inhibition. In this study, we conducted a comprehensive assessment of the mechanistic relevance of this type of inhibition using a statistical approach. By combining a statistical analysis of the inhibition cases documented in the BRENDA database with a theoretical investigation of inhibition models, we conclude that mixed inhibitors exclusively bind to the active site of enzymes. Hence ruling out any implication of allosteric sites and depriving the two-site model of any mechanistic relevance. Mixed-type enzyme inhibition is a form of enzyme inhibition where the inhibitor can bind to both the free enzyme and the enzyme-substrate complex, resulting in distinct effects on both the enzyme’s catalytic activity and its affinity for the substrate. In mixed-type inhibition, the inhibitor can interact with the enzyme at either the active or a separate allosteric site. […]Mixed-type enzyme inhibition is a complex regulatory mechanism that can have important implications in various biological processes and the development of therapeutic strategies targeting enzyme activity. Understanding the nature of mixed-type inhibition is crucial for studying enzyme kinetics, drug discovery, and the design of effective enzyme inhibitors.Because the algorithms of ChatGPT have been trained using a massive amount of text data, the response it provided can be considered as a representative description of mixed inhibition that aligns with the general understanding presented in articles, books and other scientific publications.Prompted to “write a definition of mixed-type enzyme inhibition”, ChatGPT elaborated the following composition:Also, it seems safe to assume that most biochemists, drug designers and, in general, most of those who concern themselves with enzyme inhibition, would substantially agree with this definition.catK and catK/mK, which classically is measured as an intercept and/or a slope effect of a double-reciprocal plot, respectively , mixed inhibition-mimicking (MIM) mechanisms, icK is necessarily always less than or equal to iuK.Here, we try to provide an answer to that one question employing a statistical approach: forty years ago, Cornish-Bowden noted that uncompetitive inhibition almost always occurs in the form of mixed inhibition and that in mixed inhibition, the uncompetitive component never prevails over the competitive counterpart K=1 µM, M(B)K=5 µM, Ki=10 nM, cakt=100 s −1, cat/k-1K = 25.dK=10 nM.[E] = 0.1 nM, [S] = 2–100 µM, [I] = 0–100 nM, 1k = 108 mol−1s −1, -1k = 100 s −1, 3k = 100 mol−1s −1, -3k = 108 s −1.2k, -2k, 4k and -4k kinetic constant values used to simulate the different Fiso values are given in The combinations of the dK = 10 nM.[E] = 0.1 nM, [S] = 0.2–100 µM, [I] = 0–100 nM, 1k = 108 mol−1s −1, -1k = 1 s −1, 2k = 106–50 s −1, -2k = 1 s −1, 3k = 50–106 s −1.icK versus iuK issue”, the 464 entries containing the term “mixed” or “mixed-type” inhibition/inhibitor were further analysed. These entries point to 266 reference articles, from which it has been possible to retrieve 467 icK-iuK couples  and that pure non-competitive inhibition (icK=iuK) occurs with an unrealistically high frequency (icK/iuK=1 in 20% of cases), , .iKs were originally obtained by the graphical analysis of initial velocity data, a method that is known to be rather inaccurate,icK/iuK ratio between 0.5 and 2 , then the frequency of pure non-competitive inhibition jumps to 40%, while the occurrence of mixed inhibition cases with ic>KiuK drops to a mere 5%. icK-iuK couples were relative to multi-substrate enzymes.Interesting to note is that 66% of the 467 It is worth noting that the large majority of the gathered icKs that are necessarily always less than, or equal to, iuKs. We first derived a mathematical relation between icK and iuK for each of the five mechanisms . Then, to verify the correctness of the derived equations, MIM mechanisms were simulated using KinTek Explorer,icKs and iuKs, as in a real experiment, the computed initial velocities were analysed by the conventional double-reciprocal plot of 1/V vs 1/[S].In this section, we sought to demonstrate whether MIM mechanisms cause mixed inhibition with iK of an inhibitor, reactions must be measured in the presence of inhibitor concentrations close to the iK. This implies that for very potent inhibitors, typically with iK in the nM range or less, the inhibitor might turn out to be not in concentration higher enough than the enzyme, hence violating the first of the two steady-state assumptions. Methods to analyse tight inhibition have been proposed,,To determine the iK ratio was set to 10. Therefore, in principle, for all of the reactions, the degree of inhibition should be the same. However, as the [I]/[E] ratio drops below 10, the inhibitor depletion that occurs upon E-I binding can no longer be neglected, and the fraction of the inactive inhibitor-bound enzyme starts to deviate from the expected linear behaviour. In the double reciprocal plot, this is visualised as both a slope and an intercept effect that shifts the point of line intersection towards the first quadrant, thus mimicking a mixed inhibition pattern. The apparent iuK, which evidently emerges as a mere artefact, only depends on the [I]/[E] ratio: the smaller the ratio is, the smaller the iuK. As [I] tends to 0, however, the inhibition fades off, and the trace of the inhibited reaction tends to overlap with that of the uninhibited reaction. Therefore, under no circumstance, the intersection point can be below the 1/[S] axis, and hence, the apparent iuK turns out to be necessarily greater than, or at best equal to, icK.iuK asymptotically tends to icK as the enzyme-inhibitor dissociation gets slower, eventually mimicking pure non-competitive inhibition (icK=iuK) when offk is less than ∼1 0 −3–1 0 −4 s −1  that icK is the true dissociation constant for the enzyme-inhibitor complex (i.e. the competitive inhibition constant measured when substrate B is constant and A is variable), sK is the dissociation constant of the enzyme-substrate A complex and M(A)K is the Michaelis constant for substrate A. At any given concentration of substrate A, the ratio between the apparent uncompetitive and competitive components is:sK/M(AK) as [A] tends to infinity. This sK/M(AK) is equal to catk/-1k, which for a compulsory order mechanism is always greater than 1 , hence leading to the conclusion that in this type of mixed inhibition, the uncompetitive component is always necessarily greater than the competitive component , is made up of two segments, the catalytic step governed by the net rate constant chemk and the isomerisation step, with net rate constant kiso. The binding of an inhibitor to the product form of the enzyme . As correctly predicted by Northrop,icK/iuK ratio equals Fiso . For 0 < Fkiso<1, the inhibition is of the mixed-type with icK<iuK, so that necessarily iuK≥icK .From which follows that if FicK or iuK are true dissociation constants.The reduction of the affinity for the substrate and of the apparent maximal velocity are both actual. However, as for the previous iso-mechanism case, there is no allosteric site involved, and neither ,,,Although enzymes with exosites are often of utmost pharmacological relevanceUnderstanding the nature of mixed-type inhibition is crucial for studying enzyme kinetics, drug discovery, and the design of effective enzyme inhibitors”.While the definition given by ChatGPT of mixed inhibition is incorrect, it is easy to agree with the concluding remark: “The scouting of scientific literature, however, and, ironically enough, the ChatGPT response itself, clearly showed that the proper understanding of mixed inhibition is not something that should be taken for granted. The intrinsic complexity of this type of reversible inhibition made it difficult to devise a clear schematic mechanistic explanation. In turn, this has resulted in descriptions that are often oversimplifying, incomplete and ultimately, misleading. Moreover, to worsen the problem, the great diversity typically displayed by enzymes has discouraged authors from making definitive assertions on the mechanistic irrelevance of the two-site model. With the overall outcome that many of those who deal with the study of enzyme inhibition, perhaps the majority of them, developed an incorrect understanding of the causative molecular determinants of mixed inhibition.iuKs that are inevitably always larger than or equal to their cognate icKs, combined with the evidence that in the reported cases of mixed inhibition practically never is icK>iuK, constitutes solid statistical evidence that these MIM mechanisms constitute the actual underlying mechanisms of mixed inhibition.The scope of this work was to assess the actual mechanistic significance of the formal two-site model. The finding that five MIM mechanisms exist that generate mixed inhibition patterns with With the understanding that a molecular mechanism can be established only on the basis of a thorough specific study, the analysis presented herein shows that the two-site formal mechanism substantially plays no role in the generation of mixed inhibition and hence, that the molecular nature of reversible enzyme inhibition is basically just competitive.This also explains the lack of structural evidence for the binding of mixed inhibitors to allosteric sites: on the basis of the statistic on enzyme inhibition reported in the result section, the Protein Data Bankin vivo scenario, a dead end inhibitor acting through these mechanisms, as for example a drug, would simply cause an inhibition of the competitive type that can be overcome by high substrate concentrations. Regardless, multiple-substrate enzymes remain the most frequent cause of the reported mixed inhibition, as correctly suggested by JohnsonIn the first three cases discussed in the results section, i.e. tight inhibition, slow dissociating inhibitors and multiple-substrate enzymes, the inhibition is only apparently of the mixed type: in an In cases IV and V (i.e. iso-mechanism and exo-site enzymes) although the inhibitors only bind to the same site bound by the substrate, the inhibition actually affects both the enzyme’s substrate affinity and limiting velocity and cannot be “resolved” by higher substrate concentrations. As correctly pointed out by Cornish-Bowden,Perhaps, the most convenient approach to disentangle the complexity of mixed inhibition is to dismiss the classic analysis if initial rates in favour of the more up-to-date computer assisted global fitting of full reaction time-courses implemented by software such as KinTek ExplorerClick here for additional data file.


Anethum graveolens L.) is an aromatic herb widely used in the food industry, with several commercial cultivars available with different qualitative characteristics. Commercial cultivars are usually preferred over landraces due to their higher yield and also the lack of improved landraces than can be commercialized. In Greece, however, traditional dill landraces are cultivated by local communities. Many are conserved in the Greek Gene Bank and the aim here was to investigate and compare the morphological, genetic, and chemical biodiversity of twenty-two Greek landraces and nine modern/commercial cultivars. Multivariate analysis of the morphological descriptors, molecular markers, and essential oil and polyphenol composition revealed that the Greek landraces were clearly distinguished compared with modern cultivars at the level of phenological, molecular and chemical traits. Landraces were typically taller, with larger umbels, denser foliage, and larger leaves. Plant height, density of foliage, density of feathering as well as aroma characteristics were desirable traits observed for some landraces, such as T538/06 and GRC-1348/04, which were similar or superior to those of some commercial cultivars. Polymorphic loci for inter-simple sequence repeat (ISSR) and start codon targeted (SCoT) molecular markers were 76.47% and 72.41% for landraces, and 68.24% and 43.10% for the modern cultivars, respectively. Genetic divergence was shown, but not complete isolation, indicating that some gene flow may have occurred between landraces and cultivars. The major constituent in all dill leaf essential oils was α-phellandrene (54.42–70.25%). Landraces had a higher α-phellandrene and dill ether content than cultivars. Two dill landraces were rich in chlorogenic acid, the main polyphenolic compound determined. The study highlighted for the first-time Greek landraces with desirable characteristics regarding quality, yield, and harvest time suitable for breeding programs to develop new dill cultivars with superior features.Dill ( Anethum graveolens L.) is an annual or biennial, herbaceous, seed-propagated plant that belongs to the family Apiaceae (Umbelliferae), and is the sole species in the genus Anethum. It is an upright, aromatic herb with feathery, segmented leaves with sheathed petioles. Flowers are yellow and located in umbels, which contain several peduncles. There is one main umbel and several smaller lateral umbels on branches from the main stem. Seeds are oval and flat with three longitudinal grooves and two wings (n = 22) (Dill (wo wings . Dill iswo wings  and it i(n = 22) . This ar(n = 22) , while i(n = 22) .α-phellandrene being the major compound, followed by α-pinene, β-phellandrene, dill ether, myristicin, dill apiole and apiole  contain essential oils, fatty oils, moisture, proteins, carbohydrates, fiber, ash, and mineral elements such as calcium, potassium, magnesium, phosphorous, sodium, vitamin A and niacin . Dill ald apiole . Leaf esd apiole . The biod apiole .Several commercial dill cultivars for example, Ambrozja, Kronos, and Lukullus, are available with varying characteristics, such as, density and color of foliage, time of flowering, nutritional value, and intensity of aroma . HoweverA. graveolens) landraces from the Greek Gene Bank collection and nine modern cultivars for plants grown under identical conditions. Phenotypic diversity was determined for the main morphological characteristics using the UPOV descriptor list  and gardenseedsmarket (https://gardenseedsmarket.com). Early in January 2021, in a greenhouse at the Institute of Plant Breeding and Genetic Resources, Thermi-Thessaloniki , seeds of each genotype were sown in polystyrene containers using a mixture of peat and perlite as rooting media. Later, at the five to seven leaf-stage, thirty individual plants of each genotype were transplanted in net greenhouse isolation cages (40 cm within the row and 1 m inter-row distance). Each genotype was transplanted into a separate net greenhouse cage to prevent cross-pollination. Dill landrace (1–22) and modern cultivar plantlets (23–31) are shown in In total, thirty-one dill genotypes were examined: twenty-two landraces obtained from the Greek Gene Bank  and nine modern cultivars which were obtained commercially from Chiltern seeds , while the construction of two-dimensional (2-D) plots was based on the first two principal components (PCs). The AHC analysis was performed using Euclidean distance and Ward’s method for agglomeration to systematically analyze combined evaluated morphological traits per dill genotype.Non-parametric Spearman correlations were used for the determination of the relations between the morphological traits . PrincipDNA was isolated from young leaves using the NucleoSpin® Plant II Kit  according to the manufacturer’s instructions. DNA concentration and quality were estimated spectrophotometrically at 260 and 280 nm using an Eppendorf BioPhotometer . The integrity of the DNA was determined using gel electrophoresis on a 0.8% (w/v) agarose gel.2O. PCR amplifications were carried out according to After preliminary screening of 20 ISSR (University of British Columbia—UBC) and ScoT primers , 12 unamThe amplification products of the ISSR and SCoTs markers were separated by electrophoresis on a 1.5% (w/v) agarose gel and stained with ethidium bromide. The size marker was a 2-log DNA ladder . Gels were exposed to UV light in a UVItec Transilluminator , and UVIDoc software UVIDocMw version 99.04  was used for analysis.Using the Genemapper v4.0 software and an internal standard , the size of the detected fragments was determined. To reduce the impact of potential size homoplasy, fragments ranging in size from 150 to 500 bases were counted and analyzed further. ISSRs and SCoTs alleles were scored based on whether specific fragments were present (1) or absent (0). The phenotypic data matrices for genetic (ISSR + SCoT) information were also generated. All matrices were subsequently analyzed identically. The performance of the primers was measured by calculating different parameters including polymorphic information content (PIC), resolving power (RP), and marker index (MI) for each primer by the program iMEC . These pThe Dice similarity coefficient  as impleFresh, green leaf tissues from each dill genotype were collected in the vegetative stage and freeze-dried at −24 °C . Samples were ground to a fine powder using a laboratory grinder mill, IKA A11 . The extraction of polyphenolic compounds and LC-MS/MS analysis was accomplished as previously reported Targeted UPLC analysis was performed on a Waters Acquity UPLC system  and separation of the phenolic compounds was carried out using a Waters Acquity HSS T3 column , at 40 °C. Phenolic compounds were analyzed as described by −1 FW) was expressed as % (v/w).Fresh leaves with stems (petioles) were cut at the base of plants of each dill genotype in the vegetative stage, in early March. Samples of 100–120 g fresh weight (FW) were cut coarsely with care into sections with a length of 2–5 cm and were then hydro-distilled for 3 h using a Clevenger-type apparatus. The essential oil recovered was measured (v) and stored at −10 °C in the dark. The essential oil yield  and Kovats indices (KI) .Additionally, equipment control, data acquisition and chromatogram analysis were performed as previously described in The correlation of the essential oil components and the polyphenolic compounds was assessed based on the Pearson coefficient. Firstly, a correlation analysis was performed for all pairwise comparisons. Next, a network analysis was used, in which the essential oil components and polyphenols were represented by nodes, and all statistically significant correlations between the nodes were represented by edges.The study of plant morphological traits represents a quick and useful tool for the preliminary evaluation of the extent of genetic diversity related to the phenotype . Among tp ≤ 0.001) were observed between some traits, indicating that when selection or breeding is applied for one of these traits, an indirect improvement could also be observed in other traits  . Kaiser’Interestingly, most of the native Greek dill genotypes were distinctly ordinated to the negative side of PC1 in both the upper and lower quadrants of PC2 . On the Agglomerative hierarchical clustering  analysis was used to enable grouping of genotypes into clusters of similar responses based on calculations of Euclidean distance, to acquire a thorough overview of the distribution of dill genotypes based on the morphological traits . The outAgarose gel of the PCR reactions using UBC834, UBC811 and SCOT33 primers are depicted in Using ISSR molecular markers, a total of 85 fragments in the length range of 450–4,000 bp were detected, with similar numbers detected for landraces  and cultivars . Landraces had a larger percentage of polymorphic loci (76.47%) than cultivars (68.24%). In contrast, the value of Nei’s gene diversity was higher for the cultivars (0.262) than the landraces (0.256), while Shannon’s Index (I) was 0.386 for landraces and 0.385 for cultivars .Using SCoT molecular markers, a total of 58 fragments in the length range of 450–4,000 bp were detected with higher numbers detected for landraces  than in cultivars . Landraces had a larger percentage of polymorphic loci (72.41%) than cultivars (43.10%). The value of Nei’s gene diversity was also higher for the landraces (0.199) than the cultivars (0.167), and Shannon’s Index (I) was 0.313 for landraces and 0.245 for cultivars .p ≤ 0.001, 999 permutations) for the ISSR markers, whereas lower differentiation levels were observed for SCoT markers, with a Nei’s genetic distance of 0.057 and ΦST = 0.128 .Nei’s genetic distance exhibited a moderate value of 0.111 along with ΦST = 0.169 , neo-chlorogenic acid (syn. 5-O-caffeoylquinic acid) and quercetin-3-O-glucuronide. These findings are consistent with previous reports on A. graveolens leaf extracts (−1 FW (‘KD-178/07’) to 10.350 mg 100 g−1 FW (‘Tetra’), followed by quercetin-3-O-glucuronide which ranged from 0.0035 mg 100 g−1 FW (‘T-208/06’) to 0.4351 mg 100 g−1 FW (‘Tetra’), and neo-chlorogenic acid which was only detected in trace amounts (0.00–0.0032 mg 100 g−1 FW) in the studied genotypes , similar to those of the modern cultivars ‘Szmaragd’, ‘Ambrozja’, ‘Diana’ and ‘Mariska’ (6.748–8.673 mg 100 g−1 FW) . The analysis identified the presence of three main phenolic metabolites: chlorogenic acid (extracts . Chlorogenotypes . It is n g−1 FW) .The essential oil yield of fresh dill leaves (leaves and petioles) ranged from 0.012% (‘GRC-209/08’) to 0.057% (‘HL-232/07’) for the twenty-two Greek landraces, whereas it ranged from 0.02% (‘Dukat’) to 0.041% (‘Diana’) for five of the modern cultivars . The essα--pinene (0.99–2.33%), α-phellandrene (54.42–70.25%), β-phellandrene (9.38–12.35%), dill ether (0.62–22.22%), myristicin (0–18.56%), and dill apiole (0–11.12%)  . These cα-pinene (1.07–1.38%), α-phellandrene (61.16–70.25%), β-phellandrene (10.62–12.35%), dill ether (4.8–22.22%), myristicin (0–7.81%), and dill apiole (0.01–5.88%) (α-pinene (1.35–1.38%), α-phellandrene (64.43–67.27%), β-phellandrene (10.7–11.46%), dill ether (4.48–7.93%), myristicin (7.11–10.82%), and dill apiole (0–3.86%), differing with respect to a lower dill ether content and higher myristicin content, and were intermediary between the 19 landraces and the modern cultivars.Of the 22 gene bank landraces, nineteen had very similar essential oil composition, that is, 1–5.88%) , while tα-pinene, from 54.42% to 62.31% for α-phellandrene, from 9.38% to 10.95% for β-phellandrene, from 0.62% to 2.69% for dill ether, from 13.6% to18.56% for myristicin, and from 2.44% to 11.12% for dill apiole. Interestingly, modern cultivars had lower α-phellandrene and dill ether contents, as well as higher myristicin and dill apiole contents compared to the landraces. This is supported by the strongly, negative, correlation value (−0.895) observed for the pairwise comparison of dill ether and myristicin, using Pearson’s correlation analysis. The other four modern cultivars  had essential oil compositions in a range of 2.22–2.33% forα-pinene, 59.95–63.86% for α-phellandrene, 11.88–12.08% for β-phellandrene, 4.61–12.13% for dill ether, 4.2–14.39% for myristicin, and 0.15–2.23% for dill apiole. In this regard, they had higher α-pinene and dill ether contents, as well as lower myristicin and dill apiole contents, compared to the other modern cultivars.In contrast, the essential oil composition of five modern cultivars  ranged from 0.99% to 1.21% for Principal component analysis based on twenty-one essential oil constituents and three polyphenolic compounds present in the collection, resulted in the identification of two main groups: one containing the gene bank landraces and the other the modern cultivars . The gen‘α-pinene’ and ‘α-thujene’. The minimum negative correlation observed was −0.895 corresponding to the comparison between ‘myristicin’ and ‘dill ether’. Of the 138 positive and 138 negative correlations, 72 (52.17%) and 67 (48.55%), respectively, were statistically significant at 5% level of significance. Except for neo-chlorogenic acid, all other compounds exhibited both positive and negative statistically significant correlations with the remaining metabolites. Neo-chlorogenic acid did not exhibit any statistically significant correlation, suggesting that its metabolic pathway is not tightly co-regulated along with the other flavonoids.The Pearson correlation analysis  referredβ-phellandrene’, ‘(E)-β-ocimene’, and ‘dill apiole’). The minimum degree of centrality was observed in the case of ‘neo-chlorogenic acid’ with a minimum value of zero.In the corresponding correlation network , the edgOverall, it was found that the main characteristics that distinguished between the landraces and cultivars were plant height, the presence of pigmentation, the shape and size of the leaves, the timing of the appearance of the main umbel, and the timing of the start of flowering . Almost Understanding molecular diversity  is essential for the preservation of germplasm . MoleculApiaceae family, random amplified polymorphic DNA (RAPD) and inter simple sequence repeats (ISSR) have been successfully used in the recent years for Changium smyrnioides , I index, and Nei’s unbiased gene diversity mean values were compared, revealing that there were no appreciable differences between the pre-defined populations of landraces and cultivars. According to the I index, there was moderate diversity for both ISSR and SCoT markers . Nei’s unbiased haploid gene diversity estimates (uh) were consistent with the observations for the I index, for both markers.High genetic variation increases the likelihood that populations will be able to adapt to shifting environments. Allelic frequencies are influenced by the survival and progeny rates of various populations. Genetic diversity is reduced through inbreeding, genetic drift, restricted gene flow, and small population size. Accordingly, the polymorphism rates for both molecular markers were high in landraces (76.47% for ISSR and 72.41% for SCoT), and low in cultivars (68.24% for ISSR and 43.1% for SCoT). Thus, the ISSR molecular markers produced more polymorphic bands than SCoT markers, indicating that the ISSRs were more effective in examining molecular diversity in dill genotypes and could be used to create stable heterotic groups between landraces and cultivars. Considering that SCoT markers are known to be linked to functional genes, they may be valuable in tracing patterns of the domestication process, and this could explain the low polymorphism levels observed for SCot markers. If this is true, then the combined analysis of the two marker systems indicates that domesticated genotypes (cultivars) have undergone less selective breeding than landraces but still maintain a high level of genome-wide heterozygosity. Moreover, the PCA analysis for ISSR markers clearly separated some of the cultivars  from the landraces . Therefoe.g., pickled vegetables, sauces, sour cream, spreadable cheese and dressings). In this respect, it would be beneficial for the food industry to use germplasm with a higher nutritive and medicinal value resulting from the presence of antioxidants, such as polyphenols. It was notable that the Greek local landraces ‘T-538/06’ and ‘GRC-1348/04’ had high amounts of chlorogenic acid , similar to the those of the modern cultivars ‘Szmaragd’, ‘Ambrozja’, ‘Diana’ and ‘Mariska’ (6.748–8.673 mg 100 g−1 FW), indicating the potential of these landraces to be exploited in further breeding programs. Previously, it has been reported that the polyphenol content in the green tissues of dill was dependent on plant height and varied among the plant tissues as follows: leaf blade > petioles > stems  for plants grown in the spring and harvested in July , α-phellandrene (44.44–64.65%), β-phellandrene (5.48–19.08%), dill ether (0.94–30.18%), myristicin (0.02–28.18%), dill apiole (0–4.09%) and apiole (1.05–6.92%) (α-phellandrene (61.16–70.25%) in the landrace essential oils was generally higher than values reported in the literature (44.44–64.65%) for leaf essential oil  in this study are in agreement with those reported in the literature for dill herb (leaves and stems): 5–6.92%) . Howevertial oil .Dill herb essential oil quality (relative percentage composition of constituents) has been shown to vary with cultivar, seeding date, harvesting stage, irrigation dose, and plant density . In addiα-phellandrene with sensual descriptor ‘dill-like’ ‘fragrant, fresh’ odor and dill ether with sensual descriptor ‘floral, fragrant odor’ with additional ‘green grass’ and ‘citrus’ notes  and dill ether  from the current study, could be used in breeding programs for the development of new cultivars with novel aroma quality traits  being classified as ‘tall’, with ‘long’ main stem, ‘large’ main umbel diameter, with ‘many’ peduncles in the umbel, and mainly ‘rhomboidal’ leaf shape.A. graveolens genotypes and genetic differences were statistically significant. The pre-defined populations (landraces and modern cultivars) exhibited genetic divergence, but were not entirely isolated from one another, indicating that some amount of gene flow may have occurred between them. Therefore, individuals from pre-defined populations should also be taken into consideration as starting material in genetic improvement programs.The ISSR and SCoT molecular markers were effective in describing the genetic diversity between A. graveolens genotypes and in identifying genotypes with ‘particular’ odor and nutritional characteristics. Although some phytochemical variation was determined between the landraces, this was more noticeable between the landraces and the cultivars where, most of the Greek landraces had higher α-phellandrene and dill ether contents, associated with the characteristic dill odor, whereas cultivars had higher myristicin and lower dill ether contents. Similarly, polyphenolic composition indicated that some of the landraces (‘T-538/06’ and ‘GRC-1348/04’) were rich in chlorogenic acid, the main polyphenolic compound identified.The dill leaf chemical profiles  were also effective in discriminating between In conclusion, this study evaluated simultaneously, and for the first time, the morphological traits, genetic diversity, and chemical profile of dill landraces from a Greek Gene Bank collection, compared to some commercial cultivars. The observed diversity indicated that some landraces could be beneficially used in breeding programs for the development of cultivars, with a particular aroma or nutritional quality, with higher biomass yields, or with earlier harvesting potential, compared to current commercial cultivars. It was also underlined that all the traits, either phytochemical, morphological, agronomic, or genetic, were valuable parameters that should be considered all together in breeding programs.10.7717/peerj.15043/supp-1Supplemental Information 1Click here for additional data file.10.7717/peerj.15043/supp-2Supplemental Information 2Click here for additional data file.10.7717/peerj.15043/supp-3Supplemental Information 3Click here for additional data file.10.7717/peerj.15043/supp-4Supplemental Information 4Click here for additional data file.10.7717/peerj.15043/supp-5Supplemental Information 5Click here for additional data file.10.7717/peerj.15043/supp-6Supplemental Information 6Click here for additional data file.10.7717/peerj.15043/supp-7Supplemental Information 7Click here for additional data file.10.7717/peerj.15043/supp-8Supplemental Information 8Click here for additional data file.


Below the Arctic sea ice, under the right conditions, a flux of icy brine flows down into the sea. The icy brine has a much lower fusion point and is denser than normal seawater. As a result, it sinks while freezing everything around it, forming an ice channel called a brinicle . In this paper, we develop a mathematical model for this phenomenon, assuming cylindrical symmetry. The fluid is considered to be viscous and quasi-stationary. The heat and salt transport are weakly coupled to the fluid motion and are modelled with the corresponding conservation equations, accounting for diffusive and convective effects. Finite-element discretization is employed to solve the coupled system of partial differential equations. We find that the model can capture the general behaviour of the physical system and generate brinicle-like structures while also recovering dendrite composition, which is a physically expected feature aligned with previous experimental results. This represents, to our knowledge, the first complete model proposed that captures the global structure of the physical phenomenon even though it has some discrepancies, such as brine accumulation. They us at −2∘C . Part ofThere is a good amount of research on brinicles and related phenomena. For example, these forms are often studied to determine whether some ice-covered worlds, such as Jupiter’s moon Europa, are suitable for life ,6. The whttps://mfem.org/) finite-element discretization library [In this article, we propose a model, described by a system of PDEs, to describe the formation of a brinicle in the Arctic sea. We consider a three-dimensional case with angular symmetry, which allows us to reduce the computational model to a two-dimensional computational domain. The decrease in computational cost enables us to account for different physical aspects, such as heat transfer, water flow and salt transport, with phase change included in each field. For the fluid motion, we used the stream function-vorticity formulation in the Stokes equation, including the Brinkman penalization ,22 that  library ,29.. 2R and height H that is initially filled with seawater at temperature T0 and salinity S0. The cylinder axis goes through the middle of a circular inlet with radius Rin from where a flux Q of brine with temperature Tin and salinity Sin enters the tank. The bottom of the domain is assumed as an outlet that is located far from the inlet. The rest of the cylinder walls are assumed as insulated boundaries.Our study is focussed on an experimental setting created by S. Martin and reported in , where tT ≥ Tf, where the fusion temperature Tf is a function of salinity. The temperature and the salinity are time-dependent fields with evolution depending mainly on diffusive and convective effects and the latent heat in the energy transport. Furthermore, the diffusivity rates are different in each phase with functional forms and numerical values shown in detail in appendix A.Our goal is to study the formation of the ice structure. Depending on the temperature, at every time instance, part of the domain is occupied with fluid and the other part with solid. The domain is occupied with a fluid phase for all points, where V for a given set of temperature and salinity.To model the phase change, we consider the domain like a porous medium where the permeability lets the fluid flow freely in the liquid regions and slows it down in the solid parts. Moreover, the liquid part of the domain is considered viscous and incompressible, where we include a buoyant force to account for movement caused by density differences produced by temperature and salinity gradients. As the phase change process usually occurs at long intervals, we assume the flow to be steady, neglecting the transient effects. Therefore, we calculate the steady-state velocity field Rin and Q/2π is equal to one. The latter simplifies the boundary conditions of the flow equations. Regarding the temperature and salinity, we consider the following linear mapping:X represents either the temperature or salinity. The idea is to apply the above function to each field instead of a reference factor to set the inflow conditions to zero, simplifying the numerical analysis. Moreover, the initial conditions are set to one, and a scale factor for both variables is given by |Xin − X0|.We aim to simplify the problem and propose a model in which all variables are dimensionless by scaling each quantity and equation with the corresponding reference factors. Specifically, for length and time, the scale factors are . 3θ to be removed from the equations. Thus, the original problem is defined entirely in the two-dimensional domain given by Our computational domain is defined as r − z plane are defined asInside the two-dimensional computational domain, we consider the usual gradient, divergence and curl operators, modified to account for the angular symmetry as. 3.1ϕ is the temperature or salinity field, V is the velocity field. The coefficients λ1 and λ2 correspond to the volumetric heat capacity and thermal conductivity in the heat problem. For the salinity transport, these coefficients correspond to unity and mass diffusivity.According to §2, for the heat and salinity transport, we consider diffusive and convective effects, which can be modelled with the following equation :3.7λ1, which is  in dimensionless form; —symmetric boundary: this boundary corresponds to the centre of the cylinder, and there can be no radial transport since the direction is not defined. Thus, we set a zero Neumann condition on both temperature and salinity. As the stream function and vorticity are the angular components of a vector, the same argument applies, therefore both of them have a zero Dirichlet condition on this boundary; —inlet boundary: the temperature and salinity have the value of the inflow brine . For the velocity field, we assume a vertical Poiseuille flow [r2(2 − r2) for the stream function;lle flow  typical equation  while prequation . In the  —closed boundary: there can be no transport in the normal direction of the boundary, therefore a zero Neumann condition for temperature and salinity is set. On the other hand, zero Neumann and constant Dirichlet conditions for the stream function enforce that the normal and tangent velocities are zero according to equation  in the domain, αi are the basis functions, in this case, first-order polynomials, and αi denotes the Lagrangian basis function associated with the ith d.f. Since we only impose Dirichlet and zero Neumann boundary conditions, there are no boundary terms contributions to the right-hand side of equations ( and KT)X = B where γ is a scaled time step size. As MT)  and 4.34.3, we u library  within t library .. 53 s−1 to 500 mm3 s−1 in steps of 100 mm3 s−1, resulting in Reynolds numbers Using the FEM discretization discussed in §4, we performed simulations with a set of parameters inspired by the work of Martin . We set 3 s−1 and inflow temperature b. These structures are part of the phenomenon and can be seen clearly in a video from the British Broadcasting Corporation [figure 4c closely we can see that the high-velocity regions correspond to the inner part of the narrow ice channels while the velocity at the ice walls is zero, demonstrating that the Brinkman term successfully models the lack of permeability of the ice structures. After some period of time, the brine breaks through the ice (figure 4d), and another dendrite structure can appear in the simulations (figure 4e). When the brine breaks through the last dendrite structure, the characteristic ice channel of the brinicle forms.As the formation of a brinicle is an evolutionary process, first, we analyse the beginning of the simulations. In r = 0). The ice fragments tend to attach to the boundary, as shown in figure 4c, producing not physically expected obstacles to the flow. This phenomenon is a result of the axisymmetric assumption made at the beginning of the analysis. The imposed Neuman conditions imply that local maxima or minima, along the normal to the boundary, could appear in the region, leading to this unexpected behaviour. A straightforward way to address this limitation is to eliminate the symmetry boundary with a three-dimensional cartesian formulation, although at the cost of a significantly more expensive simulation. Nevertheless, to approach this with our current model, we increased the diffusivity rate of the salt in the liquid phase by one order of magnitude, as shown in appendix A. Thus, it is easier for the brine flux to melt the ice near the boundary by balancing the salt concentration, hence removing the unexpected effect.There is another important aspect regarding the symmetry boundary (3 s−1 and inflow temperature c. Since the outflow boundary has the same conditions on the salinity and temperature as the closed boundary, ice also tends to appear near that part of the domain. This effect is shown in figure 5e, as a thin layer of ice at the bottom causes the brine to accumulate on the domain. Hence, the brine excess prevents further growth of the final ice structure.3 s−1 and 200 mm3 s−1, while for 300 mm3 s−1 and 400 mm3 s−1, the ice layer only forms at low temperatures (3 s−1, the ice is too thin to form the expected structures. Therefore, the lower the inflow rate and temperature, the better the chance to form structures similar to a brinicle. The latter corresponds closely to the experimental observations in [3 s−1 have been observed in [In tions in  where coerved in . We can . 63 s−1 and 200 mm3 s−1, the brinicle growth is captured in all cases. However, for faster flows of 300 mm3 s−1 and 400 mm3 s−1 it is only formed at low inflow temperatures (We constructed a general theoretical model that accounts for phase change phenomena associated with heat and mass transfer throughout the domain for simulating the evolution of brinicles. The proposed model is highly nonlinear with many constituents at play. The numerical solution is obtained with the help of the FEM. The used Brinkman penalization removed the need for re-meshing or for the employment of immersed techniques, and we were able to capture the phase change geometry. A smoothed Dirac delta term can account for the latent heat, which is a crucial feature of the model, as shown in figures tally in . Our modAside from the boundaries, the model can be significantly improved by removing the assumption for creeping flow and replacing the Stokes model with the full Navier–Stokes equations. The current fluid flow model is selected mainly owing to its low computational cost. However, the full Navier–Stokes equations represent better the physical behaviour for the considered cases with Reynolds numbers varying from Finally, it is important to note that this approach represents an advance in modelling these types of complex systems since it shows, to our knowledge, for the first time that the mentioned fully coupled system of equations can reproduce many aspects of the physical evolution of brinicle formation. Hence we believe our contribution will be useful to the simulation of ice structures which are important in climate studies  and to s


Radiological assessment is necessary to diagnose spontaneous intracerebral hemorrhage (ICH) and traumatic brain injury intracranial hemorrhage (TBI-bleed). Artificial intelligence (AI) deep learning tools provide a means for decision support. This study evaluates the hemorrhage segmentations produced from three-dimensional deep learning AI model that was developed using non-contrast computed tomography (CT) imaging data external to the current study.Non-contrast CT imaging data from 1263 patients were accessed across seven data sources (referred to as sites) in Norway and Sweden. Patients were included based on ICH, TBI-bleed, or mild TBI diagnosis. Initial non-contrast CT images were available for all participants. Hemorrhage location frequency maps were generated. The number of estimated haematoma clusters was correlated with the total haematoma volume. Ground truth expert annotations were available for one ICH site; hence, a comparison was made with the estimated haematoma volumes. Segmentation volume estimates were used in a receiver operator characteristics (ROC) analysis for all samples  and then specifically for one site with few TBI-bleed cases.p-value < 0.01) and evidence of ICH between-site differences. Relative to hand-drawn annotations for one ICH site, the VIOLA-AI segmentation mask achieved a median Dice Similarity Coefficient of 0.82 (interquartile range: 0.78 and 0.83), resulting in a small overestimate in the haematoma volume by a median of 0.47 mL (interquartile range: 0.04 and 1.75 mL). The bleed detection ROC analysis for the whole sample gave a high area-under-the-curve (AUC) of 0.92 (with sensitivity and specificity of 83.28% and 95.41%); however, when considering only the mild head injury site, the TBI-bleed detection gave an AUC of 0.70.The hemorrhage frequency maps showed spatial patterns of estimated lesions consistent with ICH or TBI-bleed presentations. There was a positive correlation between the estimated number of clusters and total haematoma volume for each site (correlation range: 0.45–0.74; each An open-source segmentation tool was used to visualize hemorrhage locations across multiple data sources and revealed quantitative hemorrhage site differences. The automated total hemorrhage volume estimate correlated with a per-participant hemorrhage cluster count. ROC results were moderate-to-high. The VIOLA-AI tool had promising results and might be useful for various types of intracranial hemorrhage. Spontaneous intracerebral hemorrhage stroke (ICH) and traumatic brain injury hemorrhage (TBI-bleed) are examples of acute brain conditions where rapid imaging is needed for a diagnosis. Non-contrast-enhanced computed tomography (CT) remains the gold standard imaging modality to detect ICH and TBI-bleed. ICH appears to rise with time and accounts for 9 to 26% of acute stroke , 2. TBI-Radiological features of intracranial hemorrhage provide information that can help with ICH and TBI-bleed diagnosis/prognosis. The total haematoma volume is used in the ICH Score, which is a grading system used clinically . The ABCAutomated hemorrhage segmentation is desirable for research purposes, and ideally, such a method should be agnostic to the CT imaging protocol. Machine learning and deep learning artificial intelligence (AI) have had a decisive role in this regard, and a recent review discusses commercial solutions . MuschelThe current study investigates the segmentation results when a previously trained deep learning neural network called “Voxels Intersecting along Orthogonal-Levels of Attention” (VIOLA-AI) is used on de-novo ICH and TBI-bleed cases . There aThe study recruited individuals with a confirmed intracranial hemorrhage (ICH or TBI-bleed) or a suspected brain injury (with or without concomitant bleeding). For inclusion, participants had to be over 16 years old at the time of the assessment and have a non-contrast brain CT scan available. Data were grouped from seven different sources, either sites or research projects (herein referred to as “sites”). All sites provided original non-contrast CT head scan images in either DICOM or NIfTI format. Any data of low quality, unusable due to artifact after visual inspection, or consisted of secondary capture DICOM files were excluded from the analysis. The respective research ethics committees approved data collection at each site. One public data repository was also considered as a site. This study benefitted from an external dataset of 100 diagnosed intracranial hemorrhage cases used in the deep learning model training (details below).Akershus: 65 ICH cases were accessed from the Akershus University Hospital as part of an on-going stroke project that is led by ESK, OMR, PS. Ground truth segmentations were available for this site.Skåne: 223 ICH cases were selected from an existing stroke database at the Skåne University Hospital in Lund/Malmö, Sweden  was involved in curating these data and reviewing ICH diagnosis. These ICH patients were accessed clinically at St. Olav's Hospital in Trondheim, Norway, or Vestre Viken Hospitals in Bærum, Norway.Ullevål stroke: 53 ICH were accessed from the Ullevål site of Oslo University Hospital and the ICH diagnosis was reviewed by KS and ECS.CQ500: 40 CT cases were randomly selected from the open-access CQ500 database . Cases were overseen/reviewed by KS and inspected for imaging artifacts by BJM. The anticipated rate of TBI-bleed cases for these “walk in” patients was expected to be low frequency.CT images were collected according to the clinical acute head protocols at the respective sites. The acquisition details tended to vary in spatial resolution and head coverage. The VIOLA-AI model was previously tested and developed as part of the INSTANCE 2022 intracranial hemorrhage segmentation challenge . The CT Ten models based on three-dimensional neural networks were trained, with five-fold validation and a test set to yield the Viola-AI tool ensemble. The first five models were implementations of the no-new U-Net ; these models were solely trained with hyperparameter tuning without architectural changes. Five more models were trained after incorporating attention modules during the decoding branch of the U-Net. This architecture is called “Voxel Intersecting along Orthogonal Levels Attention U-Net” (VIOLA-U-Net). The architecture was specifically designed to make segmentations for hemorrhage-positive cases. Model training was performed on the Digital Research Alliance of Canada's high-performance cluster. We used four metrics to appraise the performance of the VIOLA-AI model: dice similarity coefficient, Hausdorff distance, normalized surface dice, and relative absolute volume difference. For the current study, no new training was performed. We refer to the ensemble of models as VIOLA-AI tool.Original CT data were accessed and stored on local secure servers. The VIOLA-AI tool was run as a docker , with each site considered as a batch and invoking a graphics processing unit to calculate each segmentation mask as an inference. Each segmentation was saved as a binary mask in a prediction folder.Since these data had high spatial resolution images, a group template CT image was created from the Akershus site using the Advanced Normalization Toolkit (antsRegistration). The CT group average template was oriented into MNI152 standard space to facilitate mask overlays. Original CT images were linearly registered to the group template using an affine registration tool called FLIRT with 12 degrees of freedom as part of the FMRIB Software Library. The transformation matrix  was applied to the corresponding binary mask segmentations. An average segmentation mask was generated per site for an estimated frequency map. This enabled visualization of the average locations of suspected hemorrhages.Two radiological metrics were calculated per patient: the total estimated haematoma volume and the number of distinct haematoma clusters. For the former, the number of voxels in the segmentation mask was multiplied by the voxel volume for the given acquisition to yield volume units (mL). For the latter, we note that an ICH and TBI-bleed can produce distinct groups of voxels that make up individual haematoma clusters, which we determined using a cluster command provided by in FMRIB Software Library.p-value of 0.05 was considered significant. For one ICH site, we quantified the difference in haematoma volume (ground truth—estimate) vs. the average haematoma volume of the estimate and ground truth.We calculated the centroid location based on the average number of clusters and total haematoma volume to summarize the VIOLA-AI results per site. Correlations were performed per site to test whether the total estimated haematoma volume was associated with the number of distinct haematoma clusters. We conducted a non-parametric Kruskal-Wallis Rank Sum for the four ICH sites to test for a site effect based on median total haematoma volume estimates. The Kruskal-Wallis test for a site effect was also performed for the median cluster number. A Receiver operator characteristic (ROC) analysis was performed for: (a) the entire sample and (b) the consecutive suspected TBI cases from the Emergency Oslo Unit. The purpose was to evaluate the classification performance metrics between hemorrhage-positive vs. hemorrhage-negative individuals, e.g., for the Emergency Oslo Unit, this classification amounted to TBI-related hemorrhage: yes/no. The statistics were performed using R  “pROC” library to produce the following estimates: area-under-the-curve (AUC), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV).The VIOLA-AI tool produced segmentation estimates in all cases. A single segmentation inference took 16 sec using a graphics processing unit (standard deviation: 2.7 sec). p-values for each site were < 0.01). For the four ICH sites, the Kruskal-Wallis test (chi-squared = 19.294 and degrees of freedom = 3) revealed a significant site effect with p-value = 0.000238; i.e. the Ullevål ICH site had the highest median total haematoma volume relative to three other ICH sites. The Kruskal-Wallis test  also showed a site effect for the number of haematoma clusters with a p-value = 0.00549.For one ICH site, the agreement between estimated and ground truth masks was high with a median Dice Similarity Coefficient value of 0.82, and the interquartile range was 0.777 to 0.863. The median difference in the haematoma volume (ground truth—estimated) was −0.47 mL .n = 25) and duplicates (n = 109), resulting in n = 564 cases suitable for the secondary ROC. For this site, the AUC was 0.69, which was based on classifying 33 cases (out of 37 diagnosed) as TBI-bleed while the remaining 531 were hemorrhage negative. The hematoma minimum volume threshold was 0.34 mL, with sensitivity and specificity of 95.92 and 25.00%, respectively, and PPV and NPV that were 91.99 and 40.54%, respectively.The AUC for the entire sample was 0.92 based on a haematoma minimum volume threshold of 0.69 mL. The sensitivity and specificity were 83.28 and 95.41%, respectively. The PPV and NPV were 95.73 and 82.21%, respectively. Considering only the Oslo Emergency Unit, we removed cases with imaging motion or metallic artifacts (In the current study we used the VIOLA-AI tool to segment hemorrhage location among patients with ICH, TBI-bleed, or suspected TBI-bleed. This study involved multiple data sources as a means of characterizing radiological features of intracranial hemorrhage. The frequency maps revealed spatial patterns for hemorrhage locations that were consistent within disease groups. These maps were suggestive of between-site differences, as reflected in significant between-site differences in the total haematoma volume specifically among the four ICH sites. Each site demonstrated a strong positive association between the number of haematoma clusters and the estimated haematoma volume. Manual annotations were performed at one ICH site and suggestive of a good agreement with ground truth. The VIOLA-AI segmentation tool achieved good classification performance as reflected by the ROC area-under-the curve for the total sample and the site with consecutive suspected TBI.We note that the estimated ICH locations tended to include lobar, basal ganglia, thalamus, internal and external capsule regions. The spatial patterns observed in the current study tended to align with previous work, although it should be noted there were limited estimates in infratentorial locations –22. The The TBI frequency maps' spatial pattern was markedly different compared to ICH. The ring-like pattern near the skull is to be expected, and there were no locations that exceeded 12% of the sample site. Others report there is a propensity for TBI-bleeds to be superficial lesions in frontal, temporal, orbitofrontal and anterior temporal regions . Mild TBThe current study included an estimate of the number of distinct hemorrhage clusters, a by-product of the VIOLA-AI segmentation tool. In doing so, we observed robust associations between the number of clusters and the estimated total haematoma volume. ICH is often characterized by a primary parenchymal hemorrhage that can expand into other locations and/or tissue spaces; hence multiple clusters are evident. Up to 43% of ICH will involve intraventricular hemorrhage, which indicates that multiple hemorrhage clusters are expected . Indeed,The discrepancy between estimations and ground truth lesion volumes from the ICH Akershus site was small based on the median difference. There was one clear case of a failed segmentation for this site where the discrepancy between the VIOLA-AI estimate and ground truth was very large and constituting an overestimate by the model. Future work is needed to determine the factors contributing to these discrepancies and whether the VIOLA-AI volume estimates can improve.The ROC analysis for the entire sample showed high performance metrics, while the performance metrics were lower for the suspected TBI site. Several other studies have reported on hemorrhage detection using CT scans , 19 and The current study has novel elements as well as limitations. The VIOLA-AI tool was trained using a three-dimensional neural network architecture; hence, the entire 3-dimensional ground truth annotation mask was considered during model training, which differs from segmentation tools using a 2-dimensional model . The VIOIn conclusion, several data sources were accessed and demonstrated that the VIOLA-AI tool was capable of segmenting ICH and TBI-related hemorrhage on non-contrast CT images. We found a consistent relationship between hemorrhage volume and cluster count, and these radiological features significantly differed between the ICH sites. It is important to determine whether inclusion criteria, patient demographics, disease severity, or other health service factors influenced these differences. An example of future work is multi-class hemorrhage segmentation , 34, 35 The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The studies involving humans were approved by Research Ethics Council for Oslo University Hospital. The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study.BM helped with the study design, performed analysis, and drafted the manuscript. QL developed the AI tool, contributed data analysis, and writing. MB, IG, PM, JP, KE, and JW contributed to data collection, interpretation, and writing. MS, RB, AN, AH, TU, and OR contributed to data collection and writing. TS, KS, PS, EK, ES, and AB contributed to the study design, data collection, interpretation, and writing. All authors contributed to the article and approved the submitted version.