Hyperspectral Imaging in the UV Range Allows for Differentiation of Sugar Beet Diseases Based on Changes in Secondary Plant Metabolites

Fungal infections trigger defense or signaling responses in plants, leading to various changes in plant metabolites. The changes in metabolites, for example chlorophyll or flavonoids, have long been detectable using time-consuming destructive analytical methods including high-performance liquid chromatography or photometric determination. Recent plant phenotyping studies have revealed that hyperspectral imaging (HSI) in the UV range can be used to link spectral changes with changes in plant metabolites. To compare established destructive analytical methods with new nondestructive hyperspectral measurements, the interaction between sugar beet leaves and the pathogens Cercospora beticola, which causes Cercospora leaf spot disease (CLS), and Uromyces betae, which causes sugar beet rust (BR), was investigated. With the help of destructive analyses, we showed that both diseases have different effects on chlorophylls, carotenoids, flavonoids, and several phenols. Nondestructive hyperspectral measurements in the UV range revealed different effects of CLS and BR on plant metabolites resulting in distinct reflectance patterns. Both diseases resulted in specific spectral changes that allowed differentiation between the two diseases. Machine learning algorithms enabled the differentiation between the symptom classes and recognition of the two sugar beet diseases. Feature importance analysis identified specific wavelengths important to the classification, highlighting the utility of the UV range. The study demonstrates that HSI in the UV range is a promising, nondestructive tool to investigate the influence of plant diseases on plant physiology and biochemistry.

Disease Incidence and Severity of Cercospora Leaf Spot in Sugar Beet Assessed by Multispectral Unmanned Aerial Images and Machine Learning

Disease incidence (DI) and metrics of disease severity are relevant parameters for decision making in plant protection and plant breeding. To develop automated and sensor-based routines, a sugar beet variety trial was inoculated with Cercospora beticola and monitored with a multispectral camera system mounted to an unmanned aerial vehicle (UAV) over the vegetation period. A pipeline based on machine learning methods was established for image data analysis and extraction of disease-relevant parameters. Features based on the digital surface model, vegetation indices, shadow condition, and image resolution improved classification performance in comparison with using single multispectral channels in 12 and 6% of diseased and soil regions, respectively. With a postprocessing step, area-related parameters were computed after classification. Results of this pipeline also included extraction of DI and disease severity (DS) from UAV data. The calculated area under disease progress curve of DS was 2,810.4 to 7,058.8%.days for human visual scoring and 1,400.5 to 4,343.2%.days for UAV-based scoring. Moreover, a sharper differentiation of varieties compared with visual scoring was observed in area-related parameters such as area of complete foliage (A_F), area of healthy foliage (A_H), and mean area of lesion by unit of foliage ([Formula: see text]). These advantages provide the option to replace the laborious work of visual disease assessments in the field with a more precise, nondestructive assessment via multispectral data acquired by UAV flights.[Formula: see text] Copyright © 2023 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.

Digital plant pathology: a foundation and guide to modern agriculture

Over the last 20 years, researchers in the field of digital plant pathology have chased the goal to implement sensors, machine learning and new technologies into knowledge-based methods for plant phenotyping and plant protection. However, the application of swiftly developing technologies has posed many challenges. Greenhouse and field applications are complex and differ in their study design requirements. Selecting a sensor type (e.g., thermography or hyperspectral imaging), sensor platform (e.g., rovers, unmanned aerial vehicles, or satellites), and the problem-specific spatial and temporal scale adds to the challenge as all pathosystems are unique and differ in their interactions and symptoms, or lack thereof. Adding host-pathogen-environment interactions across time and space increases the complexity even further. Large data sets are necessary to enable a deeper understanding of these interactions. Therefore, modern machine learning methods are developed to realize the fast data analysis of such complex data sets. This reduces not only human effort but also enables an objective data perusal. Especially deep learning approaches show a high potential to identify probable cohesive parameters during plant-pathogen-environment interactions. Unfortunately, the performance and reliability of developed methods are often doubted by the potential user. Gaining their trust is thus needed for real field applications. Linking biological causes to machine learning features and a clear communication, even for non-experts of such results, is a crucial task that will bridge the gap between theory and praxis of a newly developed application. Therefore, we suggest a global connection of experts and data as the basis for defining a common and goal-oriented research roadmap. Such high interconnectivity will likely increase the chances of swift, successful progress in research and practice. A coordination within international excellence clusters will be useful to reduce redundancy of research while supporting the creation and progress of complementary research. With this review, we would like to discuss past research, achievements, as well as recurring and new challenges. Having such a retrospect available, we will attempt to reveal future challenges and provide a possible direction elevating the next decade of research in digital plant pathology.

A Hyperspectral Library of Foliar Diseases of Wheat

This work established a hyperspectral library of important foliar diseases of wheat induced by different fungal pathogens, representing a time series from infection to symptom appearance for the purpose of detecting spectral changes. The data were generated under controlled conditions at the leaf scale. The transition from healthy to diseased leaf tissue was assessed, and spectral shifts were identified and used in combination with histological investigations to define developmental stages in pathogenesis for each disease. The spectral signatures of each plant disease that indicate a specific developmental stage during pathogenesis, defined as turning points, were combined into a spectral library. Machine learning analysis methods were applied and compared to test the potential of this library to detect and quantify foliar diseases in hyperspectral images. All evaluated classifiers had high accuracy (≤99%) for the detection and identification of both biotrophic and necrotrophic fungi. The potential of applying spectral analysis methods in combination with a spectral library for the detection and identification of plant diseases is demonstrated. Further evaluation and development of these algorithms should contribute to a robust detection and identification system for plant diseases at different developmental stages and the promotion and development of site-specific management techniques for plant diseases under field conditions.

Pheno4D: A spatio-temporal dataset of maize and tomato plant point clouds for phenotyping and advanced plant analysis

Understanding the growth and development of individual plants is of central importance in modern agriculture, crop breeding, and crop science. To this end, using 3D data for plant analysis has gained attention over the last years. High-resolution point clouds offer the potential to derive a variety of plant traits, such as plant height, biomass, as well as the number and size of relevant plant organs. Periodically scanning the plants even allows for performing spatio-temporal growth analysis. However, highly accurate 3D point clouds from plants recorded at different growth stages are rare, and acquiring this kind of data is costly. Besides, advanced plant analysis methods from machine learning require annotated training data and thus generate intense manual labor before being able to perform an analysis. To address these issues, we present with this dataset paper a multi-temporal dataset featuring high-resolution registered point clouds of maize and tomato plants, which we manually labeled for computer vision tasks, such as for instance segmentation and 3D reconstruction, providing approximately 260 million labeled 3D points. To highlight the usability of the data and to provide baselines for other researchers, we show a variety of applications ranging from point cloud segmentation to non-rigid registration and surface reconstruction. We believe that our dataset will help to develop new algorithms to advance the research for plant phenotyping, 3D reconstruction, non-rigid registration, and deep learning on raw point clouds. The dataset is freely accessible at https://www.ipb.uni-bonn.de/data/pheno4d/.

Technical workflows for hyperspectral plant image assessment and processing on the greenhouse and laboratory scale

Background

The use of hyperspectral cameras is well established in the field of plant phenotyping, especially as a part of high-throughput routines in greenhouses. Nevertheless, the workflows used differ depending on the applied camera, the plants being imaged, the experience of the users, and the measurement set-up.

Results

This review describes a general workflow for the assessment and processing of hyperspectral plant data at greenhouse and laboratory scale. Aiming at a detailed description of possible error sources, a comprehensive literature review of possibilities to overcome these errors and influences is provided. The processing of hyperspectral data of plants starting from the hardware sensor calibration, the software processing steps to overcome sensor inaccuracies, and the preparation for machine learning is shown and described in detail. Furthermore, plant traits extracted from spectral hypercubes are categorized to standardize the terms used when describing hyperspectral traits in plant phenotyping. A scientific data perspective is introduced covering information for canopy, single organs, plant development, and also combined traits coming from spectral and 3D measuring devices.

Conclusions

This publication provides a structured overview on implementing hyperspectral imaging into biological studies at greenhouse and laboratory scale. Workflows have been categorized to define a trait-level scale according to their metrological level and the processing complexity. A general workflow is shown to outline procedures and requirements to provide fully calibrated data of the highest quality. This is essential for differentiation of the smallest changes from hyperspectral reflectance of plants, to track and trace hyperspectral development as an answer to biotic or abiotic stresses.

Statistical shape analysis of tap roots: a methodological case study on laser scanned sugar beets

Background

The efficient and robust statistical analysis of the shape of plant organs of different cultivars is an important investigation issue in plant breeding and enables a robust cultivar description within the breeding progress. Laserscanning is a highly accurate and high resolution technique to acquire the 3D shape of plant surfaces. The computation of a shape based principal component analysis (PCA) built on concepts from continuum mechanics has proven to be an effective tool for a qualitative and quantitative shape examination.

Results

The shape based PCA was used for a statistical analysis of 140 sugar beet roots of different cultivars. The calculation of the mean sugar beet root shape and the description of the main variations was possible. Furthermore, unknown and individual tap roots could be attributed to their cultivar by means of a robust classification tool based on the PCA results.

Conclusion

The method demonstrates that it is possible to identify principal modes of root shape variations automatically and to quantify associated variances out of laserscanned 3D sugar beet tap root models. The introduced approach is not limited to the 3D shape description by laser scanning. A transfer to 3D MRI or radar data is also conceivable.

Paediatric antimicrobial stewardship programmes in the UK's regional children's hospitals

A survey was conducted in UK regional children's hospitals with paediatric intensive care and paediatric infectious disease (PID) departments to describe the characteristics of paediatric antimicrobial stewardship (PAS) programmes. A structured questionnaire was sent to PAS coordinators. 'Audit and feedback' was implemented in 13 out of 17 centres. Microbiology-led services were more likely to implement antimicrobial restriction (75% vs 33% in PID-led services), to focus on broad-spectrum antibiotics, and to review patients with positive blood cultures. PID-led services were more likely to identify patients from e-prescribing or drug charts and review all antimicrobials. A PAS network has been established.

Blood CD9+ B cell, a biomarker of bronchiolitis obliterans syndrome after lung transplantation

Bronchiolitis obliterans syndrome is the main limitation for long-term survival after lung transplantation. Some specific B cell populations are associated with long-term graft acceptance. We aimed to monitor the B cell profile during early development of bronchiolitis obliterans syndrome after lung transplantation. The B cell longitudinal profile was analyzed in peripheral blood mononuclear cells from patients with bronchiolitis obliterans syndrome and patients who remained stable over 3 years of follow-up. CD24^hi CD38^hi transitional B cells were increased in stable patients only, and reached a peak 24 months after transplantation, whereas they remained unchanged in patients who developed a bronchiolitis obliterans syndrome. These CD24^hi CD38^hi transitional B cells specifically secrete IL-10 and express CD9. Thus, patients with a total CD9⁺ B cell frequency below 6.6% displayed significantly higher incidence of bronchiolitis obliterans syndrome (AUC = 0.836, PPV = 0.75, NPV = 1). These data are the first to associate IL-10-secreting CD24^hi CD38^hi transitional B cells expressing CD9 with better allograft outcome in lung transplant recipients. CD9-expressing B cells appear as a contributor to a favorable environment essential for the maintenance of long-term stable graft function and as a new predictive biomarker of bronchiolitis obliterans syndrome-free survival.

Measuring crops in 3D: using geometry for plant phenotyping

Using 3D sensing for plant phenotyping has risen within the last years. This review provides an overview on 3D traits for the demands of plant phenotyping considering different measuring techniques, derived traits and use-cases of biological applications. A comparison between a high resolution 3D measuring device and an established measuring tool, the leaf meter, is shown to categorize the possible measurement accuracy. Furthermore, different measuring techniques such as laser triangulation, structure from motion, time-of-flight, terrestrial laser scanning or structured light approaches enable the assessment of plant traits such as leaf width and length, plant size, volume and development on plant and organ level. The introduced traits were shown with respect to the measured plant types, the used measuring technique and the link to their biological use case. These were trait and growth analysis for measurements over time as well as more complex investigation on water budget, drought responses and QTL (quantitative trait loci) analysis. The used processing pipelines were generalized in a 3D point cloud processing workflow showing the single processing steps to derive plant parameters on plant level, on organ level using machine learning or over time using time series measurements. Finally the next step in plant sensing, the fusion of different sensor types namely 3D and spectral measurements is introduced by an example on sugar beet. This multi-dimensional plant model is the key to model the influence of geometry on radiometric measurements and to correct it. This publication depicts the state of the art for 3D measuring of plant traits as they were used in plant phenotyping regarding how the data is acquired, how this data is processed and what kind of traits is measured at the single plant, the miniplot, the experimental field and the open field scale. Future research will focus on highly resolved point clouds on the experimental and field scale as well as on the automated trait extraction of organ traits to track organ development at these scales.

Bacteremia in Childhood Life-Threatening Infections in Urban Gambia: EUCLIDS in West Africa

Background

The limited availability of microbiology services in sub-Saharan Africa impedes accurate diagnosis of bacterial pathogens and understanding of trends in prevalence and antibiotic sensitivities. We aimed to characterize bacteremia among hospitalized children in The Gambia and to identify factors associated with bacteremia and mortality.

Methods

We prospectively studied children presenting with suspected severe infection to 2 urban hospitals in The Gambia, between January 2013 and September 2015. Demographic and anthropometric data, clinical features, management, and blood culture results were documented. Urine screens for antibiotic activity were performed in a subset of participants.

Results

Of 411 children enrolled (median age, 29 months; interquartile range, 11–82), 79.5% (325 of 409) reported prehospital antibiotic use. Antimicrobial activity by urinary screen for antibiotic activity was detected in 70.8% (n = 80 of 113). Sixty-six bacterial pathogens were identified in 65 (15.8%) participants and Staphylococcus aureus predominated. Gram-positive organisms were more commonly identified than Gram-negative (P < .01). Antibiotic resistance against first-line antimicrobials (ampicillin and gentamicin) was common among Gram-negative bacteria (39%; range, 25%–100%). Factors significantly associated with bacteremia included the following: gender, hydration status, musculoskeletal examination findings, admission to the Medical Research Council The Gambia at London School of Hygiene & Tropical Medicine hospital, and meeting sepsis criteria. Those associated with increased mortality were presence of a comorbidity, clinical pallor, tachypnea, and altered consciousness. Tachycardia was associated with reduced mortality.

Conclusions

The bacteremia rate in children with suspected childhood life-threatening infectious diseases in The Gambia is high. The pattern of pathogen prevalence and antimicrobial resistance has changed over time compared with previous studies illustrating the importance of robust bacterial surveillance programs in resource-limited settings.

Population pharmacokinetics and pharmacodynamics of teicoplanin in neonates: making better use of C-reactive protein to deliver individualized therapy

OBJECTIVES

There is uncertainty about the optimal teicoplanin regimens for neonates. The study aim was to determine the population pharmacokinetics (PK) of teicoplanin in neonates, evaluate currently recommended regimens and explore the exposure-effect relationships.

METHODS

An open-label PK study was conducted. Neonates from 26 to 44 weeks post-menstrual age were recruited (n = 18). The teicoplanin regimen was a 16 mg/kg loading dose, followed by 8 mg/kg once daily. Therapeutic drug monitoring and dose adjustment were not conducted. A standard two-compartment PK model was developed, followed by models that incorporated weight. A PK/pharmacodynamic (PD) model with C-reactive protein serial measurements as the PD input was fitted to the data. Monte Carlo simulations (n = 5000) were performed using Pmetrics. The AUCs at steady state and the proportion of patients achieving the recommended drug exposures (i.e. Cmin >15 mg/L) were determined. The study was registered in the European Clinical Trials Database Registry (EudraCT: 2012-005738-12).

RESULTS

The PK allometric model best accounted for the observed data. The PK parameters medians were: clearance = 0.435 × (weight/70)0.75 (L/h); volume = 0.765 (L); Kcp = 1.3 (h-1); and Kpc = 0.629 (h-1). The individual time-course of C-reactive protein was well described using the Bayesian posterior estimates for each patient. The simulated median AUC96-120 was 302.3 mg·h/L and the median Cmin at 120 h was 12.9 mg/L; 38.8% of patients attained a Cmin >15 mg/L by 120 h.

CONCLUSIONS

Teicoplanin population PK is highly variable in neonates, weight being the best descriptor of PK variability. A low percentage of neonates were able to achieve Cmin >15 mg/L. The routine use of therapeutic drug monitoring and improved knowledge on the PD of teicoplanin is required.

[Indications of lung transplantation: Patients selection, timing of listing, and choice of procedure]

Lung transplantation (LT) is now considered as an excellent treatment option for selected patients with end-stage pulmonary diseases, such as COPD, cystic fibrosis, idiopathic pulmonary fibrosis, and pulmonary arterial hypertension. The 2 goals of LT are to provide a survival benefit and to improve quality of life. The 3-step decision process leading to LT is discussed in this review. The first step is the selection of candidates, which requires a careful examination in order to check absolute and relative contraindications. The second step is the timing of listing for LT; it requires the knowledge of disease-specific prognostic factors available in international guidelines, and discussed in this paper. The third step is the choice of procedure: indications of heart-lung, single-lung, and bilateral-lung transplantation are described. In conclusion, this document provides guidelines to help pulmonologists in the referral and selection processes of candidates for transplantation in order to optimize the outcome of LT.

Automated interpretation of 3D laserscanned point clouds for plant organ segmentation

Background

Plant organ segmentation from 3D point clouds is a relevant task for plant phenotyping and plant growth observation. Automated solutions are required to increase the efficiency of recent high-throughput plant phenotyping pipelines. However, plant geometrical properties vary with time, among observation scales and different plant types. The main objective of the present research is to develop a fully automated, fast and reliable data driven approach for plant organ segmentation.

Results

The automated segmentation of plant organs using unsupervised, clustering methods is crucial in cases where the goal is to get fast insights into the data or no labeled data is available or costly to achieve. For this we propose and compare data driven approaches that are easy-to-realize and make the use of standard algorithms possible. Since normalized histograms, acquired from 3D point clouds, can be seen as samples from a probability simplex, we propose to map the data from the simplex space into Euclidean space using Aitchisons log ratio transformation, or into the positive quadrant of the unit sphere using square root transformation. This, in turn, paves the way to a wide range of commonly used analysis techniques that are based on measuring the similarities between data points using Euclidean distance. We investigate the performance of the resulting approaches in the practical context of grouping 3D point clouds and demonstrate empirically that they lead to clustering results with high accuracy for monocotyledonous and dicotyledonous plant species with diverse shoot architecture.

Conclusion

An automated segmentation of 3D point clouds is demonstrated in the present work. Within seconds first insights into plant data can be deviated – even from non-labelled data. This approach is applicable to different plant species with high accuracy. The analysis cascade can be implemented in future high-throughput phenotyping scenarios and will support the evaluation of the performance of different plant genotypes exposed to stress or in different environmental scenarios.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-015-0665-2) contains supplementary material, which is available to authorized users.

Accuracy analysis of a multi-view stereo approach for phenotyping of tomato plants at the organ level

Accessing a plant's 3D geometry has become of significant importance for phenotyping during the last few years. Close-up laser scanning is an established method to acquire 3D plant shapes in real time with high detail, but it is stationary and has high investment costs. 3D reconstruction from images using structure from motion (SfM) and multi-view stereo (MVS) is a flexible cost-effective method, but requires post-processing procedures. The aim of this study is to evaluate the potential measuring accuracy of an SfM- and MVS-based photogrammetric method for the task of organ-level plant phenotyping. For this, reference data are provided by a high-accuracy close-up laser scanner. Using both methods, point clouds of several tomato plants were reconstructed at six following days. The parameters leaf area, main stem height and convex hull of the complete plant were extracted from the 3D point clouds and compared to the reference data regarding accuracy and correlation. These parameters were chosen regarding the demands of current phenotyping scenarios. The study shows that the photogrammetric approach is highly suitable for the presented monitoring scenario, yielding high correlations to the reference measurements. This cost-effective 3D reconstruction method depicts an alternative to an expensive laser scanner in the studied scenarios with potential for automated procedures.

Automated analysis of barley organs using 3D laser scanning: an approach for high throughput phenotyping

Due to the rise of laser scanning the 3D geometry of plant architecture is easy to acquire. Nevertheless, an automated interpretation and, finally, the segmentation into functional groups are still difficult to achieve. Two barley plants were scanned in a time course, and the organs were separated by applying a histogram-based classification algorithm. The leaf organs were represented by meshing algorithms, while the stem organs were parameterized by a least-squares cylinder approximation. We introduced surface feature histograms with an accuracy of 96% for the separation of the barley organs, leaf and stem. This enables growth monitoring in a time course for barley plants. Its reliability was demonstrated by a comparison with manually fitted parameters with a correlation R(2) = 0:99 for the leaf area and R(2) = 0:98 for the cumulated stem height. A proof of concept has been given for its applicability for the detection of water stress in barley, where the extension growth of an irrigated and a non-irrigated plant has been monitored.

Low-cost 3D systems: suitable tools for plant phenotyping

Over the last few years, 3D imaging of plant geometry has become of significant importance for phenotyping and plant breeding. Several sensing techniques, like 3D reconstruction from multiple images and laser scanning, are the methods of choice in different research projects. The use of RGBcameras for 3D reconstruction requires a significant amount of post-processing, whereas in this context, laser scanning needs huge investment costs. The aim of the present study is a comparison between two current 3D imaging low-cost systems and a high precision close-up laser scanner as a reference method. As low-cost systems, the David laser scanning system and the Microsoft Kinect Device were used. The 3D measuring accuracy of both low-cost sensors was estimated based on the deviations of test specimens. Parameters extracted from the volumetric shape of sugar beet taproots, the leaves of sugar beets and the shape of wheat ears were evaluated. These parameters are compared regarding accuracy and correlation to reference measurements. The evaluation scenarios were chosen with respect to recorded plant parameters in current phenotyping projects. In the present study, low-cost 3D imaging devices have been shown to be highly reliable for the demands of plant phenotyping, with the potential to be implemented in automated application procedures, while saving acquisition costs. Our study confirms that a carefully selected low-cost sensor is able to replace an expensive laser scanner in many plant phenotyping scenarios.

The United Kingdom Primary Immune Deficiency (UKPID) Registry: report of the first 4 years' activity 2008-2012

This report summarizes the establishment of the first national online registry of primary immune deficency in the United Kingdom, the United Kingdom Primary Immunodeficiency (UKPID Registry). This UKPID Registry is based on the European Society for Immune Deficiency (ESID) registry platform, hosted on servers at the Royal Free site of University College, London. It is accessible to users through the website of the United Kingdom Primary Immunodeficiency Network (www.ukpin.org.uk). Twenty-seven centres in the United Kingdom are actively contributing data, with an additional nine centres completing their ethical and governance approvals to participate. This indicates that 36 of 38 (95%) of recognized centres in the United Kingdom have engaged with this project. To date, 2229 patients have been enrolled, with a notable increasing rate of recruitment in the past 12 months. Data are presented on the range of diagnoses recorded, estimated minimum disease prevalence, geographical distribution of patients across the United Kingdom, age at presentation, diagnostic delay, treatment modalities used and evidence of their monitoring and effectiveness.

Surface feature based classification of plant organs from 3D laserscanned point clouds for plant phenotyping

BACKGROUND

Laserscanning recently has become a powerful and common method for plant parameterization and plant growth observation on nearly every scale range. However, 3D measurements with high accuracy, spatial resolution and speed result in a multitude of points that require processing and analysis. The primary objective of this research has been to establish a reliable and fast technique for high throughput phenotyping using differentiation, segmentation and classification of single plants by a fully automated system. In this report, we introduce a technique for automated classification of point clouds of plants and present the applicability for plant parameterization.

RESULTS

A surface feature histogram based approach from the field of robotics was adapted to close-up laserscans of plants. Local geometric point features describe class characteristics, which were used to distinguish among different plant organs. This approach has been proven and tested on several plant species. Grapevine stems and leaves were classified with an accuracy of up to 98%. The proposed method was successfully transferred to 3D-laserscans of wheat plants for yield estimation. Wheat ears were separated with an accuracy of 96% from other plant organs. Subsequently, the ear volume was calculated and correlated to the ear weight, the kernel weights and the number of kernels. Furthermore the impact of the data resolution was evaluated considering point to point distances between 0.3 and 4.0 mm with respect to the classification accuracy.

CONCLUSION

We introduced an approach using surface feature histograms for automated plant organ parameterization. Highly reliable classification results of about 96% for the separation of grapevine and wheat organs have been obtained. This approach was found to be independent of the point to point distance and applicable to multiple plant species. Its reliability, flexibility and its high order of automation make this method well suited for the demands of high throughput phenotyping.

HIGHLIGHTS

• Automatic classification of plant organs using geometrical surface information• Transfer of analysis methods for low resolution point clouds to close-up laser measurements of plants• Analysis of 3D-data requirements for automated plant organ classification.

Emergency cardiac mechanical assistance: place of mucosal gastric tonometry as prognostic indicator

BACKGROUND AND OBJECTIVES

The death of patients treated by ventricular assist device is usually related to multiorgan failure for which a disorder of splanchnic circulation is blamed. Gastric tonometry (measurement of gastric intra-mucosal pressure of CO(2)) has already been studied in many fields and especially in cardiac surgery. The aim of this study was to investigate the prognostic value of gastric tonometry monitoring after implantation of a ventricular assist device.

METHODS

In this prospective study, all consecutive patients scheduled for a ventricular assist device were included. Gastric tonometry was added to standard monitoring. Data were collected (lactate, gastric CO(2) (PgCO(2)) during cardiopulmonary bypass, at admission to ICU, 24 and 48 h later and when norepinephrine was stopped. Preoperative biologic and haemodynamic data were also collected. The primary endpoint was death.

RESULTS

Fifty-six patients (50 men and 6 women) were included. In 91% of the cases, the mechanical assistance was biventricular. The objective of the assistance was a bridge to transplant in 93% (n = 27). Twenty-seven deaths (48%) occurred during the study, 59% (n = 16) of them took place before the cardiac transplantation (mean time = 18 +/- 16 days after assist device insertion). Many factors were found to be associated with death: weight (P = 0.018), red cells administration (P = 0.025), length of surgery (P = 0.016), PgCO(2) on admission to ICU (P = 0.040) and norepinephrine dose at 24 h.

CONCLUSIONS

Gastric tonometry has a prognostic value in the early postoperative hours after the implantation of a ventricular assist device.

Evaluation of the estimation of midazolam concentrations and pharmacokinetic parameters in intensive care patients using a bayesian pharmacokinetic software (PKS) according to sparse sampling approach

The aim of the study was to assess the performance of a bayesian program (PKS System, Abbott) for predicting midazolam concentrations and pharmacokinetic parameters in intensive care patients by comparing the pharmacokinetic parameters estimated by PKS to those calculated according to rich data. The study involved 42 patients receiving midazolam infusion for two hours or for several days. The program was used to predict plasma midazolam concentrations after feedback of 1, 2 or 3 concentrations. High correlation between observed and estimated concentrations was shown (r(2) > 0.992). Mean prediction error, mean absolute prediction error and root mean squared error were low for the patients of the reference and validation groups. From two or three feedback concentrations, midazolam pharmacokinetic parameters estimated by PKS were statistically comparable with those obtained using a rich pharmacokinetic analysis (P > 0.05 paired Wilcoxon test). Thus, PKS is useful for predicting midazolam concentrations and pharmacokinetic parameters when at least two feedback concentrations are known. This software seems to be appropriate for providing significant help to the clinician for midazolam dosage adjustment, according to midazolam concentrations and clinical sedation.

[Sedation induced by midazolam in intensive care: pharmacologic and pharmacokinetic aspects]

OBJECTIVE

Review on midazolam in order to optimize drug utilisation and therapeutic monitoring.

DATA SOURCES

Research of English or French articles published until August 2001, using Medline database. The key words were: midazolam, pharmacokinetics, pharmacodynamic, sedation, drug interaction.

STUDY SELECTION

Original articles, clinical cases and letters to the Editor were selected. Animal studies were excluded.

DATA EXTRACTION

The articles were analysed according to their interest in midazolam clinical practice.

DATA SYNTHESIS

Midazolam is a benzodiazepine widely used in intensive care unit, as a sedative, anxiety-relieving, and amnesic drug. Midazolam could be used in patients with cardiac, or respiratory failure, and in neurosurgery. A great interindividual variability on pharmacokinetic and pharmacodynamic response was observed. In intensive care patients, elimination half-life is known to be widely increased. Midazolam is metabolised by hepatic microsomes. The major metabolite is the 1-hydroxymidazolam, which is pharmacologically active. A prolonged sedation due to an accumulation of conjugated metabolite was observed in renal failure patients. Enzymatic inductors or inhibitors could influence pharmacokinetics and pharmacodynamic effects of midazolam.

CONCLUSION

According to midazolam pharmacokinetic and pharmacodynamic variability, an individual dosage adjustment is essential for long-term sedation. Target controlled sedation could be a mean to limit the variability and to reach quickly the pharmacodynamic effect.

Midazolam disposition in patients undergoing continuous venovenous hemodialysis

The aim of the study was to investigate the pharmacokinetics and removal of midazolam and unconjugated and glucuronidated 1-hydroxy-midazolam in 4 intensive care patients on continuous venovenous hemodialysis. Plasma midazolam and its metabolites were assessed by HPLC from blood samples collected during continuous infusion and after the end of infusion. Additional samples from the arterial and venous bloodlines and ultrafiltrate were drawn to calculate sieving coefficient and clearance of ultrafiltration. The elimination half-life of midazolam ranged from 7.6 to 22.8 hours. The clearance of ultrafiltration was between 0.13 and 4.7 ml/min and reached approximately 11% of the total clearance. The range of sieving coefficient was from 0.006 to 0.26, with an average fraction removal of 0.2%. 1-Hydroxy-midazolam glucuronide was removed by continuous hemodialysis (sieving 0.36 to 0.63), with a clearance of ultrafiltration ranging from 7.8 to 12.0 ml/min. These preliminary results showed that midazolam is not removed efficiently, and approximately half of the 1-hydroxy-midazolam glucuronide was removed by dialysis.

[Intensive care in lung and heart-lung transplantation]

Intensive care after lung, and heart-lung transplantation may have simple post operative course specially after preventive procedures of reperfusion injury, nosocomial infections during mechanical ventilation and immunosuppression risks. Nevertheless a severe mediastinal shift may occurred after single lung transplantation in emphysema. Rapid changes in ventilation/perfusion ratio during lung infection or rejection specially in pulmonary hypertension are responsible of dramatic respiratory failure. Knowledge of multiorgan dysfunction and multidisciplinary experience encourage to future development.

Disseminated Scedosporium prolificans (S. inflatum) infection after single-lung transplantation

This report concerns the first case of disseminated infection with Scedosporium prolificans (S. inflatum) that occurred in a patient who had received an organ transplant and was verified at autopsy. The patient underwent single (right)-lung transplantation and subsequently died on the 33rd postoperative day in a context of septic shock. S. prolificans was isolated from various specimens, including two cultured blood samples. The organism was associated with Candida albicans in some of these specimens. Autopsy revealed the presence of numerous nodular abscesses in the lungs, myocardium, kidneys, spleen, and gall bladder. These abscesses contained mycelial filaments and numerous oval conidia suggestive of Scedosporium species but no yeasts. MIC studies demonstrated the resistance of the S. prolificans isolate to amphotericin B, fluctyosine, miconazole, ketoconazole, and itraconazole, whereas the isolate of C. albicans was susceptible to amphotericin B, flucytosine, miconazole, and ketoconazole. From the data from our case and six published reports of cases of disseminated S. prolificans infection, it can be concluded that this filamentous fungus is a new agent responsible for the most serious mycoses in both neutropenic patients and patients who have undergone organ transplantation.

Enoximone and acute left ventricular failure during weaning from mechanical ventilation after cardiac surgery

OBJECTIVE

To evaluate enoximone, a phosphodiesterase III inhibitor, in the treatment of left ventricular failure during the weaning of patients from mechanical ventilation after heart surgery.

DESIGN

Open label, prospective, weaning trial.

SETTING

Cardiothoracic surgical intensive care unit (ICU) in a university hospital.

PATIENTS

Nine patients were studied after one or more unsuccessful attempts at weaning from mechanical ventilation due to left ventricular dysfunction.

INTERVENTION

For each patient, two respiratory weaning attempts were studied: the first one as a control and the second one with enoximone infused at a rate of 30 micrograms/kg/min for 30 mins, then at a rate of 10 micrograms/kg/min.

MEASUREMENTS AND MAIN RESULTS

Hemodynamic measurements were performed at the following interval times: baseline during mechanical ventilation; during spontaneous ventilation with a T-piece for a minimum of 10 mins; during mechanical ventilation after the enoximone infusion for 30 mins; during spontaneous ventilation for a minimum of 10 mins with an enoximone infusion running. During spontaneous ventilation, enoximone increased cardiac index by 34% but mean artery, right atrial, and pulmonary artery occlusion pressures did not change. Despite an increase in venous admixture due to augmented cardiac index and inhibition of hypoxic vasoconstriction, no oxygen debt occurred because oxygen delivery increased. Seven of nine patients were weaned successfully from mechanical ventilation.

CONCLUSION

Because of its positive inotropic and vasodilatory properties, enoximone is helpful for respiratory weaning of patients with left ventricular failure after cardiac surgery.

[Current role of intra-aortic diastolic balloon counterpulsation in heart surgery]

Intra-aortic balloon counterpulsation (IABP) is a relatively non-invasive method of circulating assistance, easy to use and which has benefitted from a number of technological improvements in recent years. This retrospective study over 4 years was undertaken to analyse the results of IABP and to determine its role in therapeutic arsenal against cardiac failure. Sixty five patients, 57 coronary and 8 valvular cases, with an average age of 61 +/- 10 years were included. The indications of IABP were: a bridge to transplantation (3 cases), complications of coronary angioplasty (4 cases), and low cardiac output after cardiopulmonary bypass (58 cases), where IABP was curative in 85% of cases and prophylactic in 15% of cases (patients with risk factors of low output state after CPB). Beforehand, 65% of patients had poor left ventricular function (LVEF < 40% and/or CI < 2.2 l/mn/m2). An Aries Medical M700 console was used. The percutaneous femoral approach was feasible in 87% of cases. The results were: improvement with discharge from intensive care unit in 60% of cases, transient improvement in 7% of cases, no improvement in 15% of cases and cardiac transplantation in 8% of cases. The outcome was worse when the preoperative LV function was poor and when high dose inotropic agents had to be used. Survival was 100% in those patients in whom IABP was a prophylactic measure. The average duration of IABP was 72 hours, survival being significantly lower in those in whom IABP was continued for over 3 days. The complications (12.7%) were thromboembolic.(ABSTRACT TRUNCATED AT 250 WORDS)