Economic impact of work disability due to chronic low back pain from the patient perspective.

OBJECTIVES: Low back pain (LBP) is one of the main expenditure items for health systems. Data on the economic impact of LBP are uncommon from the patient perspective. The aim of this study was to estimate the economic impact of work disability related to chronic LBP from the patient perspective. METHODS: We conducted a cross-sectional analysis from patients aged over 17 years suffering from non-specific LBP for at least 3 months. Systematic medical, social and economic assessments were collected: pain duration and intensity; functional disability with the Quebec Back Pain Disability Scale (0-100); quality of life with the Dallas Pain Questionnaire; job category; employment status; duration of work disability due to LBP, and income. Factors associated with loss of income were identified by multivariable logistic regression analysis. RESULTS: We included 244 workers (mean age 43 ± 9 years; 36% women); 199 patients had work disability, including 196 who were on sick leave, 106 due to job injury. Three were unemployed due to layoff for incapacity. The mean loss of income for patients with work disability was 14% [SD 24, range -100 to 70] and was significantly less for patients on sick leave due to job injury than on sick leave not related to job injury (p < 0.0001). On multivariable analysis, the probability of loss of income with LBP was about 50% less for overseers and senior managers than workers or employees (odds ratio 0.48 [95% confidence interval 0.23-0.99]). CONCLUSION: Work disability due to LBP resulted in loss of income in our study. The loss of income depended on the type of social protection and job category. It was reduced for patients on sick leave related to work injury and for overseers and senior managers.

Comparative study of psychometric properties of three assessment tools for degenerative rotator cuff disease.

OBJECTIVE:: To compare psychometric properties of Disabilities of the Arm, Shoulder and Hand (DASH) questionnaire, Shoulder Pain and Disability Index (SPADI) and Constant-Murley scale, in patients with degenerative rotator cuff disease (DRCD). DESIGN:: Longitudinal cohort. SETTING:: One French university hospital. METHODS:: The scales were applied twice at one-week interval before physiotherapy and once after physiotherapy two months later. The perceived improvement after treatment was self-assessed on a numerical scale (0-4). The test-retest reliability of the DASH, SPADI and Constant-Murley scales was assessed before treatment by the intraclass correlation coefficient (ICC). The responsiveness was assessed by the paired t-test ( P < 0.05) and standardized mean difference (SMD). The correlation between the percentage of variation in scale scores and the self-assessed improvement score after treatment was measured by the Spearman coefficient. RESULTS:: Fifty-three patients were included. Twenty-six only were available for reliability. The test-retest reliability was very good for the DASH (ICC = 0.97), SPADI (0.95) and Constant-Murley (0.92). The scale score was improved after treatment for each scale ( P < 0.05). The SMD was moderate for the DASH (0.56) and SPADI (0.56) scales, and small for the Constant-Murley (0.44). The correlation between the percentage of variation in scores and self-assessed improvement score after treatment was high, moderate and not significant for the SPADI (0.59, P < 0.0001), DASH (0.42, P < 0.01) and Constant-Murley scales, respectively. CONCLUSION:: The test-retest reliability of the DASH, SPADI and Constant-Murley scales is very good for patients with DRCD. The highest responsiveness was achieved with the SPADI.

Recent Applications of Multispectral Imaging in Seed Phenotyping and Quality Monitoring-An Overview.

As a synergistic integration between spectroscopy and imaging technologies, spectral imaging modalities have been emerged to tackle quality evaluation dilemmas by proposing different designs with effective and practical applications in food and agriculture. With the advantage of acquiring spatio-spectral data across a wide range of the electromagnetic spectrum, the state-of-the-art multispectral imaging in tandem with different multivariate chemometric analysis scenarios has been successfully implemented not only for food quality and safety control purposes, but also in dealing with critical research challenges in seed science and technology. This paper will shed some light on the fundamental configuration of the systems and give a birds-eye view of all recent approaches in the acquisition, processing and reproduction of multispectral images for various applications in seed quality assessment and seed phenotyping issues. This review article continues from where earlier review papers stopped but it only focused on fully-operated multispectral imaging systems for quality assessment of different sorts of seeds. Thence, the review comprehensively highlights research attempts devoted to real implementations of only fully-operated multispectral imaging systems and does not consider those ones that just utilized some key wavelengths extracted from hyperspectral data analyses without building independent multispectral imaging systems. This makes this article the first attempt in briefing all published papers in multispectral imaging applications in seed phenotyping and quality monitoring by providing some examples and research results in characterizing physicochemical quality traits, predicting physiological parameters, detection of defect, pest infestation and seed health.

Intradiscal Glucocorticoid Injection for Patients With Chronic Low Back Pain Associated With Active Discopathy: A Randomized Trial.

BACKGROUND: Active discopathy is associated with a specific phenotype of chronic low back pain (LBP). Local inflammation has a role in active discopathy-associated symptoms. OBJECTIVE: To assess the efficacy of a single glucocorticoid intradiscal injection (GC IDI) in patients with chronic LBP with active discopathy. DESIGN: Prospective, parallel-group, double-blind, randomized, controlled study. (ClinicalTrials.gov: NCT00804531). SETTING: 3 tertiary care centers in France. PATIENTS: 135 patients with chronic LBP with active discopathy on magnetic resonance imaging (MRI). INTERVENTION: A single GC IDI (25 mg prednisolone acetate) during discography (n = 67) or discography alone (n = 68). MEASUREMENTS: The primary outcome was the percentage of patients with LBP intensity less than 40 on an 11-point numerical rating scale (0 [no pain] to 100 [maximum pain] in 10-point increments) in the previous 48 hours at 1 month after the intervention. The main secondary outcomes were LBP intensity and persistent active discopathy on MRI at 12 months and spine-specific limitations in activities, health-related quality of life, anxiety and depression, employment status, and use of analgesics and nonsteroidal anti-inflammatory drugs at 1 and 12 months. RESULTS: All randomly assigned patients were included in the primary efficacy analysis. At 1 month after the intervention, the percentage of responders (LBP intensity <40) was higher in the GC IDI group (36 of 65 [55.4%]) than the control group (21 of 63 [33.3%]) (absolute risk difference, 22.1 percentage points [95% CI, 5.5 to 38.7 percentage points]; P = 0.009). The groups did not differ in LBP intensity at 12 months and in most secondary outcomes at 1 and 12 months. LIMITATION: Tertiary care setting. CONCLUSION: In chronic LBP associated with active discopathy, a single GC IDI reduces LBP at 1 month but not at 12 months. PRIMARY FUNDING SOURCE: French Ministry of Health.

Tracing the color: quantitative trait loci analysis reveals new insights into red-flesh pigmentation in apple (Malus domestica).

Red-flesh color development in apple fruit is known to depend upon a particular allele of the MdMYB10 gene. While the anthocyanin metabolic pathway is well characterized, current genetic models do not explain the observed variations in red-flesh pigmentation intensity. Previous studies focused on total anthocyanin content as a phenotypic trait to characterize overall flesh color. While this approach led to a global understanding of the genetic mechanisms involved in color expression, it is essential to adopt a more quantitative approach, by analyzing the variations of other phenolic compound classes, in order to better understand the molecular mechanisms involved in the subtle flesh color variation and distribution. In this study, we performed pedigree-based quantitative trait loci (QTL) mapping, using the FlexQTL™ software, to decipher the genetic determinism of red-flesh color in five F1 inter-connected families segregating for the red-flesh trait. A total of 452 genotypes were evaluated for flesh color and phenolic profiles during 3 years (2021-2023). We identified a total of 24 QTLs for flesh color intensity and phenolic compound profiles. Six QTLs were detected for red-flesh color on LG1, LG2, LG8, LG9, LG11, and LG16. Several genes identified in QTL confidence intervals were related to anthocyanin metabolism. Further analyses allowed us to propose a model in which the competition between anthocyanins and flavan-3-ols (monomer and oligomer) end-products is decisive for red-flesh color development. In this model, alleles favorable to high red-flesh color intensity can be inherited from both white-flesh and red-flesh parents.

Image analysis and polyphenol profiling unveil red-flesh apple phenotype complexity.

BACKGROUND: The genetic basis of colour development in red-flesh apples (Malus domestica Borkh) has been widely characterised; however, current models do not explain the observed variations in red pigmentation intensity and distribution. Available methods to evaluate the red-flesh trait rely on the estimation of an average overall colour using a discrete class notation index. However, colour variations among red-flesh cultivars are continuous while development of red colour is non-homogeneous and genotype-dependent. A robust estimation of red-flesh colour intensity and distribution is essential to fully capture the diversity among genotypes and provide a basis to enable identification of loci influencing the red-flesh trait. RESULTS: In this study, we developed a multivariable approach to evaluate the red-flesh trait in apple. This method was implemented to study the phenotypic diversity in a segregating hybrid F1 family (91 genotypes). We developed a Python pipeline based on image and colour analysis to quantitatively dissect the red-flesh pigmentation from RGB (Red Green Blue) images and compared the efficiency of RGB and CIEL*a*b* colour spaces in discriminating genotypes previously classified with a visual notation. Chemical destructive methods, including targeted-metabolite analysis using ultra-high performance liquid chromatography with ultraviolet detection (UPLC-UV), were performed to quantify major phenolic compounds in fruits' flesh, as well as pH and water contents. Multivariate analyses were performed to study covariations of biochemical factors in relation to colour expression in CIEL*a*b* colour space. Our results indicate that anthocyanin, flavonol and flavanol concentrations, as well as pH, are closely related to flesh pigmentation in apple. CONCLUSTION: Extraction of colour descriptors combined to chemical analyses helped in discriminating genotypes in relation to their flesh colour. These results suggest that the red-flesh trait in apple is a complex trait associated with several biochemical factors.

A Computation Method Based on the Combination of Chlorophyll Fluorescence Parameters to Improve the Discrimination of Visually Similar Phenotypes Induced by Bacterial Virulence Factors.

Phenotyping biotic stresses in plant-pathogen interactions studies is often hindered by phenotypes that can hardly be discriminated by visual assessment. Particularly, single gene mutants in virulence factors could lack visible phenotypes. Chlorophyll fluorescence (CF) imaging is a valuable tool to monitor plant-pathogen interactions. However, while numerous CF parameters can be measured, studies on plant-pathogen interactions often focus on a restricted number of parameters. It could result in limited abilities to discriminate visually similar phenotypes. In this study, we assess the ability of the combination of multiple CF parameters to improve the discrimination of such phenotypes. Such an approach could be of interest for screening and discriminating the impact of bacterial virulence factors without prior knowledge. A computation method was developed, based on the combination of multiple CF parameters, without any parameter selection. It involves histogram Bhattacharyya distance calculations and hierarchical clustering, with a normalization approach to take into account the inter-leaves and intra-phenotypes heterogeneities. To assess the efficiency of the method, two datasets were analyzed the same way. The first dataset featured single gene mutants of a Xanthomonas strain which differed only by their abilities to secrete bacterial virulence proteins. This dataset displayed expected phenotypes at 6 days post-inoculation and was used as ground truth dataset to setup the method. The efficiency of the computation method was demonstrated by the relevant discrimination of phenotypes at 3 days post-inoculation. A second dataset was composed of transient expression (agrotransformation) of Type 3 Effectors. This second dataset displayed phenotypes that cannot be discriminated by visual assessment and no prior knowledge can be made on the respective impact of each Type 3 Effectors on leaf tissues. Using the computation method resulted in clustering the leaf samples according to the Type 3 Effectors, thereby demonstrating an improvement of the discrimination of the visually similar phenotypes. The relevant discrimination of visually similar phenotypes induced by bacterial strains differing only by one virulence factor illustrated the importance of using a combination of CF parameters to monitor plant-pathogen interactions. It opens a perspective for the identification of specific signatures of biotic stresses.

Emerging thermal imaging techniques for seed quality evaluation: Principles and applications.

Due to the massive progress occurred in the past few decades in imaging, electronics and computer science, infrared thermal imaging technique has witnessed numerous technological advancement and smart applications in non-destructive testing and quality monitoring of different agro-food produces. Thermal imaging offers a potential non-contact imaging modality for the determination of various quality traits based on the infrared radiation emitted from target foods. The technique has been moved from just an exploration method in engineering and astronomy into an effective tool in many fields for forming unambiguous images called thermograms eventuated from the temperature and thermal properties of the target objects. It depends principally on converting the invisible infrared radiation emitted by the objects into visible two-dimensional temperature data without making a direct contact with the examined objects. This method has been widely used for different applications in agriculture and food science and technology with special applications in seed quality assessment. This article provides an overview of thermal imaging theory, briefly describes the fundamentals of the system and explores the recent advances and research works conducted in quality evaluation of different sorts of seeds. The article comprehensively reviewed research efforts of using thermal imaging systems in seed applications including estimation of seed viability, detection of fungal growth and insect infections, detection of seed damage and impurities, seed classification and variety identification.

Shoulder adhesive capsulitis: diagnostic value of active and passive range of motion with volume of gleno-humeral capsule as a reference.

BACKGROUND: The diagnosis of adhesive capsulitis is currently based on restricted range of motion (ROM) but its diagnostic value has only been rarely investigated. AIM: The aim of this study is to assess the diagnostic value of active global and passive gleno-humeral ROM to diagnose shoulder adhesive capsulitis. DESIGN: Cross-sectional descriptive study. SETTING: One French center for Rehabilitation Medicine. POPULATION: Patients referred for treatment of shoulder adhesive capsulitis in our center were included. Inclusion criteria were: shoulder pain; limitation of active global ROM (abduction or flexion <180°); limitation of passive gleno-humeral ROM (abduction or flexion <90° or 25% reduction at less of lateral rotation versus the opposite shoulder); no gleno-humeral arthropathy on radiography. METHODS: The volume of the gleno-humeral capsule was assessed during a procedure of arthro-distension. The reference criterion for adhesive capsulitis was a volume <12 mL. We analyzed the correlation between the parameters of mobility and the volume of the gleno-humeral capsule; and the positive predictive value (PPV) of inclusion criteria, with the reference criterion for the diagnosis of adhesive capsulitis. RESULTS: We included 38 patients. Passive gleno-humeral ROM in abduction only was correlated with volume of the gleno-humeral capsule: r=0.33, P=0.043. The PPV of inclusion criteria was 82% for the diagnosis of shoulder adhesive capsulitis. Rather than 90°, when we considered 80°, 60° and 40° as the threshold of passive gleno-humeral ROM in abduction, the PPV increased from 83% to 100%. CONCLUSIONS: Passive gleno-humeral ROM in abduction is correlated with volume of the gleno-humeral capsule. The PPV is high for active global and passive gleno-humeral ROM for diagnosis of shoulder adhesive capsulitis. CLINICAL REHABILITATION IMPACT: Limitation of active and passive shoulder ROM, especially passive abduction gleno-humeral, is a good criterion to diagnose shoulder adhesive capsulitis, in patients with shoulder pain and no gleno-humeral arthropathy on radiography.

Utilization of computer vision and multispectral imaging techniques for classification of cowpea (Vigna unguiculata) seeds.

BACKGROUND: The traditional methods for evaluating seeds are usually performed through destructive sampling followed by physical, physiological, biochemical and molecular determinations. Whilst proven to be effective, these approaches can be criticized as being destructive, time consuming, labor intensive and requiring experienced seed analysts. Thus, the objective of this study was to investigate the potential of computer vision and multispectral imaging systems supported with multivariate analysis for high-throughput classification of cowpea (Vigna unguiculata) seeds. An automated computer-vision germination system was utilized for uninterrupted monitoring of seeds during imbibition and germination to identify different categories of all individual seeds. By using spectral signatures of single cowpea seeds extracted from multispectral images, different multivariate analysis models based on linear discriminant analysis (LDA) were developed for classifying the seeds into different categories according to ageing, viability, seedling condition and speed of germination. RESULTS: The results revealed that the LDA models had good accuracy in distinguishing 'Aged' and 'Non-aged' seeds with an overall correct classification (OCC) of 97.51, 96.76 and 97%, 'Germinated' and 'Non-germinated' seeds with OCC of 81.80, 79.05 and 81.0%, 'Early germinated', 'Medium germinated' and 'Dead' seeds with OCC of 77.21, 74.93 and 68.00% and among seeds that give 'Normal' and 'Abnormal' seedlings with OCC of 68.08, 64.34 and 62.00% in training, cross-validation and independent validation data sets, respectively. Image processing routines were also developed to exploit the full power of the multispectral imaging system in visualizing the difference among seed categories by applying the discriminant model in a pixel-wise manner. CONCLUSION: The results demonstrated the capability of the multispectral imaging system in the ultraviolet, visible and shortwave near infrared range to provide the required information necessary for the discrimination of individual cowpea seeds to different classes. Considering the short time of image acquisition and limited sample preparation, this stat-of-the art multispectral imaging method and chemometric analysis in classifying seeds could be a valuable tool for on-line classification protocols in cost-effective real-time sorting and grading processes as it provides not only morphological and physical features but also chemical information for the seeds being examined. Implementing image processing algorithms specific for seed quality assessment along with the declining cost and increasing power of computer hardware is very efficient to make the development of such computer-integrated systems more attractive in automatic inspection of seed quality.

Role of the acquisition of a type 3 secretion system in the emergence of novel pathogenic strains of Xanthomonas.

Cases of emergence of novel plant-pathogenic strains are regularly reported that reduce the yields of crops and trees. However, the molecular mechanisms underlying such emergence are still poorly understood. The acquisition by environmental non-pathogenic strains of novel virulence genes by horizontal gene transfer has been suggested as a driver for the emergence of novel pathogenic strains. In this study, we tested such an hypothesis by transferring a plasmid encoding the type 3 secretion system (T3SS) and four associated type 3 secreted proteins (T3SPs) to the non-pathogenic strains of Xanthomonas CFBP 7698 and CFBP 7700, which lack genes encoding T3SS and any previously known T3SPs. The resulting strains were phenotyped on Nicotiana benthamiana using chlorophyll fluorescence imaging and image analysis. Wild-type, non-pathogenic strains induced a hypersensitive response (HR)-like necrosis, whereas strains complemented with T3SS and T3SPs suppressed this response. Such suppression depends on a functional T3SS. Amongst the T3SPs encoded on the plasmid, Hpa2, Hpa1 and, to a lesser extent, XopF1 collectively participate in suppression. Monitoring of the population sizes in planta showed that the sole acquisition of a functional T3SS by non-pathogenic strains impairs growth inside leaf tissues. These results provide functional evidence that the acquisition via horizontal gene transfer of a T3SS and four T3SPs by environmental non-pathogenic strains is not sufficient to make strains pathogenic. In the absence of a canonical effector, the sole acquisition of a T3SS seems to be counter-selective, and further acquisition of type 3 effectors is probably needed to allow the emergence of novel pathogenic strains.

Phenoplant: a web resource for the exploration of large chlorophyll fluorescence image datasets.

BACKGROUND: Image analysis is increasingly used in plant phenotyping. Among the various imaging techniques that can be used in plant phenotyping, chlorophyll fluorescence imaging allows imaging of the impact of biotic or abiotic stresses on leaves. Numerous chlorophyll fluorescence parameters may be measured or calculated, but only a few can produce a contrast in a given condition. Therefore, automated procedures that help screening chlorophyll fluorescence image datasets are needed, especially in the perspective of high-throughput plant phenotyping. RESULTS: We developed an automatic procedure aiming at facilitating the identification of chlorophyll fluorescence parameters impacted on leaves by a stress. First, for each chlorophyll fluorescence parameter, the procedure provides an overview of the data by automatically creating contact sheets of images and/or histograms. Such contact sheets enable a fast comparison of the impact on leaves of various treatments, or of the contrast dynamics during the experiments. Second, based on the global intensity of each chlorophyll fluorescence parameter, the procedure automatically produces radial plots and box plots allowing the user to identify chlorophyll fluorescence parameters that discriminate between treatments. Moreover, basic statistical analysis is automatically generated. Third, for each chlorophyll fluorescence parameter the procedure automatically performs a clustering analysis based on the histograms. This analysis clusters images of plants according to their health status. We applied this procedure to monitor the impact of the inoculation of the root parasitic plant Phelipanche ramosa on Arabidopsis thaliana ecotypes Col-0 and Ler. CONCLUSIONS: Using this automatic procedure, we identified eight chlorophyll fluorescence parameters discriminating between the two ecotypes of A. thaliana, and five impacted by the infection of Arabidopsis thaliana by P. ramosa. More generally, this procedure may help to identify chlorophyll fluorescence parameters impacted by various types of stresses. We implemented this procedure at http://www.phenoplant.org freely accessible to users of the plant phenotyping community.

Multiscale imaging of plants: current approaches and challenges.

We review a set of recent multiscale imaging techniques, producing high-resolution images of interest for plant sciences. These techniques are promising because they match the multiscale structure of plants. However, the use of such high-resolution images is challenging in the perspective of their application to high-throughput phenotyping on large populations of plants, because of the memory cost for their data storage and the computational cost for their processing to extract information. We discuss how this renews the interest for multiscale image processing tools such as wavelets, fractals and recent variants to analyse such high-resolution images.

High throughput quantitative phenotyping of plant resistance using chlorophyll fluorescence image analysis.

BACKGROUND: In order to select for quantitative plant resistance to pathogens, high throughput approaches that can precisely quantify disease severity are needed. Automation and use of calibrated image analysis should provide more accurate, objective and faster analyses than visual assessments. In contrast to conventional visible imaging, chlorophyll fluorescence imaging is not sensitive to environmental light variations and provides single-channel images prone to a segmentation analysis by simple thresholding approaches. Among the various parameters used in chlorophyll fluorescence imaging, the maximum quantum yield of photosystem II photochemistry (Fv/Fm) is well adapted to phenotyping disease severity. Fv/Fm is an indicator of plant stress that displays a robust contrast between infected and healthy tissues. In the present paper, we aimed at the segmentation of Fv/Fm images to quantify disease severity. RESULTS: Based on the Fv/Fm values of each pixel of the image, a thresholding approach was developed to delimit diseased areas. A first step consisted in setting up thresholds to reproduce visual observations by trained raters of symptoms caused by Xanthomonas fuscans subsp. fuscans (Xff) CFBP4834-R on Phaseolus vulgaris cv. Flavert. In order to develop a thresholding approach valuable on any cultivars or species, a second step was based on modeling pixel-wise Fv/Fm-distributions as mixtures of Gaussian distributions. Such a modeling may discriminate various stages of the symptom development but over-weights artifacts that can occur on mock-inoculated samples. Therefore, we developed a thresholding approach based on the probability of misclassification of a healthy pixel. Then, a clustering step is performed on the diseased areas to discriminate between various stages of alteration of plant tissues. Notably, the use of chlorophyll fluorescence imaging could detect pre-symptomatic area. The interest of this image analysis procedure for assessing the levels of quantitative resistance is illustrated with the quantitation of disease severity on five commercial varieties of bean inoculated with Xff CFBP4834-R. CONCLUSIONS: In this paper, we describe an image analysis procedure for quantifying the leaf area impacted by the pathogen. In a perspective of high throughput phenotyping, the procedure was automated with the software R downloadable at http://www.r-project.org/. The R script is available at http://lisa.univ-angers.fr/PHENOTIC/telechargements.html.