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Black sigatoka, or black leaf streak (BLS), is caused by Pseudocercospora fijiensis and is the most important foliar disease in Musa production. Our goal was to develop a new standard area diagram set (SADs) with high-resolution digital images to aid in visual classification using a quantitative ordinal scale representing six disease severity ranges: 0 (0%), 1 (0 – 5%], 2 (5 – 13%], 3 (13 – 23%], 4 (23 – 40%], 5 (40 – 65%] and 6 (65—100%] and compare visual estimates of BLS severity on banana leaves with those using a previously developed ordinal scale. Three evaluations were conducted by thirteen evaluators and each evaluation was performed at seven-day intervals. The first assessment was done without a SADs. Evaluators subsequently performed two assessments with each the old ordinal scale and the novel SADs. Using the new SADs, evaluators precision, accuracy, repeatability, reproducibility, and residual distributions were improved. The proposed SADs is a tool that helps users in more accurately estimating the severity of BLS. [ABSTRACT FROM AUTHOR]

Background: Hoarding disorder (HD) is characterized by a compulsion to collect belongings, and to experience significant distress when parting from them. HD is often misdiagnosed for several reasons. These include patient and family lack of recognition that it is a psychiatric disorder and professionals' lack of relevant expertise with it. This study evaluates the ability of a supervised machine learning (ML) model to match the diagnostic skills of psychiatrists when presented with equivalent information pertinent to symptoms of HD. Methods: Five hundred online participants were randomly recruited and completed the Hoarding Rating Scale-Self Report (HRS-SR) and the Generalized Anxiety Disorder 7-item (GAD-7) scale. Responses to the questionnaires were read by an ML model. Responses to the HRS-SR were then converted into anonymized, random-equivalent texts. Each of these individual texts was presented in random order to two experienced psychiatrists who were independently asked for a provisional diagnosis - e.g.; the presence or absence of HD. In case of disagreement between the two assessors, a third psychiatrist broke the tie. A decision tree classification model was employed to predict clinical HD using self-report data from two psychological tests, the HRS-SR and GAD-7. The target variable was whether a participant had clinical HD, while the predictive variables were the continuous scores from the HRS-SR and GAD-7 tests. The model's performance was evaluated using a confusion matrix, which compared the observed diagnoses with the predicted diagnoses to assess accuracy. Results: According to the psychiatrists, approximately 10% of the participants fulfilled DSM-5 diagnostic criteria for HD. 93% of the clinician-identified cases were identified by the ML model based on HRS-SR and GAD-7 scores. A decision tree plot model demonstrated that about 60% of the cases could be detected by the HRS-SR alone while the rest required a combination of HRS-SR and GAD-7 scores. ML evaluation metrics showed satisfactory performance, with a Matthews Correlation Coefficient of 55%; Area Under Curve (AUC), 79%; a Negative Predictive Value of 76%; and a False Negative Rate of 24%. Conclusions: Study findings strongly suggest that ML can, in the future, play a significant role in the risk assessment of psychiatric disorders prior to face-to-face consultation. By using AI to scan big data questionnaire responses, wait time for seriously ill patients can be substantially cut, and prognoses substantially improved. [ABSTRACT FROM AUTHOR]

Abstract Background Hoarding disorder (HD) is characterized by a compulsion to collect belongings, and to experience significant distress when parting from them. HD is often misdiagnosed for several reasons. These include patient and family lack of recognition that it is a psychiatric disorder and professionals’ lack of relevant expertise with it. This study evaluates the ability of a supervised machine learning (ML) model to match the diagnostic skills of psychiatrists when presented with equivalent information pertinent to symptoms of HD. Methods Five hundred online participants were randomly recruited and completed the Hoarding Rating Scale-Self Report (HRS-SR) and the Generalized Anxiety Disorder 7-item (GAD-7) scale. Responses to the questionnaires were read by an ML model. Responses to the HRS-SR were then converted into anonymized, random-equivalent texts. Each of these individual texts was presented in random order to two experienced psychiatrists who were independently asked for a provisional diagnosis - e.g.; the presence or absence of HD. In case of disagreement between the two assessors, a third psychiatrist broke the tie. A decision tree classification model was employed to predict clinical HD using self-report data from two psychological tests, the HRS-SR and GAD-7. The target variable was whether a participant had clinical HD, while the predictive variables were the continuous scores from the HRS-SR and GAD-7 tests. The model’s performance was evaluated using a confusion matrix, which compared the observed diagnoses with the predicted diagnoses to assess accuracy. Results According to the psychiatrists, approximately 10% of the participants fulfilled DSM-5 diagnostic criteria for HD. 93% of the clinician-identified cases were identified by the ML model based on HRS-SR and GAD-7 scores. A decision tree plot model demonstrated that about 60% of the cases could be detected by the HRS-SR alone while the rest required a combination of HRS-SR and GAD-7 scores. ML evaluation metrics showed satisfactory performance, with a Matthews Correlation Coefficient of 55%; Area Under Curve (AUC), 79%; a Negative Predictive Value of 76%; and a False Negative Rate of 24%. Conclusions Study findings strongly suggest that ML can, in the future, play a significant role in the risk assessment of psychiatric disorders prior to face-to-face consultation. By using AI to scan big data questionnaire responses, wait time for seriously ill patients can be substantially cut, and prognoses substantially improved.

Dragon fruit canker (DFC) disease caused by the fungus Neoscytalidium dimidiatum poses a significant threat to yield and fruit quality in cultivating dragon fruit (Hylocereus spp.). Accurate assessment of canker severity is crucial for determining the effectiveness of management strategies for controlling this disease. We developed a standard area diagram set (SADs) consisting of nine levels of disease severity (0.5 to 60%) and subsequently validated the SADs based on the patterns and distribution of the symptoms. For the validation, eighteen raters with no prior experience in disease assessment were recruited to estimate the severity of canker symptoms on 50 stems/cladodes of dragon fruit before and after using SADs. Both accuracy and precision were improved when they used the SADs. Statistical parameters for the 18 raters were as follows: bias coefficient factor (Cb) without SADs = 0.791, with SADs = 0.982; correlation coefficient (r) without SADs = 0.884, with SADs = 0.918; Lin's concordance correlation coefficient (ρc) without SADs = 0.702, with SADs = 0.902. Estimates were also more reliable by using the SADs: inter-rater coefficient of determination (R2) without SADs = 0.749, with SADs = 0.810. The newly developed SADs provide a practical, standardized tool for assessing dragon fruit canker severity, aiding researchers and growers in more accurate disease monitoring, yield loss estimation, intervention planning, and control measure evaluation. [ABSTRACT FROM AUTHOR]

Although many biomarkers have been proposed, and several are in widespread clinical use, there is no single readout or combination of readouts that correlates tightly with gluten exposure, disease activity, or end-organ damage in treated patients with celiac disease. Challenges to developing and evaluating better biomarkers include significant interindividual variability-related to immune amplification of gluten exposure and how effects of immune activation are manifest. Furthermore, the current "gold standard" for assessment of end-organ damage, small intestinal biopsy, is itself highly imperfect, such that a marker that is a better reflection of the "ground truth" may indeed appear to perform poorly. The goal of this review was to analyze past and present efforts to establish robust noninvasive tools for monitoring treated patients with celiac disease and to highlight emerging tools that may prove to be useful in clinical practice. [ABSTRACT FROM AUTHOR]

Aim To investigate the levels of krüppel-like factor 2 (KLF2) and endothelial nitric oxide synthase 3 (NOS3) in the serum of patients with acute cerebral infarction (ACI) of large-artery atherosclerosis (LAA) type, and to analyze their value in the diagnosis and disease assessment of LAA type ACI. Methods A total of 150 patients with LAA type ACI were divided into mild group (n = 36), moderate group (n = 48), and severe group (n = 66) based on their condition. Additionally, a control group ( n = 150) was selected from health exminers during the same period. The levels of serum KLF2 and NOS3 in each group were compared; receiver operator characteristic(ROC) curve was applied to analyze the diagnostic value of serum KLF2 and NOS3 levels for LAA type ACI and the predictive value for the occurrence of severe LAA type ACI, respectively. Results The serum KLF2 and NOS3 levels were significantly lower in LAA type ACI group than those in control group (P<0. 05). The serum KLF2 and NOS3 levels in the mild, moderate and severe groups were significantly decreased in turn(P<0. 05). The area under the curve (AUC) of the combined diagnosis of serum KLF2 and NOS3 for LAA type ACI was 0. 858, with a sensitivity of 73. 33% and a specificity of 86. 00%, which was superior to the individual diagnosis of KLF2 and NOS3 (Zcombined detection-KLF2 = 3. 796, Zcombined detection-NOS3 = 4. 689, all P< 0. 001). The AUC of combined prediction of serum KLF2 and NOS3 for the occurrence of severe LAA type ACI was 0. 878, with a sensitivity of 77. 27% and a specificity of 90. 48%, which was superior to the independent prediction of KLF2 and NOS3 (Zcombined detection-KLF2 = 2. 401, P= 0. 016; Zcombined detection-NOS3 = 3. 070, P = 0. 002). Conclusions The serum levels of KLF2 and NOS3 in patients with LAA type ACI were significantly reduced and negatively correlated with the severity of the disease. The combination of the two has high evaluation efficacy in the diagnosis and disease prediction of LAA type ACI. [ABSTRACT FROM AUTHOR]

Two standard area diagrams (SADs) were developed to quantify the severity of tomato early blight (EB), caused by Alternaria solani, on leaves and leaflets. The SADs were composed by sets of images with distinct EB severity for leaflets (0–40%) and leaves (0-46.4%). To validate the diagrams, 13 evaluators with no experience in quantifying the plant disease severity estimated EB severity by using a 50-image sample of tomato leaves and leaflets, first without SADs and then using the proposed SADs. The data were submitted to regression analysis and Lin's concordance correlation coefficient, and the accuracy, precision, repeatability, and reproducibility of the estimates for EB severity were assessed. Based on the parameters of Lin's coefficients and intraclass correlations, EB severity estimates were consistent and more reliable using SADs, improving evaluators' performance. The diagrams proposed in this study improved the quantification of EB severity performed by 13 evaluators, increasing the accuracy, precision, and reliability of the estimates. Therefore, the proposed diagrams can be used in further studies on the epidemiology, resistance, and management strategies of this pathosystem. [ABSTRACT FROM AUTHOR]

Verticillium wilt (VW) is a common soilborne disease of cotton. It occurs mainly in the seedling and boll-opening stages and severely impairs the yield and quality of the fiber. Rapid and accurate identification and evaluation of VW severity (VWS) forms the basis of field cotton VW control, which has great significance to cotton production. Cotton VWS values are conventionally measured using in-field observations and laboratory test diagnoses, which require abundant time and professional expertise. Remote and proximal sensing using imagery and spectrometry have great potential for this purpose. In this study, we performed in situ investigations at three experimental sites in 2019 and 2021 and collected VWS values, in situ images, and spectra of 361 cotton canopies. To estimate cotton VWS values at the canopy scale, we developed two deep learning approaches that use in situ images and spectra, respectively. For the imagery-based method, given the high complexity of the in situ environment, we first transformed the task of healthy and diseased leaf recognition to the task of cotton field scene classification and then built a cotton field scenes (CFS) dataset with over 1000 images for each scene-unit type. We performed pretrained convolutional neural networks (CNNs) training and validation using the CFS dataset and then used the networks after training to classify scene units for each canopy. The results showed that the DarkNet-19 model achieved satisfactory performance in CFS classification and VWS values estimation (R2 = 0.91, root-mean-square error (RMSE) = 6.35%). For the spectroscopy-based method, we first designed a one-dimensional regression network (1D CNN) with four convolutional layers. After dimensionality reduction by sensitive-band selection and principal component analysis, we fitted the 1D CNN with varying numbers of principal components (PCs). The 1D CNN model with the top 20 PCs performed best (R2 = 0.93, RMSE = 5.77%). These deep learning-driven approaches offer the potential of assessing crop disease severity from spatial and spectral perspectives.

Assessing crop diseases at an early stage allows farmers to apply chemicals at the most efficient moment to control the pest and focus on the plants with the highest risk of developing the disease. Moreover, the earlier the assessment the lower the consequences, such as yield reduction, which is crucial to feed the increasing world population. BBR is the first open-source software based on physical and phenological crop parameters that grant the early assessment of botrytis bunch rot disease, caused by Botrytis cinerea, in vineyards using UAV images. This software has been released to improve the current procedure for early botrytis bunch rot assessment, which consists of laborious and time-consuming field inspections. In addition, BBR is easily modifiable by users to implement the workflow in their fields. For future work, the software can be extended to other crops and/or other fungal diseases.

Takayasu arteritis (TAK) is a rare, chronic granulomatous large-vessel arteritis affecting mainly the aorta and its major branches. Inflammation of the vessel wall causes segmental stenosis, occlusion, dilatation, and/or aneurysm formation. Although all large arteries can be affected, the aorta, subclavian, and carotid arteries are the most commonly involved TAK is mainly observed in young females. Recent advances in the diagnosis, clinical course, disease assessment, and treatment of TAK are discussed in this review. In the presence of typical symptoms and physical findings such as loss of pulses and/or decreased arterial blood pressure and elevated acute phase responses, the diagnosis should be confirmed easily by angiographic imaging modalities. Magnetic resonance angiography is the gold standard modality for both diagnosis and longitudinal follow-up of patients with TAK. In recent years, positron emission tomography (PET) has become a widely used imaging method for the diagnosis of TAK with high sensitivity. The place of PET during follow-up in TAK is still controversial and requires further studies. Prognosis is recently possibly getting better with lower mortality, but a substantial damage is present even in early cases. It is critical to differentiate irreversible damage from disease activity and thus avoid potential over treatment with toxic agents such as corticosteroids in TAK. There is a clear need to develop a validated set of outcome measures for use in clinical trials of TAK. In daily practice, routine imaging follow-up is not recommended in clinically and laboratory silent TAK patients assessed as inactive by the physician. The level of evidence for TAK management is low, and expert opinion is still the main determinant when managing patients with TAK during daily practice. Glucorticoids are the mainstay of TAK treatment. While tapering glucocoticoids, non-biologic immunosuppressive agents should be added to the treatment. Leflunomide, methotrexate, azathiopurine, or mycophenolate mofetil could be chosen as the first-line immunosuppressive agent. If there is a treatment failure with first-line agents, switching to tumor necrosis factor inhibitors or tocilizumab should be considered. [ABSTRACT FROM AUTHOR]

Paucity of knowledge on resistant variety, high yield loss ranging from 32- 57% and dearth of knowledge on management of Alternaria leaf spot has been the major concerns in radish production in the world. Our study will aid in multiplication and standardization of the germane variety of radish resistant to Alternaria leaf spot disease so as to escalate the production, productivity, and the competitiveness in the domestic as well as in the international market. Hence, this experiment was rendered to screen the radish genotypes against Alternaria leaf spot disease in Western Nepal in 2021. Seven genotypes namely Sparkle white F1 hybrid, Korean cross, Palak-patta 35, Pyuthane red, 40 days, Geeta 33, and Mino early were used as the treatment and experiment was laid out in one factorial RCBD design. Each treatment was assigned with 8 plants for observation with 3 replications per treatment. Parameters like days to appearance of disease, incidence of disease on seedling, disease severity (3 scorings), mean score of disease, mean AUDPC and reaction of genotypes were measured during the experiment. Our study unveiled highly significant result for the days to appearance of disease, disease severity, mean score of disease and AUDPC among the genotypes of radish. Alternaria leaf spot first appeared in Pyuthane red (18.33 days) while appeared late in Mino early. Disease severity was found harsh in 40 days at each scoring (57.33, 64.67 and 70.33 respectively); on the contrary severity was minor in Sparkle white at each scoring (26.67, 35 and 44.17 respectively). Similarly, the highest MDS (3.4) and AUDPC (449.8) was found in 40 days, whereas the lowest MDS (1.7) and AUDPC (246.5) was observed in Sparkle white. Ultimately, it is contended that 40 days was found to be moderately susceptible variety while other varieties were found to be moderately resistant.

Medical data processing and analysis using machine learning algorithms are a prominent research topic these days. To obtain high performances, most state-of-the-art models must be trained on a large number of labeled datasets. However, manually collecting a large-scale real-world medical data is expensive due to the issues like privacy, security, and reliability. Moreover, the collected samples may be incomplete, i.e., some important data items are missing, which has a significant impact on the performance of the machine learning algorithms, especially deep learning models. In this paper, we present a strategy that uses the idea of adversarial learning to augment the real-world medical samples with the incomplete issue to obtain better performance in predicting patient states. Rather than supplying the incomplete data sample with extra instance-level information as in existing data-complementation methods, our method aims to achieve the complementary effect in the feature level without consuming human effort. The method receives both high-quality data and low-quality data (with serious incomplete issue) to learn a comprehensive feature space where the incomplete samples can be complemented with the knowledge transferred from the high-quality samples by the adversarial learning scheme. From the perspective of feature representations, our method can alleviate the incomplete issue of the low-quality data, which enhances the model performance in the end task. Experiments show that our method works well on a real-world medical dataset, which collected for assessment of pelvic organ prolapse. When compared to machine learning approaches frequently employed in medical data analysis, our approach shows a significant improvement of up to 10%. [ABSTRACT FROM AUTHOR]

Late leaf rust (Thekopsora americana) is a disease that affects raspberry (Rubus idaeus L.), leading to a decrease in raspberry production. The adoption of precise criteria for disease quantification helps decision-making for its management. A diagrammatic scale (DS) is a useful tool for quantifying disease severity in the field. A DS was developed and validated in this study to estimate late leaf rust severity in raspberry leaves. The DS was defined with eight levels of severity (0.2, 0.4, 0.9, 2.2, 5.1, 11.5, 23.9 and 43.2%). The validation was performed by 15 untrained raters in two steps, with and without using the DS. Two approaches were used for statistical analysis: linear regression and Lin's method. Both analyses demonstrated that the use of the DS improved the precision and accuracy of the disease estimates. The inter-rater reliability was estimated by the coefficient of determination (R2) and the intra-class correlation coefficient (ICC). Again, the use of the DS improved the reproducibility of the estimates. For the first time, a diagrammatic scale was developed and validated to evaluate late leaf rust severity in raspberry leaves. [ABSTRACT FROM AUTHOR]

Introduction: The accurate severity assessment of wheat stripe rust is the basis for the pathogen-host interaction phenotyping, disease prediction, and disease control measure making. Methods: To realize the rapid and accurate severity assessment of the disease, the severity assessment methods of the disease were investigated based on machine learning in this study. Based on the actual percentages of the lesion areas in the areas of the corresponding whole single diseased wheat leaves of each severity class of the disease, obtained after the image segmentation operations on the acquired single diseased wheat leaf images and the pixel statistics operations on the segmented images by using image processing software, under two conditions of considering healthy single wheat leaves or not, the training and testing sets were constructed by using two modeling ratios of 4:1 and 3:2, respectively. Then, based on the training sets, two unsupervised learning methods including K-means clustering algorithm and spectral clustering and three supervised learning methods including support vector machine, random forest, and K-nearest neighbor were used to build severity assessment models of the disease, respectively. Results: Regardless of whether the healthy wheat leaves were considered or not, when the modeling ratios were 4:1 and 3:2, satisfactory assessment performances on the training and testing sets can be achieved by using the optimal models based on unsupervised learning and those based on supervised learning. In particular, the assessment performances obtained by using the optimal random forest models were the best, with the accuracies, precisions, recalls, and F1 scores for all the severity classes of the training and testing sets equal to 100.00% and the overall accuracies of the training and testing sets equal to 100.00%. Discussion: The simple, rapid, and easy-to-operate severity assessment methods based on machine learning were provided for wheat stripe rust in this study. This study provides a basis for the automatic severity assessment of wheat stripe rust based on image processing technology, and provides a reference for the severity assessments of other plant diseases. [ABSTRACT FROM AUTHOR]

• An annual review of patients with axial spondyloarthritis encompassing disease activity, severity, comorbidities, employment, psychological factors, and lifestyle is recommended. • The specific content of this annual review has never been proposed. • This initiative provided a reporting framework including all components needing to be assessed during this review. • This document was obtained after a literature search and an expert consensus-driven approach. National and international scientific societies advocate for a regular, systematic, and standardized global evaluation of axial spondyloarthritis (axSpA) patients. However, there are no recommendations specifying the content of this global evaluation. This initiative aimed to propose a standardized reporting framework, using evidence-based and consensus approaches, to collect data on all domains of axSpA. A literature review and consensus process involved a steering committee and an expert panel of 37 rheumatologists and health professionals. The first steering committee took place in March 2022 and identified the main domains for inclusion in the standardized report. A hierarchical literature review was conducted to identify items within these domains and tools for assessment. The items and tools for assessment were discussed and consensus was reached through a vote session during an expert meeting that took place in March 2023. The steering committee identified four main domains to include in the standardized reporting framework: disease assessment, comorbidities, lifestyle, and quality of life. Items and tools for assessment were adopted after the expert meeting. Additionally, recommendations regarding digital tools (websites, apps, social media) were provided. This initiative led to a consensus, based on evidence and expertise, on a reporting framework for use during periodic systematic global evaluations of axSpa in daily practice. [ABSTRACT FROM AUTHOR]

Coffee leaf rust (CLR) is the most important disease in coffee and is caused by Hemileia vastatrix. The use of a diagrammatic scale helps people more effectively evaluate rust severity and improves disease measurement by evaluators. Our goal was to develop a new scale with colored pictures and seven disease levels (0; 0.1–1.0; 1.1–2.0; 2.1–5.0; 5.1–10.0; 10.1–25.0; >25.0%) and then compare the severity results of CLR on arabica coffee leaves with those based on three other scales. Three evaluations were performed by ten different evaluators. The first assessment was performed without a scale. On the same day, raters performed four assessments with each of the four diagrammatic scales. The third evaluation was performed at seven-day intervals. We analyzed the statistics with linear regression and Lin's concordance correlation. The evaluators using the proposed scale improved the precision, accuracy and reproducibility of the evaluations and reduced residual distribution when compared to the evaluators who did not use the proposed diagrammatic scale or who used the other scales. Overall, the proposed diagrammatic scale is a tool that can assist users in producing a disease estimate close to the real value of CLR on arabica coffee leaves. [ABSTRACT FROM AUTHOR]

Accurate severity assessment of wheat stripe rust caused by Puccinia striiformis f. sp. tritici is of great significance for phenotypic determination, prediction, and control of the disease. To achieve accurate severity assessment of the disease based on the actual percentages of lesion areas in the areas of the corresponding whole diseased leaves, two new methods were proposed for severity assessment of the disease. In the Adobe Photoshop 2022 software, the acquired images of single diseased leaves of each severity class of the disease were manually segmented, and the numbers of the leaf region pixels and lesion pixels of each diseased leaf were obtained by pixel statistics. After calculation of the actual percentages of lesion areas in the areas of the corresponding whole diseased leaves based on the obtained pixel numbers, the training sets and testing sets were constructed for each severity class by using the system sampling method with two sampling ratios of 4:1 and 3:2. Then the mean and standard deviation of the actual percentages of lesion areas contained in each training set were calculated, respectively. For each sampling ratio, two methods, one based on the midpoint value of the means of the actual percentages of lesion areas corresponding to two adjacent severity classes and the other based on the distribution range of most of the actual percentages of lesion areas, were used to determine the midpoint-of-two-adjacentmeans-based actual percentage reference range and the 90%, 95%, and 99% reference ranges of the actual percentages of lesion areas for each severity class. According to the determined reference ranges, the severity of each diseased leaf in the training sets and testing sets was assessed. The results showed that high assessment accuracies (not lower than 85%) for the training sets and testing sets were achieved, demonstrating that the proposed methods could be used to conduct severity assessment of wheat stripe rust based on the actual percentages of lesion areas. This study provides a reference for accurate severity assessments of plant diseases. [ABSTRACT FROM AUTHOR]

Foliar fungal diseases are a serious threat to winter wheat production and climate change appears to favor pathogens associated with leaf blotch and tan spot symptoms in the Mediterranean area. The present work aimed to highlight these risks and propose appropriate disease management strategies by evaluating the seed treatment with the Systiva® (BASF) fungicide as a means to protect the crop against foliar fungal infections during the early growing stages. Towards that aim, plant tissue symptoms affected by the pathogens Pyrenophora tritici-repentis and Septoria spp. were systematically recorded in a study field in the region of Larissa, central Greece for three years (2016–2018), and the findings were associated with the monthly weather anomalies. Consequently, for the growing period of 2021–2022, a field experiment was established in the same disease prone field, comparing different doses of the seed treatment with Systiva® fungicide against leaf blotch and tan spot diseases. The evaluation was made by visual disease assessments, remote sensing with an unmanned aerial vehicle (UAV) and metagenomics analysis. Parallel measurements on straw residues were also made to characterize the plant residues perithecia (pseudothecia). Visual leaf disease assessments and UAV remote sensing data showed that Systiva® treatments at doses of 125 cc and 150 cc per 100 kg of wheat seed can reduce the percentage of infected wheat plants caused by foliar fungal pathogens at wheat growth stages GS23-25 and GS30-31. Moreover, the metagenomics analyses performed on the microbial communities revealed that Systiva® can decrease the degree of infection by P. tritici-repentis and Z. tritici but do not provide sufficient protection against P. nodorum. Foliar diseases were influenced by the soil surface area covered with straw residue with a high proportion of natural inoculum (pseudothecia/ascospores). [ABSTRACT FROM AUTHOR]

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Background: Crohn’s disease (CD) is a chronic inflammatory disorder affecting the gastrointestinal tract with disease behaviour based on the depth and severity of mucosal injury. Cumulative injury can result in complications including stricture formation and penetrating complications which often require surgical resection of diseased segments of the intestine resulting in significant morbidity. Accurate assessment of disease activity and appropriate treatment is essential in preventing complications. Summary: Treatment targets in the management of CD have evolved with the advent of more potent immunosuppressive therapy. Targeting the resolution of sub-clinical inflammation and achieving mucosal healing is associated with the prevention of stricturing and penetrating complications. Identifying non-invasive modalities to assess mucosal healing remains a challenge. Key Messages: Mucosal healing minimizes the risk of developing disease complications, prolongs steroid-free survival, and reduces hospitalization and the need for surgical intervention.

Abstract Purpose To investigate the predictive significance of different pneumonia scoring systems in clinical severity and mortality risk of patients with severe novel coronavirus pneumonia. Materials and methods A total of 53 cases of severe novel coronavirus pneumonia were confirmed. The APACHE II, MuLBSTA and CURB-65 scores of different treatment methods were calculated, and the predictive power of each score on clinical respiratory support treatment and mortality risk was compared. Results The APACHE II score showed the largest area under ROC curve in both noninvasive and invasive respiratory support treatment assessments, which is significantly different from that of CURB-65. Further, the MuLBSTA score had the largest area under ROC curve in terms of death risk assessment, which is also significantly different from that of CURB-65; however, no difference was noted with the APACHE II score. Conclusion For patients with COVID, the APACHE II score is an effective predictor of the disease severity and mortality risk. Further, the MuLBSTA score is a good predictor only in terms of mortality risk.

Epidemic risk has great uncertainty and harmfulness, which poses a potential threat to public health in a certain region. Establishing a special risk assessment system to assess and predict the potential epidemic risk of a region can effectively avoid or reduce the impact of epidemic risk. Therefore, this paper combs the related factors that affect the epidemic risk, and proposes an epidemic risk assessment model based on 12 indicators by combining Markov chain and AHP. The model can assess the epidemic situation in a certain region from four aspects: the probability of risk occurrence, the probability of loss, the possibility of risk disappearance and risk duration, so as to provide detailed data for the risk management and control of epidemic in the region, and help the epidemic prevention work to be carried out in a targeted way. Finally, the case analysis and method comparison are carried out,and the results show that the model proposed in this paper is reasonable and feasible.

Objective: Although there are different tools to evaluate axial spondyloarthritis (axSpA), they are hardly used in routine clinical practice due to time constraints. The Routine Assessment of Patient Index Data 3 (RAPID3) is a composite measure feasible for use as a sole metric in busy clinics. We aimed to test its measurement properties in patients with axial SpA in a real-world clinical setting.Methods: This cross-sectional study included 131 consecutive patients with axial SpA. The convergent (Spearman ρ) and discriminant (receiver-operating characteristic [ROC] curve analysis) validity of RAPID3 were tested against several axSpA-specific measures (Bath Ankylosing Spondylitis Disease Activity Index [BASDAI], Ankylosing Spondylitis Disease Activity Score [ASDAS], Bath Ankylosing Spondylitis Functional Index [BASFI], and modified Stoke Ankylosing Spondylitis Spinal Score [mSASSS]). A multivariate model was built to detect disease factors associated with RAPID3 remission (values ≤ 3).Results: The study included 82 men and 49 women, with a median age of 55 (IQR 46-61) years, and a median disease duration of 11 (IQR 6-24) years. Mean RAPID3 was 9.45 ± 6.7. The BASDAI showed moderate correlation with ASDAS (ρ 0.66, P < 0.0001), but higher correlations with BASFI (ρ 0.78, P < 0.0001) and RAPID3 (ρ 0.75, P < 0.0001). The ASDAS had moderate correlations with BASFI, BASDAI, and RAPID3 (ranges 0.66-0.68, P < 0.0001). Higher correlations were found between BASFI and BASDAI (ρ 0.78, P < 0.0001), and BASFI and RAPID3 (ρ 0.73, P < 0.0001). The mSASSS did not show any correlation with any of the above composite measures. κ agreement between RAPID3 remission and other SpA remission criteria was moderate (κ 0.46-0.56). The RAPID3 thresholds to define remission ranged from values ≤ 2 to ≤ 6 with areas under the ROC curve between 0.86-0.91. Female sex (OR 0.34, 95% CI 0.12-0.90, P = 0.03) and nonsteroidal antiinflammatory drug intake (OR 0.26, 95% CI 0.10-0.66, P = 0.005) were independently associated with lower odds of achieving RAPID3 remission.Conclusion: RAPID3 demonstrated construct validity in this cross-sectional study. This index can be useful for a more comprehensive assessment of axSpA in busy clinical settings. [ABSTRACT FROM AUTHOR]

Background and Aims: Intestinal ultrasound has become a crucial tool for assessing inflammation in patients with inflammatory bowel disease, prompting a surge in demand for trained sonographers. While educational programs exist, the length of training needed to reach proficiency in correctly classifying inflammation remains unclear. Our study addresses this gap partly by exploring the learning curves associated with the deliberate practice of sonographic disease assessment, focusing on the key disease activity parameters of bowel wall thickness, bowel wall stratification, color Doppler signal, and inflammatory fat., Methods: Twenty-one novices and six certified intestinal ultrasound practitioners engaged in an 80-case deliberate practice online training program. A panel of three experts independently graded ultrasound images representing various degrees of disease activity and agreed upon a consensus score. We used statistical analyses, including mixed-effects regression models, to evaluate learning trajectories. Pass/fail thresholds distinguishing novices from certified practitioners were determined through contrasting-groups analyses., Results: Novices showed significant improvement in interpreting bowel wall thickness, surpassing the pass/fail threshold, and reached mastery level by case 80. For color Doppler signal and inflammatory fat, novices surpassed the pass/fail threshold but did not achieve mastery. Novices did not improve in assessing bowel wall stratification., Conclusions: We found considerable individual and group-level differences in learning curves supporting the concept of competency-based training for assessing bowel wall thickness, color Doppler signal and inflammatory fat. However, despite practice over 80 cases, novices did not improve in their interpretation of bowel wall stratification, suggesting that a different approach is needed for this parameter., (© The Author(s) 2024. Published by Oxford University Press on behalf of European Crohn’s and Colitis Organisation. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.)

Gray mold (Botrytis cinerea) is one of the main diseases that affect strawberries. Severity assessments of the disease in fruits have been mainly performed without any aid. Although there has been a rating scale already published, there are no standard area diagrams (SADs) that could be used. Therefore, our objective was to develop and validate a set of SADs to assess severity of gray mold in strawberry fruit. The set of SADs has seven levels of disease severity (0, 8, 28, 46, 77, 82 and 100%) and they were validated by five experienced raters and five inexperienced raters in plant disease assessment. Photos of 50 strawberry fruits were provided to raters for gray mold severity assessment, which was performed in three stages: i) without any aid; ii) with a rating scale previously published; and iii) with the SADs. Lin's concordance correlation coefficient (LCCC) of estimated and actual severity was improved with the use of SADs (0.94) compared to the rating scale (0.74) and assessment without aid (−0.07). Peer comparison of all raters showed that 87% of the coefficient of determination from the regression models between actual and estimated severity were greater than 0.81 with the SADs, while only 37% and 9% of the values were higher than 0.81 with the rating scale and without any aid, respectively. Hence, there was greater consistency of disease assessment among raters. The set of SADs proposed in this study can improve accuracy and precision to estimate severity of gray mold in strawberry fruit. [ABSTRACT FROM AUTHOR]

Foliar fungal diseases are a serious threat to winter wheat production and climate change appears to favor pathogens associated with leaf blotch and tan spot symptoms in the Mediterranean area. The present work aimed to highlight these risks and propose appropriate disease management strategies by evaluating the seed treatment with the Systiva® (BASF) fungicide as a means to protect the crop against foliar fungal infections during the early growing stages. Towards that aim, plant tissue symptoms affected by the pathogens Pyrenophora tritici-repentis and Septoria spp. were systematically recorded in a study field in the region of Larissa, central Greece for three years (2016–2018), and the findings were associated with the monthly weather anomalies. Consequently, for the growing period of 2021–2022, a field experiment was established in the same disease prone field, comparing different doses of the seed treatment with Systiva® fungicide against leaf blotch and tan spot diseases. The evaluation was made by visual disease assessments, remote sensing with an unmanned aerial vehicle (UAV) and metagenomics analysis. Parallel measurements on straw residues were also made to characterize the plant residues perithecia (pseudothecia). Visual leaf disease assessments and UAV remote sensing data showed that Systiva® treatments at doses of 125 cc and 150 cc per 100 kg of wheat seed can reduce the percentage of infected wheat plants caused by foliar fungal pathogens at wheat growth stages GS23-25 and GS30-31. Moreover, the metagenomics analyses performed on the microbial communities revealed that Systiva® can decrease the degree of infection by P. tritici-repentis and Z. tritici but do not provide sufficient protection against P. nodorum. Foliar diseases were influenced by the soil surface area covered with straw residue with a high proportion of natural inoculum (pseudothecia/ascospores).

Objective: To determine the relationship of myositis autoantibodies with the diagnosis and severity of idiopathic inflammatory myopathy (IIM) using the 2017 EULAR/ACR idiopathic inflammatory myopathy classification criteria and the myositis disease activity assessment tool (MDAAT). Methods: Patients who met the new diagnostic criteria were tested for serum myositis-specific autoantibodies (MSAs) and myositis-associated autoantibodies (MAAs), and then classified into different subgroups based on autoantibody positivity. Patients were also diagnosed with possible IIM, probable IIM, and definite IIM. The MDAAT was used to evaluate muscular and extramuscular disease activity. The relationships of diagnostic classification with positivity for different myositis autoantibodies were determined. Results: There were 118 patients, and 81% of them had one or more myositis autoantibody. Anti-Jo-1 was the most common MSA, and anti-Ro-52 was the most common MAA. Sixteen patients (14%) had possible IIM, 36 (31%) had probable IIM, and 66 (56%) had definite IIM. MSA-positive patients were significantly more common in the definite IIM group, but MAA positivity was unrelated to diagnostic classification. Positivity for MSAs or MAAs had no correlations with muscle disease activity. Extramuscular disease activity was greater in MSA-positive than MSA-negative patients, but MAA positivity had no significant association with extramuscular disease activity. Conclusions: MSA positivity aids in the diagnosis of IIM. MSA positivity was associated with greater extramuscular disease activity. Improving the clinical application of MSAs may enhance the individualized treatment of patients with IMM. Key Points • In this paper, we explore the relationships between the myositis autoantibodies and the diagnosis and the disease activity of inflammatory myopathy. • Positive myositis-specific autoantibodies is associated with the establishment of diagnosis and higher extramuscular disease activity. • Thus, more extensive application of myositis autoantibodies maybe the key for further disease assessment and research. [ABSTRACT FROM AUTHOR]

Purpose: To investigate the predictive significance of different pneumonia scoring systems in clinical severity and mortality risk of patients with severe novel coronavirus pneumonia. Materials and methods: A total of 53 cases of severe novel coronavirus pneumonia were confirmed. The APACHE II, MuLBSTA and CURB-65 scores of different treatment methods were calculated, and the predictive power of each score on clinical respiratory support treatment and mortality risk was compared. Results: The APACHE II score showed the largest area under ROC curve in both noninvasive and invasive respiratory support treatment assessments, which is significantly different from that of CURB-65. Further, the MuLBSTA score had the largest area under ROC curve in terms of death risk assessment, which is also significantly different from that of CURB-65; however, no difference was noted with the APACHE II score. Conclusion: For patients with COVID, the APACHE II score is an effective predictor of the disease severity and mortality risk. Further, the MuLBSTA score is a good predictor only in terms of mortality risk. [ABSTRACT FROM AUTHOR]

Pork accounts for almost one-third of the meat consumed worldwide. Infectious diseases have a marked impact on pig production. Epidemiological indicators are considered the most useful criteria in decision-making; however, a health status assessment remains a challenge at the national and regional levels. This study proposes a health index including herd-losses, morbidity, fatality, and type of diseases, to rate the health situation in a region or country; it contributes to assessing the effectiveness of control, damage manifestation, and trends. It is a multidimensional index with a structure of triads and simple quantitative, semi-quantitative, and qualitative expressions that use flexible and dynamics limits. With it, we analyzed twenty-one countries in 2005–2018, focusing on African swine fever, classical swine fever, foot-mouth-disease, and porcine respiratory and reproductive syndrome, diseases that caused 72% of the morbidity. Our multidimensional approach estimates farm, local, and regional impact from infectious agents and outbreaks, and apprises trends aiming to be useful to control measures, strategic actions, and animal health policies. [ABSTRACT FROM AUTHOR]

ABSTRACT: The development and validation of a standard area diagram set (SADs) was proposed in this study to assess the severity of powdery mildew (Podosphaera xanthii) in watermelon (Citrullus lanatus) leaves. The SADs proposed has twelve levels of severity, varying from 0.07 to 100%. The SADs were validated by 16 raters who had no previous experience in evaluating plant disease severity. Initially, the estimation of severity was performed without the use of the SADs in leaves with different levels of severity. In a second moment, the same raters estimated the disease severity using the SADs proposed. By Lin’s concordance correlation analysis, there was an improvement in precision (coefficient of correlation, r = 0.878 and r = 0.959, without and with SADs, respectively) and accuracy (bias correction factor, Cb = 0.830 and 0.982, without and with SADs, respectively) using SADs when compared to the non-use of SADs. The agreement (Lin’s concordance correlation coefficient, ρc = 0.734 and 0.952 without and with SADs, respectively) also improved using SADs. Severity estimates inter-rater were more reliable when using SADs (coefficient of determination, R2 = 0.681 without and R2 = 0.864 with SADs; Intra-class correlation coefficient, ρ = 0.759 and ρ = 0.928, without and with SADs, respectively). Therefore, SADs improved precision, accuracy and reliability of powdery mildew severity on watermelon leaves.

The aim of this study was to evaluate the chemical control of Asian soybean rust (Phakopsora pachyrhizi Syd. & P. Syd.) using different tips and spray nozzles under controlled conditions. Rooted trifolia placed inside an acrylic box containing macronutrients, micronutrients and vitamins were treated with epoxiconazol + pyraclostrobin, ciproconazol + azoxystrobin and tebuconazol fungicides. These fungicides were sprayed at a height of 0.5 m with two knapsack sprayers pressurized with CO2 gas (drop generator in four spray tips spaced by 0.5 m from each other) and another rotating nozzle (drop generator through a rotating nozzle). Untreated trifolia were controls. Water sensitive paper was placed next to each acrylic box to obtain median volume diameter (MVD), number of droplets.cm-2 and coverage (%). Trifolia were inoculated 24 hours after that these were treated with fungicide, using a spore concentration adjusted to 2 x 104 uredospores.mL-1. Then, the number of lesions and uredia.cm-2 in central leaflets were quantified. Lower number of lesions and uredia.cm-2 were observed in leaflets treated with two fungicide mixtures using an AIRMIX 11001 tip. A lower VMD was found in leaflets sprayed with the fungicides azoxystrobin + epoxiconazol and tebuconazol, when compared to the leaflets treated with another mixture. The Asian rust control, as well as the number of droplets.cm-2 and coverage, seems to be dependent on the interactions among fungicides and tips or nozzles.

This study aimed to develop and validate a standard area diagram set (SADs) to estimate the severity of eyespot of rice caused by Drechslera gigantea. For this purpose, a SADs with seven levels of severity (0.3; 1.0; 3.0; 5.0; 10; 15; and 21%) was established. The SADs was validated by 16 raters with no experience in evaluating plant diseases. Both accuracy and precision improved when they used the SADs. The statistical parameters for the 16 raters were: bias coefficient factor - Cb (no SADs = 0.404, with SADs = 0.994); correlation coefficient - r (no SADs = 0.884, with SADs = 0.953); and Lin's concordance correlation coefficient - ρc (no SADs = 0.356, with SADs = 0.947). In addition, estimates were more reliable: inter-rater coefficient of determination - R2 (no SADs = 0.712, with SAD = 0.873); intra-class correlation coefficient - ρ (no SADs = 0.723, with SADs = 0.924). The SADs proposed here is a useful tool for improving visual assessments of eyespot severity of rice. [ABSTRACT FROM AUTHOR]

Takayasu's arteritis (TA) is a rare, idiopathic, chronic inflammatory disease with cell-mediated inflammation, involving mainly the aorta and its major branches. It leads to stenosis, occlusion or aneurysmal degeneration of large arteries. The clinical presentation is characterized by an acute phase with constitutional symptoms, followed, months or years later, by a chronic phase in which symptoms relate to fibrosis or occlusion of vessels. Conventional angiography, the gold standard method for initial diagnosis, appears to have been replaced with new imaging modalities such as magnetic resonance angiography (MRA) and 18Ffluorodeoxyglucose positron emission tomography (FDGPET) in recent years. These are also used for the assessment of disease activity. New tools for disease assessment such as Indian Takayasu's Arteritis Score 2010 (ITAS2010) and color Doppler ultrasound (CDUS) aim to better characterize and quantify disease activity. Leflunomide, tumor necrosis factor (TNF)-± antagonists, and tocilizumab are new options for patients resistant to conventional therapies. Prognosis is possibly getting better, with lower mortality in recent years due to recent advancement in investigations and management. [ABSTRACT FROM AUTHOR]

The study aimed to improve the small-ear waxy corn populations for prolificacy, high total carotenoid content and resistance to downy mildew. Three cycles of modified mass selection were carried out for population improvement. Forty-four genotypes derived from eight C3 populations and six check varieties were evaluated for agronomic traits and yield at Khon Kaen and screened for downy mildew resistance in the rainy season 2020 at two experimental sites in Ban Phang district of Khon Kaen province. Fifty genotypes were clustered into six major groups based on color parameter (h°) and total ear number. Two selected groups (C and E) with two populations of small-ear waxy corn including Nei9008/BK-24-9-B and TY/TF-33-1-B were selected as they were resistant to downy mildew, prolific ears, and intense orange kernel color. Modified mass selection was effective for improvement of multiple traits in waxy corn.

The aim of the present study was to develop and validate a standard area diagram set (SADs) to evaluate severity of grapevine mildew (Plasmopara viticola) for Vitis labrusca cultivars. SADs contain 10 disease severity levels (0.5; 2; 5; 10; 20; 30; 40; 50; 60 and 72%). Validation was performed using a sequence of 50 images assessed by 16 raters with no previous experience in quantifying plant disease severity. By Lin's concordance correlation coefficient analysis, there was an improvement in u (no SADs = 0.361, with SADs = −0.030), υ (no SADs = 1.456, with SADs = 0.997), Cb (no SADs = 0.864, with SADs = 0.979), r (no SADs = 0.897, with SADs = 0.937) and ρc (no SADs = 0.774, with SADs = 0.918) parameters, showing an increase in accuracy and precision of the estimated disease severity using SADs when compared to the non-use of SADs. Reliability improvement was also observed, with increase in R2 values when SADs were used (no SADs = 0.790, with SADs = 0.846). Therefore, SADs improved accuracy, precision and reliability of grape downy mildew severity estimates in Vitis labrusca. [ABSTRACT FROM AUTHOR]

Alternaria brown spot (ABS) is one of the main diseases of tangerine worldwide. It is necessary to use a practical and reliable method to quantify the disease severity of ABS and thus suggest management strategies. Thus, a diagrammatic scale (DS) constructed with color photographs was developed and validated to quantify the disease severity. Leaves were collected from crops and a nursery with different levels of ABS symptoms to determine the real severity digitally. The scale levels were determined according to the minimum and maximum severity values found and based on the frequency distribution of severity values. The DS validation was carried out by eleven assessors who estimated the severity of 50 leaves with different symptom levels. One assessment was performed without the DS and two using the DS at seven day intervals. The precision, accuracy, repeatability and reproducibility of the estimates were measured. The developed DS has nine score levels: 0 (0%), 1 (0.1–0.49%), 2 (0.5–1.0%), 3 (1.1–2.0%), 4 (2.1–4.0%), 5 (4.1–8%), 6 (8.1–18%), 7 (18.1–30%) and 8 (30.1–50%). The linear regression and Lin's concordance correlation coefficient (LCCC) demonstrated an improved accuracy, precision repeatability and reproducibility of the estimates using the DS. The scale is appropriate, useful and reliable to visually estimate with the ABS severity in tangerine leaves. [ABSTRACT FROM AUTHOR]

Main objective is to assess rust diseases severity of different wheal varieties under different sowing time to find best sowing time with highest rusts levels undo natural conditions. High level of disease existence is important to escape diseases inoculation and its costs during breeding studies. Experiment was conducted during 2008-2009, 2009-2010 and 2010-2011 wheat growing seasons for 3 years in Adana in Turkey. In this study, 10 bread and 2 durum wheat varieties were tested for yellow rust, leaf rust and stem rust diseases. Rust diseases severities were assessed with 5 different sowing time under natural rust disease inoculations. There were strong relationships between sowing time and stem rust disease in 3 years. When sowing time delayed from November to April, stem rust disease severity significantly increased due to increased stem rust spores in April sown wheat materials. Instead, yellow rust disease severity was not significantly affected from sowing time. This result may be related to low yellow rust infection coefficients of wheat varieties. Leaf rust showed different responses to sowing times in 3 years, so different sowing time method may probably not function properly for this disease. As a sole result of this research, April sowing will be the best adopted sowing time to obtain high stem rust diseases existence in wheat in Mediterranean climate conditions to test breeding materials for stem rust under natural conditions. [ABSTRACT FROM AUTHOR]

The host resistance of walnut (Juglans spp.) against Xanthomonas arboricola pv. juglandis (Xaj) was evaluated for 18 walnut genotypes in 2016 and 2017 at Chengdu, Sichuan, China, using three assays: one under natural conditions (field investigations) and two under controlled conditions (detached leaflet assay and whole plant assay). Field investigations were used as a benchmark for comparing the other two assays. Five genotypes, 'JS 91', 'JS 92', 'JS 86', 'Qingxiang' and 'JS 71', displayed partial resistance in all assays, suggesting that they may be good candidates for further evaluation. The rankings for resistance among the 18 genotypes showed significant correlations between field investigations and both the detached leaflet assay and whole plant assay. However, low determination coefficients indicated that assays under controlled conditions should be considered as complementary methods and not as a substitute for field analysis when evaluating resistance to walnut blight. [ABSTRACT FROM AUTHOR]