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Complications of acute pancreatitis can be disastrous if appropriate treatment is not initiated. Pancreatic necrosis can occur without the presence of symptoms; however, in some cases, it can be accompanied by organic failure, abscess, pseudocyst, fistulas, and pancreatic exocrine disfunction. The surgical treatment of pancreatic necrosis can be managed with open surgical debridement of necrotic tissue. Hence, complications after surgery can appear even in patients without clinical background; complications mostly appear if the surgical technique is not done properly. We present a case of a 47-year-old woman who appeared with abdominal pain, nausea, vomiting, and oral intake intolerance. Symptoms were present for 1 week; she was admitted to the nearest clinic, and surgical management was offered. The patient went to an open pancreatic necrosectomy; however, she presented purulent exudate from the surgical wound and drains. Was referred to our center; on abdominal contrasted computed tomography, a transmesenteric defect and cutaneous-pancreatic fistula were found. [ABSTRACT FROM AUTHOR]

Breeding annual crops for enhanced root depth and biomass is considered a promising intervention to accrue soil organic carbon (SOC) in croplands, with benefits for climate change mitigation and soil health. In annual crops, genetic technology (seed) is replaced every year as part of a farmer's fixed costs, making breeding solutions to climate change more scalable and affordable than management approaches. However, mechanistic understanding and quantitative estimates of SOC accrual potentials from crops with enhanced root phenotypes are lacking. Maize is the highest acreage and yielding crop in the US, characterized by relatively low root biomass confined to the topsoil, making it a suitable candidate for improvement that could be rapidly scaled. We ran a 2‐year field experiment to quantify the formation and composition (i.e., particulate (POM), coarse and fine mineral–associated organic matter (chaOM and MAOM, respectively) of new SOC to a depth of 90 cm from the decomposition of isotopically labeled maize roots and exudates. Additionally, we used the process‐based MEMS 2 model to simulate the SOC accrual potential of maize root ideotypes enhanced to either shift root production to deeper depths or increase root biomass allocation, assuming no change in overall productivity. In our field experiment, maize root decomposition preferentially formed POM, with doubled efficiency below 50 cm, while root exudates preferentially formed MAOM. Modeling showed that shifting root inputs to deeper layer or increasing allocation to roots resulted in a deterministic increase in SOC, ranging from 0.05 to 0.15 Mg C ha−1 per year, which are at the low end of the range of published SOC per hectare annual accrual estimates from adoption of a variety of crop management practices. Our analysis indicates that for maize, breeding for increasing root inputs as a strategy for SOC accrual has limited impact on a per‐hectare basis, although given that globally maize is produced on hundreds of millions of hectares each year, there is potential for this technology and its effect to scale. For maize–soy system that dominates US acres, changes in the overall cropping system are needed for sizable greenhouse gas reductions and SOC accrual. This study demonstrated a modeling and experimental framework to quantify and forecast SOC changes created by changing crop root inputs. [ABSTRACT FROM AUTHOR]

Diabetic Retinopathy (DR) is a severe retinal condition primarily affecting diabetic people. This is mostly due to excessive blood sugar levels, which induce retinal blood vessel damage. Diabetic Retinopathy must be detected early to avoid vision loss. Exudates on the retina are the primary indicator of diabetic retinopathy. Therefore, their detection would be crucial for automated illness monitoring and grading. This research provides novel classification and segmentation methods for accurately classifying DR images. The process involves four stages: preprocessing, DR classification, Exudates segmentation, and performance computation. In the preprocessing stage, the source RGB retinal images are separated into three bands, R-Image, G-Image, and B-Image, and each sub-band image is then subjected to data augmentation. The data-augmented images are classified through the proposed Hybrid Centric Convolutional Neural Networks (HCCNN) classifier to produce the classification results (Grade 0 or Grade 1). The proposed exudate lesions segmentation method is utilized to predict and find the exudates in the DR image. The exudates segmented retinal Image is then used to do performance computations for sensitivity (Se), specificity (Sp), and accuracy (Acc), as well as computational complexity analysis. The standard datasets (SYSU and HRF) and the real-time dataset are subjected to the HCCNN approach. The mean Se, Sp, and Acc obtained by this HCCNN method are 98.65%, 98.76%, and 98.96% for the SYSU and 98.58%, 98.65%, and 98.6% for the HRF dataset, respectively. Our method obtains an average detection ratio of 91.66% in the real-time dataset. The suggested HCCNN approach produces findings that outperform the conventional methods and help doctors identify the DR disease early to prevent further vision loss. [ABSTRACT FROM AUTHOR]

Diabetes results in diabetic retinopathy. which occurs due to a change in the vasculature structure. Diabetic abnormalities such as microaneurysms, haemorrhages, and "exudates" describe the stage of diabetic retinopathy. Early-stage detection gains prime importance to avoid permanent sight loss. Exudates are one of the most noticeable signs of diabetic retinopathy. This work presents the automatic and non-invasive approach to detect exudates using requisitely adjusted morphological operators. The use of Standard deviation of histogram equalized image after optic disc detection and deletion is the work's key contribution. The standard deviation offers the tendency of distributing the favourable exudate pixels towards the mean value. The proposed method is implemented on the "IDRiD (Indian Diabetic Retinopathy Image Dataset)". The detected exudates are compared with provided ground-truths reported accuracy of 99.13%. The outcomes prove its effectiveness of the proposed method and can aid ophthalmologists in diagnosis. [ABSTRACT FROM AUTHOR]

Xylem sap proteomics provides crucial insights into plant defense and root-to-shoot communication. This study highlights the sensitivity and reproducibility of xylem sap proteome analyses, using a single plant per sample to track over 3000 proteins in two model crop plants, Solanum tuberosum and Hordeum vulgare. By analyzing the flg22 response, we identified immune response components not detectable through root or shoot analyses. Notably, we discovered previously unknown elements of the plant immune system, including calcium/calmodulin-dependent kinases and G-type lectin receptor kinases. Despite similarities in the metabolic pathways identified in the xylem sap of both plants, the flg22 response differed significantly: S. tuberosum exhibited 78 differentially abundant proteins, whereas H. vulgare had over 450. However, an evolutionarily conserved overlap in the flg22 response proteins was evident, particularly in the CAZymes and lipid metabolism pathways, where lipid transfer proteins and lipases showed a similar response to flg22. Additionally, many proteins without conserved signal sequences for extracellular targeting were found, such as members of the HSP70 family. Interestingly, the HSP70 response to flg22 was specific to the xylem sap proteome, suggesting a unique regulatory role in the extracellular space similar to that reported in mammalians. [ABSTRACT FROM AUTHOR]

Negative pressure wound therapy is currently one of the most popular treatment approaches that provide a series of benefits to facilitate healing, including increased local blood perfusion with reduced localized oedema and control of wound exudate. The porous foam dressing is a critical element in the application of this therapy and its choice is based on its ability to manage exudate. Industry standards often employ aqueous solutions devoid of proteins to assess dressing performance. However, such standardized tests fail to capture the intricate dynamics of real wounds, oversimplifying the evaluation process. This study aims to evaluate the technical characteristics of two different commercial polyurethane foam dressings during negative pressure wound therapy. We introduce an innovative experimental model designed to evaluate the effects of this therapy on foam dressings in the presence of viscous exudates. Our findings reveal a proportional increase in dressing fibre occupancy as pressure intensifies, leading to a reduction in dressing pore size. The tests underscore the pressure system's diminished efficacy in fluid extraction with increasing fluid viscosity. Our discussion points to the need of establishing standardized guidelines for foam dressing selection based on pore size and the necessity of incorporating real biological exudates into industrial standards. [ABSTRACT FROM AUTHOR]

Macrophytes and cladocerans represent the main antagonistic groups that regulate phytoplankton biomass; however, the mechanism behind this interaction is unclear. In laboratory conditions, we separately evaluated the effects of three submerged macrophytes (Ceratophyllum demersum, Myriophyllum aquaticum, and Stuckenia pectinata), as well as their exudates, and plant-associated microbiota (POM < 25 µm) + exudates on the population growth of Daphnia cf. pulex and Simocephalus cf. mixtus. Living Ceratophyllum, exudates, and POM < 25 µm + exudates exhibited the most robust positive effects on Simocephalus density and the rate of population increase (r). Subsequently, we examined the effects of Ceratophyllum on the filtration and feeding rates of Simocephalus and Daphnia, revealing significant (p < 0.001) promotion of filtration and feeding in Simocephalus but not in Daphnia. To elucidate the specific effects of this macrophyte on Simocephalus demography, we assessed selected life table variables across the same treatments. The treatments involving exudates and living Ceratophyllum resulted in approximately 40% longer survivorship and significantly (p < 0.01) enhanced fecundity. Our findings indicate that exudates from submerged macrophytes positively influence Simocephalus demography by increasing filtration rates, survivorship, and fecundity. This synergy suggests a substantial impact on phytoplankton abundance. [ABSTRACT FROM AUTHOR]

Diabetes is considered to be the foundation of a slew of other health issues and late consequences, according to medical experts. The rise in diabetes-related illnesses has presented a challenge to the healthcare industry. Diabetic neuropathy, diabetic nephropathy, and diabetic retinopathy are just a few complications that can arise from diabetes. Diabetic Retinopathy is characterized by red lesions, bright lesions, and neovascularization. Bright lesions (exudates) are the second clinically visible lesions that appear after red lesions. The present challenge in DR detection is to make early diagnosis of DR disease more accessible by minimizing the cost and personnel requirements while preserving or enhancing DR detection quality. The challenge can be solved by using automated or computer-assisted DR detection in retinal images. The precise location and shape of blood vessels and the optic disc play critical roles in accurately diagnosing and classifying dark and bright lesions for the early detection of DR. The primary aim of the proposed model is to create an automated model for identifying bright lesions for non-proliferative stage diabetic retinopathy screening using deep learning architecture. It presents algorithms for removing the background of the images, eliminating the optic disc (OD), and the segmentation of candidate lesions. The model was trained and evaluated using MESSIDOR and e-ophtha Ex public datasets and obtained maximum accuracy, sensitivity, specificity, and F1-score of 97.54%, 90.34%, 98.24%, 93.28% and 96.32%, 95.73%, 97.12%, 96.74% respectively. [ABSTRACT FROM AUTHOR]

The application of disease-resistant varieties is the most cost-effective method for solving the problem of clubroot. "Shangpin," a disease-resistant variety of Chinese cabbage with broad-spectrum immunity to Plasmodiophora brassicae (P. brassicae), was screened in a previous study. Based on 16S rRNA sequencing technology, we annotated the compositional differences between the rhizosphere, rhizoplane, and endosphere bacterial communities of "Shangpin" and "83-1" under P. brassicae stress. Alpha diversity analysis showed that the abundance of microorganisms in the root system of "83-1" changed more than that of "Shangpin" after P. brassicae infestation, and Beta diversity analysis indicated that Flavobacterium and Sphingomonas may mediate clubroot resistance, while Nitrospira, Nitrosospira, and Pseudomonas may mediate P. brassicae infestation among the bacteria in the Top 10 abundances. Microbial functional analyses showed that the root microorganisms of "83-1" were metabolically weakened after P. brassicae inoculation and were inhibited in competition with pathogenic bacteria. Conversely, the root microorganisms of "Shangpin" maintained the strength of their metabolic capacity, which took a favorable position in competition with the pathogen and inhibited the growth and development of the pathogen, thus showing resistance. Root secretions of "Shangpin" significantly inhibited the incidence and disease index of clubroot, which indicated that under clubroot stress, resistant varieties maintain root microbial diversity and microbial community functions through specific root exudates, enriching the genera Flavobacterium and Sphingomonas, thus showing resistance. The results of this study reveal the resistance mechanism of resistant varieties to clubroot and provide new insights into the prevention and control of clubroot in Chinese cabbage. [ABSTRACT FROM AUTHOR]

Modern Medical diagnosis has a significant reliance on systematic image analysis for rational prognosis. With the advancement of computer vision methods, supported by deep learning algorithms, the non-invasive and early-stage detection of various diseases is a significant area of research. Amongst various image-based disease identification scopes, in this paper it is focused on Diabetic Retinopathy (DR), which is an eye disease caused by diabetes that can even lead to blindness. Therefore, early detection is critical to prevent visual disturbances from such diseases. In this study, the objective is to identify diabetic retinopathy by analysing fundus images using deep learning methods. Identifying diabetic retinopathy from fundus images, requires considerable improvement of image quality. In this work the retina images obtained by the fundus camera is improved by Generative Adversarial Network(GAN) for specific analysis. This study aimed to detect lesions using mask-RCNN techniques, where pretrained models such as R-50, R-101, and X-101 are utilized for robust segmentation and masking of the damaged regions of the fundus images. As the lesions are pathologically classified into two categories: 'exudates' and 'microaneurysms', similarly the input images are also mapped into two categories. Evaluation is based on the mean average precision (MAP) and the findings infer the conclusion. The comprehensive set of real-life retina images obtained from the clinical dataset benchmarked with the proposed mechanism and it is found that the proper segmentation and improvisation of fundus images by applying GAN have considerably enhanced the degree of accuracy in the Region-of-interest(ROI) bounding box for exact identification of Diabetic Retinopathy, along with reporting on stage of the disease. The aim of the work is well established in implementing the algorithm and finding the result for mapping it into the automated classification of fundus images in batches where the X-101 model seen to perform better in terms of finding Bounding Box Average Precision for both exudates (75.20%) and microaneurysms (67.202%) followed by Segmented Region Average Precision for exudates (62.363%) and microaneurysms (57.690%). The obtained results may reduce human intervention and classify a large number of input images in field-level examination in a faster manner. [ABSTRACT FROM AUTHOR]

Open in new tab Download slide OBJECTIVES The goal of this histological study was to assess the biocompatibility of vascular patches used in the repair of congenital heart defects. METHODS We examined tissue-engineered bovine (n = 7) and equine (n = 7) patches and autologous human pericardium (n = 7), all explanted due to functional issues or follow-up procedures. Techniques like Movat-Verhoeff, von Kossa and immunohistochemical staining were used to analyse tissue composition, detect calcifications and identify immune cells. A semi-quantitative scoring system was implemented to evaluate the biocompatibility aspects, thrombus formation, extent of pannus, inflammation of pannus, cellular response to patch material, patch degradation, calcification and neoadventitial inflammation. RESULTS We observed distinct material degradation patterns among types of patches. Bovine patches showed collagen disintegration and exudate accumulation, whereas equine patches displayed edematous swelling and material dissolution. Biocompatibility scores were lower in terms of cellular response, degradation and overall score for human autologous pericardial patches compared to tissue-engineered types. The extent of pannus formation was not influenced by the type of patch. Bovine patches had notable calcifications causing tissue hardening, and foreign body giant cells were more frequently seen in equine patches. Plasma cells were frequently detected in the neointimal tissue of engineered patches. CONCLUSIONS Our results confirm the superior biocompatibility of human autologous patches and highlight discernible variations in the changes of patch material and the cellular response to patch material between bovine and equine patches. Our approach implements the semi-quantitative scoring of various aspects of biocompatibility, facilitating a comparative quantitative analysis across all types of patches, despite their inherent differences. [ABSTRACT FROM AUTHOR]

Diabetic retinopathy stands out as one of the highly prevalent causes of vision loss in working people worldwide. In computer vision, deep learning based strategies are seen as a viable solution for efficient diabetic retinopathy detection. We present a UNet-based deep learning architecture for diabetic retinopathy segmentation of blood vessels, exudates, and microaneurysms. Traditional methods often consider the features only from the last convolution unit and discard the remaining features, resulting in low-quality feature maps. However, boundary information plays important role in medical image segmentation. To overcome this, we introduce a skip connection mechanism to concatenate all attributes from each layer. Additionally, we utilize an upsampling layer to aggregate the features at the final sigmoid layer. Finally, we apply channel and spatial attention mechanisms to generate the semantic feature map. Therefore, the proposed approach overcomes the issues of existing methods by incorporating dense skip connection along with channel and spatial attention mechanism which helps to retain the substantial information of image. We tested proposed approach on several publicly available datasets such as IDRiD, DIARETDB1, STARE, ChaseDB1, DRIVE, and HRF datasets. The comparative analysis shows that the proposed approach achieves superior performance, with an average accuracy of 98.10%, average sensitivity of 97.60%, and average specificity of 98.2% for segmentation. [ABSTRACT FROM AUTHOR]

Root hairs are considered important for rhizosphere formation, which affects root system functioning. Through interactions with soil microorganisms mediated by root exudation, root hairs may affect the phenotypes and growth of young plants. We tested this hypothesis by integrating results from two experiments: (1) a factorial greenhouse seedling experiment with Zea mays B73‐wt and its root‐hairless mutant, B73‐rth3, grown in live and autoclaved soil, quantifying 15 phenotypic traits, seven growth rates, and soil microbiomes and (2) a semi‐hydroponic system quantifying root exudation of maize genotypes. Possibly as compensation for lacking root hairs, B73‐rth3 seedlings allocated more biomass to roots and grew slower than B73‐wt seedlings in live soil, whereas B73‐wt seedlings grew slowest in autoclaved soil, suggesting root hairs can be costly and their benefits were realized with more complete soil microbial assemblages. There were substantial differences in root exudation between genotypes and in rhizosphere versus non‐rhizosphere microbiomes. The microbial taxa enriched in the presence of root hairs generally enhanced growth compared to taxa enriched in their absence. Our findings suggest the root hairs' adaptive value extends to plant‐microbe interactions mediated by root exudates, affecting plant phenotypes, and ultimately, growth. Summary Statement: An experiment with seedlings of Zea mays B73 and its root‐hairless mutant (Z. mays B73‐rth3) with contrasting soil microbiomes showed that root hairs are beneficial towards plant growth but can be costly. Root hairs' adaptive value was partly mediated by plant‐microbe interactions involving exudates, which feedback to plant phenotypes and growth. [ABSTRACT FROM AUTHOR]

Abstract Background The rhizosheath, a cohesive soil layer firmly adhering to plant roots, plays a vital role in facilitating water and mineral uptake. In pearl millet, rhizosheath formation is genetically controlled and influenced by root exudates. Here, we investigated the impact of root exudates on the microbiota composition, interactions, and assembly processes, and rhizosheath structure in pearl millet using four distinct lines with contrasting soil aggregation abilities. Results Utilizing 16S rRNA gene and ITS metabarcoding for microbiota profiling, coupled with FTICR-MS metabonomic analysis of metabolite composition in distinct plant compartments and root exudates, we revealed substantial disparities in microbial diversity and interaction networks. The ß-NTI analysis highlighted bacterial rhizosphere turnover driven primarily by deterministic processes, showcasing prevalent homogeneous selection in root tissue (RT) and root-adhering soil (RAS). Conversely, fungal communities were more influenced by stochastic processes. In bulk soil assembly, a combination of deterministic and stochastic mechanisms shapes composition, with deterministic factors exerting a more pronounced role. Metabolic profiles across shoots, RT, and RAS in different pearl millet lines mirrored their soil aggregation levels, emphasizing the impact of inherent plant traits on microbiota composition and unique metabolic profiles in RT and exudates. Notably, exclusive presence of antimicrobial compounds, including DIMBOA and H-DIMBOA, emerged in root exudates and RT of low aggregation lines. Conclusions This research underscores the pivotal influence of root exudates in shaping the root-associated microbiota composition across pearl millet lines, entwined with their soil aggregation capacities. These findings underscore the interconnectedness of root exudates and microbiota, which jointly shape rhizosheath structure, deepening insights into soil–plant-microbe interactions and ecological processes shaping rhizosphere microbial communities. Deciphering plant–microbe interactions and their contribution to soil aggregation and microbiota dynamics holds promise for the advancement of sustainable agricultural strategies. Video Abstract

Patients with diabetes usually develop a condition called diabetic retinopathy (DR), resulting from retinal damage. This impairment usually happens when the glucose levels in the blood are elevated, finally causing a blockage in the blood vessels that feed a part of the eye called the retina and finally severing it from the blood supply. Therefore, the eye attempts to produce fresh blood cells. But these cells are either poorly developed or weak. So, it can be leaked out easily. Hence, to lessen the severe effects of this disease, these patients must be diagnosed as soon as possible. Earlier, a number of approaches were put forth to recognise this illness using machine learning algorithms, image processing, and other techniques. The diagnosis process of this disease involves pre-processing of coloured images of the fundus, extraction of clinical features and classification of retinopathy. In this research, fundus photography of the retina is utilised to accelerate the detection of various kinds of retinopathy caused by diabetes based on convolutional neural network (CNN) pre-trained transfer learning algorithm. Inception V3 and Xception are used in this model to determine and categorise diabetic retinopathy, respectively. As a result, people with this disease can lower their risk of exposure to permanent blindness. [ABSTRACT FROM AUTHOR]

Co-planting two or more species on the same piece of land, with overlapping time or not, has been suggested to increase both crop production and long-term sustainability. On soils that are slightly or moderately contaminated with heavy metals, hyperaccumulators have been co-planted with crops to clean the soil and produce safe agricultural products. Despite the increasing number of greenhouse experiments and field trials that investigate the co-planting mechanisms and efficiency, the consistency, stability, and applicability of this technology and its contribution to sustainability remain unclear. From published literature, we collected 118 co-planting combinations involving hyperaccumulators, and compared them with their monoculture controls. Co-planting averagely decreased the shoot arsenic concentration by ∼23.4% and cadmium by ∼13.4%. Co-planting controls the crop contamination as long as the hyperaccumulator and crop species are correctly selected, and the soil heavy metal is within the safe range. Further, a sustainability assessment criterion for the utilization of contaminated agricultural soil was proposed, taking As-contaminated soil as an example. A decision framework and a guideline for co-planting were established to aid in the decision-making. The outlook of co-planting as a sustainable solution and the future development were prospected. [ABSTRACT FROM AUTHOR]

Given the immense importance of medical image segmentation and the challenges associated with manual execution, a diverse range of automated medical image segmentation methods have been developed, primarily focusing on specific modalities of images. This paper introduces an innovative segmentation algorithm that effectively segments exudates, hemorrhages, microaneurysms, and blood vessels within retinal images using an enhanced MesNet (MesU-Net) model. By combining the MES-Net model with the U-Net model, this approach achieves accurate results in a shorter period. Consequently, it holds significant potential for clinical application in computer-aided diagnosis. The IDRID and DRIVE datasets are utilized to assess the efficacy of the proposed model for retinal segmentation. The presented method attains segmentation accuracy rates of 97.6%, 98.1%, 99.2%, and 83.7% for exudates, hemorrhages, microaneurysms, and blood vessels, respectively. This proposed model also holds promise for extension to address other medical image segmentation challenges in the future. [ABSTRACT FROM AUTHOR]

The escalating prevalence of diabetes globally, exacerbated by lifestyle changes postpandemic-- including increased screen time, sedentary behavior, and remote work-- has consequently driven a surge in associated complications, notably, Diabetic Retinopathy (DR). This ocular complication presents a pressing concern due to its potential to precipitate irreversible vision loss. Consequently, the necessity for timely and accurate DR detection is paramount, especially in circumstances where conventional diagnostic approaches are either challenging or financially prohibitive. Capitalizing on the prowess of fuzzy logic in managing uncertainties, this study introduces an innovative application of Extended Fuzzy Logic for the early-stage detection of DR. Rather than focusing solely on overt symptoms, this approach discerns subtle similarities in retinal irregularities between diabetic patients and non-diabetic individuals. To quantify these similarities, the 'f-validity' value was computed based on DR risk factors and associated symptoms, which were subsequently transformed into membership function values. The aggregation of these values was facilitated by the Ordered Weighted Averaging (OWA) operator. The experimental outcomes of this approach align satisfactorily with expert anticipations, boasting an accuracy of 90%, a precision of 92.2%, and a sensitivity of 75%. These results, when juxtaposed against contemporary studies in the field, underscore the promise of this scheme in advancing early diagnostics of DR. The study thus proposes a potential solution that leverages the power of fuzzy logic to address the burgeoning challenge of DR. [ABSTRACT FROM AUTHOR]

Background Pleural effusion may occur in different infections or as a complication of pulmonary disease, malignant disease. Exudative pleural effusion results from local or systemic disease that directly injure the pleural surface. To know intrapleural pathology, correct diagnosis of pleural effusion is essential. For this many parameters have been proposed for segregation of exudates from transudates. Cholinesterase level in pleural effusion of diverse etiologies helps to differentiate between transudates and exudates. Methods The study was conducted in the Department of General Medicine, SCB Medical College & Hospital, Cuttack during the period from June 2019 to September 2020. 100 consecutive patients admitted were included in the study group. Thoracocentesis was done in all patients and samples were sent for biochemical, microbiological and cytological tests. Cholinesterase estimation was done by photometer 5010V5+. Final diagnosis was done by clinical, biochemical, cytological and microbiological results. Results Out of 100 cases, 78 cases were male and 22 cases were females, with male: female ratio 3.4:1. Tubercular effusion was the most common cause followed by congestive cardiac failure. Fever was the most common clinical presentation (62%). The mean total cell count was more in exudate (2911.820+/- 1511.48). The pleural fluid cholinesterase level of 943.5U/L was 96% sensitive and 93% specificity. Conclusion Pleural fluid cholinesterase level of 943.5U/dl gives a more reasonable sensitivity and specificity (96% sensitive and 93% specific) to differentiate from exudates and transudates, while that of 981U/dl is more specific(92.6% sensitive and >99% specific ) and can be taken as taken as gold standard as specificity approaches 100%. The result of present study revealed that pleural fluid cholinesterase is the most accurate parameter for the differentiation of transudates and exudates. [ABSTRACT FROM AUTHOR]

Otoscopic and radiographic examinations are effective tools to diagnose the ear affections like otitis externa, chronic hypertropic otitis externa, neoplasia, pathological changes of ear canal, etc. The otoscopic examination of 84 dogs revealed presence of different types of texture and colour of exudates in the ear canal. It revealed that the inner wall of external ear canal suffered with erythema, ulceration, hyperplasia of inner wall, hyperplasia of ceruminous glands, presence of excessive hair and polyp. Radiography revealed stenosis, calcification of ear canal, bilateral involvement of ear and increased opacity of the tympanic bulla. Otoscopy and radiography are thus practical and accessible tools for detecting changes in the ear canal and other related structures. [ABSTRACT FROM AUTHOR]

Exudates are a common sign of diabetic retinopathy, which is a disease that affects the blood vessels in the retina. Early detection of exudates is critical to avoiding vision problems through continuous screening and treatment. In traditional clinical practice, the involved lesions are manually detected using photographs of the fundus. However, this task is cumbersome and time‐consuming and requires intense effort due to the small size of the lesion and the low contrast of the images. Thus, computer‐assisted diagnosis of retinal disease based on the detection of red lesions has been actively explored recently. In this paper, we present a comparison of deep convolutional neural network (CNN) architectures and propose a residual CNN with residual skip connections to reduce the parameter for the semantic segmentation of exudates in retinal images. A suitable image augmentation technique is used to improve the performance of network architecture. The proposed network can robustly segment exudates with high accuracy, which makes it suitable for diabetic retinopathy screening. A comparative performance analysis of three benchmark databases: E‐ophtha, DIARETDB1, and Hamilton Ophthalmology Institute's Macular Edema, is presented. The proposed method achieves a precision of 0.95, 0.92, 0.97, accuracy of 0.98, 0.98, 0.98, sensitivity of 0.97, 0.95, 0.95, specificity of 0.99, 0.99, 0.99, and area under the curve of 0.97, 0.94, and 0.96, respectively. Research Highlights: The research focuses on the detection and segmentation of exudates in diabetic retinopathy, a disease affecting the retina.Early detection of exudates is important to avoid vision problems and requires continuous screening and treatment.Currently, manual detection is time‐consuming and requires intense effort.The authors compare qualitative results of the state‐of‐the‐art convolutional neural network (CNN) architectures and propose a computer‐assisted diagnosis approach based on deep learning, using a residual CNN with residual skip connections to reduce parameters.The proposed method is evaluated on three benchmark databases and demonstrates high accuracy and suitability for diabetic retinopathy screening. [ABSTRACT FROM AUTHOR]

Xylem sap proteomics provides crucial insights into plant defense and root-to-shoot communication. This study highlights the sensitivity and reproducibility of xylem sap proteome analyses, using a single plant per sample to track over 3000 proteins in two model crop plants, Solanum tuberosum and Hordeum vulgare. By analyzing the flg22 response, we identified immune response components not detectable through root or shoot analyses. Notably, we discovered previously unknown elements of the plant immune system, including calcium/calmodulin-dependent kinases and G-type lectin receptor kinases. Despite similarities in the metabolic pathways identified in the xylem sap of both plants, the flg22 response differed significantly: S. tuberosum exhibited 78 differentially abundant proteins, whereas H. vulgare had over 450. However, an evolutionarily conserved overlap in the flg22 response proteins was evident, particularly in the CAZymes and lipid metabolism pathways, where lipid transfer proteins and lipases showed a similar response to flg22. Additionally, many proteins without conserved signal sequences for extracellular targeting were found, such as members of the HSP70 family. Interestingly, the HSP70 response to flg22 was specific to the xylem sap proteome, suggesting a unique regulatory role in the extracellular space similar to that reported in mammalians.

Macrophytes and cladocerans represent the main antagonistic groups that regulate phytoplankton biomass; however, the mechanism behind this interaction is unclear. In laboratory conditions, we separately evaluated the effects of three submerged macrophytes (Ceratophyllum demersum, Myriophyllum aquaticum, and Stuckenia pectinata), as well as their exudates, and plant-associated microbiota (POM < 25 µm) + exudates on the population growth of Daphnia cf. pulex and Simocephalus cf. mixtus. Living Ceratophyllum, exudates, and POM < 25 µm + exudates exhibited the most robust positive effects on Simocephalus density and the rate of population increase (r). Subsequently, we examined the effects of Ceratophyllum on the filtration and feeding rates of Simocephalus and Daphnia, revealing significant (p < 0.001) promotion of filtration and feeding in Simocephalus but not in Daphnia. To elucidate the specific effects of this macrophyte on Simocephalus demography, we assessed selected life table variables across the same treatments. The treatments involving exudates and living Ceratophyllum resulted in approximately 40% longer survivorship and significantly (p < 0.01) enhanced fecundity. Our findings indicate that exudates from submerged macrophytes positively influence Simocephalus demography by increasing filtration rates, survivorship, and fecundity. This synergy suggests a substantial impact on phytoplankton abundance.

Based on the up-to-date knowledge we critically discuss the current understanding of the influence of the compounds secreted by phytoplankton species on the fate of metal-containing engineered nanoparticles (ENPs) in aquatic settings. Different biomolecules, such as extracellular polymeric substances (EPS) and exometabolites play important, yet to elucidate, role in the dissolution, colloidal stability, transformations and biouptake of the ENPs and thus shape their behavior within the phycosphere. Phytoplankton secretions can also mediate the synthesis of ENPs from dissolved ions by reducing the metals ions and capping the newly formed ENPs. However, the environmental significance of this process remains to be demonstrated. Exposure to ENPs triggers changes in the secretion of the biomolecules. An improved understanding of the regulatory mechanism and exometabolite changes due to ENP exposure is essential for deciphering the ENPs-phytoplankton interactions. Unveiling the significance of secreted biomolecules in modulating the behavior of the metal-containing ENPs is central for understudying the phytoplankton-ENPs feedbacks, drivers of transformations of ENPs and their mechanisms in the aquatic environment.

In the Base Business Unit (UEB) La Española I, cured meat is made. In the refrigerated storage stage of the final product, the presence of exudate was observed in the vacuumsealed blankets. To reduce it, it is proposed to incorporate the baking operation into the process. Objective: To incorporate baking as an operation to reduce the amount of exudate present in the final product once vacuum sealed. Materials and Methods: Two experimental variants were analysed, one of them, the traditional one, where after the dry curing process it is passed to vacuum packaging and in the other the baking is introduced before the final packaging. The characterization of the final product was carried out from the physical, chemical, microbiological and sensory point of view. Different measurements were made before and after the baking process; as well as on the mass of exudate present in the packaged product. Results and Discussion: The baking process decreases the yield of the product and therefore increases the consumption rates of raw materials, which influences the cost of production. The product obtained in each variant complies with the regulated parameters. The baking process was able to reduce the amount of exudate present in the final product. Conclusions: The two variants studied were able to reduce the amount of exudate present in cured beef jerky type after vacuum packaging. The baking process increases the cost of production and decreases the yield of the product. [ABSTRACT FROM AUTHOR]

Sustainable hydrogels are an innovative biodegradable alternative to traditional packaging materials. They offer exceptional water absorption capacity and high biocompatibility, making them ideal food absorbents to reduce plastic waste, extend shelf life and ensure the safety and quality of packaged foods. In this study, hydrogels based on gelatin, microcrystalline cellulose (MCC), and nanocrystalline cellulose (NCC) were developed, characterized, and applied in the packaging of chicken breasts. For this, MCC was isolated from the banana pseudostem and commercial NCC was incorporated into a gelatin solution to produce the hydrogel materials by film casting. The resulting hydrogels were analyzed in terms of morphology, structural properties, water absorption capacity, mechanical strength, and color properties. The results showed that the incorporation of MCC and NCC significantly improved the mechanical integrity of the hydrogels, which prevented premature deformation of the hydrogels when they absorbed moisture. In addition, changes in the color properties of chicken breast samples in contact with the hydrogels were observed, indicating their ability to preserve food quality. Subsequently, the effectiveness of the hydrogels for chicken breast storage at 4 °C for 4 days was validated. The results demonstrated that the hydrogels developed in this study are a sustainable and environmentally friendly alternative to traditional packaging materials that can extend the shelf life of food products while maintaining their physical and microbiological integrity. [ABSTRACT FROM AUTHOR]

Plants forage soil for water and nutrients, whose distribution is patchy and often dynamic. To improve their foraging activities, plants have evolved mechanisms to modify the physicochemical properties and microbial communities of the rhizosphere, i.e. the soil compartment under the influence of the roots. This dynamic interplay in root-soil-microbiome interactions creates emerging properties that impact plant nutrition and health. As a consequence, the rhizosphere can be considered an extended root phenotype, a manifestation of the effects of plant genes on their environment inside and/or outside of the organism. Here, we review current understanding of how plants shape the rhizosphere and the benefits it confers to plant fitness. We discuss future research challenges and how applying their solutions in crops will enable us to harvest the benefits of the extended root phenotype.

Diabetic retinopathy causes blindness in diabetics. Early identification and frequent screening of diabetic retinopathy can slow disease progression and visual loss. Retinal lesions result from diabetic retinopathy. Dark and brilliant retinal lesions predominate. Color, shape, and size distinguish lesions. Exudates are bright, while microaneurysms (MAs) and hemorrhages (HEMs) are dark. This study presents a retinal lesion screening method for diabetic retinopathy. The data is saturated at low and high intensities; picture intensity values are adjusted to enhance contrast. This study presents a unique rhombus multilevel retinal image segmentation method. In the proposed study, preprocessing, segmentation algorithms, morphological operation,median filter and gradient are all designed as parts of an effective automated system. With 40 photos, the recommended method produced accuracy and specificity of 99.9% and 99.5%, respectively. [ABSTRACT FROM AUTHOR]

Diabetic retinopathy (DR) remains one of the world's frequent eye illnesses, leading to vision loss among working-aged individuals. Hemorrhages and exudates are examples of signs of DR. However, artificial intelligence (AI), particularly deep learning (DL), is poised to impact nearly every aspect of human life and gradually transform medical practice. Insight into the condition of the retina is becoming more accessible thanks to major advancements in diagnostic technology. AI approaches can be used to assess lots of morphological datasets derived from digital images in a rapid and noninvasive manner. Computer-aided diagnosis tools for automatic detection of DR early-stage signs will ease the pressure on clinicians. In this work, we apply two methods to the color fundus images taken on-site at the Cheikh Zaïd Foundation's Ophthalmic Center in Rabat to detect both exudates and hemorrhages. First, we apply the U-Net method to segment exudates and hemorrhages into red and green colors, respectively. Second, the You Look Only Once Version 5 (YOLOv5) method identifies the presence of hemorrhages and exudates in an image and predicts a probability for each bounding box. The segmentation proposed method obtained a specificity of 85%, a sensitivity of 85%, and a Dice score of 85%. The detection software successfully detected 100% of diabetic retinopathy signs, the expert doctor detected 99% of DR signs, and the resident doctor detected 84%. [ABSTRACT FROM AUTHOR]