Analysing Joker: an attempt to establish diagnosis for a film icon.
Ontology highlight
ABSTRACT:
SUBMITTER: Skryabin VY
PROVIDER: S-EPMC8727382 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:It is already a proven fact that there exists a relationship between CLD (chronic liver disease) and kidney disease but still there is no available combined animal model of liver and kidney fibrosis on the same animal. An animal model is one of the important research tools in the field of medical science because it is important to build a model that can simulate the disease condition so that the particular disease can be studied. Therefore, the aim of this study is to build a less expensive, less time consuming, and reproducible model of hepatorenal fibrosis on rats. We administered combined intraperitoneal injection of CCl4 (Carbon Tetrachloride) and BSA (Bovine Serum Albumin) on a female Wistar rats. At the end, the liver and kidney tissues were examined under microscope to see whether we were successful in establishing the model or not. The results show that liver fibrosis was marked but the changes on the kidneys were mild. In this study, we were able to induce significant fibrosis in the liver and early stages of fibrosis in the kidneys. The result also demonstrated that the addition of BSA conferred a liver protective effect against CCl4 induced hepatotoxicity, whereas combination of CCl4 and BSA proved to be detrimental for kidneys.
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Project description:Complex channel planform especially the dynamics of the multi-thread river is empirically examined using the three major dimensions - bar growth, channel length and channel count. To this end, many indices have been proposed to deal with the complex channel response in the context of sediment-energy synergistic scenarios. The existing methods are primarily concerned with the linear or 1D nature of the channel and bar. The present study introduced the areal or 2-D nature of the channel and bar to capture a more realistic picture because, with same length, area of the bar may differ greatly. Therefore, we proposed four indices on channel braiding taking into consideration the area of the channel and bar. We tested our indices to the 28 reaches of the Damodar River, India that showed a significant correlation (∼80%) with the existing standard method. The major highlights of the methods are outlined as follows. •Four novel indices were proposed considering the linear and areal dimensions of the channel and bar.•Proposed indices focus on the unit-free measures with known limits enabling pragmatic comparison with self and other channels.•New methods were tested using field data implying a significant correlation with the existing methods.
| S-EPMC9950935 | biostudies-literature
Project description:Chronic rhinosinusitis (CRS) is a public health problem that has a significant socio-economic impact. Moreover, the complexity of this disease due to its heterogeneous nature based on the underlying pathophysiology - leading to different disease variants - further complicates our understanding and directions for the most appropriate targeted treatment strategies. Several International/national guidelines/position papers and/or consensus documents are available that present the current knowledge and treatment strategies for CRS. Yet there are many challenges to the management of CRS especially in the case of the more severe and refractory forms of disease. Therefore, the International Collaboration in Asthma, Allergy and Immunology (iCAALL), a collaboration between EAACI, AAAAI, ACAAI, and WAO, has decided to propose an International Consensus (ICON) on Chronic Rhinosinusitis. The purpose of this ICON on CRS is to highlight the key common messages from the existing guidelines, the differences in recommendations as well as the gaps in our current knowledge of CRS, thus providing a concise reference. In this document we discuss the definition of the disease, its relevance, pharmacoeconomics, pathophysiology, phenotypes and endotypes, genetics and risk factors, natural history and co-morbidities as well as clinical manifestations and treatment options in both adults and children comprising pharmacotherapy, surgical interventions and more recent biological approaches. Finally, we have also highlighted the unmet needs that wait to be addressed through future research.
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Project description:IntroductionBecause of recent developments in treatments for hepatocellular carcinoma (HCC), methods for determining suitable therapy for initial or recurrent HCC have become important. This study used artificial intelligence (AI) findings to establish a system for predicting prognosis of HCC patients at time of reoccurrence based on clinical data as a reference for selection of treatment modalities.MethodsAs a training cohort, 5,701 observations obtained at the initial and each subsequent treatment for recurrence from 1,985 HCC patients at a single center from 2000 to 2021 were used. The validation cohort included 5,692 observations from patients at multiple centers obtained at the time of the initial treatment. An AI calculating system (PRAID) was constructed based on 25 clinical factors noted at each treatment from the training cohort, and then predictive prognostic values for 1- and 3-year survival in both cohorts were evaluated.ResultsAfter exclusion of patients lacking clinical data regarding albumin-bilirubin (ALBI) grade or tumor-node-metastasis stage of the Liver Cancer Study Group of Japan, 6th edition (TNM-LCSGJ 6th), ALBI-TNM-LCSGJ 6th (ALBI-T) and modified ALBI-T scores confirmed that prognosis for patients in both cohorts was similar. The area under the curve for prediction of both 1- and 3-year survival in the validation cohort was 0.841 (sensitivity 0.933 [95% CI: 0.925-0.940], specificity 0.517 [95% CI: 0.484-0.549]) and 0.796 (sensitivity 0.806 [95% CI: 0.790-0.821], specificity 0.646 [95% CI: 0.624-0.668]), respectively.ConclusionThe present PRAID system might provide useful prognostic information related to short and medium survival for decision-making regarding the best therapeutic modality for both initial and recurrent HCC cases.
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Project description: Not available
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Project description:Genetically modified organisms (GMOs), which produce Bacillus thuringiensis (Bt) toxins, are widely used in agriculture in some parts of the world. Despite this, ecotoxicological methods, tailored to GMOs, are lacking to assess effects on aquatic environments. With the objective to investigate a food-related exposure pathway for aquatic shredders, we used a new food-spiking method while caddisfly larvae (Chaetopteryx spec., Sericostoma spec.) served as test species. Pure Cry1Ab toxins were spiked on black alder leaf discs and subsequently used in a feeding experiment. The toxin did not influence larval mortality compared to the control. The results, however, showed significant effects on larval lipid content (Chaetopteryx spec.) and development (Sericostoma spec.) at concentrations of 17.2 and 132.4 ng Cry1Ab/mg leaf, respectively. These changes are indicative for impacts on the fitness of the specimen and thus relevant in a risk assessment context. Ultimately, the food-spiking method allowed applying different Bt toxin concentrations leading to the establishment of dose-response relationships for various response variables. The use of long test durations and sublethal endpoints (consumption, lipid content, growth, larval instars) is, moreover, advisable when testing GMO effects.
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Project description:Tissue-resident memory T (Trm) cells are enriched at the sites of previous infection and required for enhanced protective immunity. However, the emergence of Trm cells and their roles in providing protection are unclear in the field of Helicobacter pylori (H. pylori) vaccinology. Here, our results suggest that conventional vaccine strategies are unable to establish a measurable antigen (Ag)-specific memory cell pool in stomach; in comparison, gastric subserous injection of mice with micro-dose of Alum-based H. pylori vaccine can induce a pool of local CD4+ Trm cells. Regional recruitment of Ag-specific CD4+ T cells depends on the engagement of Ag and adjuvant-induced inflammation. Prior subcutaneous vaccination enhanced this recruitment. A stable pool of Ag-specific CD4+ T cells can be detected for 240 days. Two weeks of FTY720 administration in immune mice suggests that these cells do not experience the recirculation. Immunohistochemistry results show that close to the vaccination site, abundant CD4+T cells locate on epithelial niches, independent of lymphocyte cluster. Paradigmatically, Ag-specific CD4+ T cells with a phenotype of CD69+CD103- are preferential on lymphocytes isolated from epithelium. Upon Helicobacter infection, CD4+ Trm cells orchestrate a swift recall response with the recruitment of circulating antigen-specific Th1/Th17 cells to trigger a tissue-wide pathogen clearance. This study investigates the vaccine-induced gastric CD4+ Trm cells in a mice model, and highlights the need for designing a vaccine strategy against H. pylori by establishing the protective CD4+ Trm cells.
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Project description:Electron tomography (ET) plays an important role in studying in situ cell ultrastructure in three-dimensional space. Due to limited tilt angles, ET reconstruction always suffers from the "missing wedge" problem. With a validation procedure, iterative compressed-sensing optimized NUFFT reconstruction (ICON) demonstrates its power in the restoration of validated missing information for low SNR biological ET dataset. However, the huge computational demand has become a major problem for the application of ICON. In this work, we analyzed the framework of ICON and classified the operations of major steps of ICON reconstruction into three types. Accordingly, we designed parallel strategies and implemented them on graphics processing units (GPU) to generate a parallel program ICON-GPU. With high accuracy, ICON-GPU has a great acceleration compared to its CPU version, up to 83.7×, greatly relieving ICON's dependence on computing resource.
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Project description:Learning maps between data samples is fundamental. Applications range from representation learning, image translation and generative modeling, to the estimation of spatial deformations. Such maps relate feature vectors, or map between feature spaces. Well-behaved maps should be regular, which can be imposed explicitly or may emanate from the data itself. We explore what induces regularity for spatial transformations, e.g., when computing image registrations. Classical optimization-based models compute maps between pairs of samples and rely on an appropriate regularizer for well-posedness. Recent deep learning approaches have attempted to avoid using such regularizers altogether by relying on the sample population instead. We explore if it is possible to obtain spatial regularity using an inverse consistency loss only and elucidate what explains map regularity in such a context. We find that deep networks combined with an inverse consistency loss and randomized off-grid interpolation yield well behaved, approximately diffeomorphic, spatial transformations. Despite the simplicity of this approach, our experiments present compelling evidence, on both synthetic and real data, that regular maps can be obtained without carefully tuned explicit regularizers, while achieving competitive registration performance.
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