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- Abolhassani, H, et al.
(author)
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Care of patients with inborn errors of immunity in thirty J Project countries between 2004 and 2021
- 2022
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In: Frontiers in immunology. - : Frontiers Media SA. - 1664-3224. ; 13, s. 1032358-
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Journal article (peer-reviewed)abstract
- The J Project (JP) physician education and clinical research collaboration program was started in 2004 and includes by now 32 countries mostly in Eastern and Central Europe (ECE). Until the end of 2021, 344 inborn errors of immunity (IEI)-focused meetings were organized by the JP to raise awareness and facilitate the diagnosis and treatment of patients with IEI.ResultsIn this study, meeting profiles and major diagnostic and treatment parameters were studied. JP center leaders reported patients’ data from 30 countries representing a total population of 506 567 565. Two countries reported patients from JP centers (Konya, Turkey and Cairo University, Egypt). Diagnostic criteria were based on the 2020 update of classification by the IUIS Expert Committee on IEI. The number of JP meetings increased from 6 per year in 2004 and 2005 to 44 and 63 in 2020 and 2021, respectively. The cumulative number of meetings per country varied from 1 to 59 in various countries reflecting partly but not entirely the population of the respective countries. Altogether, 24,879 patients were reported giving an average prevalence of 4.9. Most of the patients had predominantly antibody deficiency (46,32%) followed by patients with combined immunodeficiencies (14.3%). The percentages of patients with bone marrow failure and phenocopies of IEI were less than 1 each. The number of patients was remarkably higher that those reported to the ESID Registry in 13 countries. Immunoglobulin (IgG) substitution was provided to 7,572 patients (5,693 intravenously) and 1,480 patients received hematopoietic stem cell therapy (HSCT). Searching for basic diagnostic parameters revealed the availability of immunochemistry and flow cytometry in 27 and 28 countries, respectively, and targeted gene sequencing and new generation sequencing was available in 21 and 18 countries. The number of IEI centers and experts in the field were 260 and 690, respectively. We found high correlation between the number of IEI centers and patients treated with intravenous IgG (IVIG) (correlation coefficient, cc, 0,916) and with those who were treated with HSCT (cc, 0,905). Similar correlation was found when the number of experts was compared with those treated with HSCT. However, the number of patients treated with subcutaneous Ig (SCIG) only slightly correlated with the number of experts (cc, 0,489) and no correlation was found between the number of centers and patients on SCIG (cc, 0,174).Conclusions1) this is the first study describing major diagnostic and treatment parameters of IEI care in countries of the JP; 2) the data suggest that the JP had tremendous impact on the development of IEI care in ECE; 3) our data help to define major future targets of JP activity in various countries; 4) we suggest that the number of IEI centers and IEI experts closely correlate to the most important treatment parameters; 5) we propose that specialist education among medical professionals plays pivotal role in increasing levels of diagnostics and adequate care of this vulnerable and still highly neglected patient population; 6) this study also provides the basis for further analysis of more specific aspects of IEI care including genetic diagnostics, disease specific prevalence, newborn screening and professional collaboration in JP countries.
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- Hollandi, R., et al.
(author)
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nucleAIzer : A Parameter-free Deep Learning Framework for Nucleus Segmentation Using Image Style Transfer
- 2020
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In: Cell Systems. - : Elsevier BV. - 2405-4712. ; 10:5, s. 453-458.e6
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Journal article (peer-reviewed)abstract
- Single-cell segmentation is typically a crucial task of image-based cellular analysis. We present nucleAIzer, a deep-learning approach aiming toward a truly general method for localizing 2D cell nuclei across a diverse range of assays and light microscopy modalities. We outperform the 739 methods submitted to the 2018 Data Science Bowl on images representing a variety of realistic conditions, some of which were not represented in the training data. The key to our approach is that during training nucleAIzer automatically adapts its nucleus-style model to unseen and unlabeled data using image style transfer to automatically generate augmented training samples. This allows the model to recognize nuclei in new and different experiments efficiently without requiring expert annotations, making deep learning for nucleus segmentation fairly simple and labor free for most biological light microscopy experiments. It can also be used online, integrated into CellProfiler and freely downloaded at www.nucleaizer.org. A record of this paper's transparent peer review process is included in the Supplemental Information. Microscopy image analysis of single cells can be challenging but also eased and improved. We developed a deep learning method to segment cell nuclei. Our strategy is adapting to unexpected circumstances automatically by synthesizing artificial microscopy images in such a domain as training samples.
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- Mainas, G, et al.
(author)
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Associations between Periodontitis, COVID-19, and Cardiometabolic Complications: Molecular Mechanisms and Clinical Evidence
- 2023
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In: Metabolites. - : MDPI AG. - 2218-1989. ; 13:1
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Journal article (peer-reviewed)abstract
- Periodontitis is a microbially driven, host-mediated disease that leads to loss of periodontal attachment and resorption of bone. It is associated with the elevation of systemic inflammatory markers and with the presence of systemic comorbidities. Coronavirus disease 2019 (COVID-19) is a contagious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Although the majority of patients have mild symptoms, others experience important complications that can lead to death. After the spread of the COVID-19 pandemic, several investigations demonstrating the possible relationship between periodontitis and COVID-19 have been reported. In addition, both periodontal disease and COVID-19 seem to provoke and/or impair several cardiometabolic complications such as cardiovascular disease, type 2 diabetes, metabolic syndrome, dyslipidemia, insulin resistance, obesity, non-alcoholic fatty liver disease, and neurological and neuropsychiatric complications. Therefore, due to the increasing number of investigations focusing on the periodontitis-COVID-19 relationship and considering the severe complications that such an association might cause, this review aims to summarize all existing emerging evidence regarding the link between the periodontitis-COVID-19 axis and consequent cardiometabolic impairments.
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