RESUMO
At present, obesity, as a part of metabolic syndrome, represents the leading factor for disability, and is correlated with higher inflammation status, morbidity, and mortality. The purpose of our study is to add new insights to the present body of knowledge regarding the correlations between chronic systemic inflammation and severe obesity, which cannot be treated without considering other metabolic syndrome conditions. Biomarkers of high-level chronic inflammation are recognized as important predictors of pro-inflammatory disease. Besides the well-known pro-inflammatory cytokines, such as WBCs (white blood cells), IL-1 (interleukin-1), IL-6 (interleukin-6), TNF-alpha (tumor necrosis factor-alpha), and hsCRP (high-sensitivity C-reactive protein), as well as anti-inflammatory markers, such as adiponectin and systemic inflammation, can be determined by a variety of blood tests as a largely available and inexpensive inflammatory biomarker tool. A few parameters, such as the neutrophil-to-lymphocyte ratio; the level of cholesterol 25-hydroxylase, which is part of the macrophage-enriched metabolic network in adipose tissue; or levels of glutamine, an immune-metabolic regulator in white adipose tissue, are markers that link obesity to inflammation. Through this narrative review, we try to emphasize the influence of the weight-loss process in reducing obesity-related pro-inflammatory status and associated comorbidities. All data from the presented studies report positive results following weight-loss procedures while improving overall health, an effect that lasts over time, as far as the existing research data show.
RESUMO
Efforts to mitigate the COVID-19 crisis revealed that fast, accurate, and scalable testing is crucial for curbing the current impact and that of future pandemics. We propose an optical method for directly imaging unlabeled viral particles and using deep learning for detection and classification. An ultrasensitive interferometric method was used to image four virus types with nanoscale optical pathlength sensitivity. Pairing these data with fluorescence images for ground truth, we trained semantic segmentation models based on U-Net, a particular type of convolutional neural network. The trained network was applied to classify the viruses from the interferometric images only, containing simultaneously SARS-CoV-2, H1N1 (influenza-A), HAdV (adenovirus), and ZIKV (Zika). Remarkably, due to the nanoscale sensitivity in the input data, the neural network was able to identify SARS-CoV-2 vs. the other viruses with 96% accuracy. The inference time for each image is 60 ms, on a common graphic processing unit. This approach of directly imaging unlabeled viral particles may provide an extremely fast test, of less than a minute per patient. As the imaging instrument operates on regular glass slides, we envision this method as potentially testing on patient breath condensates. The necessary high throughput can be achieved by translating concepts from digital pathology, where a microscope can scan hundreds of slides automatically. One Sentence SummaryThis work proposes a rapid (<1 min.), label-free testing method for SARS-CoV-2 detection, using quantitative phase imaging and deep learning.
