Re: Anti Deep Ze 7.22.0203453
Nurit Dardon
Advances in deep learning technology have enabled complex task solutions. The accuracy of image classification tasks has improved owing to the establishment of convolutional neural networks (CNN). Cellular senescence is a hallmark of ageing and is important for the pathogenesis of ageing-related diseases. Furthermore, it is a potential therapeutic target. Specific molecular markers are used to identify senescent cells. Moreover senescent cells show unique morphology, which can be identified. We develop a successful morphology-based CNN system to identify senescent cells and a quantitative scoring system to evaluate the state of endothelial cells by senescence probability output from pre-trained CNN optimised for the classification of cellular senescence, Deep Learning-Based Senescence Scoring System by Morphology (Deep-SeSMo). Deep-SeSMo correctly evaluates the effects of well-known anti-senescent reagents. We screen for drugs that control cellular senescence using a kinase inhibitor library by Deep-SeSMo-based drug screening and identify four anti-senescent drugs. RNA sequence analysis reveals that these compounds commonly suppress senescent phenotypes through inhibition of the inflammatory response pathway. Thus, morphology-based CNN system can be a powerful tool for anti-senescent drug screening.
Deep sea mining companies are pressuring governments to let them go down there, several thousand meters below the surface of the ocean, and plunder the seabed to extract metals. If this industry is allowed to start, gigantic machines weighing more than a blue whale will be lowered onto the ocean floor to plunder these pristine ocean ecosystems.
Imagine if we could go back in time and stop offshore drilling at the dawn of the oil age and prevent environmental and climate catastrophes. This is where we are at with deep sea mining. This is a once in a generation opportunity: to stop another extractive industry from damaging the global oceans the way the fossil fuel companies have done to the climate.
Governments need to take a strong stand against deep sea mining. They have to publicly call for this industry not to start and take action to protect the deep ocean. Join us by signing the petition.
Replenish moisture and restore strength back into frizzy, damaged strands with this deep conditioning treatment. Shea butter and hibiscus oil help smooth the hair cuticle and reduce breakage for soft, strong locks.
The rigorous evaluation of anti-poverty programs is key to the fight against global poverty. Traditional approaches rely heavily on repeated in-person field surveys to measure program effects. However, this is costly, time-consuming, and often logistically challenging. Here we provide the first evidence that we can conduct such program evaluations based solely on high-resolution satellite imagery and deep learning methods. Our application estimates changes in household welfare in a recent anti-poverty program in rural Kenya. Leveraging a large literature documenting a reliable relationship between housing quality and household wealth, we infer changes in household wealth based on satellite-derived changes in housing quality and obtain consistent results with the traditional field-survey based approach. Our approach generates inexpensive and timely insights on program effectiveness in international development programs.
Surface Deep Anti-Odorant is based on the dynamic powers of glycolic acid, a fruit-based substance that has natural sebum-control and exfoliation properties to neutralize and inhibit bacteria-causing odor. The formulation also has skin-soothing and antioxidant-rich ingredients including probiotics, fruit extracts, and aloe to cleanse, calm, and support the skin. Gently exfoliates to keep odor at bay (bonus: can help clear breakouts and reduce hyperpigmentation)
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Advances in deep learning technology have enabled complex task solutions1. The accuracy of image classification has increased rapidly owing to the development of convolutional neural networks (CNNs)2,3. CNNs have been applied to broad medical research fields4, and image classification is employed as a diagnostic tool in the clinic5. In the biological field, cell morphology images obtained by phase-contrast microscopy contain numerous biological data such as cellular identity and status, which are currently evaluated by molecular biology techniques. A morphology-based identification system using CNN can replace the molecular biology techniques in some tasks and be applicable to various research areas. We previously developed a label-free system to identify endothelial cells among various cell types derived from induced pluripotent stem cells by phase-contrast microscopy images using a CNN6. Many reports demonstrate the high potential of CNNs in a classification or identification task. Versatile biologic systems should construct quantitative and not just qualitative classifications7. CNNs are a potential tool to develop non-biased quantitative evaluation systems.
Endothelial cells serve many functions in homoeostasis and diseases. Cellular senescence plays an important role in age-related diseases. Endothelial cells are pivotally involved in the pathology of age-related diseases through cellular senescence. Endogenous and exogenous stresses such as reactive oxygen species (ROS), telomere dysfunction, DNA damage, inflammatory cytokines, and drugs such as anti-cancer drugs, induce cellular senescence8. Senescent cells show an inflammatory phenotype called senescence-associated secretory phenotype (SASP) and contribute to age-related disease progression9. Cellular senescence is considered a potential therapeutic target for age-related diseases10,11. Thus, drugs that directly intervene in endothelial cell senescence may represent a therapeutic option. Specific biological markers are commonly used for cellular senescence screening such as senescence-associated beta galactosidase (SA-β-gal), P16, and P21. Cellular senescence can also be defined by specific morphology such as flat and enlarged cell bodies and heterochromatin aggregation12. Despite this, the unbiased quantitative evaluation of those morphological changes for a large number of cells is difficult in using conventional methods. A scoring system that can quantitatively assess the cellular state could be an important tool for drug screening.
In this study, we developed a robust, morphology-based CNN system to identify senescent cells. Additionally, we established an automated, non-bias quantitative scoring system to evaluate the state of endothelial cells using senescence probability output directly from pre-trained CNN, Deep Learning-Based Senescence Scoring System by Morphology (Deep-SeSMo) (Supplementary Fig. 1a). Deep-SeSMo-based drug screening using a kinase inhibitor library was used to identify anti-senescent drugs.
In this study, we developed a drug-screening system for cellular senescence using a pre-trained CNN optimised by the overall average value of output senescence probability. Moreover, utilising a non-biased method, we identified four compounds, terreic acid, PD-98059, daidzein, and Y-276322HCl, which showed anti-senescent and anti-inflammatory effects. Drug development is facilitated by sophisticated screening systems. A human cannot reliably identify cellular status by observing cellular morphology. However, cellular morphology can be a specific marker for cell type and pathological conditions because of specific morphological dynamics, including changes in protein expression and structure, and chromatin structure. In recent years, CNN has become a standard method to assess morphology. CNN is most suitable for classification tasks; however, it is unclear whether quantitative analyses by CNN would be effective in the biological field. The concept of our strategy was simple; the overall average of output probability calculated by a pre-trained CNN was applied to the quantitative senescence score. Interestingly, a histogram of senescence probability showed that healthy cells would digitally transit into a senescent state, with a few cases of cells being in an intermediate state (Fig. 2e and Supplementary Fig. 6c). This suggests that cellular senescence would be induced digitally, and a less intermediate state might be observed during physiological ageing. Under intermediate stress conditions, the senescence probability is bipolarized, suggesting that senescence thresholds differ among cells. It would be interesting to elucidate the biological mechanism underlying the digital transition and threshold of cellular senescence. Although the CNN showed high performance, there were still mispredictions. When we output the false decision images (Supplementary Fig. 3g), the morphological appearance of false-positive images was similar to that of true-positive images, and false-negative images were similar to true-negative images. These suggest that a very small proportion of senescent cells exist in healthy conditions, and a very small proportion of healthy cells exist in senescence-inducing conditions, even though we paid full attention to the preparation of healthy or senescent cells. However, in our current analysis, incorrect predictions of the CNN were very rare; therefore, we believe that any incorrect predictions would have very little effect on the computation of the senescence score.
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