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The combination of an increasing prevalence of diabetes and the aging of populations enables the appearance of a greater number of associated complications such as diabetic retinopathy. Diabetic retinopathy is the leading cause of preventable vision loss in working-age adults. The objective of this Special Issue is to highlight the existing evidence regarding the relationship between oxidative stress and low-grade chronic inflammation induced by hyperglycemia with the development and progression of diabetic retinopathy, with an emphasis on the importance of early diagnosis and the use of antioxidant and anti-inflammatory approaches to prevent or delay the harmful effects of diabetes on retinal tissue.

Medicine --- eicosanoids --- oxidative stress --- diabetic retinopathy --- cyclooxygenase --- lipoxygenase --- Cytochrome P450 --- HDAC6 --- tubastatin A --- retinal endothelial cells --- retinal endothelial cell senescence --- db/db mice --- Cinnamomi Ramulus --- Paeoniae Radix --- CPA4-1 --- blood-retinal barrier --- occludin --- human retina --- epiretinal membrane --- internal limiting membrane --- vitreoretinal surgery --- macular hole --- proliferative diabetic retinopathy --- antioxidants --- diabetes mellitus --- free radicals --- high-mobility group box 1 (HMGB1) --- inflammatory pathways --- novel therapies --- diabetic retinopathy (DR) --- inflammation --- angiogenesis --- extracellular vesicles --- miRNA --- biomarkers --- apoptosis --- fenofibrate --- thioredoxin --- hyperglycemia --- astaxanthin --- carotenoid --- reactive oxygen species --- photoreceptor cells --- PI3K --- Nrf2 --- eicosapentaenoic acid (EPA) --- docosahexaenoic acid (DHA) --- retinal pigment epithelium --- antioxidant --- ascorbic acid --- retinal disease --- vitamin D --- GLP-1 --- superoxide dismutase --- biomarkers of diabetic retinopathy --- metabolic memory --- tear film --- aqueous humor --- vitreous humor --- mitochondria --- redox --- photoreceptor --- glycation --- aging --- glyoxalase --- eicosanoids --- oxidative stress --- diabetic retinopathy --- cyclooxygenase --- lipoxygenase --- Cytochrome P450 --- HDAC6 --- tubastatin A --- retinal endothelial cells --- retinal endothelial cell senescence --- db/db mice --- Cinnamomi Ramulus --- Paeoniae Radix --- CPA4-1 --- blood-retinal barrier --- occludin --- human retina --- epiretinal membrane --- internal limiting membrane --- vitreoretinal surgery --- macular hole --- proliferative diabetic retinopathy --- antioxidants --- diabetes mellitus --- free radicals --- high-mobility group box 1 (HMGB1) --- inflammatory pathways --- novel therapies --- diabetic retinopathy (DR) --- inflammation --- angiogenesis --- extracellular vesicles --- miRNA --- biomarkers --- apoptosis --- fenofibrate --- thioredoxin --- hyperglycemia --- astaxanthin --- carotenoid --- reactive oxygen species --- photoreceptor cells --- PI3K --- Nrf2 --- eicosapentaenoic acid (EPA) --- docosahexaenoic acid (DHA) --- retinal pigment epithelium --- antioxidant --- ascorbic acid --- retinal disease --- vitamin D --- GLP-1 --- superoxide dismutase --- biomarkers of diabetic retinopathy --- metabolic memory --- tear film --- aqueous humor --- vitreous humor --- mitochondria --- redox --- photoreceptor --- glycation --- aging --- glyoxalase

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Cancer is a leading cause of death worldwide, claiming millions of lives each year. Cancer biology is an essential research field to understand how cancer develops, evolves, and responds to therapy. By taking advantage of a series of “omics” technologies (e.g., genomics, transcriptomics, and epigenomics), computational methods in bioinformatics and machine learning can help scientists and researchers to decipher the complexity of cancer heterogeneity, tumorigenesis, and anticancer drug discovery. Particularly, bioinformatics enables the systematic interrogation and analysis of cancer from various perspectives, including genetics, epigenetics, signaling networks, cellular behavior, clinical manifestation, and epidemiology. Moreover, thanks to the influx of next-generation sequencing (NGS) data in the postgenomic era and multiple landmark cancer-focused projects, such as The Cancer Genome Atlas (TCGA) and Clinical Proteomic Tumor Analysis Consortium (CPTAC), machine learning has a uniquely advantageous role in boosting data-driven cancer research and unraveling novel methods for the prognosis, prediction, and treatment of cancer.

Research & information: general --- Biology, life sciences --- tumor mutational burden --- DNA damage repair genes --- immunotherapy --- biomarker --- biomedical informatics --- breast cancer --- estrogen receptor alpha --- persistent organic pollutants --- drug-drug interaction networks --- molecular docking --- NGS --- ctDNA --- VAF --- liquid biopsy --- filtering --- variant calling --- DEGs --- diagnosis --- ovarian cancer --- PUS7 --- RMGs --- CPA4 --- bladder urothelial carcinoma --- immune cells --- T cell exhaustion --- checkpoint --- architectural distortion --- image processing --- depth-wise convolutional neural network --- mammography --- bladder cancer --- Annexin family --- survival analysis --- prognostic signature --- therapeutic target --- R Shiny application --- RNA-seq --- proteomics --- multi-omics analysis --- T-cell acute lymphoblastic leukemia --- CCLE --- sitagliptin --- thyroid cancer (THCA) --- papillary thyroid cancer (PTCa) --- thyroidectomy --- metastasis --- drug resistance --- n/a --- biomarker identification --- transcriptomics --- machine learning --- prediction --- variable selection --- major histocompatibility complex --- bidirectional long short-term memory neural network --- deep learning --- cancer --- incidence --- mortality --- modeling --- forecasting --- Google Trends --- Romania --- ARIMA --- TBATS --- NNAR

Choose an application

• Reference Manager
• EndNote
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Cancer is a leading cause of death worldwide, claiming millions of lives each year. Cancer biology is an essential research field to understand how cancer develops, evolves, and responds to therapy. By taking advantage of a series of “omics” technologies (e.g., genomics, transcriptomics, and epigenomics), computational methods in bioinformatics and machine learning can help scientists and researchers to decipher the complexity of cancer heterogeneity, tumorigenesis, and anticancer drug discovery. Particularly, bioinformatics enables the systematic interrogation and analysis of cancer from various perspectives, including genetics, epigenetics, signaling networks, cellular behavior, clinical manifestation, and epidemiology. Moreover, thanks to the influx of next-generation sequencing (NGS) data in the postgenomic era and multiple landmark cancer-focused projects, such as The Cancer Genome Atlas (TCGA) and Clinical Proteomic Tumor Analysis Consortium (CPTAC), machine learning has a uniquely advantageous role in boosting data-driven cancer research and unraveling novel methods for the prognosis, prediction, and treatment of cancer.

tumor mutational burden --- DNA damage repair genes --- immunotherapy --- biomarker --- biomedical informatics --- breast cancer --- estrogen receptor alpha --- persistent organic pollutants --- drug-drug interaction networks --- molecular docking --- NGS --- ctDNA --- VAF --- liquid biopsy --- filtering --- variant calling --- DEGs --- diagnosis --- ovarian cancer --- PUS7 --- RMGs --- CPA4 --- bladder urothelial carcinoma --- immune cells --- T cell exhaustion --- checkpoint --- architectural distortion --- image processing --- depth-wise convolutional neural network --- mammography --- bladder cancer --- Annexin family --- survival analysis --- prognostic signature --- therapeutic target --- R Shiny application --- RNA-seq --- proteomics --- multi-omics analysis --- T-cell acute lymphoblastic leukemia --- CCLE --- sitagliptin --- thyroid cancer (THCA) --- papillary thyroid cancer (PTCa) --- thyroidectomy --- metastasis --- drug resistance --- n/a --- biomarker identification --- transcriptomics --- machine learning --- prediction --- variable selection --- major histocompatibility complex --- bidirectional long short-term memory neural network --- deep learning --- cancer --- incidence --- mortality --- modeling --- forecasting --- Google Trends --- Romania --- ARIMA --- TBATS --- NNAR