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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 --- n/a

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This book is a compilation of articles by experts on the prevention and treatment of periodontal disease, many of which are full of data-based evidence from basic research perspectives or patient data.

Medicine --- 70-kDa heat shock proteins --- gingival crevicular fluid --- heat shock protein --- periodontitis --- periodontal disease --- diabetes --- diabetic retinopathy --- bleeding on probing --- probing pocket depth --- fasting blood sugar --- carotid artery calcification --- alveolar bone loss --- computed tomography --- panoramic radiographs --- aspiration pneumonia --- chronic periodontitis --- Porphyromonas gingivalis --- proinflammatory cytokines --- TLR2 --- epidemiological index --- item response theory --- oral examination --- diagnosis --- Mfa1 --- Toll-like receptors --- gingival fibroblast --- azithromycin --- human gingival fibroblast --- human periodontal ligament fibroblast --- IL-6 --- IL-8 --- MMP-1 --- MMP-2 --- chewing ability --- infection --- inflammation --- periodontal medicine --- Gan-Lu-Yin --- herbal medicine --- osteoclastogenesis --- periodontal diseases --- periodontal pocket --- health status indicators --- periodontal inflamed surface area (PISA) --- rheumatoid arthritis --- tocilizumab --- periodontal inflamed surface area --- periodontal pathogen --- mixed effect modeling --- follow-up study --- Low reactive Level Laser Therapy (LLLT) --- human gingival fibroblasts (HGF) --- microarray --- differentially gene expression (DEGs) --- gene ontology --- biological processes (BP) --- protein–protein interaction (PPI)

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Refrigeration, air conditioning, and heat pumps (RACHP) have an important impact on the final energy uses of many sectors of modern society, such as residential, commercial, industrial, transport, and automotive. Moreover, RACHP also have an important environmental impact due to the working fluids that deplete the stratospheric ozone layer, which are being phased out according to the Montreal Protocol (1989). Last, but not least, high global working potential (GWP), working fluids (directly), and energy consumption (indirectly) are responsible for a non-negligible quota of greenhouse gas (GHG) emissions in the atmosphere, thus impacting climate change.

History of engineering & technology --- burnt-out diabetes --- chronic kidney disease (CKD) --- dialysis --- end-stage renal disease (ESRD) --- incident diabetes mellitus (DM) --- insulin resistance --- dementia --- dipeptidyl-peptidase IV inhibitors --- diabetes mellitus --- type 2 --- Alzheimer’s disease --- vascular --- diabetes --- microperimetry --- type 2 diabetes mellitus --- glucose fluctuations --- sarcopenia --- diabetic retinopathy --- type 1 diabetes --- quality of life --- treatment satisfaction --- patient-reported outcomes --- plaque characteristics --- carotid plaque --- echogenic plaque --- diabetic foot syndrome --- vascular disease --- fibroblast growth factor 23 --- Klotho --- inflammation --- dermal electrochemical conductance --- neuropathy --- primary care --- screening --- sudomotor reflex --- prediabetes --- arterial stiffness --- baPWV --- aspirin --- primary prevention --- meta-analysis --- trial sequential analysis --- sCD36 --- type 1 diabetes mellitus --- branched-chain amino acids --- metabolomics --- NMR spectroscopy --- diabetic kidney disease --- reno-cardiovascular protection --- sodium-glucose co-transporter 2 inhibitors --- glucagon-like peptide-1 receptor agonists --- dipeptidyl peptidase 4 inhibitors --- obesity --- bariatric surgery --- diabetic macroangiopathy --- cardiovascular disease --- heart disease --- cerebrovascular disease --- peripheral artery disease --- diabetic foot disease --- diabetic microangiopathy --- diabetic neuropathy

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This book is a compilation of articles by experts on the prevention and treatment of periodontal disease, many of which are full of data-based evidence from basic research perspectives or patient data.

70-kDa heat shock proteins --- gingival crevicular fluid --- heat shock protein --- periodontitis --- periodontal disease --- diabetes --- diabetic retinopathy --- bleeding on probing --- probing pocket depth --- fasting blood sugar --- carotid artery calcification --- alveolar bone loss --- computed tomography --- panoramic radiographs --- aspiration pneumonia --- chronic periodontitis --- Porphyromonas gingivalis --- proinflammatory cytokines --- TLR2 --- epidemiological index --- item response theory --- oral examination --- diagnosis --- Mfa1 --- Toll-like receptors --- gingival fibroblast --- azithromycin --- human gingival fibroblast --- human periodontal ligament fibroblast --- IL-6 --- IL-8 --- MMP-1 --- MMP-2 --- chewing ability --- infection --- inflammation --- periodontal medicine --- Gan-Lu-Yin --- herbal medicine --- osteoclastogenesis --- periodontal diseases --- periodontal pocket --- health status indicators --- periodontal inflamed surface area (PISA) --- rheumatoid arthritis --- tocilizumab --- periodontal inflamed surface area --- periodontal pathogen --- mixed effect modeling --- follow-up study --- Low reactive Level Laser Therapy (LLLT) --- human gingival fibroblasts (HGF) --- microarray --- differentially gene expression (DEGs) --- gene ontology --- biological processes (BP) --- protein–protein interaction (PPI)

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The number of patient health data has been estimated to have reached 2314 exabytes by 2020. Traditional data analysis techniques are unsuitable to extract useful information from such a vast quantity of data. Thus, intelligent data analysis methods combining human expertise and computational models for accurate and in-depth data analysis are necessary. The technological revolution and medical advances made by combining vast quantities of available data, cloud computing services, and AI-based solutions can provide expert insight and analysis on a mass scale and at a relatively low cost. Computational intelligence (CI) methods, such as fuzzy models, artificial neural networks, evolutionary algorithms, and probabilistic methods, have recently emerged as promising tools for the development and application of intelligent systems in healthcare practice. CI-based systems can learn from data and evolve according to changes in the environments by taking into account the uncertainty characterizing health data, including omics data, clinical data, sensor, and imaging data. The use of CI in healthcare can improve the processing of such data to develop intelligent solutions for prevention, diagnosis, treatment, and follow-up, as well as for the analysis of administrative processes. The present Special Issue on computational intelligence for healthcare is intended to show the potential and the practical impacts of CI techniques in challenging healthcare applications.

sEMG --- deep learning --- neural networks --- gait phase --- classification --- everyday walking --- convolutional neural network --- CRISPR --- leukemia nucleus image --- segmentation --- soft covering rough set --- clustering --- machine learning algorithm --- soft computing --- multistage support vector machine model --- multiple imputation by chained equations --- SVM-based recursive feature elimination --- unipolar depression --- diabetic retinopathy (DR) --- pre-trained deep ConvNet --- uni-modal deep features --- multi-modal deep features --- transfer learning --- 1D pooling --- cross pooling --- IMU --- gait analysis --- long-term monitoring --- multi-unit --- multi-sensor --- time synchronization --- Internet of Medical Things --- body area network --- MIMU --- early detection --- sepsis --- evaluation metrics --- machine learning --- medical informatics --- feature extraction --- physionet challenge --- electrocardiogram --- Premature ventricular contraction --- sparse autoencoder --- unsupervised learning --- Softmax regression --- medical diagnosis --- artificial neural network --- e-health --- Tri-Fog Health System --- fault data elimination --- health status prediction --- health status detection --- health off --- diffusion tensor imaging --- ensemble learning --- decision support systems --- healthcare --- computational intelligence --- Alzheimer’s disease --- fuzzy inference systems --- genetic algorithms --- next-generation sequencing --- ovarian cancer --- interpretable models --- n/a --- Alzheimer's disease